From 740a04330ae7bd781262b2ea233734b7b344c2fd Mon Sep 17 00:00:00 2001
From: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
Date: Fri, 7 Mar 2025 22:08:40 +0000
Subject: [PATCH] Built site for AMR@2.1.1.9188: 245483e

---
 404.html                                      |   2 +-
 LICENSE-text.html                             |   2 +-
 articles/AMR.html                             |   2 +-
 .../figure-html/unnamed-chunk-10-1.png        | Bin 48420 -> 52178 bytes
 articles/AMR_for_Python.html                  |   2 +-
 articles/AMR_with_tidymodels.html             |   2 +-
 articles/EUCAST.html                          |   2 +-
 articles/MDR.html                             |   2 +-
 articles/PCA.html                             |   2 +-
 articles/WHONET.html                          |   2 +-
 articles/datasets.html                        |   2 +-
 articles/index.html                           |   2 +-
 articles/resistance_predict.html              |   2 +-
 articles/welcome_to_AMR.html                  |   2 +-
 authors.html                                  |   2 +-
 index.html                                    |   2 +-
 news/index.html                               |  16 ++++++-------
 pkgdown.yml                                   |   2 +-
 reference/AMR-deprecated.html                 |   2 +-
 reference/AMR-options.html                    |   2 +-
 reference/AMR.html                            |   2 +-
 reference/WHOCC.html                          |   2 +-
 reference/WHONET.html                         |   2 +-
 reference/ab_from_text.html                   |   2 +-
 reference/ab_property.html                    |   2 +-
 reference/add_custom_antimicrobials.html      |   2 +-
 reference/add_custom_microorganisms.html      |   2 +-
 reference/age.html                            |   2 +-
 reference/age_groups.html                     |   2 +-
 reference/antibiogram-1.png                   | Bin 34842 -> 36597 bytes
 reference/antibiogram-2.png                   | Bin 48370 -> 52339 bytes
 reference/antibiogram.html                    |   2 +-
 reference/antimicrobial_selectors.html        |   2 +-
 reference/antimicrobials.html                 |   2 +-
 reference/as.ab.html                          |   2 +-
 reference/as.av.html                          |   2 +-
 reference/as.disk.html                        |   2 +-
 reference/as.mic.html                         |   2 +-
 reference/as.mo.html                          |   2 +-
 reference/as.sir.html                         |  22 +++++++++---------
 reference/atc_online.html                     |   2 +-
 reference/av_from_text.html                   |   2 +-
 reference/av_property.html                    |   2 +-
 reference/availability.html                   |   2 +-
 reference/bug_drug_combinations.html          |   2 +-
 reference/clinical_breakpoints.html           |   2 +-
 reference/count.html                          |   2 +-
 reference/custom_eucast_rules.html            |   2 +-
 reference/dosage.html                         |   2 +-
 reference/eucast_rules.html                   |   2 +-
 reference/example_isolates.html               |   2 +-
 reference/example_isolates_unclean.html       |   2 +-
 reference/export_ncbi_biosample.html          |   2 +-
 reference/first_isolate.html                  |   2 +-
 reference/g.test.html                         |   2 +-
 reference/get_episode.html                    |   2 +-
 reference/ggplot_pca.html                     |   2 +-
 reference/ggplot_sir.html                     |   2 +-
 reference/guess_ab_col.html                   |   2 +-
 reference/index.html                          |   2 +-
 reference/intrinsic_resistant.html            |   2 +-
 reference/italicise_taxonomy.html             |   2 +-
 reference/join.html                           |   2 +-
 reference/key_antimicrobials.html             |   2 +-
 reference/kurtosis.html                       |   2 +-
 reference/like.html                           |   2 +-
 reference/mdro.html                           |   2 +-
 reference/mean_amr_distance.html              |   2 +-
 reference/microorganisms.codes.html           |   2 +-
 reference/microorganisms.groups.html          |   2 +-
 reference/microorganisms.html                 |   2 +-
 reference/mo_matching_score.html              |   2 +-
 reference/mo_property.html                    |   2 +-
 reference/mo_source.html                      |   2 +-
 reference/pca.html                            |   2 +-
 reference/plot.html                           |   2 +-
 reference/proportion.html                     |   2 +-
 reference/random.html                         |   2 +-
 reference/resistance_predict.html             |   2 +-
 reference/skewness.html                       |   2 +-
 reference/top_n_microorganisms.html           |   2 +-
 reference/translate.html                      |   2 +-
 search.json                                   |   2 +-
 83 files changed, 97 insertions(+), 97 deletions(-)

diff --git a/404.html b/404.html
index ad6496324..c87f2f998 100644
--- a/404.html
+++ b/404.html
@@ -32,7 +32,7 @@
 
     <a class="navbar-brand me-2" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 19c35cca6..7fd2e499e 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/AMR.html b/articles/AMR.html
index 23af9f62e..3d42e2c51 100644
--- a/articles/AMR.html
+++ b/articles/AMR.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/AMR_files/figure-html/unnamed-chunk-10-1.png b/articles/AMR_files/figure-html/unnamed-chunk-10-1.png
index b886f61460a6aea3192256c5e8c5de4ba5516aba..1c71e6e0bb071e5b532e5df68ab95d886c4ca104 100644
GIT binary patch
literal 52178
zcmdqJ2UyO3-#&hY=qn*BBCA5BGAg8Hgi9*4Xe;fhy$hL9TqH_MOS`mpL?I0grBq0J
zNqhg#cXE&4eLu(ZJip`je}4bte;xPzRbA`zd5_oYJkQtpdfz%NbDU{0>tYIp!bClB
z^elx!A4H+d&u3VOzZAGPKF2=`wWN+8#lLur9m#UVKYy8@IB!Xzu-zd4r}Jv**P&20
zQ>aG|$=f~cZg8|)HI!4-U)J0D;)m%K&J)iWR&8Ct%vyeX@wuakT4ARRO;UKhD_<oV
z=2UN}tJuhIZbuOnFl0=v3R!k~)#95eL3bA)PQP{IYVwYUK`m>RxJ&R!(HcZu%Z*p=
zSuf=0@Bg`bVn{?pw)RO=ukH4xhR<W&KlUxnYwBZ}y+r(nxivZS&(9KVM=4BmzwDx?
z6PWukf7OEWKQED6E-`=Z=TnFNCtfUhrJ8=T)h5@;#A;3oWjXD`hf~&FpLk;u16{@=
zMK;R1^UR~<Z<rb%VszYDWJhs3_woJ|o8JHMMgHewbzjq(j-nYF7bmlRpJ`RNwBKM&
z-n2r5Y~ZIiEE+NVF8v;?dv)(DUcC5+`TniLc9yLlK8_D{aZ*Q`T=NROHkqZXOG;kr
zO>bVRbMc~n%X{zGM~*q_CXLzW&2q=Whr2(Abaoo5S$&l3Y|6{K&a^)KLc#S&l{8c4
z==GH4>wWw5>S%$1@!!9H-yy%s-b-x!xlQkv$d+Oso`VMu{?MT;kM|vB@)8|>TzF@l
zc6#X}UX{Vhz-f7!fYGO&OzVYq`f|RyUK_D>4YS1j1plF~8l%#u2Yxx^@S2J{jV3JJ
zBo-rR^l68<xQ3Vbbb6*u59|G+L5G~nMUK50ZB~`i`?$EcBC8!-@-)=rm6({AVjn3e
zC>Ycw8wfiL>U-1|7Z)q2tH<GcPSDhy`X!yG2LioLG*H}X%bmn6gNL*fjvP5sK3=c9
zkkTJz(DZd+Y>AK0!-wttMVov$M5hKTGk?A32|2&f__i~*7Bg0@%2<h+ONdVKBPz`_
zXGl@nSAgoHYH@4DiWNTw#^leO39hI(%d4Dxf(-K01D0FIM`a@9HhLX*oowDTbl1#@
z{l_I-tN3)km-S%#88w^FIu7l@H>x6JH*s#?UfebinVG4)Xz7M>KeJq}hzFm&D4W$q
zhikPnJHq{9_~uhIE#vd1Gh0jjd3=h)zJJ$hX=_taTS0NZOJQ|0>2Az9xLm9(P+a54
z^(6+0Us^24G-XZ=*IN&i?CUDo*L25@scEd#HrcLUi(F)Lk(biRlP6C!FJ%)Beeq%s
zCkID+Mq9vx`X19Wwz|6E*O#oT=sZA2`7Xr~;dPr+R(vWM8%5&7xkRmNw>><(@7)W!
z#>mq7qL?o!lcSblLjLvEdHR&puOkyfKOV@Qq~%UD^r`Q9Zk^R|sLfz_Vyf<ij>o5%
z^C8Q`Cr@J(<}X}Q9-Gz}WfY&2TC(@vz`%fYjhbyon2&nJ3!P#Co%av#b&6ZH-{a`G
z%@e23EQZ@(q@$ywoMjW6YSt9@>z75#k01J@Kfh)6q}Equ+4d=R*<REzbRXlU+bqDJ
zH~GS4qQ2>Uk4@J#7WLC!8%4KHuO)YHHTv^Hta^?z7K-wVi?>cnNwxIbS+#g8b;f1&
zE_Nui9!SK5Nj=*65nD-KK_P5<YND(3U|-eGZw2*ElNhI1<&=~4X=XLIR&K8fIlk=f
zh686cChBS)1_U%3ItP|TBx)C(q%}Hp@fJ*|Ch6W?v-4b##`G^A*4<j}7jpDUpZ+CM
zQ&*>qMS04$$=Oy@+^+S*x*RMtZZ58m_)G!Ke2VmwJ(BYmEnPCM`l><0=HkJH^$`v^
z>drr3E~-noaFy(^mD^=@?A<GO<i^tK*SA(yW4i~H9Wk8gqt{<XCczxb%iG6CindMW
z>Du8b0U@EzZ#Oo5ic`x{{*voFVPn#it2Q+;QnR;Aq*C|9Bv!Q?xn(K-oX~jJsje+O
z#(t#_`FMC9l(lL5GEv-kN1en(C%Thj`JOuBqsah9E5(1hy_!dPL;f)>4F|E-=ml=6
zV^c0<Wl8z)DJU!Zs@r{|ujEs26c`yq#7WV7d1XzpkXd6)iQhgW>``A4dyDeK^+Lww
z+4cj<*=c=sDN<QBJr$xOUqZ~ByHsKe$ERFGXC?<6Fgwa$)D;!OG5R@^zlyo}_!MWR
zr<gXS@rnh?V(Yu)H6=9CXe&eqKJ4Xft#=t|upR2n>gz-#OnLF*#b869i_%7~?Fmk8
zrT%ppmJzyT0kP3>nNii+HWyt#-|ty#960{PvMi;p$x&ouPJiKghvvKc1gtyd8|>Ts
zV+*Z+jLdVt?Y_W`*J<$Maaw4Z>+}E?4&#JNfPL$_9$fe5&z~I-k_JDim~~)LS1|Ht
z2lX~&b%l6|Rjia1`Pk}g)lnfS7a`-#F*QAsx2)rmU#@gqOiY#Q%!q5(k9Q9)h%cg0
zw(5L-p`)B(5r%*wFE1aOVpJ0q8hX?$XXuf-%b3pW#pDXyeVx0CvOF_MudGukZ`}J4
zpITIRw{gzscaQLEWhK1l5+rI=E$=GEoD0T?OpTSfeq#3$*-CYo9&1Z+otX~OZu{^g
zB;KiohkO$PK(c9r@aom8sWdWQ3Q9_8tVtFvC&`NJtd8~xa2`G%tCA+e$;nB!-bRP^
zErM80mlth3t)`~-I<RYUY(UWd*TvZLNyqRShpC~Oq3U=S1)6|m>nZ6#F*WvMck~l^
zeLepQbm-(NV3a(j<+`raW=Xi?NU!e!%W!g?X^jp%2_}Jd-~Y;JFvSAe@zW=(<ouZ9
zp~=l|3pE+B>uFWN9!5_^?9Y+_$k9ROl`n77B14rA{~PM4Jf4Th{KFz6*`RV8o0wC2
z$%OB&3s=6FrYXj$t~>kq>7z&C6TMlo6&d0)zWXmd%bgyIH)_nbcj$^uo2O?xpYpvv
z=6sTX?uW-gGmS12JvT)arKRt#;Z^47_~LS@yEgHpw6u4@w|m7cXAw!r!oW4r7aq0H
zSWICkxkAr4SR_7uhDF0EaSgxvhLp(Ld|BOlM5jla-<Os?D)@HV!s3N#M#~z}(XUr&
zp~#g%+If>L{9SlcB41|v)&m=sV(1L;JtS3gbQCNTH%@d>Q}YjS>^*?A+)nOy;gYpM
zp`jdjZCH4CCl*?@m+Pdqy+}Y%rLom(g!h<DlV2B|l$G_t7oI$M+hs6h)4L9<sO)Uj
zhAi8fJL~o;8+#n=%a&`%w65%^jIhSntr=m7oYHYj(^lCg?HhU~<QUh%gDQMJn2h6h
z)&|dv$Is9>a)#@Scni->{h1Bvrf}Vh7u}sV9klIz$kAY4z?6xlr@|{MD_i6K*Gdkm
zN~*EJKrW3&6L224qPB^hcGr64{ufz(f>!OPsb>K<5Qw6;2Rfa#{c`C>?nq8wN3)ms
z(y=iLMK~f{JWFs~&+58Dx=nZO7HZ~rXEgu*KJ5o5dZp+n!3MrU&r(wr^~wV7)-^X;
zmWf`MTh!<@P(mw}re}`X7!tXKgT7TmOx$Yf>(yn*$%7=ay?giWx;7S$1GO!j?pCsy
zQ}g;*Q_Bq1%<s2(IH*n2!)Z+XtG>ur>IFJ~`aXQ3hrd3Ht-yM$#YakbpXdoo#ToVG
z;*aOEy77`dOtTsv>`3Tq&c8gLc5%PB(t}s84(H$^x6r>#`(oj-hEMhRz9uI*x$skG
zOig>~i+;4Vk(Cw|8Ts+$)kP_`ed^wi9(7KQG}YkOp{tuFB}ih8%l`GgJa8=~sM4-G
zv4|iW$(BUlX{*eR{N1bWH~P$@@O3vj_EjUMD4ad}j7u)OeI#!t$HKzmlq(77tc?MW
zA3tx*aqPl%3ETe)&9v=nH1tQjXZ1Akl}Q{^eNRiqcT+2#NqP*vV&qRgUxNv-cv)A^
zb1p~Pm3~$Cx%iF+kpovpf8Rg<kFPJUk|>z0U9^<#fW=XuE8a)0;~f#a(`G|`^!-=&
z2GP|Xj#tm&h&-|=N&1_$uC8vmwJ+a}a|#O9FDEd(4qq=X;-Kb@SI5^WNEsSNEnK?c
zIr5uf@0Sea90%*KAKIVn)jKx0WFJMMdzWz5`<)Ro{<Kh=DK6X4kuZuIqZF0;5vx|f
z;>QU@C8PHbxz1#SpQ^l8byt8s<mJ3_O%}Cfse#ckF%JjhJ?Ixs)FjJ<(nm*k*e8#S
z9W`|Q+*o8%?7^S=ywADMQ90KsW9f$do4txE4eJA3`wvIE^55L_l#VHX4N}wgjxYOC
z1qB7M%S}d{-CtZa%e;9+Wz#C(^@4^E$sQzdIMC@A)o)Wp9qs=8;B|X-^}oNqCG0$&
zEE6EIZm`}I!8^DkCN_Mt@G9MItW=g(E8(G}z^PM6Pz0Ah<dP$}iva0xQC&pNa>o39
z^j}|w>*&Xa7mdB)cL~1Fe*D-EF~5W2=LG#4hJ9`<Wvl3OopD5D=+G0Ncmf0uh`|?8
z$#tV(OMK1x$4Z$8l3e*ER+f?&De5rD@WDUDusSNCHw1G}3kALxZx`O=^z$-D2cyR5
z;S(br78!#-3zkXI0$j#BmO6LUPhLGP&}K8A@;V}%-*%r#-Kt+}_YPy|%hOf)N}e8&
zBY7k$Dr#`5&$X#4PsqUc*mtM9ckf;{c)Hd4&}#rAsl{d+zqRVkU&QF^$7i^S*QQ=W
zNc2<Q%tT&Sol(4kqN3v0!|rE)24%D7u>^@SV8vW1_*U*ZS>y^pZujjPqk2zTZmxRQ
zr&yy)^(=X#lGF-J2h+^O*zoXU@!7v_hgz~+TwJZ7q!zYO)Iq-{PR(JWC+)~aTP*ou
zdKb@6-m!F){%C`|^h?c!_u5PZj7UJfTijCNI^E{l85JFOR!1i+{M3_*;I1FZEopgE
zvETj@avdwy;Goje(_3UUFx$sq15?|E`Hy>UFsM0qqae7LFY6SoU*t6w#P~qq%$NSH
z^)F2)io+w%u2^wryRd##ixb;e4y6YqHD3rD_oVYsv<C8e`*HD#q%yH+_T!$(k1acq
z)W<<}Lb2o*imerVS6ce%?!LwyR9`XYOtKUM%eLn=#S>;g{*tX8PsmkvcJ{q43t@kU
zANP-QaJ64sM+g|=k|F^{k#Z5rPJ3Me7frsTpIIh4crVnl(m_DYiW$4*e4430I9AB<
zP0q<`%@q|DK+vnG70X;lkK`crU7jx=UlR?`oHJM<#T(wfYj&jkF;Wj?#UjD!I&i(2
zZ2^w+Dh~*-^iBuA!w?Sw$1fb7k0~E7)|h_wIZ01S*jC6SG&C%%qiMRQ3Bhw_dSnRb
z_<3roP(n7Q61(Kqqn+oUi8>A^vx=L3*-xGQ0(>#G&2!nZ4f~hZ?vs3g<vlhwMhk5V
zbXA`i>09QMXg)q@z^j^my7&?A-VHS$n;4gKw_;uM5SEI78=@Cre=I(CJWAbpNTFb8
ze7I*lgO#7-=(iiz?d4mhddyra5f7&ni}{gwr+)e58QZEUsb0E9UN|b3JOId&dfx1T
z%hY(scJVQ4)}~FA@&g}y1*O2$3@rV@``@)#&`&p6T?iV8zqsi(!Jpjx{54(id2t$(
z-&wAA0WWD7^t&d_?hk;D61@2j-31#xo`;K2QRRtXpIAio6*BP+aGkdIe)uq1c{r!f
znSZ(AvG;7YUxI-E4aR=8nDib8i>vQUtvAh*#w}M~p1;Vdh|M<n+^2XAanQ<+D@9uO
zbt9&94jsC@aM{KfwJaNh&Z@}RN8&&8=R34M(OXhvGYd+={9^ZSSbBGpxca^?=2wM`
zYlEcygb0GDLUgt2f5T$bULMSP&?W{SV{E7M6%b-I3yV=@xU@r0N=>rki0PP<n3~I2
zOXkog6+wr=_BD%!dLp}mZ)~gpm|Ehaap}!!2e7iimy0&(7kRRcw}*Iv>2ASB$<7v0
z!?UcS4#{!p-){#g2`{9)zM&rw)`HlFK+}cWtBg{JMhfxFzh=(J$SCMCIjUHB_(=*s
zJtf%-iw`7uu>8m}`YUfFJ=R>aerdz5G4@_bz_@l7@A(%;>r+kSOiW_0FJ+6$>dlnG
zj%BvL)4E~l4N$XiGIT~Yan+w<Rfaku0=W+yIFChFRUUlA8W?m@+=X&gz%GKD$wv_}
zgPWThJkhKH>6OspA%rN&>_*1K%IS)4=K4s|1mG&htH;WUjU5G|=ia%q3cF$m5L!&1
z(r}+F${S66BPwbO4i7*hP*Iylp=^2g;e&jdX~Qel30%d(@V_XO{9fS|@6GQl;&B24
z{q(KiMv6^O-OX#CDZ+W4-F^F+md(jrnu|fzem~r}5FF=z4hQI+`|<JCm!0?y@>tGB
zI4ubH&AmulzyC)0KP{yZEr~(R#wN9z^K}fs!$}(A5;qSIZ`?wg`^bA(JX8`<@z*!2
zUoHi{Mxf!KB2q?0M<=B}tNme<Rj|<yuYCCMA%o&daXdY*J~t}rB;dmur3D91oIH8<
z*s<#zk&vtKF8nF$>)sbb`wJOmwX~k?*}IpM3ie%HT}_^O`|jO+fbOO~LbYS~774x7
zyV!MU;lC9Rt)jU0PrBsE_zQ<2H5g0`_ta(ECm9+U30{1=d}^{yd|8JWdD5Fl$q*YZ
zDket0Dc9K=0LSph+xs9A;dkkO52{;fxEy`?L7VP!fYQcJo4_)biqRa$k<P@TjYf&n
z0iuq{dSx49)v^?mbW6?xfxRx8n<5Fls>txK<{7Gl>Th)Vd5CafNlD2qUZR$*cW&Rl
z4L~YjP{F~gk*iAB^Q&u&)zM1)JxzHU18rqqqvz&kwLL~yfg%d5JoRI{_{ZmGLaJg_
z_JxIpx{KH#>OdXK?9J@50>>@xu_^v$zGL4$C6K{KASVU}k)E5xH-1c<`=s*o)=?B;
zRj4h|im@~f=>U<8>R%5ZuDZK^pA0P-IrHY<@A?i!xs7k5-EjAL>REO5fN#P|YS!Ky
z2FT6K>lx=h&;NWxCc}kvnWy`=P&?~VqE-2Pz6pa}_;9f8H{YzKU1s|=pUxQ^`FinL
zIXMjdJ)xrJR~wctV!3+Mt#T~B?<;7*;ap8F;WT%5_ch`J0n7oK6R%HmaBx`ZJBb+O
zHmq2=QYE`AI1Kv6f~hanpVL+2@*U3~IdYYim33#_4q?8GA1j7BtBX7LXw3_}A6sCv
zOm<*OZD3@7(1G!J(xZ2bMd#^Lp@uvfj1Q_k;lroKK^+<z!kArd?Ri@}U{afK=*^oq
z*NYv7I>VGSoQLL>EnxX>CF~s#U~0WCA-JkaK3d9+32NNHOTklYf2Dshwe{eiw>)52
zm63g8tVW*tgKg3;GM7AVM>wp4Zc!&#*f!81>pK2`iVbo0>{+V<rhVtsj!}xXvH1X!
zzkYo*y4bA*AzEEy*K56tM&?!#u527>tM>>jyX!2b@pXo69b@fB#Om*@AD-KJKY8+Q
zVmZWq>@XtETCdx62J1A%tVt}o80vo9a)~YtSJMUopCo2rz|oAc$Z@uHj=5WoTyMW2
zJ{kP&MNG`j>QA2*vU#KmIv2DIjQ!}h+Zk8bNcbJ(RTZ^`rv|4d`gp{}n>zOx^K992
zh)Si>zIVx1vzK&OU3V5b>+GD<+116add3Nxb+MCBzn#b_-m2?gzNms{7j^D=_6*nz
ziQs`%?_M1l8P7MXC5sS)>!-$^j7*l6^^F{#OpjvMtGslFWh209T0Jk-(AfBg<@|Y>
zRvlY`U4eQsGlipzFH8SicVLh3i#&F>PZ>#cob+Ok*BdxA3%jbn_q-$81pXmt_w#E|
zF%LKQ6}}_&zUmyhPG7r?eO)hHDMkG7tU0tEv$NSPE<V2Ps?lTLrHc#BXlTS278a7o
z^HfI9gTLsn$}1CK*}=eMm=t+|rZgo89QNOj>R};P^iKKLbX6nGor-$vLJ~IhEMi}7
z|6Q|ls?t^$_4HOs*v`9k>lW`k{Z#FG$fO*3O_-81_r1MWGX%-{3+q@PIP=YX|3e?2
zn%+y>WuD&jtC4}CqS<<7uU_fvlElI`v5}0MGF((j!y#$C%TB|e^$xY^^YQcha18Y}
z9MsaLJ2?J|B{wK4iaWaa=4~LDK18O69IIEYvf8oBgHw8-V!njPfSt&tF?u;|ZS5}`
ztga2v-zjdn?U(;719zHk-`M|4<Z|mV&kgI+T~-SR-np}63UJ+QBt39uvW?K5zgEBG
zBeE;MbBC0o&z4&h<%$fmx43<I3>L=aImmSEr_ZS-ejFX0(ea+O=kh%_x=vn-mb&TN
z=Q5H-6dg{g2Ma$kV@?b&17*2jN!gm?ZL3OG_fEOwA@EB-FKl!Y)A%CNXFiTlXtg9T
z)4I28HP&zZY97UQZzc7QSC@&^YT1MKY!1zYp1>}&1`*{`ddnk~!J&B9hR!{jdx0_u
zB8lN+2X2pUZnckyh(Ly2Ze63{nqz2ah$px~fkg!c1a|D+z1wup<EpBvghB#cfIzI=
zqZ1>5$9C)*QoeBE!k2DF=zcMLTiht)fu?y=HnFN1XZx}TJi&|OkWHYqQlWV{bVf#0
z)z&@-1!!q)J!Z92b05NLU^VB_l9}ngnT0OXE&Q(Q_8knSXIyg**P>wbDdywMRMX6P
zEGCX^+fpCT7gpy>upq*9g%p2E<`N3U*r5OW8%3Yxf<U;!m%h92*|X=d`uBOgIV0)~
zj(s_bKFq5LbO%3~K29SD+YkV4jnXPt1pS_VJEB<Z{W%kUHO~K_ts#Vy%k|)JhtzBv
zta%H;u{&>V4=4vMPPXbe%d3`|{N@)#TX}*+zct%j+)1JAssV)>LNIuVQGK@FH2pG#
zl^Kh)D#y_lq3D*d1sy4O1<0(Hkm8O1#nwHnspQHgpnnh2RSeQ&RC2QXcG&=u9tCZ>
z&pCrE31#PNaukv*m!N+5@RehCS#_+}Nt`1xe4kU>L2^q=Zydq@Dl(cUG>b<4OCU@t
zAXgzwQ-T`EjoD<S=$7~{Z8(mI5ia8&1uk>Unp|yBRivCCcmi4f5YWz;<xQoAS}F<k
zHDskBv>bPF$pz?fYhhi#e(1Aa;CTcvC@==mQWUoYt0)SqM3G5Drb3a|rnJmE&wtv9
zTn>~vdGdQ}RQocKpAui(&$@9iZ4^0ob<wg^Jg>hvkh8<rDDETSnvBtOl%o%jH33Mf
zb&g)E>0(lq=f}b@O{@QY`&kH7Y(*am<-!)Xh2@7K#$i>)VsQdf#PV%1L_Uh)Ban?*
z$Z6D^>^X7)wGfw0Gds3XD+t=RA80+V%1jC3MU_+8T-g8eSQRB`Z+c(u1XiYDs%Zo3
zs6EsWqMz}`Y;!=RK+_udkwUow2w47Vs7Axg`mbH*A$7;jeieoC`qt><pR34A@K|pB
z2MLx_-&9&%l&;wug}4~q##U-|u|L_lG%!342zia-N>Ll%a8C*=_NF0nqatnwH!L~O
zcK-Z%uD~+A3xXfzQ0YJclq4AnMbqu~Yf%Z%0EyQ{$Qwb@P*FQv)%`i?IFSXab;t{M
z<!|_V{nnfyv-T=PyfTm}GEoG#V50C?zJSzx+p8Q#dSjtdku*fAUVomHGbb-1kN-mH
z*PQwA>`R3&gV&aIu6TyW)}I5|TP*v^RA_UfSZ?}faB!LQdJ;wVNby7M@CPVU#>Zca
zs?QIWl9pD0E>*4bI`sPW>j_2SVPRZbE_>}@VCyqtxZdc-d~oQRzu_U{AU#d@T=E<J
zaogRY;o*gyE3_2l+(^k!NlEXLpj(O^zkv!i2JOKEP69XB7N2c?QrRJ|rKNRFyDUI7
z8u08``c^C5?D#Ef9p)d~+Q7m#_QH3gq~piEyAEsQ-}E20T~NN;{i}eJH2;dcrZStO
zUz!EWh0^NT7=qTbvev3!fK+k8%S2!QG_t^g=tFu%U8`P}uiv$zBe(r6-2u_t!_Vd}
zvZoJH<xk1(;6Gk~*|EZ${^w-l3YqoUhvw}C(Q}y3?EtEku5a50NXQcql|_LI^yu|>
z!)FB@OZvW4+`hfIx~l4EI;$hr-qJ}YsLDu6h^+)4R_mPDI!9*Old;~Tu3uk>jZXf7
zr1lwLVk<&<%q76}O30z?DyNpz<~XL2_zU^Lc%{T%dXz<GANi~P%x5{3Y3(iv_4pdZ
zw-r9ct3RcKh=zrQNv5;bQh{8nIT!q~5?Gx@l`$c6LG5YGLUod!ED3<!gA+Q7m(G3c
ze?+|gVJ!ahfg>!fJ198#(1Y!=OAQ+0oqLQjyW&u4OTE;5tRzr8ujCR|04Z4&#4*o(
zu@)2EptyZb<GoK^HSrqJ^^-0$^hFa>(+PhFpW8V}3NP30-Di+<{>q#j?QbUX42t*y
zfYm`=VENL`g?IOc{Pu)gA!-E_IYcOK1giPsfnv_WrUAAN4w<BAdyXOK$WQ1rCupX*
zW4e$O>D9_QE}k?Y7b7tU@GgOhCJA8C&!3-$Ku^L0N%sgluMTTD(1AhlD8)VGC^em<
zrfxQ0m$WmE5-~tUCHm>p4Qdbx6n$nL91=V}>JA2ea>up~>*(q#gC)CPqm0i3e&D70
z3K-BfEIsbm6^ELVS$03Ty>KWnE7OMkr?m_3oH=ypC4JG!vsmTj9_xfRc;4%s&r)b}
zG`m@Fxp1Q+@cwbfa{jr^Dam2f@iFAauYmz}8`p&G2Z#8(um0YrbXMyTrc+Q1qE;Y^
zy1pFp(-{4VAry9*Oh<vogo#z)1R$ER<+MK40ZPAus;Y5%j8eS1!%($S{Tb0aP(V>$
zKBu-&lZ!+OD)kIXD*vrWVN#2FH;2L7ds|G@O@uf(B}W<^;}<hBdV70wLOekUm=s|_
zM(VKQTTA>R5XSVq|E$YL>~5w7o2Gv*qE^)84yOp!@a)`aY^{QDh3p%K&0z>tjGLFY
z8r9~9etzo*U0x(5RYN=~(18?8qQt?d<CfBT6=PG=L6uJ_{Z~e4sR#UJ4*0jlee41)
z7#IVc62h|yMdMCl<EwfO=WYI5Y>H-cSk)t0me1f!$8J;C#4NGs63XUmq7L$43*0+)
zFig1o`t?ix+_~p$2W|BG8gon*mZvq?_?c$)q|O5NS8?o();)C!6&a_Sk>J-T%|GW+
zBT@fHFtE0^#v+{)4EXmC$?r-@NpZS)EbC|Pb<K9BwYRJ^1lWw+ul`enEM@rgF){|r
zh9=-LX_uiIx3obCtDJ)hw9;*^Hn*>q^DqQ4+2yDcf=&}a4QXkU#`*J4IJkItq6<dm
zI6fuu3Z*U|34wq=D9g`LjAYA<hfZYx&50H|IXQXa;h)H}`!ZgON;d};+dKDbCY%G~
zC@4n&Q*<I7UalA2=!I&q^~}_W6pd(AQhXV&pI(`hbMmjJGkmj$icX|9YDG0j=gT4B
zX2n?LFokGEdbRCvw?HIdj{aL@praeKhmg0KN}zIen3NA=T%fmi7|a#~n-NhAHzcyr
z%fix9;q>XCk0Hm6^4;hx_HWSj`Lldd4^x~2`kRZ45I1^QANcu2|Fv?PKG5Vgg})8K
zjRXHya{6yYzW<vqK9P_~apw~iZ9qB>sO-3SaBFWrtG-`x%i_tWPoKuPFR=0c*lLs2
zV!j{VnceOqf(wqAA=Qq*l1bStR6dt@$FGERuKjLh@ydZCfsMJ(jww0T{%42zex%%A
z6H^zwbh1Kn+FAbnJ1N4~VGubFOhIxT$Gf(HL^kFTd_+l)4G%A`G!5K)79mI`zQbSO
zRf<W($_E?1V=meL!A|4IuEB+Oj%M=vdJ4We<n=74ecjq%e$Cf169*@bew$m*ZZg;Y
zo|gafRP=vyLjD)svRe!J$yeOf#joo_fT#-WCd)D>5e$g!Al3Nuv-j^$4)xS4?Q66P
z0e$5{ChwsA*)*RRf@a?JjTCncshj(24)-=h7j1MzEsfv2{@)02oqyJJ7Q35R8Th<_
z>6dhyy9fSBb%XeI9+hB{C*@>C!*Dr9)01P+g!e=m&W&}#rdBsGugcHU(l2yNSnYGv
z3G2!P%50fkMu@Y~sDTv2S%rNF%CRq4>(4v4&ud?b1*4o|2+i!*k9V<;B0@tKls<1v
zzoad1^vBr9!z^K24(em|Z&bE3e-IZXrf@@f8>#0s+BGw=30s{2OlRG19z@DJSFX%E
zu)$NYD{%j%!?datBPEm~3MSzF%Hrri>9=+Dp9mmy1Q7r?xyVHfIS;B}Tw0Th%GHHT
z6(64LMPZ^!YpOCrR%%@7t#K%r4lQ(i+yQVT=+F?%XOT7PjwR-}goeSl>|V)T89{;(
zm66aEzz=r5F$yv%iKqW+ljZ&RvHk_Y#Nxh@8=|TBkn0-b^Ipz{@I*O{+Q>Dp7dk9F
z*3P&Vzb%)TkwXq8Jb3k*H4v4hKo}AB>vEm5U?)pKc!V)X7NL;05FGym4QTpmqwPTc
z=e7ZQrl4i;0^<4gd|l8?qkpdNX~QP<yy=*|@Nk<&#+6*X5EJQ%B6H+zPL7|<^uf)}
zOavfnMdT*P@!n(_Tym$Jh5Yuly*Ch9Sf$~SuWu9_50Pd~xugI`B6xT0ZpZ)&KN$#1
znEEcNy9q`(`DOBh@Z2P~e*lWQ=i?IrxuB;u@d%QJ3A2QruI@>iC)>d_R!-nmp_Uh{
zRrD&PpYA_{Y$B-o2rjwz@86rh-$zmnY5~W)r@Q0qfA%5&#&9?|40lK3e!z!}chLGc
z{m-Ce0TGeI-*INxP6VoeAB37$0lA^6si|*#((<qUJwd9XlzhP}?D$9ZKle7pMrU=C
z6w8p5+flgSH7$dJ&fz?(I@$hFow7=bPRPnCIXF0!w*aR1@E+WO${>>2vF>S2*#-MS
zNRng6eB{Lfy?R!Mdw!rR0p|Mn2Xp-|ga}`VyW^l@h2q=ZzsI%HpdMQPc*{J5+*n;}
zQlCnt#pRA04ltVBu=CYON=!Uck#QiXSx2nKZhVSp3MiNaoOpiN9Lz!D=F`ByCaotZ
z5(BKIXuVlXY)B69y{YVskB+WHIgW2OD~8CKGr%Ne1uq)-gfGhc=V(<xKREzd02{w2
z?De9!o7*E%e8__n(dy^dmupy74%&$j)CB(pKb2M3>N)genqo6V2OAh`at2BdDrZ_n
z;tn|d{q_Klu0ybvho0n-5i%E=f|8n-===LcSX4(TV7DujTLXV8$je_$-w8jStVq8*
zTtvZ0+J+q!VZ4tt0E-<Uh_fs2{e4lCS~j}m(vs$E^KXaY1TjQ9$6k0))C{{w9BTXm
zaIO-5v-AdgyYmvjR48ws-h1=FP!|RK5rySSs(HEa4;7VU7tDQVxjS?&-~qu4uNFu@
z-nFHB0>zV$L|U=#tYT37Q%_2~_5X{Uf8fC9&K1J+sqEj`;xDJdx0*cqq-XU;WRT%n
z)D@QAJwEx(`ZfWQ|0W6*-5VUVik50$h3H|6=;8D9sd2Qw%}xV-eFn!Em|y!PDdt<|
zn0j{=i%k@L;^lzL%;WQp;kOM9Z|-3lAS6WO&(f0e)E_@C!jW~72F3v^DM8gJAMg-f
zn}`w@N)OC&4F2nWOA7Z-j7Zwp*wkuwoJnR8ozFf<f2Y>K^*Dv?h?5v(N2|q<CJw64
zHyN0i;&st=ycRKLvqW!aJzLMA-{|~bu1D1!)q>f_e~;k*zlS7<bl_$U_oWyV<jj!c
zUWL^I>Is^cK|^OBn?3)765b!u4iV_(hVmJ!&MGcqt?bL2$%RELFY?8UvrnEpVTDpj
z6gHy!9x6hfKZDwt*}ybWvB7_jXBz+h8cG6Gdr}Gd)L`4x&^cTtHgO8PmzZqf<i&>N
z@F}h~6wPbmkFTk3)gGOizF9pdc~BDFD9SX%pqtF>WIGanAp#t18By`^H3;<rw!Nwd
zY_pa7wA`WCppm^Mbpj}=@!j^Gi#=}6yB8&gVx`U2@bHb^4<4}l6cgr$^>TOZ?!)3!
z9kQI%Vjq69fm3=B4@zc*KU}-?iBm_+;dnhMyzg!y>Owo?{_I|@`E*tc5Zb@C;UgG%
z<^qyiEk<igZI*332NyrTtXihkUzv@W)?I^z>&>7nA3=Ps05TBN{je5gg}2|#RexR*
zn;zA6hI!(8aYbe2P#C{6SN3+dbf6sQj6z2>l6kRue_9?sJg=VT)G?MGCIu8{SF9Ml
zd8NExSdxI&+DQIxc}A`Ib4xgqe}86{8xTZ^|3M|1Rg?2bN=7ecW4`z%aTiq}rJ8!P
zyx2om7aFvFc!C0nq2(kP@83vwCLFi*=S9D(!8BaWnSZ_AqnuRWeE2UIl1*uBVX=o!
zpzhj#h~Tq?h&_3zN)E-7DkKi?z(BM${MW$78J9o9tI0O6eKTsvaArgk?fvylW}>)U
ziBmauuC_B3rNO(bf+ta3t{&{DB;gndo@?K}eeF|A;N^zg?Dp*iAQIHD<e6KW<7TD3
zWZo@XwwQmibar;WQTpSqo=im28>?+Sq)zp(nqXvP<i8~JWIB|~#s=EDkl8A+jD~>6
z+y!nV-G}A{7-fh;Tpc_OesqImj?KU3|EAwjbp6qBaiyIrG>;5Qf_9@!W~#5R>3A)Y
zec*0&O-*!S;_fTe$YsC7B>1+~54WfPLbtgDu9ZQpjssvKJ0>DY+b>c!P>dFe5P0m_
zTsIQs=3fiSO9Yk$-dM8k8Jux%TP&<cU5e*m0b4Hr+=jfl`qayQStv;ZttFK(I|<nR
z)FhR8uM?wC4R@0bMZ2Q@`cFI)W-oP9EdFU<9&~6N_BdvXQB`QvO;f$L!g!+x<2HMd
zjl@a`VcLfSFj<~-VsH)p*?(-87AA)l+*oE*aGi-3N_q}Zxm}#u3$tz&hCYAhA2>MM
z1GKO}etEkZ^_$tpZq$uLaH;C?X(cTsF#8&Odv%Qfhg6e#c}2x-UPU1&h+;qK54L|~
z-KFUUQ_APCKi?g@Tte!7adD-upwX+1fqy68%rT_Hi#)W}TmDZp5)nT~!gbU*5wu8Q
z4P8yB(jj{M7T%%#J!N<El*5feflVUvUwpimUz`7<N4mcY5fsWz%Tq7sBF%3doeN9=
zk^_<OVYvQX1Hm0#@}vkuU6KKSR#y8X63X1++CO{0_|HGuTCQ;I*ScBo4a)56wS*ve
zlmmUG`bgIH>Mt9o|F^h(vzzCZ0|yQ$`nat<LWC*iO!ydJia=TS)bA*P;4v4Yev3%|
zlKaiASdPpVap-nmCkg$T32M71Wn><4_=`HG9yw$vg4)2zQ>T^~0lMlTmv2>N7T()f
zvwTSo9sB)WkL2X!grbTNef~L4Q#p-$zHYwD^SAh(o)AvSOiaArvw9nZ`{7!M+_0S;
zOJKFK$(CWB`wY(w_bUJC6Fipx#=!rj(#Jof$p4WRx&K=Cng2Ub+GO+$n0$UA33;=#
zJ}urfyZ;#JjN$Vs?)){k1U5%81hHR)TIc|eGX%(aX+5YWISzM!tV=OMpVv+VeU)=t
z=3af2Mb7+aJ`x~0AvjQVpg!0}taGpqrx261dX9qu1a;^Oxz##zLntB7EO9&^yY9!3
zgLQ*-O$SiOqxv4=zz4WS(+4m5rqH6rChqd3^X%NJ-fYMabntE8h!FriE%e=k?P<yZ
zG}<$mW2iJC(=ThQ>*g*f|Kx&lHiAm9S`=xh`Rk>Z!kNlRt;@7Fs`?|_Q=%_1zgkKP
zzZ{V$zQI{^z00bvQH^HxHUF|uUvYE)+vfhYe3rug7Q$n>l%Cne{88ve!N>1<sY-Uf
zW<}<OFB(2>9Avx_yboHC^MwfI<?H__L(YfbB=~J_x%c@W?Jb9{cA_mUGCjT4{5*vF
zK~(Z>5|m&2p)-UST0XM;tA-sO&i$WNyV$C%-25T#Tlu$MPAY3-WR*BxPvexrrUy!(
zv>VS!Smi7?bq4tf!9J|9*%!n4ykkjhxP8!FyPU@SG86yl>5MXG8Bk0i)lYCzE%ldP
z(+8sur+=3IMQ!bnH`V!ks(eac1wYr;?tsJMl4QdCh{|=N&&s=o-lOoOdd9f(Z5NT(
z${1Jn8SSc=bS`NdNEtQ0JzA*u<E?(3=QmAPmW?+GnD?(~b7m?LbQS$Iet4xA{M5%u
zVU6wKlP6qMVHC5=tvfqAIs6%;>xC!A9;ujq<W2js!pa_+!()YKm@QhV^L`M<7pNpj
zqACpT%|S)S*0-w=e@)X=3=9ltr6nazgBv}EFJ(1cu2FJ?*Jg#gI~UYGJWrFClPkE)
z!U2(-`u^?Pdse-nw|sqBJ$SCD5vz-Ds`mcc-fvVym<=8^NWz(!b>DAmvLLKMyoGv{
zox40AH`M-ix>PLbOwn_UJhcMu-!%f+kga}uMhwyIIs0&{#z61j;o&jAUIXj+lA6C;
zIFx6_aJLz~zS5A8sJ<DmJl$;vFT01)Hv73P-yE~>%pv+is+847JI|(RNP1XDmY<cY
zyApA;_WjvBdET3!qTGTQ7`7g!3tnMECwb`pW~tg5iBC^5^2B#lzW(5TQP;HhRO{IO
z8<y8IM)nT)`jvet`!e!nOcgao!NuitFFqR+v5Lb0mF<>CZD@ahAA%MN*-p_&Xl^Sd
zte|h-oZjqt@M6;EWW#E1Vy49;(D*)l76IT!8bQ~~4o=QD-$Uo#aoRbZ;lgqbbjf^4
zzeF6Ta1(@`Ptx^1-xza#FM!$mXI67B)nr;+eyAe&$fBIFRzKf;W&v=5SpE7zg*pBt
zx_RYLGe8wB#*<<0_0_lMSuIEDL<2~nGT}l&jOU+-{78H0-wm3^*DtJ9M631hN0rE*
zawI7))BpWtJN_ZB)BUZUx_I&5>*S~ZZtuJ@4^{EmNB*i+e^=v!{-OQ(fA^!ezVD<w
z&jxra?Zl1BpY#{Du1dIYB(JU)c0)L656LW`xE&x(RC)ClcNS3?ST5hOT@_St6L)zW
zl8n{$3%gqBC|l<>vr#s$*!2%}*#E;9`rjLaXE94Dn@6X|Yw|iPWyN_d150!(Iek>0
z!P^;$<~GBo+#Jac!*B?D2C#Yh?$z@|6Pup(21>qUm3v}ERAL)3?vYL$&6o2K@uN?g
z@VxIfTBp5}^0=(U!T^j@Io0?H>PEqqZKaW>4Vi|O;deo&!to>V$txBuSRRf^7DO``
zt+<8T))kHxxImLHzFR?>p~jmx&Ft_i(LMCaHv-YPOK1_rJ=}MfqO#!dXq}DsnKQZ?
z#cQ?c@>LO62or^WJ$TQ^CUn@K0pTMqmXk{Q{a_(Clsrng;5kp=^i|S%NqT@esMjy-
z0LLRHdt#y??kWU&MObxsW&WbLS+4~ty#Ha>64D1LjNVz&hIT^P1Jcv)a{)+3=2c0R
zH#UwzDX!H2U<54F`e-_QQUB!4YByJMU*|gZrWy<Rp_3$xbc>Oz`jTNu+9@qgvvC0U
zrwXGle%Ut>#p}(r&9bfLPX3hTeCTKhLaxCk?ttIxjH>EWJ*ar6AqT?s_l!?1^W5VV
z66qlnrlUcyBpt%oR>KDZ#yK^<t-i&g0}5U_Zazh)c#S`#Z3IwKQ|ZkfkV!HA{Og(5
zeSiO0&>$m}6i9C6%NabY<~lXRDq@#lh_(>ZsR~P(fP=O&eK{ima3e-Qcj!bXYczVs
zxzp-nVf?4k9`S4R4%xE+Josq1oG%j{c?!Gx8C^Ivs=x|JdkzuXQCP1=qsDVERyA@G
zz&6={7~tJ#G}Y+TDF}$`bbP_~A+3vIGn1D72P}`7)FpoeJ&Wdv&#q{**|X#bJhPQB
z_o9@n@ov`=baBeShCnO`Inz_H%o2+^r_`f%2<`Xb5<yLuxYF?3dBj1UG?yS(S;}+j
zmEDRbb&A|TGe|2A^an5sIikqt&tLW&Zh2ZLc?x`+M=_=xmbR)mwS!Z6Gb1xjrII~H
z-H{*cxajqo0v|kRwwJ~QcYeu8>dl*Rfl)$%%TkitP_DXvDdqGhbRKX}AsFwJa}x5)
z(>@(k<mk=D9u~zGztGpCe~BO)z5eET^xAyIO6vBKU~W8?8t6$NEgrZ9{n~^JnUGv_
zQU#YBA?+F9&MK|bSO_|LdR_jujYNX1hFgD6u~h3*Rk#t+i*wfWNm_kldV?uS7wfK{
zX`P;&bRu2ub$PCNZMl$Q6`NcpVu?h7Bmgpr+L@2zhuf_YJKyCv3>iXNPyFBj*|ZW|
zjFczZb0>OvYCd&Xb&~!b{86!W8d{7YmI~Rs<QrQHo}t;m%w=G`GbTl+?xArL5WW#O
zosO6Q{jZS)ZoWIt4T$;Np(BHdUaj|cwgfs4KM|Yg-b?x)K@vV4Stc5a>Py)#Y#@CS
zThL&Nc~kFAbiOO<&Bi~!VT82D5jR2cmo%FmWm16#0j+@s!{eW^b#+c@l&3Al?FDpe
z4z*g_g}<&DDbVj(IOdqGR57fjeQ|S9^z;ucks}-k|3`yR8>Z6WmiRuh=njz{*6lrp
zS(3+w>bA*3@jcnPOOe>PQN*c1<9tNn-Ss|32hAEq$dW*zqz2y2gCymB{P>Y@QAo&w
zm%bmJYTcLj5x(VBn>K00P2o<ha5#VwrZEbK1(e@jz@Iz%Y`Cu}9&^@-!vN$!nmMTG
z^sIsx;fBQ~iKy9Kg*v~GC>l2~E{c~fC6Zb;i@Mz*(ws}omYmzRJu=vf{v*Qa;nuE1
zdsY=dGPMGGekekGib~p<@JH0?_RIR=YP+K2bI)RR5yv6ifah$o)NpbFfYJD9vsbZ6
zlEicX<@8E=?U<b=6`@p8lC#E6=Y~55E=$sGL%QsDYZpFGPd^9$1F`PMA!tWEe|~~=
zz=g{O#-SSCg|?U&3r~-`K9_qZG|(BMW?3T0dcY!d`v5w+H_f&Y;X<Nd3_xa3bTLcv
z=-11hPkE?d5#A4uGl(7|wPj+XnqjWND;T1^#JO47=ZmVxau@DBI#2iBqOB*eHN?i-
z*-KFGq*)=tj82`yX$)t8$mQeJML}}QvY(LaL%7>PC9O2?Q;N&VY7jY|30rrzr_`v+
zVQ5yZT-j_Ou9;6EaU6DwpJSC_JaOrVLyp}&i!E?PXgMkWkp&9bLEXM(wV+v}>Td^T
zPkmYq_S}3IPci55_9uD){Z9E{xo7fRrbH|~rU%#eA%YvuA~MwYq%lE57Y#qZ2%AQe
z%lmKU?E_`vq%l>r2Q4d{RMKfw1$<T3v|U(O8PSPIP{pe=r=}d|Nl35!koY2*ge`Ku
z2cj^qUrD&T`vTFi9}iyC_Xu13xJjA-fal6;*Q{S34TGK%PCcl?&a=j`4cy{wZwiYg
zMy7n!Y@XnJguI9EoY0O<WH;KXhlxpBr|-<}mX#|Z?8#CycS*iRK#3%cKxhuA`qffQ
zs!VbLHJ#1NG_)`7@>Ghx-M{3%8%dZsKVLBt7K-+-Xwno*pnfRz0SA>>7m0c-7m$Wq
zr}3Ry*)ONz)PD`x5bgUL-xL-)n7D#DQZs+<RX0PkdvK8J{7D?7a)P#yi8YjzcU2lg
zsVf65%@rS|aS3tQx6W{@9eu<%IjEeuYgtE`%W#rK55|Isom3h!QCmjdk2}|pW*0n6
zJ1U9D&R|<Ds5lMXi{Ca784>~>CpB;M4S%+$38(gV*m<HRpI*M><NbL={MTD{^|JT5
zfU%rvk9DIG63XM-;_qmB*;1BA;&OhBwcO?amsRyx!&`xgi_hxIRe{o#kc;jn!(utM
zqD{GlZ#6f>E3R%{x0MbiOF`8R`{zir5zvE3^O+pZLzvI^9bLsrb0=OreDELy4eEP}
zM^KuND*;5{q>>PW%A5MP<{5N#srTifg>dRrx5_)srbxN%fnBQhE#7sB+BcfYJ_a3{
zA3x&b<frD)SXS{QyJX`0R(AFyknmleEOUoT+Mi@oVsj_0R-~gnIaxqZzdYzwpxML#
z3B&nVQ||G*H=~=2_K-&VZfy_X%JDA#u&E9f%kWfgc+E{uPet4@>B8~Hi2zVd6&Tg2
z(<HY3B7KjDa{l73>Zs0E<MN?LgdxBf&B+b|II6(9_saqOGJ}@>8}YmMtSyRGcw=|{
zgajSy^OxkXgUV717p%iUf*xL3XREn?!+xUu7&`1C-eTn6-rnx9DCQYbFE#3Cgfyq*
zJlC=DpI@WXFU^+p5uVG6c4jPp1Kr~Nmwk@I#^eYD0wdZ!Tz>p`Ez`O^M@c|H4}P(T
zXWr~`(Dn2V8>_4Fxx=Ku#?IZW)vOaVF->DsvaiHwLLrIx;uHPyN15Fs%JXwLd!#DY
z*%AF9>b8yc5vA~!LZX&I%l*g4yR|1Xw_V<+xXE>tmHq0aY-NQzdN&uW3cAetA%=1F
z26+5t0Gkn8LP+TW2L&hr?CPY|>m+@KkS8+0IwMcLY>O&v=Ja^il*6=yhELkEJ)9Y(
zf#Q8hY8_k6tbZ!1PDcW|QMoljP0NEWqhCu9-PwpavZP-Mt>xs<2U3F_N2H8}Q;oW~
zyYDZ`x43N@RT0l>*4%M+g`c~O-&8h0go`TXn!91A!n!^RML{V-hVAY~(K@+1VrX73
z(-@L}^tou0H<#+f?5%Xl@)ho_vU*y)Ea?5oR;g+948(#^qUZ0kW$=Q??^g``giJW-
z&2nP`xu*`js-Xn>`>XZl;sA#e+^$>lD}>P7teF|b^)A_dKsT`K;Uq61KJmrcT3S_6
z3Oo%s@_~2&0&qeFKnW=Ww`?p3%-c@cEJCl{)MpfT5~3&ioTSE^vLES2f52S)l<jz+
zc4c>(>t^kHdB999ef@?B7P|w2!=F)@aFs4H>sPl-9bbjv8u~1RO+XtYU5eYcZ*Tt$
zcukNwwXyAou<LZT7y_onh|dR|VQAUt%*p%m3(OzxK+(4bJT`AuE1k$ph&@@#!PA4Y
zHUdnX(GRWAxm!xT4HXvA5dvQ*?h%^Z6M7#We;aBmnn5V%pmub0z*WJxIcz5Tt}TT!
z<AlbBfclrCTD$M<e%sWX^ZwU}U3HA=9lhsVK>f_ds8*ARi8)gUvmru9D4c2vI96wP
zgA?KIXc(=8Eb5#ped<&djy2(?j-xjs5@`}ur?RAMWLojbpI3EDpLUGU&ydqDsIxH%
z9VRC-cZt{FGX{~%9jA_slWuk(?=2Y9h(H%*WO-m_$R`B$1)$45@5O>=koQPch?7bV
zenJy8JNk$Ub|t&cxRe=ne2VpB-DlcuFak%bJ~Tl{JCEB^G8;_EK?O(-4+ry>DW28D
z?z*{P+S4a^=h}&BFkzW8@tJIyKr!BqS5L~wC<&liPGu=I-Ych2bdVxpPZlu_XIe&J
z3q&?P%NSz$fikI&9zj|tu8B)bEccY7YvEgoqRp<JBiuaqniX6vcL!`)v1^gQ@|Q>>
z5VA6&nKVwpc#Di4ijOh?x@vheLx30XmQ!jTK`&@wD0#B?I3XcrL+UUIaJO(@sy9h9
zz`fQb>3Q`6)b1%(PBV=qtzE>fP}7TE?yICpoZJd#Q{#yaI)w?g5HM@ZHq2SkZ{|9o
zLcF|9P6Kyc(6{kO#pSb(k9Ylb^jMPvK%B%{alBF#=?n^(Su{@KZD~{go`+cDIBv_F
z9AlxJs3n=9Ly|~DzUHG@!20W{OhlvWjB|X^@vs!r24xbyiyQjN)~;(``=hU4T?YuZ
z(+nH*=sc(#Q6F@Z3~)Ara<cw?0?IbIj2;36l!Kf`yph0_ildib;x%(8Hzn(T+)DcD
zP=JsFEd}Cz2Kdzp|Fj%v)YN+xAP0oH<8bR%;an)I$<a$G`X3MPYp{Ai4lN_ihU)d?
z=qinwaUZeCpGQd3C6<(7U2-Jx!T>K656C58E=IkJH=(ZSD{LJ_m>YTL_^)Dqa8Oir
zREXyS+9b5%9hJJ6?oRZd5Tb^&Z#vsQhTQh@QbLR&9uvaybzFMav^TA!EO)t**w=@X
z(TGBTm~RLTiSRT~QsM{Yfje=q=JYJnQb(pA8m>3%B2*W{kZe@LN0PUYpEPj1BA%^A
zA&+>F^tXRsAUw+pyq%^u#E<)dhuzQDU3>k4HG_O9Y+Odx3pPI^*bvHdG&!$kkDNMX
z2Wedaz8Z|~fjw9R{dSb)os*&lhrh2~Mp-U4Zyjk2pm2XfUmbZH%3TkAeGQ?EkcV3W
z@*l;8bk>mLkKVs~*RMJmj6J;`$1}yd4DnDau;-Oc-_MoE?TR?|P<~=l#u8T<>Dj4i
zA*amX!=n$Zp8ZIPQ3OJYc$89n0CB{;@nA86`bJa<NWawp4#e`kZrwWQh0s)H-@>q7
z)qU3;^UI4V66s&Jx>MXvyp&{sqT%-UBFW*U|GR?Nf1ooZth;{U9NRo^OMrrCcK6uY
z+H+Leg|+YnzSz%nSklTWxqFzgsQ^x?TMb({-l5%=oBJf~hm~-PMaE8<RRPaJLPRGT
zqu!U5>19oD;W^-Nn!Kv&&a`@yOIzLNWi`aLx9d|O711g?lJ_$orVqS-m3{Z_kA0~d
zn#XQTu{v>0jU@rvz2actbUL=wQ=%~I`Q}!)cklwmcrH&p*~r1?<I%(51@ut1s0Qie
z%+D(vTzq^o{r&wkKBwI;#R^=f*94)v{=mIKITY&k@*csXhU4KpR&H7K`t|D_X!5MM
z#{a1{K6nB7?tAksQnOrVuUl}O|75g6^dlssB?aYJzcHRym%r5e@s;6Xw)Q{tNElYF
zE5ClB9d++<=s;*3K1Rr`y}f;BMVJS{vPzx15m;l<@MHjJgU$93P&sV9S@s#yz1L>t
zytnR8jYe87WN|sRQUy5_&0<8!#?%MZ_D=M6@g_|Jmy!c#$R6g;{&}S=aOI*3(ke(E
zhR;|9>2ClslOve4UNaD61w)9psULzgdY5o^6oFUd>^;ai0&rvyT7(St3DEya<KI3e
zidDg_7vhr3%tRB~u+<G~6N1~GE$~i+BMaGI&9Nti99^@Dg=ISk4XTVVR2{;sLV_X3
z3ZAb2+np%jPNtE&;6RxzK_`Ft1^&}uQOrvVMb=ml%lrGvDODF&1zCUle)-z9YqU@>
zbw{-bz*8FO$X)bLFXANP7p)4w4_q<63(ZC+X~+mGlT+tD>GT81^K6?}Jw-;Ym|6U$
z@pVy=6pgf&1Hrv%m8hGp0ttu)n+;U=!6lr+`5bxtzfPL(#tq=x(u9wlSik4uO`OB1
zYrW*LK6WIGkI`rS$cZ*!UfVbi?^wH6_tfc`LYaulzEn_0v7(dmi0SsGHIZ^fuQ-Cu
zUS6Rm;}hdaw^FqK^-2dE>=a!U4&sfbid|gHhYtaq4}vnCV=K6-{U9G54qQS)pE}oW
zi8ZJU7ltRddbcJGCkX&H#=q~`=-9m*bh+OBD8p)U7AJtg1?wf$RY9C;qd{DeuMR~I
zUQVd*8J-lsm)dCYqou`O?NMN$Mr+WDY3+lGJ7FWu$jDGEa=W#p;Q6nK{0^%Me#3My
zn0s~N*UvsOe<Kw0xVS|tPqBdbU0B6koV@F5jfKV~nbP1GntM^9O-wCHC?YA0RU(ke
zTyhp+d<q{c6=XE@-uo<m*6q=%)z%mGKK#(qjk9HbeMzmK*=I@JY~sqjWy{O9bu2hO
z#2c6RdCh8`oQBVyt3B@C9eSFgQRzL&eJIns_2~2N$0fRH_0f}JH@&>P9=m+A5*FQ5
z9zr6=m~R}^lPyWE0!!F0zHndfSJL{~Cy|L~4Ye7Evz$G1=6)z+pc-DvD<dz8J&VsU
z$Eh`y{Gp87GfuD!vt7D$X-!1r#>B+LLrvm}R(EW7cSlbty=Y)6xOZXgnuxzl5GI%h
zo}>l^<y*dz4(+a)T-V3JwQburt-|bzqG`O3t|zU4iX!Xjq9+omDi^yi^KC^Yp^<|U
z0hOKhd)&UwZMo1Aw@W%ziD|N|1(u$Xj3ujixVp1c`M}WLe-{YuffSu8d^qJiDzr;C
zitL~gJP9gna_LzoA%-ApFHV|_kMzYK>`gBSJsR9H(U(^PKr;(g6$0WoR2y)jmqIVg
z>RTQj16gma;m>vp|9SHwR$;)^Sa7aVo2Y!0TvzX&NWt-l`Vb^&p~d`pst6#d`_ByZ
z>*cNCnmSBFZHSW^rIwY7FEhL3+qv>Xzsy;Q%G3o1r9#0Thc|eztXTs=)N;-C#U@Db
zo{#wtqM7yg*-F7s5S7^?lxurz@`gFdAu>4JWbw)s*8;GA9OH^@AP-1>uu{SMmUix6
z`PL?c5<63pMG|+Qr2rA~E3O?VxKjN)-sGQxQt6S-ALcI;0n@vd^&LZIt~}$zb{=;u
zOZm5g<^qz?LV@?=yB<DevIVxM|A&b1lrEOhGJs@CB4ZI}5;Oa;hvx&}S3yFRqQO7B
z?oVvcg<@{>`qWG2TU&L+wB7eDE*H;4i4EXTgdAB3o}39{vl{#lCx4R05E$A}rNmir
zC;!B>$JaU}=+4C+TOGu>crnqh?4~t-F*RW~2<Vq@E}Ozxe&x`pRKiU_bP>lQNbd=n
z%L5oK9_oLJc>?y(GGssBU=Ah7N1&1B=9Bf=gIyxvIeOd;zI^C*!HQHYx+h<-xSWQt
z#pjTBVXb;iM|tp7oa81At}aU=EodM~ArS6~<4B!<*V;RCF;K$#Gw=&~0*o-2L}-m>
zm-izuZFc=#4`o$!^Yri_0{nc3JLrWb>L#m@@wt@Lxi$ee-*DXFTp3k92CyC@K|)9;
z&Y`kiu<TE*PGSK9)?J)bsf|#%gO|6YlVPJx^qk2D`Dojp7g8v4|A&iuw|D&hp6VXt
zT#`<!cSpU+uxt}a!^CWDwJs_wRLr=0VJ&l#^GTRim<KX@Y)W6wqXcE1A=O2R*JC(3
zESfZGcCM}F!=ZjS&PK85`5F*A1=D6LVfGgqPsLruwA5{(0mOQecYN#8n9DJ4T>Iy<
zN~}BXwNZg|U+<2R?5KGHT_`<$KVzkQAly??D7PsXK0r+d@ct#)$~c5HpdJUf-F{q?
z17M5RyTZ=35-A2%&+o&JL2}J=6wnk*wUa)#vk0n&IyKIxY<`GQJ6#Bb5Q}q6&$ETp
z!9en+lcBm5lDPt(ge#2eL?@!&I$Q%4kDQ$stu60?yK<i=u=OOt*d>BB=h)_N*~ZO%
z+XWN4{Ng{Gm2TW8=WnpPi)J;H+GO8^=P()zicB>wO0i|+rC^O}2uzv}R%}@{hyxie
z4tpUWP9Ck3g=!v9kA-^s?%kAKOqw~TKgVxLc0ZfP5VrH%Ytxyg3@g0C`1nzg#`!<D
zJshW}Z{Kxx962fRv$1iJ2M!a;sAkrDffEY2(>l<c0obc~nAQC@WHhnoUT9dDG+n=X
zwL)wD3U4KcEL6=<Ct|Fmd}_Abl>Jwj3-ihIuWps-4j|L?{PUMsz<)wuD&)TtnWvXu
zIJmW<f1}&Jm%S`V1h;S9>W}V0nG6RoUG?357AF)qlx{35DN%q(|K=9OXm{&NKg|&j
znl_Vb`SGchY;3W!C;D8t5DcYE@6y(M<!-63(mU?jK2`sX$p1fD-2M-+{6EyqB_CGP
z9X^EH2I*9ev|fZWw2*D`J~)#T9DoP8jrW24lV#d>XKvXqH{BsIgL;#KVGeX#pRBT7
zcRW(}Eh&hHvtjnJJZ^`C)NM~sa&V6`pSZ)|^NsdDn4#uacT~J-b0D=I_(`;m{qaRI
z9Q;71_SfvmI}JFalFUv<KtpNk+|atMkUz!$YS$7H@o4+A*3ErOl-s;P$!-{*0k5Mp
z@^WXl&u^ASp)jofK<CWNxMt@)4uW-s{9<2O<B*#uQci>cl+<HDq^r}+G{Pbx;NzY9
z%qoFZK|G{$1qz5N%?cSz&1wpK&CzrddOESut5F`RhGha}rtnu|A)E$Ct>o~K8XbVT
zazg#C4gM@NO^ZfZK(PotK#r6~?1q~j9?c#q;*ye6eIaUSfQSBZ&4AK%ts<8xnEm0n
z<PM#`wui;%tzu&<u{rv>S(IXSI8x)ZTU<WqG6xk_<;b62q(f$VfZbtGLE^<L(Un6F
zH;h-$QE=%*a<4*Y!Lc~!zYREpQ7dQLCD!O*$ZBwIUB`^aWsY~cNx$)>TbSLle6qYj
zee&v$NlJQig}xHUHie&IXVK$D<h$Jg3DFQZ>;IrTciZ_dvfEyMRHG*+i%=^-UwKsd
z+1W$BIHPoS7Sall2WJm*g^L>9JKi*!)=9;=QDqQZ7393%?q?9`VFo?JkWWnQCuAOC
z2PDT(z!JrCZn-#E%^E76of@fvKun+TXRm<OO^QZp@ByMmmso%AyMzhJQ>X4U>3wZ&
zYx{qgdlPV~+jec7CJoXYm7%gkDiksgO|(=fN@YqBks<R;^CW5|l4Pt%D1}(2@{~jf
zk-0L@nKOUqz3{xxv)^~`?>+YS-~aD>k7FOlezYvV;lA(dyw2;q&I?Uuv&GdS_U_&L
zYyS`-tf7gyxs18=WZp%x+4-_pkB9gS^CRB`IHE<s0imIx;n>5uFIJ8$bzG)Whq5gX
zoPh<Y(BhoPRA@Sb8qaof+fdr&X)Qnn5%C^*Q$y4fq+_+I2e5#}cP5fwPA}e{Q>V)L
z{h>dslbfHq3(7T#RxUY_chMoVM0n@MeUcm9V&*1Q{^h-X6HZsDKbNdSm>)`%eug`<
zf*$O2A~MrAPCYPBt6j_$H$jQPm$nstDR5sfRrEnI$bk4V(R}^<R(d&3eRlCm0Y#T?
zh&A?-o?7QGb_EZm!qF>8x~>l6z-k&tF&4ty7O<**6w+%95zsb%k*Qz5gcN%GI!L2t
zEFoWtuHDze?7FqsY{3e|Y-=!C6>?GV*BAY!+iu@6&u$Jr3hYXWLS_KIC!={7{eq;p
z2m#S+gRD8IX-9{ID58&(kmo2Ud<9drjh#Aucc>u@^And-)YOke9f1Cv)QnIF3h<rQ
z{k~iDSKaUbO?}Y+{s{d~R832a?3gt5z>9oc^6t^+)2C$CPp=hw`b6zH8BVtHvx#e;
zT<3h8M2EyzWZvS{9K8rC{WsM=Emc+VIcHA~>bQ0-d-4PcVd7GP%nnS)K)bz6<&q^n
zgG{OO>@x&}ga+v5uCA$JVZW~}W!Px(GWngt>YrSGZ*ZC(#BTYzm1*Bg;q&CDk|_&#
zfk5J=cYZ@LJC*_yk`^%5E1xnyf1f_9W)@u^WXSKaTP`?Xz|yMs?!JL+9VI^%8ak&C
zGPkJCI(l>KBJxRJPP5+^<y(UzBL(o1A?$-2H<pd|UpH8@$h)_WR_GdTnLKwEWQmMg
zwAs^JuvthN$aIQ3b-`pTr?zZ-%F)F%!1qfCSb=@rba1`DK14J``PS+ceWt@W<}MCT
zuYM-8S(=`Ywi&4?vajm7@~29yg!M1@W{;w=A`gTcN2R6~Ad~oh#o5gf5fLXErhif*
z@|R!u>kFOn;6nH_%vuNss5Auz1b?N4VxS%DRnfW}BYkT68=mb@oNbaNLQKQ<(U4b*
zS$$UwZWGc@+>ho`Ed<!6bQE;kK;L{tn0Q8t2KF-ihEph6@xT)G_YTnMlC+WVzhIUN
zo1CHfB^BvU%Y*lU33@;nB$lO9=$vWs$r*x*#zsa?7K2^2wN1{1jBXl0dFwS?&`1n0
z>hu8TV24%`#h=rcxpOaW9nr1Ih7O8MuS^O&Bg1flG~Y2)1!szOgH|HeNwz5OIhX3b
zHaWQV-1P6SqlC=pL)e!~0^`|i7X%|DXA2)rsrbBYAc3GPa2%h2<s-||X;*;7J8_jB
zjH1fCdGqwjjzKj>i9#-)0)zV8EtZvuF--$VC1+fpP8jB{V_p%0T&5%0{8f2s>?;^J
zQ4c|km>PTc{{80UF_8TK;p?-rvt2A?g7U3%$#eu5ol*}G5s|!7OPKA!gMYhv!P_5D
z@i~rp0}%_@*PKPt63_r8K~GgDiX~;)BCJ_9bb$8rf6V&`KmixRi1}p!P_W>Gbv8q=
z0|!?oizG@FJLnJ~i!GcHTZh}?U?x7--f-$E%{{{$#9R$<QP2THEMM($L`^3PV?#qn
zH!t+W7IXk$vn=Ibd&PjjsNZq6FGrt}VFg#Yo%dq*lp%l~p7M-<n=>&{&}y`sX45Eh
zn^~Jt>@dl@kAxU>%riVI&<8<`#%AK$9<ktZYmtqdzdbN;K}Qig<@F~bpv&Eoq#1Pp
z0&g7WAoGdgBWVW#kS7uZ;z9<bV+PsCr@TC|jp9@Ha_Nz0zNZDgp7=jg$wUDHoG{OU
z(EFxE5!X>Y<D)W2$ZP?(A8(-xk-jat!yrk^?e1n6)e8CVgMcE2+wnUy7%l=rVDYhR
z!~WYKD32WR^X8T_-Fb5QxgEfp=OcnxvN+@>^pVuSn7(Cb0<!^r*bFbE9?x)3Yf6lB
z3fmQ6z(F+alr!L38vOVv6vzBXp#b4EQ(v`yN9OEiB!nca5oBZsYgXq@OWX&keURY@
zD-wfc9Aca7;U7KYmPQ9?*^YqPWXRc(V<QuZh<|TC^<9xIGak%L*^R1;65QTFED-e!
zls9Zw!w;F4#b~p8&D}v(De+ImR1)H1Uy)=Phw6f$oSZJOXVow~?`~62z|U+lQ-wu7
zg_Nt`HPs|p2E&ddP3R`7CdZE-Kdd)D^+owv9N90WVW>h<rf*~Kf;Yyf?B26yc8NUg
z-uULFD^@i0zgn=E`nO==z;^ZGKZ3<v73P%JsKB34Fifa`Yf94q_y;mcg}A#xyH|)q
z;?r0+Fx93Ok3FX?f7aFUQIT9hgfG9~E0K1gl%FO9KlWr4w8jL}R{9zzRUSs|Ir<cn
z-%r5P3)R9Zu`_@8Al1c{jq$4dfeNSJ*CkB3Bz016We@e`)7hh}eASI>#@yu$s5N#W
zoX&#+lw)JG>VL$q&QDgs9(HqaY(0|<IMAk3{z8)U>Q0qluIV=YA6_&1d{T7<h=vNn
z41dCTWb=6F5DEknaL`XJ3tlMn@U}&-CD2P@Lk|mH4r9DF%wPDG@*F8O4(^QRHctmW
z<Ipu%LV<~HB8@z$KZD!~X#HAaUfBJz*v_$|+(iE2`bGEXQ2|D2BJ;SH6L}9|^+&UK
z`VLNj?<%04z_P{zTgg9z5i>6pIck~DZYD4aU6fzpEgb<PU8O4qkpOX|?FrL`^aZ4(
zl2gJok3nyD56-X9Po6;}F`8cp5hsnbV2zyB?_u$|apT#q_9isQ#a;fj6#QYFsqJWF
z{ehqMbntvm<f^A$0D{b@1$yoT&;#&?*qtnJ&+=x=moHcUsQd>30D!;e(M7|^UpqD`
zva_?l;#$&8VzBz^u4j*#(xB{x=i8gAW0Kg?#YK<zk$E(x2zJDj=^3xLJ`}!DxJ+Vs
zZPXcu9E;NO?d2)?CI@*O7?dc8D;rs+xuV4e!+eby<HKgv=d!ur!bM2U8n;1R((6KG
z7nmDCBy^L`FR<t>YL|4+#ngugYhzOs`(1?g--iS<p@YG55=+(V^QM@mwXg5%d5*(a
z8?!ke8idYu8!-qZB^9EXC1M^T%7L8)g-W~xWW4n;5UG9R`lpW{-#d2tRXGX8)FRNB
zxe1>kBK!Fs2Nr}zVp7w1`;xck{6Z8K>CMtaGE8Y9^Fj*jNNHGwhU{SVUd{|6U?o0G
z;M24lPhJ51O&Ugtwwj^9T`g{^89N3uyLAYIDE6OWuzhPeQMiP})(`SkB_*YRe{{z`
zSzyA}OvHnzMF(D4#M9aP)0wYWL(_JJJ-T08-d&#c`~=69#)4qd)HtHUkH!hqaP<7i
z{EPoY*)JFwF#W_zX4}rO#W6%w0?4-7K&K3(<aZ?13Q@`A4Av}xq$&|sVunqV)i|b^
zgibfS=K};mkdq5=ue`%v+_Ypb>K?^pMhhGVzu4T<wFEyCLdG_LRn~+@hGA0WSLhW<
zdE>!0qZ?R}&$?GkU4&=<4C$HDpww{xq_RZxIHVR!Y6}4;+k(gY_&?v@x{C-Y<$r&l
zNo106?!Jq?Nc<8p>Vyorp&52?79x=#-ONPEHFye@<>>~53-_nn5Z`G4<7K~pPYUY8
zh{ou5e1Z<8Xq@K6Ma4J>wyq{9xINQ&>TTau6GJ9Tk>(4ctWp^Lu?U2)KM``1=4XJP
zxxZ&m-2~IlUHE2y6QGpz;nqz=<4U|RiFqy5SUVnUJ#__SXf)0jvNMPx9^&a(<g=fk
zXNCrVn6@BrYW?V&q`6T+{<X|^#%nU3WDF{!(%b8Hkv3q45jhz-fs1bggwz6LClvrF
zS6JFMPG8Kr%^@2lQ=yXxC4U88mU}^F5!Z*WFc3u6#WE5#6|$JtZEl|3yp+t23f*;;
zsDDkM8a4eAcA1Q6z)2U_?>qHT&&4N?Gasjs+XuWMhUY{Q@WGoani%}UbWNi7%%2yq
z)`WmuQt}|CTx7Zvz8+@Qd{j~ZNS_#YA&UwuQVE$wz{2CIj;hmJXXmNErFd=L8{%=<
zkI6t9MC|}gH-dBo=}JH=0+A2ntU-!_GBUAQ9D|Oo6~TBMwvn5F8wk12nl<->+4Kcn
zx{aL+Neq@tv^&NNw15VtML-A4a2g;+4=8{V0X#w*Edu+E_^IHS_Jx)mkG=%9(ujuw
znf_DgifJw%AP8QFE<8gu38w?XhnPp9@Bp8)$QLiv(f)K8M>xF7kE60$U7UaVRSO>K
z7ZPHj+&hEYG6kCp0UzhD1=C-9=?bosG1kZpkemdtlc!F}<-+*T7DDSbNJZi(EyPOo
zQ-Yd`3bA{C4GFidbq*gqvUmRn_&v~3$w!4WKe@mT7<B`cta!Ir8<@hPt;5Y3Z1Bv#
zgK0>Xw@`^}yuc18<7O(>!Jf-LE5NcqjlAymbnB_-;-%`$!?5qZOI&xg@pk=Dt=nuv
zuO@#1v$J@pMF5gBodl;}=l$#RBf3dCLkNdJ3Cklu#o&xez>tCNBN}o5Mn4Ks_N&SD
zYpTzf#`y2Zwo=}+hxCyUn-1u6A^0cGhd2**0w_WlOKv0qI<)tH{4s0Ov@6J3)>*c5
zUQ_Thf`3<|!O8vi4;IpofR+!e5F(Nd3!C9SO(ZCC%s*}?3Hpb$b3l!LvH$k(>)4Un
zT<hAg2NQYMb9XU2E^8;X#xaY)`}YOV9yb+Drz*LMCSY^^Os8+&B=VcTrqgm#UKn13
zFFXr$vp2=+T;AW&1$5{rNRF9mYicep*?b3N^M~HHSMT1v+s1xv&iW<Den}oLRr-s?
ztwaH^5_=kYg_h68EW4$V32*TIATz(vfCBYc>s*8A`U599=KLl9mlEhxMvkLstggI`
zUaj|pN!NOZ$=X9x47MWM$CmK%>)zf%8lI=8IPg%hn%TBf-k^AquA%=QbW#1cS&|+9
zU2f-EytHDZBnHbMcYFe#vke04FQ7tUs2>5IvH3}$#UYdw-4b<?>*4s<{86U{eFAz=
z=GjkMU(7v*@;T{JB_6oRBR*U*PtYo4^8UsOGSCoYDn`g0w2;dZLHSM4l`Tu;f*<?T
z(gzD>LWLxWm<SSvE8b`ysLEV0zYqfv)dm?62bGZtR0$~%qPfcFPv>B#tf#(Xu5KRl
zmMAdA!#LjJplv^d$|(}b+u(Mw#!%r8C4q_-0fR10$0x7;PKj?KjaN`+hu;Yd1T{MS
zor}O=zwNMhu|VnT`2A@D<sFNSqSiYUFZig0cLiTm&<|kqulexY)2|L|O_TX^=PeHr
z4#Q0er}fb&Em2P&J+rF*PbhIE)M!^EdJt8xkH=_MTRieyGN|(KexG1;Zkj?0OE%6F
zWr|(yo&=R7wgu0@8L-+iw40S_O~CdfBM#E!9FogdBo$)pgJJl}u0T~E2qF~`<1y$%
zSPgt`a|W^C{PQ}$FHkj*3{D<!yk~Gm6A4@TXwN?4G6nRsLO$4l!|2J#?=#cEcrHJf
zX%W_4<lczY8Y~W*?<jM3CtComC6ADph^i+MZ#s{5XA_AuU^)WP`zGSns0>+QcgkjQ
z@xwy?(XGO>XhIK%bS`vU+e<h*kYgNImZ)nFM|t1?Fs=|ruX|xFbL6~l{tWWHD0}tc
zJv{wBpHJVdcK<;;vLfi??A`PK-0ylES3=Rr-9jP*nVkbWhm2kqwk8ylH$fyzRv6yq
zrp=0rCS98cl8N)FyHVzZyZv|TWYT{9_f$bhy5Cp0sUX<DxP{g9(cU|tQifJpTUb~q
zYJ@V0Gf$pEbL;u@k>c!DR(5&vfQ<D3>$RD%eT)J?cRu$YoZ>sF-9s4PLxhST669*1
z4@aY;nqhS@hj;zdGQ2c<e&J(ZA$iz+?*aZ}Ywqh=iqa}AqQ1V~lV{pL=G|-!tI>}l
ztmOSbVML-wUz5Uwl)(EYJZFX@MSbu3D^uU8z6@3spH>FT0E0sQe<bC%E13uh86KQH
zh-0YSAp7#===9x3CWSG&%1I#JI5Y(o)-ESj7wE1wMTn+Fpbbj}67%llPEZ8TaY8tb
zTmA!apS=qRnU|OMefG3Rg9u@*S+mg2_{_@b%>SP8mccrF6+X@H?G0tx5=G$X8>`UT
z4vj;Hh)@9dLg3moWLulMmF0h|%k}@{e9lCFbWM1@V6JPoM|2leI%3|cxr^5-&RIWe
z)&|E_w{NOklFGccwk}d6f0gy=w*@k658ex;?5@w-`tgnAPtWjt)&<MOW-h6Zyf&Bj
z7W0mkhO3s$6Y!_hKe~O%R;#SbXMfVXsQkzDXVEDmy+hIT0e1%lm%&$q+Fcs-8piyV
z!&3oD@$gj7u=<KAL4oETjhLAy$`X>2%A_wD#D#{wJ!e>A!=|qKxHn8)fc1^Jwzl?c
z-c`B}0s}|fxgpm&uB~mxv9RQkypMJ3M&Z_tem^2YfA|a*iAzYxczZeLGk=PDnyT)}
zt@Pl*C|wZW_Im2uo<23sG>5}<;;N(ZZHYpo65F>+;}?cy$uC4cJ$mJC%m{sF=y%C&
ztx_xkPapM!xvw{P3Sw2aZSj>DY-GOjLyOOGuIhL=(^sP<)x^wf-@bjho)s3~UaYIG
zu3mA=__c|pWf%RTD>AN@*4Ag|cH`*4x7+IVd5h24>CLv^#>QsD{`<En<*80Kn0};R
zcP%7QXCMC7uuRf>#uoX<bGbalg#Q<h?tlCv2NKv$P@r+LLlE!>vnc?!^b?YF>xwf^
z8$dLu!h1I+i?KgA4$UuI5pVJ(%(Yb`7&RlcTC)Z0LU|}3+cX6^G~U!7J;``;>&R_p
z7H*rZJRAPDLQpVL@nMMDQ>!qcG8rNsYb0idNF$xlVThi!vJ&b2<aa#RJ!aBn2DPg8
z@@SrRO^=@rhN_*xO9)l}h=juiK=mn{eu9F6Ku;fw*~K6=IJV6%yr?O7&l+4mKQ&)r
zVzk2#5MjIV!*fcsdJsz-Mc>GmGnrDEoe7B^1X5p|x(yR~<J(d(9AiHX6R|Ql;@?bu
z>HzuF^P_ht0b*wk!KObq_7#A^1}bKw2CTcuI1@ukADD7wT0yK+LGjqU&0iFXU`h@i
zpvLBvBU>~l`kpF$2El}Z$Y7L%7P&R=y6EKxPj)XJh4fMb#RF4oZ}8as_Vg!D6w(vc
z$k?BSgP|!Fy`G$IWh76(FMI#;T&tqOU)e4#*cV*NN^(t14J8_S0*Z?cVdw`f0$!7~
zc8NlggMEE{+Me#{_p(M0p`Y${Sme3>To%l+8`F(K)xZ6in6ESwzpE)N>^zcv-o(_j
z;>qn#nPL(WWzw@ErQEp5x0YyZ&N_@YopI_c#}>wN?RnR1aod^YV_L7GKe;XUn&G?0
zBbC7_*u?A#QO#L+BI2BGIHwb{msqTsFRw6%3SI*`5H3-2V5u>zYmC57O<W%B(3jJW
zYDy5O;g{|4etv#LptSgow*?m$7d5f!e2t9xe6KYUv0ZF$k$((7lcpx>Sc2pUG|cUH
zQRWdT%4lqCWUf6=N5!(Kj_DTO?3OKCs5D@(r*t~~!;*uG@O(t!oJP^Mc+l)-IX|uI
z&dv8HQT))VGr1C1QzyCjgX7HNk`mv6fdPv4lawm>2HfHe)7uYY!OJ_>JwCJ%B8n(G
z$&z_pcQ&0884H@xw-!m79+gItx7wi~C=wn}r0Q?)vVRkU&g9czFUExQ(_M$wuIHnw
zJf<yJ=%(`cP;J5cljj00H;Fi#%k}S4Q}Z$Vz_jr6{Ds+W-xGzZ*l+I1zC)>WpII=p
z$D@9axn$?eSN*Tn>y$P7q=xtpeoQcyzqX?lWbnxH28c~J)AJ$vrBN)MivzE079uOt
z^SFj5cf`)n+pqGaH&Lz8Tw$K%iieyG7LXQE+cH@RP+C8`dA?(lOl<%@iOvjRO!3@@
zDa8*BbA4Cgew<S1bW!Zc{g|Ef;SMA3W#t0s%*3oyYuL0kqJ^LUvJaZJMY2&~0}Gd}
zI(mEJSMOmIiqzNO1h6hSsN4`(J9?U&82S16&vlZwfsV)pb=Z@blor#THAx97ZNsHt
zC|TMz)W=|%OD_#&?68uDQ?8AX^Boiy@g6uZt~>Ma=9PCTa&mIsyyQ{pTVTTT6Lo<S
z$@Q%}Le-mdZ{o~;uvKy4!kH6Zm9Gz9EDp@T1g;)`inevAgz(a?jYGV0hth#SCVc_A
z?wA#v<KZY1AI8en5LkXl@Lv7w%3s{ciSr*HiqdRb%eW2>r>SD%zld1>Q^&5@S7|2u
zYBYYBa?N0Br=bvM2tYW~lnxAQYiD;@ay?U|H|aRbG{4o&#33}NxOO9+5l~mG-CU*@
z`&1qmc`hhKCrK_LNjR4<X=b08Q~$w{iIx3gOV|IM`#E8C<NZm@2YWmEJ6j<i(cl$C
zY3=B?mKyz)WdlIoghnY)cI_H%A}k%4ubvAk;(@khnmBr!s$vphN~}fvKby$XL*jD$
z@!YFN61=ldBrXuq`5GoX7ML*<n2Huy5;?YEqu;QguYC5!qxr7qmlLnOnj%@<c(d-!
z5Mw=seVl{M^N``!Uq-eqj7uBA)2Y!$D>KHl*<^oZ9=~*SD+c#$TzPY*X=QQo-IFix
zKYH|NzVcz2uhQHX%Y`znG^5jBAKmO7*=8<2yvS{-NYSk)-JJJAEe+os^1Nd3OjEGo
z6wk;}(&WU6n@&des12sxKS@Xs#{xc<;qFEv1$h>#yDpa7Gks6!d(2dFLw+vDf#o3v
z_ldszm1Fl$F>&#RX#FwMii(P1KNxGBhMTz*Yy)gyCRHl(>C+Heh;`Z)x2hy~U#(;H
z+7KQi9P`Aqk7t;2f>O+~b@8y$l})ovIG$k6(-0-+i!x8z=fHcRaDDh$g$^1gsKZ~K
ze1D@oV`5wi{dm2x4r!Yph^e(0i)o@`j{5-&PH42Xw?B=YE7f@al=a)$?1pGCGMXsO
zZ%h$fd2<L#`(TjTO=z+DgeZC+?lcYUuL@#^)1T{S$dlRgJs5RJMtl!F^%#E2iQ&hU
z&tHLJO&_bq3G1ye{+onva&FQnWW|~%Rr{eJAc4Zw$0f>~cP54uCXBshdp@p{;~q0l
zI=Y#>sRG2!AoM=L?5Z;#O=E!)m|rC|)>mv(xPzsm=-ix0{{BM$4LLBdw?>_!33-SU
zIQ+X=ixgzZWZd2>5~-oJ+i|F4Dv>&f`C!Zi-iH5F<~+AMU$!^zq2tf%MqPf<doo`4
z*H!`OSadXb4@-|N6{-3fzi4&do3kQi{d#oKf!3Cm92{vh%3zg^T6qVQHVXO}=GX)8
zCn5faRy#AgkEC?%n^Vi9m160Nmu8JpeiPoqXI$Dg#6j{vr0~0BaSYITayosk8#o9k
zW{IGD|Ba$8eI3hg-PrLXQJSAH(;*o1AI$^H*oE~TLJ8djTI3UGN`KszYhXiKukJX;
zv3}A@@4090N5*bDflao-G|Z;a-h$dr%TANnXjBFoX#0e6P&XOTa{>F^gFf?ySbFCt
zIAljB3X|K21vB2^XP%aiBPI|hvwfhHzsJC<;0E+C9>ZLQxX0n)yga`%S>hoTPHzBp
zZNiPlG96HaPIYT)B@N*dXpi73Km5g~F^4~Rvx`?cYzB;>Y_IWU%2M4jQly6wryfvt
z(?2H+LSaw<>w-r&g;E5%Txv%W&5En5gVcadoNz28IgS0e%9D><d`R9N%FiA4vu_>m
z+n45Hvvwpo+`QG_^GB?oTj7ZhMi-M~7oBcT;7u!dv+<tF*az)f>6S0X&hd?WpbTMb
z`~!6AnD<uTOtglCp$Sbr`H;%=)(oaQ(CufAWn^}$ldU^goC}ViA$VdaxL3lXS7O(|
z@+$mh16s#(K@{{Zn)tOSV7vKo0E$=e#u+6z4)v(sFFx*K;N^2sk4-e2xEJTjN9OT9
z0(T^aS*gY(yatcf^J6_XP1J*fEkJF}A5XBuS{CHbXS|VD6XPRIn7->;J5w6fo*W(6
zy;j;q+^h1(TiA}aQD@k>1<4IoiC*4mK(~ucZhVm1B-fRLhgS1CDT#oo<R4eoSYxPa
z{f$krdw~E|C4XGf0rTvZU58KU!J+Ph4rF0N1rwtM)y})5@7Ep77shO0S#19RX**N-
zk@AAm&?&$ec&~$lgPv>mdkk1t5TMsG_2Vg@iNhxL_(Y)EBG>OTTbi56AHkDMcSTV&
z4<p<AM;r9VjVdZDlg<zlyOF$Il<aK?YIu>wwvQ$YU!Ng7v!L41Mu}ulbL5}=#sYU$
z3oo=)7zx*(p>)By`QjW=p*0`v++LiDdmE`#EAego4bANotsQ?U857K{A#gFqS%}O0
zh$3ST&PYLcwHT56duUrk4FGW8-eEfAVbCXXSB_PqqoZT!*Hs1J8!hjAJit_#W9Q?g
zq4i?W5M98Bhv*nsorhB4S8P&lbf%~K@p?<H2Z-FbeEup43mtMQxVE3-At7~m>_!Ph
zdJj?f=narOcXIyaok(eV**&`1hj5}lq9kKhrM=W6=xG{JO`rlY$D*1_0>PQHXZPbj
z^>B7s4qkVEJlIe$-d<phj@AB-eNyBVkE3uPk3(1u=?uzHy`hWT1OQ`>rP!Vsz_G_z
z6P!MSm>`e0;1-YUV)L<h!@b_h9`3z?m`*G|@Y1It<RY62{tPY*tCSpy4L@%R*ThID
zG4+QXace298AwcG1gP4p_i>K_5Qf*lZ<s&F-eD*fNR32WTD_V6$Z@^Fl(K%^CY+On
zqq!NQ`;hDOVu{K^B(k$SL&|roNO90cxo=<QUiC$cO-Ogqjr-%9nTTNZ;NdWZSi{=;
z6y$w+s!|QU`{7~}XUU1DNtWek*Th)HT?(0|oODK`ZDK+ART7??9@?nZ@)uL%U59$A
zoSK4$Fe~u(AlCU1f^(%Td@)vx1;TveC{m%GGLNycGv?-7==oIsAYa4WvZPPR&0#k`
z)kxVPY2+{ONeD+CXXpRZ2@B>f1rN-=Jg$P<`s2DW_rsA(M|#dLay{|X;n>(${jsn1
zafq;u@F!8FrJ~(en0~7!V+?Sk%W6b>O<s4iT2~N90hWP9!b}b}(=YxOF;!(|cl$mY
z3{~tf>#`e<v2-&VuU8v4q}${D6-YHbyi0Myx&Fz*MPTHy9-SU66Nc4&<M`l?i>v%Q
zI_=VylGSZnt96~M?z;TcPP@2$@kY+KW4;bgylXa?&&JUR7nesM?iyh*l>??O5Ow;;
zuU?5}01EXY&^C6V(A0~jbg67koBeynYfyKjQBc7-l@7yzE~{!dCB(M3jk(>5PEMA(
z%4xlq;0^-D0sFgD>sq*6JDXby;Sv-$(&<-moNvGg5vul#7d&tTN1T<Fm1%~U>|o^I
zg>6BN&wc;f&?-Azfj`Lh5Ce@dPRZ{V7d7UI7UZE`Ckm0(gFOHF>SuOU;{j#JhYhmT
z#59HNA9oBAmS8uEL%(Gsy>`qZW6#adc7OW;iIK_zt}nvOywoitA#c5M?FUozuD>ih
zp_$SZecD6zcKXXQa@*MVGCs0Bd&z+W2yFgpl#IUKo>4{~6gVZ`Kj5rmjRE3~D39g=
zRt6$ZY_tg;(P4xpOl+R{AnxTu9PE5K+ZS0jt9i8b$y<d#1e?<U!<h1he0!9XsqtAX
z3;WZf9Ai}4_u1I=s#Xp-T(#|*2+x?vRcwso#3CpFGuw;N+cM-CyZ43j^t+kX6cy3X
zWh6}2`9PITLACdakQ&y*`Y8$39WzT~bpt6-o$bu1Y{!2AENEB8DrNLlm}{L39OgSg
zdA5f~?Ebm(PQ9{CTL)HqsSLW^YxdbxT{}*|D2%o(JFJ4r<TGECPOvL20+<U0{_`Qn
zfSaWr1Xbl-{Lks(gT>#74GTeWEU#~&EHEG=QHOHp-H6(_i9YrMRLMqkpJuRSEFC#y
zxkzetUiY^<xOXEE`UIQyj?_-b6PgBYGXW^RNHLHbSzG1kbDqN~p(yw4h`uz3%$mFn
z9_=&j#RRxJ$)1;3?xxO{Z6u>{{iT+Np0*~}{QgoNkSo1IrAg^1-21s=9DF|Hhubg&
zTTDzWLR}!@=;ndXThbCt$MsK`rH9Yq{Z`6Vt(Y#Pt=cvE_F}+9REE?Vr0l(Y#cKRH
zLJG{ow|B8o<Bi<o$NMCjj_?gUu3>F-Y`=l;-XFMqi^q_^M*v!GxAMTM33;4N@d1g2
zj)6@5&sV#(oaobQB6IHt==rC7<oqZ@#R_A^>#j%nkxM_)As<Priw|1W{LQRwjr;H>
zF=4$_$M5GCs;Bj5r+KtZj7*GY5FCgg*X**`pvWWCmEwIpN^O3j$b~U@vv-UnAF||x
z_d@_>2(jN6vWjzxmA}c66t7=9l%wWOqXcX{r3CD5e?~jahHC=TFOA_`bg#ot0XS<2
zh90_!=2LgXy1Prq*5h{;k6_{fPv=9o(rrZ$7{)i}_@ze=cD{}wGa-^Y5-X>&eX&I6
z5S%46)4?70IQ={F6tnffQlLg}MeDIyoPiGyc}#Ro9HxAzK4>|1u1j;{fddWv(#^LT
z5|n=4zdszov@oj#Sy(Um)Mo;}Fp<Nu_slOeDfGZ>tsXV61GI<MpEjaNHg9_exw1wq
zdl!=DR}a%kK-HgU^WdY7cXo{n5oZQWZz@c31p;y+EE8b2F|is;>aMIE->(1r9eX^+
z4^y<|4JOEwA<DuWKQ(iQuTYa%7g6~i1KIE58CKUrHsGuB7<5%V(1e{l1RYv-`mLW&
zAi%-I=n1x$oD-BoBR$WbKIO;!6@tC)j&|Q#kZE-&8c%J5>cjiRsxn)sKt2BT*xQ6w
z1YLmjp->O#O9g8PP!haR;|c3Aon6$7SzfdH3t0|Z>m-U^T;&pU?!zi7P1yCv0upg)
zTgt|aA2~gVt6gJ?l?`#G9Mu}LVVfYp?;JT{fQmOkTs?R)^t1Fkk{h1L2h3c3C91Qt
z({+$u``IC5q?#+%;hVwLhPwcN_Hp3humLOvHLG;H9}TARBWKT#Nve5J;MQp~7CQ)6
z`l$9cvXTg3s6rxCxn+dJz4Uw=EjJLVpU8CD&3dVlXmuM&I+jQf<LhG8T!IJU%~|Ne
z{U##W;SY%pW{9jrcYpX0jSod<3`a9(FHql<(7unQZdvf}-6SI%g=i(ecC5PAJZThX
zc#T2u_No?t&pRR9!yzg@x%Q_+PQ~Hym8k_ZfY_&?UarfjV0$i`O#6$6u&0ob^Zu%?
zHr9>p-GgtEhii!=0BTxwj}H#UWXNlB6S=ORI@#)jxUK;mo$Yv?!Z<I9-RL2@1NfJb
z14k=RFN9|0L<%8==x&2Ztu=Zk2g{i}hkQ^13>W}A^g9GAAl!duX5qYzG+&5>A#yeG
z9TV9Tzj>s_f7jWNg@<nKa*|!5=T^t+?QHDA4;I;ke{}^!%7d`$4#`<T0_2gLl}s~m
z!rKbs8EaNPT<PyDMo=*_+6S1KBu+9FqDCP(EcU{FHaNcI!LrcOtxbDm>>0bFT(l8R
zMxZEy`g#a9BA40w$1=4Dtk5`@>NaaQiZvq7Isix(ll6Qx(#WMHyLdEP&Xwo0+O~hK
zsCW=)k+PAnrtYJ4JjQT$AqjDhw?ir~nKj#sl5)n+mz_79TYVN>-if>^5Hz_cQb%RL
z%VfZ+t&qXw4K)P^&s-$?qIO2#Ue0A29#b^dH*q4@h}q@2!wJp*!9*p3muHOPqgI=h
zrywl-c0!yH%$z{xxz<Yfa50t8-!3=u>W-XUx9!AQkV{_+VkUdByac9;HUCU~Im(KO
z7?K@&ocP6xJX__@yz4X9qywzxKY#q6%0K`75e5yWYlhLYyO=z6i%P_PC6ULB)NX~>
z9H!R0Te+FS&(G)+A#Bg_t0HGn!QDtbw0Y$#&}6HXccmdr%E23pMmdI&HDI4W=W@SJ
zNO+u<CP&2q8E1%mN!;7Sadn<&N=%Ho`t0^E&+%{O=H^ryYy={rqoWCN3@vXw260iS
z=+C;3B;zrDD4ePsD|agO{22?2d~l*P3Tn8Y5M#|am-L^EOB+xLRo{!09Z9|^7n|KX
zm->q7p`M4ghFJ&;-cupP6!($J+D4MyVp8;d=nLv`#h;DkdjoVUK0sC^3Wpn6DCx-M
zBkS-Zry6()Ye4YmQbyJLM8v9tvzwK<ohZcKCdnlYr7iyo;p49bf`?0<CLZ&^34H90
zl>sb8^GSl%KDg=q#mPoxk)^jHH%j8GY~Ib|uZMrDBgvgW0vP*O#{PTt7|m2ht6k<^
z@h~VZxg2!l5%d~;HB@u3XYZn_+i6{iefAV5`xZ;5eL`U8$m3TRAc!&<zB|R=zTF2k
zyjeT(d6+q)u&eZJ$zf0K$U{-Sa3aM~3mVKa34;<|O4WIZ(kMjQ@qW@&!^?1fVR_H5
z%zs&?i9D)J$Xm}l`<bN)2}Gj+&XR<v+>Yc>lpN6lS&f&841$;U80wCZEKq_S=*ixB
zP6`8%sI;>+sZ#GxzKr`Odp|LbjGw4SzPFiv7U^Oha+0LGat6j2hCI3Q4wey=)&L_;
z^O!7-rI_iBoRLaH3|Or!YAfG!$Hx#gmztRE#PM#M$M~q=9!-7xc2VJrhTd`zJP39r
zl3}4+!Aw3x+P|VwuPa%6aZF4M&bpYRo5QIn7uFVFU80jY%0R5&9IrJQ26<*&A8rdQ
zpYPVCpj>R%acP<ANBz<F`t3&oL01;IHi{m;5jid<K6Xx0@_5Yx)aADl$}=XbStI8q
zA%J=TK+;ZDMjLDEmmxqhcF5}0B#)?pLZQo^PV(FG*Jm_Vtbe8u-knlfTI#3fF<cBW
zH{n9ykP|JqThk+vdxZVwd&KP3%3@+dDa~Od&rM+Cy*|U1hCQ^2YA^Y%I>n@_xVU(5
z<U~b!DVi7qJSGM`gz0Bd-An#{VZrd&n)U}fLkkDbGc~y3kRtq^ZktGC7&sx5q=Jh`
zdK#b^w!XJMjJq9=l2Cn{64PqN&#gs%o3u(+MC8VA83MhNV{cg@av<yJv`9ehmB!9n
z1^TYXXX3X{0$aEmU-kw}pYhM&t(wiQa~o<{tE5UdobG7-G<&vqN&g9u3dxwoZcncU
zO>h^#H4(XUcganM^xhz;pVxRzxs;Y_SW$-R_zlx+r2Jp?^FK>-_^>c>OG)^bo_PXH
zBJpFOOCs&&mXL8+rP8xFUa)XGo>1%nI#U0!H<Ewm*aVwc@QJlmdcIqNSMvR>4E8W7
z{i2hIxqchTB-s!G;&U;Lt5~9%g>H$cxi`Lntq&EgGRX(S+p$bXTY|^&IysgCxVHyp
zkAXnv5qHRTu(fR;=&wio$wN}C`S@CEE^2Lr=i{T2FLmb;kts^8<LZhuQ9!}#^=I3~
z^iv>s!%yGvR9S-vS9{I_l#J46EgH*7{QdC8lthrk=>HI*T4yq|lzi6O8Xc3C=9Uy^
z1}-~%gD|b0!Jd1e>V`P<{G8aXa2PNAtb11T@`Ix0Ha`R<tCIRI2y;g)9tOxdS(BJe
zRttowG9eF#dKD&m4~jM@8XFs@q@)y8RXvo*82X&RX1sU#{L9P@?~zy9z<lM%y0GD1
z+(!S`4sWp0J9Lw--X-xaY%K4DcOX_9c*{-H9%Sr;GRihx{0(ti7B70zVa5eiFC#nT
zFRY)QHPEhubD2n)xRi7QQM--V#jy<_t!vo52J8SyQ&ua9+TzT&ACbL}OC6~7m>~N#
z@ez+D)<*d0j75^)J#Xmou=L&v#uh&)YOMb_;q~jj6(Pu4Sj1O++M5KD^45c1m$tB7
zE-oXH<z1ASDY3Vn)D=fZMV-;?aFDyjBDgBoL^>ns(P>khZTQZ+=Ns}s$ZK8R+p5v!
zQiv%UPA``c){v}e!QC1j1g@nAPY`yYq(lz%THBKXQJ&q2RSUn3+<pY0^s-$V9nyPg
zsVxUApG_Zsd0aRn?JkpAASl8?QVwjuQU7~;t^{HkT*5Zx_{D{4J;Uk+{O__-?Y(2z
zHm39P<on2gHRiDOT7{BxOcpsOmCz|VF_7<&E=o8~x4~+E{GSB&nFWcD;A#l1Na%Qk
z5K*U|`WuOtoqGu_2RYUuEK+OK;}h5nJ_49}d_>ICYeUy+WC+woMlfbgU^}%JDgcYX
zVC7u*q@<)pk;D~6!t_zxmyyw%CtpgM_M8mSs3omF;f<?~Y3qv;BEa08CkHkiemD{6
zr3RE$2D6(9za2L*95>?Lm6i!t+HkNiHp6-ogqBN6ZP-%zItX|NBCJm&gN9eXHjIms
z&b}H2WA@3&U-!sB7uypO)>umk(k9^-*Y=V8VUl%_6Zbwu&Yl%=C|iYhCNGBIBfQa-
zqxoV63@=*<xp{BlX^v($kzSKu;5k&8Q;rY2RE(6y4G#8|g?md2655kQc2ZVp_;imj
z$*^v@mz36FZp0&U^m?MY8Vu7iNVU7vKz$Di;0bIbHgfPOX)*(+Y=QEDr(`?ObsWm2
zGBBp#E=p7vCR^(GRmFQ90PN%!Jm<@N_nb+1(lTk*XC@&MG)Qd2YXkc^#^*R^)e+na
z|LO2|U)0suu%M4$Ei~Ru{6S7=jUsQ*A~>{6+H2j*{#_36+$}%Yd$|81n{;I9y3E_?
z4p1Q#nd4V?aKS;+8I^vv4rCeSXfHD3nsZnTXdd6E3^l0dL1XB%j-|UgktIMVR=`fi
zLu70{lwxx-x$n&*3yewT2N^t|6hul$SoJY>9GZ8odd_4jVIl5kN|?{%hha1&(yXof
z!A!fu?TK7q5_DJxU)3TwlDk$y3pnCF1*II&LtW%AwY9dc79Wwr#wNft9NzLwGW|Fz
zn^2i}ZpX3fBn<K06oik(Q}8Oqy-qi8KKf#<rDN6_Q!b(w<Mm?v0+3n5b^b?@_=QeV
z>jJB<*(P#jx3Tfl8Db)>n0eRWC7O?a);zdt*DeBB50d+5S_^T-Ua)9n6LB2G6oGG1
zWEnak578k34mbN%ng~0U*5b<TTTv|gj3NVhjD0C(X|bj2@NXhB0+<+*63=H-mMbBG
zQv(Si=TBL~J=-}czWvKbAU|QE$D$>$be-2+Z{F}A8&7M&Aa>La81kq37AAAIDlX;1
zrh|HN4SVF?zx%b;SU_1%T~)5(GGKK)Hg_qJV!eF%<sVY4=xDP{bE3R>vcdA;9O9^h
z=WePRtSrS)qb$g}_%M{yX$Sc)C5J51cWKJ??gd6PUZRjhd{S^RDRwGy`hugL2saX{
zF)<c=uHfndldVY8@SZ7D=%Rv8zC22#QGHHCXw}SR#(Ig<$RTIdr#tMv8frv?A+qg0
zJ?C4Prl8c6<jYo%wXsd_{mHFFiRE>=&&1puM9tC7(zUDliC#<1>7d$65!sKcKK*#p
z@MBsF!1z9=AL}mRX=8B2Kj$L<M!NDp^bsP9i)2ADS_-t9!<lM_v^HIWnG&5ku!%py
z%;i;<Jt4VCV#=I7m)w9z6<j@%PQHAzuZ_$8n?YsT5y5-*73rFS3k4=sB0p4^r3tD;
z>9e-02ZwM2pcayrk6?K#yPO$`w`84{>7~i9Yrj9!nZ3%P){RO-law5iu(8;(!tYiL
zWk}zlt<~3o*ij?nyG{>e;CTCsGx}5+2|lVb@rZ|{BJs>_vN<8^id2ZhPbBAiX@o`q
zSeDTlys9v2K>R;tahG7+K0>)007TtEa(C6r4cd(l^uWiI8vzxUgBEe{VELwmt)+4I
zwXyA_uJ5Es@gWfSM?`2}jN0mIzI|(6_wg^PJ`V1B)`pJ@7#hFey{(0$Y($cEDq){G
zY{yW0N$F}L@rV?wThu%rkkcBSFz?x#P!Zrk8H^<OpM(NYCQAom=v3<SEiNp)?d|P-
z{3^3}182xXm8LyfT4kP<-f$F&t=YU#3pCk(u{~sdVY~BDy>So|(N}Fh@KE3)_g@zS
zq-;)0OG_UTSRRiGqbx!VgIfTkGekcva<qK)%Zz49b({HxGQWblT=}UzDBhc0ZNuCI
z8+sW|S$_>xDL$gOf!76%WLY@jV1Ci*AVYo#UhJ5CIk>kT7)aqx&c;BQkmGk%#R7ez
zAHFD>C;O3eU=L1O<OMjqF**3b@DmY0YNF}8#e1RR*;BJM{o&ZeGNl}Ir9O4iL?Q5E
zNEl}~yAvgb6WVl1{tecI)lo*{$^IN*gI*+4hG<!l*{m20a#af5W+S>#N%b&iZ>{Km
zMC3gf!K`~L{CtJ=r1%r5d8Va0tVMhQw{Cn8J&sc6;EE85v)@D_qts7OaDG7}dQxPj
z^<OW7eqLdn$)ypXjr$a-w&$<rjEO|-S|le%jkj+2v?H4#3KY>1Q1m|t(ip`31}dVk
zcyq@VaBEbz1LF*}s=E37LL*tQN(9&d;|2rsIH*@1k2j>k=Jhru0MU={-K6Ys<?@i+
z->4<~Qy3dHr4vVA&n-(=ULtDQe%Hma;`K&gprMb-@-&JRY+U1E{d<`I1lg~i()H^u
z#l6H8$l`fWX%cBI2V`_JAR+-2C_t-UO_wx0RNw|;Be;^6{6?l#2-DbV!cEZ_NFPOK
z`xJKs8jCkSt`KMe3hY>_<2mx!&^%`h9M2%v{#c?64qj7+F*nFFB@AC;9AtMSU%Ksi
zwJE`E4{h?L2xfY=oZ)Di`D=O{(7y;U|3(b@ANojGR4Ma|HUf@8{Ip}&69F$#{Mza*
zzxkdyi3>Hx)vH~V<QHr6Bw+}`Q%%9IBC8|i{?swDF0#I2T|awDE6I!F72xz61|RRI
z`Ks_`-#J(yrAT5Jq6p3>K)|D>78adty%5GBkJTVSskNm-X)F?&UOfkvo!;^E>EmTw
zmD!ntD14AwB!c{>O-k0nA5Dhew6f~+j?kz=<e>Z3M9Bww!*q!&fJR~*1|`K<%X0lr
z*1pwzQ=z!uA-$H-|MdT({_Nj~WZ>pJox4e);DK3e`48#o0Oalu{~&3SB4D%mqTFT#
zvjwS+*TvR7IsB02zAReD*e)gFNGA}LK-W2>sCvM(+*b|!HtZZ)TnN4KSJrAjD8TnW
z89O%8VSZuUIU;ER+WHo=ohXK|DKOCWE~5Y!?BR||Jo@*GNIEht95y6vD>oQjH6i2o
zXAX<6UTr^dI}chN-~>yjTy5(gm)V~HT7x;j5t)w>HbEagd?4EhvEcAE4zlAy<e{;H
zF7Ok!<0Hl%Ptul$NGDz984nW7b+;HNMu^|?5Ejxl=!92%mx4oYBRzjj?e7ni9AWg0
z|0xa5V;);gNO~KhL;-QDhOJ95bt;511t8RM^e1Y2pm~W(Lz(U|9$fo&d~}MsO3P$K
z*;e$?o5_L;0e;m^+>DO6T*A+A+Lat6A_<(m@t=n$iLLcEe<QYPclML2gQaWT8Y6!$
zt4wB;(T4o1E7%juz{x?rS}M&Nxp7hteB=F3L^Px$wdpjj%HgaY?58r3rICyN6wLo6
zw5ofKTcU6KGmFCSw|NhxM3KH={}3{s8@+ux&=okLxo4Y0XBD9u1MRW#FfpRE)IBM!
ze{Bky{A+jg6Ucyo5TA=Qm!;2hcVd&|whE$#E^^v>e}-+S`t7GHzH?mY_3UBj{#RaM
zk=iBIbAfaLEDxEe#1|*In4_;6iUW;p=n*6OmzMVSJg^|!aKfeAJ$R7F`L4z(B)8mo
zaeBCiJAe=(ueN<TPEn{p?NfMsllr*~rXX|c7U9+{kACd*V5zj`X0#(zY2Ko%{D&RS
zzCU0aA7SBnePn6itIRb#A0gAE8G_*%ORNRHrEPiN%H{U>H}q%vh!cbT^wbP`czH6T
zp`gbg6Vq2rITw~3>8k2~kpLuteg}|SEG71&i=eYJ)<iaZkBf^7IZ@6AY<NBxcwFRo
zOD}mU{}h4g!^!OQd@fA#pwb{hvFII!Z0a6`Q8yb)>4rPNT?)|xtl(wzltl{9FQl6w
z4X4roX3X`vNX1j+2RyrvCU*R(Fn-2CauFs)w}YNFv(cpAvS~f)VQzs&*fk`E(CWbo
zR>pP!d)T0NmXE56U?!-{IK0#qmWSL$>3aiqf<zB=3l_a1Z;`7LDFY8eB8@`$W0LoF
zRAlr9b?pZfB^(3v#e8T*RgE{wZ$xL0ERJJfqsHwv%8XOQ%a(iR{Vi&jQ!)F)2Fm_H
zdOp!0V9Hp0F6pm88{Re|XNEJsUlkJl%FPPRAs30-=CIglax<RJPnm+Y^h(yk|AZ_N
z+-y9=k+724NDcyQL~Cf|8n5ws?}ftHVU04Eoo4#bXUo$ysZQkcsIX%S_G?Bx$fdHf
z=-W5ROD>FOs_rtsaKao!HwmWJ`BF`+j%#4DtVI-D@D9=v*~a+$KXN$#Pf8^J#!3C(
z`N(7|4paolKUrO%KEhU*3SW%u5lgeUqT)CSUr685gDWd4aM&EmG6@+VsoFnstjWA8
zlu6Eu!)uXVsJa#BWER$vDgbG%MJ4q1IB4f9i8`Ov{au^Fu`vAJbUV-AxAoOKSjQwA
zC7w3u7}yQ#ki&H*e<qTprQ5&HXvX^{wEGlDe2NpD8V{{01S4N1*GFza^e6od*{f%+
zQ!dw?1^^z$Qv(nGpF=n$H^Is^5d|HQ=rO9bI3yUdQ5^A3*)lbE%F;UR#uDAYj#{Hb
zrrl~}IuNT|okXxnWaH`5x1;R$!#*wpJXSJ<<2EQrG%mDs+yy_WyLbZ~rJL@lK<z9u
zx(3?!liib&e-_x8Kte{uS`XorAO$k4_{*0&=usi{5hBM)?|Uq41gJR#Jw(u8T9@0H
zMH7@}`Kyc{ZoPD8GSUm%DDtm@PXwbnME)kBt|q1XXipI*D%tUFo{6ngYxJ6S{!(qa
zGUw_tvN=NhZ?p(WLUxSvPU#fnIpW@o{=E~9>NtUrD7YykB_@`NES=m*S)7IR6UZ#6
zG?exY!FHjZp2agRg0$6v(5JSxw)I3DREcsn{*@OiIr7~ks#t(5>Lv15*Oq-S32}wi
zq2@;l8V_x23rJ9f62GT7cwCI^e)uBa06o$EVvGQs;IVdNBY#p_VMn3aLaD$^93q#r
zw6wo8@ZcU9?9?QL9C5<mPEURxsiju7y;b>N5HD;*`|vC}_7mQ<b?Gr<BO<siBh5eZ
zzuzu8jW<SQmpC9v1`AJxCOEw0qH*R9WMWXGNXd-4z>NG8gw#ju%fTgyqOoE}SFwqf
z!1Cqd91Ic!Tnbn*t4eHMF>$Ax+m6bNd|#t9^IxH3j_Uq!<RSa?hYt}Y)kCXy^N7N3
zSl0m;QkjOD=o_LEAvC5?ay^Y;CPX|!@{4CN-c}^KL5xrx(m@N?5TRyMe=M~8s4$wP
z!0O1>L|zrX2yy^r(lf@jGQ=2UD5RHR?eU3;i3k~|6}R3dO*lDz^a}Vhp<U<CO<z@&
z$|zo-j$0|qbYE8j)KR)jaM`~@9GUg43?`yxBm8wB5mnUV<|KS386sKN&{ZX8k--%i
z?U?m2*~b&|^F;21gNXiut9LSy9Nf#liH*aYv}NT@5}i+og=d%A$fJX&NrrJ&{u4Ht
zqjikSAW=x3$EE#cGWH>hxMybqf(?bLtehQ^G?kIQnK_(sPvvo5kc<S$?ubydNi=w9
z|DGEKh`Bi=8b{P#<zQ@3uic%V`HAnF2vI>+qC@iUROFBVb+sRfXbxvVLRbHP46{{i
zPs_-TVd6Mb#T-<BnO*NA6o0`KCm?j28mFdydMA;Pa{?8*W%#Q+CY=Jw2(TmOqHe=|
zKvz$#nHf8qbtb_0LExp|fBZfa_4;!cz#dV<6qT0;z$(oUfPz#HVV?026hM86hT3PM
z=-(Nt-cdM-UonK1v00%EpJ}1+m+VuJV=DVR{r~FO{GyGtmClJ)7m)lj^%N6h9Tn|l
z1hj1_XWgpStXZQWc#rh!{n@1Y*QnOYztsL2T<-rV#sU=2w2;AKPH}!Ero-rn{c8{2
zUvgKD>V;EnKUqh*sAWilAbTv8TphWkY$-{GaZ*JjCp#&clX?%x)Gv-oH&K{>^7t_y
zH69H(whj(u|A>rpL^J)v+l8jkbW{df$&^^vv&8+KU1e)L`BFfQB@$r0%Q$-p0=|}-
zQ};xY!^F#jWe%L#`n57D+M7?#UbXF7gMU=1cPOi_r{sbet=HzpbZUuovt+&5|8Bcz
zb5;D=H$2C7tH{V)oy&0}C1?AFbxU`12tCi^;9VCTx-D^=kp}ZdSm<;PDBCFS>r&|&
z8R;Bx9o^HJu2AFFJ6<(1GUnQquCMU^Lc8MUwJTa@ME#Bua-7S+s94;YOZNXcl$i7F
ztUsHv|KI=du2i<Q{zz(-h$9bTl|+!d+i{@LQY4<SXox!e!|GexFUAhWZ64mQSQ!x&
zMJ1Y}6*u40;>b8CGQXD0bR>rYywV0mc20fViaqK3iydZWXx}{!#od!npHxU`3nI<&
z2$VmMz>x|(M=5kq4R-gZe&@U3h7j`?7*Mh&{2-a}2+iGLjC{oG6r@Xipip&wSRExU
zLAU~ixXE@~{A!s9v-v?;?lP1cm8nH2(N%Vhfy`_H?|YlVC25uU_1pA4?Ax!XZHxAQ
zJ6qem#9`mxnrKju(MrJK9-sEE_%6=9)8q^EFYBrB=kk5`!|Ib!Z2GV%wFnD(PE{$i
zk0EFki*SvDT`d`TczN%-$<e_DC^f2Wopbv}wcV2W0T~~-*sdCJT)FjSmU36>QyC%V
z3zr^U)g0AicfT>a{QQF*f+e#Lb!+J2^1lG6V+f(4>SI_1(Mrn8qfEpe3|z7r?5utk
z6LVllk4n8x2C8U#tppW~IRU?<Su-`fm?KU)=mK*F=q2Izc3rW$cJeT0T9mD0dKBKY
zZiT|v%fTmi*oU0HJ>Tv;`MVFRkNHZz%`YfWqwnvU*K~CA2MOADGoWt0t4zUR+nMTD
z%a@VQo>dxKPu96QyVbH~g)8~eL+`y=WGnc^L~N+*ygN<j`0;1>pKq5gB&Md8mJ+M@
zFiwZkFDqspGO=k^F3h9uo{A3--_`9{Rd?+>%MUBbA&tkKofdWu4lbMr2eysejf;&H
z>ajSar>EDm(*dO|=t#1cyj_GQQP`LYUM~>ud%rbaZmE;lMH^WgXOCE{h<zEUJ1uew
z3j7T?Tw_BQdEe<UcwD;`ul{FSJHOha{q+5mrFTk1kI5A#1zTc38W|LGrgj1axC2Vp
z|5$td{Yhzu&WG7wGKDdnuBZOI8FGu@n|tQQUbt|f{*N=DMS4Uz172EN6@B^gc2)p!
zHq1G7YcJ*9JSj@Qj@5xaI<7fL!J`JZ#LmfEnWa*|+trAXDg8q_ygqxI$J%ULXUt>#
zLx$+YrL}u$O5U6s8DmI`RQ1N7BELrge*2Q}W_mT6k<|$6YD!$HI~u4q`V(7K%EI3r
zjEoVV`UuNeZ9|SLD}{70&lHB9WnE)?DPb@QQz2708ce4xX}<H3or76ltFDB)jSQTD
zg-vUFyKjk2L1SyHDIU8SpdImW%bx79MLz1{I5%`5ai)Z&+S6yTv88atc@5t|`7V9n
zCucN7$=#T?QA~Y1>-7N)-s(qtml<6Dq~Xd<tW?1F!tK!sbHG)5&IC@~*5c(jD9A`R
zXEgow12(#FP1`Ctdb|ANs5JPauKP{d4klF><P`AW`{4D3n2w(l`*ridgVkh8AT0u$
zkj8R(C#}yvV{{LcZ!`+=@H_tE#Q7v<=2hR`oC_yIEvQ8Ru&4zu_u;+3Lo#&inR^uC
zD2P?sBf0+f4xA29z6o4EWoQ@-C~EjQ|AF`>r*sxq=;BI&W{CkY{%Bm*d9^-yC3J)*
z086x=+x<3ci{#?D=(mrhw4ff<FS+!04oIoH317;~8CME(F21&(pd)%ESFaU?U+3OZ
zOKt5yvdTaY-nHw-9K4!3lkfc3QzTsmo@d`-3LpjorIy&xNpCZ?yg8-90RJ@^TYCBL
zx!E6U>Q+ulAn`brMVJ}675-nVS<5EV_y6L@tliQ~nF|5tGAL0Pj$XI-J3k!J$RuS7
znFq(MJTv1yD(XF=;m@Dv)tPwa5#Fr4EjFc9&?o%7J}N`<L&^5gwL5Vx9KD2R6&)R$
zB6uu=44J|msR66SMW&_c!o^VU>I|MA9OW)bvfMr{Tnrup7q)iLYQglH@#MYl$$;hf
zx#Wd;D3&w+_qQLPyjI4{l=i;v9BLuoN>bqO7?0+)=UCsTU+dY~C7>mxrYV7}2H3T@
z>4~!L!i?Ucg%VXENTH%oqSt)r{`0D9)A3jtX=%pzU`XQNdR_!eIy-PL_=qMMuL>wB
ze)ii7-?k^8_6UM0{X(+b*KKH;*CFl>$(>kdVZ=rkd)^rgZBggN3XuZSP7Hz`PPNDA
zQS$fU6_I2MH&igWa2+B}QN(af3}!LRNdd93_$-;XOw0)(k11TCrDGs?`hK>gjSp9y
zH3Tm8boNbSs@rONM@zdd=&f;Yb?TFu2&+xL>00SyBWs$aHm3S1|55fVF<1W8w;t$8
zth{NI>Ml?+ag_<jZiBt*BiSf~W>Yv6Kag`tBxOOE1r~P?pD!EAf2@NcdHSHJHBdzh
zE|ZawvHSIX2bZj)I*j<r%F+kK4<A0fk&|ukn2@fJ(8`<dhto=3s-XjI#>y?maXqqy
z-6xwRjmI=gc2n}cYuR;V;4D%6x_3cv0Erb9l9{hi@+E-q_6;_=e)Q1$Ww(^RR#iQ3
zY&?xjlne_J6c$e6l-QPqTmE{EnlAjJqKnciS713NwB*!nvn*jbANMB9J{gBQmLpc)
zWfC9OmR->w)?M#+w#{k3VwM)dJVaW<X*Gy`Knv6&RPe=JfB*7r1LeFQ+3^}Ho^D4S
z++{Jwl^oX7i$zQ0V_kbt*JKcNEc^A{3?D?)a%0#yB}0%2G?!{ya<vCH$+&-VNNjRO
zIkF<%jZTRQ3)_iYJ&6=SWfdh66})*AVhKrZYGP7|wi|Ic68KiO2Ho6`^VHj$t)nVg
z1AP=e$qGs(S5_Rc@mJT^kJF{wQ)w^;v_nm=6(V0>1CE?cjVP~_z-iC!=O6P9YiTWV
zylsC@7c0gM=V?W%(<?R~qZ7GIObZ05yLV{fB8d>(8C3=i?vi(_yhI1TZ57IKSR^wN
zWSHJG^thJ#Hg=)Hfb4M4L}6Fep7f3^DLwri<VP|a>CW7Po=QiW<-Q+sySZT*QtvP1
zvFPYL$GC@+nQyB`<g!G>P`ca#J7uKlw31$p8#?uS(yKr9lX2Wc_LQ^8O@>p-n&Nb#
z2TcRJiG?pT#=gP9!dW5+J1`i<vbG*WKSK$+HEbbEdRqPH-ljDDu~scUD*RawQkHH_
z3$l$|*t;#K8vfIF&moUi0&e>nc`zK3I(!KUF<_~qVq;YSU9Pfm6~W<nPMSwlQ{`OZ
z@d;w{9_B3p9dIeQSC!PxacSt0>|G;cvt?*gZFwPCLc)Q=LB1mi=e7#FaE}SO;8EPm
z9m>D*C|gQz*zL5odEW=An(tegZz)P)QYpk7xcWZ0zwE?0^&}Cb#98X0efw_y?0yrp
zu5r0ckGVtaG6gl2{$>2HDoJB9g-RN{#o<%3FIE&|OS0YPkcQG$XaqFGRv+LN89KPw
zG)sjO=k#kz3x2xh#S!9rjXncSnx>YPajUTbfGKgOrYN(S{NPgXex=ix<XYS{%3EfZ
zWxCm-Uv^lxv!{|H*UmjpLau=4g8G^xKj#ihaW5;J>s49`h@7B8Bi4-=5b~ifanxRT
zy+Zt^TK9tft32+J#Ick18sqbP!dqS%<}AIOWsrtWDpPawe(8tq7Y?mC9VF|t?U)9Z
z^dO&qTx6s=EGB@dVD^&cH3-ElP3ye^>9$RkAH9CAF?MZzpu4duZfU**d7ctmKUA&o
zbuI4Q#_n4oa??h3L&%cFUraRBWV<h|zjJM+u-<cE<np2cMyQpWDW#>bHs*yWxjM{z
z5sqpSG)m#;p~nCW<Hg^j07HDPGZ~(L$oxLehs{w<U6}TmQs09|M4EFbR3ed^lNRhK
z8;i3!Ix+DGv<uir#UJnRexAKh-{4{LCV`<RwMIe$2?m$f;MDM^bDZj&e<;1hDPSJH
z^}Q7LqIJ6!OTK<3?x{F+%D^gEI?Lc|;8y9;V=N5*;^n449-dTAf5lgZWFdo(ic|lk
zc_4A&JaFJZ#h_NKPQd7yL((V{`Gb(@gEj$JK?UY=iSLc~+&0a6zWV?fRd`X)81^ct
z)|Mz>r0wh6zSw9-*+-+Rj$=j!JSKLKSuav{x7iK7L>*@H)vET%<Tw}fnq}O*pt0tN
z<d@!NhqrGzY_x9oe3sHXeL-{0>2uyV;%A%yu*V$Qf)H+L$Cou1hklN9f~?zxW9OxK
zsm_V*wdD`m{uuL}lX4@Vm5PgcUxBhth25N#g5ndMm&I+H)rs-#v*_qzGMF5MwVpNC
z)7dfG^HfSo9zP1H(heI(^=6Ka&|%#@>z5FpOMRFMVOV@K>-x()Tl1?{M7YX)?V^~;
z75I#)&Oc&)^oM3acx{9Hhx_ykewO*&bA(M23|2Nd?pNf^PDASt5LJThveBB0$uKd;
zTo3}m00`Y|_;Q4wH&*8Bz|Bw_Evx%8%9d%(@_^{f1Tk-{W}>P|{wWf(?29#Ocx03o
z%ve6-PyM=hj`Z)NFHc^^(wM`wOB!wIUm=yn;B9#~a7w>kch7oBZboy%p#8p*T1AB=
zsc&L`c02ZD8ln-380NONwQZ^9;i>5<M#i5}FB6C(Q>~y^15aLS>d7Y;2%p+4FzVZN
zw$tfhUBCaW={vfbkeE2><_2)AF*84Q!;)D?ddab%M(5i5Y!LEV(F)Rj$*23~sfHlt
z7VjOWgUA@#U1{4c{cGXXEHb=@wCc2=o#iWZG&8jm1EWGpuMn5+yA&=tXWPOyio4%o
z%yF|1xY>qCjbOW9f4IoMq_p(O@USzP$xG}(WKA6NE$cRy4Q;F~uj-<>I6u-K4qBd9
z@#5X}N6Hzw=I*Oc?f9PLYHU+b#~BxQHfG@~$?Q`zR!a%4zV$36<@lYwyNQW4sz>|H
zuWtH&@sx6UM}9w^u{gy4?s5EVY*4K}A!x^M5Dl{wY7zS8h4-w#Q-ANXZB=X(NY56u
zuolCPx1^}ZI_G!$%K8csGZd3(5lC^H@l7f1@wsz!<ni}jzC6enZWK__bW5T}fJz+H
z!@fkm`|%>GBXf;Ar*U$gCH8IquN1_sn1!3FI-UCH?TtxW{n|$FTRQ74igVEe4%nRE
z9dY}q3k&BVZS82ZFGr`Q$__;wmE;fX2{N&@7f(f&y&+hTjyY%AiD#aokCQY&(ISS2
zhj&zK=cyw8y&{p@HRL1dMcF5mfw9#Hv1DS?<HwKv25Y~5HF8>GrM3OmE1lb0a7D?L
zT@l|)udL8RB-I_r@!RURDSl&ZpKN}2QTIMPSf6<&^&yas9f->qPl>w9w%%q(L6QLn
zhBaTN0Am~gqU@0j8wVPV1=n%>>LQ*}m`LhWS{CAN5NjaWq|(SKf)n?#?f~2)O3<N$
z;o7o2*IJ@0x>9?+BOb8?!tOP&VovH?Nt_402Qt<i#lx7N#0Y8R2=2oXT(rQMalS1p
z{J8b==g-*SulfpiuyPM|ZnbOGtVTl!ErR&hW2bpR4Lz)5)ZW@kf*YDWSCrL>vhq4z
zb_G<5ZNFRbqVdBT%l$RWI$+1}2=*|1@FL$Z)Nuvz27729niI6dR)0myf*5M#UW3Io
zHRuHFk9!su=T8A#JhwPt4~KE*`r1A^;=5T@GkKXGIBeL5I(HPb`Bc2v(A?x$HFLET
zUw2;-&T{ky8LGEI>Y$HrcG&j(;huEi-)OvxLXE!_4VG{|t`PsOmVkk%ceNBWj$udx
zp6i^dT9nel)J=YsEw+5~V-E3j%=YG5(<gmIeN8FkYNs%Y{2?*f#PkePV84z2+VDce
zT!{1gxG@J<i(TQ;DfD(cd;a`2x1<iRC$7)HIOA?RMrRDlT(5X>F^EJE7etWtwd}y1
z><I?Cw+p)Odn&1t>c;rUAvMJVzkYI`Prl%)Pt4Ol2R;QG6OGkAl}x}MRugkIP(5#f
znFdKFSX^(IzdO+zKy^iIjzq&o*SD1F|21GJz2-R_kea#_IMBZsc-i$U;KGMX!1J9&
zd)-Wdc@H=`?h72^-|YsxVE09C+@Jc7Gtbw!0n5@^zzZdSr^441rphz}`wJislmk8R
z7UqGwAP+>Nd!WbSDzr<*P{8n=U{?!A4{Xp2Kl(qH@xN4g)WmX-Z#`Z8T-G@yGywq9
C2|?2U

literal 48420
zcmdSB2UwHcwk;gQUciE)R7(^E1r?B9EQpb+Aiao4Z%PR@fTCCd4NW=-Ql)nyK}AG5
z(mN`>O9?%2=R*C?-e=!^&wuyb_dNgg`M$@1$(!}AwdR~-jxpBzL{VOP$5y7T7z}0y
z?#iX>7|e!942Jgo=8f>1;s;&v@Ylv$GSZjeZ}_utk>mz{ZMkzr-3EhUeT4p}@#&j1
zz+etyaF@<2JA{sQI{W!if;fI!+3|Otn!~6cI{Sq2@{=ESKQp4A2v6?z{dUDRRO?vv
zr!UomhJ30=Mk-$g8QY4PYCST(EGH{3`ol@@#W&&0o4nrMdhx2~nmym7dzk~xC2r1I
zN&DuN^92`W?CNu0Cw`w6dSnyq)UDg7WF>LKs<7K>X>Q-uH`h<Bjq~*T;Y97VR~XVU
z8osqxdk?vzo5DmMUSGTR3*`qGCiKGn=}t*n^g?mhI%D)VhVueu2l`u5YI7tFdJ(_*
zf8!#QMsD};rN&g(8)LgMk<ypFnEb`&Ix-dX-Im(BWApPJ-MQAem$$gBQkO)V&ah!T
zq}*ohtN)t@_va2@liN06{4($Md_Jj_ciKl{#bKp;b;-?fBsq7gYmO}7HY4rk=PNIK
zLMc`j7p1sAt(3GloHSh@vBHP5{wl?2(3WLl(5#tldPAI&pkUDZ<z*1BQ8m6oQ&V&L
z(;kK1s7Gw&(=`$i_Faw~6OJ{`*6(RH^?o_xQ~6SwEeNK==Kb)-n|E6967^waz8pqP
zZ`DPjUf;8yEMXiNii@b$w(s;mqyKrv;lTUxL_+tSvZH~$F~0Jgq-tLU@t~q5QjrW%
z%dyX2XzI%md&4IU2bYvL9--k=v~gXmkLVvxN|iS=GmC52B~TjQ!MKA2taOekgoj!Y
zMl=rySa!IJ8p6Mq_H;V*cnMJ&l<I41(>k4C_arALw`Ler;47MvH3MP%5~)QiPI2w3
zZ`EGz<x=TQFJ+S<E)PmelrMF!uFxbfyMNGYr&fA5F~7|*Z>=++cAlbb(axS=Zfk2R
z=hjWTd7ey3wmOO<PlrwPrWMn*2lZWx9#uN<e0{B?+(Mw+LSTpEKp2jw;PPXS(8$}&
zDe_z)4~0T8F*B=3ypfb){PlH*b;YTCxCI+ZqbmJiPjh4AoBVvC)y34+V2RafiHXU)
z>j{25ZVMHTR7!JZjrdY;k}lPBw%w|T@7%fA<m6Km@22GgxV3{Hu{j;Lvon-f*P^X$
zU3L`Rc9Ql+4FCAb(nJk^vzG6z^jGSch6Yo;WsTfhB5PoXRSvVQMj{^vMp8)JkJw!u
z;`&N%opBz|H>wuwdG0b3&0ckpUX1S>|8k%1ih(6*EC&rX=$wUyCmm~B^xf2VQ&$5{
zX$CAVE_T2~o0ysoE6>GWi?*Ks{>W-B6w^|;y?5rCN0~iLk7*$0v~h>?L}3uWnac3U
zh|P4BfKdctHUW=o*!g>L_dtB7<o$SxEz_ko>W-a`w}2~Jhxfw-a74JAo$jxyu&EIX
zgdb_?MNdb}f8wz2xLa!Hyb*56cRIg@z%8*d>0>hxX5ZgoQ!{priUvsReQ}X)h(VlI
z@|0SRxq5$1L_}P;6DOC-+b=EY`Zf*Mr2A&-6>7*!<D||-YHMJCKKZJ9cs_NueVRJq
zCLt#$mrrTZ%xu<i-Z8JgHW50o={oGY3F^6)+T(dW-X}G(j#pJx35})|Pr)EkDtJ?J
zS=&20LPf^&j-bdxdySS(@L<G3rI9s!q21DaMZl?wj(nTx1|@~QDuM1w9p^Fs9v=zu
zh~-`m-dmp@k2P7r7C8|YO2j(CY*us)ymOqkF5V%~%f33vZZn!*8jw6UmfJn<kivY<
z{0j;?n`)sreTjM483W^_iR$|swoUbiSoOcBrLW8%2oLd~6OOMKsB5MgxaH`pE;TeY
zH7!pC7FFu|hy}z&xC&d57YtD<JK`;P*eKl55bZGT2<f_|?rk%k7^e6wC{k1)POw7L
zmAp$X5~og`T4-w~2f&+S>&FztD9YhZ!wD>R-qYl-ERK+NJdvOdDGYdvjDPWQTkctI
zji9Rg;B&@|uJUN+oV2j8h;7A09?Qzk?jv<m$MKBS1lZp;-GsS5Sc;0mxvush4F2Hk
zn+Zc$k8%I2Z`Ssnm^0p!o;_UCsxbQ&Bac;t^J1I?)x<LIyQh3bxWkVpqLZKY2(2zp
z+rY-Q7n+55uE=iIX&)-21{sBy@z#j?PFIWYL{LYKa+x(b{jKZe1o~jM#%-4%D`c3r
zW^}kNjiD_{C-}`h!>2tgCnqN>Cnr{!(?8k%!~G5D)<mjushJVflM+j<HB*HX1fFPV
zpK7#YA#n5c7wQ!x!q9}OYTMoL7GE43W6P9Ss#q~EO_@y)=yK@6FD>==_ly2~PwV*Y
z-lntqpP#$*^G38<YPq@c)GF=X9eM2C{Eufi;#|=x^?<mGL&U_u;z()@8argn-p|Zd
zmBsWDp%AZvxm`|X$7El<VAORv6R7Lzm^%kI?0oz4^8+{{`^wKpVon{iR=&+|)qPhf
zMJuW*sXCnl15p0^szoL$LxpUv>*_{8S~px-UQmZz#^yTz9o;`{Q}N8sz*yRJo(+)$
z=PcU%+*StMsz+Nhc?~OHq4b!jHvcY}*)s2t+j6N}C`7fuwtXkR6j9rY`bKnDort}$
zA(rJbJcSK?ElH_lVfNMeQm7uGC)Lsp(u>dEG^`E{!lJN(#h5S`fFFhOA^tMwx||1K
zH(0W{{QQ94J3UZSqgu4sn3a{)E2F!3{hVco;I|q|!AMF)t5HOl<8;3PT9eh4rRg}g
z#W)<Xf}3=bQR~imZ?V}H`S#b;S3ldkSMaUdr071aWbm}7bL}}M`^6woI{*!tUSj!9
zX-?ttR7HJ5gP*2l{+k{i``jB<-P=g?P-^y|)P|)N<b57)OtM*}&T-%%dr$OvurJ?y
zT5=2iLm{$UQM4=rui4y|Cs8Hp>}sAFsz(<Tvgw~WF3Sch$o=l;N37=R@<=6bO=SeN
zHKMQ0EVT+2vEwdY07J^<N_}^hBh2=C;3}JA?@Jozx0I3QfdEZQT!vkdL-~og+bwB&
zCm6P#*!Qf}EVU5rnCEO_1^She<1#dZL1-D)%EcTEFMlkj9p9!bwKSTkfJ&1=<*PIB
zQ4@3X%2M&!beBFZT^zA8m_Ml^oqgep^i&^@n-D7B*N-#Q)z-dHk{3FPMndd`lrfUE
z^4W{orpHUb5X%1opoTz{cX#aA0arY5&Rk7=@`*xkh3<-Va7YL#)G%5?eDSMKe<}<H
zr7(uA54W=l^SLeMx`h-J6uebSKa6JoO`uMji6+|eW#P`_L9m6nDgt>XY~5(Hi$=%r
zqt)t0Vg7sM1rNb(;?mVdTXJAao39M4E=S;qC_RRKR$iL>IDQecqv64<m6EhKOY6Xr
z!{Sh!FV<~}!;Ofhq5Ty5nOm1)6(Yj1j#FhEq{38BPfx8oA2#4}Tq6gc4AsXCpdGqP
zn&l-{9WV~63S239ha(mn;-(gA+{9tir8+IUU|RUIS6eOe-TJE2hLeuU1YmLALjA8;
ztCw0V56->8%*ZZf?LrASRxSkJ))NkB$RLgmMM&`N5#^oQjY^iLkFl)NJVhe5RG8}^
zNGC3UVC41n^;^?Xi29m`ZM+>xU5Hp+z~}CLR>ETa<q(coH2<vVxO3FE*<eT*Uh=v0
z4J#SB?dhrF^B?GswF--U$w{O->&@xW8Al$2TEK=Q!aOF`%%$57)nQMFNT`w$l;Zkf
zeuu8@Tu%2PoD}GGv0-x>j0%P7en7%?nXXheTvP~(nnhEpZUp2GA)a?XFYG&|;itR&
zgVAxJFL1iqjoQ3WZBO<srW1KPps?Lt`}5I2EuxZwm|dKx%2I@UF1z6DC&>vJnwE4P
z*U4v&ka9C`ez=dmV&OZz+pBc_QkCVq9CE?Rv$KaTdftZIURC!-Cew*BEc@!T9?1@N
zmnz<T$@Xb`^_x&E8UV2uwtZQ0ZdaRycF{tW6<*ER%I=2Mk}i#%-P>l@`wb+I`Fm};
zvv{Z~6+A_uo7fx<$^@J&gRp>T&9T*3fq2iUUrj=@^L;y-9jGL@x>S$W*`H1g@z=Cd
z>Rr!&up4O_`?&&9OP+bda}>7%v!Ab;y`A~o$xW=biq)dim0C7c{P`2)CE@Dl<k5_Z
zjWgQ?FdKcWjZ|-xw`H68#;CfLi`6b&$KcHeYoqdK>SSz|Crbf%bd^Eikd#tR)Vm&{
zShsn6O<7l)!d=kS=xSLN%#U+*OMjoTlAX$1TacJUb)1=9*p5~f>I^$|P<ml`Kq7)P
zUce<2$Sr4MGmUSb?PTP6J3H~P^AT_4z2G!RbR4aVUSo@t5nl6_G@Wi~1^mx3#=C>#
z9v^e$1;0N)FwF7C6OO5n?&a8adXvrrLe{sr3uo#=9s7e2_RoOH=PnxC=zg!(wMoT!
zXXhUOmcVss`gV493rj_-OHhvuzJGiu1OVQq-6FS7Y{F5Ygw3YsvzN9GhAf7eFxBwv
zGIa~Ja-9wlTAA<J)GMA#8NZobXKLZLP|lUw`$%G0kBNzCYR+wSF6gYm)yvQKzl7+A
zWB_FpU&&RNe05bPGHmU3VEfrshVk2vcZheELe&q!E|l=Ne(e)3TJGb4Xr6!M))KvR
za^JHxoPkA~u|Cy`kW2lR&O3moy*byg(l-vMsjKG<x{F#~K(B$?c=eTepH;z;rsN7n
z^3R7)SF&+Se7h%_>gwvwc-2d4@0@g}rMps>>-Zp7j5#kUGN1Z-RzxOwjHjJ_q-b?+
zHN3}Lgd0lwc-~IEIy=R#h(7@6;e|u1J#NcBShPV4ij%08UGIcYIg4vw8T45>j+=t+
zDQh(1ZL)5WIN(EjVlw0H;e==(abf4NY}IgB0#RsZ#MtI6i&iM9BCcKPsyQ2P&NY&C
zwes%h`3zXRZ6GtaheMuUK;?{uF!L1vyIeK2Q?A+^aU#%?%vFTnoSs-+nZOaDbBjqx
zFp*?48*WIz<&5hBhRoLSI>oRRY9oQ@7xBn}x=2~bg#^69*t#t{r^q~l^9`B2d}-5I
z_B&04Hkj8LM;fx=Tl5y{C8+h-vhJ0!?tWB&(WT^V<dby@g;!DLMcap!{dt?$0p4Q(
z#jT0G_qTC>fnct0Y6<{6zPenqTDgHy>jj`n7N}uCf_HEEj3g!{RW!q`Vuz+aZm*G8
zbs;ZxPV^%n1~DW;;9Z3RY6QD&VOX^Y@?{p=7^C)jcUH%rA2!+4;W+w!JmVQ}%==#a
zXaL~69C5j-ds)tPHZ#JGB@*(eVae0I3zI%>9VSn`uZ}?_%!dXCC2?ss$J+pP!T?;P
z>I*-zpxCX}j>9<YP;cQ)m6VZ@S)q2XiXnhTOg5^g^Fv=zM9p0dz%G1bB)^bmC-6a1
zA5<d)!dUJXC@N#M?|3;7;pR%3j0?kS5PP3;rF?n1my2jFCazUrYf?%!F-pv)_CXus
zixud!Rnl=9dgINrughs9xx;0;Dj$YY;XN`jvB9aF1v<n0!Pn0+x?Pw05U_*Q^@Wrf
zH|pejOm29yZ7wA{m2e8RE-O6}D@SzaKQiWMl2PM_pue+r^Pxv9M4az2yX6yYr0lFL
zTTLqxb*@{Yd;YV46LlUl%9QeMs$2`2l+KjtZyxlqYA5l_MbwXy6J#Pm!3E~83WjbC
zfMx))gkh_lI5bW6i*9dbp`K}_t5y1|>I6OGAqy%<P;CL9y>nSz>YlJo4qX{#C-yE+
z*VM=fboy=Bw$D6wbUI?yMLJ$cag8GwYs=1uPO_lW2W8LpPCV4)0faPRpG+qx5EAP%
z&^V34?3(C&N;Ybo8smwR4I-izvqp4U7NBK%=M)cNNCtuI3h%KRn69f(<^3*1y)dbd
z?NeCkRhVkg2Rf>hy#_Ucx!tZJ6NCw8n@_u>d;OH;%Yc;7m@R(~2n9iS1jzs|NR&a>
z9D)k(PY?D=4gf8o&?tn_4Ld6}BXAk&wz6Ol$PRTE?WR!Om8n4WjN3RJm&ua18rhr>
z^?NwvE77FyQ4l@zh{gI!psFy0Z5nS)-P#T(&t`|ZE)E5W*jomiRIj^xr~`_xA@C>d
za@%es5KdqUl}_$M?fn4i+@;eTDo*9j4Ba2=b6M_TuR*OFY80O_@>F~7V75m0ViU2X
z6)PKb_MlICGTc1kDWIcpLMSC-b$MW6%*rj~^XJdYAJ~`Adkg+}>@r`>KAz|KA%}^X
z*=A`hcdhG!dG))Iq+)=|ZbDpmFmJWs5s8H|g^lmJSQFk*4WbmkKx-7TFg-AUIxdva
zpF?;n#mQG8aE;mz?Txk6g+fV%z0jB^v`N+SB3P8Dy$&<;1={?KfpF(plTj<6y}V7<
zVIrF-9DaA7OY<+6=q^j4Cb=>~!tMP>^PA1h5!A5ypNMGtUomZ$2-(}vTW${ILHC`c
zy)G@}?#6!uxA%C_lu@z<Qdex-(d`;g%4CR8iwvP2-$?P)IAh)Wg^!O9ivx<v7t$A}
z>ZMc0rl#dTzZd&rN19V>0Hq{pef`z|e+JTzNPAb-MqS*L7Tj41?-0qM7TAqI>lm>1
zF=OK|UtS*Z#WD+6Rzi(V(%O~%(WrOW=6SQgMbGVJFlR(8Fa<yf*(buk-5PC4N4Vbq
zsw1!=N!oP$Yw!Mtd&Vc65QG(IO3@C{U0rN;98QRv0uJtWvF8q<<)6jV^s7JV#R`0~
z&6-gsg#d3R+?{MN-wBkfy(L!W8J?9q_1b_Yx219PI?W96sLWKd6~T<u-rm;c#JBR0
zQRfSQyE+ppWI1t^s@D)P^d(+iUbN!-)|N#zOGfU)HQ364IQzMD7p_|7_XR*3nOUk2
zxoiwx@j_dVXfEq!ZTq!09;l5_0AM0Kg)`Sv0NK#6udQ%eQrnw#gNSKBNz3}<7LMDH
z2GPVii{iW9Iht5lR6P=(i3$Zy16ZZ2uvO<Ldu$qSsJ>A<Y54WElzP5(pS$<38@e%e
zoyJxd2!hHm5doYvpirw^7eHk~V_Ee?>^4Sjmb7#<ehld>jctyO=$-mlIUMoH9*zL*
zfx&1WdEzp_F7`j~$<Q7?{D43K=HwU-RzC1nx*9%g%bsIc9B^#HH}$(MhU(rp0*zV0
z4JAU2PGb3|Wof4V!wVlu4h|`*&Mi%R1Nh_Q^5Ht(0*Jma*V*u|0@`DHoL`g4WZfQn
zqjt+&0K>LU5&Co4V@n$8YBS`|c$dp5Z0&_sIi1~(y3t&V_wnLKR3k9LWx#Xz_s*Ta
zE95+yE={}*3>Zw!m~q$2b3!h(MJHjMIZ5Udg>wiaWO^8V^loTuH2Cy*S0lFv6CXfC
z(?H1`rsn1|AF_QAfjN*4d{)>x-o;pf=8re$zCz0fjge{Kd9TR>`A+6@E6itaU-8_2
z;QH9v_%m=%BhNBmXml^#T-{!DXn#qwFn%|tPNW|AwTfIePS;#ljh>9z2nE&yBRV;q
z6E{3(G)iIxB4Ht<Xdxt{;<680*be4%wn72p*>1&+02hG#;8eYsY;0~mI)C37!V&iW
zjg-e4in_Ypck$l<$1mC)n;3tal9Ezmbi@KMR)~NV+qb89ddTan5pvLM5+)02A%=9a
zWg<UX7E+o_0~PCCF0<{Y3WZdk(dmXcc$1KD0tblihYc<3EYu9ZpMF>0n0Mr*xAiF=
zS$l75_m)WC=S=*ZxD1ztDeh(yw;rdtcYf3ZOO`;T8q<l*)N%)=YGfx?7}%y#rLJ@O
zyL^c*&PgqU8<5xCm%d~Q?BwQ10jsW24>R^`c81xK{;H7ry1KLOjichj!^6S^q48}D
zczf|VyBee3dxzY+7GP@YPPpf=nly`VF#SO(nOJx(dU|~=l@CAKf-PgVdiS-Dr3$FY
zE5uRK1ex|61YZ0pMjZ#ewL1lt3usgfX|Ii>(a6lcF(XppY@3-uv-&%SyTsmC`rZfr
z?BcbZ&T0oW2uFkqIg|QON`mSeffWMEa-;c46cF!IDj8UcY(5__x{dP2%vWh%y^jwN
z)-!=j2K@9C6dDoW`GSP)Oh5auOGN5NHO*(%Yez_kpxDC)Xmh$-T3S{E2RG!snycG5
zg}8m-KSV57%T>uldcxqvu+QEw$B3OmeMn`vm`J8k&3M)_E&IGAH+lXCkZcjZ<1-O1
zCr^YN%-W^H>0h2=13+*fr`@sws47I2zXdo77zr4WP@<1Z?-2u@(}Fwo*908A6iFG=
z39JhsHp+<jXK&V!1H6Wa2@|pVeXpOTHvcEBCmr$`4d$oif1Y5B?S}*ZSwcF!VH27$
z?sD7PBr;i6I+h1d$mWxSM&>_V^#4SKLX`?F%uF{VWVNx~@o7NrM715o%mn}SJw}rB
zhaqT^@LN1|($%t9TgM!C%ygvb6`x%T_cO;WS~K3%=syCMbkT|Z+Mv*cnXQzSqWmd}
z)Tzdg-y)5UOY5Sa9!8VS--ZUEkz23Ul>P;TZw81(oT{{qeT6a3NTV<}mnE8az021<
zKa8T08%o$veT!VkIRnU`v|77JXMbI~T5?Xxjr_JLdL|$|iiL1qC_AfY4~+7DJ=x6d
zrQg+~?JqmSn<eLhgM)?F4MLmVqC@yqO*M1OaWXO$7?t9b>Jh(`!5!rX2FZgpH2G|A
za%Z;p4Q?w3pb{qZ@0{ZQ9ii`!mi7;7AzCNJZIDoj04+(<Bd$}v5tRetbNTYQr$B9k
z>ZhIClTt98h}dvK$}Y5~H`8`Wt|HaSr>PyJ!CX?ZGUwPGmjvZurC&;>hd(6z7l$sM
zwd#5YiAO8{EJ|@(A6`H;hPzn=vD$4WaW`JCX|?X>OId2genJ5|hC{SlY*bWK1!Z{H
zTpoRyJAQXP>v8|9K<>&D1xW|c?;;u3*NXgvp2`q#!vPVzx%kg}^vWcbwfCPdW~mjq
z15oiOu3C8HEu>4!W&(uYG{^_N&=8ajevP)NJ-?|BM7nan6H0i&b2tqRje>Wt^r938
zzY@~)bB>TiQtyRNcWH{{h!jTY4nFOQ4AmHZrjhvY!}%ITZ!-F68;z~NIrEkmrR;8^
zxG5ls93G7n0eiwKf$o<}HKp>1k0{ZDUR)6M@wwB|9AzMKghLnXubqR~VMJ+3QTzZ<
z^Fqor4Wzlz$6sr6@ey4WfQKaQ&yiVx#l(GBfpr}oAzX?Yc$fNhZy{{S2F7gVZ5|%K
zH)sQ+_jti%udl)}9Gczd`_H_tpn#|qjEF3h?NHeQ<PDXp7qD~rTlLJT(af5D#M@Q|
z@!G2lp;^P+;BX)Ryt>l8DuSCTQ*g~_r^L#K6?9s_1Xpa~2z>!vS|4hZUrRw9U6^{#
zE<vtXg1jU<+wHcRQ31(26{zi**G@T&uE(@tD_95bY0-Az8Pd_#)*c?t(K#Fi0v~{?
z8!5(jIQ_2@*xH+#Vikt=QZJ~^Z0y@^t2;B0ik~@N_~v(1k4>zv)pmeZV;VZkxTvT@
zpzaP$Q)NR1&mdfnVBg1WJhse|5zw#*QD^E&T4dA4MDgZy5)=q6Vsg}HXw8$fb|?q_
z3fQwQ^FOg=0ECc|%&E#J^;pLLj~76YpPzs1&Tv8sB%TkCjGaoBn6$q#`|;KizPi5s
z%IM_RRmbZW&2v01VAkG_R;`q~@53f`S)y#nxl<*#(sk#u0W}`QAyHzYU~M`#!uB`-
z3T=>tn=27DbVGAgNEat@XPssSUAxy(<NKS3+_`&zLx98~gC-ED#3;BHOz%mZcY?UH
zj#It&j?<&--BjEu8F&}K(J@@L_|hnA3dypwVENl7i8bmF@|aym7pQbn&-V4be#U*3
z$a447F30YrF4ds>YdPKY#j~B{3c&Yh>5u&)ep7Tg{m%kn1z0n;!VXE!5Mp#{UTfp`
zxQAKPW&@~(ExTLF8VDMM{-Y5eHZftuz9Me5<Qbz8$j>HFG6B!>8kGB?R#XV2Af@9B
zoZ7<ZDw1-$B#SDtOzO{Cv}Ngd0a@#Ucx<4oRpU9)8{9uY<f9ju|62x!f13CHdGbWP
zTjT(ceb>fzZ!9b*5dNL>fnzPLfgU0uH8-iF6)FG-4T;(tJs!{T7rYjLt+Dtzz*O%}
z?eC4U`0*O$;C0o32b6m^MKVa!OoLrHAB>_D(+U^kl{qPnZ^z`Vh8X{+pS*w(d;C^2
zH|0NiyzJ~&@6p+J8w;8N7XZb>fdlCHdmp2{`vy{`f<CwfUIhdM(4;)p4*E4HqsXP#
zRJCZHzl$qVj;#m)U~UE;{%>}2{6qEmYZb2Pj;%8-`S@^~O{ZP69jW?;s<uDq%#js+
zX3Z&80Gg&C!=XMmh|lD@y!b*d3os(UF4AhBMYkd;w+AXxg7Q@}vk|)Hq4I4!?_S7o
z=VKw?u;z+Exp7=s7~pkVSw=<-Km#mkPQT(J47Pc-#OiV=zRjeNX&SM3{gpw;(}1)a
zD2XDl;_B(Qq{!4Us{s&v%I%u910hgU;!V*PySM4n4caFpjZlvv<Aw%UN}}iq#|J`f
z<bUmuv#!hYT6T>$V65s{#@AF*wAAlC{q^0xn1TrCh5M_+La;)ha^-4@+YZ)x^CmKK
zYvn1Kt@(W7kI~plBx{q-g`BgvN`&N>J}L~Ho-MI=Kw@=4K54CciEOK+p>iYomd)YY
zJz&hZ4v0C~(g_bnhT7KQ5Ouxv;Ya*P(L#R+jtI#QI?sT@nm?VOM*hcN<9QBqvBvgA
zsHSKI`E#lE1EEs}i7pgrk<hV(HMs%rPFxnh&28i(HhTp@Dj`U6+8Z0`#by){bnw&8
zfx_00l+)DkZS6@wn*xmh<_%?k$ig{=`y`c=lu%bzV!AQ)dIHc8s2yzOAw+R?HUE=3
zJ2QdAHzelp+6^~=UMLJi7E-D}zww89+6Np@72<zjS@2A`P&KhQ5Tyd=3N5~M00cL{
zP{C!>|AZs__*#S6#l0LmxAo!uu`iZ2W|0FB*MSqQwyA{G7Diy*DHQS7wKo53Fw*u#
zXb@wTsAEPl1)?U9Op9`2UqztN^86(0NEZ9S+!|VK1d&Q>+QZe?!Yz$!kL29@Pfzxs
z3!GHXSW}7OFi6V55#giuLaqRYaXFrionbh8;FkNLY@8syC6mR?n%)k{EMP(0?DD-m
z5=b*vANzj1+!;3qBpEO48LVb^QAkM08n;thTRSi$1IVOys6w-4*MH#JrS!|XgV&s7
zegl{`=oy`ji>R{|P)d=OBA@HSDsm1t1**KQP|-Bvgt4GxTjlzk0SR;sSM(*RBoAA(
z`W<SxX+OYUTL?^~yhOVWp*}(=(!$<Jrl|IR3Q+nz2BTnAj^Y4{bPmmajh42rOqha#
z=g&%VnEZh)J5$P{^kM6-UHA-Buql8?w`<p7g>cdQ`ix(U=~3J`FneR4Ej#kg%yqlD
z5qrU{l62<{ia|{FMNHW3&H}rI5A?1tpgqg4FIf-MkqLd4mhD$591+no6VBaBIm~X*
zA;u^k@BJ#xUIiwAOz5$&nsJqR0NkM6B?8-t4;ERo+TBz&q8l_1i?bf@$VYr=;0Tf#
zLHa*QGKVG02d&*^=uM#E>}YEcSUKYQ*yrDGvIaJV-ni=5sN^wW$<u<}K$@e--^Zg9
z3X^~wQ9Y$TH2^%VLES>07FmFuQ{r^u^WQq~?G~_!<<VwpOj0$fi@^!?`6*4k^l^z9
zE!na*L3o38gWZ2qO6L~-wnd~>TLZ}Nzul($clZPUh4T5&or=iES8D_L6!mFb3|j$a
zi>2NhZ)|S1Uu>Nn4!M=yGLR148kMPLy!2NOmGFif|3+g!%byO4yc3$SBbT4Yqn1%y
zJM4D|Ll~Hu#)-WG(+l*AA<&!=c40(*5WG+y_7B&%V=M3&`tJf%UXe1tMxny?xskFl
zvEp}70S?5L0f$}J(V^A%bYmP))Y}i7jZx5V28|hV%70a3*VlSn0e@k(Ov}+0@6mCn
z+^U@Z!c=o-N>xsKI*&d?%f){ZE!-D2qy6)*89M)k1zW>APRQ5+4BNxbJH<O1xzSE@
z?|k)Fr~VI?QYbhgs4^of56a97KCAOS&{`aezU)<wN(SU+es(~pBo@-0?;o}7JMB8V
zR)1IZn~@c3_SSgyb%R)|LW)YY&_L*zr5o5YGC*}lb{T0_J7r|8xs{&x$L!cCemOyz
z>38AYNK0c&gwz0*uX7yX4r~4uR2e}0ZGGox1L6hIEG7RKQX)Xf4ypmIO}ndRVIU%+
zUS7n4Lxm1au_CFGChRs)j0>Ly$S<S4g*o+0z2(x?ARw<m0YT22<<4%ZHnA57-{D!=
zfdb)>J?MjxVV|-#(TR+vLGJkK04fEv8R+-#za4ZRzXw^-JA#>+IZ18HuV3oX4YE0W
zf5=G0BKZYS_18Y9|Dsycu@8Wta|3$Bd>|e{cwSx9T{Y_t$B>d^V`C8&TF2Ol7|zlf
zh?pEzc7KwwmBNk#XR*KPmrmS2V@g<CV182a`-8D%z|6Fa6(B;pv<5Y-<^EjL;Ct-_
zrL7t{i-1qHER_Ho8j_$|4Zvt*kD!;1O3k?C{Lk-=-1nGbc$`ww)4%GHXTBn~Q#2<%
z{TfCt)d8qqCl71W1Rm3beH%B4T@+fpkA7n5w2Ou|uzO`Tca7JF{>hg^ft%C?E$=i4
z=4-7T^ykn+Wat%pih}VIeRnuvwp9ys+#oO_$-A!Y9Zcg9_wfq}%H_xoiact_T7x7$
z?&dIIyJ37T^t&fHLhoESCKv3tjXUq)vwf$^r}`>*0grJ{3?O@NAXp|~a=lrCz@#*{
z@+3{O9%1(aRm9#l0YM$I^oPBzw>?&v4z<D@Xh?UiTdMuPQVUQ#`vC_;ln(!WU9AAV
zB7eMKWXf%IVzug;G^>BA8+Al1xHvx)$a+H%xz}Vl*b|ntgWzU7j=UOy6{^V^jd4sZ
z1PjFN9#!<1k@|b0DPrOCIjgQv1o7W`YKJwS_|v0C&{~UKwHi85{*}>BNoI*uK=W2V
zu$E1Hb<{|rq9H+v*QUR+p^;+63k-xSjtHfN{?Ze)H!&XfF;2|wo;Aoai%3mSS6sU%
zKm{UVk#As*uvT-n?~DC59|qz$m{ZOH@>4^M@^Z1na<Th^8n8Uo{3v<m8&mOY|2lNd
z(Wjv_Vb$NKQ5rx}oETk1GuTE^Hb8D15$)3nPz2F>NHJ>rVTFD?K7!1|sfQ_RkhkHm
zktByBYW#uhK8iyj5Ng+?uASnHJfrk0-{^%`{<d{J13Rc1n2b#+H_^AdpO?f?xqtbi
zJGr}?5Zi+cx=3mPav63hqV0jd5C=k`3_#&4<OF+oA*$}pudio-%z{=4#2W$|_>1Oc
zBgDn(;}qE6C!*_PHVW8CuD$c$<iY<JQubdpHa-<kGouxy2=#FLStn2H8L67RTQ{^P
zT{Zvb^<~}Q`P-n@LG+%#jIiKfe$>TBhM+Pva_cL$Wf;grl+v+_i~d(Q*y9TXo6^|s
z{kKtZem0JB57c%X5!eU{*OksNaDe?PnF8Rtgem{~l6eaf2!Sn0dg#>e<Y3~);m&r6
z<$1j(x{ng?>`k>m*F$E5_Lkx}3hM?M2<Q=MNz=k_P;%K=z_Vy0new++7&!c3ZHfMW
z#wN7*zl9DCVz3OI2;etsx{ZL{La#4!TE(fIR3V9?VY`Q2#fVAjKvvKK(>wH_BY9&`
zv1-uS1SD;PCrK8{(q69h=-n;(vLDW1y8b_^m8OAfwJ1$#OXQ~a;J>bGYYRq_1{7YK
zI^Yzbluq`P4kRX}Tm{<%BKfTQ%1r~&VH-?Z{pQG}1Z7SGK*Bgg6N3hWXiAf~umG%j
z-ktCA$!pawG8p8aEc{p-WXTh_vv+@d${0b$AMiSY(l3(CjV|VHYP0L3s}n?|FBa+7
z>1roQ)epApA!5NPHa1HJUZWl`7hZjhHgwUIoESuDtrPHKm}^eqqG9`3OJMr{l9rVK
z8BG)C%6>L56N&;BhjJ!BHg1n$*s;8Sr!&^qn6`F;RRH94lx}NwK%)t_mW)5(QMXpl
z@ZeWXNS)jDFnpSP84;BY*L54NpW04`Ht7cRlVHI}+$`-V07UNQ)^m3(VcE{QceNrn
z{jW0pKIQW-HZhuKf8`7hk^;Umj>@hZbTyT@m_%S3v<B8d#C3dpW^Q##cSQ!}){F}9
zLf58dmf`I|s=#GXUcZE3KpH=Y)*?E-@WUUeQB}2hQc4dxO^`hjk+>hxtfZ+KLeOCc
z{Wa90yBU5jOgLvS*Uvlk-niSFpy$>s0O_trtz(I)y=|5<NRbkWc)95^oa})yG4Y!!
z6K^j}^=YRdehI5tJUm|Qy3$fA9^gY5)z{|M1&=9v((R7CyI?>SL<a}_lcH3fvk099
zh5*c)-LCDMREby+XOZKZm~1nhTlA=21b${sP3>w+)AKkO<AaRvfMc*_U@c&Ec!@<K
z9kLski<#U4AqQ!w-~SAkG*<;ug0Oc9$djSqN{uVudyNaJVz6hgC-hRJHvJxbHZL>f
zApc+Tqk%*Atr9qZ0evH>et4}i#h(K<#S09_J=;^kv6nn53;<D{$YN7@=CADuO}Y|@
z^uSsYvEY20!&e|$ZE2!N0*8)hj5V~OZH!68VZ<=#YzgQKqKkvks0TzF?e<7$RiKWO
z2`CC@5nBN~dAa|uvM9=ZK@fw^C4k*i6url=COoV))5u<9<sQAuL2$m`^-o-@Eb(+S
zGZ>-yZyw0FXaIud8g&FdFvP+mcaXIB)JrJUrh#j%!dg_<!VwT&XlKAB9o_b;FIN6b
zZ+Z+!iSq_&?cEr>*njs4uD^J{22~QqY0bcvErW4qLzd22FvN*_QER|=$0r~_e@6<V
zcwo&gIgsu>iP?X0F>_6UL#~~p7|9a<t4EhaG5Bq>_LxyRS<pP;&;Q+4X?_*X3EDDH
zsH>CBIyMj2U*Z37XV=b;+NU3%ijRE!I`aN2r-WQxsb`eyQQ0wB8x(G&`O8qRXDeDH
zZ{7UVUG1r&V*L926P#P0y1O5Gpn34nT;~GyeC9F3b696Ryzf8{OZ5T6{>-JWGNXR6
z>8{oq4CdoU6FUs%!UP;PiS8oIbuvwxp9_H=iG?!WsR@c)Sb_b7<uzNcdgy*)lQnZq
z;pBiRw7!ksF_`MeGrd=ntZU#@+8e7PstQ<&uU)us!K1BdZ)TA}uNr2vgimQS0A91c
za{o3mf%Nq0(}vel+w6wwq#*K?id?C-<M{&`tjFAsOVKbHKM-Z}5oJ?IUVsB3<yBRU
z0HN(l_Nor%7EzO7R2z1k6W_acZ)`gYW@ix7#gr`0x%qZH<(g{cO2A8fnuG7;Lj|M2
zJw5#Gg9m><yRhw*M|7-pAj}-a@f0m*wB=ZsNj=8AX4zmIxhv$f-u)EvqA8B3x1W=n
zy9r!dR>1hmB&nu0(YwuG1cdSF$8j1=8eRTwOs(Gm&nRF*l{e9|H9(b2h4PgI?c38Q
zPhK{U+_-k_C4QnS8A!}>OCttV%)Kbg4!&EB6D=97=2x#=sRiF(3?zCJuxvVwWjE)=
z)4c>SOO16M<{;*4C|C5Ex%Ink_*~^NkN0h&W2p@nbMCbjrodJ`aqe7GZsqqo$4{KV
z5f*2zMTiqlKHn{w6%E%vuc~$T;XZDy_(JEo@)63$8M&O&9$=aXG2mx11sAWd(@Z>(
zNNmWp((SaZmm6+L?>V5WUlT5-@y;O~4yVKb<{o}GU0vT7sGD*v`f>uWo7qjN;{CVb
zestWZb`~vZ=NA?h7JJxN<H>|2_!3h9Zp0Wc#|4omz??_{eP<X<h0X3Xw5BJM-o9-B
zc5epyqCr>y)ApQXka9UukMA_sY0t0!dAnSw;5Cm;^eJCcQYKF%m1=8fB<9-;41;c2
z3m~}RjSOQakWvM3jE8E;QG7Z}uXy7r?E+;u?iRGYSTWbKlh0-T&JGsA+L?)gNe8I@
z8mDy0TDmTi4>`HG`~w1FSEx%P!DcDfNcb_+WcJbSJs2im#ont4hIGeIo@{`FWB$Ir
z(La9Nf-R|lvb(VD;6WTG{1b=)*-jKwRxu}KAZO}Vm)ursCe#{MtQ81TZy190-7GCr
zCV1g1hCv5x09$L+^XJbumS_bQwDIwE(_l=6?|gH|Ca9+B@IS5jzR#F$>#S2MS?IcH
z3&k|;?#r6mmTT@CgCiB68!W)Onm~WR-w#(ah1#b<cF707MRdC*sVXa3Z@&|$2iZVl
zFip>mwnmFN&ki=K>NW!10rL~cRFU*KR4d!mANUG&v!K?j1F6kiKBY&BPn%ZqoH+3n
ztSYJ0rMabk0yIC1Qx!ZEXa;41f`X<TM5mkzvvo4j%>U3K;gjL$g^GTmMSG4(dyYi`
zh{SflEi{{Is;eg;FDFEcrq==}0d??3v@{@$WPUDt4Vw(a-fhq%8}YX(TXX|hGy`iF
zzkKlh1?P2=`V5J8Ba*D&!`UBx0HI7my)dL^sA#F+7CB#5R#pHN@`i@SuqN4VM8%@B
z;06e+H&$1uW`#LL9ZKCU2S(FZJMFt00s3j`cR|U{cBMLbv5KgutE*GI^SSK^)t>z#
zAi_*eP7>wWMLZThW=GvGJAY4S;zqWZlK1w0C;k_CUyO{m9+#w*s|6l`17`}xi}xJ+
zO-xLJ`AmpFKfj5I!M2X!_cAh$fDgV2460Gk?GI*Da4R`EIVI;nzgP!bqLEB+rh>`(
zdYpV%uXVn%meyMU)W*=4F9LrM1q$d1ZtfJ7C7@A<VcUIubw(f3+p`yYw}iVcnv8W9
z>B96j!Z)Nqyj>vzT8QcfUv##-B-1d;w<&NmuoH6bRe5<8DXE8D&J$vCL1(4Vd1zbc
zKaZa|lYSWq4G>EfDCfdSy)Aa*9qPm^`w1QDxP4K5b(jbRERBQETK>!iiS!u=w!GO^
z-8#^xsyI0njN(ZsG=Q!LPCz$1NBy*$0>=3Hg_m4OA>xDra3bzQA$L_)RvCwT0vI6~
z4(k|0$B-BwUkhP91C((*+$SdwPjiSBIRVWQ2>W$luxtD#SnbE-AvP-kauPTxTw@?B
zF5OzGaa)N-M;OsQZiM6mz~sa?3;eNry=c{L*fX(9xN5nrOh<sYW#72O$oU~{aWOiD
z<-jP#^ddwG!|D5?wGaFt)PO)}T3(nwii1NsX75w^oo0-8F!5ahY1^;(`%kYBp_}h9
zVj#v7V*m(}R7gbPZghg$6udXk*ld8)Dsq;&?ai*x{W*b23+=kOI2lH5+m}F#oyj(D
zJ$w7fJhbdi;OXwWBRB(*nLt>YwH9Q7|AxB?NuUAlopDAo9{N0iL(no;6B0Sa>(nCE
z>K!N;v5-Whi8GKB;(?$fzY@le#uvdE1^E0T5L+{F2a9cE9r;Sc8E~8=YLoEfBA-#!
zr^|o502O`LAy*+Ao5sU&$<=U7ZOenRsw(zX{LO=K{#X?nh})U14D9Tw;H|rYCuu`P
zyRM-Td`v!cu)^ZZnKK1&a1GVaVertGS5%n&bPhkEyU}?RWX5R<94d*rT#3LZI1xB-
zhswh2wqQ}Vg7t2Ib95(%W_IoZ-@UIT9Abr5S`H+aI%v>8PLWk~-R3J^M&;>P9G^N6
zyl!i)<dJg*<qS|3FF}*=6;)XCmeX<(;@U(w?@Ya-s!Om_>&k^$Aj;G4nIy0iW?sJ2
zNd=doO39~BuX2juG!Qmy<v?l>`07M@yD@Gsqr{k(N2ECq9g1q2f&iIk^~fYwdbYgi
zWnN8k+NCZ`tv4H*5sZ<5!&|1=AWH=SeRcweP`#CFBkMMj{RACn7pEOnjOJilShCgv
z6so)?(MQEmI)zR+B2oxn_2(ppI>7pGEHXO1kRe{W0{NhMCS?@(+RJrAda>o@<?EbQ
z7dP9HY29N@;kX`IH|0tlj4BIuhZuCt1$_aUwqUXyL^#@PGSq8?-c9H%Lvzv&De;R+
z?{~6D9#JK*Btrj|2+6+y9QbxXzHZe1)Jd{W1!*azDnx)wKtL663xB{wdv2;q2&)MG
z3K4|g=pvAL?Z_+Oa?lrEg-X2b^sUuIB4MJdkeXrx;*QzU>=;dv^wq0Rhn5qqGLw9k
z)Wwcv-jztYZD=^`CD0Xr_ku&KL7)j#k3~39fQ~<!!M&D$`J#CB>eVobZo1xyEf}x!
zf;I+w-GmMb38@qPf`d&9pe64oGAJI(6<;{}O24S+JHHlpAe6#L81o1q`>QfCW@_Ik
z48~+yOhdrSmr-zvU<jDKHn=5}!$Lz@z$_=_JU8Bm>J4C-L5P6ut<D;b)bBjfo<E$j
zqkbn~uNG*xKjH(YVfx;}ab|YA6#dzqo*+ue5QjXL$u-@=+<_yNH0Koc^rEnh3-z&y
zhtEeYhl$Q}S%!)_UMKqb`Ss56C$U|E4*v<$G}x<CQ4WF%LFp9wh9{w<08N(0DUGc7
zN#T1dXfHhERaF_7P}B8iW(qzs+#|biwng8q(IcrcCML#sZoD&_thM8f<P<9=(sIj|
zEhx|d<EDgI7T`D(!W&S!MmQIeAdQ&s`{#|#>?_u{kEf_IKXvhpbzjF<`}pC=`3(rE
z!LfPz{7y@hiIu>w((qQD7ph9D`8h{Xw(h*o-Ksafg^4<5a2G94xpzsl)EM9oEA4Ft
z-ETQ3NvBtgx$<d6x2m!-HUa-q_~=oo*49=g3D<YCW9@Nc?YU-<&VxnlQxFuR!tyEu
zraQV}Zf?E1aq}zoVWnoHZ6)bNF)^Pe7;{U#S$(lZ61sH$o+!%}^ILUk0sTL-HyV;u
z|D38AW$*Zi&muz#weO3fR_eyxj0@YnSm&l_jnfh*i6|MHeS3co%FwLX@$-qKn{80<
zaZ$`XBTI=9SLS7YI?s+K@TWrRBbrKVPAh;tz3G5<v2qEx#`YDBC{s^y=Y3mO-v{Xt
z`0%<N0v(ZPk-k4-4*<9TCz&-y_}2kCz`RyE?oD0!ocI>7Vv05iOAtsg$ZO<)l(bHX
zY8a^ae2iU|ZtDy{r*(xoJ8o$8^75%LMAGo7HP)Df$xpLiJE@gdYt>#^DT}LvJd)JW
z(ea|xFtgPbYK_cUcHa@$rHgO^L)*-?QO(fm-J~Z3g6XLm=q13?zx#*r2p*o%ffFuF
zP8`Q3jl;Xi76f>PM#BhylK23iN5V8hF~7#iL!N&<RO7P>bAN>hPO7MumX?FBw}+}j
zSgh}dyPA@F^y68{O>4!-W@pOYL5gYKkHZmB>5+iW^}vi=3rxZc(8~Pr+LteTVuzq{
zXoj11VT?fA3<P0*w-uKni#>bx*g@+L2@4Lm4uk7o>F(WhD@RAieDXp+a}}8LzinCp
zfkBoy1Gbc55HE=GJ~i3SbCzOYfQs$DvI^ELRI8N%xxpE(OVB)>-F{IgRaMt@;SD;x
z>V9j>Wq5D`7(-NXL~w1;jCB=i!oPd!83XVl*VLuzO1z1STWTwWgV-7XE`gFAU3v@e
zK>ZVt6Ig=%tzTO&_F%2cfwBvCM_0k+fX0&;87WyeBocvU$r!W-qI`GjKKUJ0_TW~@
zvFc7iM-l-_hHTxs)l`dX@4kJefUnUm$C}heo`3r6S<+YW7c2+N>9+R5s^8Do*}=z+
z3eIPM%cs|{t<s1|u~A*LG(Boa0SMIt$}&B*iU1fuqeTlhI2IHSqkxn2wGg}G_J!!l
z3g_?MK*yob6r;P%?XZ^dG6)l~M-)8;Tn7!Nb8w35GH@7Qp>(~06X}JYGoT0=!>BZ}
zjK8WAc2C07Lu6P5??|Es11(OIhAZr|<~Glzxrt^t=`}dVUn4RhN}Vs2n59az6mekq
z@b~&FuIziNzY1xT0%yL=D8r4}0iAH_ioEV(-mg{xHA-Mv*K;B$YY$Y1QNX`a(6lUR
zeLGF)c<o5xBJ6+u+CK=^_;g5BEaYDk$Oal2x1XRr0m$V#KzgWM*I_<JMD@~I&6z~-
zZxNgXa3ss_Iv<6S$g5ZTvrXzR1EgsLg)J7a;wA=NS=r}s#^?s@D^-x;8lcoQz_}c4
zICfMp`I(slrlgRZXFfj7D7PVQQzOSb$lAuH5!wN5073=e>mf*NFayCt^6rL@V|b{`
z>T6gBJ~;x<aPWtQ7`8b->eSdo)#x5YUk(9aX!i5l2My>t#8>9^`vwNMxVh!E3v9o_
zZblt`!+gGG8X!~2nJp#htMHrxL=EuSk86QCb*-eNWEcu3keqas!LLU~Bch@f=rD?!
zsXB!-KvM&-60ZaOEZb&4o>j>D(zy`1p`jt@KLR(>vw`&P^l<cY=uI0Gq&uGQyclVe
z?13mfk#@jSPmOi`@Bmb%EaCN$IA;JP6zFD0z_y!krgwvT8+6YIT5W8=casU^bQ&7W
zW7~UOe24COZNfZe`_L-_o7CM2Jvjt@u42fR^VF%T;X8;W!eCyn+hB~DkGOw-9VlHg
zz-CaymZRRi(*nO}11QA2xH<s-s%Br-OjQ`nEB&1ZF!SuG%}J^{j#6-n@#)YM<Rde1
ze;5g^`4=(ngTRqsR^q_e;RNV_f*NfAaSb0lHu=AL72j3pYzFdjd1a+vm0_l^?Aj>Y
z<3C*79KD%79)r2GjSa+pV@`+7hcJ;Rz8(6dUI0n{_##~P*PosHAlv?at#|%k{`I-y
z)7z6gB&iI0_o}mAXh1Xx78sPNDeGs^=VR{VYh|=6xY4WX=y20LdVzgZd?>E|1P+RV
zUujRe;OjwXwOmTOJULIC&?9a7GGtr+{mvC4Cnskw#p#sy=9lj+b1V9+aYP`CptnK4
zxo^{3dqq}ud)}#LhkM`iHo7H7xCQw8tMA`(c+0?-s9myT8_&~p57bRe5*iO(N=^*>
z)H9jJ`n?BFB5<TgNl7uhJBWMg<#j_#D>SBFSN2ZjX06B#gBclTxv{i<uU-v`q(G;b
z^!oM3ACGr#>9EZG(bID@Y~_|$4^!eDZx*HxS}!s?X?xJ9`4@Q-Wv*O#QDx|>w_o_~
zj|Yw44_;V%Pp^pQx8F01c_0;CK-+eD`(6fyL8v3B!_I;(7k!CBg!m#nKSwvU`S|hU
zwa`o=5`+~xCRA9#((nehYll6_Rj_T1=LJ4ezdn9zEgbb}LXSnzm>v<ysqkhM;e!(?
zOe0p^WbO7`t7gzx`2f8aNpR4`p87oszRO;wo9O^8)6i}-28KPD*T_2~GjpUfCoN48
zpa{?fOkz%1`%bDwqMi_}N2cB6LQq`ZfT#A+yT2}@;+2T$gg%eD*j&^UdENts%p%)W
zMRw3w{Gt4Vk1-Lccj@Wrh!2yKYlN~3?Uo-<G0V%#FLKt7qrpD>oOo?-B(1@IPjD}t
z#6fmT%oY_F*8x+wFBSk9_ic;cTQl-ZG$y&8p8g~kmoF9yS4c{Ik((Q?4s_-H89Bdi
zulCh7_W;|O!E3nd0H9gu9t{aY|4)nojQi;AUxPQ4PojOi9_q2*jCNFuENlR50t6|W
zPpPUoUF_H#Iw-UV3EH#(R5kn}_#hoU74Gxi6=VVXw|}GF!3!<s=eljJtur3JJb1mN
zv{YcwaoxPUfq}t$RM@HgR~wQS?<ND_-go)_6<FZ7mKL?7V#x>JSz3k7=f0JE{>*vo
z*yrKXI}asRKQuqbyu|1tD4oN$!59Y}15w$iF46VSD8-4eN*|l7&H|}0wgN$#(bJn*
z+S4s_JMkfnhYnVPt&>?%Wbty(KI5XzjNXkO^qIpClxBbCd7;lteLeXsCbTqNn6Jg#
zt|zZ){^i8YW5g*|_zL-fp(;iGfXo9vr9B}z?M2M_*jG8-jL>cR`7zWR0H%J?JVI&T
zXO~3oZ1pxCZrKE%Tzsgvf7f0GzVude^0PZRhu*tlY@)^)?X-Kswh;L4+-UDSy&ZDv
zT|W10!GQO&*glc1wU9}PSR7y)*X9!^PQ0`&_@+gr>%ZCNs;i<BkdcwG%q?Nv`?%X6
zUh{@tchBS?xV-mC)2g9nMCFnDW`<$cUChi}ffaid+kr0H!2dv6DFEV<Stlwjt#(f4
zRAj>8fe*6rOAm`e9uIqnGwlzo=p*gp;o?#PF0*78P16sxb-XYACLA_Lk(upB-~=lF
zxJt@6!csAGtilieW<xMU+AV(92P8}M3kfkRsD{q@C@xUT8u#TtBJMJcMqH^3%b8mb
zu!z*n4hZz~8wBgmODvF+r~huBQYGNoMbSWBBXS3DvqE2AAK*8V=-nTeZm6hm^71Mr
zBqSWN_+17tUN@qhHtRv6LY;H>YEt-Y9`IydEJAexZV;peP)p3R<gQ+=L!1?mra*Q$
z27?*cs}GKmBaYs1k^CX+??US60%7M(%7UlftCadoBLym?mu5&TDE@%MhHgZjN!T_X
z4!>U&VxcRVV3yVV9YV&>nQqVspF5`lk-TG=ACTc2c#@Jah(1Qye?`!_=QKNhzkl<#
zt-lQR5X@<Sg8$$7bzCD&<jU?ndy?r64MPW)b0k=Fmgd!y7}s5U_C&8m?BlE4ffWL*
z5Ws+{wnfvtWyF=9yX1XknDHfp3}1SC5h<{`&pMs;Yi~L|Y<dbz#kH74siW)kGh17{
zji*rPG*CbGOs?-}8nSK5OS?q9(QPRorvxZu|Bjnt)@+rRt`|LyQG5NQ%5Yk){ci_x
zo<{g7i-c>(@XpsNkjrA~H|&BD3d0Du*cCnY^73yVql+Jdg@g{^K%GPI(@&o+b$9kW
zeE1MpyfneX?=^Zw(OcsSnl`T|@SWxkl)>R}#26@WkYv<ZcHKPXef0Dm+=6%ev)^lm
z`9G-s{}u@GFJI()mazL3#cS81pvQDd7BOu&-U)|F`1_%P$b+#aHM0}SPa{z2?)q!i
z2H2~{7ikvVKzAx^8UcwQ+0j!kG`&!7KTXN+Fv5I&x-JVFSn&ovwWu8)5s?Zkk}BX#
z&}1e_U67=J3roB6;Fscw!CZTECUWFEbaK8}ZFr)CHL26JIkUYr^!MB<K72ZKV817#
zk8!*01=d7ZsN=MX?&2QMp(N(zsU>S<BVO<=-I|#jKKFW~zGTD)kXNuckZl6043lg+
z;lYc>pdTRRf#~boSm<nz*f}w$8A1KOBG~-6WOEc;klp3yJ?s$h+af)SaN#Mr0l?rA
z61sriuYn}QZ&DW{T<Q}L1DvTzW8#h2itE>}+d@~$f6(bwdi$6ukZ+B8n1EaC?m@8S
zp_IGaWj+`;O;RGw&pS94>ik1QB&;kS4Xh}^U-B&#$MC*v;DLdF$!-TRMx3zNupo_E
z4A6bzp_M<%!BJtDnE<0iY6A!zGjJN#6iD{+>gtqfDtTqmtPmaq5?j%hYlUdg<3H(X
zal|S^C1_>-3`%kC8EA{{?ZRQzOb?aUf9|tZXI*zw=s%*$zxex$;_qp~+uvVjkwH~#
zgQ~rq13WtKC=Ohti8h6mm6aOPyLa#AGp;=dlr@fC^rySEwY6VhU>qE}O#8IvPJxwp
z>A5b89Zp~+)3J#j&uvd;6LVU>EDRk70PhCWhCogg-d2l9SMGwr!}SJ0(sCX@zFE*a
zmK!l}0AlT?dU4Mng_NYPH@wiv(_v}<-u_#FpWm0B$p?Jlv4_RQy99bwp>q9#w`u!?
z=+XNUQ~1}NXW#L>en|P&t=DTf1@OQ=>Gf*Q^tV9vAV*`yO2J8w#er#g^WuOg5RGU=
zc3{8~ZWaOajV<W9^V+UJb4LDfzrBAWW6z(&bokXFK2bJ$DxYLysf(e=I!GALAp7Cc
zFZmAFJpJiZXfDm?I7$mig8I?5|3)wXq-f|va6~x#$22oC)|x4O{Z2Q-?;S3g6@{_5
z1-&U?`UZGvLmZsei`3!b;ZcC@#}8|8`}TvwYc3>s|KqI;k#g^#t0cxi#PX&%|M;{8
zbxeqC1Ptgh4d>O6YKNGOH8C^WvQt=;=1<hDzqNgELI{AiICVw|R|gMcu>MThQCtfq
z!EA@gTSX(cZ{I#HA|i78%pbhW7m1;Y0Z;9ZinZe1yLXMWBw%6SbhV1Syf-{5FCH|v
z_^d2tsLR7&qaUFz5)QyMI$ZgE1q|N?hR7l#=(U*OVQC(1G2oGfDP;nG!K>H5-{U@5
zy#N2vTKG3L{Eh}IX1E5$(uIc1R`Wl5VAxeJchd@ME`(H-;Wj-!Icdt+_#MHM&(}ZR
z4jTuR&9#jqRBf4?`F9l&d*VbHRI%_*OCyojW%`g!{sivll9IBaUWTJ;Po6wcd4y5(
z4&8*Z#^DnI#(j7a{Fh8eo5oPO8(sZFqc($(>vA6I<>2(f71RmJhl`s0ft-K?p(4&R
z&w<kGC>s9abG`DX(o(Kt$1W3<)zsq4%db8=vhJ-^wFmn4L^ChRT4~xITVc>xd2!c)
zcaskjHn@A~Z{X{-2hS#JZ;HH5_%-5f#i!R<j!>M2;%E{PFvbYc_a9Esy>$KL*O<i@
zO1&cP5RX#Vi~gp^PTM?8H;A4*(c3r!Y|g_O%I{FYG;L1Ah=EcX$#(X(=el2yZNXsp
z=(hi|o50&PKZ_i97Y9Bi{>PsW@tr-tH`13#ys-CAfrxymw|UKH4sVfEQ~Y(-4qiWy
zQoMMFuK|!^)}8Mc;0aqgM~@yY`1x_09TFIi9O*Il{L}CYZoAJZ$?O>gfb}Ag0W9Ua
zj$EFH2a=Tg7l6H3`P#MbAp$_B$-O##9`yW(aJLo5*0GKke_)=Ml;YlQ(4M)txMV=9
zg-1B$E~n%IO$k1?tjm-61>im!0dAIXI_l4D9F|qwFbHZw_Y7!(1NMdBzAY6HGpWM@
z{jH!2ytmhag7`uV*gAH}3jY3(`{v!sG>cF=e#+^Bdogx|MbtrYEu29UAOr*54wWMs
zK>c6|qizG7)Ed#W2kz{{gWnkg^Y|dl9X#7bzk3FrIR^+onBUB$9u$ex^h(1_D1<oj
z>MFE+TaU5){z}R9TM{u;P~jJ1dwP20n&BxbZ?Gp09XgNBVkP%MV@Q#+YR@_K>NoX+
z!R*4uud_XW{+?BvVP;TD=B<*gxH_PfObX;7b6dd~<087hzd8WRfshnPfZ6Y2VlIKr
z#1Cbs+8O>t#y1re6&_putn<$Ep&$6hAuYkfaXE%&iZ?=*)zn`AZjVq8mKt8dj^fiU
z_0VY09}A6ybDe9L$0iKGVIrV-*FZ)F1~{oCg+w%Oc8L_&>O<hetOE}S_oL!n!!Les
zhJ&v+*ln5t0^6k?Og#zN%amr_IXNRk!(j4db@d>t0w5JXJlMlfe7bkiVR%`!P;G@E
zf*-AgHAsfjx+v8{4~#57U{pxZ1reQJoa6U&@@+WZNdqv&ST2|kN91!|c0k19a8q&!
zcIf>*+Te2*!7=p@hllmWRPC4N?{?c#Htp6hfFi~O_IHmqhJE`uMMMyP4M<1@`o~+h
zZo#p%Lt^0H2d_^cocc`}9UVO_^=D(g<cJrEB-xF`g0guP@kk8_D(ycli5xU&vpjtY
z%L8YmmdTeXo9KZ>k=TAL^hH`bGZ{U+h7(F1Jd#xz`9*H&>l<XYf{2y~6AHA{aSje?
zu*m2=-v9gF4l5}_U#*dMCkiPd-K%hdClxFM^DC<$2QRX^UL=Yy|Ge0pRdo0Dks!VO
z$clqK!Nrx-GzB=So3-&qkHMeHrnJ+RrT~b8MR?3IzeO7#^p}q01>{Zv@%;@vsxGH`
z7M{{GBL_AS-j+Mn1wb`+&sPHyi5;1l=+?bnbxY*WjN{LH(P$rq1PCtJ`n<b65X^^2
zov`B9Qgh&N;by7LhtMWPZW_?F6oJk_iWod^Ctliz4OpA(CJAs8VNV=AS~6iIFC*iN
z1yY?<J=gU=s{8VIs`s{C4b*OK@7<)RU8^)xkr0VWtHF?@2x%gUGLO-)RlA~=N-|c;
zObD?Iwe3RYc?yl@A;Ti`x$cEMz0dQUbKdtk=e%eCu|FlO-|+o@@B6;)>%Oj7V;=xR
z$KqE5&z(1K-qfDuBDzs!pGS`CKAz0W@jBzz`>D<HFPOK#?`cSH+|`15QEAID55E5+
zH)C?_YR_&Ot&&*1+jmaNEs8rFrkwow5XekxtIf`zPipVzh(4Lc&uJ=Cg_cPWCsoS2
z-OsP}m&}tNKYnB|QC5TEa(*(#4Z?v)N9}PV76dv#J1)sgHi$wB3s%6Nvc;fU+3|}}
z+A8X$DXBd0ynK0lusqLd1;m8mlJ2=l++h`+D?Aq|NyFiO*1UTQ7$-?>L9VW@SOdfb
zLC5?QJ}XIg2XHgP=fUwWov&SM>nkssoUI)mTV;7=^i_&J<{8*>a{8`4AcFxRLjfX0
zy_>h`>C>O70ec<x2vE`1_THzeYTvlO@}Zj8wC7w@Cah8DHTFs_-u|ih5MpAiSRwJL
z)-f)KojWx}89>47JRUB*0SsoN$C~%-+gRZj<1fXpFlARJ3CCx=eLIYs{ymMXH0rJ#
zU(;r_Ut8Pgk7&n^I^ZaSbP7s_Vu_Fw>xJ;e@wra%7R^Bz1#cAE(sK>PM$UodQ}20u
zhar21(9j|5`~Ve4#a+8t47`V)Gdi3JmXcoPRRf;x?x!>+hC?dswv&_dP+&O|oB(ck
z1v)fb65}Ood>@0u0=eQ8<Puj<{(2dL{;@Aj!Ij<zV)T)<Y4wjUoQpf0G}dhUcHWAa
zdj+wyknu{b;;kca5-53e%dcfvjZoT#nw`x~>IOkY%WmVZ-|a+qG}^l3B+}uKH~j}f
zHJB`Fa9P^bqM^xTq47OqP<Hg(le;`Hv%|+>FwO`|2ZarlRN;wT4`PfegQzPExFx>k
zfgyv55Jx*RML_W-cEA#5bW5Qk^-u4Mh>MpzZ~h%Nn!9AQ%uM^fYiuV#+6l~jkeh-*
z7i@q?k19xgg&6p#ghV`1!N9>;!$cS}d6DwnPOOOVtDd)Ss~mIBI$3aa<`M=ITRx;H
z20;S)_0Eh_Xxr*KkLKNr6%bETu1xni=j8zLL6#p0cAqclsaJAX$<)ALxyXm{O&AJ>
z%Ay-6L{<FLe^?EsH_~1RD9>c>Lz|-rx(yo5v~SSHMhR;RaLK!M%tXiq=iw*S&|^fU
z#D7d-bZsA-Q#d!5Xih$ZokHMg6}P5vf}H#V8do$4x+vt=Px$WOE0HN{v=a`3{@le@
zL@N}#Wv?63kb1C|a`8*it=^1?75=6j=yE+|c_*LB967mW)L)DrCyDeNGvBwU2Mdk0
zcV=FY_u0x4MWQgzNyRV@oH<ClG1A5Fye_-v=@~+a1}1q_(CVMHaG@}}G7Q1e5ZuKF
z>rebb%&clvi>yu@B=nHPj{Y_gbg(p|*kIGzY#V5|fVZ{>xUBbUrI9zsQ#={j8>#|*
za1b7VHbsXc(LqVP{pQie*7My`i6AZ7K)8_<&>EnOegG^E921@CLOyi}uEhw@OAkr$
znHd-afyEMtwi{nRKg#@kbTdUE;W%T*@T3_JPu>cID~w*ynQxDNBj_Btm>Rf)?M9KL
z80q6pOkDe-vP0UV)eEgD%GC71{OHXxKK~dH0w^_GTa$;9XlZv&7%f}2IU$b>%a1lx
zC-R;RXeymwQ>HgUlQS1|cn&st5YCr|l5ihvj}R<RK-_BO5y!58dx<_JwU1BXA(~G!
z9@#8BS7m-$A-kKE@*x+bFH@pn5D^RNoolEK$)&NS3RtE!TQs7jSAZOEPEhRVGu`&g
z*Xg~8DVhcJF*e{H&O*uwt-IYwV+z25l^ZxPvIra}&Tb!Tc9+f{@Q#S87!BTmd0p;S
zxCFewarZ0d-tXV~eGIUgr3{8XnC#^V)=_9a2g6CD5zW{VY7k_{j-oo-9B<9?#TklC
zhC#z0?4kcBY4z0?ClsR|!Y278Iu4<*JlI9W{@S?D-$N`y3tZ7DMHQo|<l;TKy!vR_
z5#@{cc^DchWLp|jqZ(($0fbS!*@vh%B)k+)=;<|rL8pR_I|omTP+uBMIWc#~a+y)^
zchSZ1YK~ntiSg(<a3GbxhnJU^<@fx#=#wW;n5;>YCyN=q+oW~Ahe938FXl|eO5`6{
zwQ46$Ji)ko=+;Yo^>{*C&yOAmvJA>xG5mfCqOZG{%sk|}`@y=ULF1YloVa_S)Tj=g
zAk?ghSM#kLyS@^s``etH5*(i)2){wVgdVIrwj+1pD84mg9twpVkfm9j7`vm6IXZl8
zYY=aGAx#tjwcTWv-w<SxgNtOqT&ANKiJ3XT2kJBZJrl>HXXZl#_r0YTwm*|4Gt_UI
z+;|vg6!0D&jHb-^YwjP98)o?t$yG}TX(kKp>noBvRNZ05?z_LH)%IZa?PgI0k2+$T
zc6do_W+SHoGfpMQ##b=WG(Rn3Jh~pPuFpQgUyUMydpu%=(R-YBMIIX;9n}J4Tu&)e
z+y@>qZJaBNBO{AW)(q91zJEa=5wPH?0rnqWQ17=-E*ucD10Ow-191-;?zv!zAvKbp
zwPEB37fl*(KH)h76e3E6LD(ro#Ci;d^jncBPo6$)z?yX{nAbUq=J7mIP76u_W{gO-
zA3;r?!}{ipct+*9>81sLl;<kIfzMU|1(sOgJYz?J8MjA;8Phd?t<ZykY=fM4P=N2q
z3N8RVaIwPm@lFsysj@~w%Jg68d`LsX;7^5hr4d>ylLF45m$UQOsa<G>wwCB2dW#9p
zm@2ycuQ)UA8b~2a(V;#oZrlUO5sZ1P*^gk&#z<ZcP>Xh*>-1^_B1HP4Hs|LH%Y}uf
zH%x1InrReUvRqbOdIrCKP@1*K59k18@=9>Q>);(@9yDKl>TJ(*a`B34Ud{};k;lo?
z590mEpa1{rMF41#^`nL__B3&-?9tzs1j0f5eY9+dozYMp9b6p)4p;$n0vqiHMk>08
z)8>!d@#5L2JcwC@RPm7EAWlnSv_pzo1H(QMh`H#w8ieZ2bpq1u)v7ypN7~2HHj%#U
z7!3fb1t1ZLT75kNz)%HD?Z}E|qWvPyaA3edijxnvI3yV*4_G9JLq+T$I7v;bcZ`A#
zx391BU5Ws;hYmw?Tz@d;1h5lOJ>Yc6BQ7yC<`1x*W9VM8)hy;Oc=##l!>HxK9DS^E
z@Sy&=Q50l`08rygXhH5x#WJ1CohroLZkztbG3*L9!A@3wK>?bofDZmC7!HU^O7p`=
z43ypG%VW=n9MuMrCk!SzS$H+Jlt*WXdjZw&@3#v*6tx5Uz0i;8ik;ecdI%KyM(}{H
z*GGa;V~E+8sW$|xQLWrzv5uP0b$l|M#6EiTXz}|PKvyP`65GE!ux}r$=qH|gOVE>K
zaIWwCWvK@Bc`?k<Ai}fz-0BNdV;rt5dWExy$kd?6P!<AM^nMsMUneZIQwF~WbUXn4
z#0H~j4NPa$gI-evI@TR8FRj`>q&~-9xU{B@hWEV70H&RE>!9(KhkQp2<mgKDDbAN<
zWoCwm2_q%A#5a|g?8ND&!WCN>-FQx)ByulxK<|6p5N6UpwTbp$;!?VTp9$m@Qa1~D
zn(o}K(NBE%-WbL9ISqgruysJd@eRRTsPFbC7wa$Y)jxtJXH!>!%k!Yd9cz69-w$_O
zm&<yHOp!<9r3A^AjuynBA%%Q?*4MdIsQwi)2M(VPsb6j{c&F^T_iP^ltIdMoKD_&=
z&to`zA={yTI`4{ido5}xDi}^}-n$);N~>NPx?NkR{ybKcZ}<$FSR<6(nfLO=;kDJu
zsyLsJB}7suj0^s^dr4nDCpt;(buHvwbcjloo!w<0#UB9T#4Z;8H~W_vqXofAWyi3m
zHf?brW;}p8yK=0XI`T^w#tI1uiMo=JhwrHWYQ}=0V{@Ir<akOk6UjBvI2N)#nj*ab
zsJt9AqDMyq=*^jyvcV_Y;`DO7Dbr89o+G5_nG2A?55qjTtxWM`;LrE~o6olu7))R-
zYS{QG-)|Y&>f08#INl?2xdEEKEI(+R?*SIl#crXxf|%5U##|Of2%wn0Gp=Cp0uagN
zoig&{Dvo12yXQLn<dUjd+une3PRzFJ^q0?{X^WTR-oXF83y1@q4Sle-2$(s&QS*iT
z2n6MLRc?&NKI9C*M)=S66&CJCcX}TLo~P*UANhb8m2=1an<+*A>3-mUp2Y-ei0lAB
z6AePYE1LfZuRu7)8bNbo{;xY9%tko$aK3(>no&Qa68~e*KPYXkuH0Q~688tN!OL5>
z{G(ZW?Ihkm`nTw<jERk|Xck+$TE(Gg^3b#^mzu9GbNK`Q-X~p+r0h>EX^GqKZ-7o2
zGVLb=6fQd@wPxu44ozQ2NRO**aV(z=E)n{>er=+&a4aQBrJ+9t)JK;p{o#13Vjenk
zBDO8K-&}SEPQ|}&5`oy)Bkz;v*NeX+(~DMPu4d%x|NXyA!;LqYKw{A+@I$~NW(ioT
zt~8lOi8KP#fh(ifs@^Q#x*26Cc#w213NLB&h_&ytI0Fh5$g>5&2Y>wGhhw^zNACTs
z#k%nd6aNB67Q;L2`SJAcv%IA>fnXEaSJb}OEI4Hp0f_o#tl7lXm}o;~jG5fP7Ff_B
zGQ2$*wL(mc2BFZUR7N!ptSfZdtdS4O=AV?i6@*@t&-WNO$%-@t5hKt`WwLP0TOK$z
z`ZiXnI8NJ#TSeE5!TcQtWOagu?H&DM<hQ*v_|f;$Kc+?b{OOyB7G*I^i@II*XIhl;
zLmP}q5>Qbe36*gL(qg-n_baMr1gHa7#vJ^G(y~CE{nER~OszFLZ)t#tCLCU3s4MEo
zSzKT0ls1z&u$Xo9+A@ni%kKQR!)yOo+2A?sB-WE3C>CIq3vaSb)K2EDgTvYmpn4m%
zifJ4R-fL49@7sxVCAh<wSCp5Uk`3oo?k*hn!A#Qd$9*%w{W)Pi(eVqd^5Ks<0G3;;
z*v8!bXGcRXZ(zgNID-ZH8_>rT*BbE9SoA#zu_X;NW<0E$&%;08*-%728M8QN4_}(s
z%bUFnZvd>TVRTE&AK*)V{ZVr**v~l(aQHW!^{rozPVmKx7Z)u)Q;zd=saMjtM1#^F
z7yb0pE6XeIQGWIWN;14;s@|lg?v}Lg4{Myf;~P$fu}R}%U%a?Kuuc}xB4`3EKjaZ$
z1!*jpx%dJ`gXH8c`W1-p-S6cwR;@M-iv~e%w!#mIK{3Y`y?8Tuq!a8{Ag_h(u8AMo
zj?4pLcuES*Q-VD(lbDecmsQisz-$kL33=l#siY2+Obn&~SU(-n;P(cS`T(~rylC;y
zfK*;x;FEFE{q|H^Q0b=5D&P7bnNB2OU+&NJJ>rymggqh5=0g<`D1Qp95&l}5{C@ht
zul%RGU+#A}Fg{i6n|!kDtnAyNsa7+#(m;Xzb=F^aROyGVkCwY3JMh9|hH|PBucWKF
zS&tn$VA;!>e`@D~aoZ-&_(@Lo)!8!MxF2-q%*{;`SzdSJxSp;+LY8k?)0Ez;Y4LpX
z1P?4YE30QUaqLOC%eq^~U%oASb@^oLt8bSDI5HjIrAG%F4qdHW<|paa613mEyS77p
zUu}O-mE=INb&hpoVe5~RNRMK8?tWtiiN<i}?&H0*xf{xNOTJ^6BX|eG5C9|@8kF+4
zZ_XEAR<`-X8zf=*RaM@2$R|7N^2CM#xFg-`CYbR;yKXTJLm?BmHzK92xdyVe1Eyc@
zsuwSpq<!dEp6eKP3*6n!An<9D^b~6E<LFYsgYz(H2thY0v?*&?gROVN)VfL3T!ym+
znb#Cba;tmC2OA~~=gDdxCe_It&t2pY=Dl(@4Dg(tr&ZjBC{5rhacGr6-N<0UOpdSe
za<ebnp$M8&_t447+adK#c~Dh-X0?+OU(aq<R$Q+8oNLE+4W_L#X)bG#K9;=05>kZ_
zQQq~0m2hO0^KHXmWeDj;Pva&$j)SQ`4mZd4|2*Qlp!RT>1}3YfKO$H&i+{KQ{%`u0
z*S;DPUS?~;H6~OZ>Lu%~zP_KbP$og)n47SUH;{O>O^bN~oVWD2ZmDlOSZl@8IV&;H
zl0Vm;{ALU=YTa}P3Xq@pIzbGNIzP}mNnt!sQJ%*SCZpf_^gZ{4^j#@B%zzG<G*zGb
zl_0s{T*fgdcSOPT-}EhfMUp(e^q9Tv+S9q_sTg*|z~6%=^d%)=zi9$SI$14bOI7z|
zYg{Qa1U38w$y2=p5=EAF7N?ppfaOEFN8~Q52>rg5+h5qK8QON%SdZxq(kRR)4@uHD
zGKHQm>F&(3Z_x<YF`(?~c;SezXO7A|gC+KN`9wQ)Nt26oxX3I?Q#$E*I*Vi@0kB!I
zK10n=8!Y!^jmrnup-_NoOH*E{v$Hc)A<WFo_HPO*4=nDg%jy{0xjj1Q35gUp8waaw
zDP;I2&)->Vy5FgMr_PfxQ|<RdLQ4<!kNrT1a>h(pa0@_KheVz4B>-nww$7yK27Eiy
z<$UL3QPKNnCh$}#uHp4PP`K#GqC7_@nFgRX>7XvI2j*ngs96mKR>P7=sG-oNBl{v}
z7)9~#I3z(+wuS<7TDYudUKcJL_bti??n-w(xM%7kp~4-#E2rw$J=R9wk&zBJyNIG!
zZ(A_+@dy@wtH@NR3POrxB_L7n=h{vpt3cJOPY$f9Wgm_+IYD|W5P=dRo0C5H`yEDE
zLn?`7CFOaC)uHRTrT@e94H%l@-Y8Ua+(=6U<E5d!wg{7ZlZFObYe^rQ9DXpNEe!>_
z!UK(@6WBO_tYIU-WivpItObx*<w|IAtw~Aa8vxM;vRN2x7am%FMZlues8X;}+88=I
zn0@gkUpI^`9WAe@;F>~W`X~$apgUHHgMZ8hS?Rzl8V?v`SxUFi-|4?43p#A3X+K^0
z48-#-q%jMc??ZZvoL)hQ$tG!A{8b)SLx8_5NP{#|J)`K++A}>5aP!qqy5kZx{MU}L
z*IHw!Y@D586Q$God{wQsfPesL=dwN{_gLF!N52X~VYkSRm)y_TH)Hdoh3oC5!_3a~
z!#2>Hbj?x4oTkUleV0(=lfg5r^}RH$q064ZKCd{1jeXb~fSx3y>@Y)8QqrO^ClZRb
zM)CDe-(i#jYZmX1JY|7F+oF5(hAaU!*R*=Jwv*{HKL1%!B>CY23Ov4+-4sc_h7}1l
zT{ZC<$>;59NDn_9&=^78>K+uScPSnKlnW`hB`;Z1kj0FnPG^{t+2mmae$A}7epjD<
z2AKnK9UmY7-1D1|%x%DcmTMe<v3HDWpQES2u82{Ht6c++GPs1gVi^8m)5ds&7c7(0
z6PC*QsAg!C*ONmN;GNj=ZW#<RTLT<7L93<)1GaS=kKv_uh&yzM)0bmx`pFNB6cOd8
zNiYCa4{V%zQ={-nc1cru-@7W)pxv@M>KXin9vzWYO(nLbEz#0m-7lcyQ~5^oW_d}4
z&EfR&`;6+|;@I^dpM(l(;t`ObFZrvIh%dNnkIuZ|bheHie(u@E!gSD*SV~$dW`im%
z=DfrBRSjAl*&sl6kX;%@F9;Dw0><U4)X3P|$aJ{p9LKEdyv<f$H-0jP6GwmQ(6@yh
z<P{0FhaM{9`{Z)kqcha#@P)i3Nx%+TK+R^G0UG3Bvq!cj^>J-;N~TJZUKMoE&v?jb
zq<5R(h18NYC{lCsRC=ns{B%?{jz%{uTM>+Tt3Xrp9?<JA=*eWGm*o*07QYfd{T7ee
z2Xhn&hPGT;^EI*WxB`yk1{%M6f?r~p;Fo?<v!mq?J&_vnP{oj~D>Mq9$HvxzRFXlS
zt#|FKc{pMBqMsL#TN~HEkzPL}8A+6iat2sjg@3{6tvJ}JG$ac8V}(H`m=r#^+O_0u
z2L+Ob1u$)8vfN-L>CkQI;6~aDqrTQ|_F0u$ZZ^F#?8j@z3~ZGy^sVxA;~Y<}sxS?&
z3fR%yct$$x_?bN{+?y2FHW!{&w0$6sbck(n2AcdjpE0MGO*cp;T$pw%GdR3vXzi2*
zCnK-O7(j0-?&B}8V;fA*!>srRc!MlV$NK94ysekcTB>GUNsmvny7vH?!Pr5UARBw>
z0R+~dL_|W#bSlWe&-WynHkE_h8ekEark>P(w+YXP{8LYzN_Xg08d~Ag_pX-3ARAxB
z)F>#YZifF7q_H!}5{)dLA?8MZMWCrVlav?UX={zvoDU3MBO4R%n36tm_+Udgsp``Q
zL;VdVn+{%h9~7j3a#<qXiYe-P0fV@9*d1;_vilYkK+BA-x@<ai80i#unv#bG@i8R1
z31w(96F6*9c7knoWW2Jlf!gj^Gx$V&LEq(ciQ9enO>yPFw+wVL%4$|tgYEbQ6ZuLk
z?o+U>>hKO5=-)a);0{^&l2jT^2-bikw5IO?J8CVb)85D>ethQyj`AfNQ|*}GKF%&r
zTh5LrhB`|d&Mh@O#v+AZ@|hwr{;={*W_2B}wx9<%%mv6L$#K|gb~r3!p8uAz&`9iQ
zvBMm`X^2`WBq1JV{M7X`M!?f>^0|ed=LE-r($z7?p=}PGTxxkG++!6XwI9F48v0Oh
zg@FR_va<e;P!rN#j8s?jf?*AF<4}`6)}_>}9AHO~2FJ0JF<96Ih_I>4U+%7zFxiD$
zSluo1f&cEAj)S;Sa!zBUlPzv!PF_zr{H=Q}d>nRT*HqRge18lB5p2<EzQO_9*HrF{
z^C?+Y{~)EkSt!)2uZMJ6Tdh*1V&;kCO_G5elZ8+d)MVP49bb4#y}Gt5V7IJ;N+b8&
z(y9zs|M)Ige5F?3N)?w?hvr*;CR<HbR>7syf!wE|dRBI!_=spCNxN#Jq9;LDQokb=
z)&%G#_XH>R2HSObRA*A8{w1LbMLLT?^Pprtf7-H=a)lEMTU+!FLpF>&1)?CnvKsaY
zSXSNBKGkpTc~@2Wa45y$+jK4U-Dd*7Z#@BGbfxYM;FT-;tRTG0I*uaXgj*|4d1hom
z);B-PipNqU6q^Jq)|!Qb9~P3Jd<}vD*S^4@FyKL^+a%>vZ1z2)KsJA#HE-)ZQ<Y^7
z?{eHcu16N2BaV5|ZnKK#rC#Qz6vE3Y``!tfe^;kuaOm$#Nv!dllEE>5Iy}HvmIYMr
z2IBD=oN?s_J;rFjh7Rg9CQlxB1sUECU^`^#UJ<wlU)t3)D;9;ut6fiqPBy)H9F0K=
z9he1zAq1nWp=N4jy^&Y;e4{%XaJiVeKI?zI8L6}Dake;r!{z`h_O1jsh^h%VH@8fx
z&rVjr$>p7?Q|8f}=Y`G#*S?TW9i%D&jhY=eFlf1xcqSV2aTyU~3q2~a;hqPJ9YOb(
z$wC9$tk2U+><HL~?9O*Z0lSSIT6S&r#|~164%nI~AzV>3V6!wk(dKb}bolq27x|nn
z%1V>^DqD>#y<)3hJ{#R1{&Z9!NQ5@NKY@~}@_b7CD#_%V1Nm-AcU%=E0~K;2K<728
zJ$V?m_AIl$4xg5fl$6CbnyG(X)u!Fhyi5UWxq@zdkC&vF5A*~((7M>z0atC>Zsa;h
zS#k%-h(QkC-e!TgtaQ+UHO)5#yfT+Lgp8?>TG7&2xYRefLO?8lReM5e-!;?lmW0H>
zc&S27>GLQA+bdhoi{pstVJG*oVa4$i@=GOod3jV^C<sd4<!c7jRP)As7B|RsIdiDa
zrros6M6z~zOIE*9WmZ`!YER%tE0`9&({hpX9bsj4xSIR3C1O|N`u7^gq-N{GD+5HY
zb}~eT-JGTgrfmHVuC75E-`Zw6wlK<|Fu$tqT@I<XHd=VmO)^=Q$$gdVrB5~Xxj7Do
zln-Beet~KD8sTsCVR;G2ue>J|%X1F6NMTix?<e_9BkR!;U%V&Y_)d4K>CpyPEVX0Q
z17Z(q1&+Id5_uVuhR_pm&Pm%i<8mBU_H}qs&}Xt>3*Z+WZSL<KY#R27j`S>W+oH4D
zX-{&^;+#W&vker->yXZqn1g55&HhDN2x;sNJZy4WWNs`8%MLDx=`7YXCPRAY>sh`#
z#}<pR@kPXBMjI+G&B?Iv^RLxL6+Z!Z0ju3~Ml!~ld$z!!bwA^cmWnawg=x^8t(V>^
zndQ#QsNjs=gbKayp+T6`U^5;DuPCA@_7!lbY(&!ARJbr%P}r0}x3<clAAHpN=*~!^
zLi@5du?4nLP*==wV6yDU;s|3|_s|uvSX2Gt+%Xhu)t{YV#Efb$Lnf8PdhIyDI%F=2
zK<qSt?CM+?<_;Mi$<dXENaC7wW8u-7JlL3=jd9Tj$jEWBliNK-@1S1pD_5{YXReFl
zsx|T=c6-R<$4kiK8R6WuzwITjuW+Cm9vXh}Dm~A0J6NC5*~)>&Q9)kbZT!M&h**%!
zJRE{dQFiKK$cw!utdglUw)c`vz}CB)g)44XdD_B*^bnIp0f$6|Sy_DpKTn=xQIuF2
zTGh}7Z1oQt*D&GPXieV*<v{|(MZL0f18tu7d!ZCx_ZW#R9dMs<6qL2tpxbp8e#642
zJFFBG6n=UAaiity%6a51hG%}8Y@I13Vr#X|ul}~9EL9kp`#b2FAqiKAIn+fVPwTa*
zo;`A%%%MJqAz{+`rQj5LubB$CPd7HS!+PGgskAD|&8E<!StXm6Q9qn}Rbw+iWUlTn
z^BFkg@`*R#u10Zw)i?6rBU=kve;sfqGV%C=h4a$FP(ufm4mS(i<>S`8Qz<8r+ymcX
zz5%+Vx}<3ydQSk!ocm_$_Gz)l#0piVy+w58st|W-P|;@~O^rD;=$>P@4C%UkSp@fi
zJVseuNPg-LJ#G%^w5&2c#GzLAMYsbh1#k~IjyFN7#U`+C`+RkYbHKMs*5se%sDPo2
zlgzf;!9%NsM&233`Svi&#8o6{uDH4FH%cu}q;j2z>7{b*O)=pVg#>+pd7(-iRuDPY
zX*uhm&l3kFArnFgKxeatOdZ!~w@_6ni}W8icjn5g*$Q(4#48?q(p7S;0>E&nos;}+
zJ32uRFoj-wOMY59I;SS+YFhNE3Om{yKv6Rv$6Oqw1v=pJEJ}rO2WSocSe>n~m>}E9
zT8K=Nhel{h2tUHy^oECgYP|`k6>Mx;ihv6McaX-ORsbfj@C&6p7bXbapML)9B?&O#
z+EW$|KKmHaa>FJl6+4_t20Ee<y2Z%{JrdFI1V~$P?=McK`!TO@Y;mdmt<Z-gDvKm1
zT4d8w*0{ox*iLTm;SdDGA(Dj5_IdFK9yL*TQ6wNs0NOz+;#wW0V`LK>_o}{;5R87G
zFs1HVRUx}K?hY3(bUeTPpW{TaSNJSENMoY-Ctylb^s_S@E3;<(SMaeZLFPP72~g+8
z#q&s=$o2JJcodSB{!rc&;pYiZ%7&cfL{Z<qTY!3tB`?d6kN}f~Qv~K8C7bvgUPA)w
zHo`I^9U_a^^oE2HMn{Vw7u?NCzyV2Ot2dW4c-WZARYKJ@n6FC~tg~lufL+Ffv5Y$)
zx1vpp8@8I7no_`Jq?6$jPGS1u&&NKOn>v?lAYE3B$gs#Sk3#ItC6Ba8ycKT>n%M+H
zgr+=H+_WQ1T6LH@uRLp@q5Tn#Bee5Bll3&${2MY%RL&5%(+>mc0YzyVbE3MHG}{j)
z*;#{a=|(=iLSAP3fO!U!0VI_3lk3%|)`3F(1+J@aH@aAwnKc!Dg9G#D>#z|x(3s=1
zWy@$#54UeNtQ_=vAwKMj*!%dr7_R~)yO<QPYcchK$jCc#>}csM1;tnoJ*4`&x-^Jx
znL@?v3x<2M$g`D-#Sz~g^|oPm#sRft1KEYxZCs0NiWseSwNe|7r`_@rlmbKq49)KS
zxP@dROBRkOZ^&8z>-G$81P&mc=odYIG;&qJ=ZhR$0`YwCG?y|#F~4}`-gHz$@P<CX
z`iwXcP<oK=95B~7Ne^Se7ibeP(m^pq`VaTnWlXRi>qi`I>vBYWU1{)&$%6Xh6EJm}
zB&m{O9Y+Q~Yd#mG&-Iy#b;jZ5XoIE8*U~w`WB~;yb8ImW+YMdKlyjntiEN6*VYmSq
zUezlpT?Kf#t_LY!<H}aA()OrL0Bd0*%D+QCajs~c%aaI5vQM3w6x@7G?qTky2nY<L
zPz*dGI!*y)SxGWdYLaD6(GQrTS<HU=bXlt0=@bCdr7hA?U)tOU_$zPEF*CVw*p@$+
ziA?tnqe8l@vi)V^<HwI1hZ5)YSqLY5l^aCi6W0_;Z-o5NyTnQ*AVl0ac}(d(poXFH
zzSE%fea8IRui1?uF4B30h0lxTjOo0wOy<iJe$lkr>u0bm1u7EB_2!I1j4lB21xret
z9_RG88N_DCd1k<4No%O8y`s44#?Lk|ewkAwIX<WfalI~YQ(Rs$M^xBXI7+8+$R^U*
zb<Ti}Lzs1Y>`;`>;|11RB{=(A@$9KmXGX%4#;Alg8!KyS?V9i-Em@_pQdrie?=MWa
zr?nAJF05Xr)SPQ%o2Bj>ekkhcn>^VwWp2ufPdbi*dncV_V~72P<rtv9dE9I8|JX5o
zltvY~y%(7gXnjYuKA~=NN!@o6Z^Kv{?ks_^oOgp4pBW1r)MZTSU>ViwYg05oU9gBg
z9p}^l_poI5<G3wtv9bt(9yhoF(bL8myw!6wS?M(=vFEjtt*tF0`Kq~DFY}<oROD!6
zY}{J3e|hf00DPvBwTe<iu6WwKx3}le-*jcJl0lZ%54Vz$2%ac$hs({dug;%WtdRef
zf7Fv9r{n#@-m9_>Psa>Q7;$*S85Thj#Et7YfEJcO<#9JI3fg%0Vf&+Q#x*dNbtnq2
zanuI9HQ1jsl+ZZjB`#uvsACFht)=u}T#o%(GSi9Sm%?FB?(c-8Q?Ccjo72&DAQ0vN
zbw1Q(Zo6URP-(!S!l<ljw$Di2{bXO6IXNM6&2%J8PXg+41yXPVJV!2iAl3XlQhaX6
zZzH=hKu63MtDB%<ZRu37Zpq6IhUF(>hfSI5j5Gfaw^9C6{hHxm1DsvsO0ICJ^At0g
zakQ$I|2Ronup&2ik&>-`MzfLB#Rm%3oI7(kKTfv4<oS@h(C^Hl77P--Lya?f6W+Yp
zQE_`r&hQdKk=oE!3OpCBt1HVBAsF-(u}sXrMnzHj%XaD4a@C9A&Zw=7zx^D=mN|~f
zFnZd<9Gbag`5}EkDjs%DR9UlYDP0S!$9LiIaCs93r4Yc+vu^77&fRQv7838S3`{vQ
zBMyd~+or5(Aoyj!q_AX;n2nrsWbo%HU(#Tf>Tlyert(T>jluejtM2ZKSu)N$hljq+
zro%KGA+xKiYtoJlXEDTTHo!i(x;J6N`<^LuU4Oeuj~U$VnDW25^Yx#4WxsdfE*S{O
zN|!!ArApDlOQTzUVXrdz2ZsvU0uV9*sriiMJD_zQz>)n@ho@va#rSg_lvmHTG)P2Y
zW95;$>q-8!RJUDE1cyiBbJb7D3c+CfEkw-NT1jU0`9#+R-Y3zXwgj0v=FG{`PVMF}
zi>h~lsch?Into)O0Lps`fwBq?KmUm3s-ommDfAX8oHNdubE&Z}*XBA_U7)m|;y#(C
zpn;K$jEw&4oL7T?qzsR1T8V(!9M*^Fsv6{lX+R8Fz+V?wl<JaACuO$+&U-iJh$W(-
z6`4T^vXDc&5POiXl-$vMx8{3a$Qn7)-0uY;2wuEBWwoYJH<qgS){`$)J8)XIKk`2M
z(D>HK+1dW+{Ng})Q17#p9C`~F<m&Mvg`<N95auAP6)%c0yDUgjJ)rh3K-nUTS{FqY
z&~b+hoTdjY%7B(r_0uHRd8YtA;<#FkD`UjIY><_>n|&Qw700RxXLw`7k-%^ocng(x
zeDII1;@7fD3rc@rIOei>J>Y{@i$rXu&#FGX`{@M4@5Q4bF9_;qQ9ooeBthMQ$&=SA
zTaHV536Pjt*Zd9LI3Gq&?iHEml^p!&i>zY=6=8D06F~eIsuRgb8i5L#Nqj{u!Te+l
z3OgI<%M%M!uJd%@lP62lYdm(rGc95?K4F94I0l{(no{S=H<iCo?B{ggwE1~?Dxa?U
z#x$I&-Z2nhrfueJ*m-%u3H9`mSiw;o7eAj;@$<ld{gEnwI$hV+ff<NpWN8HTN-}P0
z-sr1@c9iGYU%#tdOKwg!3sPlEdVZp`3_z9Pf~ke~hIcU>4{YrmBhmB)c&{$%ec>o@
zZ>_0ysK~nvR{mHwy>Ro}Kr`*mn#+g(d?b`~NR8*1PKVJU>$9h6c0DKS7P%ZU(jlf~
zBfvFPmDPDTMr#LpZ&Y(I=)e#6F~)Qyv%#?4nAWPJK>=J>QT$O{>O!sA1gJjICJekh
zy%sXK0QFOScog1IX6v~48{}KVWWhq{$d$%0So@OlEuosB%epCqQFx(M$7GR#6m)l$
z%*S%)MgruG<i2gB9xfSpaLvjsUYtj}6)n~EIR1=ExGrvrUK$Z|$IzAaj-6T;YYj^f
zR|Xjufg+!f!l@`PHzE5Pj80H|CTBSGvh;v%x}%V#_VNNCIoRh#(*)?3_=f4rCTD?0
z;R-5{p4%JBTx)$C&J#ktayyq@oZEf>pO;1dr#tNbyp=z9e<M$+HFcmu<?A}QIUe6;
zpE0x!kdO<yfE%OF&u^zt^@FBSEC5Z^RYfBF0n%bFCJifu8&=Hj<&%(?Xun8*qbiH4
zAyiqx5fST{aJ)aQF#Oy66ZDOZm0+1;KGz<jeX8nNhWI;Rj+;v^mIwLU&*wbOx6X^J
zZvK06wlESr07P58XsWl+?Hv7$*e)%WwntH>oia8KN6MTEtSNrl6!2GSk6#|6l=axt
zCdk_4j@HtV;8`S<JUrp>f05bn&!7A66bg_Z9BttCrSuu2K@~0Bf@c~?4@Pd`2O}1v
z?#V>6;4X?)J)}|u;+W(!g{Vl$|A|{}7m7`-+XCk+q~TJB{iQHtk_IG#=m=)g8{xg0
ziP{d}jt+I;Kx=+-w2d2^3#G8^Q5IyF6d%%S&cFY+TJ9C^96uangqxAo7h$X@@3e4Y
zYCH+x?wbY|3E%3c4|Pk#(-HhoICqep2I)iLm;u*@ESJeWjJjELY9cnS+{Pu|D*K4~
z+LE&kMn;FXxW^Z=hL3;Lix5&88o~Yjg)BZZ;ZQsRjOFfRky}g?*KLgT8KNjvXU+|O
z(*s;Y4Q6jLVJCG9nB!8U))Gz6QcN&`ruXaR@32KHLWa=xT*jm`8kt*UP8{Z@gqUy)
zIGFfy_*QQ4byg%mYC~&C8?+h%alkteC3i0JU<aCD96cDM7R>68NI#s7nB>ZUe8(P;
zKVnqusp>_wzlPnU)oP<Y*tT%!oLkl83Ip6WMn}~eMSP#y@h$hyw}yv|?Pd!wPb!Od
z#TaJ@XMlpHowCzc<{woF50TsBnWX#6a01~!yAHR7TcIR?fPi;apKfx!b)mZhWZpdZ
z9CFBAEXvFJ-f4PoLw#>%yN@L55bfrla6@PcQ4rsGToW0c*GNW(^Z3U}?a#-amj5c@
zn_%<yy3H>?CvDAEc24m1w0S!ji|YQSb00UlkU0-^=LlIqt9UcDl+aMzfY%#l&NsC@
z;XA3?3v2Z<T)#FCw7~>&Jqr5B3>>4L`vRY*bZ@oR)Sh`#YJDyxiiuygC%!IF53qbD
z<};3tm^&Up=k(H5EbfkE$y5$n{)#f4e2fVH!*VVS8Y7L0gh8Lm7Dz_Rg5lv&RME$5
zQ`pj*X9qB>*}~dVT_NfiAmUw-JsFO}$WB0Lc3h+GZsZa`6GUNWrQd|1FR<0L^Q`!O
z0Uq!KC{KFLsb3`>0(r;XeV{;TN4Dur?^}<XR*sx$fmKC0(WT}sU|;NFp%r#II2H4s
z?hG!nJP_9g9U>Nk`VO;(hg$=L`E#Yn)PPCJSE!&de2L=MeE>u0fJGAkM^^^M01yB9
zn|0ehzSi3N8R5@3Is_J}2(U~R(Ksq@XrUHvL8F$D4l8)&FFS|V>J=2M7)h+bn#-cr
zeNonUa>oR3?lW*jSQH05c7BVbBr5CMKjiG`b^O8EGZM4pGUsF+YL{Iuvr|t4nnJs>
zGK>uSu(G0?BGHiIw$AZW>xYeQjs<@KA;bYXX>WgdGrXhm+M=ulYYF3~^+C`odL$t_
z$ugkhlhG6xw9S>Dz89{mT@wG3LQ!<%#+EmhT&}P(pbZBRtTpP9N+55u?Oy<fJuw~D
zqhg#iJS>_k?*895wjzZLHJ@m&EiZqmInA+lIKBPXaF1^nWn%sb0saT;=8u+U3Av6f
z@}4)kwZUB!nD_P1hYG+C9J3)51n=D(TH}!?qD*kd-=T+xsO_+bLyCXuq;|@#$he;(
z(dFb<7#Dp)M~;?1c8Z|5Y&GT{|CAw~I&)?(3zRHL95O-wqIA?7F~9w{@&sL%e+uE@
zS#XuPZR>uX|0477sB-AIfH@GPPD9<{MkC?W+Mq@+Ay9PO%RphjhI;F<$9$|lULyn5
z3j0CwU%axwWapEV;S^I7a2vIB;B|i7BNdDz4glFD)Q2(2T$g$rkSN~9yOW84C&@{*
z1kricb#9*QE1c`72nTx;<eP-{rxR}iCxxh}=yBjmWM-b!cGLJcQsOx5-VT^aJg=zz
zJxsqz8$JWvBz#@L`P{7Y?bkWnkA$qi2%Q5WQH0d?y9~8$0i_pJtwp$*k^VCY?oUeA
z-*36xo!Dy6+^F<80{$O(_mscpitD2=MK3MPA@=$6LXq$3i;hVt3v#7&G3v!+fiZI&
zkY{CB_rEAGQD#-n;`+_WYEj(^c(jIkRjkh#tuAfhdY+afyCJ+nKduH7p;^SpCoU2?
zBNYNe=&^i(t^yOLr=SE;ZLW30JJt{`Wk~G|1o#T?Aa)h1Y9r2}SsGoe+sOp5t~DgM
z+3J~&(Yp6V1xYu1RsQ3uPXr7i0Pj^pTJ1PsCAbttj?<KREG7E}Rd-A?0occD;S#nw
zGB<F$G-%G~^>)^~ldq|cF`hyk3|lhR&c~=M2*~-3-J-d0ecU^BG+hSODKM36kk{+q
zlD|_`o;RmjWX3;=?H`)L<^%Pw{`N+^op^C*9_0WlFK>=VRrjtY0*#6q&?Km9Z36Qe
zDniIZ6LZa4S<i+SfD@nDJp2ku$p+B~kWO7-x@WM<cqk^-bd$?}C=Z1|qy}^Gc~d0z
zcgW$mNQHH`XvL@31ee3BLF<#gZ%or}%2BxJuh|Ut(aLyp%}Sp8Ug8A1uJS1fIplgY
z;vf~P!!_bRC{=sUNP6TD1(0Gl@dZG>O0>x)qg71YW}sM%VOJqAxq`RrU-5;M6~aFr
z>8Oqs-!l2i;LBm`0rV$nLCw{(3lOGX>GL_SskK`>`?^f)6e~j7{{Cw7*x3eKqE2-5
zkg+TRl#Th=%HN}}_Lh;-6s&Mckg4Jq1equS>pu(4lWKLpo&?nLnGlv|#wT9;iPpTV
z!{<qLrz`CsPLtB{MA-h6zX#W(H#D~D0g+3PjMi~cW>EeVWnk_i#B`Qy2xwLe=f0TS
z>K9`r)2TI0L{4Tq+#rr)4!UyKMU@i7k|98)C~58@)S643gmFC<n8BCf;<av^D01rk
zE6;R0w>{Z+DtIV1I-1MbLjt|tvgI9-I*Dov>2?BiJP}nT(2qp69VEcz)5~h*IV!FP
zXq)LR%})IU%^EM1!CR;l<$cV}`|B$H8w0CbFZIXS1S>BGvdVqmKr{90dMw{zRm{3^
z{79JzTYTe2JfDABfi?(zZxIk08j1*Kjpg?TfQjx%;im|)+zP(*B#HQb(bL8lC{mI5
z_smz%a)c@`=Tp@4un{N<Qhx(NQS84XER?)5A%yri0+Wa^(hQ+q1N5QbS?<Z8-X2;3
z=ph!~8%`*zJl|*hHNWkfA#^)Y{g2X^0GCNMM<bRi*#=PKraEQ`P7U?Huk{bhh6k?x
zO;ZH_H7s>>qJd@+a(%!E`B#N%@dxhSXga{3JBt7-J1`XXp!6>}Mm<N6KTJU*`(mim
z2T)SOU@mtbzHB)$$$8SjL>qIIoDUF~h&6?1rrrYF=pD$>^KcAn18SrY9X;&_+Pg<$
zX;1|-sW5k~8MpA8ED^`irUf0AcxiEHw-l6?-baRd96nvq!*gXvV)GnZFe36#8;Sp$
z^{B^;u6(4y?u>b`^aPxe5k~xTZn^oQvX)%$z^Dknqg4zmSi*AW+-5P*1x6hEQ&VZ`
zROumSHXsx<>MrI@SpXQe#d-IUMJoRQgq0f???%bUXP3P-CRBDY%D}Aw))*^vnP$=m
z@QlnL=T3k;{%(2sfLf8$7fMbESTT(A3<oYqt$A*}wBS)^F*n$bE%d|!{tr!z>=v8A
zj`3{Z2o>frr#GR{e6jdiyalS7Ldo$Bci!>z&hHaLr>pZsyPL^pUNjXfgr?SMHzddf
zo5FLIC|$|1-L6#c)Gk@rvvWAqLv*7$WjW=Ukv&guSM+JFdxoOh(terv=~B?{_kX$?
zFac+P))qAbphiFt(T``_^ty%vZNiWac4HxDdp9anaCm^I228--KV@X3cr^J<@q#7l
zJ8CVOzN@uD&-8rg!PQ+2Zt9)F<mLL~q%OnLuh*HJ=zLmrO167n?UyFZ7PeS;f$O`p
z>|IC|sz0jN(93*b<d%$5TKWbndpZ+q7e$ilC$wd$8(DIp5vm@sy^&Wh1S2g8J{OPT
zGR%Gx`>FT@Aq0%vMU4;f6>|YY$k1hfC&Aq4@S*%rD>ZC^Y6icVsUl1lX>8!#Tt!R)
z&E}HR3X*4f8qKV?3Z<sq&GWD!2o}x(n0<n5YKcy3jCC|%d`3Eu{udP=xd(dya3F01
z)SJCE9CR^Ov9qb(JkC}h*46D4=g&>tkIWO|BMfE>>{-v;3rB|t^AHZK&zFBt{PQ~J
zCC67h9&5D9spvcX?!~(F8VL`7jB=p|M1Tb9y0CM8wC#A<E*>)uyo4r`3C3&mpd;rF
zkrc$Bh@y6^@GUf}bMp~5%?y}|2`&kQR<6S;FkFhFn7O+Lc>M&qGdDjkh;NDE${UC%
z>&vQ>9?wW;@JaDQ?xz)cN{q}U{W8K`35ZGV&q&_G;?e~Uo9?~&_@VYYm@W3e8Ra&E
z^1gS=?=aG7EQAP8T*zq=tWi#$8Fx|ZEYulmC>wBPVC*!D4f+?q!Zv9uYK#I)U&hRq
zYz!efc;LBhg_m$nD8WvPxnM~_Hy1FHuti7K%&Ya)EcL^;A^LEfnB*Uv<{Jo1Ge4Sf
z>@(5kY_TxaKV9V$R6Q%QsJ|NAELR2q>X37cLRaJzajV2Z{y#{o3FEbmZ?CH=N<4X!
z_c9DKIqLE4mY8f8b9S<!Y&|T&3cPT2e5;U`bp%+e{0d{)C-DthJy0GiE$5Tha^K=`
zo>(c|E&gM}RS-}%@D9)Q{;y1?tqUj~^Rne@n+viR8YL*+^nF7*26P?}H=RkebAAQ+
zM5}Igb!BwUCID>+j7<;lXp@4A6F&*G2AmTuyl$D;sHN2xO|(S1@{e=flNtnCtrXM;
ziT_AnGJ)n2G3Bx4L(P{L&P+{<R!55VL}LOsRLBk;n=~w^@h@u0U$#b1<B8e7@-B@f
z1t=VA1%7JRYFnK_$Lj7+G<FEir4gr;25xAMC^!|QQbGYiwj?ibssda-h6r(<CH7Us
zR6Wy`=aOvDEj#q=$W~aEc_S0&W@so7sHh@i^`=sjibtNr5AA`?LsfI>P_NFAA=Qod
z1p0t8*5vON&LY;M@KC#OWs=joy0|JMP&b}pvIHK^f^g>M^4h=)CBh-GG9ZuOposqX
z4Y)wFbIP=|!N)j3WVf38$GB}mBs2=^3QT46*h))f+%N)XdAfBC&a9=<;nti3t$a2{
zg{oPqtqpEAv9@JZcWW0Y;R}hodrxK+I6iIZAL?q6@e7l5{`9TZ>R5Ksq*WoO<xIs6
zoctKDeRZH`bSX=^Zm#qZWnE`pR}N3sfo<OuX1J?*6vb@ZYUptK!mRwW>m%mwav1-;
zL`|m2x`zQTW7reIW4C;+>M`R~nPy9S+vfCfN^)&;dU`6mYim8L+q{36IDSvE<>}L_
zYr6gY_6#kOT{PqVIjNMC6n;U$1Gcto4DEM<?4K?&rQnnKrJgX$^+oYfQK~pR`2_^L
zIyo6`!i``QNkZ7Lp`oFZL|3pi+I#F=wdVxRRz)b-+gJWucMHT(F<f#V&Kx4m=C6*3
zGz{d<uQ?Q`wNqs7enZ3HuGZ=;&9?UT_6?1Vs-~t<aEcCr$HF`Zx1ZmvFR&VW!Lhfm
zuc*2@{NBCUYU=86rz>fHF>`*6yJ<-F?gsn2MG3B%dfpZ7YejbmcROjWcS$f6Z_hqd
zSRv#2X|Ld8H`|mo)^qrGKRO{eIrr?W6p=!Bem6H_I_LmMJdD7&xI+jh-faUUC?G8C
zW4`d!5oO$CLtULRTvRdJz9cCoGIAegnLl**44fB$xd}PE#>dA$Hz-;u4NM_#&?@ZH
zQyU?h(CLDk190Z<BJrzeIH8<4dD3b649sC{nL2G6Mq85h?cRMIH}@RJ#JpIsh}jXC
zlt-zZk0G#Ngh}^4-EGDPb)zkQ!qY>TaAlC|D#|=EJA(Gsw_kcLjGI97@Ltlk2h`Nm
zq6Gc+Hd7l1Bw8Klr`^J3aV>2DI(oMG&Tf0P+0<E}gJt6qwV{>K;a+<$!8M_BzES4p
z_H6q!DLobAzO&Ba?s!Nyt%a5zs5v)XpK<xx4@OUuA6-K?l#NynDY?OgBuCO+=zI>O
z@id<AgpPOli*t|t+FbTA9`^*qszwjua6xJJ^sBJFR6lC+593^sP$}KLdsidgED*gU
z_@rRW8{jaRd*W_2@rcQOaEVdQ+q+f%KIb)9$PXCF)y6hbx3NB6?_*b0RejwQ6da5R
z^QTDG4#A28PfPsW*-NB)jY(k0Nd)-&D?p6os?AXTH=QLA&(??Z8=1C%Uc@(H!rp<?
z&(zoFY>17BDrT`8ye3);x#uKodn>h~$M#rI51zu>bGIzBch%Sk$<>^5o@4EEt$6+q
z_=EIp-w!LsjT?uLhck{1Xf^P-l~b))v3&V02J)5_bElL`{N{I5A+b5kyzUcogZ*#m
zO<g3GI;@tMkdPoz9ffYn3;QW2p?Y5)Zyr4Mg6ucP^qUE}^AEgf1Fiz}*{D*|F#Pz5
z6Q^LE$nwK2sV<z{(PKx?jPd%5<Lr*qn;om&<3g)Gd)YE<jSmW3S9MpYS}Giy6>+@w
zjg6~y%ln_Jo~@r2?(->vXKK#edGqq{q+mJ;VOetID5x;0NWA(kEzE|USd1rA0IA3o
zOc+Re*XG?=1#6R>+5)r-#n!K1U!CIqW>unY@z<|sU-*MMDN@@wdGh4DEY>61=g_ZN
z*J_Nj!cMIpoP3v_fwMw2?okQK#_ib)ggc#PDV`LxcJsV{EW3SIVtPWU^%L>Tj|*IQ
z^{uuonvvU5$^O#aN1~R}FmAsW(~ZM_H@{C>dhg`?Nm7l?T^c~fcjSLld%IIf#-bcF
zZ@lwmOcL{~u@-f;4iwL9Xl->99W;e?x4CIjRc%K-lB!#b0d)IST7~9LX~2!C*xM(r
z)lLY2LNhr?*YHhM*Y%djSjAb^dbIX#)w5r(WiDxD%`we9q#Y#gK4-o#$LWygr?we#
zR@;F3^6~M(K_wVPcvYtb{hI9T?60ZX&{1gOKe{jCan-ebjhMjFVwnNYDT~Q^@PHpO
z@<JcGQSr|k)ZfP-X*%p~ay%ouNhn&x+{~FoAM(%}0u<JTt?Aj>2P`dP&ttD%4R<e9
z$646F4ltQ0sWH+}=RSeY*7G`WeSwyn+Z7|dt&Osz(xno0w{OO&)-kQ-epJGK<LtJh
zpPp2STsPf}H97{mYN|-J_fHq_H28Sj+oswJI##Ekc@g6*Ht&9@<)dOrvn@}`!j5Kl
z`hD!|G=o9hqK~#G**U7B-M?x)J8}W#*-|l4(K=F@gX&|gol=5zsCeciPT^rCW$)#M
z4s+HXzRxk=z`0x9s;;Eug5=;QNI(51vhT%Ay3`=c1YGJpYoko5bWL1p0xos;`OI~C
z)}d9NpSI^1ChE?vwOTE^B;5<|n8V@RGrG9t+<Je1|5Xrm!f>k}?Ao+Um!_`?DXz%Y
zSSV$&2Tq+{U7Xxmukf$iuroZm1_0_QfjVwf*vBL}U#~3=G9L|3PP$w+O$M$5Y6lO_
z<;-2YV&%$wBm-uU<R@d@4*t#BO;e-fq-SL$R>}>CS1644?@|+)J7wX095n&(O~N62
zuf@fs=gU*;8eXG7%f+=`$CYa0R<tr&IZm_M(r`TV#GP=q{@J6!E6qJsqRQ-A>HL%D
zoKwe&vP#p^uA+6flo=Ehv|j8()1>c&pJ^Dg4Fzn<n@^wY9jvR<-_w`=_ASed<E^*>
zDb&;<TYdfI{o5*bHc5Wv^w^PQF@_0zm9`A*$28czH5E7Ss@tZ7pq;yurRXCgz_FEE
zm=XWUS!!|8Ndp}il0ojW9_L08W*7=fO9SHLB@qu(W%M(pVzZ)-*CGO$R581bduz5U
zwx2XUIMapJyN5^3{W&i}tFC!kBOcxh7=0Z~-mzm4K+;>ggCP)t0mSvu0%48{sm?YP
zT8-eEkXBPclAz=!NZgg6xqZTV3!=L#V*uN~y>H;dDv#TnDpes#_6z|$+}m3ar(%|b
z4BI+RJd*~6mogz2RRp_jb5zsWy}h!Fd|O(KnE7CpoH6i0W_^dj>FQcJV`5ruhkN<7
znk(~b53M)VOiX8=>+rH<S=y#d^51}^blS+{SbFwN&a#d-S(Y{Q+dnQ)+T~u7IPTm&
z9i8x3ueL%O(#hw?>))7uHdiiYhrP_m%$%}CEhaQH>)x4iXh3f#dpbrSf@F~G^|JJg
zMBM`?PoDgCVKMatJi}X?n(#F5hsD+Kqmlh<`(XItxTDtd&mLHehk9!{P7hD)LUy9=
zYdbpQtiC_ty!84uoFJ(Z+6<%g&Dm`S9d~@+3nIQ5<iAy?3v6pj&^AsHfxFELCMk6G
z_V(6TZ!4&D7#bk^<HCxH;IOc@sa=hnDv=D4Db1x3@~p!A{70y4w%awsVSc~5x)76G
zDC>jEG0*Ah*EYcet_=^$`D!N6mJ1WN7MDV<GfPnq7xaP-T^k&_X$e)Mi*t{ILoztc
ztEJ6m<UOwX)ZX6y9HEAp4<E;e(CjkM*VoTidxMvNKkP)W^qifYlkVm4TjFA35|K(a
zDG$)QbZWiU1?hKkpXx>Znc`3>vVQ%en3#=f`}Z%OF>yKUSl~ssuC*3Hy%#M4JjCV9
z^2igDC*1z-vUSbshP_qW<%|!0+E#Ph^4d)ad_med)5E#y*_*~%t9Rr0nM*r6^N)xG
zK#X@g$D35hpeEy;vP+||F9-$KA$YoxCS_4^@y14lXbjiPOzM?z9X@vTiU-I$5f1V?
z@(D;Z?s|EJUbt`}p!yhZqHZM=(MZ<_2|8tW=vr(@3WHq>Xx6`aPMD?IXV4(O36BKI
zq6YTw&rZQ3EwuIYvKA`yu~w}0+Gbq(Dw}rBbW{98-^%Q^Wsk)}I@%?WKki(KWNp%r
zZJ3a+Q)a64SXW+762vd1!3=?EiZTxOcs9GJq{JT$VNH<4$&C`@?}Csuv?=y4b=szx
zbMOtxG?8^_jFI^FHYt)=DUditRSH~D9`(y(?EO&e7VvUc;2D!<EW-1ns+z)~>zYP!
z+o7`t;j4YnU0M}(V~I`X20fM5a~3$cjB6ykEWc0GoxrAjvld6NN^9_@RF4}~&$2UW
zt#;fp;+E$4-*02Oe|KE?jd$|!U<X=BFOK}-p*Uyt@48tZFTxvg_&?dh&wkgeygGUI
qaE*_DI``@i#jUjm{~!PIp>Y##89o(^xU+;Vnz>zZTbSIDi~j?P`ghp?

diff --git a/articles/AMR_for_Python.html b/articles/AMR_for_Python.html
index 9b28f59b2..929aa7129 100644
--- a/articles/AMR_for_Python.html
+++ b/articles/AMR_for_Python.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/AMR_with_tidymodels.html b/articles/AMR_with_tidymodels.html
index 34ee48de0..6f99ec4e4 100644
--- a/articles/AMR_with_tidymodels.html
+++ b/articles/AMR_with_tidymodels.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index 7c48af51b..dbda819fb 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/MDR.html b/articles/MDR.html
index cbe14e36c..95145c156 100644
--- a/articles/MDR.html
+++ b/articles/MDR.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/PCA.html b/articles/PCA.html
index eb166725a..e6f5f68c3 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/WHONET.html b/articles/WHONET.html
index f081bc581..23e1ffefb 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/datasets.html b/articles/datasets.html
index cc26255ce..f11be6ab2 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index 14caa4ac9..ef1bed800 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html
index 02fdc3046..f65a257fc 100644
--- a/articles/resistance_predict.html
+++ b/articles/resistance_predict.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html
index e1d06c49e..43c6629b4 100644
--- a/articles/welcome_to_AMR.html
+++ b/articles/welcome_to_AMR.html
@@ -31,7 +31,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index d76e1d9c6..2599a67ee 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/index.html b/index.html
index daa9d9526..8a943df6b 100644
--- a/index.html
+++ b/index.html
@@ -34,7 +34,7 @@
 
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/news/index.html b/news/index.html
index b2b43c20c..b8f4ec84f 100644
--- a/news/index.html
+++ b/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -48,19 +48,19 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="2.1.1.9187" id="amr-2119187">AMR 2.1.1.9187<a class="anchor" aria-label="anchor" href="#amr-2119187"></a></h2>
+<h2 class="pkg-version" data-toc-text="2.1.1.9188" id="amr-2119188">AMR 2.1.1.9188<a class="anchor" aria-label="anchor" href="#amr-2119188"></a></h2>
 <p><em>(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using <a href="https://msberends.github.io/AMR/#latest-development-version">the instructions here</a>.)</em></p>
 <div class="section level5">
-<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9187">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9187"></a></h5>
+<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9188">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9188"></a></h5>
 <p>This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the <a href="https://www.upei.ca/avc" class="external-link">University of Prince Edward Island’s Atlantic Veterinary College</a>, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.</p>
 </div>
 <div class="section level3">
-<h3 id="breaking-2-1-1-9187">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9187"></a></h3>
+<h3 id="breaking-2-1-1-9188">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9188"></a></h3>
 <ul><li>Dataset <code>antibiotics</code> has been renamed to <code>antimicrobials</code> as the data set contains more than just antibiotics. Calling <code>antibiotics</code> will still work, but now returns a warning.</li>
 <li>Removed all functions and references that used the deprecated <code>rsi</code> class, which were all replaced with their <code>sir</code> equivalents two years ago.</li>
 </ul></div>
 <div class="section level3">
-<h3 id="new-2-1-1-9187">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9187"></a></h3>
+<h3 id="new-2-1-1-9188">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9188"></a></h3>
 <ul><li>
 <strong>One Health implementation</strong>
 <ul><li>Function <code><a href="../reference/as.sir.html">as.sir()</a></code> now has extensive support for veterinary breakpoints from CLSI. Use <code>breakpoint_type = "animal"</code> and set the <code>host</code> argument to a variable that contains animal species names.</li>
@@ -112,7 +112,7 @@
 </ul></li>
 </ul></div>
 <div class="section level3">
-<h3 id="changed-2-1-1-9187">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9187"></a></h3>
+<h3 id="changed-2-1-1-9188">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9188"></a></h3>
 <ul><li>SIR interpretation
 <ul><li>It is now possible to use column names for argument <code>ab</code>, <code>mo</code>, and <code>uti</code>: <code>as.sir(..., ab = "column1", mo = "column2", uti = "column3")</code>. This greatly improves the flexibility for users.</li>
 <li>Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.</li>
@@ -182,7 +182,7 @@
 <li>Added console colours support of <code>sir</code> class for Positron</li>
 </ul></div>
 <div class="section level3">
-<h3 id="other-2-1-1-9187">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9187"></a></h3>
+<h3 id="other-2-1-1-9188">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9188"></a></h3>
 <ul><li>Added Dr. Larisse Bolton as contributor for her fantastic implementation of WISCA in a mathematically solid way</li>
 <li>Added Matthew Saab, Dr. Jordan Stull, and Prof. Javier Sanchez as contributors for their tremendous input on veterinary breakpoints and interpretations</li>
 <li>Greatly improved <code>vctrs</code> integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as <code>dplyr</code>’s <code><a href="https://dplyr.tidyverse.org/reference/bind_rows.html" class="external-link">bind_rows()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rowwise.html" class="external-link">rowwise()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/c_across.html" class="external-link">c_across()</a></code> are now supported for e.g. columns of class <code>mic</code>. Despite this, this <code>AMR</code> package is still zero-dependent on any other package, including <code>dplyr</code> and <code>vctrs</code>.</li>
@@ -190,7 +190,7 @@
 <li>Stopped support for SAS (<code>.xpt</code>) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.</li>
 </ul></div>
 <div class="section level3">
-<h3 id="older-versions-2-1-1-9187">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9187"></a></h3>
+<h3 id="older-versions-2-1-1-9188">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9188"></a></h3>
 <p>This changelog only contains changes from AMR v3.0 (February 2025) and later.</p>
 <ul><li>For prior v2 versions, please see <a href="https://github.com/msberends/AMR/blob/v2.1.1/NEWS.md" class="external-link">our v2 archive</a>.</li>
 <li>For prior v1 versions, please see <a href="https://github.com/msberends/AMR/blob/v1.8.2/NEWS.md" class="external-link">our v1 archive</a>.</li>
diff --git a/pkgdown.yml b/pkgdown.yml
index 3aed5287e..f7e40cb3a 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -12,7 +12,7 @@ articles:
   resistance_predict: resistance_predict.html
   welcome_to_AMR: welcome_to_AMR.html
   WHONET: WHONET.html
-last_built: 2025-03-07T21:31Z
+last_built: 2025-03-07T22:05Z
 urls:
   reference: https://msberends.github.io/AMR/reference
   article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index 2aae77038..3088f8bd2 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/AMR-options.html b/reference/AMR-options.html
index 0b4fd9d75..fae097fdc 100644
--- a/reference/AMR-options.html
+++ b/reference/AMR-options.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/AMR.html b/reference/AMR.html
index a7a925892..49d1cba59 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -21,7 +21,7 @@ The AMR package is available in English, Chinese, Czech, Danish, Dutch, Finnish,
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index fbba3d3fa..e0887749b 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/WHONET.html b/reference/WHONET.html
index 8ea915bb1..497edee85 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index dc523e44a..0e8a7191f 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/ab_property.html b/reference/ab_property.html
index 6af9d85f1..84322284a 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/add_custom_antimicrobials.html b/reference/add_custom_antimicrobials.html
index 69f714bee..268dbbbae 100644
--- a/reference/add_custom_antimicrobials.html
+++ b/reference/add_custom_antimicrobials.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/add_custom_microorganisms.html b/reference/add_custom_microorganisms.html
index ede7278bb..cd56afd20 100644
--- a/reference/add_custom_microorganisms.html
+++ b/reference/add_custom_microorganisms.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/age.html b/reference/age.html
index 198cf295f..7d29eccdf 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/age_groups.html b/reference/age_groups.html
index 687dfef55..bfc22f587 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/antibiogram-1.png b/reference/antibiogram-1.png
index 6108d886e99164f70b930846cec2e740bad44971..3379452e87515022929a8110d7f6383f74dc7398 100644
GIT binary patch
literal 36597
zcmeFa2UwNavMsu7a~o*`0s=N5DhP-ONVZ!95fK#yB?}^w1tdp9w<yU51(l>25Xo_o
zF%Tpt$w`Tlb53{6Wrw{_di&gS&b{w_Z}aWlD6X~s|DUsF)u>UUio2&z$!_?b@p}q|
zvO(^+^jQjJr7eZBTxZQH{G`$E*)YDX(vg>y#&>+p98Gt@x3$-fU$mr9nD3DPFZ1pk
z(W6jyQRJk5I%gZ$-|X<9S}#*-xUBd9{h-_H&3iSE|1=`=DEffeKEr!EOAgD`7gV0`
z{!KNXO~p>s?t8)8hXW54j0UQ*tU2~m@%W=1oV(ag6plP`Z8~$`y?A}G-qPZ*uo|zx
z8iy-m&01X(szpgcb8}9Ui~W3M9%asBjv--teG4vsM{dvc)4o$_<PQ{z1^eff<ooe`
z%lOFmBPZ6{E+c<9d*ban^8NnV?=;DG*9Sig)872zfh#@v!LFApGRSwzzTN-uM;Q8E
zUv_9<qE<^WZ3Ts*dLiG7`MIn&i%612vhl@p%F4;tj!`Ha10UJFi&LW*{?i}-U${dP
zA709?k~a|%x}||4BgFz`Iq!d2r#sM)RPpTSox@#^blV;%ow{44C?(NAH4C}^xT7Me
zR+A|vYu0f7%O)4MXpM9s`%+KgmK|f=Wv!hel($(WDg4b(7|R&eefP_ex9jdd-xF-h
zW|}!s^ExW3d2q(6uO=6t#;ho{aJ|WSZooWun@SFS_H^#vef#8IpB)X4j^^#{?X|W$
zrJztE8@!y-pYFj_6%#%Z7`N;68mzp?bajmLwk>;3dAPZ~yR|w$_R@z?{j%UQnwl(+
zPja@nEG=9}yn5zj;9-8}xyDwRp_a_{@hU~d6hjJSQm9>qGOF-)UF@Ch`!C#Qe&Nj`
zA-Zb)j@agr(9qDYpTd=Py3CK5&yVEiw=OMC*)A<i`R;xA9siZ$M=#_llpnO4QiiUU
z+Lm6}{E-@ZlG9f%Z4KkpY+uYCjqhS_o#fo&uVH;LQZrLDYqmNjuhU;Ec+6;_N@_uA
zzgCv`t5ZRYL)lBtC!ZYJ*H!#@i&0JF-A2=N9$bF2m6g@V`*nOd#g7Ga7w2c<tOpvt
zHl<pw-*I4<<Wy=|E}iVoIJKB;gQ{04Ls@e=ll}E)E?#8V6LkHo<4lp3*N06~;%sa}
zCJkGC_b7Vp32{7F6Rp7{%j$I9aUw#?Q`bx6ReeZQTAJu!`cRf_*JFL155I0O|8zz+
zclgVPcHfZs4qh$YiqK;t<wrN=T9!%B8`Va821_{P^@N46_^VsUUGJ}ps}M>`O5(Tb
z{bbwmo2*V};Y0I}Ki<g^=?>g-^v*`ld{!6nJP(E~!p+x}0`0mUHzpNzie}IDMwkyI
z)m|~9HjXG4s+p$lJ|)umz^%@%ISE^wGW>qs^NbAfR|)|&9VU;;q?Q~Vha6hn$)>`f
z>ey=69M}9e4$2Y99m0b%`i-%dj!tGqL`1aT-g3&Vh%Z}wc5<Nc@*MWTva9$m7AqG&
z+zFfTlAQfYF4q1@Nzt>dEojOwCL+T9v4DQwb8kt;n2gaApK5AkU<+*>zKULBrDA;c
zQ-hgG0nGW9B@F9iZjbDW^=0=KKiHjoW%#@P8{4>*b9$>I*xA~A<arHhj07WVCvvtZ
z=G4XM+K6ta8!6ISJoxJ5<M`${Jf!V-g=~9;EURZkbQ;&Mn^?1oYF$PP_UK|o&uD3d
zNKE=?&+lFw_0P`HuxZ|=k#_xVjAJMF84Zmf2}zg5`A1SN&K`_{T&E;wIs&B{iGhrN
zt!RuAN!EUM^FpllPwIA^4@YwOoXx|M@uY(pW2KM8?MAK3_*4`3Zs$?mn!U6zq&q)7
zLblt)CgaT;uB;JHvz*G8@(E>`i?e+rFyDOErRn?~hp%aWs;)M%VP@>G527xpi$zBy
z4`hc7rVOecJ9cb6<DtD*N&^}Avga+xArZ^8md;t2YGsA(@?ZXVKS)!|-)IG;Un0-b
zVX~eDuh=1M+GL%7rR2%BgexWYB4V;cf8D^8tkl@h&@eq{lg+el`~JGYneJc*-e8Bp
z)Wf~6pX-oCIddkic@Aq~H8NVnXIA^_(oRA1wwy<>?o(f`>h^u@65Z}`fMtcMpDVrg
zrbES%Qsd=E)r|ey<8<@$?7D;KRWz*5_fuP)&zw8=K%xdtHf5$OpgpDC%e^#6OxE1o
z9Nsq1oiA$#waG&M<f8*Wo)Q~K{8}BM;v3_V&uo)JpEdl;4oOGb>tD-%eDmf_8_tYZ
zxKdbNiE!&qK|w+OtL0Bqx}F&6WG_s*N2yWsmTwZ@yoK|mTgc*g*vRKwn}kHiF4;6$
zH=3AN9HY-w$r^q56^s4p)2AFIMTu=vbM<9ywb_eS>zPFliC+I2s}ZnI=~Yfgfp0qw
zpQp==zsp@or&(zO0|RoO9t;9H*l+V@tJ>TL`;<aW8k6g-sV(+K`!&)IZ4~LaKRx2@
zvU4@7<27l=167l&rKaDVy47mZX%RFxIlv@vrP$5Pc`RV0!6+sd_Zcgj&Zgx!lFN_t
zVf*#j9Y*GIvz81I;*H?%8x6FB`vkwEOtQ4+NeGxWrA&Vclib!iQ)&|*v(PQIBw~t3
zPQmHWee1fcB1|zPC_4Y<%5|Y|7Uo4f)XVzWJenDYou@yr<{8CgZYRszWKqOZFGLP@
z#{6iJIWC!(z1Wv66Qh|Knv!zZ(8x$P_ujUVe5pnA^uC4%U0y5MJyzfF^KCTKbX7Kl
zN2069P}^fYepk_v%`{^q?`uO+->F*#@_d<xN)NYt;IBpx9z2kS%_N$&O0L?#xRtDp
zw6ysA@P~BA1#*&!wM!Ex^7{2&yqvyuwuVZstsJAtEMY(H7GRpTZ*H-(vy<O(die0y
zr?+(4^E~4WtKZ<X=}V41PQe*T&^l6BSg14n`JMS>y}qn;{~^mz93*G>UYW2{L2c3L
zgZJP`MDe2dkU2PVkG_~}Nrcq)yL*B^Caa}eo6L9ex!m>f@tMYVhC3|wSIK?L$;s`s
zEavBTo}bpJsSoVS5YwVCTK31~%Ury8QGDW)Qe(+Yg98qe{cdj4P2G~zX8M=vF?5tz
z8wTOQ#~(I|?ROsa4Kbc-2wCXYZ~v9a)XlBVK%8pYoJL$U8yoGuoEK*G@?B(inoH03
zs$}28EzknM%xGu5bxh`@s<gDa%fduOy3J635*H7TXG^9-ezl5LX4Q(@8U<GScU%%*
zlelvk-&>Z~Z{FN<b2}9zDri?)uOE_G<>J|Vw&pXp*35}h%Rk^U$8uIvpAHmCEw)Py
zj;oVrvuQG?qZ@h2=M-KsB{h&(g<A@;X{m2-U_6w(;;3}a)1Pj!u_ZGZ%Sva!JHs8a
zk^uU^-(R>P`C0VEgexYsyluBGZuTs>FEai4mZc`WsZGpSFxS3)cY#f0q;nF=B-5)#
z<a{}gN#~??2d6iWrE0QMw1*CwHVLR`+UY${a48cT%5a<3ejLN2mV7xR1nyK?I?$@-
zqWRlptNTAi>*jlzkL1$JO6LTMSjLwL7sosGMNuuHlV0xF^c_#`4;v^BPrppQ(ikw!
zXrEP8kf?nMsFChimg`QRPlCr-SBX2~fi$p+b$%=0j7IWMO_bX6GMk>t-E3@WHbX66
zM}FmFZ`}A|>YUP0^8s$1{KwxD^dv1!oR9NDpVrW5lK=jTK2A*sj+hw^#JjmEVqfE~
zeR<F6Ej%UK-IfrtV0<!IT!d5M$=0wCXZy*SB%|8A0JAIx7;s&<ONYHD#Wk}i#k{OP
zW&Y<|m6er;VgKz>)RubyTYAFb#yge<44YeTEMJ`mkDcATsw*tWS{1&9(|N>QD<|?|
zLK`vKET=gcSdWFKW~c}`hPytmtXsZ8tXHV1q52Vnz?I9ad^q6IDQ)*OvaY~E`R3y+
zFLDoNPPJ$xw^sYRy1g=EGTQN0NbtVNn`YtcnG)Nv5@8#A=iX1@VkN=NRkhATnNw_R
zjLmCIWE+DM5TV`NG+Iv84(Gn$=P?hmBvweybjD~Q_ehJwP%hS!o{n|$SrKo#ahTAb
zdz)acn{GYuBr-ycYC2M*W@aKRqK#8aM^}+*+0EG~X#61g(uZFMf&&hl>ySkz(WpPN
zD?UCxWxP_py;7d9#H(O(Rr^m*cdxNsHhJU&qoMnDo_jdH8XKkApRlYjiwIvf60cX(
zhMVxj+opkZcPz|Jd3!O7$smMm-L~zRhK7bUPh)2IrLcC#t81<<O=vAyr8BHgZ#0EX
zC<G@AymeNS)9ePWOzDp=YLi+TlhTE!<wsokc=_N6`8(anmnf^o!U@J@6bFCv_Fzw@
zh&OMZ%;(Qy+m1;aQ+NKP`(?n3@rvTmj_+*xYItP4b}|ru+%`aM_4;KUecpIaReRRl
zpgT@LuFKMtOMbrOWL)!`^i;Ba+8Z=oCCVdIV)~<us$MA+^*IPea?h15&6k<?hKI@c
z^J>Hg9Fgv~KM<fu;uki!e_Ttxe`9m3*l=+4HtXpxcVu%)1CmXQS~BcBB}2xoB>N&0
z*x2H4P4AZX%Sp59ix<-#+FMJaz6PSckg2huLKC8U|NhUvPFS)_?p%^snjcfha`|92
zGo3GmgMg*{S{0^9Vjt(pN1NGYJlxXK(xw-NT(TWlV}HSsD?v(Cw;Xtb-=gCq+#aBQ
zQjwrWK*X2ri9#lf+4VZ39R+p{Loe3vJbZwSjqL1&IGtQms@Rv?Ti{<7=Z4Bmuf|6a
zjMln1NtJ07ni3T*W-&1CZf~x@xj8)gkzIG9w>r*oX4Dq0mMwHd;S`%iwhZNMi(XTT
z{viB_jQ^JDv5=*mbaZ6T@7=rCKNd2bvxlwi7XO(K>sw7s5H#mf6#-?N-#oi?GWB{-
zWpi|Ts_wfrFsT`&C=OQ<BI}z?;b0qf2wXY2|6=^*bcOwfhK4!?KD+B(eknWFn&kul
zSt>T<A>8UHAojUA#ay?^X{wnYVCRbCz4xEs;ihM6vsviqwB{OW+rq<wiGlWy5X|AT
zTfPnZ-Z&^<xoq{$&9_!<xJOXcBsH6vTRGAV2zX**f`;s<Srbv5tqG~MGj8l&Zf<T;
z3q6V=d>SLa@Hzg-CRw*{=Gh&Vdv2Su6Z^kyq*I<}`Vhdk8!;#EiN0FdoYew0OY_}Q
z`LEAB#|qRJH1^_%23nSi(9xNf2-fDPnPqOn<$f!;tkYBZlKe;-XCv@>rCbG!&p4fA
zQX>Q(!xQ^5r&rh3<^w$w7)v*D$u&((RfiU34tWdOF0&XOFmn-;TpaPvf5fk=(~xM8
zo-r`^NZ0G`kjv7X!#oSwkalk=Nu+O^f%K|R<;SECrtJ{3?q|t1eKz-=K5KJdZH%`R
za?_D^)}@q!G3Ujx5VNUn*7?t?&8yF=<T9CM&}q%L(No2&hu^KvXV4F%Q+jo>-oPon
zOS2ovk1*${z#Z9h%|mTA*>f5sOiSnRP@Cc#6OJ*KI^a;Svk@fF7{yP0PK(A<h{nML
z-V*R_3v6#AYK@%s^3i@1r>XQ-PrV|)T<(~RAILUZi#pd`x-)P{SvBptK@+Z~P*ahh
zU&g6lD)ef1-c7o^TSvy=k}OW2JGYfy{n}3?mEuRHINhhU#JG;0*MoS5#$=P{iXoEb
zP3HNzxc%QWSoix+?U1m)elkQ-f~1-9e*5ntFVA~>W4Uf!>?M+@ZJj?NL62i)K2fcb
ze~eyZD~_SJeu$G$3w-QeK0fbTn<NjDRFp&p>vo@20EF%iLz%)Xj>GRBNjlj_UWj9(
z0nTE5$t@u;hy?0aB!dQFQ%0p$lEC1!B%{@7DwiC){wv7EuTZ5daj)%(Ri{X@B_n-V
ze>QQPAWX^jyk^UHE9rCO_5^L6YPQiT4iXdQ;^rpE(tM`VUv_D6fyqQkS$TVVzPGo2
zupK9h^Yj<no|pciSy_^^6Mgjbnzon7;?%!QdS{b07Ib*5Sb(_Kkv4a}yeEQ21S!)p
z+|TcBwMQnD8d`+A=OyM`IPl(f^VQ(c;^4)aOEGo}6Scm)%d|tx@rQhp6OesRPFL8Z
zZPtfu4kpSTmR#h=(>N%k?wUAP>7TW=$e+)<rz(v4@l__ppgPdye%vZP%3MZgkw5JN
zOd1jkM9zUna{Za(^GfZISOBBx5;Ny5IF}zyuf<{i9*NnQI*tXjg1(4xS?CjMezh80
zlEIY?nv}`4I7sy+JdBnwqx{|e>hb;&pq^ak!SBQ>lF&$OW&|t|fj&&!D$yPrnMHF(
z@>yAr{<{7y0=x{caHSsJbHqKqKy_I{0V4>Rd`=?@rT-)OFTSGg{}*X26VM{R(^$Z%
zr6yuly<4%|Chx$o92Z+=wyfy#K{@|eMQ(0xci_ci($eS8pErij{Bo~O<*QnUd)Bcm
zfIL~Ca(1@R`1pg^`7P0IuPz^C(9IDyf@c(xZiPEEZcY;<Zy}i&7Z(>B8=)=EC@J|~
zJJ!!V&NHgln3aU(1+Aoz!uCYxE@349d)T`F0-g2;XGjRY65<mVW+z7Sn9OnruR;=G
zIPt@9hwZXbvlXV9c`#<(!Uy~K^-BY7fsR7S!7df)&LPyx?%lh2S~ilWdiwH(=;t6|
zGtK#qJyP{OGqba^&4x?85ixmFNbm<rwNkEK+OM8+;FV$s3rR6UGcq!A8)$T9;)`>9
z6{pXh{eeUYCexH3X^~gft7#xPK*;3YZ|t%%PEJnh_B}60?nur5Nc@LQtCP)6`>)SN
zx&qAdm~EOWbOy*xP*hdlEN@x1d{xd=)>J0)FM+nFw>I7bSv6qY2rx$=k>hMth!>yB
zOcCsm4*sSh(_w0dpy4_A(VYJJ1l^J+f;^SKKhjIl&n4$MeeWV2+RMg9$bqU6d|2*G
zcL*yT9pMKP8flNvaWrDRZEcJet6H-0T{kz)OnX85(t<r92+(DcbH;zZ>W%QlqG}ux
zSQU`rnX_jrzlx9?&&>^KVMP7FlTkpw+&oXF0LLI?NlqO`_tS+;B>NY!&}WpD{j>%9
z<zZJZ6B7^3rKF@>XbsJs&P=iq6!dCpPR7IVT-YGiFgc{(7_#ANbX(h|Zk30AezO-}
z<r+6q5vrb>Rp=xyxab%d^jJH2zOUqx-M`_P;n5w9kCIIqk*$!t0OaP06DKT8Yac94
zdoQ`ojCBj!8&3~^G5@eZxPSImxem<3@$qgxavPw=3X6(-d2c!0&wA(r_DzHu_4@T@
z(ZOR5@%gL!*E+d78sM;0?vC}Yq<*g!;n8AbXvoH9J=3YzsGXdZm38skwv9eJzc}Xd
zA@a_z6W5uPR8;(tX{?l}wN?<B>rZ%Y)|%zX!gN6U$hq_92jV445(ao)<689;)+h3<
zY+AUHW33J806`YPzo;oGwGZtnJ0UNh;3Bobrt>HHx@0qx`uN=2yHq{MsebrSZ_mMA
zwVB#jt|PpvdT$#6$lcs7BpIIf?LKrnf_O*kF7-x1J%pun@kMYegQJIW$*3_<D{*#X
zUAMV)w4Ry$#y7it=B%b>@PHaN(i3ZPo4bMf5IzIDaLNQp3fJe71h9Sjw|u~{)umY(
zz26oW^R@ih2WzLIvV%OwaOb$?_<Ciyo&>+gZO;Ywd^UL~f2couX%RUoaMz(3zER*4
zfFjVR8TEfI)v64Ta0R7jFUIK{d)`d~6B`?0Ew50QlQZz~jxxjW_HEm8@Berd9tmhv
zj5!9Wu`G)n2VN*K@#(J@rmixR&XJn`Ae95|hKAr+B<yp#LFw*=Ys4uNcE)j$F)=YA
zXqM!PYy%Y6_X38WUS9xbP^rfe{o(}&R-3);mO@Qr%;b$T&t;rCf>DgQpZJY`c2hsE
zqLQ!Yui>{~iDP`oG0<dnyKvLBqvRmAgfD<9*-ALs=P}Jlu2L2z8=0CZ?6uYog`7q+
z=Mi9oM6H1Tu4pwzy7`5Yg;y>T>v`d;Gt!nT=l|0URjh*0w5c2jk_s?vfeog0vG2M>
z>}rrK_H*uG5;jdNVB~oP3b=mguCt$k95ycAq*XQBIa7{e2aBf$HokIRWu_DmWHRTd
z#4s6`&%%_wFknV=dZ0EgP)#+E#_U<OiDhTVOtJoxz_{Ox#K=m!`aS4n$e0rqupKKp
zc^Fm*V~p|>24nR)I{H+aM@~twgqUgiK(d<~;p-fM^AuA;-w%f?I&s}|S931<Uch!;
z07FKH`$a!m%z5-K-6jbiNui`Ng6yo^z4i7~iv!<W3Km|-eI><ptHI2^wdB&2{%wEw
z@jpWW(5b7kQH~p(od4;~ek5N<lD#RvE!W+b`<1H)lQ3`34;0F0_Q&u3(dtn(XAiBe
z^mH?=W&r5HsUh>zRpe@0>0J&8C%$^cdGPVc_uF>IXk|N(++v;I)pO9AjaG7Ub$cBd
z=|R9e*(7^Xr{H~d2KW55+8#-b4pLA#|KCy&Ci>e!gzRpyZTDOev2Q2nP%rQUFN(0d
z8syQUYOqJTfH-pgXHMfNg~CDg97L&uz|uz&4%U@=TW`baa#0&Wz5=H9E@8U9-1Di0
zQ+pwQmDI$pCgFsJMn>)@@7%jtPfw4~gD;Bqu7PzGN=kye(eVF{6L+o}MG$t+4|}!!
z^ifFLaL<>Qw%)EyK)vKo|B2$;pUZdJV+TK5&kx_f2PO(|a-18W%ECkZ5c%rbw_UN;
z0Yt;s|9pjyT;=o{<!E&z)5XWrJZ_>k^=p57cZiE5RtR@=0IA~co*x*$6%MhB(a~P#
zBb0Y*$h^CVgFs9YWu@zDJ+U`DU3;Wv<o2s2V_APtQCHvj^5si9x-%*&^l;B>RnF}o
z^%lT%`Ly-8Y+cwI+Z)@*NS?K{(7Lo+W7W#u<VI6@Qf_qBK*FU$vhJRPdr#-){*oH>
z_!|af3r$S?6m;i>mn;2Me!>F41YQ&!Z$j}&x8vh)$nfg*{Bs|wXg<Dn?3ZUwbCYuZ
z<QeC{2$t)3iI0AKA-{LcP{!D)$&nkYS$2RwSHD?Jdqw<v^}{slcJ&4jCO`UbEwm=d
zrBSq7`E_#K$Q=Nbmq|Jk!w0@ttE_YwG2Y*@(PfV6;*RA_(iZGF_wOtq9Gjd!Oz-|0
zlz~?cDZ0!mfoNtT$7*(_%q93baR_(gq-oCo$O)Y8Bk$5tv<O8x4f`St+%{d|?Yxza
z&R@&fPThH?lWfiYGQ2DC3J&D=n%T|^KD^I%8K(zZP7OA<V~0-0GUA`Vzp<Nq`?qX9
zo@TK3xsVNzjDegaKop=!Ha2oy@CZLd@3qU#%_XSiIzwzN?ew^wL(KYRgT6r}!A3~%
z6w}t%8SSs2UF7~*)UNQS@UyC_jQKk)Pm+WEkXt1WJPvr1*bf`sfxULCpBddK)*t&q
z-Zk@E8T>`zPpg@?DoRY8BX1aT8gdv3Xr1R|W6SeqWkuz1&z9&lb&Y>s<PWzQFTK26
zN7HGd8np2V`0C81#ku-?WU{5F7q|SO1-OeW?#FkA-^-$gbne_a4h@3f`;}H?9C5#h
z5^khcwq(!2?+Xdj#Ke+l4fIDxp5e&4gumI2#7BybZCglMaGhc6Ydk||lQq0F&jmSq
z9u!IDrhk)Vxr-cp_XUM7C`qYT?#4wpWZteze1ww70pg2ru>P=X`9rkT`m5BxzvA@U
zwCg=S*VoP^g3Ie~A04%zdLIp*36jY|E+d~ctBkl?O7b73fkvv?JHxs&<|B73wNWi^
zjTG#-q}DhYg(OWH)#6yj$=&!H948~wPr=*T${x8=U8}3BcWb<tj6zKU$dHYV&9@+>
z<-Wdi-bZ=>ynQ)Y=e4x>wiYIs>{<4ywpN(?{vDGZwf+q-jg>P?e0BaEefp6K0ZqE9
zNW2;o4U{T(_gBH$#3xw82{Hx<o1IEwtE2wS?b_MdA^Yn7WDT#6Wvc>miUO39#S;KH
zqpp!`+Wds1SSU~357It+@nVnu<LwCpQrnGz^y=1(?hnz;QNMWWe_IRjXGr~*dFUS=
zee#nV9XmNQ1bxj-dx;IYk*$KI<foXo`|vKCWF&{xjO6<u{Pt+bwBbm^dxL1o&L8b8
z+5*?#WZxH6K|&Nl9p(6KS9;f*ROBdi$SYyU4^R!r@#9hR;eEdA^1;WXxKBCmfe=r2
z|7_uUoZA+pAtBe-Zaq*WfS*OdVY=Q_QSbkC%M)c(7QeZL5l;s4hW{Nw$iKS?|G1{)
zKmQSL6e?-1xSt$|+!u1bzdwI}I|3HR1-UPWKf5j`>&r#c#M~E!l>zQtQ}^AJvN2JJ
zcDQ$)hna@bLcvcr-n@Q&JZ(i?1Wgcg<P9NAc+<XyV$t`m5xPk8ng4*>C%H0~u|J>%
z1n6Oe6LMQy>FH&WL`qA;>Sb|wQbIc>hFYaawE-W0T!MCob|f5v?vhJe|NSSJ0yc~?
zx8mDBRlV|z38G^yZitGB@pf}V<*VPgr6v-H8bEDw?V{p3xDw5s*UNv3v*;`&K9;bC
z?WCCJ=C+!}b{Fc24<O6w=QfU~Q49ZpAjmkr=KD;5Yf`tr&Ty9-g*0;JrKO2yJAWQ-
z^%AMc3|uFn`u-1R1C_r<Xe}7-zYq&dxA!!WAY2E*Pw*2$`3AqM<U1m!fUVlIpKh%>
zRu(Mb%gb&~bQy%SV`C#u`YTf6_uIDpN&p=(V2ZZzTD+bV^9e=`cAo7?>F|+<cb^|C
zv(caczT(J}8a2V0g!BU+@8(7ddti@mbDtnwAU$H6X{8>8qOcjh_6Ro>N!fG_HR~bq
zuDae5$`D7bEN1*&p`=FwRiXtU3rQgr-uk_-q9g;bBl4;*AMWH|y2I>u?O1`>@Z05J
z+9-7^$=)PdY}>Yt)U<(nenKXAROoZ&belT}h0^26yC|;S+W#<@6D}l2{LtI1KahD>
zNr1o8yt~#s8_H!5v;}|BZAvkRcfW7{E&fsda_#@PiVGU4U2944-?%QeS#%1rkGn)~
zGS`iAoai951scf=AUSqgeL7R}hK_8-=jVxquEK19)27r?G1MHWbC#UGGardaqZ<me
zU29SNIG%R<<^hO2`rAj^^Yf7-{)Jg7uAKM)DwqhGI%Y)Bs3r$6jDb+NZFl!Ta)lx-
zGk~RY)`?>7TT`1N)l(17O{3oIMsgas$~*5!7Inn_!YwKt3EwDt+O*gKFw}e%$<5_u
zCU<#%|Gv(R$T22`A`?r?X|6$a^|GOjnhpa=B*8$nPzq9*pz9*!)f5H8hepGLh2K&E
zP$`}Wh@52(z+G)b;R2s>3{|BzLe~R|i_K0AWfxh~-pO<9Z8}NgN$nZ-2u8TEGu13>
z=E~#<$XbHh_;vF<4o}wUwu5srwHRnIS!korb`x%~eO{pp0{Dvh^@?K-rzH|SpyGhs
zYHlxrV+Hl2zvDy{vYfz1IAv)R1yy4i>m;0OeF%X-(%E0?s4FrW9|`E6!t>j<-6ct^
zOqoG#w1zsKk^kD~w?v&m>Uqdo#HX4pNim)f%|v!VYEa&e*`(4oKQqQ8K`V)&@Hxo3
z%Hl;UjieygnCVrckuyA~OADE-|586UAHDk<al)qceViEsv#D<73ps%4g5Cay>`Fn)
z+t^3LgPP|t=u3x4I<-A9ioRQ)pr8Ndd_<_j)Sz9&HnM6*-p(!YKsiS836fJ~yDSB2
zK39p+B)9ztWMBsLRjdrdi~agiM2aG0ABhi*1G5#_>)KU5L<11NuMw%dl|MUr$^@ir
zfZ*~S7M(Ucv<1RC`NU*ZqcRrQ_`U5?c0P;4evonN8GZob6v-}y^o$PKWMfozKdiyU
zKOYo-b6}sVx}d(sk-BYv?ZQdUs>tM7ak;;_?$7;dxpMTU*C46TnH}$;qvN+7{(L2~
zCiIv)fULYfZ%H(KF#MKE@n=uBmFu=kg9;h%uNSZ%|N7flU!-ahS668e6CE8M%Ordh
zuRLo23b?)`QY{@fe1$a|h2l8dk+zO|`!=WYsw{syxgvvZ<n8iJsM#R(vbJjhtJsFj
z#4VvzlpCK)2e^+MN<)L0FJt&;4`wmAQd0F`ovX_qRRi`raw8%-y0oPeG*6W3!|Q8T
zetGW4vqF0+an{uammY!DLu;@vEHJ*vQsD7PaTGp?YEIDZ6qq=YGV!RglSewDtLpSt
z3_2S(x5E%}JO@5Z7pLNp%~9^iCrs#IQ)=ARicpdP4mPKgibp%dmmVa$26Htj(}Pc0
zX+l&+D7Eb&QQ{j+ja9rlv1|zJlg`KcKc+i5lbixY4=<E24tt+phB9UX4&wA&>r(J_
zhLy-aH`M1xC`a9cmI(~N`VI0`og`HUsIDQYnwa4P%_Um{DVCaUFTO+0KFO(&Mr#qQ
zoPIix7M&@Sjy&KZ%GFLVtn_;Vul-!9cls%KdEkY=_3IU2D+uV?wfegj<*P6qB6CAb
zjEIaZmTUo2XWWpukAQycb)rGL3IPd67>}E)KFeO5h#}UCP`uZjp3e&!3qZS{E`Uo4
z@*)7??EBwK82j{AnQ>o-f<qdh6$I&b=T%jo&>$J5pL3ZR_aW`Kt}o=Dx>^l3i4ax>
zF;(Hum`Xzj|HEX!e;%6sPl%5Hc^CfyWcz=N{sb(SC(^*x(`}W1=S|z%nz}Ebq>-q-
zJN92V`?a>~E_9a$`SRXSKAz3~M-&*Xrb6syX{v^*h)Kha_X$%o8uBE6B(<=uI;{sq
z_iLkWjjEdJj|*NTu#x1iK&2IutP+tIQGjR=N^Fl_Bl)B?iT0-7b5F!u3sdgTJlZ9X
zo0p~j{atqLPdKfm6;tNB=C6-LKB?y`NKV9mi>&>h7WQAUu>Y5xd6Oqq)2LK>z|#EQ
zK)eP;baQA7DNa*jV+@twLS#|*Iy~GUT$SzTZ!HqB>3<SGB~RYXp{kO2js`3#{m-tf
z%m_lIJ6x2!pHg@9U-@4_qr1OKBq2aSuxwHMM39@PZHbhdX!{UNr>@YFYs%bhd=$|(
zqjZC8<@IZ~ACLZe$Zl|HacC)TCB6D)0Q358qS_!zlXfU2?3*A2KBI2@Ge5cX+d)I+
zP*6CPJA{lctY;G1Pb3Y1t<d?=gdD%5H>S-Erjvr~8Oswi2H>jIlWoJLC@;)S-(1Ig
zbu@!JI)!a+8qm~hDZeE%|Kj=DxptS5^#|6l?RfE(e#rLD{-f!y!|nD7$jcA3iCD)c
z?yuVTP|p7LLB1g03s;z{J}G(MvG98G%#K~KnCaf@Wd@I)*c|&Qde!pc;>Cq8X4V1+
zmLC*wxUzlBak1SxrqLm0LDR^kE4RTY8^TD)9Q0MiU7$?5f~pMjKH82vRp~kX)&a!7
zKPwR}m|6v^XgLW2z$*H5yzo6$czf-Z_-bV}J%sVHZ7S-&c{}y%cG>}QcX>Ua<>loO
z*T1r4xS(oM<P8bZm~!`HQ7gll5uzB{UWL6l2qa8c6geL#d;|%#D|^Dek>ua#tuT)o
zQ@{3ZWjz*JEhrf{rs>*;4R#RJBtW<5u~X#Wot+lPCtF>XAbdU-dYSBl>vqwt!;%0)
zo%zdGt-op&EH;?JE_V=EwjUXk(aBb;T8$!a@Tv8`S=7jfpF45%=m*hmgAxccEdZ2X
z#KpzUH$nRoOnMD)$Cjm1OY;!!TN`GQkEcZRoqn5+I~i@q&Eshf>YecEK!MuODj0&M
zl=<n$`oTTNMFuROt>^~d>CiaF2Z9j7L3v$WUD6}46RAviR8$G%%YH;VOiIwOt4HST
zdFNvxZoo5aY`JCg+*624Dmo@cOLrDz(-^AooN|-RHm%CF72@L>sTO*&g$Dk;y%UWq
zN#%`^kue^^(9Y?0Z$*GSrSs<>A~EiUT^(0YP(XI)SF7cc`SUi~rewT4x@-tSt3vO9
zn@wH8j-v&m?a+`#R9hX&-te9?z_{`^3$EyMTzaTG%K0E2xAyG8V(!)2;0W=6wSft_
z$s<Em&s?XF=JQHrfy%CZv`EBUo%zVH46KMiY_C)c^yy+}Pv!EcCcai50^6n<)D3D^
z%clc+u-<qisCd?v@S{_oC1$@q7l0*j6cEE8Y)QQdwb_S*FLV4XFJ!wMW-H&73?(%-
zp=+qfJ7DRvstQ?oQkTns*plJk<>S^!8Fu2#Aw~YgIJMrn-BZ|y1GrQk&mYm(sN>G#
z!vS?yYi;TP%Ehmuqob=00*vG30vcUURN9=o*AjYmeEY+*;Vp)JRPGk9-d;lqMQZt<
z|4_NbAp)z0EBYPXByq?E0`z$3xBDTGoB6z2>Jhk&lR=_M;;kU`TDwG_EX6jgmK?s}
z^`MHEZ1Bto#M;5w0|}+DQzc(3XiqziU@Yzgt)AQ>LtFBzo~#^bZBD`<Ra?hNYU?dI
zdo`j;a%rYaA3Y0w6RJ-IjMI8vHEVpwv^VqE%db3KFVALHH<+npXfT;1o<Z_czL}*V
zH`VV%g&OUF>~+knY_Hpb1JNsf#BT>drVqy^@!=!xJz;?sCjzMY$5mSjpGM7%E5CPL
zw!AQD1H(ZMl(H|PTZ~Ad)GZ2jKSp(q>;Q^f(HX@^9ep^EhMGiE?VdK))zz^pIgwq|
z{!sqK)y7vrr~paF7*vho=$tABdlA?X72!}OG1cS<5xIKbXsw2g#>@zwUre>Mu}1Z)
zI1c8{Yj2k^h*(_CnoNq(gTmo496EYff;l=Pc0Xwqwzb-@VMAwrb+u7;?nrv;!XTPN
zURN7{nmUS{HMO*MS&%~OnSJ)|<f%`*w2wH)l{ClI^y{y`9ug9=wm24RTAK8?+v`k!
zW1@Or>(CpVLCCKJ5O0tJ8fFe|n1nT3%d8DD1hWsS@+2CveiO0mda%3&%|d(Z`dU&=
zE@nB}`B2|~wi>t<<1}Do6hzu*CiXFj9;=Ev{xqS7hwGTxnSBp?qhJq7&g{k}$Vv-a
zxo$;wsLPWL`~6=LkCGhfleBaiHX`e6-eoEeS0TnB2f^WKXH(?Raz0?hl2Xo6Q)@LH
zc;w>2`l+P+OqTj7wQt<j=IBU&LK&wVGU&6;rby8`i(YD>M@QK|04h444rpCne&I$@
zjzY1AR*D-5uG&UJ8<g2b$FRX7ZEoy7i5WK6e4!}o_ER&J^XW&^md5FQ<Q?S!Ru|u<
zBjJU9G5aIo)^HYu8jTYaBxftUyTD5oAh%9?<6*%=<nN@fX1mlQWk^0ieRqO&k@x9`
zzO^208EX3-&?N8*bU#u1fGazf6*mf&!xF*f6-0J$7W-PMG>-r2S<z0}nwm~vgW@pB
ziEu&Zk*QzkH9vx|8uc`DWXC#~c!_j9ir%~D<=K5`K`TW&OJkru#QX)|>uYSCp_iP2
zg!+*{nYe&2r$Ql^XVsIfR{~5dW_!YH>`TbAQ6eI2-_D^r)&Y6jNgv`cX7H4c&<Z3d
zH;TrrF48tbdQga)ek^FD41A)|<8u(rN9xw~dXI%nR3U!~Cj2N^R|ZG|WIP!klEMLe
zks4xxs)jR2oz&hp5f)I}T!uYgL<1E>)sKnaSj~LBeANg$x>n&q%Tz{Or=W3_^HE2?
z%uJqeR)tQ3L(5pO<77fe3F++fJtevi1=EjaE_011{2y?T>v@Uxz9t;tfT}YLMFc7@
zOK^487GP8HWu|y%@SPas3s$9;h{}89NS9!;NSS}u_)CrgET-sLnlS1}Oyx6I8=#X?
z2mpO>ScJNJ!1Ve=nlCkXJ@Fetkx90Y22;MCM?~ES&(J!ZCwvg95pi5Hs&uSC(fObp
z3EvM+ycB%pf8r%!{^Wevy$*-6chc@<oQwH;+YqP8dP0GKr6_<BCm7TialX0YOCzjH
zzu|byf0dOgM0?Z&QnV)#*EF@$FVJOirXHbM;L`is9Q&4N7UF7!*xLwxO5OI$FLrq5
zcqsNeLfv@_5n2vHCORsvW|DNC059YC0!};Zx@FBp{Z43-Kce|(gtYw?fjE8)1!ET}
z!6qYOZp_|A?$xy=R8c?jGTc*%7W(9fP)!6NVhr|lSeDu99O*|OzPBDx`yf^qs#xY!
zL~j8g2|Ry4tR8(*M=j8N^eQ<yS>PMO`zk29`?G@2lP6Cc(ScD0`ZB29ON^I5DGM|$
zbgN{~o?nEdUl8)WqAzz?gNaBeL5QXfDUY!Fx_v`bz2jJkWp|KG7bJL2;5tM29KWn1
zcQFbFU_JSYw$S!Tr#9IJIL^1TmO&!?gec?eaPElL=mtgO43_D9xB{(EKF+mmovrWX
z&u?#}qFG7+2hbQzvyTs4`Wbcnbiy<}Y}!vg^hiv(!7{V(bA_nV3!tP*urui>VPs<R
z<scPWXkp7bAVw}CZk`APXP}F||5D!Hx~7X}IQBssS7&&7dd{F5>9I{lJ99mPa6G8L
z%ia=`AK`T#17ef?JAv9Jf|oB4P$$A?*<8z1)_j+6b^#hk#~ND~g<hTw7to}Ucr>%6
z#>Mf|xHRbY1<_M+!pAr+cRTrX9}dFz!K){r5*+sI*^hGZ6E$l3IE1O_raKFch%jHP
zcV0U}s>U0$wvs1xZ6SCFOm`5P<SwI9Kp-j%6SQAU2k7vEd)v52LHAuoSg`wiW3>ge
z!~Xtgq9`OHs|>Vm!7b@pAj(}vhgvao&!4`~(|m8a+V#DkGf^G!;Q*`YvJr(ZKuNza
zDBk&4r{Au&0A|@O=Zw=H6*mtJu_fAAyWmd?;I#a3P^@3<q}`w<xj{70_;3Kt>IRG%
zCzJ@u`Jm%Dw8U<*(JY%ZHi@9H@poq%8tW528K`)elnU4kHgOb?SJqfn*VJ^OQZsb?
z8{3|F;?8$1q#0Dshd424M^6B!+Nt6{!C<lp6qED8p-E*ApxKYMpp$6)Fcj4fpf*3~
zEk1f2bh<b2_hop!aI6VAK7>=1i-$dRq8E1H!A+Vu*vj9D{yYG|0E_MC8>C?d8I{4+
zs~?HXTO1#95&AuJvsnU#!**HZplA?{xI&(vQu&VC*7pVUNeoiXj-}#b#o}oBu!G24
ziR<A(bkrGBo2?<2{t!TW_0w%Zep!=Hdl86WUB`OWCHD_4z@<B-s$d)OWmNHuFtPY5
zclw>AS^0ZWbno8Z$-AF^@74eQ_Z0f{>EDG1H-Bd&w*KC$|MclUKjQz=S~#VpQyPTO
zE<4D3L-Y7wI8d>lK(O(l*6j46r}Vvd^OrNN`W-Xxne=m|u8xSdz9Fcg_}4qWAxDRL
zfR%rV{N>*^-a^}HO1k`0`oGH+fFTuNrwvKQP$k35Ru^o0mnKqs_AfMQ1b;H1ncRJ>
zlxA!hJpVMIe;y~wk^i%<W%jzwvSvMWIzJY%yut!#Fh3FFQi5Ds0HrbyOs8O-`6wIT
z7oDD*)&%Ay;O>?^(N@i*HORWfuKUD;4a9a&*KjXyK~G)>I0miu8RRFO-Y)aHi)gyL
z0$QLHAp9(ZK03i?!yf=oCPinT<FfjlHHqqZxXcWR2O670cEbJFBdZ{g`kac2K88Fz
z=GT>R271v43YckyvXC_OMn$&+kC)<=HMtLIyv4jyOvdUV^z)834>=2?Z^-7?jm!X7
zPauAHnVc*Hp}AtIXkR2p0fvV(c2%cUS7%z((t?kK+k^ORL(Ws_z8O{#zYvgON#my}
zQCo(SLka1V0@%jHO?&ZGHFBLaj6v8VcM#$~<HQP?s8RvF!Uv?|-VSwJa_=PdCk?|y
zNR1n?(n}?)bcApBXN{7=humXm3_C1mft)*8q!pt`G@R!#1fd(lS;|n_CdEe&Rw+pU
zKeg`U=&adZ62qfTH3HeiWBNw4{zSPsn#jy;{NJuRfs_*IZO`4z>KHAl)&tMg#&g<l
zY`FD}vH&vy|8x=o8_Z}1>CYg2*vM7<1-lraxF@o5(uIrO9XV_P^hkbpSe*}(i5pkS
zf`iuaWgGxIrPe)((E?JPz^Eoxs;HW6grIg~Hs=K`G{X%I{!n-1uiO@;aBDs!2(9Bi
zx@#0`Eu&u&>Lir<`ay7=n#`T0Y*+x%huC2%1$d0P4$P{sh#2!kPVL#fYU9pF%Qqh0
z#V+erT#J<dWPlKlWp`-_upYOD5Lv=<_qDcT$|+*#QEWomSdWb!3mS3^)0nDY=GT#i
zAk`P2bQyzW`qtZKPehsNp(7AW2UHY~taT!KndphXbL;4K8OZvc3eho`gdlag$N)<o
za=Tj*5YD}!LKNOKsORAHwsGs=VvnLrA({fw+noaAq&hee5J89l6pkY;ThLi<7gg0=
z7ScK>`ZSYx{N~-UH!8o;EnL-V%nU|ChYo$UOZz43`QI$c0IP;6<;k7&A-ggM3zGZd
z>u8@Nj)N};_NB+;2b$qEd@_pp%=7R>Dr^DS&=d_Q<H$6BxdsH6`k3VQiL=XT{2L`G
zTt)L;qj(br3|vRI&2aMjX4Epf0a}akUVrqaJ_x}ClA=U|DvJh#u-$T#XzMaYZnZ6f
zl`QkFo|h`aSx5qqt2=-j!<+#NY#3E!D3@L<Fe<r$t3d7DFp_KmyU`Az?pDbE$*Pp{
z>>%%cx-IkIXA_WwQ4{`d@-NTMjG$O-0i&ZaIJnVZ<b9a*p@+5;qFUJ~KK!;HDFz^7
z@4ZU$w62=bDyuReQ2D4J-25q8?+z%R=U$>*B;DGhvXWdR*4e&O;+tGfXvIGR=RX7I
z|Bb+z7?q~Fc&fJQPbwH3F?gQd@_75;!KXtsFG8r;Z?MuKU#z8~d3X7`9x)o(2frPD
z&J69yREK&rwY^YeJ!+d4{Fnb3&uKX*+W*dvSEJ!yzw6VdM6nkdXpZsY(`xzG(6apL
zPXn6Dz@||b;zIu|Y-%zU4{aq$toA4`H}n4Ms_=TEKMYT11jvwMMj1~2y)ojS7WRMI
z!v24pIQCXGuiRP<Dwz<a9q(4N7867YHQY{%k3ZJCEG;MjPBwSaB4ehjE0q~aY+r%W
z!|dRcI=jhG2Jyw|_EZdBvRs_$4x{0>Z<P0P>eUO1gb4yBt*oniL`WaPACu`mgvG4~
zj2pwaHvP7oyUiM$<Or0nTVq>Wg-ArOE1B#hg3qo3*$8MyVp!_uo9h5?eF?=43^z1*
zmKMV{1iIQtfB+<uQ{*h5QH<tyLVLMLf9wZvJ}nMr{xz$}$B#>X*D?`{x*_Rsr-4f4
zZoAgG7Jm*qP=;xdv<9W8HlyrDeP2*VJnlnE$RJ3fN)vLot8vIJLUbNu=>&-t+=-!;
zDvDb0K@Au8C5HSB&}dMTNa7VTp7r{h0?=C9+7FoI;4?3fV^Si+Wb%OHB8rEo7dEWF
z>Dxi-*ep)t70O{wsL_^zITJh0lUN23UwZlqq0@gARnX|!T(l7lgif<N-d9Zo8if<|
z^t<1y_+n4GFMVEM4@&HwtWLuY8EvplFA}U(ulgH+G`@Y_xBbQ+H#ffh82e|9O$sXV
zhnnwD1^=eX13tG<Wn~Py05378rQNau*T%Ja7$0h2Y~0^<ukQ5kl@$93Bdg0xLPA1}
z?8+BF$J^~}EiW(c;;Ghl`W>y3S9GYUV(||k)!`@rnTeVK<`T)Z*elgoO$?GDLp42^
zDyUWV{Y6&7!=nD_(-Bp^2m~uDtdjHV&)?8DDcK_R@ZrF9NP$dPCGk?kAL)NTO0-3-
zT+a^z-CoEVXaL*n4Xtc^0JqN3I8;Z&T$UE)d_Znj#hnZzWva0j%w`C8xg1S;x4xBv
zPOMXysw09+cW$cy$m42#I6&Ar`B&4R(D*6x3`jdhtG5#&cf=T(!yq>byT~r;rBQSC
zdzKB^LuXwGHRW#{se@^l0+59PBxF98oDT$*RCyy>9%avp7m7kIOv=Ql`-q`h!qw_)
zgrR^UV0D#1lR_wa8CKt{POAvTJ{yB!8vMrv79}fU=ts=hKAYUBcrM%hCL+3UgK*P*
zoEW`#H&>Dh-$se?a>6KvhllqJ(jc#^=8?CJr_k700#Wg!EsElC>GHG#Z@+KqTSM3X
z_u07f=eZu9Xx3N}rIulfd@lAU2XOym0OPymNdC%`To>xFPUlwK<pLj)1j_lqB#6st
zR;OdShg7u-oVcP-26CWcXhT{`w#7ZWfKSpy&4`1>6qg7!Mh0*<#7+z}3KP*oZ9I}M
zDX`tG1=}i`NJRh&XLh-GyzVkE1MPE3zb0nm9rq#JDFYf+iK&O$nGr4Mz@o>E>P%z?
z7t!$lLuU21ZyD0ubsgsG@m?8mk&W1>->OcD@yLzVmpKcOSsdx9Y(iU64Jn2T@#R<=
z<y-P0L*Z!GB3;X5q?Atw6!SV+5MbI^9y8v{g64lxlaco!n(3KBm8{B=Z57*ZobJ@X
zxK2XY`*4uvQ<6enCvPC5(7G^XPQiy(nZ($nX0jNh{a_S@VO}{}{(-!S<|D`ug90+p
zg`<G97TRR?Y2a>5%-v}Y+4Voc;hukZ;^kQz`en_}P<SBCDx@78?Zjc^ct87=M}Fm*
z4ntmyk~60qsb%yU*5;Ipd`pYBW}e^qXZDgwJA(f{Tl)IvMQP|Re8=FLMdeU6pXVT@
zS7DV;f@p>d!Mw6glH$DzFjbA4h%ylyxcDl8%)LXFuVnFl?Vb*lyRO!XriGS9m-}kU
zf|tbLlA#V#N=1e?3d)8Zs)!;MhyLG)@E&t*j7ZgTf0b{qRzP*1I$GkQvlnNHcY#+_
zG|E0>htXx-7$Rb{IG4R7hFNAM=-{Xw93VsfNU6lV8y|2GO$%g%B$+x#xCg5m4LOaZ
zsL6i!Xwm%h_N<B4gJKKgRbwa!bP~^iduhlV3?3F4PKt5jA%!JEf?yV@cGQ@%g;u2e
zOKO7T+%qw&>P6F-U{RU<4^PZsQebto9vOEuKG0|rH5TbSZ}lQX=(go$he*XB3A>$D
zzJ>xa#^LGtZW@_p={gQU63Q)`OI?@>cbGd#<tw++GZjuB;FoDCx9Zvvca2uvLM#tK
z#OEl$AhRc=&JhsN%ZNShtko#^V|m5F@{mdec!7pYujtna+~W^w$&k`{FeVBhUVJWp
zdH;)?4>?oBHxpS3sR%Nl5fCR_RWmy5Q#Nl&pE?3BDaT%^kJmHOcy*5JCI0v9*|X|*
z3$s`8WhGUDRy0C}fM$4%;vFA3S!(k)K0TbHMkH4UJ5`+TPB}4jtESWm!7wPk1nXB`
z<Iwoz#Gt!X__%eAzSQ-w=!VvTimH6KhfQV?tS-zwVH>{etY_&{(pO~3AAiu`L09{!
zSvM!Tg{l#kS<1p~wx&CKgPuo9R)}uIkoyKb$45KZCszd}wSG@Oya*jZ8Nfq4&Uz<i
zLt%u7(kQBVc0nparyT*39E8yGy`EJvBVGQ-0?hnc>cD`&<36bM4SmGa#!<8%aSb_k
zi&X)H5g)WkYW^C$LTiV<)Z9go7Uh{MZOTbW7GiQe6p&`TF|Yz9vg93mPhqq!5e*zm
zUch7<0oKLgJH8nc`R@>EEm5ZJ#N5s@!d2q`Vsk^;F2|CatH$%3zBElKPOdbn^38Vx
z*_GP!Dgep=P>do_82NHQy=H}mt54N0w*`;=T&C-B)uXQ3zR&sE;@qCOo1wkM{5oI0
zYat_K<wnt$D@nvMxUke^Ku_jHl{vP<(7rkJ&x)oeSMz{rBeM;mRZxr?InSHc0<U<6
zC1nY(FHUcnh~8d>Ng46=R{}cG!e0h_n~|py#?{Oh^wKU!QG7(&rvu&g+N6IaFXLf<
zQCD9yVmEX+7CCgXmoW|PF%*l@F!t@YYNDI6H5N3Z%d)OUBnLb4YG}MHbCO(LcmJG@
zN8R|tG9nYW!)$#)|DkG4hCi;_kQp58xSxJzXT4vjhi=*}ew{LAcI%0(fx-c+nhW(a
zLm~E~-G#j#bUhm-%sno=cD2D0rGfxH3q*k6t&cD<^MQ5oK7M2xb!)0?YQG(VmL*XV
zfqx_Thp<VGT>>EmAaw#bE@IM@DeB`*AnWWPy`q)oiQsOcMM44IlQ3wg2l7A#Acq4(
zXk{|{g-ECB+B}Jti#Kz;k~k0y7u{5Icp2%cMS)WX5}(Ip5@&!(@<qFb##EaU5SI4=
zj3iJ*zCil0fY}ex>d|PBPMl|~)ps(hZK$bsT&k_E?gZsp2HnNEtirCUkj3ldrjw$V
zFdhOL6~vXOQ(7Ul&3<qI#n54r8ZDtjiKP;07^!UTG|!={$AMNY)`GyL%xTb)$Rwf>
zorE@}Vcyi0_qW%Ipzq8QL@%d-UC2WSlx(7SZ5mCeomhk-GCFH2bI8f8PcX6D>X2Rf
zkP>R@Id`}Ca)it^b&p2dWEw>cxqxs*PC_NgN$1oaInow_h75hr8!ls_WDEou^+^s7
z0?$EE$7G-<ks?LJm|;xwW6YbJxv&IM+@BDmfN`XrPwt-JiPVAQ#!)Dnt^kCAeLDgC
zO4KN1{3DN7J$l0QVchr!E|=V4j0x6r#x&>Mc6~4AmKIx=iU63a&}+eA=Cm?gu+K91
zc#EjYA&r5GhT3Ux7Ej1V$c>^i1m5gnEg4*@UlVz8ru>fN12TW3(<u?{ykszSt-7T`
z#N4Dn$jnJ=h$oU*6v^>KaenOUbMGr~pP0y9ib6XiXa1KXqLK|KoaU#XC?w6f1RlZ*
z9YmE6@%0J>bDdLq8cylFq<e%@F*uN3%aI=gsYg}kj4_0G2Kuu=ER^7^F_|yo1FepA
zP<Jkwk3a^u0vAV<0yR##p5*wmO`Ra>ZD+JD)Y?dK@OXbAbAk%gw5&AZFVhTwz=(Y=
zp53jB1Ffp#rI2?%CU|Rp5hv5A2P`;h)(KDou4fTX$xVHlq&tNvwsypfI7Bclu*SAo
zp;2=oLRule2(mXKnn?UQ0!)@xGA3^cUvHAYr?Rw@OmrK=22vf3Nn$J4BFc-T#{%+|
zhxw~aCTc(a#$E-&9i?vpqw!fxit_>2F92OchG$o!0ePJsYA~j8LC_fUVJyT)q}4tz
zN-R(MzLKPA$vW}WMZhd%G7l;y{rD?Q_=Leran<HDB?A%`i_Ee)g_ctNub3kI5Jd~8
zs7z|~!h&^g&&Nk57@`{_DI<>N-OBRv`+0eJ_dMtV(%~U2Iv=Ve?_d|wS|li8hWoP?
zz??<hr8`N}8HRn9DLpCnM6(M^zzys{o@Yl$vfc|luuug66ms+loXXYj!JJJEXKykI
z(7(0))naDbJ?>FZ@@csa8Od|Yg@foJcC8}QD$odOfIW`Khmue`Q<pF42Oc}R#Hr=F
zW*sRRkO?iMrHh|aEt9$$Rfl8l+aJy>>;FRwpa*4A0R~{T=tz<7csVlbmked#@wx@_
z`^_=MGMFC1EHUQ9ygn0W@hRYI2)Ir|0M^o*LgmhP3$jw5{KVe|Ep0q5-UTMzXkuca
z)@4D(T{rW!ZqBRh-NN;keU)pg$Eh8#ZlA=R?8$<X>Q3vM&+F!3*t0l^AY|el!CPoY
z!Z><`-oCyuGOV)NyrOq#025g2ue5STd2b?*e(2Dl%Q(kGUZy&(H#Vk))9k)b9#+=q
ze(Fiy9ikW|J`tO<6J<W4OHpNJt$`!Nc>Mx=)MFIIjR8*>#BHwyLu0jlA$8W-mQ1Ll
zX>DnweQj;bm}S~=-5kt~g6dH`wb{BcsoqzvC{lZ3<iwb{8cyyh(ViDOAra*<G%edP
zg5LQHebccR8bJ<yQHU(|m}ut+-$FPV!Z`R)vpR>#h-G90mm|I;^6L~WoDM%3Bk{yE
zwJKv%+F7Fz=3l<IWyNiSPa(oQa=3`6@)T;=ixGBO37}7e%sQH`m-%3Sg)z*ZPxbb0
z;<SlqpN!%sPMegwMk#!DeYX!H*pM0<2@bUvY#=fMfYy?%5VQkNGYZ?&LnoaGlq5=O
zY5^P~kUm*LkD~`~Nc@sopgQ6fhNJ}&b0d0L_iI!GHIwFm=jXU+=mXo=)HXx(nZ&J-
z-#tJ?O9pY?qGQ2FjMP-B7F~#12o5drFoie-G+%b3c*>{c+(L7lFuQs{X*KcIU<4tk
z|8+bkiUWlCP-D_3V;%(!`&+YJTF`IMO(u?#*}*v8)U*~92KlkXGf>Vow9Qxz%)N4`
ziyV?jtC_Mo6w^JBp@^56*&P`n%PrS9<f1mD=!@P&BhRU@a584Ri}-o>8H~>f#<7cu
z3w~8w+W~Q2=}Ujjw7kA_jFPQ+{c>Lh;@c@KM^)P+bhn#L+-{yvlEiw+on<+`glns|
zdq+)3C~}wuag%^ZN{`3@_qz;4hIb))h2^HcNhPishlnR@W8Ju{k`HOH?fe`oSC!Qa
zQE8z{=ph4V@gyP|Epl1xcDaHbkyD*is(Sju;OQi!pbz|w8nXL(8>_t+y09H^_UemE
zn1Pa-J6=5(2~ukQMw4pD?gBI-(2^ghI*qLy$aHph6Co2DG{h1fq;O8hY3iujoDSkl
zr&C2!?}8nP&rsZ%j;N1G?NyPYPqgljc4jT3K8Gq5V#`)S8{+Z(Ld<$cFoi1F&e<S7
zIZGTKok+nFr+22nRj6qwR!M9e0);ZF9Urx5=&a7c<FIg)b+)|GexanSBFbcsTtN3D
z&Jrqt2gw)oj~+b=c~o|XTI;N`m&9aU)AcHaC@&@DDeKa?zBz`5jMD0<g|x{Jjp1Pi
zOasgbg4O(x-VrrzkdVyDmRa2|@>;WJ!@TKShHtSx2q9225Ig9f??x*4sv44V#FvuB
z6duVK)xO%AQgemw+sQ56ucju^p!$)d<jPZxP{vxI)gp3a9?EQA=y$_%9hzzuZXkrT
zjM|yQ!N{FmT<QqX_3lz`?N(_2fP6%R=J`%O_Q9%jNetO3Mr1aRubu#{<WJxys+jv&
zSCbZScg80}AxGiUOtM>E)l%ENmb4u7SyZeZE{>FC?vK0o`DUR{s0`;lol6sz`^<AS
zl&Uy|^FHxcoT*yl|75??3I&0Q&KIFV9K4U^-PLs89`X}CWf=aW&YHX0kJh=nO-<!H
z1$~%_p3+R&MYU^*QHfEppbpuUS_XHINwiu9S23brLqO{yZ}Q#DY$3r=i99`x8aK3v
zWU?*Y9T=OOlsR_nmk&nLBbsNuZ23eiI*z}1_3DIXZRMTN<npT4$r?Az=|cbeO0C$p
zn#*00?T)O~lxVF!D$m;0-947woMt6|@LkdDA$IoVm>PF}p#{II;0H{+QeMdPWD@2Y
z|500#avd!gZ)Q*LR6;{_%C#>yI}+?w8D95?eEoX0S$yC%qd6eV6O${d8iCv&3kxfQ
z1TJPfOJLxIZAQc!nZ!fkdt985qrYkIz=WZ?M1w7pgU#;fq?H^0@d)D?V<Ux<9uYRx
z)DO=%ZEgDNmQmDKFsxsN;clD3s~yMGyp%#C^tz)FX$NHV8~OP79{BmOiHI<SxGahQ
zOx{F6pZ(y$ANcwCUt{<UrvJs+3~3^euEaR?x@41pws0~H=UP`W!}RPdo1`QQ=9t`m
zr~MX#e1_pA*aQT&U>1_Bqoa6s7Y52e%eM@j46=7NGH>3zneul20TAdI<{=)z1b*ZA
z5)6rrby;#gEGDK4W(2=-M4+j;dF1oE<sIGK?I`<ia!P-X#)5Ka8=zBIg|W#;?d(J<
ztEw<D;{>@au(G$p!ou1t(JX%0Wl<aoNs(Nxiw?<+t*xwpP{%G@*pZQ$dFc=%rZL`x
z3);ocPlq`^@1O+#Ft?6^6wBMg0}FKRh}BJMO3IEsrvhEm)5TogU#L3wl9|nK_U_7&
zw{MSBFaA)`8!|m@8z|wh9Yk7re+ZPCWT`MMt-XB&)Yb(|g`|AOWNeuW7aqNEf>MJQ
zbC5CJ><%XXL*~34Z@7b{!u)_ZJo64u8FAZT3VujdOEvN84J?!{nyIHjOhxQSpxh4(
z^uQbowjY072Ik;<UiB1Wa?ik-x_NucYHRO8{&SbKPwnO6I=e<pt$l^I*S1TSE@7KL
zpqs=KgEhHyR;tZl;djNctL=-opD<jpYLx|(LXhY-nDJrf`NNnI<c-k7Z9Ip*ruPK}
zYaw^>8F}F2a}!-tD^{$~hKomNU8%0FzL}o>4y(&N-PBO4EJj&jMh{ngaEf6ym#wYs
z@Ogdwjs<bFO8l~c2|^7pNc<7=LfqW)wPomErBL9F$w0w}3m#6ZC_<)9ZkYGO&FPQN
z@|JLz+_iV_8eF(8!%hH;lRsw%6K&d_-0)N4F;q2%!D&6kP=ZIIR$SC$1_t|X-numc
zVeu{zk;ko%`kPX>U_ZaKN5#f&R(Nvg9Ww13ZEZS^04`sf(}mFn@?Q7Gbh<}f-34M)
zQ118)t#(B_{0Nf_KTC`nbt6voN^Q-T_;o|8?|LT$>`R*dVat|V@C(fO-mJ3t{R*3z
zm?DOYaD=~e<=Axl*I(OE8OX&X_G>evdKfFqU}|dGbPB&rDHp{FtoGAkGV4&cuO{l6
zg+&{;isbz8(L*K;54ye3D?)zBQHCe9v_D_D!s+AdYySD|GPoHs$I&Oi%EiS6zes{b
zYJPaRqmvWDh}l!HdWUTWccRSeg+&PWvZ3=Wxb5b)CMPfNeM!j%bj<@<;B@0qy~P>X
zDw^Be92C<VFErTbbCe9P!~oP2{ZEq$Gt=zW#A+m9ZP%e`_d7~-eg8|kWn(3F3RiQW
z2pOI#5^RGlfKhguyoduqVHUH_L6t1_a={PoSt}mts>9}@H8a20*Vjis(>Bugzvb}A
z$Ov&`h8|7vdhTrg@vV?N{r19}o>$!sr%fA-0BN7EcIxlxDbSV0sn<UE_}cXFr9;Mb
zn=wC3{K_J7ELZ#zot!QvA>;dBKR|)sh8*ZU*7yFyhcAZ*Dl5+ds<AjbJ0nS2Z6@%)
zs=MxgtlO}Cqo|aTkz`~mlzQA!WMq^il$8~lRzhS(Mn-mpsFd`Oy>8hfgvJwP&&<k}
zk<9n_KJWVB{R`ecK7Ath`hKtLI?wYsj`KM8(An0_;}B|s*HohnF=@i~#=!(hh+zH#
z;<O{+nL8R4t@Q-DE}67l30p#X<RlUK@S#7{O{cqVBkL#KbPa15tOa!79}=?djw|=x
z^4QBPx0BRbaMIsv`}d@`ZD%E^sH$23c#?4%-QMTD;l(BI$$@imW`4e9p%?s}mIYmN
zearlSA04BleV?33nXArM1z29C<<wx2IV_CnB_}68#~HNE%M}K~!Sc4)NPX_(5=ho)
z(YaBD);rw=3ur`dj<xK|dQ=-c|L`HJe2j`LaU7y43&tSy@Z=1ytyS>^i-`>+frZKD
z*2PR71Pbgh*+pJI4o4nUtR~iFNhvAaXnG^u@VGb*;`YH}qy|Bv`MDC!Csza0Fwu6^
zj|^yziRrj&#mdXeTU%fM65e#85)yTQb!MUN8o;lEJ~#v$9y`rS!7#(WoZQ2M^rs}{
zC+PEIV70jy$2%7L>Q7$t3U%6#N*}(QpCAr5+@8L^K1q)4H*eprM+RXo$)T;SO~Psh
zcCrVXpZG6l=Q`jsK6s5G?EdH!FVfAqQB5v6S0++qTwD;u|Dklgm>9Ho-@dJCFPX9T
zVlz(7%*<2)EhNRgIR|0=5X5uDDIq5(CwY6yB;iR`QBhoW&Z#qJYUe&HFyar#c3PRE
zM_*vmz>;0u)6+9!{h#>f*apPufp3evJm!B?kMjX<)=Ep1#m4e^Uvaggv=?`gm`I>q
zdB+P+FuSaY-r;wsl<~@{!O%(3qA}%^mX?93X5+JH2DF*AQLF29@mao3Jew%LgT!vH
zHoqL$2}K}t{2B`vS13Y7qw7R#`Ys}=BT0%Ja$8wlT_wfY%K_v1bFI$CLW}i5bTktu
z=QaZa1DwnJ($ehc@zdqb_i;<uVSeY^&54df2ogX@xT>qGB{{>&G>KA2C^6mo32ATV
zHsJ_1yeB!~9lY`;zlz~ALQR6pqDXF(Xqxhg6M}_o9}@lj{ZB2Ymz8m$bK2+4n<K<B
zzHvmljf6R34zPf&K_h`+`<G1PBYpJ)0|RkKorJ+srgHplTgv1F+mmoPpc5ypiG;$X
zMCj|~NOR|}M!_$86*Xueo=-o+@NQF+_SBxKsVU4=V1##bT227Pg~^Us{-jYoFGu5?
zJ$f$X)<br<GC#T~Vw_UfeV#wxOT<H*)={2}#^h@|U}=M1)RCc3zGWHnMc(3wzn-~|
z?NjxfotgQTee&c<n{YS9c)GZ!9aKhYFLnNE3toqF>^u7`VxG;m_qcUqqYQ1IiPPHJ
zdOr-UMLGj$NH~M*fs2u<dKdwu&nzy!18Nb5dW_zu_;brUSpgXt4s?k4CMNO{>&flc
zW%YG<QW>=FLoR`flYeZ=fE;*O_Qy!U*eG9>^e3W8VgP16H=UdwT$lE+QX%0<vzWs~
zFKP-|s}#Ds0^X9j`He(88x90q{2!wXQTVVt8rZj(MQ{+B^TddB9LUzzuV4*#+VS-%
zxbFyh!K5W9=XFG<^c>trERYFd+7#z@TAG7&)t193wb`VvEn2K`v9kK3PwpFVx3r2+
z`V!&0|GM|)0vFxH`rF=K(?bmCHoM~F0S_W#MR6(I;X;bJ?KlO4iAa<b=E*|`#`DxX
zK^_-fwC^fVKY!9cFi^HkZ64QnM2xUrYz`!DW6NunY(H^p6$vj<>+AOR_5pS_rv*w%
zOEs30(S#EOPh)j~!?Fzqxj%m@i-?F+oIaP{qN=Fq^Oj-I|B(qV^(dWAlKf?sF`qwF
zlo<IZ*ml2P%*@=x1PT=c-Zpq>Lm^3Len(Q6x8@g-@J5*`Bz#*3*P^#D0Am*_Q8NFy
zF+xo5zh_ogLZ?y|GrFo84X$Pz=2Ax}tjj!k`gCAqBmlN#7RV+^AH_5m9FJM4tb9i5
z>t0-AA?*5C^nQ5wXgCcC!A=?JGDdOvPfXbMPMa@es62V{go&M<w#vlm<fq*i!leEj
z_A2Zt$2lrz5#s9MF~=r=95Awsot^z2;`Pht&q?T5u7duADo(_qHo?@!+;R;5mIa2&
zBNmVVZ)vT}Z`CrYo<MXWeSGkmpVE0_anl@xywR1iN00IyJ7%;%exBzNUsS$sk<`k-
ztg%)bUH<AkS9-Ss@;N>32$;-~KAs4-qg)PRIX-x;lae!@`abST&ZH@_+w&A9T-;$k
zUFSjnQ%XuyX08L90SIv`j!1uPXgGC)#W-?>l+SE#<}BE?2=T5@<K?$VoP{j){Otl_
z?Q?gnSs|2^T@(-VV0PGC4WLQ#)(>r1v9iLBeUK$rvDNm)$Tc`lQ$3&0M}F6wBw_du
zlbsHmY-*kSRr<o6z82%Im-=gjLnNXeKMwZy--Kl&_AK=;-u4pa-;0Z*gHKwgTTYm~
zu4Y;(09B)Jnr)Wxp!<D%k?1cJ(fo39T$pa5Q}*|fzRqb^H#e##o@vKKR_ATcc~%N;
zg)EgCU8*Rg_)!)MRr7;6bqWHGOYtY9!T>MzP?)HQxOk{NpKVJjb0rl!If+v2SI6O|
zFg|kb9aJdkQnQAS<gXq&*H>?SD7y2>g6#b$T0f;cJ0shCTm)p2RJ!kOD;{HLVW`ie
z&0GxUI(kk|AWu5?QGm0OfKm6g@HA#sZoX0TStBk@fxOl)08wLp)I>+`tdMPBH?5T_
zNJvO{TS$Jzd_n%<?_deeD9ypAa&BL;mT%%LE1A`jaM3s5rG8YHv#{R5-g_yy<ZtGc
z`JXh3R`hKt;*}r1Z!1XM7v_o_@djNnO1C!ITR*BcE*?yIpjNQ)<e5OycP$2;r0UCu
zEfA?m&$s+T>qiARdxT8%<p2HWxY38&3stkfe;ar`75V|w&&K`>lFjKu%sf2dV7)^R
zHtr&hWN=}3=jU&cs=%7gpn3KBomlpX(vd*t7V@yxJcoF3{<1DV-0m708WeFfu)Zx7
z?t1#*fgfIJ0KjUI%OvNtQ{k_`Okd`oh*qEs{BUq;WQ~BrQ-(eF>e^Z}60K6xmt@pK
z+p&0gatx2PJTF;$spr&MSGSppimKt$r#hrOI);V~-SyTR4Kzne*%cHNOe#E!-(O-+
zEpv<FUFG`)dY2FRgW|%F|CVuuBS((N{TY7nqx;Q4?D~{_iVA;*SymC1d+2JrPC4?L
zSN6h}kCmN034R;(Duth<jvl?B6A|k59Z4=J&Pz*WJ_V!wW;YD>{Ek`GUr+eR4tpx1
zutkD#ZSJ!FvqZ9eA6I8*XSAJn?7@q)dnubsd0aX+mG(V(!X<BJn@eN9n1Cx+R9tL<
z7W!?*4RzoH^Dm#L6p4Mua}dZU@yTJ4=##C&mdz&l{j@{rE1*bnzHl=C)fH-#Y8tVm
z{U$kCC9g|ZL?kfcH^v~%O1!qt4e#`Q+JUK4`>@Yi(u%g|w!NMMI)Ow+*DI`Z5O!NZ
zVPSfdqxYX04Viutd3@F)SBooA@zvSRN^b>e+YWQRo@}d0?Wb~^46{3jIcNJf){l_#
zujZ=?Jd^i|=XDf%`|dZ;GrkJ}wdU<Q++nF|H*ZRweJ0nqW~?b-^gtGP0ZDY_0?$y3
zR8Gu+t3h6?h~gy4`s%d#Tn+*3Ys&k&j;hr)bmVXIS_1Ddleg=$+}!q!6_jzI$cmoh
zIE)Tp9h5Q8tu~unn43G_8YDioi=Dl#L@i^gC}W&+7I!VxAe#pPD<Ia)T|J70WW~Uq
zT^B`8>X5&9@nV5px4wbE`vpGj-Q%@$R2<kwk1HEiYT3g?+m;Jb<UOG$CtsxBw(Z2(
zv*Feg54)9Gf@0?Rw5{kRyyCDv5!CsQALmwk8Kuy&vp#2uiRz|jSc5RfNV~vjE^>}K
z6RNN8QGAGsiZ+GaZfy-B<+#pTVy9Iek}bI2OpR={rM$g)&v-KPq(aKI9dwL5!(w%+
zEesv3m|0M6d8UJ=yk~%krR?$J$3;%x8BlI9b8w`(mO~o|Z5a&%k6aLLr<FXJ#eS!1
z2dQP-s*(3KElNS53+Yj~pu#>bNd+q+SbBGf^^aLeOq6CGJvCv$pPl%jNuvl)D*5<K
z8aa5fcM3^83V|OLCo|*RQ9<E_9A*bqS5cS973DnI{cCcLtf|*soztTAvF}Wil}_)v
zMR(ya9x`+2CEM!A<fZnv(Q112;@bNT%<HEFX}wP0BSInybKpa7F)fcO&L-t&uWNp$
z2vipVqaFpe!$U)UZ{MC!Z;V29NS!LTPj`A{xL<7ffuZ9G`KioV<0@Nv2g+9%AI_k)
zl}}te<FwePg4OMk_oomggC*i*^zs8%`x$xUzPZ&K$&$_)U*}F1IcZmR!RGvNm?#1Y
zphJWP_<e?4d6>6d)-;~-^^0aD=Y$$ZWS4TEJY^5_XoPiP<Vu>EiMWKs2+J|(iJDq=
z+t&H@y{BOhEO=KMuXT9k&(hM~BS#Ddc4dgy;<!owLj(ii0%hkpCiC*E4{TKa#O3J=
zP{%Na>Ufx@a~Ia$ArGg|w@M|w>Y>sN-%Yurf8T-M{gOM(Nj+nugZlWy9Bbv-FC}*n
z0bxy3leeU}cs9YL`Ci7<>(y^(Z0hgEXpK|a8ynU21VZf6G{de(I9!M&Cy|d*X)jY=
z+SH=v98}o&!$_LrJG<BxIB4H(EO59o>bJTcng45};=;m<zL0g~mjWUpC&@0>ok~1n
zE&r}rjpazM2(I#VC*82r_FWk*_f}`#62;tBVeg!(oLIHXxiy@!*SSshC&hkM7wGan
z`<ZioaF6n&^DL9HM^daTm!)s)Q+Z=u-Q*I%gdO`);iDNA=u)13*8SrbzIIns)xI)H
zs&t)A5@Z$<6x_?lSAF$+zS=k0{kG8s$Ra}8CpxEkdQ7lts#;swz-}mEw#v7Wk-9g_
z2D-ZDa05t7By(&h&PVvn2wc8=dGE1foS1#mUXeET;GhP7ci&_V`(pA@5fM6+<?%wQ
zY26CQgF|SJ8Lo8jaRdhkZ`rzaU@}3@EpJR;*hRwD_JZuy)*B3lU)mB<cXpff%235^
zE~e!2A2|}MKot=dRtKK2&dVISka*+r4*UFZR;`;?)`-Mdom;wURo7(l`DSu~WrD;V
zv5oNfnZIaDt(fhqS4|57p6vjZpRr_C&hgoHV0f4)($U~di(D>W=H7@)j)Ayg$*q0p
zH@OQ_((5;}FUe<#SQ<VnEU+`!5krT1j;XsHv152-!~)>l3=XdXtsD0VGy<kh5bDsv
zHB`I{e^Eol^nLPc3G?SqpGL%ZQ2GP`G#l$O`M~Zj|5{q!$;s)DekKlDVhr1xv}~Kx
zvMp*nJELq=Vq`Zk>#W*b(lC?xWm1x^hHx2pAsM${f?%bB31wPHduKJ-(KdL^y20RQ
zdYzZXpKYmShxEs<Q8+}BcBRX&&7W82h5ZjJ#yP4wIvz&pcCB5$O<9vy9v%vasH(6@
zhlhtRu&m^il$!ebleu}H;vJrker+o>&=5Fmts&6XR^(|2)Hg9Z`{1v+xj%Q$w2f^!
z*&1Z)xGS|pzU6Dvpndy8wF0$WR)20_{?5VnB$rQm3x~?-CMINCEmL|JjHI={2KqP~
z9YvM~yz98Mec#9N(6KO!lkGw7V!;=zvXQy3ANjL3hzYMgK0c(@#ZF~`ysg9FOE1W+
z)OU972ENfsDSoCe9JX|mC;<<$@dMTJ14=mnX<aomE7p_l^NuH<W>*`dX0!Q78dVb9
z&|*EBQK4_wROaSHdPJ6NG1odX{NbmDh6XY5hnhqnn@DZ}sYKcTRj^tAfwozDL-V1>
zXJl^uV4|j`*7bXJyxVJW;sQzW%$e=*8MazlTAE4f_Zi_-0SZq;l7H=i0u-*QF97tF
zXpK-!vMC#6ndH9Sp}5d(u>)X5SsT5qsY%$_VuVq-N}Ig&sGpyo5Z1zg1&&)?y?5d-
zQC~#c=qP7A)qN^cZJwT-5zIE|3}u)40F9C4o<p+-L$qC##TB+UnCaHr+r0fu%4hoY
z{yjDFjBa85uZ1Ysw`|#>sI48TR~Xq{ZI=5DrK4fdO+H^=U#b2YPm}3;F~LrYW;PaX
zKGn-7Y<XFZ8cjav_O)}`0Fcf)t+%oURPGzbdz6TLxL-*VXWt;vX<}10-Wqb{2y>EF
zNaYF$L*w3d@+)p@0mefuQv9Vlo-^O>nv#<;8T;*m*Bmjp#qBk5AwLz_@4HQUl<qaW
zmcce;t2A+`S$=Wi(s2~y&(jz?lc$NWkA-$agHqzz7D_`-EPPe&7-nc_xTUHrte{ZJ
z(k|>nAQIsf2(zjTUL!?m66PMb&Z#rM!{di?c_6Lx1p#Y2w9!RaWiD^pU!Q0+kzpkZ
z-j$hH-inGO@6;U~e?Q{Z6tLzF(K9pq=XP80g2#mwPO|kTMfcRb=H6iqJhTlOVGaHC
zaVu}5KM@sOZAri@y&7_W*;MKrOPTmq+D)6jL6}p6n5Va~f(B4hoK`2>xKNwmgnBa|
ztUvp=MBzi28HcgO(tV`ygp`hm?8xN;BO{rRpwgL{nRVn{q0gSZE|DSnn+Q1xp<H1Y
za|A2J=a@s3mffA-o1yk}vV;S+A`Dm5Rg`qw7MTkvTJ%IpW8Jz90-uwZ^Nw1Anp7fo
z%#i<F=^|cpox4!G*TvAU+WcDz$-XmB)v+W!s6#=o_;nvhlcdG{HoWEsk`H=2W(o0h
zP;w;v3d1ij$CNR0$@t*Wb>jq9hYr0zmFb)!D!6T#;cQ7^wo2ktygPUD=Sq}bkawR?
z9pf`>eRb@Paj(qt(QBk7f1jSuhEyBzg{p3D#nuy8I>D!lxzqO0cWCyY7A3DmS9_X#
zs-((~$Y(ocoj$2D>{b1Xw0Dv|*UMJOuc%ASHnE4@(fDU4Kbg!1|Fs=(8Vkb361#I!
zh0DWw$aG!S$ffm{`n?_zi7>a!MLp+<)K24M>0eCBngWQF?4%iN)?=Y+tQMS6oxj0!
z)&Tlrb?A>rbnZIU_nCN3(t?S#1f{v&Iwx2r)8)RyC~n};+6J^%`})MfX)^UJL91o>
zYrsZTum2(oXKvxUk(w_-8D0}F0(!DDo0-FtnKgx3P|VR)ncREFv#j0H^ovUuL0f-4
zEla+KViT)b9H;Zu1$R1pY%TZ45I}_=oU<r_)4Z>h?pa$~({pBCNifOoFc!$$Oma`J
z==96RAj8zI6QO?Y(n4XUNXx}9|IwP6>d2)bNm{pv_C9t1kXXSz2u_FMXb1GtL>yLp
zdr(Rsr^6Rf?f}Ep*3r2O9KP{=BKlRoLAK?(I$Q77xD}O+nU5Rj<aiOz^nss0gOMHw
zU#zcOUpRQ+9Iu?K55e<bI(B@(r}6!RV{GJ!&KoY4Ln!)ych}K;#}J38Cr>^anEt31
zc2Yo`sJNobs^aLrII)#<>h$Tsz|^=i!k4dIbL!7!^C+m{L?T|Vb??~`d!GI;by=O4
z4-a62xWeIviiCPTH`%+xd9t_EAm1An%sHNa9M_i-&4kp`;F%G!Xk~Ys%`2d_1i2u4
zUVc8Hh`Xz+o)GL(0aQN6o&RfkxpHGg0mCuO0kAh<8knGwOA-CcGza9B5`{SC_3O9I
zCjS6(dCFGMd;k8fML^T5(=dsCG#?8hEt^HgYxx!;nAj#|Y#f)Vo?t}X^e)Yav87Bi
zuGEmk+|M~Isf4UYqi=d(;4VNWDoJ~XGg?})8Scfmr+FM59n<XSt9qtmY#oLhnDJIb
zC#qI05!xF*epI=0N50U$KODKwed$jP4d2p*3V3pYgX9_F><fqG4-~3LpvDWn7(y<o
ztzh!Hm#Hh`IK1UXU)k&Hi9MA->WVU<Kjd@Ej+2F7zCYk44iHkcD5jUj?5;W;owyx$
z{mRf9SUlj)`NOy8EklhaSQx_}t|b5X3tH&()}fTNqb)|VEw^~6*Eu+K?kI~{t1SM3
zPS7v0q5S5+iQuzMb3#>RU4h)}QXY%8$**1wK(S?5>?lN<vue?64f-Wd`|(H_j$^Xn
z@d;Emt?kE;A3ubJNE$Wr#=)ShqOu7TCR>F>IF0^vLY>y4nTwP0)5Q?ZsLY@*_XDwk
zC`S+Q-c!4r{;4B80_cBw_xB*qS-i@sq?Gh-U2ZSY_W#^8{P*b`#YbeW@@7i>KseOt
zP<nDI>bqrCnFcAfB`}E_eD7ouBYLZ-;uyziTD(l2rEr@H<Z-+e-T}9a2$(n0Ha0er
z_A?&6Drubi`H*aC=X)N`sMd|vFRDHT{<SF02~G^_v!}$F9`b~!hl5`~{7h!vs;F?g
z<tJxZKtRB%x;P7!J0x)&yLa#ID0U(SZ<&Z`yt?SMpof%g00A#8<scm$-4HOr?;91c
zdV%C|Ga${nt^dxuhoUOSYsCeUw4F#kD9gu)l*`wbnqU&3Qg}Az(K2?g<qF!DHGnt0
zKoJ)|ooi5v(IkGrTnB-k0_XmFZ~=}!=13Mw`m1+NLj1d#<O-6-nb9dYJp3J65<*Z4
z9Z=bwX+(L8-X6i~KF*0|1{(-(562&|4~P5rugQDAHqrYeUd?Dz0J8EHCOvwU-e?=^
zC~|n2y#Dtoq+9@~1-<8HXPe7*GkI;6tlwqxE`wA2&;}IBtA8gDhXV$(5ozhFec`*U
zRa|0mB(B4kgIaTwG!vx3G}Qu&!65Dvy)J}~Kctdk+qn}g#TPCuV7rEewvPv=F%6pJ
zgHUGt`$%hQR=-gQjov~&dUT#c6%bO%xJP<><;J=!a2<i%%?V3abX(aE95?`+n;0C%
zmi@sl7uV9Zzv5|o7C^TuD1dV8x}`~@k9cqZ3{Gz#$Iz=hc^Pf=s~fHFq*!c|ZhF97
z$6<BJ0YTFo0Bp!-@KwgnO;s?A(_3={V-`*9G`r?&g`}$Stj2z^-v|GY8yU^1Kk_d?
ztUAiCyUI<9Q}CAO7Z4Dtq#<qIyg6-%+vi4T^IRkPU`0Lih!6k!_etz+0LH`+U=6eO
z@$t0bCXh`NedWdj9;o~YU8Ic!Be1PCKB++*$V-~KN=kQC1mw}av^obF0s%AESA2-~
ztXthG+0P{+D0s@u8-`h=jx1)#Z5#&PZdT>xmAP4!Wu&~LkosJ9K`qLI$e5UWNC`-z
zevEWGI0fApZSqZc&%{fq&w91mWt#|4chSXAEw%?yUNhbe%x11r7pRivrfRVZYnT$c
z@GL)8o|SwHZs2Kl<4J0skdq0fC#hyMIvBxBgLB`$eQ7Qn$8M#j$%kT@feBz;W{Nyp
zrq|9uVyzHbF4BMIq8rxn#{QxwoTLa&gr#RYmVN+jWcTT;?xdG5-L~%bxUM2iJ5qT`
ziZj=y{QyCC;d)_e+YZdz5JAps0n#;ovei$Dzq;u?x3l}^gJN-guQYb`er4HBFd<l0
zFzYY+k!@vSY8n+8=>wt0t%(k1Fnb{BRI!#NaZo`A8*<SDY8My}lB&u$l{0-7kybP#
zWyfhQARtgPHV#_P6^kJX?CP`Q9!Y3ULe_g7a4lvwgrOGYJi0meZ^#o);7;O4nI0@x
zvt9OVrxiCV;{YnG1k?&F2SEwp#$pCtSDMH}yo>0hW!nW`cCcIEj*qK_a@+;AQGH#C
zUr=xp`q56DIN@9O=mxo!!T3%(vbIy|>Pi4sNKTVvziNVCe2Jvf756#Ed0bz01A{b4
zlDuXwTNQA*-w}lj@VA=o?mhqf^AACqLq06ko#GrvS8CR5&_Mz3LR?ZXqalaV0}>MU
z=x3V|l&V=lqMds1{reAE?o!*{XYrBJ&*^{gl1&UBz}x-(*@xGpEh(uSj@<`P(}6H(
z;?{xypawmBGf*+Vy)O0Z!5N5aB0%PX;Wuk{vDR(3rwl6Ut%lYmfZ`c~vqH;iIqX$H
znXNlGIVnT>APEDj{oR9*ZDPX`A{y~7OJub{p`re<vAeR5bX9tL2Xo^cfD*Bg<oGr;
zbje9vJKZDuUV6G%b`64uY(T^ZFRlBiWz<)9O0N9G9Tb{AKev0=u3dfK2h*&NZ@7aI
zV!}8{;6um!o<2#}ON`W9eeX?2g68Af@bD)5rdc0NlLE7m#A0r+IHk-|k^@8C50VsN
z4PwWspx5|9Nl7WKBIQb8{CnjL`95A%0qRE&GS3L4c|EI31)Xp7C|kw**UXGh^L799
zbQ=nJu*_`^q}RJ~2$e4xWb&l|;<Xoglg!vDn3$iRpJku#P-PjHn3y<NP8{g`eH~xS
zfS~)AlP&GWlovfHnam(G2^Z!L)sroHVWML-;ICe<-_e_YVWOP=bGjsrd136?*oaJw
z0cFe_`VLbII{mqec1aOy&r%sXr?ys=<>ek*ZZelN$HBN7zu;*irX`z(2-*Vb)b*m|
z9tI9c#VC4}MBwk=y)R*oPHM%QG)^52-uILr$aL=`c+5EN5U(h>(Q9<LIg8Fp#rMS{
z+3mvR{5+dxBV|07BoVX%FzTl+xW1Nmk4wljMCeJf78)H4orTdQ*7PA>k0K?u_(opR
zFo9qPuS)$cocliJk-dn?icD1>vz}TPP%I>$kDXL3N!^*ZSt<2=iowjzjZz}a5vZt@
z@D|^ti$0bYg)Y<k?j1JVvkPjY{u@2en{^dQ`R)zk47jC3{GWEp+4gTj>$i2%`&F1W
z&7KVWpNFvQ!t{h3@PmlEe2_9hVnMliWs?#Unf_<e=?3;qFBmjucH(>DYoMv8!YhH-
zus)Jk1<!+@%$^JocHQc`cMI{gqzuv@yxP63EW$H2w*UTY?>cl3Hvjeb|9loDCWdX?
zd20VY{=a{+Y2bO&ri1_U-M|0v_dCH-_J1DvzwhS1ep^OVs#@d!^^^VDxE$h^ILqK2
QL$t`MtDaTKI$`enAA(JSm;e9(

literal 34842
zcmeFa2UL~kwl0hsHHiW-MvWkl$g(1+NRghzC;|deqzl*p=}PaI7)7H%vQUt215}#y
z4k{|r5u`Vj-g|HVGgrz!`|SVTbI-ZwoN?|Ln-K>D7T@~5_nq&Y&wS=HC)a*Zk!Syu
z>r*y1Hg<)>-ydURTkgolw(RuBEAX8Lzq>>DwBodq{P*~bkLd%M9{BXh`NP^aY-~K9
z^lynz`>-J!+gEG~-+z1DF}SbE<>oEBY`LMyJ$v%>zSsHo!<Lr<w-4P871{mh>?0=;
zj%}h+IwswFj|g5r(WxaMP;|*zD|A=M$nheP8yENZM4Y;P(f{x<jpomHU;XK)i+jJa
znVo-cWxsFFrDDT<(nHQMRXRcQ(k(J!PBTyD2gF<E-F9+ub8|1FyZh$-Sl7T(`iiaV
z%O$(%^Ecmq;<$vq_*C)fD*F7x4@>pw^TnS&A7Z`qi<=iW&^PYf`6!D%vn}89ZaID4
zzWx9B635F1gf6DYjK5WD(!)jd%%5IdvQf-j%k!0xe%5|bQBl1UYuMPDEC#bD2Zpo+
z{u}P-uZMEBcUZ!9FvYSdqc})>H>dQ_H|vE?+-@)OkMvkv*eGFrmP2N&e3Mh#Z{0O<
z1`hrT8}-gVTkiDX+P0A*g9YJBD>*I4gOzq?i;Q-bwX{jFJxwXeim$B?EDKn*RCn2u
z-QTQivMH099&pGF%E-uAUlykozj?C7!($+Kap8nrmAb@iU!u*cjm8fW5)y)Th6nfb
z_No~e<QCYNnwhmsY+^gvVA!nXYEjZzy!^+QA1`jWv&?-ekFTRz*GidrgGWlNHQ%SK
zRBAA|r>93mOsr$jW6@>3pw3NQt87uZ=>pY+2FuL$*Ze+x9gml=)m9X9FWD}Ro01aL
zdU8iZAxy;N*dZU@rVow>4<1~>vFY|QPU$@>c^ywKOr++v)3qFK7HWJ5G>h#X_?+#c
z<;mu>{;p!z(i5L;PyOW;J_dw-s{UhFpZtjb54Nv2(ax|nk)7{W?JV9`);DA}-;g`6
ztMA+$ek4p*#$$1|#iqd`)v!6sNjt?{J7+R`&~U8l&7-)uvarRu&f-uRDJ*2!NPAIY
z9*5Lf{AhYAUoKQ;ve70{f6+_4`L$qbBQxEmGa@2l>q<V?^RnZQR1JM(CJr;RhF@_9
z2pcN8<2{V0N80N=7UxI1RXrs6<-PmH25E;It&0Sm-u<{T!L;geUt*c8#Qa$0=)z>K
zhi_c`xi|NY1c?c9%FUhk+iegU;?OK1&+Bwn1&_D-%RP6h_S)Im1<1NN52UqvXU&Zi
z)!EhP*)(3rZ*TKeEp2jW2{DUJ-|DYtuk9^6)3v@;(mEu}@x!$fFqZJR28${6LV?({
z&7Qo@=a^oTdb+8WdMeVxFOF+!8o#=+(ZZd5RMlffEobCb+}Xa``1G*~rOY<2B~4cU
zKj-8Jjt;N>#U_my9rc;I-s+g5Z&&i%+*c;OZ+0NlDyS7_eV<ixR`Kn<{62CEjyU^?
ziwm=zFV?CSzxU(|<&_c3Np*E|b2BOn4Urnm+UGIfYoMm7>3=HkO8c(|I0}aI`NEp)
z8!Ybc<u2`%+_&M#Y1xm>&;E5V>rWLhx6l?d+uo|F_P;d~?XR&blbLL*H;ZfP(qg(|
z^~>HqQq$c;&p41ZTH5H;86wdcY-2ss9ceW(PA1Ta7b_n)aG<-EZ(+!D?mef+Yfkr}
zORKp=wi}1oX;jDPYD&s_EX);$$;oNzI~j6FwEfDlXqjkSR{B7}SEo-R<>a%Uc4<BN
zDr~;bm`;zmNJ@PC-WmfBw}>MF6(6P#USDtc=H9{S=`xSd-q=hb95U7Gt!6c`C7rU9
z=~+`Bu7`JqI`=%1hDVz2xAF+ud*;=HoSdAOcIPVI^ft~-)}J>>?pSosc%NgI?J`+<
z%9F>hF44FXi&OUFO5U_LX4?w-Q(Ho3W@dtID-^i1?d4<FusvzNEuJ#ykco}h=QPr`
zKEI<lz*oYiqYbN2G&S5R&ZUcm-tE?{r2XXNx7R!Q+^614&kg0(8Hc%9_b1mxI1Dxm
zx;Jmgoq8#~Fx4vfN~GkzW%hVA_VUdA^GV5Wwo;NCjufx_F%};9oM*(X@yx*-7yqXA
zDXeD+ZJbT1#5Spcv@<(*?o`ab!p`kiv^!UN(5cKN0jKGKybtfAM~@2b`^W~8F^V@k
zp7GxQ;p%EW*{qV9+?k%J(QB%%n<K;Aoo6No!;;NvI<9bAz8>qYw3%-Ak4#As^W?GD
zDwSKD>%$q)&UJTlACEF<f5mN8oSHk|KQ~j8YiZY>*D=*Rnd9%{)^|ptH#$-HT=|{1
z)zuw2vjasAIin$SUGj2;1>3@c7v^VEzuafn^Y%!9@D_TGoVoVh#xJg}>8^^>G&MC9
zHmi<45-766VD7^PdKqpbQe+^rW!_TXb@*jpMsMu&EAgD)^z7@l6zB?A<W3~VS%^$q
z#PZiTwYW`|L~AFGVE1KJWv9>5Z6^1|zK#sGRwT1Gsf<XLylaqXq})*w<e%d<YbmmK
zuuF6)Tgs-<R}%r!PS&zRKXSG)m4tR=Ph?Lv2SvqZ>?GrYuMAnkf5_ZcX8kU$U!PrC
z+4+k=?DiOUmx1)o5x&J?KBtj_Z8k60X|~b#y{0R}7H;PAc}TM!Z(%&ncxvc<_nZ6j
zaMlTD3z&28vKANg8Y=42PvVr>hInU>E;WxN|NX$mAf&c@7AtuIu5+Wb<Io}Bf(xy$
z+8^HcE`0awlA5-5z%Rf2GS*wOn>Hn9wrOyyg8OJti}}bnj{TJDfK6D@#U;y*9X<LR
z9<XDT!PIxoV_m<6EBPDMJ~@@%mr$fAmz$9xNxR)VUFu*o-c#k&eoIqwE|bTmcyD^w
zz3Ym6-#mW&SlGU=c9TnAyh6g8!fRv`vn`A66f5}V-mN4btER55K9j8qZ+61=%|UHC
z_353V6=?{eotz$12X+bwjLr{w$k8I;Yr6B__~kpWfemLvLefivMwi?XUAgHzy_q$(
zE?H}N<=KJ8bOol5$6TwR>~^~`t|e@`eokj*v+$Uu;<?i$>*MC%__&K~FmS!VG#3fe
zjX!;m{4%3}+(=()i+lKVOQ_300J)b((b2_-Yrp#IJ3kT`fax7!(v8+xq&6wmJl2fU
zr?0G=ojNU&*iNZQ{&B+i>4f2Nw<_RSYda+Jd0qMrEt>TU7%G=a_C|9kubynM%4Jx&
zEw@lx7&ln7yWdF<8akP^O<|-!)jgpq%;Uuxd4__%+jy0IP4$Ch!7rI@Px3gFnCpG}
z8PlU-i@{f}T%myZC^pv8&Rk8;rlZ(ouaJ}3y6?>d&f3=*`g07fN=>gx&&GvqU|8D}
zcwL8{d3m?MJ1a1;w0VM|znkqYnC*0*v0Ot>PfM$hoNR$^gKPG5M^JcYXIWSnW4hc^
z_NGHtD-X99_nwpAs7SQmWE5mMjVhLj$0WE-)EP^iFw@-2_ff_#f?RC%ce{I)gEBIQ
zugsbykDu^GAawGT8^x9u;mZKTM23>zE(Hq<i|ehsvxc5;&F9=G{Gh6HB|Fzm#zslo
zb5Z$0vNuG+4yS7B%4M3Uzqlf>E)<cFQGn=un{U2LKFGDdGt47rEY0Mn=IUlcg%Lzx
zxm6p4%tsxK<80(z$L{l$+}xpN4V*yGWtU8@>-329Xz@NE#@a*kZ{C=F#AdFIAei`m
zVwJM<c+V}FiCX?L*;#XqXsx3e3Wk+)R%){g-W&IAg(bLL=f2H%;l-NtjzF`Kmc{v&
zfPKa)6npz%kQ;^19`;Q2?^-?j?Hz%S9hczYy)%>z`c27yDEfrqU<YG?f?P}Vvz?Qj
z`aBjVJ#0ox#I3C7B4j^eSa?`On=Eap{yrt<0>KyDW10Eb99)&Sj!Z*yjr;s~jqigh
z?z<KbrzNbih7U2#M|y3Q`HtvRuB%Ds$3CX6=9S)0A$WAAH_kYp$Kl9`53gi@Ql%JI
z<zLSkBtFsKkXn*)W%WiTLPg5U4;SGhw<*}yp7IpS7j4>ZQT13|Ff?<#TGyu4OT1$s
zb5JP8H1_f1!n6ym!j_F`6~X38d$7Mh0YCBtPW+&obnYlzteC_a<@(T%#A~X5b{zij
zG94C!!+dLT%4hNQ!=v|wPd>c}0LH&=-8$g2-Tj7{TSS|>(>4Vzt&hVk3u^2!A4n55
zt4?xK8NKFa)8rH)uydz%y-8%d$3mxvu**bWu<J;HiMe_4ZujBqjKwKaJsY0vLu^m|
z44Z3noLcLX%^K$NXVwX5Fchx$f2_00O8jk-vvb~Oj<0|HI$-~W(?aQOOjy_|?X>f!
zj7oz8bS=}jq&C^>y3bUGb?6N`*BDhbX?I-uM)S!^Kw1i8V^zr;_Zi(BHx}&|m>pbP
z7?ePG-G>lVq5Kt7S=Feho1zu=R&2&VvymsGd*Eq*lYlwl$An?!hCr9L-{f`Nax&va
zJ!Qx4GIl6`2=iE&j@F95XS=eG{cnz_!^@)o8iKi|&ho}256cms>6WmCu}Cow_e#4u
zv$$*>8GS4yLPZ;)Lo)nN)@}vBkd<4UE)yTz(6<@4ZH&yT{_eedo7ZkrIl`P9@|0_A
z3ylq_8e5kXDx<rY@Dhiha3V3Y2;rZ}aA<LJ@Y|)GP}j;K*>#6O@Iyc2u7V8?V}ZfK
zh`&a@{e+}=46jLlX@1a|p?A;fTn4ifZM!S704CD`f|yC}zP=AyYd&VHO(^DW=!^)m
zG_{lZ5w8+g5H@}LaBQF{b8L=64#TSCTA;?1SgKJ;V8x(Y*IiFb4R&RlLUOl>y#swG
z_P|+4sy<y+u5w@AN2sZP;-^(YuSIK4_|mz4sk{rh%jn!_ng7DN9R3MlV@APvjX{`k
zs8fW<d%bLDJIl29R~nsrV?w3`Q&i?>C(Gbnf*8}q#<IS^PP>s0hPU?C#FdSf$%SFl
z!(VznAonHF_ayHa5Y(#OS$oI_ojG%ci;HUnIJBMOTaa90VvQ59C>9e3-M}+7cIw48
z+27h?q_(I6+84;lCYe9HPYW<o%q`whsVWy@<<_gKoo;Q&Ow|}@#!{q@6bkreB}=BK
zQ*=j2QDO$e0-WF^E6dA&MT$e<QDSj!6j@r>B~ID>d7hlRy=Dn$!dYyK%b77X8K}lf
zDbiLvP*7M{bH~y<_V%i+*zwNVOGw~^VAFlcHTvX{gwMRXq3qso+4lOk=^oA8`hl>$
z*osY#?|vkRSPI<i<?S(i-Jlq+Zy2a)s(1UaHtcobErC(WK3k?<iPuXH<}UQ+>dZ}>
z8O-X?Z%8Ank!ZcKPiIqup}(H>+oSS^J#Qaz_gmG}^%B<JpW5tf=01@a)^02}tBv5|
z%g{V=;iXaBdK1Y(L|0`d<sgC+w;O(tk0VqYAZ{5tS}L`hAP{9&R<?b}o`f#fmH^5c
z6n=PleY7TbK{N~-C?>gpRM+Kxdd2nKIk$r>Gai*l+9S4#X_lr$2z2~(ZThQniKY4%
zIUYWI`1+G=!M_5GN+R$T@GVZ|?>dpTM?fR`aG0!{@aY#<vpQu43y#Y3sMqsF^UIyV
zVg^Xq7)7Xtg~H5ak$IQmU<kX<O%WK>o&P?YAF!7KdHWf@SuJuHND%iRMt%1<Dk`x^
zD3j7ji4Rx#6~%>2DpIXl8eK<ArpMzvLJ$&+kk+tLy5hb0Z3YYN28sr6xc8rbcStU7
zUyajblS3KuJ%92*9@9kzisGZ^%*`A7TGCa+Wa*OR?HD^^#=3hbI5~BA1$F+sR^|1x
zm3+bUJDf6OKM<w`1PgZR2-qlUdOYe3;hiSCnsy`<udRUHq$g_6n4CNJ+I_a)f32$Y
zUU@z@+a2dEtP8gIw{_k3DebFE9K~m&x&%XDVO5x97%>2RGv%K3jXn27_4cxogVIOm
z%m*_$bc@wgWvZICu$k`@%ILZ$J=1l6x~|N<`h%G>n{GXzp|E-F6AmxEEXNDi<K8d*
zqG=oW0`e8K$o`E<8}2-m9OV?Ac(?py&}4<u?)EqGe9C3W&o?~=_w%&+@Y7XK#ZI*s
z-kKiv@d)0c<ag8Ze7yVojPzLff$6zUxiE$Vl6LW#KwiB3ivStNCAC{cL_}^{TD}Q9
zz#+kiME<otmUy*7CDx_jveLUFf~*k)+puoMNL?sN=zO}2vshtU$W2Po?%lhWS@aRe
zDWUeQX1aUDwU@EgPNWrZv9WPm`-<XPY!~}~{g2aCJ#|gX%Tq$^>mOw*f$+KxWNYWx
z_w>hQSne>P=oM@i@2wrOe2JVoGxKK)9sHPpFhe`j-l95MyRu|md`!9S&FjtgPQc>!
zA$&3vl$DjS@fqox>nmz&&v|PZ&CiSrT<(g}j4PcP@6Al#oU#rMP<605+m{ig5u*rP
zC9e4*<vXjRLM1L{Sl&lbQN10%oVyNaMj!vFG@VRl=EYJNaxpoSu}alWFa)K+5;n)w
z)O;tw%}kdq&Xtuod)*SyG=d8W5Vz9T_n05Q@#Wsl*rgNR(j%{rYiQU{@25Mv7<;Vi
z;^c4gyaj~Ny+W<dovV!=MqcoJ&P-KY%6^ac&oBQO$p>9Kr2u;e5-!khvgti4*A>B)
zI)IOhUT!c5=C;b(i>TPx7f+~Zt4i=~VC5U!;zGGd68E{yj5VImW3O|YY$C+f))N)?
z*Og+*mkf5jxj%X<&UK4CkKJ*~Z6d5%a)M;+L9`Ihv{G*BnQM<)m?G9(4e8O{1D+j)
z0&zzS4GlX=LxK#8{Ok(;cp#0gi>ddq+xsl)lNbe*!Amy7Z@9Mc^C>VJk^6-3uB7|_
zfuq6jX{L31VHO~i0`q;IXR34Q*Pff045y)IE2U&O=tXxnr4(Is05PS&{QAHZ;`^#>
zC`D&baQW*sL`OEALNbQH@hSz*PF8_A-dht1>~QPWEgs$)KLy{d?T;cuO~-5Z=5O*<
zQ;{&N%>6h7iQZA?6MA|fp)#pOxg0mY_2FHdO5K-zP_sh!rqXD0PgAA?pmLR0mH8~5
zNJ=^dWJ7<(;79#6t_$=~6?43jQBGEPuQ;{A<>kRJ;=oCb#fACtY7O(uxwBfjx<PF*
zocR_e%`e#5*^yQC4W*?>Pq+C7$a%O&<>Mjv_+nG;@X9zRO`Tt*cMulreBJtuZaZL7
z&HH%V{lW#~Rn{>(He?@6?-DFLXl2oqQdH-nrY6=O*$@aSrJ^;0Q-X)*uO*$!0c(K_
z3VEqm867fa>UT{M>S;VYHGW9RA1Cc}JOEoL_+_1dm_%qP5eF+)fB7qea?sxoUCKJ#
z!lTEJ74aOtS>r815sMw)7<b1AMoo`f#svljs>HB$^pE#9h=b)(VNr+{r8Rf6$JV!3
z#2MsPh<hPF?9W|myD>Z2keydLE$>V!HZd~9UXhsv!o*_0Hxm$xQGo35P4@*0lTU9n
ze8^LG5^a%+03n5SYSLnF_#6*xUaRDMJ@$i}Sy4~%g5AR64ml6L+Rp)B#@6e-n#J2?
zrDm}fMCPLv$fOV(7>GzpIx?MiJ$V(Go1DAu{!3%ih2i?DNbXP&HY~j9<sGM&Ddy04
zL7v$*l*c(Z_PhC7*9t9bW=wYijUPXLyp0$b0^-pIxv~l)V2s)miiDK(^z?8pd~pcu
z|FZfwwE@acGwL$zdI`<nT4*dU|Dm4m!Sq5JAwb!wXPlNI3-S59MSvI8J!XR`4VLLW
zkE0ZW-Z{KXn{Wn`B{|QP6!ZA;&Sfh(j*L|I^dd{JwK-@mfOWl>y4@fAM@7lkW~Qc%
zn-Yox4F)H4l1*=M@4E#~X}$ukmSNTOLxjn<D{aaCl)~1|>qQ833l@euS;d@4uTZWo
zG)IQO!>c<L6kSzQliNRPVOUEHg8PVX*jaK^QVy2L1%CqoCnzJ)ar=x;_B9>jSFB2h
zZTNR_@0zN$pXusrJpaQ;Z(d)QrR#@(;sTC4Hbm`W8L8H@OhY@P<%9N_D}S$*y!kh4
zB~<9ZQO`Vbd(YiWrIP|;-~k7O@V3pkIC+8pNcH5eTE&070Q0}R#KX`gJ=T4h3vNAf
zyS!=3lXXELz#ROatod&(!~7RK+W&cZ>K|68Dlu~vTMEzGtw*{BKCk^nIaE5G5CPjo
z3-ABQ+n5!CG}G4M6Z4l_RYMzr{n*%Sx&L7sQ_j`?G7bcpsZ<-GuA$+_JK0xPIetZW
z)Zqr)^-ZO8=O}p}@3{Cb_*3Tzk;}UyJ8DwlkX{C_k-YD;TU}FgHuH$TNwbpkszNKX
zxw-dN^BQm}?oE2n?eD{1k{@>PU(z5lXu`rmg;lz!o#42ttG8d8UH>-IVQ@g`M~j<?
zFk|a)wp33|=k=Wzn1DyJbNHko3GcLe)BZgQ3HJT<$kZc(1b(szniN62I4acM69eBt
z^_T-^YNEA$>+9=<Vy|`pNonc?BqF}yK5~KtPV2tO^;kSMm10`8)5E`8*m+e-v~|fE
z&(#|%-p2#clEMEws+a#@9{*4P9q;?StXgmO{O3<l;?#{V@ZIL%@X3>xTF?ILs;d9?
zef^vM>YtbRPpNZL;sBd^(jU0z)OQa4Pc_U(+6rI?-@MoUSNnhB;F8QgwE+K)3OgBC
z#sMS;uYCA&wOO?*31D9(j~o>DY{siJt*nc0Y3c`ntc+AcB?BK>A^pPdl$pR)C6ZRC
z-&qcKg1$)QX{fEOeV>6KCH1d^)VVzSjYAVXRRa5riqDNsB^VZsAY&8*|K~L5($df)
z(HY{PFFW#^&&{Y83BpdsYDbzWp#GA_n-!Bbm@~^{pxEJx^Q$8hDbMN8LcLx3#|qg|
znWp?f;aKfDyfL!JiEp1~{<tm7RTxxHok?T}m8tC<B<N>s7WG1t@0xDHL2Xlb<;Crf
zGKW}MCI~ERyuA^4%8erDemLa4Dd3EcjM)7{UiEE%p!Ln~ET}3>C46SvRetZ<w)8UT
z(Ief%c#s71Iv@>E0YV8#B1Xg}2P_7FTZ%wa)b_-q4DBX?#I@n5jI9@vSU4i{`+tBH
z>v0X^^~;W{t6Nk{ugwUY)M0rd;L18K7p12S#URp|j6t_S7fSdv%DNj;t%TCv{d5EL
z^Q9H+im0pui;e)7b%LfS!c7x`H~jG8n$_&+0IEwUa^_Z@k#Qb-jSRbK*kFa^L!f>b
zPs8@#fpYkXZ1`M2=xpb4#j?<dA#<l@vp-R^p;@0`VxEy@W`7E!0BJ#ehXg34x~_CQ
z1*=$ebq&Au!gQxGDk@ap5w`6rPpG1_o9(N2^NUoNr)oAZav>;R$uTbQS!Li=M!>wc
zUE3Dw*l=<3`KsN;JylV<GT`5zi1~ag(Jl4o8tl-tLI|;UTsHu;SW#YH{&L;3hEaW`
z-vK_$VgI;nrM+YH#mPn5LndQei0v_OPEvD{;LD78A*=ws`oW@%N5Z;ot4}V%08i{P
zNmHcB|7r>J^qStskB^T(iizoeZhCUmF)keOgM}w!;!~Y%9hT@`g0U9|OYBpiqLoT^
z^}U1FnG9?~kL~XmCus9K<ph+D-&R!_!k9{*oXV4&lZ?w6QRD@_W)uJ-H>kI>Kw^^O
z)1WrZ+!=M|*}19VAZaU5>@23EGt@=MDrefLd)ltI+7PRn4u;y70V1?Cvw1vrWZi07
z+s|BvjPM&yeewBB2CDvDTgxnvXD69d3L}>kE2{&YyYIq>7eX=6z2Gd0Hn=^NA`Yp5
ziuAH>Gv*_W-(FhLP6gP;beqxg1DsUQrp)FF2T$P9`-j?z5?YuY48!>lJSs_7c@okv
zu&kNV0xWWcQJ5U|I@r2!2kJd)g<9+AB~Kd?|G={UExMn7hL``8@baIi#QLAHBfsmx
zKu1I&Ns@Xnxcv`K0Isg;zI=X-7ST$lwmg~6syX&Mi*U23x>k2-b4)FVR3Bw2JQWTs
za0w4^EVdbRTklY9bGA#Qv8tM5iu7RepFd0xo$#xBuhEAB#2}zN$KSaw+<*Ysx54sS
zRC?`qes#rr<nLS;k3(a8-kIfDy76!<z^whh=G_0&=TKPCcqb+z(&dzK`ETEbD{4H;
zPDkzkMlbh2W0(GU7yr$tcf7Co9cc*><@D5*m}gP)(3^24V`6J~tg<|-M&HE(wYAL0
zevCgvnk9zUNr(!yX3yJCn>7vGlB0U)|Jm+@NUj?i0H3O-&f?S9`p^FP%0Ioy@yL{O
z=i=0(G(aKkpsILvtX|M14$18RX4X`;6Vzs1`>EJnwIx<4wpr%RPcRffo7z+;Y)f_;
z7<8Y^3_kz-6GUV|8He7O)Q%{FdDBve^-h2#?tYW^(`mYuizQW$E>42hEe3<?%RmL&
zK4{4x<aa^wXYx~~CB+-hA7~!-b|BGA*Aa<+>&5xWPH?)-Z4xB@qB@6$4~QROwMdl`
zpsf)ql|v{b0m+9o3bqlTQa5d330#xPA8kpr=`0nLk$wO3T9n49P9>8AfHMjZ(`Mx{
zt9!jOcRa?*5WzWQN2DI8P?p?sQB+JUMREdw=e4$R$So*@fR*$Mf~;}^F0b0qLHa6x
zaL>f=>h?|2Q?aEOgy=k*MRnpPFQ0Asw=tokBSpK7C;I9Xm{5F*8!XP$DCJvbbVuyc
z%MgJ+%F36q&$zUBqjB(?0A_tZX~N_Irb}d5vc?zJ)>45H5+6h04RGV772!&wkfuoy
zhK<dc={XX-KmLX~>oIMumk*+FrsF(U>X4IIWrI4cy0a_lL`nNKKn$XCk~P*DamPW)
zbTB6uVE!=leni`KsD;fOcb*&w0cKduBPl}53oN$JVW4rBZc6l#b*!srg{v(YL~&Hl
zxe>guM%i@Jpu0bV<gb*YdwCC%z9xUw`d!#8!<yRK+gPnF3MPZ^J7y;Ob#>TXv42)?
z>S>KG_NzuNZASUPk5}{f@g(O`J%^@S3>F_JL;BkAe2>|N7D+XgI_z&kQtrUe`#fEl
zPUsL7n9@`4F6l;tO=Dv_Is5jJjN|NBw=Xc)76qaYQ9xOrUju{nH*l9wu%3GFnL5K&
za-aqLwL?TO2Nk^kSNX#BU-ffbotS6AA%ev=A0PjVg>RWKUPLu(S9Zr0)Z%<dZe&>&
zw^Z*QxbnY8qgBm<&jWiQ2@D$jVf(hf3pW~gV&Jn|DVrT3n@hxvr0idS48V_<5AvOu
zoI^NC-NPs<NvWxLlB^{>zsOIp2uTV_-Zq_ox+ER;N~$W!955P;#g8xOQvHcxb-4<T
zAt?Cb;#j0y$=vj)u8ci2m7O21@3yQNT&j0>`muADB8zipkvOrjv3X~rqM|AjjUyw5
z%D?`*q#;Jd;>~%WQ$JoL8*cYoNmtQ54B)Th<`p5x$vlga=O7Lv)k#UjO5zYIgOG|B
z`o!JV!!I{Pg5ia9LTjsX;M>zjkeo78w^m!2@OB|8-i&l)8336oR4CS1ifsxc=-32x
z8Og?c8P_-L7FzxQ!YcEi?up*GTz%)pxhrPv)jIDQ!M$&?dwX=_-qYWc-mo}CN}NzS
zL0?9A)#oJ#y4gYe`t#;C)eIs>tm&v;H`||TfFj&zO72QqQ8)R2k$|8gVT%L>B$2qp
zhRfE&e;48@*>g0<md#JL$eqdeE+Hu|Xr&vQB!u~<o~}HGl8Sbs(XG;19JR_0Gm+f*
zdb#;ZIlG8pp?$7XLzL$ENRJ#QiPN#CnSU!-<i~=OFX-s^5koGb%<BPGyk1Sr_F4q*
zmDq?>7T~!Vl`1xF@g!AES66pNW23pk-*$+AP{2j}M1)ZQ8At*=|E&dS<I>>u?+2D|
zI8oj)v07o|-6c*x-qd7OD~DQm9A5@%=i6mcD^;YnuvBO31?+-!NvWG^$z(b5A5=m(
z99ME|(yEWMNBScq0}c9YCrbBrpbN7KhSegD?2wUtcX64du8O}lG(ya?<#)af??gf3
zct&?Ul_Pj2k%??kAWgim=Et<d=7W+vLtCxfO8Q`jrO*N-Vo52%Lb-3zC)>p*@p0^A
z{$k)p%kMsZxMQd3PW@%dBEluQ`h_=-sVVi=792}lJ$o$RXL&)T-ZsI+?{*zml;9LO
zI2w3!!|^Yymi%(tFZK8r>y}?$Vq~<q@J_sPb@9dGVk52M(P{sIY@x7m?ZNT7u=&&d
zqAYdD(WCLt?dY7{R1C*|Vokp^hC=e$^x0bqUhHx3LT90WR0v#AIZir%D$XnDxTdh&
z%r`D3{ufi@v2n3ub%{Lv(7P4X#-AZ6i}*DEx~i%w(wCDWoKf)W*9Q^`wg!o4r^+TF
zZOq&U9l|d3G89yFwtI7l?j&LZx<andhTr)=A&VJ)vxDk8ZwVvoBUk>?Fr~$P7G`Fh
zUPGQOY_4q^=>kCTgblami#vH-7fg8#Zlrp^g0*{E8j-6VXD9ket9+IswxpdYX-=Ra
zXSFJT6`6n}k+ivjvihf~oc`e#0c%LmjPYtGglFON@1AZ#sG{RnGbMGK65Wf*d8Vrz
zTcFWC0ihyGmWayy&hTS9r23v5pnZ0Q-obJtzgWYQs`214%8;TqKL()on5XD3L6J7y
zkZgf%u*~!mN0e+|ZGz$sm20z+8Pb73lFZ+No`QX+p8rheI_m!KRFBzZ`$c}UqChAP
z0PCN?YT7R@<9J=AX%GUpu#0>PEeE9Wl?qH!iXfJ4cR*teKB~D4IUUO&cVQszonVrb
zom>g&CLwMWn*OjM_cmB1L9ihTOcL?6Te1kLr#knQmkgrFt&veo5^e^`h~In=Hkhri
zfyxR+G~GZ}7%YF3wBub9BqBE5;r<aRDJd@v!EyL8iex-e<7dV*K~MX2ux`b6r^TnN
zodLdRAa)Acle#|Pu-V2=CW8d+is2R3gZ_%)!PqVZCY1Kc;lD3Hh+@#vW?~fh58ZeT
zvU`N&Ir#c82y<UUsOT29p>E2OvBQ|e<j__@^21I)UCRP&nLm$x?cMivnW4Xvj6(~_
zJKps}UrhWH@gOh<Hxhq<F#pP-$xbs*ILIPJn^dIHnFASAcP8;X9S9=7M)UX<_NdCu
z+mfUbce+j2B3V6%lXc4PBz9Hsa@RozKY~sI64Gpxb#;_Qhe-!n1X@LANsCPKPJo<X
z()<UYxdg!E*0D!7>E5ZWgp_Nxz>hM4EQYe&BuVLM5Cf5Jx;i!I7Qc_Qu)$1)zjYqF
zcw0x1RVm@ixf+joO<H%-jCgLcKJ3!}IW77fHM2^7uE4}Mhqw&^lUarfN8cu3Fs&#%
z^TwwGk}|VFJr<W^Yue8{R4ql12w2(CYq03d>o)erJh@6^8ZzRWBwjJ|XKiJOV%^ea
z230VKC4|0#zm6;bD`i|Hm|A>*Q|5SS6YJ(}KkZ*X<fj-;Eh`AmWe|PR)gxkKg%lDH
z^gAF3H6|>~Y8D}>0!b3k++r2i^gD=?wBT^;?dh?G#Tr88Uj%7lFtwe)lx@W7&iam_
zEWJU`mXw;ng8medNX#TV5$|Kzn0BGNCD#L*L%HzdJCpO{|7M7|<VZl7>sAtnjld&p
zQxI!*Y&FNBRA5ezb`o-?hJ$0M=QHTx_5uE%uqhFx)&QtC0&uJ1BxB>|WYtOT*qEq2
zbpgZN=`A(zdIh)nuP_|}_(Eb6p_Yp~3tozvYDg|BJ@j-Lp%?DzDCoOLp8zi*Lpls%
zI?drRNmyR*`pxf)eGt}9`HliXeq(6Al_v+9xSP?`qAR;F?9&Oys>GawE>6dC9-P=&
z6j#zGn_Uv$U*R^wV_2B5PV_gC6%iL5X2!Y|m=-B@5pb-vr>`>#iUaoPo-m5bo+u<2
z1U#E9Si&-guH*Gz3=v_fz$8uhOGD}jVAPpL#b~6(Km^$dXl|zTyO)u?CAK_^C`;y9
z#E&9uGJ0idhV)T3CxID(1_f(qvr`Q<S&y{qzLKtn?C^`V`w;a>b5)0vK%!d62_qr(
zBsmOJoJ>aDg?t{u5B4T|Sih*0+|AJ}q?ge~YB20i2xxTbo1i8vUXQt9C_fo+1+0!G
zFn<x~uIaBbV*P5xD=A0c2lxi7-vaVM(#$$x$JBP-sz4pgJiXnoyCS><bq9Rh+1T%w
zW8Yn|1ubHBDb3Ek6ZH(%4+P31<V9Ftm0$RuRgL|Ja@#-4TH7BA@iejNXlU>7FdkO}
z6;^-m#9N}m>K{LRc&;+TG}Qaf!1fKv2h$%478)u?3oaStGKZ7t?b=%Y$s^GB7Dt6l
zuUqfXw5WOu(REX(j7x;l1~o?wxif<8_flBD8GS9;&^~QN5ad1b;y<b!Zesn)QK}G|
zM!`I*zU~LXo^byCb9>7*(ZTV4*7zCSNTIqe<TRG!8B3c*Yw;4vj8AO8>+${<KZY2|
zYCHoNNju@Jf>g?iX1>3@?aio^`p<`AojAQ%#`w-{&3|4W>OUE+|7J$(|Minf$Lyjv
zg;C@BBon8xH}a#9n^W0`B)BU$d4pBmdejIP1?pL$=1avO&9r{%ORPhPDSTs2MtpHa
zi151*s3#FZ)7tgJz_qbDY>AJeoEH615jao<!hqe$$;kqZ?vIX})=aQ)hDfB^iY%01
z>yY9lB4aQFkXLERaicz}m)AFRlHhW>Gc4?pT4V<r9u`N!ce_2Rv!K=s2{0&{HbtzU
zw{Txg_EIW%jOk~b?!Q9rWrV~)70?Ts=?a|IKHw!1jWZb#y__4iY$_~Nsk5V-f6Dc}
zV%-fNh#nREWKB;k%uVw&a<QJO;}gfAqZAHXaGnRB`q4MplA`fO4u*vM7#q=TR1H4O
z$UnW+TZ+}t)3UhW0GcR>+Lt7dYEpk7)M;69^U@I2ljuEhFa`p3YLn~bV5`5Qv>|V4
z``zC?61j$vhjE34ilVBXKm6tO61G-X2iDx<NI@41sr7$XV5T*KVr&>{iOXGZCe>;q
z1l(w(J5TLfw!PIB;j@wuD35~Y5uI&@nk@)wvFOvicl&XB4R4fB3Wopnra)ji*#r2!
z#6#|nPWBr|hm*5~oAeirPbuc$My69lEez;OrtZ2rQgG`=7?*`o<&A3F;ZUGH0wVfp
z`6^Z=7<%fG8yoLhk5&As1=wf(Z25+>XanFm4}CNG@{Kp6JbMc{1GOK3s7XMYz=E!c
zMg3H`&aMUp#_D0(fBt07bcvM_<%BXW_UPTreeN~@>{NH|zE_}|6_}AScwvqCZ|-=#
z<@wXQ-%L!=aoJ!=`eZY+hIG*<)~`_6{wpjbw+~+Y#4%B%F&h?b188R0RepCGomz`I
zVfPzqp&zH`YA5SM-lD-ZL?A`LmAFi;cCl}COHkFmAM{j5@vke|0r*#~oubxUC>#45
zH)~1!3#V`6U8@tbxQf&4+RXV$q_qV&J$^VBDczg~6u1<aFp8+`Kvp(V9<9PNB*x+3
z0O))J$u`W`qP_zM#Q$uG>pNjuzbE$HNj~Sc-%?C}_tPqC$RCWT;2U5ZTGw&j0VFMD
z&rFfol`NW)tzIoM>r(24G%3fSca$p*3N*Um^;AYCvsC7NNYdIJY7x}*tjt`!QJ4{A
zo}f@y&q?p~t8Impqk;Q$dm}nX6qqQQZ>HAq>qYv|?$?<omN0j*U)cr`^R>0xQqtz&
zOv32NtS+%mX5gD0%bH&)h9AQ|Xim9jqg~So9lu01M5u%WQg#TmO=zElq4N_ByZ`9$
z)sU~G3tUu*g@;(SV)bif@us(r?NHAO%j1-j_T+PSluBh)`cGEb78?H^MWfmOiS7O~
zCR+RxN&H8U#A79S@r!=s-+7uolKk3Ep5Q4to%Y8uAYC=$8qnhpsZH`3vv>1*tB(;z
z1*j|v)I#l7R12)gKO)hD1YtdB!>j&+lc^tTlxivciz@@%ra0>n0)F<>@_&3|q<oQR
zjgQfBfVgO>zU0q;hh!~2ag@3Ae?0)@pXTx(F_-_!ku?9W?Fb#6I}e)yEvUFkeI-=3
zBc&`cYg8n~xv{eyrGRu%<(sDspC%fW6n003g<|f^m6}lCL2A~ZI*tu`<icGh2Y70~
zqgUVAH+1!ix(McN5XFQuCIQ-usM<!yRxh;E9ziQCXFJ}Y<njjn+BYREd?LAI$>2^D
zH-m^r%ATkfpoSotFhElY23zFhX6wq1f%l-8N!p992o{<(lfbVbDpPwele*gU2j+1+
zJ5b6{VIB#Uc1liRi6^>TKgx=F2Y$AcrQt$!J7*t2GbI$5G(#mhnZ>Gi1<(DKtUd&;
zaI1n??a5160nyB2;?Wq>C~G8F^b3ME2|xCzZW8bsEi!Kk{l4q{Tl%Z5W@e^rvB6CI
zZ;r_9G!DGxGZOX9ZF^xsV9}SMjV3unXWi95`L^k{%xIw7Bi^V*3N^xg=u;F+TCZaa
zG(Fh0^@-oHzfxdiw=w#uq=&9}`9)||+233e-Xnk~_9xJe4229p@(;pZ%bKuD>sDm(
zw9~Mie{rF=>*qXsfBZ9be!Ko4|2w_q;|@x1<vdd*kSnk+>S?Eckl(}!P4xTNcZL>E
zo~^LR$h>q51l+lg-+>H7$860r!P6FhVCaW2RZ)WT=v_8OGa48r_R%SL-t~NsRz&@Q
z)sE^$Y3jO%Z^0X(yGDTthBNb}AyrGxb}MMtHD#iax8u_m544(V#9xm71%q`A(Z{Hq
zAd=((pe31hFk0{rcucMp52(OI$rkfk?p)GMvhRgV2ZV0*W4jGPCu)bHgbPVD&hDcJ
z@HCkK2+v`;#_T$$>$_!?12EfAkh2r4w8RxF(o#Cg!Up*Qip(#ui=7+K5XgCXb8Amn
z{9aJbTOE4Ds6M?#A*J3ls;Zh*(Ya#-CgCR_DzQe3VQ`fqnZ=k*<I6zn|E#GQ%d3}5
zV86eGN|RLuPRAwuU8q$Jq?s<05rz(84YLN;p(>Ssn)wUWDX5zUJ@%!LGjN9%jKki}
zzI}L<_)`L{sFFCGS7ITv<Pf=plwZJ1{%@)~-^Q@mV9B*htORHn09tVm#^x@}&{!0!
z>~Rh1FeFjN>|m}3j94<`8r{q{+rD~d5srf;w=MYmcPjhe|Lkqaf_mi&PAmQ!2YdZj
zdeMi!9D8)Ur|VF87DiA+-Vb`B;ew(xRpYiPRHOJrlr~SpSJhmj9VMjWz0IgQd5N7E
zPnHFN>+XcZI?Qw(y2Mv}h}+^Voq}Nu%W4bv*H>hkqY1qZZG=*32hCDZ^P_w%MrRT{
zwGxsdsnn~lXmrQ#9esi+wVRP754>9+pW;ZoH|B4_&@fMG3>vmex-aVQU9hk~Jt1b{
z#*c>CV8E#b>GR}dNifEdRlW8m`S#_d_134g(;>9!U?mZ*)YeU1maMylhQbh(wgPiE
z`is%Cm{ZBRTX@Khq)HVfrYmOHl%(d2$FN=vAGlo(z1+SG*7i$wKft#I*L-}dg(@+`
z<|{C1C;*WLc)n;sq0GJn$i2T-7zt{b+j_v0qn{on4eh=*sJ1W^s0R#PiMHpjYw4cA
zd>vLjg|Sh}-W&r;BI)A8S6Nlae@_@!M+Nqny!cYd??9I$hh`Fk4JKM?W$M-B%fB<0
z_fp=;;YZZg&!8Xe6lhv|VD^XKz35ww3mD2l_Wr5j7>n!w-%O|bbb1?;k}Mk5qd@Wm
zV-!%k-mQ<}r4k&qjYDn}fj$+(hUQ-;Dg*86i>SpI4U}6*wvOXp{n#Sf+dh`Vtl|QS
zs+~0GhGq>>9R{vRpCU{jY6Da~Va-!QH*$fb*|Ay8vvu}4NX*g)GJ1tEs)#0$nG9GR
zr}WRM=TYQHL(75~RrvH>`m1{84e)K!rvzq^SchG#_A5w~3u%fM;+=bVzffdVa$NP;
z%O0-Q!lsoaUT$bN)Pc(lK$HGDl9xxNW^Z-naK*Hr;;6WsK>U-GahW*rKB2wu`mG(4
z?i%5Ik`+BYgVj1}omW9A$#1&weN=S_S`#wt`!k*!`eiTFA5%5U`?8KJs-jfXi8pa;
za>=InN8X|Qu>(1JJhNtl=_B=f7GeZ#UZ~G+(oNZ2?Kgeuw0ByHS((J?_e+~m(!>1=
zA^a#XY0?}C##W}9jz4_#$YlS3wWZa;S(6gU{&uu+lPrYhoM>2`u}iYC(^B(pjm;b=
zBr8X`cZ&kG+%o<wCAAT;fqy|rkBOhM(y~%ESu7N>?4%h3URJLBP#tN#m&7yu&Xkp?
z3}_z2J0#AFKs-&QINz<xg;?fWB*<6J`6{M#K~HbiRM{Xpc5iH>TX|zGb)yQ+o>(ip
zQb#hiWQVaq^pW(*#GUiyziDWCq`nH2mEUEYE9f$x93EPIWp9G0MtF$Xh_sQiNsz>c
z5lLt7w2WDMyS}*W&O)=isnnF>HJal#=5;w*x*eX^Bu*Qb>(6VXU7UpaOOu96QRR~a
zTr{1?6=Plux<lB%Xra0@E5ISCmlwaxARQL@d`1C=V)!FF63B)0Xar2%9>6gtRD4HK
zWi>|8Z@MbZ{T6tw%ro4Lhp1Nj=O8K?xk5bN*0nEbJO)h*(VmzO8Fbx7!UEr2^VUy5
z)X!nsK}oy6K`7djj6vv-DHkRTiYgoTtvb`<(X~qBta2#72I3p0gXj#56Tyf^08C7R
zE9`E{ob0jGdB1T(wRFeTj;m3+#{DZ>>9>A)EYo@Q@+wCw1WiT(acRJ$U(uihmfC$W
z%5BXYDwTR?QfrzmbgN0wj!4~(9u2L5$P>}-C>fjIPFrl-CRd;$)&I6_9)XVBGseCh
zuztE^<*v!fN7gk;E+&noaGzThNSuWd^qF+SB5i-KR6QIcfJma=NXAI*;^>1D2}p{?
z?l*N>tIymEPl+iw)16Q#GcmpCq{T6BiPQ6{vNW@56N=&eRc}{#nWf`Rs~WU@<Jvd-
z{3f`gP&$JDHh*lo&5Nq0)F>N|eo;-ah57uzx%7lxHs4D=58N-m>-f`5;1ya_Wg4Ie
zHW=8Z6Sl1s3rM_9rI}kUk?>0irgsF<<Rq9cRA&c9F7I~zn&vhz87K&sfw#Gd4e2Zq
zZz(2nh$h69$Yw_BKRRBBqBxUe3;^?r@S->znJfth`bYwUS;7kHJh=1irIA*?MaT3d
z=fKaBB7=a`4PF^ZZp*Y&c`k#Ub)vg-rcRRxhDOWLGC>dJ4>_JF4X)w#zM!5m=^k@V
z*K3xhg4O6b=uu0=l}vM4Osp}+O$#(GV6;>ygF;G>n}@o0<`$g)fW2o9(I`t0R{Ycz
zQH?8-kEG$A6dt!0;k<R;KI5&BYT0C6B3hH=_(mDCSYUVdc+^}B?U7cf?2M*<{*qV+
zI0{2tidE%|2i$k#Ibh~}zj;<h73Y0}zT+uo8?Hr0lJtDEg=lM>D@sxFK>7ktw*}jj
zYFmD2N#h|*KodK_g6#)=M#QhTWY0s47<p3H3JJ|2)Wp56n9Wmz1+6iL;qlW=(XbL4
z@sbXbl>}BDAS<Ychh&nF&(K^oGwYI2yK3zKF$*1#=Uncm9YI63JLJylF~ctn;NS#8
z7Z{0ptb*UdM~@_;gjE<y`M=C$$^_1CY=Lag288T31?s#(e7s^o13_r8<OJr=%wZGH
zb=_aSYW-P)pcoJn*x#_*_5G?kqaX_nM;UdTOhAtk>LQ{cnBYFHuSa9FzkXneSVMH(
z=+xpu%VH5Gg(NP_^crKzCJwCwz_&0)LS7k7NXXC&pkxiR6^c=Kzv-(g>vY-99vnSu
z+9w{;KPnMu3p30bZOUS7Pq@Cnyuq53y_oo9Mulr-u{s8Tx&tYLu6Uf%t8zaasGmpk
zO_<?wc2wh_6(k73F4sCuBbc@(??X+Rjx&4bAP#~ORGU=1z;-==_E7oNPaUyMq?RRH
zVzy1S`WOtP^-gB@sb1@e^QSR9q@?iWq5*}vn+&*uz;R!<OYiG^N&|do3LSe*2de4*
zXS?mR;R*JY?!SM^viz7EJ91+%LnSnGsoiv*G&7hUd2Ht`u8B8}a^t1@TW>%Fn^fQq
zET2PC4<~Gl?}L|nBlO)u7zL0B(NIQeT!S1}1spTY+hXA`u?F+XKIwKcMFb{Ir^dKR
zci97B*hre^CJ>!{YkYFnL{+!L9J^yWDe(bhd=RoMM7&#u-nF&#v>4rzQm`q0ct%xb
z{CJ+~Q&oUCaz@IsaXK@bVsm3jrb9C>^)hT}+8VY!+o)5KhQiQr5v1A}JP=-B+>PwF
z`MmccsCn_=)|r`k%-Rbp^H>;FE*<_Ql=Fm~&;*1za6E_6Tu*97GA^965@XrX03x>q
zC!FTf_MlaWG+W*E$!5M1bqUE$s4ipy)6j9Z0R$B7j)WO83W!h;Z*fg5uzLcV6n)f9
zwhqk;C2!XG4O7koxWYt>$KsI?$>chx!Jc6Llp4GAh|)DQAPx|-4aF*}i75;sFc3+v
zzAh;ry(mP$Nh~vFW{`>XQmnA`uC16mUneov6Uc9w^yZKgw!s<-6WR*MDIADb!0ZMZ
zC24A}pVXHQ$%=z+bi;$`=~MKOU?eDpQYPh_VJ^LZ4mGiEPa(Z#1KVSAa;_+*2qR%6
zwHeJD!GxizzPTqf3lZ}nn{h2jlw`;|(<lPcQYiP>)VVtXkN7fZnMZLn!XqM#!ow;L
z2J+_s>?!zp`$9=*cwIC=q~ue-kT{XhR643ObQNPjDjt-?1n}cnga`!lX<*C=&3eu2
zJc?Z<SW3fAXiVk$-TJ|2yW=*=f;yo|C}v{8V)Ewoob82UI`<cTlk)04X=gf+c>jkb
zB>oNr2wTRn|0}X^h=~)yRJ5W0uMkeP>BFVq-Y{)p%pREQ5YILF+>d6Jq(kg!gX3(3
zT)kvRWN^ivXG(q?+bbiKi%9Yg$8DExQS<g~TXbTn>6cwSGKdpSbHn-M_JZLPx)Ix$
zZFeql6k>_|Ng)rDv=B~%6NA^4pwb;p7To&Yaz01LSxg?mi;xB+^yvk?km2aiC{vpH
zwNc#igs-e=nekG@oALQYNa)*85VNpeLQw<~zt=d;HRorYY$#lz6UZnkCdG8b*$g2k
zcX58uzkz1d2%A(qz<f}Xbg>D{sH5SMtT+@XDnLUYgPI0t>Ke|RD~82cj?5I!<Sc9y
zi<-F+t1jdHs^3YjvwLmW)c1Kme@G0QH)r~lc_?*8Hba~r-#GoqDj%zH3lpQF3r48v
z0Gxq;gUbAH6>GFBj$XUB74EetIb1d?K-5eF#m=g>z!;!k`=^$!(WKI1iX3@>Lm0Mu
z($Gvj|JvDZtqX{LG}n^iMqXKIPlGFlb%|_@;&Oq$kVu-g&Wqy)fy}mh*M&?!=IE`O
z>_@q{_WU-*>{m5vN+dg-gFm$*Lg0akOqHr)&o)zvxp~gx^O^AM6ZKZn$9Ga^Df{9g
zdJ!NYt&@zxO-+4c8C%M1LoY-r4T*(DIl69M56Oh`gm!KV#HKWl*P`pXdcUXN%xT;r
zyW5-EgmEW$iFRl`Ek|@(ci$}XUS6V?StOf3IuDp%AP0RGPLLt0;_PuXHI8uVaLzq3
z4~1y_DCxkIWmj4v6EaFwTfC76g>2j@;ksjPqB%m$h~yloxN_rKt)vf$r-m^Ez4wlD
z4bpH_<rHlkUMirq%nvx&pxn+HQ)eHV5IwnXAg;&wT&ulM!=ym3=VWE*j9Zd@sYZ)8
zGHe1U{bKS*{M@yt>T7bU2W(!@Kr!|>4@?9h%7?q-YS_%%$Ur?g1sdXt4qVacEYn#1
zAnw+l{Ga_wZRXS6NW+F<iAJ*)=)g$Fz%zfBdBo%rFaiy2n^MPI-vOkF-?&b!3{zyz
zV<aqH{!9_P=iGUS$%?2KlZ38xppb4?m|N6%+|2>eWQ}3rG=hk|<{8E`S3Y3(Vhy!&
zZOPa5aBVP8RHoeSic}FdpW6IL6bhldHSK}Y@~-G<@SmMCb9DvIQGu^`-+9$5S4k*p
z*nvj)N>ZJE#9e7L@0HE<btSnCF5XkP+i(a~-(R^rGs$d271iWpN6q7z0u>c=Lx{X)
zjut=!%1fJPY@Dgb1~vEti4EEC^3dexi)7{^K^791JL0#nq@0dz-SnweGgFfHq0JyD
zI`dw-^+?$0D8z+Em*M+D_(S0q1bJ=dPh?-v7$SslIiLVDVf@fCBX+|fCWJqB*IV6*
zdFn^0>un==yH4}OZ<q?V1EAGXSnf#i8g4$bbm8pHrrj*RMmU+l?t6#&Q@F);;SpcA
z75Hj8``6IKNeWP`(PET4QHzYarrs^eKM@o3K_XCZvunSZffD2qiI}h3Xj`dF&V~l=
z&7rU3#DIZ1?LGkG1MmQ(wk81_(Rm<>UQ-1h5Ks)$<=hW>aaEOfAPr@;G!c73L!Sx=
z+@0;tttf+=IdEll0nLI|bQ_J7o8N(;Xp>>v%_I%*bZx$@A0Xm3%H9yX$X9VJravd4
zGR+Jmp)w(567?Z;83^S}RUTXt`KK1(15LW3;pPzHCO0}`G%Ag(On@l~Iry8q@J@ov
zx(S)TAoZ7{s!nJMOKt=bhpOFV#I8V+R{ISUtY3fa*#^rj!`So+^QW0;ekP;?hi}KV
za5=tF$7YjPOrLZW=Le$LCtO9VE2e2(sGWgk4he>a!I*b}Fk+EUS9*u3HXnzH%8UYN
zuYJ*@EGm%$j35qxPMHXu0%$#i+-4^zCqk~DL1XsNXmbSV3n@R)8)HaXT6BDb;-4(e
zi|#<6GLv{4l1CCUU`0-XcO*kbe7u1DxV2>2iZ<#opbiayUm@{$-Kq<lwIwxZZn*C@
zRaSEmPKI-O{Jn)zPh?d|3UbvgJ4Vozwv!&O>pt+V1lq$&pyR!XF>gD_%lP*;ARU7K
zW+z(Tt*m9d_~c~C@My1TPFWp>##f?0l!_xrRF8lhr0D}hzbU(iIzoN^0L&q~LkO`q
z8zIIZ<uIcFnc8el&jdlePkU(`gaVUV@-ZpfBC0pqJ>)NPt^IjB4x0o)a)5k43=iuB
zQ(cT(tveq|qhZCzUOt{U(3<lluFV=;i~L^#r5x^=djKu?T_<9x=#L4o8yukphxx?5
zcXk0kPh==-ST}+*Fjg$3j!wV;T(5q5H5(%i{b?2mZvx)F{o7bG7_aQU;g#PC;=PlI
z(1bA|FH-&}n`SK(9k*#Ms>zv1MtUdZgrT6MayEj~E~;urmxS+wg3hO4di-e^FEuK{
zP|{C;*&!DHfE@bYJP2yt!nW9^&txDy8TFK#`x>yC-657>N&$HE#-Y05^fEW07K|a~
z^1c8<lqFKHQr83<Z&~p3Cu^<IdMG-bkVCp}@M4AFS1ZviMAK7?w+5LrX{ZDpV1TK{
zzVT))pb#Y7!gx{|WlKa$o_`D20qXve!1Q<jph(~yNp=Uj?3N22>aqt?0c*)|D9_vt
z&una%k+>N>6KsJyneLQZ3}CR^q)Y_zZ&k_-_CMq8_~zgS(OtQHn}*Ricuva$4GXNl
zPoRgP<Ob7)nly|uCdrQ(ex?zEnGtZaQb6+sH2xgSiugzSF}|V@X5Q5Ir34ObyR1_0
z<TzNLCj}C+Xbjtj-i2UHsi$e(&65%XP^IV9UO<?NK$wT<o#<{FXK-cjD5^%3VF!+(
z&&r_rtowfR+QpjY^p*1JXHU%qDSUb0t1Cat|NQIKC9Bu$`CR0jAm^5^J_~)nBW>iw
z`V;H4zWK~)yFl!Bho7!obu;Rs;^jzAEe-bP>p!c0x!muV*51t@9oYQYo{w*P?k!%}
zYbWV8*zaPzb>Al&C+8kVGXn~W`zs^MrY&0rDwhr8(N03Xz29vn=~-$~Vc{P4Imho0
z9lD1Ta<4>$<J`-!KvA>uEGL_i{b|_JEX&=>UpjPGEXVZb=x<eJAXrO&yk>r{P;bH^
zSMcmVuYX-!{5>ux+K+w<F1C-d4n5dE3{w1FLc+04`YX>3ZZY|wQhn8E=7;KXjhTiP
zuHWpHmwcnB_$mCn*Y4c8&(SvasqFli8ld7Q>|7#G#+uMDSa)(|UH<WzA+(O&mU0lX
zwy{y3ABFUOZ|y1g(ESbro6u5P9<H?El5AtPOX?xMW*}0nEXTd*Bvk^9z65uq){}?x
z{2sFgzY{1}+pVpweR@}c{l^>}uh2wSo@}NGdt0_~<Hjk_$llmFet!PEUn7?t<yQ$1
zUfx+2wxI>{icFKvy?K6zv$eHVH2sc6L&|Ltlh4stav7{pD~N~hT3>l7w}!-V9gVyG
zngi`wZ)$%OQFL_Nk208#m__}a=;+m`O1y**xK`x9`wVndjyT;%=9!|WONzUfZ#GKL
zb5Gn|tR`fTa|1y3YX;+c>G@?R#0xG8oJ)84C4|ZR3Gk19*RIuKLF{Oq%ty36m00(~
z@#8-~e7GG0Q&z26v*t;i`T6~uzLa&{kG;EMRugLyI<e#_nmT_C31Me@{rdG&EZvT!
z&(JOZV{!43+J5^@lD3BvH-9Z6@&#hoNAKzt!O;B(Wyfhu_4t^b{UzXgmURY``FZ^F
z&HVh!z#e?|-GKvm<IleM;%AJ%KZ&7jA9Hd}PDz>lgc4#aP^-M7W7-od%pW}{^8nom
ziqj6@`q_S{s`)v$LB?_Dq-}rwIxjBK?|%5<*M}`{v31fA_pzO5xOevR&p&^6e*dq&
zzV}ZkE2t_U4Lxr5;{(b5UoX_C=6mxjV@nn1*O)($^5x#sKbtJRc=hTuD73)gT1b>>
zS}xl=#nUqr2M--udcMg~B}hzpF2+@G?EAg@_N^Tr9>&6)t4r7tp&WDt3$inR*SmV7
zWof+o`x;U??K63>De~s#yZY=i&s74D%9pgLi>t7en=h}-|I{<@>(dpOKqBH%^FU!O
z+f(fK<M`ipJ#PK*K_ul?QP_)@FHL5m?%i7oCh4G-)|VKAxbBoVml@k0%f|gT{r%4X
z<pi1`E6eO^eevRZ<Ri;4ZcpTfi|U-lOZ*v=JKCp+C!KCH9uvo3iCOv1xp0@7zP@)Z
z{t&^(t5)S<YT{>ne7jxm!;HHsBRL`9yKqTRI{A|Mb%e+z=VN**8-B7p-tDa3O75?#
zI)<A|-15fl+h2mZSdV_upbnRFRl(af?LW_^n_|AE^~SGmo6b-BH0NIf+AsUe09*QU
z7FE%RGrZ*;OA^-d+%ZgTP(BmVXVBi>{-I;%zI|%ZcZy3(Q;K=smU(jUJogjSEsryh
zD-qhnhGVrdXX?W$wpjP8K0fkzd}Md-Xt4Y8VpQ4j){v`SUN3<<JYT;){PUG7ry+d|
zoWlgnyKi@dhlg+9y?b*1fTp-7lPB6bFwW`YA(g~=5a?529M^pQ`Mo#h3l&${K3l(D
zgP-eYZlGfI)g)&}{H+d5f!KhX-s>=M0*kNR!4ZImU85u{EL;v_fU3dZMih^f{cY25
zC`8SyAE6i4O~ma8W6Kt&s{EvqEF48glzWDd_CGaByk`B`7yPQB(k4$N>O5y8wr*X4
zHRDzZ-hX$@LMM5@QSp~=jGQ)=&mY90U~fH%3bKA`D)t&Cwcobuv%;bxc4yPh(hxRN
zRsNYXC#ns-rZA#)Rjz)jWymA%!$*#+cJ%s^i)&}0rg=+_-hECC?(``Qk-9GVI^mE=
zrDwtwj#aDn)uu#<(5Vx3tBBU#_u#<;$M7`;95d?X8Z&#gZ$BK(6cMmn!^30o<dXEM
z@EXsHz2Dx9<}~?^=e*1vu}V**a%n?%VO%s9*l*?upxb_@5e8e$rM>fCR(E!PQ+3sL
zUgX(4+F%66-=h+`<V(@b;}+-7KQYW2Rp&39*ecZ}`T+;yMPVPtyx##=0S9>~?+W|g
zbLC%w++w?E7XC<G#n`KBQ6l@B7dst3D*kBqwS>2CkH6o~R(|i`DiM>4k6ylf`MRhm
zagO7$@s_o45F9*`Z0D;Qk4o@Y9j@G68QDJ2lwr5_6Sb|ob{&cSQo?oWNk=wba}php
z+js7K4m*Ex?#*VlScOf~Mm1{>A87(tWoh=X@yMYYU*2^M(U1#Ro`@7{>3QXd!na15
z-Jf&-v53a4T)zBkQBn12-XGMZl$-TE_U$$EypidC@9f~f7xD(P#_&;v!?&5trBAoC
zor~J?wd&Yw52c##YL15NRVtr|#i`n1@fi<KjMK;MO2NPGz@4DEeHf+RB@gS30)vBh
znSWq+=KG3!Nvyhjf<-QjeSe4wvYsRS8iwZtvfQtO0R;e9GylP|vFNqH#~5h&V^WgH
zgYfWE=*m64dHuss!Q#1S_+z#k?i0{e*Te{JH-4S+(3|IYypQ>}0$+b@mCx<Qsr*G|
zD>C52s;cs*Pv3igO>#P|&dX6t>-J}z$8#%JJB6?KdeQ^|O(sJ>N-Hw;*0u{rJ5a({
z_4(Shd+mCT=ecOKhPX$fX_2isKeKt8VaU>lXTkzhe)Fg?FRn?kA9~x|d`?W8zc6LU
z^U#B9k6~k6l~<i5ZM(io`3y1Ycq3a8*PF`5Z}%Q*Dbq;5d00s)s`|wlyP4akALRI`
z?`4{+%&Y9JFiJE@Q7*Eumkg;*vEaV=c#7j({*OK9cd70%8+`p6|1GZH>c1CQ)%(+-
zv81x4FfGaNN}r7+e-wD=K~)8~hsv(Jii%_LQm?u7zUPu;FkYN&2&}kzlXKkdgV&)4
z3R(CYP*duB-e2y0F1+yQbY$GjXAcuDr%vWKckw#B74@o(ROMqkr29kIG54CXb+?bI
zaSfw=>^X<CSJ(en-IsVn`M>Xv#H2)aZ%ekaCec_zL<nWenl($4eJg9mR@o(mgsg>T
z?0b?W`&P&<J7o=HC+2t0`+Uyl_x=6}-*X(tF*DCR^UU*l-Pe6x*L~m4d6Tfw%n<V3
zr5%(A-yx_P9Mhk>$daPTSK%^Ag$S8)EFF27kicu71acBx_M~;ios<)rdU|nqjCv0k
z_mhSM5{(8nV8J$l7{Q+~*BQkje?F9&Lq$`Q3UMEBXcYkIJ9V9u2hK;9>>g*2=&lF9
zk{QT(1xZsA@ZY_~*0-Ln7leEM{{7n;3go0bSD3%0$%`DM7##NqGQS00ZWgl2dbq7~
zJ|)0VArjy%RrK`elO$dKR61-uTPl0%I;6D~*gT#=Ub_=>uX_LzOD<_?7BVt204wBB
zc6MR(R(ZfyP;$XVF)>;oM?dZ)l3|_Aw~zsCKS4{B@>rrrOwK-|+_q3vJq5LV<{*kQ
z!Cr`BNKck<`U;IlF8bXlh<?9Ia<B^R0H#36=f8ZJQ8c#Vt@ChEgLX_*?IZBPAWUod
zcn)=e<!vJ1TE58C4G?%oG6BUvg4qmh0%}sDitwc{+KNj=M5}al4ZZ|KSw;GCMkDWn
z@8;%av-d^|RbZ!!ySw{2TH0XXD_9&em|W1a-S0H?-3-6O%$W`72%YY;`OB!vHNz-e
z&_ZacBOdR0n>ajzO#W!=b#Aw9$dd0|Xf*_iqydTj{e7z%*ro3|s_8dNmyo?1_TO62
zfkoy+&lH4}_JE*5JW}6K8za1$4g!64*|lcP(v1SVTAu*@;oJs=-eMfFy$9$?lClwp
z;`!`;?KcB|CutPY5I`9uakZ0hLCwIaHgOq>p=#w17I?Y^F9a7fGDYWdOGq%@dcj6>
zoZlCgI`;x@VML4nV5?L-xx|m=XFprPch8P6WXsd!=oG#C#~Q1Dxs@Eh3(=f)YZ_+S
zaCVpznYNj2paIFDKgTUd0Z?ZI=G<-+H(0DjM@8w`D@7c>xon2@h+bH(%3EgH0R5>7
z=7hLyFX<D@6GKuEwvUSZgoE;E=695pl+qz@PXM?{9fURFOQXKgAE$C5pm#T1l0~Or
zdr=GH4H8LmEfXEx)4zZH9<20_DGY}F+2YcEaONr|F)b}-WyNuBZm!<BGBVT{&z!))
zjR-2Fu?yA-xbA*FGA2f{i{Yg~s(gSP5ImQNz?KF9q7wH?`mSV2BN0CWZ{LYGZ?%U?
zi!DM*Dxb~6hmr9!Vpvj%2T;-A+?cqIH2B2B$?L84PWm}SJRWZD6T&7HsG3PY+(h3+
z+qZ8m0Ae-LlG-K1#ZZXu6LllYTY8<24oK~J)q~exNrqmB5W{57mxKJ&RK>~Fb_FX*
zI?R#jSHbC2X^+#IIlA{=4^OUs>*!E~=BV=z^_u$u-h_vRotpdgYX+<$^bOAq&<mB{
zhfo7f$mxQ#wuuW1UF+omwqpsYd3l#a(OtoHwV21kaVV?|I!MsXvRqo(Uwzdf1rZ3$
z(-dT^kS-$;hN~zXtIMb_9@_rB^>67?nVz1m0s<*}t_>n4Lhl{D{Wj)cE&fRD^0f?(
z-PAqdsgmb4qju1_W)Iql^gs=deLm{y=GFR()th)cs)ExETk`yPQquY7IiFTt8HBM(
zBK!NCTjCS4<sTUUawST-oDY8bwAgV(?1BG9j!J2>q|wDc*%oBur$~*TER5w;({Kgo
zjVP|+4BU!=ueq}TYH5$&!=4BvE*?oA+ST&8gRk8mPLrRX|H>X&{PR7al<)i-2cCJZ
zi&K-NI!Js`>5mc666c;eu4f)NM6a&%?%;X~`wZ=rygrfUvRuXb8#GJ(gA+XhZ%CM#
zx|l&|-a_o5(TiAj4xH76FWG&eTwGi;yoMEA1J?&QQWh7bu58-jcF4)d1jI9cW{>uV
z9W%f2xX0uBiVl%wfvi?p>HkOvJUap5%04sag5f7r)QNNNN7rg$aWratq_xX8aSTVq
z%)sBjR?IVLM1ey{@88{D2BSKe)ruuRG%?;Liy!UbFi(t+zsTFLMa_6Bz)mTGJ$1NY
zW$NxfxRh@!y@W@;srMNsPYoR6xk8SKNTund$U8X`Q`0=tR~k<{79Y>Rx2meCSrEUR
z$>(4&#-wVBrNuZq7x2=2$jRZketq2G_omVmQV^v!`cap!@yW5Hmx2NbB$|+&TzRtN
zc;Ui@bcnK6xPS)_F81~H#RWK&j({$wO|+m%d9QOqSN`e8(#os2CAocfzLKMHDy<B@
zZrH&11q4Ke1!f!waR~`5R14`ly<E(Yd4@$s(yb<oJ&14@qBA=aEv(g=xAZIURmFf(
z{7<yS?8zzGxzv`3;NTM*C;foNCwB0BMh&vvz+)WKk%U=}U$D@2BX~8$J%$lOx^4ps
zX9T;fk|q6dgcnKcSA(66ZA@O)bygIWG(d+E%1H-?*>*++1%+IQT3CG?5xrGf8@V6@
znuDQweFjE48bO!u#T{w9n-I-SpV9FOGln>1Ah!197%>5&XJoVj92XW9g(}l>i6rLk
zGG2e0V70`)dNni12-Y#9qWeL%X#idn6oQO4uDDL!08Xy2?-#nF2(LD<cZr|>oJl!!
zl%xTuKABZpAh4><>Z7VsNF(5N41*wpyV09QJlXHx^WWwrW)iURHUPBP0C(2lx*WS{
zR%`Nbp<<o71;g(6yiod5i_yS$EHj@q18yRKfYIO(F{`F;jp1kor<f~9;s@n1`Pz$(
zz_*=&I0~ae5HO!97?N7p06Ar4e-@Q=zjjN<+i8tCWYdI?_Jx&;Uv^1Qi5p|0p$US#
zIE?hI>;O#=md4JVDYsUxtgi>}G8Ct>rNs1h-x-b0<{!$e{<U<FI6&%ops5dAZ9^Bi
zAm)^Jc03&&-3D+;tg!zvd8p*KxTZTO;o_V0MukL0b!xKfEc`&L1XQ2zm?n?n*5H(f
zRM`3ZF$HiFka=Hna{ds1k7t4n;xUi<^R~I)zh{BIs)r*!A$C?CENB(<>fdLnfh{Am
zg>s5T>40Dpdvm+o{wV}zl3Z+1MsRB4!kZs&;^U`5%ta#dOh3`Kx3{--cQcGpehIRt
zU0P-#lh(|kgQ`H4$B)azj1Ef2;mRQqL>307pUQu2Y*g-bsmGkng!G;?FtPtSa4r#%
zk-fE;C!IO?!2O5}pbLBl$m@4Ky%hng=58zIMB-qR)!U~1h(Y~=9!lb?S3gf@IAmPV
z2f&ST{%Q0`Sa5Dst+t6G*g(yPR%mW{>F1}?24oi}YwX?|W&ZJV%haTJrrJFxjlF-S
zXfpvMk*AlFInm+lOn|dcx^;_(m$!w-WMOrx&}bdVTq{sM!otHvk0|%62F?nHKRU?E
z(LT?aD>Ane+a~IeHo;ideFP(lB>fDw525$Y4Or~+?RfkeqJY)iOG1)$&7j<Bkymj?
zZQpCG+~K#I#Za7QZnUgYE{c1cf1=8*v5=*AEOc$6$^SYGpUCW2a(6GW`|0&YCJ;Ly
zci=A(={CW+TkOaYuv5$s%_?cIbTet|2;_e4yLY)AKVmz7w@Yd7mr1S;I5gdUbD6}|
zi6>t;+Q?)UkG4~?p9HeBNpJwbnD+;s6B;}nwbRrX+wGq8%MMDVtAn)%i6dC8bo;*_
ztsZKOS%PRN<^NYIGb^k4rc=F4KASB|?}~O_M8xUhQq6+N+p7+vWdexRjNm|mIQMnR
zo;NJL0gSHO&t;NL<{uA+d7SaWg*cqX{nYu*=Pf{?a_iPCl%O@eV3&y_K+`CSz~wlz
zPv%w{idqpnf5wFj6x!nA;=)Gl^-RyZo?VBL8`I!MVwQAfo0*v*?u97F;d3r^M-{mn
z3j%s^hd_9j-cbosv9#n;zkR#6(#2x_@YqpuAouE+oTABLS6Eos4S=Dp8#BtpZ3I{%
z#Qjg1PibS9MsKUDH+gwKEnHncncU5*Ndol~mEt*{JKBzP-z6^rv>noZ5<iNpI=#4@
zmXaKftY#b)TUbqu&zSq-FmxHtl6gWt;8+$Y!sa?0E1ykAM~CbShLY$Sh0A|9n3!^k
zg~u<gjej=uIh9bjH&a>LLK2@wa*pnVuYA^tV{xZQxPlK>fJjmLFhNz@a-uq%73O1n
zw4j}v6(o?G%4rI{)WfaXY+)395IsB3f65uRa~4*_j^$K7pD-|}7%q8mW@l|O1_}o=
zp$k>WWNd6}s(sCJKx<b4T25^N(>I<d>0sf}`l4e(tUNaKRJ;nCjOQi96s${X=4k$#
zEU_zx@V7PS4<S`+YJU(|$M<W$q30#3_V2q|AEV_A6zJU0s|mAR#N6b708ts`XK)^g
z#x?!Tr}_BoHZ&ZHPb;MO?`_3#y{zZ3oKKqjB`WjBB6O|eLsWJ2?A_$(U@=a6x&+ii
zr$y~jBh0IN5+50!RM7~mV*GAvNzC%*<8+56uq^8BORqXdMMT_P;`Y@jG=#UYBMBV9
zy7;bdgP&c<iK|sXD7i$%2C@9_-2{;KG=f>&K5o9t+txOBn5r10RO3$-fXBEPA9g1_
zuc?uR#2J3*>FMu7WhcZRrMq`uq37W?c;dRZZdv9qq#2iTih_cMERjjr*v2=rI08y3
zrvP^`3~hi_MmMaVRnmD1tUSXtKBab+o7E9DzRJ(#$29mP3QQ`gV5eXV+5}$<7!02p
zd03KZ{35p9w(88i6t)Eh@H2>)Z{G?~>9|S11EPYic2@Y<Hk9x2%CJrfC2VY_#UCOI
zMgSmyF3|#12{mH4$}KB9V4)hyOD#v`;qWaG9h(n9H;Uv{gA4Y(N3L|WprlYc9|zJD
zw}=QeM87ImaNc^)o`uFfy4TR#liT3rCWrqgUVTV)`pg;e@B}I^E-WiwAod4DX_r54
zc`vmh<eu#0wWu8AWR^I^uwCOem-qUUl3lknR4B4^QV!&gw)S?>Qz2i!Dmso;oaNI>
z)f??TZSi+dkV>!h`-02D$t9vfmh?RoI5u#fB1}!#9=W@taOVEU`wt;-96Fw<A6Wb2
zSp4w&c`$0QtlaP~b{>~b0UQm*9rfUV)zQ|T8q7vgpQp!N>^fi6-X=#?<y^5Pce%1X
zA|-__+M+|edam{*Cd5DxTx&?f0#ng4tu>2`jEt`aP+>phXYh?3!bl5H^}RSl5vaUX
z%A4R#Ue1{0>Q$dT0?x^(&i~6VuRN<L!JT&GttHIeowsXsgo)V=-Ep?1kL>o+)FgFO
z(YbXQ@#5S^N3fL$Y0jCK*~Y5y8ST?f8L!?9CF=&F8~UQv!wpm9KCg}__>qgl(QIQV
ztDL=$Mp^}P&wKj$)!>idt|9Vt2-EUoNWf#>zrXP8`SXALOwJ?q){<xVFbU4i7`wte
z8aw4Uec7!_mAq~@pQ|YDe2FY}4lpL;t1v;iG%#&}hw5flJEz4~hWX9qN7`nCVf55n
zRh@1&9Hqhwpp=LYE2OIS{<f+wzm3T43oT&>o8jL~>Pp9v6ZIZO9O*l5m{h9A(W;`)
zc+Upx&oiw|)^V^(I3lz?+C&v9GV6F`avkTXqgA&9x*yY|3v-EL4jx0`Cx?g(l{U=)
z&QKrr@`!u}dC@m^k81KY0BB9Gjc7>>paO*%zO1GSr?D$g3vGN)@Cb5^%%Y-mpFVx+
z`0)nfE-}Fl3$Up1rU4#akc}#I`dKqP-3l%YET5|e1*YLJ^Vr*u9XJ;y9UYpI($WoZ
zqX@}oVOj6K*$01LZ;|EApCA=xr)?O1rlY0pfvBG%<#qzBKUZqxfpY@2y9F=9-|G*Z
zbr6A2?9hh4@&-u38d!D26{Z+x`y=}-d>ij6c2cKeJiUSc)$PIR3+0)AF?N*f=QBdg
zpse)?K#a1(oHzr20&G+pU{|tRa_O)^qq%{4-ZTb9VX-@KMJQ0H)>{m%uWfY<M!+Tw
z$=-AY(yf&dI)I<_g+8o&2*W&15-FRub1~M+ii#&#P_}_-{s$C5Vw5<xf)f+%oe$+q
zL~>c9RoBPo8qnBiZwxps5P@J>ZNN+F?=4mSnYrO{<On3a?N~+r^fn}K#8k2gRQ~j0
zGL65q1Lexl-d!7Xl8d()X3HiK2xNqsl7e0W^Tg1Y6}VZ6TT`xCd|cc!klANI11Fm@
zUKD-w>(0hLU<1LrTWGKY&JgsilTIdI`U&NJYM{cL0+NG>nx|bL@w+Z<I|YP_dL0{t
zpllJFv<%?`n=^>aKS{@>zJxxD*T)Fn`ipmo9WYPP2ok6eM1mG@UD6~;Aha%;_eS3T
z$2!l?9cGe~r`=Iiy#W-{Wz(ua(8jG+N6V9T^AD<}uc0npBm*yCYQrv3qykQ=auBcW
z=C)C&$ny=xTK-f5O3V&51AYYVV}F>2g)pW5weVTC-EdATaQT<i_3LJv$5703s{Tnd
zGY3ZubZ;2#X$A2Qj~moh5aP^NzYQ%H$ziqL05pIYY6t}rM4w;CXx<PKJOC1YX@!7?
zl<NQ1i`5fYiY2zWTsOrtW$jLS=kNqp=hG~jzrv@~ZDr(TP_s#;mpPgBhY_ob(E4Rj
zeAm830rSfvBy`&@6qwcza-j7$f0rjX_Jjs{&U72fAg=5stjwZ)q_xGZKz-Q&UCJ7I
zcUlD}18GlwHoY?5yb_#&5wv2*Jz*&F?K<nqv0}`j=SiTFxwuFuC@A)zMVPj!X%u*t
zk}HNz4~9`%Cde#poi-OtoB#%+^9Nn{-kvwgwg&+#l_9e(+f9lMnA=vUN9=*D@!7Ly
z_0z8D(5Es{+<_75ll0wIiHT8_D_jbDN_`Sy_qQx)=?nXM15k(8YpIgELqHR!ef#z;
z%yVG^!`z9_<1c{5ngM5*3L1@$P6;;BrDtNA{R#$tup9}yeEmD%46g!z?tL&^g3|K5
z8+{X$NXWy2LD=uk;){b%lazT-=G#CS+89Lq{y`wJ1%$ZbDMLhXK6sn=IXR!r77cV)
z*MC)l>mw3~kut{-+Cbb+5*!WO>^-oX6OGp1VBVoT(k(Q<jwon!=sF<IplbnN$P4J^
z#tFh?LqmhAy82geE8C39l{U&Q_&%`6a3~*fJaziCB7i<h>%pj(FV7Ku44|IA;vdyA
zxeIY8=OZbZHxnPiDKCBUhc%;+qeQZ|uR34H>*!oYhK$NpASjEinooCbl%cy>zy?eY
z<_#Ip>7?4r0tm-MQ4^M!lbsAh?Lq!Mw%iwKIyK;MBq3&(KPHo<+%k*EwzYr@g~a(O
zWbOK6j1an@jF1e5U!Sk6Fsg9;=hf@i#GOvZg?L03nPEqXlSpJEx|9o&!)2p%?32Q;
z)2?CCenc8|uWDkqiy3A6vPFdCO&WZ5<IQXF-}`ZfT3erLu%jJyss0CcK&^*;@2J)6
zY5#=Yrm&kaB*-AN;ljb~l>zgKGi#9dG2CT>JH1K?f`#yQFwvD2(F3|qVT+cByX~Dj
zQGL5zE84m~VN0_|#wc!W@ey?vo$X!vEbtUA=+wSC5&+2-WJ{{h#)r`$gPUq_{P4y=
zDb*2Hd<*W8$ox4V`!w)mNTkVuL^NrHWo&G0$ai9Y<q+>#jVIeIz3j(>VdWpG7kBMV
zcs!T-DZnMQ5&0q)$D%VB3LjMO*x!Q~_HbKXW&>dc9$NYCZ1ei1(!((S;>vK4{V|UB
zU}4t=qxzW-G1Q>~A+j6Y2;rbf++@G%UMiJdv?{J26jCLNmuHcb%=g>KO1>*8D~At-
zK`@?w2vNHm4j*|8^H^Iz^icjp9w7p{`oNtslLnSpt}ge`ZWu}Z)Y6$2wr~OB0)lYB
zqA82^AMX*Uh1;nl1+=SBClh6}a<bN1!C^+q#)S>u%S4fLadD9uz;y^Uhxu*8w*Ctq
z;$8%n`(xF)ou2p2^vK47E&KZy8EU!+A|oRsMPsWoYF8VtbP-6|4zc%5u25#R{-~OI
zEZyum3ov7_<%1|?V?TdhB=NDRBx$p!Z}thw`gP%!%)f@H{`<}sE(5Hx0u_+|(j`a_
z#Xv51nC@T>v9xC#RMsyNW>z_?wJt{4c!l^w*JJwGQ-jUu)&e!R6+DMIWWW3?UCn<U
zZTu?;N<I|*kiwuZPO4IP_}^zVhUUSk{Cj}^{65L=*T(X{{&VF{;Qu(#f1XQn`v1T6
te_tuv(D^?P1t*ohep2NB^uam^2%!L6diy(RYho#)nzE)+f#Ut({|B6MkjVf5

diff --git a/reference/antibiogram-2.png b/reference/antibiogram-2.png
index 5f9c45897695b90b53510c46d3949f81b38fab13..778b155d7c0f095ad2386d93eccf875d9559050e 100644
GIT binary patch
literal 52339
zcmdSB2UL{Vwk}#~tGmr8QIaA-B?(9tuv>yilAJ+7G6fbnV`!CJO3o-zvg8aZNdzRv
zA}CREjzze0;qJZ9z307g#(VFcamQoq-7w0kzt+Fj4B!0bH-GQ@@=~WyTs(onU`|Wl
z6<5Mwjyhp5M-2XP3|=9GzFUM}#|&ho#NjvmY~3mIgkQfuzN=}A!O*@!|38EuUoygA
zE@7m_|4?y?o*i@!SLuon*eh~0HT`((iugIQ0q<ka8pWRr<F5EM-pG=^iDRa`5XKl_
zY8>y)%v@*0ZS{$ghf80=Tk@RY>ZFgN3Mr!x!*#E#xnIr<vQ_NtJ-AwyM4dYQj8H^e
zai*i^R9%bR=&a~+pRRPDFFCpfKZnrTZcmdk4<7u5u}S!N620_V=|nmDjk%We-}*wW
zwBwVB=4e5lDGm&#xz~MT&^Ca#?#S&KRXshr3fB$!QfiEL(<wc)lqbPT-;bg{?p{0k
z-}%CI`BGRwp>cDh(~lPvBPEluzEd43^&_>u3AU5pa#}=*HGdqZC4EKbVrn<td2Pxn
zmn_C}Z;Q*Y&aZf(k{F+xo7>Q%W7ELIW%M<;x$@W(yBq5%+O~$dOEj2%j-z^SHSuD5
zPCLtCReq6?k&<{$&0NM<_w_!f^*)o)ev54XycSX6Zvvx!SFc~c_ddl^uArbmQCqvL
zuO~ylYL7=B7H2Z6P&HBaXjBlBuY02Wc8~{=FkBbTWyru_@U!(DmqCqJFRq?8AtQrr
zdS=G3D@~Qlb$ynS$5?j2c`~N0$a+{{we`-0d0xzGRzoq&ZJ}$6hoi#zuv!zpEtJk=
z`Tp_z7_Up^+qaiW?WWEX=Zf@ai-tXx=F1l9(p1v}e_qe5In6)t+KvKaddSd|&=$zm
z=X3@$rE*_iUq8-{#k9XEf+uMH@Z5cOclT(AezTkhpPmU!M%#{L<bC^El4<QWO1<>s
z6@6VXZqxwQhFhrEs5{A7e(od%hoAoLw0cBPeSd#{xRAZMY_tGZvTVe8Ukc8EEZwrY
zw|_hz84FSf@YvmUHzu;GXZe*aR0=c8MUtwjs^;wP5$k)Z_SSmsdvvcb7>s`n;c}Sm
zby{v<F<cs{&Dj`otrwh(zCfJKQ}7)oMDkjUz%2z##=3HZa_ZB$&gMu(du&hPJ{~%5
zS?J%FXHx(CtSDs=i$XxHFGYQT@H{0t@@TtuX+=fFHcK5jIXORSehxa1%^#aT6YvHp
zF%CWncu}F3XX)wiBI_ChkE>7Pw#LKi;a-QXau9ZJfrSX{@2v2InR{$=52R<6UOyxB
z^R1VuOLHZ!MR(2bEYatTjEuNEb~cSUN8QIm)JMejM#a#P;u@I3#;{_ax(kW(Ws@`(
zekr_t-G-i7qkAa?1)Ev@N;hZb4RlAo`a1^E_T8E}a9e!Ta3J)Y<%@s%(Tj=ddG4;g
z&(6kIYzzs@3;g&?VC?ms_z#aei26O<-E}qI7e?Pm(RuR^yYZNto5!nX>*2mXJzmFE
zPjkVYxIV}@(SZy&SN(tX%eQZwS0-wo9X-jwFp?M~Z?33K-Q(iAvs6D?M{PYYR^o3}
zG9Ky#7rH1?g&b<^jnoMG8rv2TetEIoRbzrD91nW4UukJ+lWohb71>)WO*}XPwn(DL
zYH%cKc*i(HGmm*?C-|DO!PnqxxMtp-ny1I<!dG0Q1aJmAj)V68LoNAc9R~gRW=nI{
zo(HZYXD~A}Guo|f?Vn><&*$Q{KHG;}V6!7`kCcDVPI)$`o-V3JO<lddYM)qDKTu-J
zgGXV=_**oU=kAQY;o|pC;By31eaqlR539{e(F$h$>Z-GR1;+ZX&d`p0e||Q=`vObd
zOm-C=ILI)wyN}oAO58pT_$rrW?+3a57JIQnAI1yUdgEq_hd=Bx*6-ERxw3hzL=E3`
znctd>!%xO}ib@91P+gJq>t!rVPG<JltfsGn8@VVV>byJ_e#_Ygtr9ot&o2pId2zgK
z@$<>iwH|$I(mWgu4LCELsz=a>sq(ML!UvJ%jep)(hau<5^1;hQw5Nsvg3RAUHU@gF
z%I4zVzJ1%aQsJ`Z6Sp(Qk^_-SvL{P7;lqa;X=!OAUF!P&$RMq%cGoGnAKlIKl&IR<
zo*C^_R`Bmo5EBt{oTusP>f&-){YlAhr2|(G^In_jssD7Go@O$}QO~ky&>_cau(TMQ
zaCDtujgQ;knOq91+DU5A<k22cui931o3qgOwT2yN%hWE?sPb^XwY%D}1P06Cu~9Z$
zyZECT#aDj=I<OZ?VF%0E@|AB(3G};Hw>K8(nV6UqRaMCpMAlT$k>emT*Bv=~>l`h-
zk(Ah|zsEiCErwcnE?-&4c|4>oMIp|s_Xc4-w~>P|HkQgnbZ!yZ@Joqxxn8*vvrwaL
zT|u@n?CII<*eSYHbHQ+=#(N2ll+XFkVwbgP6!zoG%iV%5l!eZ+U<wBc>4@4w^HPk$
z>-!V@Ro<MQTa8Q4>31%5tY1*LMbe|^iXCv7$>cI^efx8`YM+^C2&Sm0sY#`-Yp*(S
z;t2#E&b<|}eV!qBSMhXO`bb)O*4LWy<*%$u+cVkq--H)v)OBzYtMh}xCr+MB09%-7
zj-o(z$iF4uMT)|_dx?Acq5cerN>bvD93p%9obFRHC@P3U#0X+xNQt(&N8`}@5PhwL
zgoNV#`{!bW?0q1D33zNZ>Pw2(+&L@KX-TZ7v6bB+R_(j0sHlkl@y8#*VPW+fl^YdT
z+1UJ!o+KB~)~gs<Zscqk<78#^MF$Vpe5H|NAkh>jCN{a@01jLyL7^Kg=*-T}?*H!2
zt2#v~S~8NHhK#7`>FMI}Q0;h`Fb?FtwL`88v`I-x5gTrwe`l$I50P4QXL#4sdzLHC
zLNXnVFp2e!kB>*6F#r<^fWIN*`9LOQu_~QPZeHi@*)3;c!8dPxrl>pzS6W)_P~eZ0
zC}>NR4uW_CiNMM(elT$Nx7!ZjXT$9{F^0Yz&oEsF@3`Gr6aRkE`Qp)$5ns*#uii(u
z^l~3b-`ZKMCLuXkoSy4k2vrqvZHA(1cg{ND{rju2uJa|Ei`8d&aMl%TYU1KLc5M<U
zRW?;#zjn>L(w$iM<;@lTtt-nDlr4we&H)y=n3|Op;N``kTY8finGhgk9<(1>N`p&4
zHZB;aGF#B2N0Rg7eBH#(Ox4~DGoFl$thSOkPfbE%66<UnwDsx~)p-&UUvl=Zsn#TI
zW9LtikQ}FXdn_)VaG}7<D^)o~Qdd_OB~V2*weCl=bsi#+JfdnOEyE=`I!~pGx^It_
zXMGQyozx+PD}(=BVHl~U&>tAva$Tt4XJDw@?#kwKDQ6=7{$w^kImc6`J^{jzwI`7v
z_iayPBHWO{pjt`Jq9?2QmMB0KFE4=+uPX*9oH1s_$1}*^TB`B#Le9XAw~jUvhSQ5C
zMsn8KVoV3w+AueBm)eV)2dRi17J0lIy^_MP+a?q4kjxJve3-t6AhP0_o^0#Y9pXaO
zu%#uQP9)N;T)W7MgrqIcq=h+d*olF#$jGMMJU%3lFwv2xW84%@LP7#)!*yrgv5H_x
zu$^c!TV3#Haca(VryxPOxn^~$Bg(y!i72REPwS}TM=Q)X4>=7X8Xi2SUbz6oznGt0
z={?MGZ;<=u2GxLVEM0BL+3#rNv{X@2I$t(lYS9$Q%Nok2c^(BsFNL~==1iin)8cm&
z#0P#J^Skr%lsC0i@#Q`672#NbcaSkJ7@#0RC*m?zUM?<!tY8gUL7x-<gn!skzVXzQ
z*sqpV;O#g-a@K{-fq{X|qWdVpEWHxjd6<{oHz57Umgg_N*qxh>=R0)<93dDvf$8%(
zyA2fMqMD+c3+Iw#!X(9SI$y|FAg)@dW$93n<n%Ri@yGV^B_+WvLoW9J`t_^RN;ALV
zRC`j+paVgoh9xecLtYpg6n=2Od71TlZhlhI0lVYl<IU^oQg}KpI6Fzt@4Dc};^KC5
z{RMr+{`~i}0W~Dl*ZEOhKpTE*z1J{8vl;vh_w`yz&9#*1Kq}>;qbT_30mi&S-7_2|
z;_9HHs)}7_WRBmu@ct#AqxS?@>^V=!T=Dt&HxGi`u~U@iz|s{pG$@ZAJqk%QO0&g5
zyU!_XHuIq&Zrz79%k(CZDXO<CU7ep8`I6=3X{M66lP6A$<afyXMp;)1GRsF(R=+xn
z-`?IX1{A|E5+uVzZQkH<_>wTeX4cw&)I;3C(9_=DKF`xq7rYw$W&9tJ*M~g%>-sw4
zI1X?8Y`Y@qf00aV^V8Xp-Hl;9Lhpb5e&GP^A<x*`tQV`B6x-iopLUd?LdTeUjI;Ib
z!cYZIm`DI5)$~b1an`J36>;zYB!R(3M0tr@?maTKDA`>njKV?pk5#!1Xu6HYB-v`3
zSoP>S`g_S)4|KOSa#jtDjY=;}UI`FjU|_I4FJSU5S_4)&ab`NM`&X<Q?~Pk^>WsFm
z+neaF$xKacXgWMp>|s^IGP=Q<UEXfF=T9_(bZW7@)!yC?8O8Gyz345Jm^b$}s^|e>
zETxF;nl%nRh($JNk8JST)vI2L36c`xHORf2vnC_YQgc3J<FML(_6!h^Ro4!o|NE!s
z5Fh|j_yT4#A|uvvdT=o??Cng7nOJ#sw3a)sjL`3IN(JSx_q9-)(DB;$%(AOOIsu67
z%BJz*k|;z^r`0y8EAdlNW8(RiVuxPGU<KEUUf}it9Glt*K4pn@f_p{=Kr3YT<<?#j
zr~5SqK0h;RlTE&SNUTjn(VuUh7rsABU3H-;M%dAph`n|ENukVBiryN4_S}y5L?ObB
zzNEJD611JTS$gQ}_S%f$w`jo#Dw>jhz>Khdgy|fIoLOu?=Nr`eP>Q(N`BCvCOUxDV
z!36_6iAA2iUS8JjGukLyNI_XJv1LEgT^lWEi_U*&>^D~kaCnp#0p^Z8JIUb%yCDIU
zN@rJBEfg-JuERvVJZJ6PIFuYnNMJjMwesz1;mj|RP|xS@0#1;uVPUEGb(-siUA@EP
zb-Um}!daEu(;9`An&Ot>CB&Kw3N^evIu}T&jam3rU>l|A-R;E1qhamH8{em=`+|Xo
z3p-k&m%vS^Ax3&42ykHoj`td!=gyKwzNw<|PA0q+QgaPdH54Rqr1y*dIw&M977xpn
zet&k-08GUfg{nF{eN~?f-$tKFT=8nVjO|yR2OppGT378NWQ?0HnY3Eoc|oB^$v@;|
zD32{t12kmLrd`Me+frE!SR~4QFdHz399`LLZsW*+ZHqs;DO89`wyaHcu$&Q|{eYl6
zpC)|{6LBkDf7cwgo_QWB#^RZ*vUtd$3=CPN(`oFnW~56E8HrFX!7DG%(Ds}9e^#(S
z9_&gmn6*ARx(BKB0G+eMx!D9weiJ3m%9515qu*P6wFHQ=2EqW<@OF0*pR)&~^S(zJ
zQLs+~P|`N98$!^xbeOPI_M61CIzZtncO7hh6fY)vMr>!%X>V(Cbis4qRW?pk1ci6N
zhkkpz#Cnu>U(q^f7-LIxRuF>ZdieP-gk<5!S+29PBEWx8H=CuzXzhCI6`nY8!eH#n
zo27zmcXnpk@N=A%n+D|oZXp+wyKk?0LycVf`tlua{tC1A<>j|v<zhAZd&^<rHy-}k
z6vfX8X*)$=`OB42{k?I0?l32Zsdl5F=#qH|_d|jR-xNb7H;No?Lh=SMF`5+%0kpz(
za7hC|qa-8|5eC|Z;9?b6t^*g_9L3`yn2F89oj6S;g~g|(T|ajEBB@D?z1k_t8*dxJ
zAy^boB}W|)7HI1M;uIh=t61-6Jt+9Ve|e7f_<Jlqr>}vUl<oqHyq~(hJ1;qh4kg+r
zk@emya9S6oXmH|rGP*Zwp{mEK|KVPEMTpS!tEPX=O`&V2SiU`-j)R!TTiSHtOI0Z8
z6~Cp|q6pKtZT@(LHnx1giOjU(Yh$B(*fFKri{%(>%`18j%IwN*b5aTlDFB&)^Jjp`
z`EdH`;^D+Exkim68&&)6Xy0s~2P1!#;jADX-umVUIB$4wZkVs6=>ojpG%DlM$=3`J
z`y-+fZjOIEa@H?MUdW7k1JQ7IeGYTp_EAw&tD|)sAUP|x^SFF-#CNGpie6&!TZ|#Z
zPv3{tFKZzTCIGcDWpx-nL-2~qC@`@4co>rz7L{@6Herm};&#8ra|-x3%%vn!rvq9{
z>E6@-$1kJ_Or2>hpC2q^89a)4z<T3=)KXFk1lA-s{v*MstEY!$@P%3uz^{m>4h?}!
zit6fNkMX9dklhg&hv1-5?u2U}eD?xOky|1ixFQr>K8wUB`S|)K9H6Kihq-=@5*wq>
zBId$=Oa37tA%OiM#Y2*ofY68D3x|*Z_Q}A2umpUWT(MNX3cM*8-YZ@MJ0B0_#t*tS
zrNL5r-;t5iOizLBppLruf%8dzj`$I@54Z1uo1s4vgAa55|MdkGTsy6)H`-O$#B#{u
zTu@}AaFKu<=?o3!{+j-Nfdm#sL{xzy+-m>`tF6j&Uts%Z!f5NAv;I(`l-D1a#F$Ll
z$#TxB9h;q56EVWr60jHSK5<-zk~%I^N&b3b5F)}w-CbnEZ(R0(8dqFXQ*&qp?Hy+M
zT9?dC1lUpOOrh_F8}J~`ahZ3%XAjVYqx8GTdmSpNkzi$cZ-@|_b^g>82~zj~6gCjD
zErzOmh5R_W(rfEfj>R(<Z(aqGXzvON&rqw;qRQ3RupUo;fD}Tq-Iyx(*0QC5LI4Kf
zlFRxmRgaFX99jUBQ3gLgzi{?GIOQ)zDilt(s=yeX72SLwF78LkWe_yk*w{GQYl!#v
z@-m5YFRaC(@5N}}DPvpYfQ{<T(uHaUcv`Dv-s5ym@G0bS>;VTC?7gVu9#o*i4v?II
z0eAxvusXTQWMGy^NKgtSuje&^uScu_bKDG2FnNOg>Cc`$YjOa7Ex|L$Lv&%PXVE10
z?c0xB*dr%SifsvhFpSh+H!>8pz$#Q5YH4Wv3@<!%y56}yAG^7_r3y*>S%sPI#l15}
z?ml<W`~`R2l>D&w>H}zj5CjWHc@~x-SkF6IFSdu1jo?mdm_$TWF|3<^U$Q?3Xh!D2
zqs#AMuL=oPWr<~f&;gniOvcKZnVAKLhB6QO^HsM<tq3{HhHr^V5L%&5``MYQ+&*{-
z4|FN`_m@1lqk>|8qsCAp`_UCQ;jOWt(f0`fUuCp;d3l}9jz7>g3p+Xl?1_{(vWvWI
zGmUVR<iWb4M`3&4vZtq|`M{U6=j@ct6>1Lw7jM?Vak9L&mU?KtZp|)A2~c(4kdw1y
z7Ot7<#)I2-KoK@fBId_9%-S^j-o1O=VI-OQRYOeT*xS{H(<?)KmeD^nfu7w;pe#YS
zKiXwF)q7kFxX{rio)$l^)1LV^qmpt$n@rb*vvWRw{@k>Y0VJ&CaLU~jSXYzyD~Lak
zO=M8`z~OKcJ9Umrg_nniYlHUY)nPeBO9hpcqC6aj68|XM@h@0f-rHK41b`2=?A5y{
zVLTevp)(q}vb*5vnb!i?rXFgzimCnri{Rj3aq*h3A*?Z1^AAA;L(~Q2!w9DPceBA7
zehyW*id$|zn+q=-Xit(EZD5I$W!t~}iYBm64>+U@of7Vx%6v%?m(?$V-7QjvK!<?&
zkdRRCz1CAz4M7ogd3pINCnw(P^e~>E|Bq*O@87@wqSF9OkGbcn#gS6`nb2znSvn=T
zwGu=q**1Yp3RIrOd+v-P4;B|koVg%7RZb&E*%;Q1{o1v@*^n|!rX2Wy729n#t^D_W
z_BIr38s|tzP+{r&>j+ud*|DqjKP+#S*MB{9oaS5{abR+}i~WhiJ6&?8GW!{FD7292
zhx1$OK}Bm4)Kd<i4cLj+KaLj&oWa<%6<G8j4qQIxid29fg`U%80PEX5RaWcA-ZiIY
zWHepl2U{V?!!pQ8NozhltRC4TF5m&}*C7(%=N9a|6XfG7@k`^WC3S~xbNKl9#5*nw
z3D)xIj8>Sm#0nM|&Iv+arQ3SN@sLIJ-d5bcAmo~nsg$??AVXO4D2c$<&_E^c+}YV#
zgAylv>fhl)p;<G}SPl^s5uVoNQ{BMOjsWa!y5+G{OHr?0xf~*yqg&>1z$<c8?JT2e
zZUkr+vsJg@O83z~8IC?ildNAMWh-?0uyxH#;R4U@(dNO^t0(n0|3n+V1UbG21uDpp
zC=^o)*_qNfkG+u_63=A9VP0qYSa?P%>$JHp7#9lT_vBRUQooe?Pb-#;Lm%z|LE8Rl
z(VbUk5T#CD&<24@U}L~`6tL|e^FP10-KyuGU8ORW7WCH>tQnNVIJgCt(EWB9PXEhy
zUNVN#Kk1e_00D)q(_QI?%VQ1R<NBp`W(FmPOk)5hJZZ^?!xZ@b<jDEPeSd4WE0x5-
z1?EtC@a6(2&@hExX8ZCkG-3<SV=@m&AVf0B=Sb1ot3V~h4@WcN&p-4f)&E<J@FeN+
z+XU2i%Tz&iNoUtMC9%N)tE($N*Xe#e`NNGO*SzkY9;w}Z`>Q>CaAGz-N1nK`0!szh
zA=-dC;CV^mAxvB{x<V)N8QZGLlG?dh-fg^K?XJ4>215H+7*NGd&ZhBNSr~dV^0$CL
zAt6CUQ$6Cwl!d{#EUy=t-Gtv54C6QG!I>rjJ88H;*PvTD)vkyPM*+4(D-~|MHrV#t
zT{h1({>nERR`C8pKc<TZAN)6T0?G(}@_U&^UB*cHEpXDtL=(?+zX07l>q+%$v2WM-
zZ)?B(C{v~iA=<ipNz!0TY@?*57Sh^l4G;9uc7>;54(X+(LIJ>2)hzG-loIoi;V5Cu
z*#U@Rbv<j>Odj{5F$~-LKE1KTKSXRhJ9-3TS_X&_efqVM@nX9b83jeXH%n}|@+`8c
zy`3#^I4EhQ03haB8D>|ke?9OjjAbIMqzmXxRA9>Im_*|wlRpyk;&$1fA`NTF0u11m
z4AzfWH(MWeww<wOLjVDy`-?|>SP?V%?Hc)$9mNBCB29x5U-p*tp8#9ITsHqY08xqe
zA3l7D`iCjK;HXVagF1w(9BN7!)6+YAV7gC8r=jpK2Qo>pmd<^(ZD~3^8%SIzxZ=T{
zooA^qgh05?2Z_QsA-1BAMm#)GcZP0>8OS-yH?v*byl=GyOYHyfxy9pmboZFdH&o@f
z(;ovdRlm|A7I$|Zh$B?jyuOQ$>q+SS??<&>sHUsc1~SQ<Z>WIMT6`H{zI=dut=H}5
z2Lz!=$_vmgbHHZkRR}RKK>kBTLsO|g@Sl%pQO0k*2xva@Ga!`+<pJnMj-XN+h*Y<w
zTEy2+<Y~j3Fqx46w9y~`Mw6QH=!L@jut9Dg1d5`LWM^kKT2@6EQtPT^JU;t7=HjoN
zWM74*Mh%zaP8B(HMD^*9*jkP{?%O#5KT<BSb;wlvmyI2qH7x!k`E+-t_Nn1aeS<C6
zwdoMBOf8m!)^YEjyW4BNt_rs}a&}nH3S7z=z~sw^nxa)fcbUiO9c>wuIEK(Q@*bP-
z$=26_Qp$S_b@okJN#S2zQL8pYT~C^E17dF6UDS?#Qdo2TG`axhn$a=Nd_(A@*{Z0k
zo6v5c5VpV5vJYtv)!)EvwUqipQpzxdBT7ddw;}h9JD9`xzqJ5^HOp}a`S}=z4_vH2
z2l}1kr2J;>iA@erI~ja@a%gM%JiFb`cdzOoab9Q;ftFM8)<o2WhSM1AAav0wP?O7G
zOUjQD`Qw8Xh*15nQL7FBKOC>!RQtj4-gbo`(fSf#jU=F(Zrra<GeEl9+FC`8@ZIxs
zwY4uvF}+%61jk+jIsaA2sx4juw=w7_C0+wPC33{T%5MRC2O$VkC~7i_!G3{kX6sW1
z$!rv8Aulg4lu8gd%^*A;IgZh8f&L$r6rJ;<Zd`WNo=ft_GBEObly0M!CztE>A!>s$
z!hQz#drW^AIILa~(>J>^-9Td0N3le4+U;u50A?&itonbcNRSO}$^yV5sq)}2`W}%k
z*0(oYLK0)UxsChoKyt)Z<}6rfD5_-QfVbxu$&p~U{<)P@QmY!8e+yFSZR*62C-YAT
z4$~NXV>z0VF1Br`BeLcl9f^DX4>;6I{s&QBMMsAQH!9j-|JK#arUcAZiwi93I}aH-
zx#X<w8$13d4ztCh0lq-8TzNCFzpAiL3K>U%0p2R=iyXgoS{1sEkVC|XFSI{F%l3T3
zNz4Z(RB1j@%LcACr}~WWBibUM%QOniRe`RZYG*=V)1DfdD!ScVkn^F?MHli1>N}-4
z%Bu8Q7W9(5yiiz2Xs*em2!lDF^6})HMs0yu^R=wm5VMH6GkCG{WWHiwGLKXha}^07
zLuFuyzcGdCa0rjo|F@b7n;_mNe2hKRlOeJ((S02B#Qk`K<(dJZ#m3>WgF<Ai=Kq60
z1F0=A`En=)vFQ}wcr$=Vlu_v0$uRO`EKig5?A=T$UVh=rO<=OFcw;NP)L~)k3$`W%
z>n-#_^aV8jqVCQ^*Fy9S(yV;Q{1r~1d~f=G^g+54V9f-8rZdIRtQD2S)6tTURBbg|
zqqe$cks_4j_NU-uiEm(2wIu*P%Hx1HkC=m$jId{A>2L6_*nq$D+1FEgruB8@Ts-6%
zdNK9`w)9PnmSVgnRv8fFOa1j?glnlZrBFC)eSH{3fhulxX3v{d64+Y;D}zptC(1PI
zY4Z&^4|e+rxzcxRt&sh6upNa~2!h!T&d$#A{MP*!-KTnU5)4BU00B$xD+C&b>%VyM
zLU5tuCI4MkR#scf^PK?nnjCPrWqqptc9VDP6=80s0$HKK!M$SGk13vEEL*~f`T6;Z
z`fimr9gCD8gy4cabADEi*uBW3qpM51G?6xNQLE!SWo5zjct#=_P5SzKAGC`_JI)g(
z)TS+gV%iCsupiQn?^LfrhC3-$%{eW-X{X=rlHuXh`c@;qROrn>+>FZ5^%G^(No$lI
z{o9Gci=F@yY==_fI9BsR41>}c@n+SB<*J8WrOjmGUKFnCEx-HWbYDkj;O7Qrs&g?M
zR?MjN9UER&Tr5x^<|&O|I$i(t<>(#Pkg+AxO{!R43$>b~*+u1T^p$UvdlP8s{Smp#
z-V%3wYo{+OGqYJ+8$)<@*qXKQQA1#2S!)`!H+WZ!Z2EGIyuV%4sM-t4wrdRIRG7=T
zSgBnlzS7gxB>|Z?J}-y=-VE1Hz7JY5P{9W90m0fVkQ(Gwn0KY|*;?9!jB%lkiQP;$
zI|Bn`lCTE~e5TmA7pMb7L+BrpX$QO&A8$~dQURz0y6ETUN!XEa3G4?Ne~w#F-CUTF
zscz{2cULB^gsoCm0alVdgst@<Qw*vWq3jQ2#7kATEG~~XUYM0u7E><4`?H6%#LYn+
z@)JHi)PJP6AB6@}+gRs`h;SYgd4>{Hk^?&+&B)(ciGst(&#}<ENb_=`@$^R)QYW(>
z4yrJMe)w?oe#3c^9?=|KJ7*E1g9k4}SeqhE^(xw>exKRVZocpa5o5iY=Yj(_vlQyR
z>hsQ8`0p*^`3<mV!h+@W^9AyD7lren!_n7LIhbHmwrSq2h|#XsbsQuct@D>*bc9qy
z>hu&F4@Ciq*uD9*a?Z0mHvdF8twI?Y-Wu(nyPDF>`FFxHIj|<QC5j6BBB5DkeMTRO
zWFtbA2Q-hYBWw(iTlOi1DXkExq@C=~WFgwXDrZv67B9${C~unH@5b>MHyvaV28J`D
zo8M6h(2Xq)0)&b%P^*wmj#Rsd=>-qIu|`4mw%2D}9kOK^Id3Ziml>&yTZeqpK<acH
zC>|tKs4P2{x&d;XI_L<UZ}eR2Qa7=Jv;@c{|Jv~0$dkk}06jQBf?7LM3ZD1a_7yyq
zFMzk2ezb1@ul|+Y;g|L-l14l~BXo797%JU*kcmOw)ZcCo0|c1$<yw_})}gm3Hvq8F
zfYuT!oiyv4fo;wkgvu`3rv~#e`CoYDzg2Cg8f}O8qTX`X;b<y+xv=m|ags&1aiM-B
zzgVT<kGAOL{XHN33DV;jOZS6?WDevX^)+as=F#q~BeJU15v`%xTo2gZCLd3p(}rf&
zj_3Zaf3!iE$D)?8>&U`4zt1DN83d<dCvh4m?FGk=-xWT%^S2V<HWc*b>pkQ}JPXY%
zHa@QVyU(?vsV`+BtHx-REO?kz2^h5uo^RCG__@xGY1KO58uM+0)$9UjS%p}7o<4gv
zW)pM*3Vg31&pii&Ed^p&jnWZD`KJ_5G7X5S)ix)i#rX4_*E96RWp}avg1_h20h&oc
z2c4lr*nXOnU|r?TQ1aA{m4hRIBW@;6W6x?(RGy9hb~{?hTQWi5_za=Q^d6(*G7ukd
zTWLms?-D<`(gDywsF3h<yfLhPcyGO^W_h7%e=i5R9;n3Y&xO{F*hEVlz-n5D-plfW
z<H6!KMMg+2hWLq@;-VoJ)`@R}ADWs!K0T7)x-qX=gmi=mHs8)Ag$twp4v2zaRZ#mV
zLRD252>tXc%nGs5cjrGnKhC;nbre{@Z`bA_Gy1~4h%hjq9w)HsPNHQY#WHnkU#`s;
zOvP{$JUt3*cM=$iz~KkBV==d99(g0+3V4e9c@gc;wgf31hdu~(L(on^$~?dgV}Ua9
z#R5cp6EKls%2t@~dAg539nCRqi_aL{gPvnOG#=<lNLXU*l-$Ny^&!vEiP%K_4wc0i
z{;;+1edunXwt#%jXg$7#)TwHtVnG7<#^pX<%-a`8g)l){1WdK;Q^)<?4LncFEuOeR
z*wsQ3nP1iAzbk?N!$dP8i2>?4{z4sRl=t5Bak!4Wd9NYFnc|?f_p=JYT+DLyf5JQH
zMsBkP&g4=jDeHZ;bff{0G%{{tYhIsAjloO-;V(bt5`RDc@gK%zE2qWq0kwUekD?jj
zLxkMj&WC-+-SheKw#L3*D=7d)$T87iWg1Mr>c1+I180I0%teGe_S-=Ty|I_B%S~u0
z@8FvJ728K(<=V#oD^q^}hD_VX8iEX=@2^>J|FiWnq%DAcz<YOR>&Mws50*LNQIlv!
z0P|#B60sbn?a-h@jaTn6QIG|U0y;ND5Vrk4mN)=$5`z>(1)<FrXg`1yc+;J!P*2y8
zNY%&-&js64Bc&w*taAuk!MmYDJx;nYqrWet{CyXwEIOc<+(gTcVs0OSxMN9co#msU
zALc&r`56C7gfeZLy~H}!j@_Fw+?^5(IME*=wEC9e0FBqXF+J2<qXPpYXHxw(Cqt7^
z#eDN|5bxiH4xwRmk&cA5n<c2sGvyH~Wk2#kt@E)PCm)ySir$8eh0ERc@sC+@Xwokb
znkk+X3lHmy-Q0VnsI3mWB5ZdZZ<rSVpInxPJ-JkC<zb{%zW76m-F);@#6sP3p5bPp
z`6n$KDv-jS{8iF$KzQ7~7Xf_3YXb_}8#l-^`k@8@fE0@g#P1hSY`w<kR^;J5p&w!D
zfWf>4DiEqmdL#ji?}-IvDXv@7#At7KHm{`)lzvrx;TBmX%+QvLh&b?++r&1Yfk{Z9
zPSaj39dsP7XsZIDqZm(Nr6{rNJ}Sa7kJ90)GWYHo>})PIi&j1U@p*H-sEQV}7+jCP
ze`MC@u^Ia!u&%M;e-IS{mBa0hYbB(_c|c7cAUF|z6?n^K-WM+%z!9J<$Z!yR?q~}t
z;!)6CdU=`h@Ytah(`V^~Xj|&)8oLgCRc(6*u|pQ8su~<t*2srGHWqbwA*Fn+TN`Qi
zka>Z8ig_@mJRAfhG;T97>{cdmgoUry=Icb(eR;kKB-8?3(f?eqQC(d3%!ZvtjsskI
zZ@P=iwQo6t@KZ<YB5f|5#3kty<#F6r1H~X!8V^=RipIVk5eVE%U#{S5kP-zov5<f)
zKxq0s<&6g)jYluK0F#IGzjC5QE8m2r;zZqZJ&(wlT19wclK^vqFK7+OR~vENlaW#O
z<SBET4az69$Aw@&Eq%%Eer!qG5o#gek^eHuyT6@lP46PmYz>waWMl>2yB&cWi{oNL
z6u#-{+QIt+Rid%V15?CVs-oRnRl@|Dj>31)63-i4G~xSwZdW5ed1F|O*nY8c1M}$4
z)1s$<7oDIf50dRKuP;Zr&j~M-2leR{KYobHpjasXH<9d3Y^y<`@cnxiD70&UUyXE|
z2Zj#s%ULPlJH&5|qzu*D>)&LwOL~pMbfJP^$i_MVS(81-ba|M_NMUi|TUE|KdN2As
z&HUjscBj4GF5xB1Q)!&6cGiz&A?dg#sr(qji*JTE0#{qyEAcYWj!3^wId;7{uE%Y)
z;)lR}Ev?Au-5kaCh09ZMYa_>G3haTh`V`CP$;J>ULYMht*Sb@UsHChMR8)v5JbHBK
zm>x9Fyu2s{9zV!?eg~?VXe5mXVbxDS0;Hs*;?S0qjlFfy0e6}!=ovwJbBGJr#w=?!
z#0p#QyBHX3H3kTC04akpvlSm6$|?V3bCS?bQMhp6NH!@S2_Hf61#k;xq`YjZTTYA2
zc(gD?Ga$jtfyx5tl5jCkkJZ^e+q^9#%05o>_;ygsY04XyVDEE(?oKcXhH>aGBmkin
zA!C%5p1zVVe#eI2vvG2%S+(7k4Ll1v#cx&FirOv)NQpAK%krmLJbqr6D3Huv1Zw7=
ztq-fsqrn${Uw`zx+d)>_=15y1cb(MOWCe7&O|m?+P=i9hs<IZ#ELbO%Z&@|4R$#c?
z8#FsU8qP;^97W%h7B?5XnJD9Ko*pz&A|fi_y5hvZ)f_KV>Phvj4`%xP-?mh7XZR_P
z-}M>R*q^LEE9Qq6-M$LuY6v`#U_o1%;{xR^r^n?x%Z+FTp@nS`X-A^m_dpc!EtOtQ
zdiisN+v$8z(E6V!-@10GAcpf&$=&T|Y5qrty5I8B;N9+Shg&6nj1rTk>v##TJx(>S
z5Khia`WOX9Av%PNBH^&LE9bGo7l!?d_&<SUncEAWxYHiz8yR^K&b<Hgma1re*I{c5
zjmQ(1sMXHwi@_32#l=ri^JPd_NUhKGa56BUJQ3!BN@L7Tsi*vWO*fEM0^UR`lrMp8
zjujDRV1Q;!_sFmJSOy%n7MG3UV@Jgqn7^N1h0FNBx1Ft((WEdvA1^P^HS_2X9>hG;
zKzQ!}fKuozLe<b*X|=UHzC;|}7X=Y&g$9t86H7>852Wvb<j2bk$v2P$EVJh!TF{fn
zK?NGn7As%xxA@hA!LAoT7YeC@>p(2jJ8Q8HT?0y9a}_;eZzyVqK)89WYzfW83Hjv4
zP<Eu|$simEt)a^f5ExyxJ9&`8i3Rj*BqXnBZ0_34_Tuug=fD#{MJR;E7C0+bm73tr
zyE8N|h`6r%9XfLCK$-!ob_8#+^vz0XkC*T@0NkVirHJQt2c@9RBLj$*knH37-Jd%4
zet3uk=j<NtEwLh_(0=g)O)`#k9VsFJ$Q2L|a}w0x1X0sOuvR`sh!@FXNJx;t5vojx
zt{o`~@{rph9q}rCH;PKj$nXO@8Y6XWmu9JjSrqUy)PM#l!(^TX=JrVu(C2e`?(JlZ
zLZ1<_(Ih0OktNBWH>jjrYUgc;7vTdaf%#PfERTe0TX}=dhx^R{2#v}R5|~qZh;SDC
zT;fN7<uwJ;BGK5j{-Ief2f`iH^ClrdG8B0%=sAoLyNrE`KIx1b@n)GI-2h=Z6JUt1
z>Xqj~NAH0!1O^-VA2=Gg8)<?9ZK(G5fs%-3>q>X~nofd^xpCXaS2m4jF|7gNf42qx
z>(UE`lwhgf^D;C`ZcLkZo6r_zxooizR2$>Xr(OYtq@i<><fta-0xVCWi``D&__?O<
z&t$8QRiqB8-@D3;-&O3!Rjl8-HPS@X3Z}+9u=tm<0-*&=zKe)MpV(I+Y}noTucG=;
zykb5gMji^Jisx0LLO<x-q91!;o+STAW((R|zKH52;a6x8eXKjkLIC(weB%`#OuqaT
z0zJ?-%50c?iG$m-%t0cz@|;{F|BSST5`XCIhK?V5!Y7G3DnN#+u{jB+^hOq>^AOU>
zTfDuVWe_xS6GegYxBYq+ofFB4@_a)G-MIbnvZCS`scia4<$<h557ICl8)@H_Bbbkc
zcA%xG?|M!r$L1Nvp?kjZH1r(Mkzuqmkd2Tv#M~n%FI5-7+(f+<RTULd_jQrWIXl3_
z%di3e3J3W7Fz1{a^y)|7FNg(e>;=8@j6UclVfv5t6ry+hhn~TIT`7>UprN)eMH?xi
zl#LTDUxcjs%K5nozQz@l`%Yt#8HB4-_er>R^z%Tj@I(g)08Hydlgm_WxX4x|c>$N_
zL`}GutwA?#oG?d;OF*|mc=<nBSbwz0S!ycbGXi{X25OVWy~mDX-nt_~;}Ykuu8gDT
zH%mRi$qn3ir<Co1?Qs)CHU!vQ=HF;LE{~2LG-t-1{(aH^T4i(~D%u-!do^cd{fKWP
z+Cj3$fk~6UZE?OI?aph5{{;X4(>5L8)Yg2NVLy#x4q`Fcj96bKR=#H6<Fz~Wkuvce
zQR0n%RRR4{77U_Vgj_!I$@`Z;MW1@75s-BLg@4s0JczYU-d*^JAoWL+8~+=0Q~&iQ
z{J(ey+BYabBnS984b)WEaVtF@xgt1R4Y8}QsIB7WNa$=e#G*OG1bo4pu`)1oT4++|
zrP;8aU+By+ay|rH^1rB>gMdYU#W7%063bm8b26~u<j4J%p_o_oZu9%}p3Sn{eXp<j
zs3fTdzjqficYOamO*vYrtKYZ%dEv9d{Cg_toq7^yD}QFCc_f(&7W`JgYo1!reuu3<
z{HC`o_X_cfx+DY7n{a973oZ5bCEHwaMBCwomVtyI=gHWO)0o8@=Ml;M7;m!|8-UKd
zc=G;1_YD4g@n@OC+#|lIHIQ*OcD!>^k(KppGR}kl#U~_)(4JYQWOX|E;rsUoMQcX(
z+6pC;--wUNavn`I^t@oC@;9q8<fgo}U(o;T2L1f^DM2|C?uc8ohi#{iN%)ZF&=a?}
zU2X*4d|@P?^m&DjlPtse8e7hdl%jKu?6DrYkKE|LZ0>#kJvR2FcZ>Se2@5v0^pySD
zlKMASq*lkn^v$bZp5BIvCL}!k{p;7~L_ipsrkoNGdDAQ&XpB_1t>vR5JP~CYY~-43
z<VlH%PC}&k+&;@(4Yt@bSA41YtxN&g_m?~80gaGkSRc?}kn`TMFBirULof1KzK2&+
z;X3Qlhvxl$vp%GzF{E;$-Tj=*Cxg7c6HRB&H%a}djxNo5EAr|nJDvJtPUq(u0|}RE
z<=Kt~rNo_7z%Q5`o!UET!NwQWjCFVZ)6K2C)NvsTu!xbM<KB*QjF_h$p~$)i2GJO$
zS=BP`!yv=5u<U^0`3o0#9cDF}!?~(|Y(XBKf!>!^fq4k1pjDD(sVbo0n1=G_sfMLe
z>znWFA4f(<2fNi3i`If&=gZHk8~N9^T*2KvI_kVMHuGm`$anUCh}|apYwPiGg%-2Y
zPL;FR+~7mD)1`m?^;<-<vGnzGRlA&~w$qc28w;0TjEm@9*H{S@ByXvxm&p?0Y?@++
z!x{Y4k$uv))?j~M@6SJ<Nf|D!>fCj!yr-@qroXkl{dIU)c;+h$3rjlSA0e03_bGRt
zzJkUAnO94HAZkW~C|>~tTFpzmlPz)T`+K`aHz+${2<Dbw*5e16JjmcMw=duD+;ela
z>FMcF2WGYvsIaWDu}7MjYDvk-*F{D30PwW-=zC^D(dUBYrXB*3*U2<MZ^>ixAnQzo
z4VeK#{GPP*n=bQ^T7x!V)e0OJ^duxD(}2UL^Xn&mcm^Z{{JcH42>@)i9vCy#wdpRN
z7=FrA*Od~zd`PdwLkm$H;6}s3Io#QtK1-L02$R|2o%ebbrGT$eT3cJ$u<mYdcEj!)
zQy`gDhH0o(7_OZH%0b1(#>V5-T!_Xm2!Q!MuB4=7H_=2Ttpw8`xr~mspN;cUZN|TH
zM>U&6O%jmh3`2O}SgMJ4Ne0WEc{)mVi5rcUeDJ>+kaYwm!Z3?DOmL)ui0JU)!z!SP
zk&C=34dZo<if$k-3j+1qJEf$1*8~JK6hyZYLGGMK6XtD8+5w{pyjBAO(0G?Sbm&kT
z*iPt-e4J<|v=8r`rsh+*fB&@)1!or6x7qwa3Ftl>*TT{EzE@08dH3g0vTI7>&5^tv
zP`*bd#oFoUL?<i6Wr0M2_3G8Ts%a{1i1n<`W#r=GTIWhgNXWxRfdhh+`A?uzLC`3|
zC?+QM;^oUU7%PbsaV->?7=X4{dnmg$qcq4s)m2o2tH-jgNG2#^35BYVnQ3D;YHo4p
zR}P3y#OPVWO}^=~bkNfKthfW6%Pt^|*5IlzjBwZgB5ylLhQZ}Mjf6vMP9Po}ezHw&
z4rx_87P%g!Jaw#Msg4@lqX?ZB42`9#q$<f1wAsijKxPe|e-U`tP#l&o%ANCgRC6+r
z=El;!5>isELPA<FmD?RF;!49jo|wP=8Ky@4)H1a~rVz+Cg$+s5Eh~g#c-{$5Wtgj#
zsa0y1ie`9ZvE0mQnQH0ao~uw87?}sXe*Ko-WAhJCjz8Z6X_^HzW~TcKW@zj4`z*C8
z-AbSf8<N@#3oNjoQQL)rp$)n+M%)pkw6vKoPgBEWSi{gf$mBaVmqvMQCuC`FIo$`{
zU&Uy6-xf?kb^&l^TkA43G$f;@PKS7625`L;H~JFVdh@;dl3+K5^l1LOr^lrJ!b-T_
zJq8xA#W%~JXdoXQJQv_mjz%!x^n^{Zy-mZ@Gy*r2qd-$-2Daq_MwxF|e+G=FdCDmR
z!A1C4w1LwqFz*Uv#12>L!ofRswZI%7R)HLS51|k)lOVFS3Bo2L0+Joes~g@k^DZQ3
z*AkHPf)%pM*zhNSdC!2*t||CbMG!PMOr%fT`<m}ls9)uw8ThOyPlFAF6;YKSnG_Bv
z0ScL|VbBkX*=zur*ZBC<{HggJT*SgS_0>}3W99|)9449~5|FN`VIo2;T@}W*gBc@a
znc+ac%^4=H)Z8lVK<ZCMN1jrT-BR<OtjykCjTG$NUsNWB^f1gY6z&#-CIza#R){J#
zAs<hkI@Q_w?hcbQSi(0Y?n?<UL?IQ}nQx{n0wJSsod|=K=`gpr4OANH2olY|965gG
zJvd=tB21H;cO=VcW<gF#uiRbNmNxUAh1*0?39RLwynOf5BgZ@6{&8d%<^ct@YNM<`
z?{xyc5!4g2yM2~?yU<z)OoV}xX(%BTl8F^gOYuO080Ba{iN|F>KhWn<31QTcE#?Mp
zL_CB|QwhRQ3=mhB)4R+$-@K01pciq;k9Av?W@2PCh4Boe9CdGkZBK_(7}!3|mSUY2
zrVUf9--3e|+LL@ew#F%#n3=zt7hGpzl7Ox4PL_*Wg<k(y={|Hf3HXjivN1A+rUYTR
z2yO}HAZeH?%-tsxJFg^zh{cezz#vDXGev<u#r^d^?d0lj(05Ms+$RnPwx9nKrh&8I
zOw&R23gKWHS`j__yTkiaa4vo8D=-i$6PTq}F@5tP=!Z4udUNgsuEHSBAQxX=Uk0S(
zR0z-~&YTJT^x!ofk2mkmNQHRON5}-FP-6i!3-jm~21=BlA3Lpd_paBeW6#~(-Blq-
zq6>ksR2tG05g}4Q^5Jy#qykw(x5o~2qESe_u9_C?Cz;^p<_6J$DO0Q99&A!ru$m<5
z*Y*WaP*T1JA4`J@19WhK@cmU)RizGdX`s!LzNg(>Zqh=B9Vk`=38e}c`<!8SnyL))
z_6jkn-<P66o$`U+bMe#hZ7>(r1j*A|L}KHE8($uAALI-u>kA+a=!B^Yc<z<&3b-4U
z@DK{Ic|8gCt1K+knd2L`N(nGs(p6-wPpmo(djb4~IW+sy6cb)1=64MO5A&*eM*43<
zJ`WI~2Q&p;g;;_tzP_%`90cYrKmx9UMCd&%SqL}sSG6de^B752macZ-P5Wt;S6FaO
z#%tJ>3}A)^xw5V)C%=cT@0pfCpgY#bX2DXqV`T2$ZG|AkBn_cK6$&ca^@rRM`9S>&
z*S%>&Q5o!5_*NK1yXGi<nS0vrW;1A>eR|Lea<bWUYcXI@E$gGXjePC}U;VMDQ>U7U
zrk*;}R9RomU02V_0MC;SJVPrKJm(ZJQ0}Z8=ebvsm_KH*&3Sa-n&B=1rg=>Wd=+$=
zd18(u-p+O@Fq$ebNZJMnVb5AlDS6C8(lYm@8-Ty7*zJ{hj>^y<^lOEzO0Tc==+c}N
zN(K$n@Zjb__;Pv*W@!qyfTHC3{9rnKmnO_QbO9%Tl8N*^R@eJr3GTp%xBwTByUGO~
zW<T4jIP5{p1Ql;Qh~`^7IGC7fV>e@NJbWgbEbJ*-CGx|qY*nt^Dw`(~SAcWSR}4!8
z><Dm3tlr)!&STpwWs*C92(!Y*1O>>s$Th$kQKnH#=j}*#q$;o)>^Bpmx=^(beT(Qq
z9?DZLFrAOWuWq>$7onG?!O0qE{4cp9U9{X*#S|12lk0mt%Mja?!5;EN^i$+%iAxZ>
zI<xhw*dVqs=)+mbMUHIp{{<bwRbAaeuE$q?KS+}wMc&<g!(cvCUJ}V~ou#hlEWsBg
zhq6APOJ3`tThdC9iS5C3*wWd+9MeC3yp6&LP+owA!*_uPBkH@x_qI)_1tew**w+mY
z>jwsde0ypMvTPro-sR^zOX3a6Q^-mk7KUOH!eQXy5ZZw#y(<F35SI(X!tTn-+G(}F
zg&v?h@k_WYn`Z8#!qFs_`+4baW}9A|q?oM-e9^GBCa4HNm$(F@5P?_%1f{RWc}6Z7
z3UQ(l(-X#!(|*cN@rJ~3UGz`W$cdMa7I+5%TgWPi;wfUB0O390f`zE027-#mV)zm}
zFK^eL{jMJbh&g+$VGsbzde=w?F-zYg_+1we5U4jC_9tBMoTp)7Va3~CPhl0Yy{NC3
zktP?mB-ose<Ia*CD#U6B++%+f92Jrm8(uHmAU7hyV*5CSFXOvJCBlxKWzqF@+3MM`
z^>qgwkhgEqsK+V<0GWF-A5X(Lg-jICa6g;*hdTiS6+i^xHEq2Oc(|zNraMs9%eTHk
z8tKEgL<=S?E<R4d0z^4Ock5>;jt43X5c72OS50wupE>i7e>|2s<;-NrJ_fcq7qH>L
z50MN7C)g|{a$kukKCBW{Yy+a-Vv23)c$fh}TNHYa11Ojfs0$CLOyT{jqh82kr<gJI
z&le}o5MHA@0J(W*qwViQ+XaLBV(bTdST1+(p3knp<;$NQMsXW`eGi5O%yY$%=f1l;
zkTUDp&B(*$`Cr-qEWS@%0!eUJrMqhi7EdoWzp7`$I@+^G!J!k5l^aM*WF+=%r^D`W
z+H32+SsH?&&pRRjSDkYM5!JX2semb|D?-F}!mol<$^$of%b3j&VkB8g)G@tEH$fTp
zsaZDtN*#zZjxM4?Lj4}mm0LgNPOP*3(wSkb!t{*)2!vcSlRpT(?#t5K;B};cj=p@k
zD{U6_Vf+>3Ai%^LfvccH7s{M8FVO0$^7Jh7r-oP$F;F_tV`oVU+m~BwMu28E!Q%K?
z8i7_U*BBXKSpYC{f%$M`AeohN=9Y&vKWFQ>G$H|hqXP)gB-U@-Pk54R^g01xli+N%
zaURUl>3vo8qxam&37`|1Uep0rGr6ZAuldBk;`U*H;+@dr&xQwATnB(7EiJw7k`7L}
zw6wGhxw0$AP|6)-X6rN1x4D+$&Iikf$4gW(rpiU}Axr}$I3)Xq@`{L4+{_6u5}yrY
z-qR4$0^801K`&?{&aRb@0{EcNJTz+O>e;mmI{`g+@Zr__SEw#rNPyuRH69ZH18POZ
zAmO+2znq=OGR<TLQ;RO3MYSLMLgJdK11K4;Z9!b0o9@Yub62hy053;JX2ci8Bn^E6
zH3%BKE~}=y@R|=E-?F+b3z-9Uk&KEe6$A-QryV2ebXU+wnXBeYkUviYbW{b2Tw=cX
zEW8bwjr;agN=ZNtoP<`1P13HjyqsJYC=|MiafWk)W$J;QrS=v`t5zQS8)E-Zz4!qJ
zgepK#3&0@a&pUU>&YioI(029tC;0Ds$StW*jB1rRWbQ^nd^FbX={CJ-4$aHJL{Q4g
z1nN1DlWlEnjn6edYK@mTDFV;y2xRoFjR)RWPr{cx<QL}nTwRSII^cg<I4nCtG1MlC
zP^Qm%dcq@kRAG{ixI#rvo=nE7*4u)fIdfetBb-pzB7?uR>;+-Vc%SG)#!5;@$C1}(
zSycrjiOAmOs8+etk}m$j#fxcxI#NPrRr>GEz`Rcy6dUOC;LftlLGWLI^C}?L1mvOy
zPuU5B;|4<CP~qG#yJK?#AR>712RqSy^=I21sPecePc5&lslt<w3OsgQ?C1JbdCfb&
z;M^d6+_$mG1%#E#AmP)i-vckVLXpFmgk{_SG%hgr{GU7eQ3U2{|2#>~R%}=Xb+yqp
z5ynQBuQR>^XjQp2K@C0rPi9dNo4{V>pFVwRMe7P^36g_GAr=-4Pdmy2gX+#SoGbu-
z#vC4$1Pf~Jz`-U$oidZ}_M*$Yd07MWU3TC)kX+l?xr#05^j+t8T^o)|^+V1HVO5_<
z>t6ICxpLF6?o`So;LS7`ub76RHoN|p|2p}{qjjg}0Q%(_H!DGZLKQkOFaG-Li|I{B
zz51{N%5X&amVMm16)vi8-n?1Qjy6dF)NT#~REhc7#U=yA*Q++Sw#=ZW1C$)Jx7Fea
zdBk9OV?!M>UxCw-0j<E}+pwut^gWNb&7jag@e<K5@J&!-96fd{4eGINz(0^Mnbv0e
zI_3-~U%{-ZBm&U@T-HFWk7x((2-)kJxry*C31%J1Q;=%WV1-#UL?aYSAZ$mfWoW#E
z%<vwnbXY)G!=rX+#$^E3`3Y43o<pzvJ$x0^_`PzDQ<cuEYinKb0E{(2E$M)U;c7|`
z9=wI;=wv|TnSy8%hQ}*p86YG97FyuB?{S*$mIhoQq~Q@7#$1CM1F;JT$`-nhS^e`t
za-?_RsV(NcAKsx{KjR;p8x$P;XgGQr(pA%vJv2dcdNeA6;1-OoQ+@aP^x!^#*sfe7
znG`G^wtuWJoEzm~hAUTiy0V&6qXW$NqHJjhG4niZY;5)JWb9fhFuAA*O>&#gA+k@O
zK23wyCkOuSSPj+bH`AMGfFP!!9_iqN#P!v_3JT43s1bM_=LZJrgJ)O|93Jzy1tzCT
zp&%lIkPXh|B1D5JdU*6#IKbrIm6mRU@!YqdV7+%Qy)P;#@C;^|f)xqE;O7Cq+va1c
zQ^$a=GVbrS++6%Y=Xc@>592@o`E@2#nI-leteE*b|73|Unw0*Myq3N9VW_YTTH`;b
z9%BUlIQ>t`p%<R(qztih)sFJ^J5_*TP`?$e)&I>%U>24BzBJ~e%$t8!M;HvJ#NSuP
zl#~9#Ww5yaEpHwlojSNvpm8_$c2*P=UOiU77s@s@b?(WZPstFyi+;W*{hOcisoW?`
zx$`AwQ+;^V?jN_`ef~eV`v0qaaVkI*EyNha3jmr=dGZ9Knx&)p{P@{!sC6FY_0i}7
zV<8MIW^G;F#7~_1xN6l9`H_Lf`i@_xj=?xupwD-t;K6QqL||YGM=R!`vohJD4t3)y
zc+m_fP`-558RUF1$ayr?(Knj)HQl3v{aEq8Oy_$D$h$M4z+&a)RXuX-RO^rGS0SJ;
zPVY(sf)W+&h~x^O5sZg{3TGjmOA3DzjTk)>h$oRk_Gye}P_KdjST%X2va%Ai%pE{8
zbV2D1IT<{}x<9`mGiSoSs<<fUZv)Y*1-cN*{lLWEFEBrno*&}4apOKL`YUd~uPYU5
z)-*^6BO@bdism%Es1C@FQ8Yl+CG<_prqxkxl?ZE1zZY`-#6SLV|HTQin|43%1J`9@
zW|5<j|F9Y+{cqldn5zcG2@GcfC998exL~Ealh{E+01RgFI?yfc5j-Z0(f}}X3B;Bx
zc`P79HgaEhFa>J}r@kHv!_X8sfBt;FQ6qVQ^{^fU-ID1Z^8$15GgQ&swCHAo;b?0B
zLG6f~bez}e%Ft8>c=*Mx8Cb_ofY<CAA8x~Zu{sdU1_lPJkec2@LX3p^aYo^{^22{S
zi&F@6wC@H2xQ_?QA79g&tuF=y)_u6v>n?Lu1A}<zL^DWV6n4mfq1({W%4GQ%6zm!-
z`78jZ0NI(>Lm~>34-n2D-87aT6@dpB9Pl|n$wQj};4+M<Ou@4vU}W?)%<Lrq6H&4j
zt9avYFMgsfX!r9qV=A0Bo$otfC?ka(v*xSzJy3Wd64L=mP50%cFq#2k5}an?;E)IX
z#2d!Bp$cty(oZx10%iay@DM#Hb);dZfQ(Q{o{{vUd<V_&bg06YmzLf@h|Mvm?JTi1
zK{aV$B1k!lmjV>;|9cVMczhUR$W_J9hFtKpqV6A~KpcfK#t7gTq;_}UxekGeaQ+C|
zT6t#z6bcN+iaa2t6V1}u0mAwkR7AQtcLO1ePIT`bL<Y?LVp0gyfp!49@wMkJUYuS8
zeUq<DAe^gBXA6KRMrj4HUArtp0k_Q$<eYjL0EvDAumol+G+3=eJrICy<c#PKcb$8S
z-=KG4_Gzl@UvAy^``NQ$cIo8VEa9@uukh5TUh1sbLhh7Sf<v*jAB1P8B_TT})D52G
zQM{q}clSvz3B(0~?P?!jEO)TFt(du@N!%r#p9P+3kqqzu0tr7|Ewdd;syXURh{42r
zdIQutTA!k#DCmCO*7q|Z*Hx8DY{pls_IDNF$xk`fN)!I=K~RZjccZDb1dBq;J_hMX
zNG?bq#WpFTS$cS@CFwWo$B#dhf&TH}$EN1N4O3VvIB`f_0Bt&KY$}wH@&pzprYX=f
zG~y2PitPg18^(zEB&h%zMR4~8r)Hp{7^T7K&$K8XUK{%Jmuwb;af$&x+#Ez&fr-#O
z&;gj%+7QGtN0dMY2pkSfM4dQ&x(mXd2vwHy<;#~{K$gWM4fEYSFrH#G{1%E@Rd@<e
z8$4r?$33VL9dRQ3iz-OcKUk+ZY%LqXBO88#qN#DR><LRr{@-zBF&G~AmJWTbi`<XA
z;K^9;sCbM8EY;K1*bySxqS5^ZE0za}SF8B(I{+xtACSCwt{y>O@N^K0iKRm4K(qf_
z1dkLjWX*X_iXX;iP7HzUja{esK9K&ZfWKDZ0Wi~`3*Mpvtt0w5@%!^n@Hn1hQ*cXK
z`KE6WW`VK5uckMzv$4qnK4=4D%(osE1(**}Gz9>#D3H^0eR)y{Jiq}y;*L-OHO<<>
zu)f&-PTsvR4y?8H4a$7ns3a)hrC_^*+Dtp)`F(F;uKAC)wzi8mO<x~hIJVWCL{<)U
zmA=PT5@Q2c40LU5`W>KoINkd0xqC!8BdR_S3k)}ks1PcD6~AALzCHij@#E>%!<Ey}
zsmsbD027==7nTT$WD|l3RN9b(YzDI#yE3)af&cwhC?`ZuPk)+<yGAd0#KDpJNI71H
zstd^RJJ6m|-<Tf+WOF@b4oo`ff3WwSQB`K!wkW1jFqIJufB_H$K@>%hVAcf!f=Col
z5RfdA<3bFG5_FMNl7S$SBxeu<AW?~uRRkm~kSzK2saSjec<<hOPP^~^xaU-B)!N=w
z7OeGsbB@q^AAKN!=}GnJC11#&pMx2@xEphiiD7JW!0K!WEk@gLe-D_{AxTUQfCyNJ
zh7Bl(p1osb7zJU>f)i-|ufNhG(|tcT=Q?Fgrz2vnXnb2x&<*Z}=Bu>K{`Cz;dmxeL
zK<l6zJT(iGs$P@4$-aZiujf*vi^Cmh#umWXpS<_3w=cgu)KNCt2JM`#sQzviA?GPu
z2%cJJC#i8aeu<*+8^_k-6K=RGwfL{UM2(Y>Mkophl>}35cf?De7h)>=F7KDQPxngv
zUHHO&z(2i(e_aQbfD<fDQ%f_Yzm&PZ?RanD4!ID^fBPyw)32HzI^+U!f`h`t!$VpK
zO9??i`GkaoUpJ-cBKroP-1qO_$KCMZB@iA_Oqm1%!2AwR7AxC6P$>j=O|j^dCG|gA
zv+CB_85`27bNTQH9Qg&$Kt**LVT#C*8vxgbC6`qf2MC0AO@S29lZ=D9?u~@;{=n&U
zv<2iB;q*jBMcEj)6_K`f)hdFxQ+tuaMUe_X1Rey<SX@z25j!yV8eh@z<5LZ)3PL{P
zQ>1{^@$Z^KdxRej803wFX-l?&q%%e>&og$$EklV;Z%nH&pZxjL)^o$&6W}ul=86N;
z-q{%m2iwb_3J8pgL6rsg16(Vg&3z39W}_dtjWy0<hSl7x5{{o{#%2+O9_=@IgzoxE
zfm+rE{Qer}AoDjVQ`$lueLO#rhy5NsdV$;a0;!7h97r|~_vi}g7OzEf1MOKC;=bKl
zNUVST6=u$pzln`44kuQMhO`feGXg5J=V&;{(E>feLDCoiz4Qb%lsOuVJ1P)~onR(M
z3qwlN7hz{%xvaH3C^vD#+>ac*Bh9&yqYG8_8E~VPsLCjni0FHQslZKw{PpjJ;p^9X
z2iJ7%;zkzN6X21LJ1^p9LRfi_3bJSuDiMc*L%;kw#4ZksQ_*Jjq6-<=zFu`r!#fkT
z{qdOaLogjVgb^7(-_%SST$^rm6{&SpXm>~3K+9xpl#PR*g=Q;tHU|R<vpgIe$8dnp
z;$jcOp5*<7-Ioj<5J)KsK>5MXZ5~yI%?qUx&HCW}D|6pvOe|ncHtieU6X|+k?gWx?
z&qhz#HILI8Gb|zx9!WO>%KDOBE8A9{SwOQI-P^ewdn-?NNlm?^dgmGNsx(thb5_PB
zOYU)S0)d1Ova$0bIKv4@EjNb{*bIsWTC@Yt^Pn27(S_PPDwS0CtCIp}#jcHCiEpYE
zu2@<c)iZwqg0zH$>G=J8&YK3Z83Eac`TFGVY47lN-QG5Ns9<cdTzXyS(=E-^i=HSg
zGhzi8idLG=CU-!P+Q&b13~gkN<FVgN3R3<sQlk-A9`Yn{R3aeW`xH-!=jrY2>`ZR;
zVAP1Nd1T^hgL1PW%`Ehd1u`7^Yb#T;lBht_9-gtO>1pu;_Iht=4|ixWE99T*jo@7}
z^kqdzNR{QHt@$NEnfz@6A{G;O8?zNvIux(==h#lPIvsu#@Ot%93;U%BO<%j}ju(tw
z^XOk0^0_PH-cLurxR(Wp3SU!m8Ma8;I*WL@9eA;H^V+pxU@{2aL(&=8Sxc00emrMS
zRK~WdMb>?>;y|V2eE#p%zk}uDAG|4fKBVc9PN}mMUHJG+hW?uDdLHGtAe@Itl$PI-
zvqpcVevU4vLS^G+PM`Mm^<S1-Wf-|?`w4CdiIy)Jz(cwD`TI;?+Z6faDO+_qrSCN2
z%V%>xx|5~iZX~tUG^zg5ZWgAu%8!{|JEmWc>=e={(6Y8;g6aY-&8NJ+2Q$0Y%k8{u
z@o2^(znPn_e?TGsQ9x;eZJ)U2Rl40pSu-9!i|!SWz(Bh)nYPcYDgDbw6)|z<)uHi~
z>uM{MxqPAy`GqpL9DJX)&<q4U0bhVpeFrN8feRS_7Z~n^YQY{ynj`PXdrYg@9fMWz
zM^yX0-l!Un(8!Qq<cO8P`SdN67iXTlW-TNTL~19z@+z8c1nrWWVNm#+HoxmO=UzGL
znNxB<)-POiQKBio%HmPn77qHuvvs;t_D^dJM=o#}FqH&J_2n?j&1MOl8d6Zv#uwP?
zuP!#7S@culb60%4gFQcogjV?W^$grBgPi$UFiDw)C1&a1m)%@kCx9;2!DwU{m}fL1
z`uHkYuOJC0HQFA|kfTSAKq2856chs!-!8yFrB|B~QaCA40u+ZHnoD6#NrTy^deQ2P
zV0!MoVaf{tq=aW5r8h>R95DbCMOYmU3bJq=P{kq9pv@xS-o)kA!==_uw*7VeFfwvY
z@;E@_#Pb0VGay|_(TL#L@%Gx^_xx23U&Wein49@>kk)_Ge&aK9&=Vomh*ltXcE*7;
zF*G6KRkq@!f?CFH)TKSZY%7v4ssZoB@f=2fB~8Of%QI+6PS;7`69c(xNYuRxC#@*-
zlN5vYwSaIVeSO!WLaoqD`>k2Qya<Vz{2G~Ipb2yv^>Ei{VK937Xb^}-#NhL*YXqI=
z{qpTWnO+s9|DT!Vju#jNA}e1meITgHwsOUaS_}g`GjA)v^SHq_1S#=vEr1^oSKWvq
z^pE!7A)45J>v0$un#xfEgZ>$TsDVJjD}*=ua3t09@l2MfR@$h`wfgOg2kdq^-*pj@
zci^>HO$-}A5FP~sB+~VP*p|NO=60}oDhi5+2|!|`;pT<^ymH)mc?38xmg<B8>#~@8
zkqmv`6kLT7g;AST!CY||2GTrYVq(&PAo_a{4r@svv|+<TP6NZFdLg^POO(XAw6c}(
znU#k;v>Nz#R%Y%o_thJBUvPnA<mZl}xhTCw|NM)52Yf!y%_0iu+0LO76T15C6h5x5
z;SdZUfF5>Z^yM7R%07ci4oF~a2zm@m3xD*S1HF0b#hkDQlDKQqjG9y`6~|1*Z6&)B
z8Vn()2}|59*+5~l)HFM!MN)sD_Bc%#HjIeJq2`N1CrSEJ&^E#toasV862DXN`RBpq
zb6@{e&g<~i%ew#sztEyip94x!2PY6@BwoObK&5vnpd&j6`9Bm&6r8{hu>2=PHP|sC
zESf;2MzS<1L|C89eeqY~Z+0<iQUFA56t#R#cp`%Olb*?@)hQB`TQ_D*ed>*sFj`mr
z<mh6X<-e^xQ055*NC_QKfE~>=+DWw}Jx|2+5k!Y*95_*@iR3J|-^I==@fWC&A#1{&
zvrDBzknjY6V-&73Xx<<qUBUq)FeJbbpE5lQ+u(X*|9azG9a$rk3q%f87uT%Oy7V(s
z+78<)!n&f^gHcHyVQQJy?ff2_)<S!y{XTl@@8@*I8(81{ciIqoSvBYZ-(g~8lm{O=
z0!d&uuuXz8473&QgPv@kNrNI04WZ>pIuFnTx|bj>mnxJe4(hMg4D#jkQUH8T;QJ9q
zj({0!xK4i{04xfS_V$;*A7XkTMibQlL@OB@zQD113<?1gI~0^@C`2bP$GFd7)R+~F
z3n=Cx%k)<o7V55bAc2rT?s$1ASz0D*>*&z)4&#}`$%cqUq0=KEEbQ6tLqTNsuCOr6
z$oBW|Iz&r0Y1}UpEP75sjqxVh{^#g82Zx4yz(~Z$#{;GzAP&(E;t&j6dHbr_LL}cP
zMQsGt?=N`)o(EAu5PapylP7pIAAvgi(P-PynULBNJ)=CXSBhnq0?CtFP9rs}0dPzB
z^_R%Sr21+ICMcB{WCm3qZQO_|-|)lZ!{e(q-rIqiRxMU(8U~XeP`f?HNCJgc?7u4-
z3Kl6EWQhB4qe!8RTA3Dx@pE$5iLw!R5jlCt)ZF{`*S=H#{T^TC^ugjx6r>+V7hc`y
z6`?~d7CiY`j^kHJRmLgpPl_c#ZDAz%qjbxJ@C23vPjJ=^Kvt92My~_vG6D(&rIjMA
z{YiVuBP65<O@z((t>0hjN`FI=zBfk{!NC|A2j+gwLA)|bB~nBU_#aR~^%cMo2}MXw
zDF>(l-jWeO)e|`IBlw|R!1?NsUNsnhx#Zz`q@($z(7=iHe>vqc^#y?o#6FD(N+M10
znm8y34b}}20L|~XyUQOtcH>V=^qGyBon3&kQ~v1<x9#%=-OzraCzhsx8d@1?QXVQP
zKoatiR405Yo;`ai{pL!|E8b|FpL37~n$2rr5j#JpZIulR1gqgt;_9ZT(K}<5qF1m>
z#DMdYrv0^&t+)8h?<eqz6>(TP^W5FKb9goNA!#anG!CEl-~0z$ny&KXAH4GU5NTn7
z^eNzrgV!UJN=?++lQT0jh(?dd4+Zf9n6XJiaPFwAApagY?gP?ytCb|=mH`|Z;A$JK
zf3<2j5-lStF1$E06F+}m#_M`SbQ4@Y0Aj5^znVvH&C>!~4Otnixf`-<;(=ynY0q^v
zn+bQ1uepQr2pGG@*ic&Dv<waIf_^4hKqsUK)9X;DGcWE64{oqtk}4W<Wh7Xl1OPil
zy5CCh0G2}?GNdtUZEa;um}|`Hk#31{kHet%Q$hon$5grxjg}0qhR>TA*U`doIU7rM
zH}!$gR!h*n$<YNnRX5&?3v9~T7n+g&p6HD2LCn?w4ImhvgoH$2_Z-?(bpQVq)^S$@
zS`7Ue>A`U;Nyhb;K;ayZQEL@2$dN>oL81zDSTtXhJDBmSiPzGi;ezbI@8AjTTT$e(
zjPLjBWD5QtK{%>x!Vgd5n`Ld>XBdW`LzH}9M)25b@mRWRKV56O()b7IfBYC+E`dpi
z#swKB3O9`qRsdfJAPnrmq#>CCAoHw>%7Zr&<gNpQ=tgfq4oh!c08$=`=4z6N(82QV
z-n~1te?|JuKYYUvQBXT6bq!^*z|tm0>Cu1@TWSvAi4ja6x+ZJox&?NgfBrb=i9h;0
z2A6<s|0G<^J9FG0a5J7i$n^i7KV19|SMX|IfMNWA<q`(58~^jq1NYfqe`)rm)NqQk
z9`TZ)Pen!^WnfyTG^fk@KlKIvCy(Y;^fc;f0&Wr#5)3p^_X5i!xZwzp$8TMqehY4E
zQ40)O1~nB^&SRvTR0PJx#u6tw&L4K-Un=_Nq5qHq6oTNY9HkIW>L((D!{{zFRMX)w
z?jrgYMbwN$4glSD2W1&B95lpsm1(?JR|mY)<~ftVT-}-Yc>AAs8&A?1DavSTSFL(N
zpfdayQegk_afD17geaA`0yW{%%)mwEZY}-B_|GR1MXgl}fAQWz0-~eR_|2LVB%b^{
z)H5`YsqjKdMnYM6QsbpDjxs(yYLgL5?kn^DVHDOu;Xn{W8j<Tn;}*y^-g0y6!PXrb
z2hqk#GcrWi>_p|8RNtHI4M`^y+C~tLj}|&Z`+=-MpnV!q+kKi8L=}XxF~<Z|8{#OO
z;UaNi3^7v)H22&bq(6<EB3qcM!q@QfA{d>GVF-0ZTa6MNIAc6ynE`xsi#5u!hCkrT
z8c#Y~)QOKlf5A*y2kSo42uSZn-`moYVbLiBW)qkx(MJ=72QJn5I~!F0MHGnG4TgbH
z;drn4bn8JBG5%nJ5i5Zj`L$048-9$4h$sgA1%*{c-{}4>>Yh}Y_+43`&(FoE$OC&)
zG&YVwZXbbm@&)kQ(W|S|HtjR?C44=~lkcd=rB0nX0|H25{M#CQZRxWSr_F68McjF_
zCR6K;5ruqTD96WvU?u!FDb64PL65Z1Z<o3fdeZk-H70VLVVSa1D>oakdN@Q%M0o~i
z0Xq2{RN(!hdYArT5;Qr*6Ub(y7q$amZH)VSdZK|GNzqcxTKoK2kc|cKHqmZSXlNOB
z1eWQ|`bcP=ApHbf1xR>9f4Fn&*2DN>z8pF5wh~41^EYC1{&^82-uj|w3xRPVk%C$q
z#%bopV+DWn5$WXoos#=tlDaUtdB)Z@9o?Mog9n${9nb)Q15pBSY6W3}TisYsogDDg
zYMkIpDQaXm1*Oa>8et7kF=dE%IIR2U?D_ptqmT&PW`r_$7iC4s#`M>hh4d!OtO?MD
z;2nhwkEBH?ygcV=Ayl=LN+=`}&+pZKyTq!$MFU-rKja{gY2+loLANPDQG0penzVUy
z9~w#M-ETmE`2<lKMB53#eQDm}7hW+yN+rk0$Vf8W8woN=fRogX8V6t?QM=lY^~FLZ
zB6T(BvJGd4!-k6WzvErD6vkLMqR?WA<Z?NM1A90h^450^Zr}=^fUc1nX~y+W-@bjj
zc`P90v$bMf?K)gi{~E&@pehjPe12JmQfe1v0vIG~2s?VAH-!gn`uHdW+uXG6=CMB{
zg=Pe_inK6@kUM`q8m2Lz5VKo-Ru&a$$(J@~XJ;ZZ{Dse`ma|JZs>Ejx-XZ#6qHqyr
zhjAQ@gH_zVI@UbGY-0VYRrd{&l02wLUPj(!_aRnu^N!rnux9n@rj0{6rT@f#h72g&
z&r#A=-xs;B*}7l+em5gxft5W!%1Mw3s)h}@@E$-Fb0l>dcP4YYSk^E{5{6A`40ECr
zWVn3Z6fXbkx>5etrGHYutLSP}RSFHoH6DRhB032wz2OVt^ODzYFJ{AeCfMW@SF_ZM
zi1=c^Fm#3nfa>vf>hRWyK4$WS5n>hE3sd(V>#k^HFdjpv1A|2-z+gQTbDt-2@}FlF
zj(T`>^*mHc**cSGLU)20LQRB>UvA0YcBc|hf?FNCnC*v~m(AS0ysUrPLJ;J1(;S#@
zueVu!;&B@3Uc~`L5Deex**;3<)%H8;Uo$oA=TBq0w!~-Vi{>9W!{>82XJG&MC>tf6
zv*8NRl}yLZs%nVoC`7c;-qRBp9*>@da`n92evqHi{(R;QRN#tXy~>>t4$~ubto~xq
zf`xMs{tWu}WXX`0PP$+uP}fJ~m!kgWq@Zl5K_O=0G~IlaoWFlAOfc3#93fGRgVgH4
zV}4z;q<W6<SW|RGxzOmzgT_rUX;cDjWd2DRqc)rrLcEd~7+w?ViH7Xi2~W}q7TnJ@
zZvY3CXtIMcIo4kX8ZR2Z7!vP{tgG!(IarMa>akmoi1sO!(eb%22u<Sih72K~o(C=>
zp7z!%3p_N@$J5A!4v|Je1D6&mhd2j7n1JdKq;FSt(l7LT(69T?WAr1@QYJ)9l{VZF
zX!Zh-2|#09bJ7){bmv382tdmSsd&#u*2vfRM=>a=ccR5WmcXAwLl0Uz0vfSlY$j+l
zSXLo9o6TVWmwz<Wgh8%1t_2m6luY>J5r`iDT<*}&P>8{Jcue-D0D9IpYz?EVAp$o@
z4bI`W0p<pDsR%o!9B7_VetW5Zz4{GHhpG@hhLPvHv}Pv)=16QXdPqv>EsTIMAqy1A
zQ$@ZQmX7WGCrL}8z(@F$M-IWty{8E8gn%Pp@rTFPT5Q{v2LtKDu*?%^@q<R!{QdW1
zauraTlVpH|PUbeC>Nx{Kj7W^CjNU+!2Ys|S?5}rfW~ZZ@00Gg<>%Nx&2F}wX%1~pS
z<&^ThhwqP>^(PQXbrBU)WefOQzzyUZ;firVBnWTuL)c^IitY(o+%0Ku+o5dwKopty
z3|)Yp$aEN<hawhc0E?3Y(ozeQ3n?yo^GLv&xB|!ct24!OElc(xLZP7S0aP#%H^~i6
zFh~+u190hz2&2SV&MI2nl|rg;0L>OcvcR8Emke$$Y;S9O1~cHxpn_La=m$(?pFNM8
zklFSVs1m7t_CbAha4~Ne@@##GSyhCmwBUB$KN04CsmT0v>9>O@L(x&22HRy<7X1-L
z{eMf7_F<$G7b7z^{uh|vWXB%2&A(J^Jo6sMGeNq|X)t+j?yvRuJyVFDs3I?CcVygB
zMn;Tuj*r{@M@BkP?wR&1Wf;8PDS7Qp6xPF)U|90%(`Nk2;*HYxPJX?|R50|lYj)lz
z<NfIM0o|-Tsj$1_4Xc=VWEb*u<k1VV&0D|_La#?-Dp(xsx<B&pVVhYt1(lYzaMyhT
z4|N8b9J0SP480C&QBdiqW(6({04ZU1zObO+$7cz)zrktVn9%rTe;vK10?<d~-E0EW
zZkw6Uiq$6K?IDp=hEi@LzL3dzVOzumLL4<trLE3hbolUtl{P<ygFtLBYIamx37fSf
z@31|4_N+Jy<q<wa9Ea`F5{GQXmmQoId?7M_We#RzvURP$h}B}bJboNpI%9mGuy|I;
zrz(rB1LHe2E>1fldiMMxIQ|UO{J47d;4Cy;47N+=3~oVP258Tz-dvb@Pi<<MS490Y
zr}h7<!t9?C?0>s<?f<%;JVhPt6PJy=Iy?JwGQ>x4S)1c+S!1_t2QM|cZ<eYzS=Y6N
zlV?=V=3bZyZ|{n%U2Y9L_m@mIDsjJw=QfD^+u(x`=f&;Ug9<!`SoT`X&O9hgiJ8o;
z6EAdVN-F3Xx3v{3a8l_}8?%b-NvhS?=uty$cIDst;(yZ~{#!r&Z!*MxYpwbJ_3Y38
ztCdXumpJjifAF8$<3IR}?=acSd(F9h`%x5eq|w6`*d5`Kk<qA)41w8UTMrko(xw3i
z0>(o}HKZ|0=KcU#Ckx$~i`WeQOZVhEVEPZD7s<~fKpcOu1~WkVUjU&dy%qW+CyBNZ
zdp{o^pZ>udO|w%azpTk|CS>`G7RDF0$rETF9W<2Rr%>`k=yFC$$sb&*5Qdxx(u(bR
zA*cm9(>;)go`W6f%=q@Z+-JB_zDQ)T8EXCzj5C21lf@4?bLmo;7@Id~Ng+}Bj*W}=
zIm)oe@%!cG+qahvM$Q3*XKvjKxBSuAI-{h}qPP^&zLgdRb3gSJ>bb=k85y;$>vk$G
z(V@)!<1Oh+udZluMZa^LMrtlJpdSbJVvs5dpjrev7;ZFfm|O}sP5<&&%H@wo*t|O(
z*Uu4TuNK`|7$1t|#4<q#<Y|PzUo_nWcsDk0C7alDXhyq<+yG4_nXP~K_U+pAIMVc!
zLFzUK5N1h$r_|q8F76cb`7wSUHC;i<<Ws)f*Y*5i78a@jH}7uKeIjvWYm=4Xu@fh5
zWi6>TSn{^8P@T%m73Y^as1T`eIgPVv(n<A2z@WnV?%~bFS7p;1Z;G$@)OPg1jVykS
z@_3Iywe^!4ISilw;;=QjYGE+~1H7YMU0p;i2$*91kB5*@5pdD4F*Ozx{I>yHGCl7E
z;g>ziW?=YFQBD|CKbKz=X9jHu84_V_H9|@7^yv|0@h@TwjP2IGYjp?K^{u&?7{*t9
zlH1o$bk|afLwEAGUU&O$q2yv|kAPyO9N&P%Gm)KV{4HKL^Y@x>X=1+mMuT~Er}7EA
zUDDRp)<yn{7&?#EcX_@k?HMsdN!gHY{tQZ&$3QDT4OpP0T5cN)8WM7fq>aL+G3e(F
zQ93r{QS$)nsW-fvtBJ16+nyht{f$hYq=51z$A_$1O*pZW%s%1AkRcF=?-VY$+F%Lq
zVYBJm9v+HnYHIgO{yyD)Z&%d!BN?om>s0(VUgU98-0vK4C3Pu}S+yf-?`%!82hs}r
zySJ}>tr2-bm1X2muzwS@IfSgE5V`7W6`GiCtgKNrxAQU{0YS~3>LXv({eQf(jk=V#
z)XHAgE~@Ix5}T}|;^M>zg9E7BR7zI&W!^iGx2L#g96mC>R?8MLm|n@>VJmZW4Ey&K
z(U#!sa~o-X_<9+<4X%$R#2)h{)t?->Iln}S%{QF>m~Y9g32y51DjKg*-bGbjIm*jj
z;nx#yoKW3)P?zUmZzFqO>$awzm1&8|)4q!sif?_@-%~Qr;p!M{_-oN2ON)h81GV7v
zi+$fQM+y4MG50RfjIWEvo9{vgqJUr_1l8W^9k6R?jsGR4YFqHjrv;a2i!XoCs_fBk
zrsSA|hdaV)11TPnzmm~z35!*FdV1td5%ijq0;F6Cgq3vc)SvsO@p!u}4i<jTgcNNU
zG}v>D+#Qyjgkm%bTdrt>tKYc0yGxxuy=-C}r93%`Pj>B|`>zkAZ&{OVtr@Q@A|f)L
zYX;!~A?_;Cgpi38GOzLQ;lmeTz@Ywn0wVi69BuCvwu!JGpJP<6T+6~wfF}6^O3ynZ
zV7Pk#9BHCnR=zF$dk&p};kd=&@;}%iL;F7<-hVJQ{|j3F@BH^oeKqxu7T}*J``?e{
zzs#eM|H9AUdiTyw_L{T)sn-*?uyk)v7Gsl3zk4Y7)*+<&5n-#1YR1xCkNKX+`xncq
z7JJ>kZJ~3$cVO#g{^3k+=|(Nh5^b}7q&3}0;S(-5Zrr%HtLJo)zoO-nCNJ8b6uMYe
zcdNPL`niCeE;q%|EwmP}a%`TCwl)dY?cuWFgKOHt6VC~>hx1i;iTJh$T}iw9{rlw}
zGY7{dZddAVz8&|I*DRq02eT&X&U-a}$Te+QNm0>~`ox{Lj48*Ni|O1^uM%(cyM*yg
z@qaO9ruha2M&G`@#4e|0cR-ji(&3FPS<j%LpexMRM3-L!XQ;ko048=9`6wZYN|Br*
z_xb*nD7aXq2D6YPeFH?7-^Ay@{Hz+K1^<m(3g4HwrS3z#&$2_bz%JgJhNN`uBF2_I
zG1=$$@kuuxR{eX~$SO76tp;+z{z2mMa>2LOH!6s7OJDX4Pdw|NxLsJdZsH(vuE3wC
zr?9?n_d=Ja7qtKABmcYR{!iB2{~g!=<jMb<E#A}D8VqU0MMWP_sXoWpHjlWt2Ep90
z1?xFfSY$Bi3;!Hd@3NGEu|@k6q<3Ti+D_a`dpZcwUtBhfMo+;-lZGDW?}F8H(t-59
zcOO5xip-AzKUOd`jhh(mdImbs2&$}Kq#VS{hTzH{iP>FEzdI+R`aB<D>NaMQo(2VN
zCfX*<{3-(tMFWLeIhr~%6ne+QqZ(C`6*P8NnAiQTrVat$Pg|0$84oisP6{^5D!-b<
zf;<dPx^Wl)f$U5%o-4cf#W-y5Zb6#~Vb6UI6xOc>_F~6$KL@21cJp?;bM2AK(lskp
zd0nxci$XLW5Ycu0eE5qegQ7<M^I&<1IB$Gi&ri)?&7GW}v|8$aqN4q4b@uTqXjdtn
zv~*bvGjmhmgsdPdEBoQtOOEMw0qRFkQmn0HInO@FpFdXn*48~Fbv~3(tN!V?IC!>g
z>*sP!ys}&APrgu8{P||7mD*L%VJS0l5XZ1c(;~C0qt-T<FpfEE`FDqB``#24cS{Uf
z(`UVE%^IC)6YrH@{+=u7jZ_npl8&bGF&tnQYq@ar)T+-I?;<mu&!Ltx1a$2cDguWE
zSf;R1Dlt#+`1|+ok4rYe0N76YH=FO`s;jRb;686D!x@!fmE#vgZTGH>_rV+OG21ID
z_y8QP$}`t#Xs&#sZ#nl?Dg9{lT{(K4t2^0ow^jFuz0<_ebf3buguY`>WEc2-d+RMx
z5s(wx-qC?mCtS*Tgo05WmBQC3oDWBGUU&H9{Vu9%@Y+!H=i5ganr<yLTlGNw{%C(I
z`|w_v_xStTR0gyTFQd77&EF4Y&iS{i{msaOIdKe(n#W`H6^iki8c@{?+UMXZEU_^^
zgGRY9b07bX9d4q#78Bm`-n}h6X5CLnI?FhwD~$tjcUog~r54)pGlbyd*}L~F1RhKl
z?q?Km>@aTP<7?xl?!18G^9tA#)LM$8pWm@nWd*8y6=q&E8B&!kTrg(PS6j`%-80Cp
z&BV@SZjNO*kDonQvH8FjN+yhiE7UXQ4(<5|r+%zIj8Q+Ub&w7^d4;M%yvh*;v2JJV
z`9BByG!FKwwt?pm@c$ZsTbNCSfRk7v(891Jp)cEi4kdYY(+$t48M?|rTQTAv0NGe1
zkN}F^P&<aw)avW&NlT6UwyA?QcbU2SZHv@aor_gFM<@U?;R%A;pe5T*qF?Lkd(Mq8
z#C;$&*O+|92Tfy9OEr!neo%z1z>3mnxc@wdnzp+oCkvfQL{U-E73NjHKl`giE(^^B
zYo5y=1-)O1si)T(b24HEX6}xErh)%!#^`@3e5CCV;DnoQx+H;G0i>c^jPBQJ@Ee7q
z0E>-vtgNgZtDl4cAb8r_YiuXRreW9a_l19j6t}RjGU1guh(ZDK&R@qA%_R{%aYPY(
z4W=Rwq!m*YF^c!8m9t=yqy%DmKmnN|kAL|3{5IceD8UhP4^t|!*)j&y=Z%bG+)>0w
z@Kb^gEewH*cmMwV<aQFzGyHg37;jwqkyWcaX7}I@LFgBLg?Rx3ljlN#^JLLC0ZBLv
zAbugPgb$=|(6Ej>5KGV^fa*YGsZ2gO+35=Lw+}}YPTK@VPJVquf(#PlCEDO<9VFd?
z_?>n{pGoAJG@_3oDircT$fVdK5sL^8VlPsOq{c!X5N7$pVY6|XHXJ%TbrNbGMsjV@
z*;j!R<fEwN*zcn_FkBJj|2PLKxcsM~VDGw~ky}}RAG&x+S67vK^5n^qwt*W7LQ57o
z1Q)!0dn18k$s%ACq*k7dlx-Hkd&1qrI-<g72&uaRmbClj#G+>2xo6KF@_i6gdGTk6
zvM>Dhk^;Z<W(eDKWm$hkKVAlI$$vX5=Jj)_k>P&<r<M=Taee#t7#Rzs4Fg^2##&Gw
z`Z@m%VD@Stk%$K*5`}Sx@YI+K{p&gpK1qaCB!S*G9=P(DxRLd?`&x@s1Q4w>2r3x6
zc@p6%;w2gU#0m3@!<M;U@NHB|c*67(wGN~BoCtUzzV6(IZwol@1rQrVV8~<w5v2Jh
zlQvseShSDWkH93atC%;QZ**e1<erbucAoyIauBeJ(05noIN0ESmUbSLZOnseiM;QP
z5{~H@=1_UMM}()_jIG!y^T3Ek^++#VQ2+<~alqnS9R)!Zw5cGy_Y-$e`*uI&atEr8
z7WK%HBMAZGI6>3U5!pl24nY$0i*0U>(@z>&zQT>E`j`7C84Ik2{%<IA<_hC+Eo7{k
zEE_XQ=R^Vgb3!W?Iyhmer9BLd?j*OmC5pRkD-Q@@F-v_0AvlrN0;{ydfZF^vM*|b8
zLE$(M8(bYFO!id+lB)pbSpJTzt?GFZ9e34ZK;iRffVGE>n!dTDgM?R?`#|U4ZA;_%
zeDghf&2yh)<4rsc%Mq$X6Mg*18BH(sGtPd7t~hvxu>7o@R}Wq0zDq^-D$naaL@Q`w
z;$Xnf`^n+mxa{*77?U7}BG#L@`=4!S7h{K;;8M&~Nz;fSU(W~`20^+(B!5UuVxfm2
z2CovWyFBSDYxY=Ny}CId36>eqrslosZ<JedRP`1jPxLaPVTOVgyQ7x$ylpU;%+ZX8
z+vgDlt{umx%Z_HqF-PX!%?v@kxoFCoz}lU!Kem3V(^{S1Fg~vEX~fOY;coF;I~|NX
zFrRi6_H{CFo~`H~dD=Jj#JJ+g?V<Lq1=5woRlvvRS#`A{MZw-aGtBkR(*$(A{%3RL
zodLaRe2y#sgXb4pUe~|AZ`5%sw?N@8?mVe|`B|XKA<H{W1b?V?i1|_X7dLkPN<Z%O
zD%ht#h4L&Kb^%}brBuj4BDrm3HbB#!g8xDuC@5fRKZG!hMJho%RfA;_LckS&tV?}>
zk;Vwj=1CPFbSLZvHt>q{OcT2S{JGj$$SLAIJUs3Nd?8v<=)d(ULWlt)(MLQI)VYO0
z)o>`#x^OI4c8Qvolh1+-`jYj>ht?b3?nBTJ7nhb+<KvCMmHp-cty65?Cm|)d&q1X4
zxRQj&N6Zd`QOYS=T>4igla_2%Xk&QJK0E%Ye-QTfjZ(dsbC3bRPDZCuAjMwY(1`_0
z4#aBy1x7qU4(y=V!?h=tMO{rW7uu{^i9L`UntXbBjfEQWciXkENriS<C?t5xF*2J4
zU58}r@damA;5d$97{#BX8IJnDhD~u-am)g26(iNju5#V0s^JNc2Mt>iBN?bwtb?pS
zAAw!E9&d(2y>5U3Hp7xn;P;aN-T-8+N_M4vzqQG9N&0eyox%1@BYWspy@3CTeQf19
zWsPvy;cCK~=LhA7CUOaJw^9t3^1J7sPt79MK8OT7pujOPk|sMNl|)~Bw%Y1eRzG*K
zP3ep#M!Q)>Ex9l0a(7P%49{?#KOGbg{S=w5XliO&x~;q)v4n^z;rDaI-!i3t2P&@R
z^@&moCN<a`f&vC3wk4&xxqfvts5xs;FpeN7U3@nIbLLBKCxowS<7}mq@1o-|Hrq6=
z;A?ej@<9m8%5N2?m+rCsaRJ)=XNt8K%_?Tj?taqCbkcF&ou}2SvjMvhWqtdAEtwL+
z0E7_|CuD1fukCOi9oHd3ZioxHF=RnR={>cvRtR1_2%UbuHjgtdy<bg+><tVIi1`zt
zt`6)7@D+Z&8-E_qcNOR&g8h&YS9nNfV*SE`6>JjI!)M9F4spe@s1RrVF++w#ApF2K
zLIa3nL8zsUXdbpm?uDE))N#-q);3{yuq02A1Ynvwu4gpKloj)IXX%t9>H#t!PmGa_
zI7V4*nx269=*;THZ=le?m3;o`ET4Mt0a^qYPZRf8DGEhiP3;jeuE61s!XwV?O)Qz8
z>+|qo6jaeWF$gycBO=$osxSqHo>dm}9UpbP#kt<w%!EORCgKdKmM-ZthwJiYh*YNv
zyb31;F|CFuWS-L>pfaeiCMQ23gy{DWx=1!4Bg&f8X)7{GkKlw;T!$qWYb<5oVzIb1
zz6Ti=MnRK&`QtW>Lv>-~2&7IB$&M(7h<^&EwS(brvJHt1a2nBFr8Qdz#$mgYOFI|-
z3)D}FXothH<t2zEAgO<%Fz<wAB1x;6$&5DsKyQ)<p@|TEN%l-NZ8qe*i!ci94T*Zl
z80z$Q9&8gZW=@9m3YUq*k%@sqcE6Ai)3R0m_^~8<qb?+N8^p~Ro6}E2#aM?;;!)7p
zO3|>_ECy1v7wBq<3w2!Xgds+uXV2>CMeyhT)J6o5A#P=O3D9<unWSJ5ixV3*Y*0FA
zijPF5xQUt>@q&;PiHV7pC=QAB8?MHv!5FlNY2ABv?UKQM*-YY`YL}y_fhEMGV!#86
z#hZrt#u3^uMo@Ug#hW8z7wU|T5I-?!qf;uJZBW-j>?C@DDu<mwcwEHs15as@MyjPL
zH6A0I#IFS=9)_6Zt34G8PpG1v5jBh3=wKS|%!|m)vg~?FY=tpalIrgOjc_!abLUN$
z!SC(~Ce2i4h0{E_xVQ{~MxgFc+9cE?Je^wC0|QdeVPGF>PE;Q*8Q=T&<)1rWf(JKo
z2qm6&6uZTp9iftYO|DTTiJve4SNJ1H(FQ#w@w958d*x{Nyc4Yueh2+LnKmOMR)oOD
z`&WbR?Oo?ssz4;VIC9P^Cg8&xgNTWw!`Dhr8LI&v;#1d?QjGZ@HJmOUJV1q(_xA>H
z`PK7mFd0Q+H)t$5vOU!o6elia4-PgaU|b>UHZE(ogW&P8;0#gST3e?Q@(7i@A*5V~
zy_Z7z8v#QZBNV#?XaF$?N<0kbZ_!lwJI{d*|9<@2XdIlwZCB0P95LL-8lc}fVjnvy
zh@uE@Fj{<<R<2m9&MlNO2B=|(rx!*NMG+I;w+(#9v}PSf0Xtbb>4l4b`A3jpQW&@;
ztUHJbfQ+mfVK$tMK;!=s1v?I36y6Iy+f)Gc>sv+;<5~z`RS+{02M3%I(sLquDS(l~
zU~8{MuMIXx8bb0WAt=GiD~-jz-nbR9bwS!w$g#s{5;TzeU@u`EQ;2UvMns4j4iTIj
zJB)drP-qEyxp*a8H6;Bh$T6X<&2PuaV??t-x&=JO81x`y;s#OY9MLHwm`+ahg>UGL
zJ{PT*QUX~cyx(4TKf_TsrFUA-2s#7Xx^uh8LCFFmZ$zhuDE1xdKR;jL#Bs=k$GVhH
zvr0mQS8M(gH9O+?+I4<5Qmgq2VA`dMmowx(1P_#i+C0>xCe4}WNI-?=$q!)}^@gg+
zsba~3G*I{;A<;}{#mrRllsAS7UeI=gT7h^IfgYAYxAqYt$~)`Y&MGLx<XU15P02yv
zvRBUq^OhzBNtjb$__lGKpyr_G&~f(x1<l4*l-pKb%9xQPlR5ZNI~6^&NSvM;l*V=E
zYGo0iAeZLu$CO%xwj(rl)h{m|C59__9g9qp+~vs!#k*8Q)+GHV8BZfJe{v^CO@vZr
zB&)Hfsk$A_mOhI07?n<ag|3iEW;geN(Nyc)b^G+cR3vwjiV3lmT$H}tNz>^L;XL!H
zY%1%4V0Ni@2N&C@IdgY^;wd;-kFJr-07A&!Y@Yey%(qKN?jN0Z<Oy;lCOq%e_0%jE
zZUP{6nS@<j^J;kTgrTx>+@UW#R2YLS2%u)<;TSQqBQu&L2%;|fj)`x11ilexXnkou
zK1B480y_%*3^Zh92o)m$j+(7-zav==y>1UWN(ER%#f*_91)yAcMMPAI3U^mplXecS
z44M2SC(8cY{gaNgEIlXLw7s}M*ivwm-D&uKMGp#s52(#a<60Jcu&z^>X~a=rQCY<p
zaoNJ(V~oaRv;%1n(I-}2DhS?{JBaHFX8-zz?9Gua!Q21E*9lvV+8|?DT@O#sa|j<P
zdSx47%*pxvjU;g$!VPK$uqhGGJUkh=zyR$gPOpkZbu&`q;WgQdOiqG0zMi_3jE;F>
zsJd&Yh2(ko36fDJXi?4iA~~F(r$8PteYKxxy65TcSzN82YWxv(Lp2;HF?l8VdGB}`
ztAT8_N1djF3`r8=XjXeZ=U*=_D@&2(o(kAHo*F>pFNp9&6-vx90qAv;q23YJNwI6h
z1Uq((9Hjv!5JV>Zv+;pX0h~~Ka$r7O-<brsHlagxSRjB5w8!R=*)x}lO|n)Qu(c%V
zA2@Lx&VJ0`BN4CZ#ye}0;%Dh*)FHcRnAFs^q7(Rl)@}ZZOBgUF>X!9}RBunu6wRcx
z1(SQGI}e?flT${Lk$`O6rQNhKwXDH<a^iZK!1~@TO|v7oT&mQ-+}83uc0tl3VLwuH
zXVU1J%{yc#(=sl9(fZ3_%DkI5DKXJe){D~s;T6;6PPvVf)Y&oWydFvDB|gyUm;?Hd
zt5)o=<qKv#ubU=)J2=*XU=W+$1Xs4u!4TZH&tu%&1+xgM^QNPw4-!c$ip=>9%_x@k
z!Hv!3fEJBKV3C<kIBRFwKq(x?wo#Pkmn26*VBC>K-Ze4IoTu9YWT(wpKNlLOXHdAS
zt*qJW6mIwOm%<l1s6~!u7{(`p^uEj<evEND9WGhh`>7xECN#&!=wJgKJ<+9W+Q2kI
zkS|Q!e#pQE!ZNE)cE*;rJjsJKI>;>mDj#DyNHv}e&my2&#vGk>ewFNCVSbDTj3&~h
zH7EJ&)Jb-yOPmqY!Qu|Jk#dQJc9AS*X0t@3RN(*%SIoBz)^iO_lErBfp(FM=weMOl
z2$HR7sjHht514td2yS~kGo>T#EI5Nb3L|#1uHQCy(i6@W->t&G{%T>aolOnZdVr_m
zJyd|E9&R&V?y#8o`)H|`^p5~G8lR%#R!GdQ)b*eubHrq+cw8y`i9&UeM>4-ukR*!k
zSDPC<M7HG@zeM{-W}yh`fn0LO(V5GyB_mIL-46YWoz)>S4y1T7&6^%|>HnlNg=L#z
zmLeU0v;dARNp*JS<@xlImAb44-`Va!|8WLT{;SKqXV{`|ZgZP|<H#|CUA7p1p<QA<
z<RHe@!!!(SG*gNPSu~LYqyxhP(Zm7)6Vi3;Gd~tn>?|G<%L4@4U}DdLe`kcIUg>2B
zD3e2HLQsezE&;XMM?_p?;2nN$GuS*s%1MImamfaKA7VuvN?LcAevrAg3hr=}#|C6v
z2nL^~*#iYfX7{r@{<tPF@qIgSvvJ=M@&S!nB>8Qy)gt9CvfG-`#(SFZ9&NU)k|9W(
z$#g2FgfRJw*d_XK*alEDqRw%|eWvs~>x=1e*er9xYjYIlWQaKzsR76U0BJ|jtdgQs
zPGdvIx8_-w(gPq9pbt{SxYuLl154j<+{b{=V~iiKb2!LkuTqb$mJHaEp#jKgCMu{i
zIw|iE8pxde;XDk)oJZMN)<7k!5gDq(BwFH$c0lcyF(X2{4&rII$u#{iMC4?G`B&0!
z*RUwtr<Qohowx;4661!I;x(J~Co#UelZ+F)%88Ta2YpxVAU@3&p8izMAyyr@$4byS
zF=V{4SyZo-g&26P6#L;)VMC<!m<g_XQRmQ?agr1za)Ec<-3P|YAw(w@sbu5fmM`o;
z?%>^4Bibc6FALw0q?Yyk=bz(Q6Wc6_)`##$l*<y%8^M>h`gok#075U46vMJtiABWW
zxafJ>hF~50lg2wDA8Z=>(H(G7NW<<lYBTxH`*mM`hfOvbB~0k9CMi&%g%Vqj$kOrs
zkx^Viu37(f(wE_-DWk5b!gP^|jPBRGnL;*)(|`*;LP<1?az&a(?8Y#L^&Pwcjbj>1
zxCd3KkUDYlJnrwWi4Leq1wPgJ$@`MNzI5@X*ba6xGDs4A9!jVe6%*T`u^cr^(&B=q
z2__`T4Q1$R7ku^3iBD!%)WmZqW~)|MCwLhSy%IfUFj7s)=@_$vzmwxc0L=8e(Z~>w
zlb8`Xtf_q5YS|s#QxmNp8;yp>9!5#At)C<+EBMk#MQ;uWOBFgfUjW5APPr)A2n0AH
zZV{M5^+=1?qLF$IGgdBbuH;*yr6YbXT&EvA2C%7xzw-!zwrxv!zzL54DarD{wheo`
z&B+HV+{?99?H;NvL#%xvGh!9b{ijA9EWX;n_(LtBjmXdl{e_5%xpM{V1q>xNZaaZ2
zkW~)1mTh>7d16$wt%Ca-AL<ZHM2>t=Vno~%Mpg-sT{Sf}r-ManB0V2|OB62%)Tuxp
zf1u#!@bK{Cgi1|sbhW^6Htv(c?IupQd^~0^T&7Ip_f5OxG`HHec@n3Vn3>jLR@poU
z?lwo%3L|6;Z|Bb9?f_J)5oq^R&9Vn>^`*D1$N|=(6ne>f2n8z;_}X9@gwTl?d@g~{
zwxu3QzLzw1yWhE^fUeUi!t#gr0Br3@2OVR(ABWv2&p!->6^a;e3#jeh+ka@okfY{v
z7VNh@LJW%Sn}>O3%r$9fPb0}=L(G~ds7WIcY2CpBZ$wr!pa34`XTB`Fp8qvFr?nS0
zw!GTemi*<Dg|-es!i54{9U||>aRkT=Xsv!@ijneDmVRoGJj@FAtZRd9K@28Nw15$%
zuBM`tvl5dW^p+o;A<Hi^QJWC2%up7iuQG`F0NGHbW})Cb@w{TX`NaW|rCC!AL#P9=
zGQi8Rj!t#91<|11&pfudX^9nUqfYjkHEl*UFT$nyGv0RQR31>`HC?f2iJt=HM?`oy
zF{r$QnqdV4hw9=&3`FuNE^b18#LYegk`G)O8My(sN$`6DgdhkLxD4IPK0GmKXo)<K
zz$RX>e7Eou-@g6LMk^t>r@%qw7^rqq++l%i1bQD{SWAPC_N?HRB<6Kh7`5W%>3=+k
zUUCBUpAgI#V7qAmMdi(wak*eoUC)RIFulSClc#81@Y}!|yg-kJDIe;gBR!6&M19D4
zL;z9CGr?1d_31|rem~&QaqLZX%BpU9E<Lr;ve~MR2by7g7JM7>d;6Wo4iZCch0m8)
zQ^+y|Oob&wn;Ob{{nmyYbDRzT@823dI=Hzh=O56E<lwDDJQDHzj)74n$D70jqnsIK
zGdk&9$v|%Hj86P^iyCvZkA@A&QTT~Oy!>la7jFakd)4mBSG$$>m93k_$*j<yEbTXR
z9U;T`NmMDj_Zk+PrqJ+W&PTC_gF+)gAKO3FcPtr=CI&wVfJws<?-u&fvGX!vMWzFW
zHIdHhI^8=!%_8-F$C*$<i-<E+i`qGMjXh61E7p;`Q4%Nd9Ip~|gg+Vo#$+uZhz5Nj
z3aT9fiHBO7IeTLf82m<RIzP8zZEn(N27r~)21%wyCA@Fl;I-(n_g~1{ySQg&GHX1R
zl(o`3CuaEHtDSJ3yr1#&BOD4a+@uI)IdN?u(AUu8+}ANwkP?qDkVj@q)lD<rsm%gP
za2=h7CEMegRI=Cy74G7xh>rawVB%E(ER4Dfqi}e3YXz&}uEt#FuBMq?!JBf#;qrqy
ztP-^um<)VhLj@1>rAisD*&o$rvGn~JqD~h)e9xf&ajG>tv7mu!HapXf0aIn%uxea4
zqiJ}Y_*IO;;J?tzDdi(hxXgpyhmo|Ef{15(?8u`q&kY%R-X$o>KbkoYZHhJ}gf3>C
z$-Hpw1SUl{>ts7kL%&=Ld<n2nzDwKuz4qOH%9`>3q%hWfyctU+$V~uV&f4b4t&?rA
z7u_{|I$We+P%=bt>g1*dG_w>`7-vBy6X`sgxV;LU!j#+kFw26>0`%A<m<{0^5GWWf
zW12WNfa;V%t;a+i4AM>t78%U~>s^hl0K}^UCBJ`R+eZNIH8^ZWs(`1)40H%s3QR@L
zgQOs$FTnzWkB`(|v$X|x<c+m*RoR?M?EYAK97dK^j%kIxZV}Bk_*js;JjYf0YH`r;
zWn1)_cVz3FdgET~5Hq^;2{UY9iS`mJY*HG4JTVxd$eOnp7ouH?8F!<bx8#~m4yZs1
zR})$b&#H}_2M_~B=#9z%CnC=6=(y4H)^@T8r*Z{ZhowF%TqS0kqlvM|H)^u5gJ)Vt
zD)V7@cd61_hqbuMy^$UiG^zVBI|+~3)2MO=-3KE2ae4ahu(ri#^@;NGGmqNt&O2ZR
zfjkB0B0X9sn<EtEvuj}+qrI06>IdS(TWj6}aM%h(j`q8sq`BOz@0-HX4`7`@E+ino
z*^FUMwhvxGU$e#eC5f@e&o6F%_wIdOqa(AYsZ_k*vJcLhliI<d8=i=b*+kR6@p-&X
z?z{kWwB5q_dwhE5=M&F+KH>SY{V>p;LhC_K5)ZeEQEJ-E^!Lti+UI>~Tc*FPr2e23
zV@Hb?;rZ<C?O*M3lr1e{$O608Q#_?%)7<`P;K9dZqw0#M6NyU{QEb1mA?irpL!;`c
z9x%@Np4w<y1q5{%)S*h+TTsIVo!`>7kPT2AWG~X;&gVQ!`?7~*3+t$3S*;X>4=&oK
zfqS?A?J<<m+`(ZCNM>F?f~cA@V{nV`P|u^t<IDjY#Lv-oJbgqjsvsa&`pPCGN7~?t
zz4a&GHH=HXd|iD%D)e<PuY%^b@ZUo00MncirO<C=B_!#j3P%DzE`g(6@~E7TBUtjB
zFbn!SYB1V-iALSL3&ZI9jNaZ}RXYpAfJ@VKYT!eTo`sdB063NEdwbm@?(%1zqM3;|
zn-{W5Y`;-zW@xrJ-_+9y_F1ewiI1Ji^OjlbP1ELg986uBnir@Zd?z!H*{^DX-l+It
zf){rP4#32Qaspm*y+a%J&X0ZWl&6|UVS!erHNpAxB(_MLLtn6<RzReX-9b`U;ChBk
zX<j_v<>?NNl$TSJl%ZKv+9GkSyUpv_I<d+w)~A|m87i=gOgHuh;TE{xq*%BBb>hJb
zs-tc5-)F>?*_=M|MzY*(XMuD$dw(zeebg+O_f2grz1jEHATMdGu47xE<X*{_PI&^Q
z`#YHf=}Z>A`m`6Lx(`M7J4M)4v<;{{<HOz;fv)o&V`vYDm(4IWM0_o<o9s7jP(u6o
zAy+~XS4v#3;o8?<IG%KI)&Y)o_D;H$sdt_pzuu(TCU*9C$(aFf+F-=7L^Ge+q?I%9
zuT-Z7vX~WrX4kp9x{coW5gRk2jXSgtBZcXBm_h&IcWOm#uJrbT#*!`QEls@#2zrk8
z;d;UN*Pym_`wAk%!qys0>+ULO)G84uXRe-=DyKL_vS>ZnGrqJPZKI!%{?>JI;*0NY
zf_}>^VX?@~XI^x{mRZ+W6nb|BPZ+R9uM<XFC{@(?8J#Zy^Er4vyWlH7$MGSwpFh9G
zi@d3JfpJi_N2N>s)=ttntUE3=&a5@Gc>1ulNesYf<yY)a0E|*GFbxcxP(ZN2gy^ZK
zZk_V^`~CZW?k%ylKXpzOYZa{u>&o1NXE&c!Xelbg-6E?JB+`;7;YrQ#IU=s!!?=;E
zD|g;6=bUs64g!$riRp!Er~|*B4S|4%_!41dsO&5HtK+&fR=oxnn(;~J-o@vwN25K#
z1b9u}nKg>=BK{RX=GPl)3HDm7BFG})w&4qOk~pZt!(yOb!s6+r6U-=Q3L7M`na}Ux
zLj$J*O-a3Az!fBGICi{S$_0$IbAK2p-cUOs42n9LZrG>nT*eravm9nA6UIaMu)E%;
zg`muA+xw!(gKz)#oYJd73lCmP>bKC#bHFl6OGv8bH%h(CE!BEC-RzOe(><f9NGLBz
zp020PPSv&|R1@6@QYI;~HeES$*}4A$T?96m8!7wY!)|0MpX9t(`yEx*ybVH^+M)yu
zeI1CS0>-8d$`bOVJ;|}E;PmAFp+ys8e)+ZR*h+{En?m1@pPilI<mD~V*USeogqNaO
zJ6p#6<bsnGBJ}3A><t;p=xPfQg$EHbW1LLk{$AT}G%J3Z_ByJS_(<t^q>b6hTAAcp
z5UC_Gw%6pXvmULG5I}&%`a^Y6V*!`yw^>btcXpEl9#u{L3YvN5_$iJ85eMmGFkv9f
z3@&&7fz>C94+;*q_R|G((-Z1nvQO3r=#I8ypex_LHJ9&fAvs5=+MTqgzyLWApAWE6
zL+%5R22>Hd0@NZdU2m8~O?eE1gz3%*Qc79E2^MG+4tvKRS=UK#JnO5Y{f1he4}kzN
zo+J<1>1(!uFqAeia>0#_PtSS;gke%fZMJ`QTGA+xNgDw}>t&&x19f{lds}$+xCIGO
zaAo+80zYlSC`nHL(j}GndaYJKz9VU~Lw|X^e)|)Lq30VFCB?kyXFjjr+6fo_L|W}e
zAu!RSHM>!$yt6JuhFzQX2!L`bYMpc6S@1$kn$IaA2D^*|q_9vSS1DvM6FBQ^p8MFM
z|3a^M28ZAl-_Dtv33C#XdGU?xTx4_P;%sWoIpy*5mzl!PeB)!P3UPh6d{#f^0FRNC
zvG$bfqNR=z9SMcDvv)_r{d{<2(JF-B+hL1Mt#L3~AoifOn;%)%Y1jmlni!5>MgZjJ
z4PC6SKRMg*FnIr`8qUZ%8P|6bg#nUP=ATynMaOVrO@z!QBh3x7>IF=1__B7dT)i~%
z!qEG6o8x9^KR{G@S**KdpVe6w$oyl=B5FQc5q{<Z!qpMt5b}{&7j4--G<#mF11dPU
zdskXsRUn1=%N?<eJ?{;6GIcebSGyIpeNE8Pr1(>n=&>D7h(X?S0yK5qab}G$germW
zqYo$E1GgQOOR5OGFF!h}U27n*#yBge_RH`rX{F^4%j)_B_3(Up)6X=tTVh8(o9~70
zJ?O*nA^g^gQF;Ybv^wGsOirTIcJ1rGsoUK=n{RpZ;zS^sN`%WD*^QthCitE?a<o?~
ze!M1_((y?b!&W$0=z^{|a(m1U2CtBkaoA07EG=tqRThnHC0k|N-fLZ)WOm3Xn_-WM
z-#_%}ue(FgY6_MIrVhiDQYF+%w5SY!2^X9^cI-f|DYYyru57Zy#<D>e^bl}P<<AnV
zz0;L<hHi?42u>zXe^q!{@$)VyUbGOgZ`~v#r(32%Fn~&|JzbeK^Cu6z86L=QK<z!6
zC&P-}!+W<BU}Yc?D-k2?3hofdrYLr4K@&S{mx`Ju6pl}IH&72U8IH~Tx)n*-@PH+*
zLtF)|SKcho?0ymLTI(}=J-yfDBTvC(d{wRtJcyRre!8a`cX5J$(n!zn=7rWIYAyAk
zYs#!E+n_3q#f-W|^P&&KP4^*FuK`5WTA0~@zsa9C7MZ%C_rNX{kFwIzWK%{efB?tZ
z{sI;O<i4e%V=D@>^Wb#i;>I{pBJkIZ!KS?3OcqUj!_F^fwyr&LKZ*Oa!_MCECwa7>
z>(b$83sK)^ez~<NS^FIm_@|o8lESyPk#5C)O0p3Lrg!}<wN>uNqs8iBk&y>i<O?5u
z*E7-$wDK%2)e((_Cp#EK@_bm=SYB^7O~V4Ho}IBAq-TIRBzNTs^)qh0dL7@2Bvxgu
zDyFx<yN>8h(OY@42p&OBRR%>EyJi;Pb?B<~Go?W<5Cf{wf(^S#P94T9-y`X`bmhU`
z{L=FxGBPsD?bTsAb5!98TiDqTl$#g3i?THzIK49vIe~(@1*$+7x&sO#zx(%}ka<<&
zG#h3y89BaO<{_Q_E~aXD>rA8WiQo-H;seu0A^q~rPN-ogFnAk*<gWf~0M%m{J{{85
z`;9!1{yz5bSVoph+y8XW2_bDEal0+fdZ87sDIRKX51>0%T`oNP{V;ZFJO^w{{A8f4
zVYF}0eC*62B2sV9GzuXQn%wJ1MU;poJhwBJ*BVd-JkWuvy|06oM<H*TZg#hNri`9X
z3{XK)ud=js-|*yHYY2S=ZhvOjOW;7pM?-ACG8T?tGf+e4HFp4CWZ^P2lX^ArIWi3a
zt*Bu@5XzDY9|UrDx?@#t*^&jbxs}vuYuW9)rY8f?yaM5_oFL@35PXdQF{@1m?8wV)
z;3*GWv&G@aJGRcULC71{qGp01hq#)cjlcS2GV675eE*&uLb8)5Ub0ZDWP}$v?k8C?
zxHnHLa5^{ntK&s4{nJhhrB`$m_6CfLA1I~f3FmbZ{ORcfy#wzE8B1)y$bJ{38fHt?
zeC=%al^yfz;SWwQ1>ht#op!5c8ZUK!oV`)Q)_Hv-Wt6@<p`fL27A`<ts6vXy(w3YH
zN;8v__i~>O(CJi^Bdr+Jk;7y*7-uQ1`+dv$VD0Q#QF`OIWGDOv4x>jAuix6v(r<ft
z)dE==f$x~g+4I>@deknu=)it)bOfWCU30Rl3ZZk>H{A*Dg&HP=$QF=wrO6=cl~$hE
z1MGb)X5~DjDoajIc5KT&>o95-2*k05*neXA2pk`?U$F=T<HDO8g;r$5y!(l>-UIZL
z(12?}Xrlq`0TYUKC(s>u>F)GoA<Iqgy>%ICj|enH@r>!~Pme@992t+7Kiy4_dfDk_
z{p5hnu3bbzLUvyv-iS{RR~`2nIAph~Qp6Su5OvA|INopkcxhH&^3aO?{i+7LS0-M0
zbiHb~MU6;v?LCxM%?dLBE75RUlc;4CXn65q#%tHIH(g4YKMi#neaN5reOuvzmuF1V
zL^fbErCT+e9?AQ7^i88!a?3`b`ZNySs4VCoFp}Sco~m^0PSje*<XQxB?P-&aKz~>7
z^Gn`YKXA)vS1!1~c$^yH)w?~x^}U(q$u~`U-}9Vqi4T~J%At_j%+T0)S2c26V=Ar0
z<E1TR6^_U=#*seX-Pr7l25OJ)&3r$TL;m*;J0sKlggPI)Q=wmG1N{|2RC2a!)Cinb
zPpFXGYzUa*+=uC$-Mu5`W&7&ZMoo8ibf`_%JP_2084n76FgDeg1in@55>Y&7I!}Jz
z0Pd6Q@g#|I)~vrz$0&E^fJ<H;voeq0Mw7o+CNQxsUzfRfb(Na_`jpZKZl0IZxR0Ju
zzhxq*dZ{5Rk><IfjP3T_`>N}eR^FB6-J2k}o=e@*@1sG2p{jUT+Vu3tpsFFK%$4+<
zyq?-gKcT>u#>wfBe!HJd7P*}c<v8-~F89^AYATq8J&C1pV+C`2V_a;qgIH2-Zp&5S
zOFEO=8g>krwJl?68~>D2V{NW^`SRtL9z#!^$*S_i@<k0T<0a~ipoXdQb{CEx(#cL+
zaprkjwq&dRvCfKgon(QLk&#4tYT!I#NqaU&x$#l$TcP}&5-;kS>hx9L{cLDZI}j`j
zukejlR#t=eP1@|KPsKJ3$$JjCNR-c_>XN9sc&^W>Z1VzF=FXbog@eJLHbpxsM2&NJ
zF{%{W9?A=edBoKw>WloSsgun;i#R>Jp{*t_SlBFNsu6%_kz=C6C6P~7;?QL{FCIzj
zyRoy}ZEwL{iQ@oTuE3=$)V$?|*rt*Q+s*3d1<V9BgRb8mdVPI!Tb5N1m#`TmDV4jn
zUbc~aaz{S@_Pj40M*9u8Q>;tp|A-eGZ<JJA^sIm5PH?M7&!69n#xJ|T_DQVQ`@a`i
zxhV4uH^Y3>5je&H81T1O^fhPA6LPW})c1_Nz#`#zz$IeQWNhD!+M1g8DFs9T?hu=h
zunJs2y3^!-Y(2dT#2Nh9y6=_YONsWO{^D>U@+^Z8y{V^Pr}qBir%&fYsgV!K$S$pw
zdYSKx*JqtP^|p<r1=~ZmZQVNW+_`hlpFh86lx7#Wii52%S#&ltQ?yy__M?c4P=ql&
z(z8`5`+Ba*e0IjE`LTOm*Ojbhf1@P#lN;K;el_m@u+d#?b&1r^v&z}4_OkU3QerF^
zh3e$YWsa$DE51AM0JK+xrF+i@xQ1y=uk~(mgh^F|<7FTS?K(=`eTjw=QAu~-X2u#y
zkZ~*FyDI0a9zEJvV)Qq6R^g-G;yVW<F68lFkhY8NKQvUTnsxG(W0};D(hMrq72bu{
zVR&}d!9*<g=X`y>imyMHFJJC&k~i>^=&<;7(5)RdHy7Nqcdzcl-Tsu6CY5*WBSSTV
zyY%j`G)j&9+$rc&Z=2&u4;yGL7~U{zQl~Tvkt#!}xu)e>C{>I$bRKuOAk14GI$Ew}
zZNkhTvOue>=R$$)mSJ<vU2;lvWettsrW?ny>+YltE_~2vY90fW;=zopiBYfV<wdl@
zSkEDALCw<Yo&5a%x<rth>M>v_0a<Kl#E4hTf%w!(>#moXEily_d$eMUt+}R*9JuXy
z$r9y@X>x|DYgr9bysGiq>k~+-;Fl@TC}ieS`Mj^-Zs6&ex3>FOyAL%G-G7A)>!q|i
z130LIM)llUY6>k!*LA+x&~`lZE2b=pP-1xubq~a9$EfX(ov>}u7W=Td^Ih4~#o`5<
zsQOk;)1E`QKX>-Tc7@9K6!0z7YJdNNz1{o5aQ@NbRYDk<THqp5pl%SYv**}h*3W1M
z1zaM^+_IYoNKy}Cw1H{Fg%DSw_*Kn^uKHVpf~Tiv`qg$Og#3AUMNl?81$mH}CvM%#
zQXV979ZhIaq5QL_PrrTbCVnJzS>F?A_#S{h6!WgER1h&rc=CjtN*uJqoBp1luCjUG
z>)*5lNB6`!*k5w<^7_o{g{)vuUYvS6zX_s{qLW@V0>vkuo(&8Q6*yRd55O%T5YT=S
zj?y<!@6NldS(NkQg;cH?It~UG^mWTn<=j6ke9zK1AmB~=)%QJ0T3X&;ziNy0l=A;j
zmmfN{>@~K~Zf<XH|6wckJSOHXB-P)ZnONT~TB_}{Mn}SZ_Qe85!|AnMtM^u5BZC*A
z!EK!I2AI+sO)^^QQ80Y`^ohGCvZVbrRF+=`Y+phw)0}AyMMy^6^<CvM(?>Xq(V{k5
zZW8?V?b`(;hzLgzzFT3i|E*2_>!bOv&>UWhJn!p3-CwolZK)`=#1VLJQ_KaV^$7bN
zaR$=*EDq}0Cwos3*O(Ha7n{m&kFxv-j-b(2XE4ga_obpORK(&hz<~=Mn@t{{#%~LZ
zqo*BoR#n|_o}U>qPgm6B+xG2S=U=`Ivx7@ZD8~*f;sqB%Bs7m9K4<ii?gSRt$!lor
zDj9<%-I+6I9#1;%n<0J=4N@QP@8sn@s;S9_#g$9a8X;sceVm@I!lw1@9a`?s1GcB>
zVW;{*Df|E<+T&$*I$lG4xllz>u$WyUfBD_=PSj51Pnm0$zB+jI+wHiPzZy&@x5Zr-
zERXiC-_+)~TSx5g3EYwZ?Kf;~V;c^aPNXFoRB@f{{k#4B9qzkIoAQpGJh@$5oR#6F
zQ62wg$ggwrA|n0*H)L?g&X~D#UXhG~Q^K7AZHrQLDxZ<XSYV>#c)E(Et;SKP!1lg3
zu(`VBws#+PE17F%|0NXQK(+pVsyp|8sP{aM54M~xl8DJA-HznioSjOTvaPgbq%w?i
zL}ItxCYmM;QJaKJNitfLu1YQ?JMK{~L#9lOmLoG^a$+1Z(Lr+T>)SrgAF=b>_&&^c
z=KKAAKA-pJ^?ttJuTz>H*-Xhe63PD1Ur3oP0k~TDp~YZz4wi@gCAX8AJ;`?^Ea0tj
z(CU4lU<{zS*D+2;T3Q^GdOtu8Y~OI`I+f(p@ZH#IszH4f;w1=LPU=N+zYcT=F>e9b
z&9>(rSa}rrtZn}GuTQNV0{}u=rM`J*wjM>D1pJBD({qsF;h`wXdywq6vGRLL&Ev=W
zwro_m&J0?%E&_EQx<6X`5?PZf?I4|@_zOLYt_Rx3qB(0`%6f$i`+;KQ(O39GZ?6_k
zVM@Jv^{OvmbTVu|zZQMD*)$~-I!#w0=2lz^J^AV)Wsi4#H<P#QgYSL8Y)OK5Fs1^5
zz1lD2Pz&+z3q6eO84da~9u(PwZ|fFILxyJHBD;5f8Wy2w_jeW#-LG%UG|>=ty4GH`
z7=ST@!RSRa{|tF~`Xe+-)g)w%(FlxdG+I3)8KRD4+XBfZ=ot+3uJYq+iR5x^t3Qa1
z^6(LTVjz6^o0G?|3auGsH9YhYi5#KNH<w~XAp>&9)RusLsF^<E)>a|L%Bk2^1gk34
zlRHShaR2q$GMhyWTn5nR%%@Lm$+H7HcBmrS<^-3hJVUd73N{b~l}0*)hCq^uiZ$1=
zY0<i%zf|tY(GReZa9-;+blSM>KZD7cyByq}mgbuij*uGd`asyDU#*va2A`^hwVY#Q
zdvoXu+0NWg?_^ssq;VQjN+z9wZd=$73k%azwtB;xYRin!rrz;SN@By*I-$)vdt6wD
zkv4Eq!#yU321JpE9YB_k9dMGbJq|d7NJX_c{9$ilK|^EXttU^GL6|NnjQA(VAs%_`
zN1thymKt~jGK@BnFuyn8*`^@SUuR-cH^k|OAdnlb_t-V9Yt}G8pI$~7cS)3w;I3pQ
zR#*+n6o-x+F+p-|^>8<Mxq*Uc7YTi24z`z;qB#uXu!+I_(rD{LH3iArgJ=wq6kCsh
zmy?()THc8>H!T8K_Z&ACV_u`|t>w{ZC~001U*~?G5mU95M7iy~PA@OZaHq{nKH-PR
z0gTyj2A2}Za~7)xu5VmcV}S*Uojb4hbk&FmI_hyWZ+~lJj0Vc)p|=v14ukUQFER5s
zmMs7gGf}bIEiiBcBt4f}R~MH}wEe17>wN#R`p<Roi<IcrbF;EipI8(uEGE`(;8!<J
z-@d)3wk6q}my(p!cF$GHc;m%mzHNs1KjbrmVu~>Yi*gVu?7Z_YU(%(~9o73pF|d?~
zX{}s*B%3K30QEzn3X&C@WP)Hl(p-Oa*(suD?S>rUwhq^pALdSudP}T6dr)GL<MXWK
zY$!TZTa>JXnG@S-47V{gE6e=Q_<CXfNKM$(*kSH5q0Q-!GMcxSMdA;B%y%(b6raU9
zVqh#azvNfXTo^}~vLMo^Evzw;#7-;10+=(*)%U+aY3;<?UrM+~upOG1JoQZDU0CHw
zW9WC)hbLH*!Bh9GTpgHxq>2nd++unzoU(_e?jm_F({+%zao~~?<UYsyY4ed=Pt3%1
zKnLDyTy~h3ues6;P;n_mw#UmbZS}(L#Xed*Xyd^Zvh+4Uk6K1X7ZGiX1o1nb4{_F5
zTWf;zc4V`OLdaf@Z7Nn&$Q?R5I)klJxw02J_B;~U)Rs?6Uc+&m7*?3rmVy6{y`GR8
z=!r1`OA88mKqED$wj@CfDw<<dlr8@FSHwT23?}mq9&ZTnKI)3N^kXg(C?LkG?CcB>
zEyv{sMq=}Mna@8DqKO-Gm%f~Uklfr~mI~w?+hXW7^sLPq_{ekhea}EkDiOb}wYGL`
zb)wuDM!-8jUqOj>kWev*R%66qcw6q>6%=Gf3^)|@-a;V|B2gLvd%d+^w^V4a=oXk$
zU2xuj&LSfeyZzl;S*nsUWs!esS=^xTU#s`tcME3#%fEzmt*$Su+4?f-ZC<DAWr)7F
zdw|jee(-y!GYgRKPN?s5Ks@kXN%)VWhVsR(E11}bfK}@6)c+KaH@{Z96Bep9iLmz^
zBGaO%ghJx|!4@kTE_nk{v~H(P$uflu!x)Y6-qlC6UkeP`{UcI_7xQFECzq<hBiw$}
z7RlR3pP%BBARz7pwM@U$Bu~BWn>t@9<KKM|bUi|OXXPPj&ru}fo{Si84k5R<w|{+j
z8rPg3_nX7G)k{T+_cXfL)&bb$6bs3xJEBZmF5&x!gjgU+5RaKwR9S=0P6!6;uli8J
zW_wPEhb|HL;e_Iib4G-4;wQnIhw#k0Hh<djN6d*=nnD8t92g9ppy1#EHl1i#TW|vN
zoo2c8w|n4A_{V(~Gc4es<nv))<F|PFRF0NtA-PsOCE=c^IGEd2jMMRqx63AE3PsRX
ze^HUl&Tt9Hc{QYP(z6niPDCs6?)S1QzV6L&pVeYb!thAW<Q69@{caNFB&c`1`Y1YC
zX_etT+EN&pX=RK|%>bR*=9Ju(C*<a{KL($-SlVL~>Tb2kB~CKr2sIEug}OANpWnbu
z{Rp6pGLRO@La7o_IvEp-<5)(atxeOMFOK`&1e-q(k5lCe7PgeJO;f1ksYVmw%HP_}
zT2qYdbO}wmaSD=E1>FU6x0j*3q@?5@Gdcza24OP+yz%}nFsb8JPZvb+m>Hub7YY?(
zh(rMwxS$Lqf?3FG{H!<A+VV#2?PgWuo;balo*aoJ3i>y%Fd%v6{@kUTX#XpkM0An`
z5I&__A8?mYrPl&jb39e!l8pyMB@|))mKAEt3${g?{@fU8&SKHee@FVakWZJcIbcYO
z(t$Bivshf#bMMO8P!xSm0dIbcC}->AcHMJ!-hJC**L@ceqdBQeTIs~flR>y~Z5D~@
z;ng&T;;i@?PKcl}QfHJJKQooOs^=uFr)mA|X59^x8>Wo2mGiA>*;v4P!))G1c{+B4
z$5+)P%uG3I-YGrsQQyE|;EotsyTjWIZo{f>II29w)iE_0|Cty()?V-I{gId0T^iOo
z8D^X_c(_AXalJ|Y913{Cg$oPs-n}~|uvcxXH~Hg~4P@7jb86L&cy*W{jCMZu!#c2i
z2}2=?iHWA3WO>qr%`<1>Fe@`R5aACQy4KNdo69)7M$g}VzxJ^wPQ>AFFP&TEFZ2Oq
p{O`5@TtCVBk2hNXKkvR6x4KRKSvX{j4!0!I?wxxbi+1>){V)4qOCkUO

literal 48370
zcmd432UJv9yDeI_t+tWwHqb`8wG4oOqJU&WD;9_#Ib$G5R&vHR(MGgLk`c+Nl$=3P
z5s(~8vWiF!l4J4aLjBKu<J=SOIp_WNIfjBtRqegj7v?wTT>F;%X{psKHm#sgD645F
z|5Tt*me^A$i_ibG6#vrX^LPkfmY$c9`V(LAYwB3G8@?>RbW+`lLfPt0elPOq_@Ymt
z{7#|$c|^%Ru&>1~Fvuo|Z>Yl0XCLk4PamYCAKoARt%O(l*^gSwwSNBPXZfF%v~t#I
zhilcp<|<K0EdAkXU)Y|jXMbPhWBJRzlfP<o@%@o>efwj-6K547TO=(z_j&r!0%+x8
zo)a@;C3gxP8Yjf^{icFCtz0`Nyfid5H5ZdhJN!XOIg5Nlp{U%u!cM-1hW?mCzEYO#
ze7}Tz-Lq#AANhLt$a4Ed<eL@J1uMx{dHEl7$k!`Ze;xXPe6!B$Km8W5k=08obGvJz
zRI`4hgl=v!O)lfIi1S!>B`+>7Pa?oHi55{xQ9F0y|M-pn8`oB+ltl@((9F1WF7kAc
zzxy`FJr&{;S_WnQ-p{WsGj!i-`$os9-`i!PMd;en)v?xo@h74JEv19qSW~kDcKM}4
z9}2UzI^Q0sc$;EW<CD3D@+x$YFKu92Z%&vps{4aZsOChglZETBXGnNVOibZOvSCH}
zH8zPy%Q&3(vFq5MyD!O_bpCQj_mqu|%?&Pn8)I|x(^gg^{7fTLQ>_6J%BagU>tk=C
zTz$ym!}HjitkIE>c`>2pOULMXeea$LfBbt#(wEnp4GXR;GBPtOa~jO`-FU#@)6LE1
zZ`@d&ZU!S|LKS&41DYRV7hO?~7+gl#vuhDwWwD~1xN=@Wc30RPe#g<SHKj5U3iaA{
zEqzI)ybb!k+QV)0b8QZTxf5JxxLjWD^wm=6{*}_T$i;2G%2Pt!4R@(zY-VPbVEv1=
z+4N|)H}7Am@+KxG_eFZ5bH?g5g+E+hYuWd#C~-J1Z#06hV~nX=aeevP;Z~=?j@O$l
z-qyw_j*K^1bVf<e8BKkBmYFFmC-&vfU9$dOMm3T8_3`Q(MXWEL@Yohq;WA*=U|GuJ
zn>{}@(%CuP%c$_;RIIM7e9&UoCSJ7FPAl!*`8(9&*{Kn$8d;$Z+-YgNdh+l?4`=G`
zz)MGM-<@;cmY;TGCHrUc+GA|uUJfIl^_$Gn%c`U}g2cz)UGSTEwEw~h^W3p|vFWZS
zegY;cg01%Lec6L~!hKH*`SspBFi$rzHZH;STjB#h-4kkkV-R5GBj+|<BmC*XF-wHY
zC%O!8ab{<5z}!SzexWV5Q*a34Bha#hvx8yGJ15XQQXwhn)Ss$KPwea?p|(b(D|FRw
z`+i@$`*h#9Nsfmk%RX-;XmDXiN8TQA>KA+7lU63!6hE9cZ9h#HXqjLb=jV^IxEm%a
z0)!aOeYhh7rnX(#v!ZPQ{b@~!P5nsj12#RirveY{uaKBAMY4~ORamywT3%xOnMXuy
z?8qD=ARyp?QFVB;Lub$w;wUgOGV<;gVScB9Ov5!|Lj|UJQ=fy<)Uyz6g(YzeM#ZiG
zQ-yARipry3at<#$U|9ZE(<0ZKN*2a3g^`xoI*>cjs^!=n5#*DaIXhf>z;$+VO({FC
z)+5ouoC9{P4u+>iy2H1)j8?S_%#*!|@YV76@$nf>l4O1Hm}=*kf5gqm;V-<oy3{v+
zwqvhW#?u_<#uOtzK{E~BjQ3aju%>adjU+Jc@Mc99_B!;$L=D#}%5}V4EBBT?fAZDo
zrnI!Q&$qb~-^dEJ)P9&k?CZV0yK!oa;TBZNo4vQmJlibFjD4@#1BOe#i9>%Xv!p^c
zL_)kKO0L3fj_IcV_R)SLbMt{qyp8cC4(9V&KEa-hxz20aWeq}?-WT-Y{`?LaDFnNW
zH;~*2GM1(XSmX(pm6dH2GS`ZlWAr%uaQ0O~cb=Q_%-r}4L$A$^#Dk&ajJmtN=We<3
zPsN^A!^OKh|K4%GE5%GvQ890>-=Z@nD!6B8_KAmtzZ8edL2{A2xueyqwj6p|_~o={
ze~Q>lU-H&8quyl}86R$9OP?toYE%x1mt1y*C0nr4x>7LKv^M%ouC05Y3}5cSW!vn}
zl7)^>Z*}DKmfq^)fjw=T8?Z|rDHk>Ds(c(18!LV6*s);e;o?99X6AHHTrz^hdWLiQ
z6}xWW4i7PYT!-~iwrzn+R&Mwd8<$AJeYj=NRp{QmdkrWmBlj$vcl0Kt>uliD32d8Z
zO_28(dBQQbC%xHH#`lSV%1e?ETbzfUPYwI#2c9du8S(7dem1e8!>+UK9G%#!<7<#E
ztG0<{6biN%c~oG(yv{s1(GkMx4oRw0(Tqq-6I{J|b>=*4#;WQ#dq+ym>C(wB?aAdL
zz1Hu>cS}yuP=`}Z(;5t>SQGgnc=^F%dR1Cypw;Q9J5j-bok#=Ti8eR4Jk}Va^9H-l
zn?bkv8SlI&<B#39`O`dib<}HG^bcPhLXK9T04eI~wspTq+}*kS%KG%k8CLN43{8KA
z6*AXnI5m?$KjYl1*~T?E!{#=5j5lZKh1TU)>o<x!TBkJ{$DQtM#|D<5^joo}I(_pO
z4Gov_b>@tem#*Z?c|dZ#GsMl+A)tuEO^oE?iF=#82VDl88uUD66_k~|k#%nag%8Y+
z>CV@6iz};+c2X+m6B@HOg|_*l7SV%U#_Wwa=DMV;KKmIOOb&g}*7<vj>*R+9i~Ko$
z=gA=@Rn;gZF8#{TBMV#O88S<yS)!)dWX~^V6Wgad)5s)s%S$?<mdWf4Xqz>Njg1|~
z?dyMf^H6xA*{ZaNW8c}s_a&ygoV$*3l%UoH2(>v|dFwV|or?oqr@K4uFtf6<?rs$o
z44JNtqKDT+2aw3XeO6@kCI*nK<(PUSrJ$;+>RLu>%xTHFQRMGF((T;M4ADMGipL}-
zmUsB-24kZmva&?#>gpQKxo@>Zi{VMGR~E0}w*2xgD9E;v6W!0@{a?#O?uHp3&@b_#
z7I#WaWUgUv6c?7G#0R4OO{BERaWzf2O%`&LV^0d#%&^?uCfx%szurizj*$!&cd{MG
zXunS__Ey#L>rZdXYj(59?C_bIs&EVL>FE(0tvu#1H^wY&${l*Kl18tQlN3iHJ4|WS
z)z>dH$>QB1&E3a7=~sPgv?6Lx&mN)cwb(HGZG+@odK80LHe)s~1$9}&zN%AU=V$uU
z<&r*e-))(C!Jb#yorw?WLkQ657w!v3mbMQf!Dw_+0=<o8syo)Lq9tKzcGKKfrcxa~
z-r8MVT}^*-UxaJ%(p6oSO=(%X<bs=xn)HhI_$QuFGt26Zj1yzLza7}Ad`4S4=-3)z
zDs6bEkj+lRwf&yZF4~$y@2{+>nD`{N=&9wjLR)^=+?ky-thI7t&q}NENjaeJR21OR
zNQs`E1uUUcLndGDPI|s{vvs5Sn+JagV=+9biczO|B-;c_#!-|tIy9W_u(nX91r-lc
zwx+lUn5)~p)^RjX8wfT@2E5>#$l%MLwH&|=ijkfvY-O-%^JdY#Zew+-*)6s&|Ncc=
zLelizQ))4Q$GeW<w3Q9x?B}@SvM;3Hi&l5^Dn(L+P#0>(9o^aUelxBe-l`(AZcR&c
z?5x^$7H@Ovd)OK5l-SfEE?g1U!@>H%VM+Qq0Sg4ZxVi!qIDC1<e!%k6o70jzzJ`VL
z=$v@J2Uo)cpZMI-fX2o~(`T<XICMw&@;i<9k-k>EY;Gdy^W4@02B*x?+SUl2Z8i*X
zu`J%{`)PhQWZtvbb9ZOxwKXNSO=iBo>^KoWJo>BnV-(pu8B%0zg+8Y-me_W%W(N5r
zU8g#Ht!BRz@mYnWrlyu^u}XDW{7jT2ex`c;aXq^o*Bg0Vv$B8cG0$eW&GnLN6&05}
z_u~5L9fqp5ZC0P((&)SvSJP4=n0}IOuJpYly^qY;)3m$09dwjQB;I7>2$r0ms_;8t
zcp9miOXZl|vel;cOi)^PF?vi#e0N^H(DW1O@wu_Fv6gy)^DqC-8LgJtD)AoJ)XtN7
z%zaJ2ZFbt`h%x|#LMwt06Z58fnyo6tJI<6Lzl3Lp3autTxcko33LF81bMd{x-W6)b
z%0<r%fBg8IANzjV1iO4cPcOGW?m*WH%`!4Ek+(C*PRF+Sa?z@d{8VLSWi%KxOx_B^
z?8uasolZ6vR~qI|<PO^WHkZWTl!xlwl32rf@ibRDxKkm<i-h<*Yj8f86yOKP*3f_w
zU1yrwc1^vH^Xng52;h3Xwl?;eIlYc)7HW+3%(>pahGft1LhB#3Z=}nZY!=V8{z(kC
z8;B-6RiB>ct*YxHKtQ^A{@l2h+uT?tB6K89caCebSxVK4x)#N0D@s@P+vh8-pBC>5
z@X8-Qbot}UI@g>u2mSdk9P|4*X5JO?BU*=L|D@~du1es=8`=!y&$CX?JGPg9G_&*3
z@pkyUfGmS7n(JFHc!<|`jCe~_kTjx)JO46S!p+bd?`!A~jpj=r9mG3%*9=Q~LaIgG
zFE2k^_;P^1f60?8O`j`dCA#uC+-AR+d$if=q_sNs^7=CC>h$9h(n|ht-$IXL<cZAp
zy3H{T3JG;G^I2oJa|?9BNCeUlf%075mg5bEyJ@bhkq|yB31(~M`1;rW{)zeWmyjI^
z_3aF!3$O3e=wy?Tlb@x{vU?M=chN|&53j!57=SztMm6V6?=N;R-|SFZr=6@?i@uc%
zZ22i6y|tv5ksplmFG^zy71JDTgDGmlCP{;XY;!CDrdCad^$(=r(y5{Y85fqW*`nsk
zLd(BM-|3>!^i$h-tK0nCaBr&#BUdH*X4=G$jNI|Y70d*JQb+=k$znK&E+^dKrG=bx
zASvHWA+UgvGc4`!V^M6Qd3HaYs;YJQxQ_FeSHnwcNGFO*%;F-^>oMNO1Y9_zJJ+M?
zN!6VyQ5`N=%!WvPoa6lE<16xZmZ<1<Sp}_UudOE4H7X{CGb-58*q}VnufD!MB_%}<
z!^zZVq5Ova%TAWTfGVZbVvIR;Phz<5iw(b_$_%8q0M#HG!|aR}ib{?^-Nb~u>pY*^
zlnkpwm$lQXvs^Yw@6!MWYsg*e((@%WpS!2cEzp#wMx!Kys8g?n@*eu?ICApv@Q?uv
zi{WG8+`bt(Qa+G97%VH;5T4UQN<uSW6mYj)$()6Zj0{2{LCd=8I8EXwdDZKr^zvB^
z{h%!}R;!qd396G!6fbYB^_}uA+iLsn55pjvdakX87(m|gX7|x?mt4DptQDd<qJnHS
z2zFF%w9~cu^iaxRpgD62z|EJSpO(Z4#{4uRC|DFQ$+DWZyMx?T-sCGjTeY<A$N&Q*
z5CK`DWghQ5N1O{5En65IZJ64=h$CLKYZ@Ju+wF$brkxyjrLzFB=+p~E_xtxreW-S{
zM6@`Vge^o1x@RDFV#aoqBHbu`03T=Jm*#^_Z7f^XG&BEmg&WH%Z`hk*t-<=h=9ZzT
zJJD#u=<qj5>`uFnfhwcvVY2r3#+#+Y<vrX`;VL*YnD2(R7$`Z{E$1+h*-5H;Jn&$H
zaa=+YLAJPh?h2mt*rH6#v6g7ahA*x!CvibQ(%p>*c1dly{HGvT1_`&v=}FQN3a2He
zJ`sqTIXB)kf_B<jr>a|aWzpg!vKFIVRSoD4B|^DI46qTX?Plgc5Y4VL{iNTK_i@G;
z|F#oVdRSEq(^t#sBCWa+G2_~3E!zhDOxASo$Y_n6uf>lwjg46f3@7YavBXTDCu-kL
znnU=zo<jgLIRaG+0hSyxXKLH#GC>NM12&EfwN+1~I^SLJD7!B@@UBA;H9HeS$8hB_
zwvMzW^U?zqVxAbDOmW-7U7^>KP-}e`GX>kG>(j~ihSlLT2aFnIvL@kDjMoG_yHNCb
zZOG)G>5AIc$5Hg>RAX%PoviC0eI;Q+&l21wHucFfb0Di%#JOy3my7uF9iw05#fr9A
zozlUzk^9fJY*!8)jAbw;bT;i-hd3Qw%gARrl6j)GwpI_Q(wlb~s35h)V3C&a+YL0&
z7P-wzArIB_j+A_Sb!X(w4v$a0y7P|oPpH$zQ49w{9nE<P+b>!X9uX1YY~__mhCMP_
z)?ZS_{~eC_3IC^uJj?v-Z?Z6Xe{kn0-)xcdknl3T+P1|Rm)rRb-aZ1%mG)hZm&E^J
zo>cFixo9P2RNFjhDc(LHe$nv@YyQi0)jwDz0dL+b0}1)8X>Z;|6t&f(UnrG!z>O+d
zKVI?P{=UCZLsOGm`)CNibyuaOlqkX!rAH}ayQ48KpUhrpdLWdPyDhsOE5s<RN$~gg
zCyTT~^-sAvtlzt*=V<ifcQ)v=W~bICM$cCk*&RGvaD_Bn8VQS=H*YTdD3ck5z@+iJ
zv1a!I+r3y{rFiV-s$v8%ol43Srpp`LVNo|%Z}#!`-}$frOvtEg{~`9nRgc0JIJ|!j
z%lD6E`!|U~V=nE(aw%zP3&jdRicY3rOv|S+qymej&yGmWkF4mwfLgXu($$#|bLeh*
zq-%mQ>L%n2CX0cwe(XAh;wbZYacj{wr@fN1AJ|02+p4RRjy6fo&yEQ9#wUx!&!anb
zAht>|6$Eq4PZn*&?87i4@k*%-SRsD=7!qw9)D)Q|2t>6)&t(jyjdWI2pdbVx<;d{0
zP*j(%-L)_sN1u5z{P7OoXZ#*W{dN_axC)ZMqHR;Bk%IYTw32plnOk8Y3iv@9JopZK
zq;>}8{CyD$oSV%v&JmUa<D6lM7pDUXn3KLJxvVR#DqUCXlv6=%01C>|w_JYtOOOu}
z!`y+8sZ;b%&zEuZeX%7!Nr@}e*f>-kBr0@g1CKP_G^u!(c49K1^e);qT=X`Jh1+~8
zvTk~pNt`i2G>s0R8;;qo)aJzfEq+u2mmPDo?KbRHtL$I>!Pvw^k;~gB+apyuM#<83
zdSk!3vrPJ9K^_UStm#9(Tc^}}-BOI^Yw}j-vJbkY7)q5dlRs#pUoeASw`=P6yZt31
zz44w@^&I=l{s94s+S<7#O*WliY|1c)WNF!sR!Mc<Jy5PTzEb@T@P{LqLmkxnB-BpT
ztW}O@WM%IO_8Tr;_1l9*i<c^8-E}P-l$z-2?N#l`TQTZ_Xpga8KAQCSm^*UMu>nYi
z<Xv@btrGZ2l@bl%<Hs&sxBzf{G}UEalVFLha{2<}t+utk<d+-)0T~_JCg&_|0$?Q*
zV`F=&Y+E)87%Oey)q2;F#OBg<OhQ0Q8sRV%$6qcxUpX%7eX)iWmLLmqO>|Z~{1JM{
z)HNBiM)jHebKIqEIrkgq<5N>oCLiQ*M90Q*<+GcEzD2f^G;GXgXGYFl>x~=ecE1v)
z&b_!bjj=9w>T^I#V)EJNSE(|*t&O~gRBW;b0<Fr0F;S%F&zbp^G6jL;WkIgech;D8
zS>GX0<68~+TppxJYRI^v8N$h=#x*oDau>)mnIyfQ$KXd*Gs5!pF^k3JB(!<l$0EwJ
zHqF1dv9hMJQvSp3t{C#Vg9rCoUHS3VKJWv>LK{0PLim2}TZW3E!g^}in&WVE#+HNt
z#(F~gy(0^%N%>MPI%qArsT&)H5W|cm-nxdw-dP(p+!d%EZ^?TEYWns9Q|+M90R!WT
znieWf{2By@CYLwAd2<G}m~eDPQ&*R)B=ww1BfR98{nv=EJD0G^-t&_it76^{a|34B
z-}AwgCEI`bNR|1VD-PD8rnc5=|Ap6jg{y`7yuT-1OlrY_7(NgWQd@15rsg#@G)mSM
zrsqFz*k~B|(8F!EJt)l$EQ%%YgKg$unNXV;Vcmgc-<dQe>=jb)jh>UOH>e2i-(@m)
zcqSme%wHg`x})+~zeQ4gZSALB3Jd>=QTmN+y-zhdN_V@cv72Y=&6`W{;$@9(dS<BF
zXgq8w`NfURnN}@&2^<&k1M0~JnZ+8S7#2GM;uo(wA{()?D`vplww^ULAQDSBkNK&P
zdA~31MI`zCz+Vt*X@PN>{j^*3`pevBTPgAJf3UMc&ll-jvhBFPnnEeK&1qdA3SyFQ
zf>JYT=+9OJ)`feX*4XiN#YcToZNIg=u(kgm3MtBp-K2+K`6z1j;_usAXQJF^6V4Yc
zjXt>Jn_g7%6KT9^%a8HS9(i?Nd_IJgiT@9^;J^A`R-9sEtQS!Z?>8NKnQ?pDd{B#d
zT-O(b+4T1BpZOoRo_~hS^>1E<5`usvj!M~5xw~=ip0zdfr`_JV`3l=_&;PU9+QPEa
zOIqiIE-yQaw+el9_dhMzk>xwwu_|+DnK#e-B43s9Npdku41fHOls-#Ofh(@zABD<i
ziYvIBobe*H(*OJ=e^(Tv{iVsy7bLCzdIiha$rT(<|IgpRzo|HqRq1Md|NI(h2MXHS
z+OA?3D?@=WRGw~fJQkzWGt?$MJX4<y9yAbTGcPZ1HX|}}*ka>!O@)%1BTw#Q7Y<!j
zRo_^y3qRjZoNK%L*WzW7P!_J%Rc>xfH*#@t(a_M~QY&jr;2Zm6E_DOb|8SKO%lMwT
z!;a=NGe1A&`*HQ$5ycxGZR}TD1m`*?5~|G_BbB(u0<1KQyiIGy-ArbVzON5)jhwDA
z>0aGGa}CX+Gm7KlywDT0V!7GXoLSNnU;3M#Mn)PWuoZAMY;)<h&~r<O$jr=SjxTw-
z>tyltQ$Qf4tm_i`AoM#<JE2&gSUr06kM$C+7$<RK(=V4Cb(?aDvkpij!zW4+xAuvA
z`C>uB4;@$_=YYhy)rx<0>7TG(XcK0-PCVX%3s5uZQ<Tex7dMP9UW~qUd{hZ{Jf%q)
z%%6ANz@wq`;r4AtwaYACegHpAm6>*rjFgrt_f{?*?~-vj*{PwDYta|eD#nLu7ZVrP
zn>x2B-dsX@0ekG(v%_9JE=c&XWyrqy(Fgg;eeX!rjk5hyRo?$W5&K_d_d-<~mD?<~
zKQXMx%`m-*%W)w^*8cl}{mZo%h2OmZL{=vw3uP3N*>7j6+80)86#3b*|G!=?|IKvy
zo{|5P8b8s|QQ2A^taOvFYOk1&a!Vw|`K;N$DlaAL(8(&@AK6~p{_g#)%j-qQMYCH}
zo?TdcSeN=<TSjU4==0+X&Vu-Dkrzp<z>Gi&e3xB$;J&c1qWSJW+!hMIn$!Pz5&ECJ
z<$o1am=i;P1u<^*@)xQ2$}E@%)b2xL9-fz+e19!-^5Y%f%(5lyS`W<9ThgQi7VdFx
z1@QYW8YKS}3F%<vtS9F|U3Hi@fkG&;`HG>UQ~BQ~BqWr956aeN5#b#2=cyQ_63}P$
z9U{9!oGm3r{3_xTe_i-_pVBQk+V)UW=u{#FK$ytVii$0XSwO;EGSZn|_=LM0k`lDL
zl?z^v>4S>~6?E)B+yFY%1Ep5+Ilw}v+j3#)vr<*cpJ(q~yX(|RIvImN|F$IBp`vsG
z6u#A+pJuMwEVx&+@98m#u^2ghBFTULI+^4iFK8kI-8RRr$5g_DB)_%MB{W}qdYh{#
zq%2!Px%NTln}U&|slPe5zr}f^Yzj0%V@FbpZ4-|ZECkX@ZFZ|S9gJ2T$1tJos->$N
zf~(hQu!tC~ci>D)V>>qe!hL(=6T7h+4kf8Z6$v1SI*J4YyxmGGLmF#Mx}#CqH8q>~
z%H<yF9<!*`0hD(YLlu+wA`~8VyEzi2e{<R(2iG|>i#vIv({(>o3ay(DQsMI#u`#Ca
z{Lu5@k7HOq%-zKH0cqKjNSjJr5sx4L;UO`75o}zN7$o}z3JQZG--JD&s$8UOKz&CN
zMn^KM#<9-(Bt<jFQ-)QFHW#3^j8982Q>63vpXQpedN_LTY`U!OMUPhbZb7pe_8)Z2
z8m*uT?U6L+C{UJmGpY%<)0K&E8+6Ln1~eDFe(iXj=SX#1qxJEn66=mc%r8}*?hJF0
ztju$ZzAl}9ym~&7p`wh|0lUdU>-fpOi4f$TR1%Kqz2L<nYpmd9DFLzLn~>JX)i(KB
zh##U`4rY^vONFadH4_%L{)g82LE_vXoC~^CALO;T6b}%ZRFKZaAjN%%%$d1pkTWWf
z4qJsyrYXd!a@=sXw2gHHJ>=O$eeZ7vFUmW7Du6-5$>{b?8>e#|agjnzcFM}OeaEXC
z8~qhxYIjFAb*=2L+RW7wX@;SZTl=bD{}R%Arh4x~eqJB1cPp<x5`<)r%SpHHcF$G^
z9)hYLs$%3V2|J#<Uog?q#ko3pq?)7A8a|kgGr=o2?Cpxk|KpWe4OTVe&g<8&hr6aB
z4(ErZDrND|S(jbvHj6N;B@-EFoc#7f?{%6u3VL?EpPL_m+(aeRlH;gAEl+wgzlZp^
z8s>xCW*dlko%+1<AT`rUZ1yJ5Ee=BrZ|0s+2isO+R`c%NBkcFL$n3r)t-B{>Nsh~r
zf7#iiPT1N0#eR81Lh1P_M%CGjh-yeDUo;vgmcURj7na~OylMJaKw1bRH73R=!Q$)1
zkIx;BkQ|M()}+q0Psa+4GBu_Pm5vlNMhtsW#~0y+gw6C^pk=G_z=u7gZWJ_C^XOeD
zFKQbWSuC){va+%b_U-qGex}@70l~>nYMTR<M)GV-#bIH;wSOJjH8nTr7D6SiBA-kk
zjFDjuC?*cjzC_=yPQUcw#VJ4jorGei>k+#2;*)zSy3ZG0ynmU+=du7_!(fM9v?Oq+
zJXS5p(`K;TnV_??wGFfT(@H_7w8VX_d)E31Z1%fh?4VA)E0R#jbF#7{ycHq;6(dg-
z!;(NJUJgJK5|YX7_eBln=O!woB!(YKSk>$ypBd_=*}J&)a{Fuj=8VhSIV?zzkKph*
z2oG16aXj(qQO-_51tQ=4cU^0E3zr%K+Pl6ifc9-1l+d64{r>*n`_O-dGXAN}{D0Tk
zFzd0-UEBRyd318H&sOue2OEXrefB?`9ll>TnSIXoD;A5IW@ihMEt$LZ%Rj+#`2VwY
z{P!qPN=sa9566^~uH#nY$D)l-=BpybnXD~7(h)0ElKylvR%Gap$;%9O$)v_b3|?$w
zQ_7N4YibJP%Vn`N&>&tfQt`Qx&bC>?*`CpFa)!}w)G_wCw~I5?TB$hX&Zlca=UhIl
zR{CC!z!gso91j?8hMhyxRSSgAb7`M0J~RK(5DAHH&Hid#<Elsw`O;-vb5>P(9KXSk
zw}d;!S(BQ`XhaDDgY9UhOEEdx!6@jiC)M3d*E?tLU>UPYG00@2!b1^y-en!GtxU{d
z7=$8FuwQtO-W%^)?QLEoh@$1qdn91|Dwu6pRCd#VnHC0<1X=f0>SGe&`J-QGZN|R$
zQ<V}u<Q#2vQo5H-%LI(yl%Cf*h~6$);DTbnjk|8zj#3_(ucHfPdh8acf9?|9cnQ<0
zu+|x^3{+^x5t~t^P7SV!rQv*mzW+u|zQfMPyDSF+-nUjGzTAFBh9hJ&!M0mXM!&Ei
zIFQ|J`Y%>ButK#YJ1)MXAg-EYS~-j7SEj1jbnlol=sHIK%CoXvqH8~s*gnj*<x;^S
zj6$XCc|$j(eHG3y@}`Y}#9)~5jS6wis_uB|zj2{7RaVTxqf!oLDsX<rZQg=d&GxE2
z{hgTNd^FAdKq(u-h*?ftsKgOi;hdZ#bWl(*^4YV?2_Z6>SlN$l8C@mB`I$YC#Z4?w
zg!(-Nmm9k0Mqx`mTbNiD)Rp&F78BuYB4)GjxXnT+UU5AS`ij(hO9)yv8smU|pK&d3
ziB-~VCf!YjP6R69p6Dm_@V@GUCVl|6ayJxDBi~X(bQ|I}#dQ{=3wo~>YTidM0Z8sc
zHIea@1q<<E$)_Ok0Ohthz%9kCy<&YDfX2ivrzg~6xYrgaz4Aa|?wm5xmEd974{jXh
z%@@Y;iEjJqH)1(MIWmSzxJy5nW1WfEnFFsOp_nn0S2s5Y5^E@YY;U1tTD@4lTkn0r
zU+WY7S$#y{hMTVTk7n@>FGWz39(wp7BV%J}Ow0(m119zHVywybsV^RGfuO~ep7wnF
zI&R?uf!0esh;&a7>Q*0L=?Ef5Ut7jjPId*$V=e3ZMk72mRRE1tZ_}ntP|5pK6d|J}
znx<5p2&zwri75s_eSm0@+r&lC84ZYCa<5K~y{xbH$*<$@IxQrk4m3+@F>I(JH?q0I
z@Q#ogWf3gHeihCJH`0U$b4Ezq5Z$XP?D$q6@h>meBDQ1zh>1c+EhZ{HEC52}b@kW?
zVpW{-b(`{i8zgFXqbC3kMq<r1efoSUan`Ppn0((1FaVlB=dojUtPx->v4tfUt`XHE
z${|LKHlu8^v&7UE>@?ta<l0hwSY%5VE!*;-lrLXw<a3#3_k}zx(t;y{C+*#KvNS~P
zgskxga%pQ?kdTEAZ{{a@A0X96z(DAc*|YDLNxnh3@O5|#(?zQDTO@;{UoS5LUZ}-d
zkzD27O#;Lf_2$H8Gp`Eg&j*MGS|Y%R=TwaR^w+X&C+WoI=*O=w?fb!8mB2UKzIR)!
zE01XNGX4~0LG0(`uyT;JKt9-!(I-eJEN*(ijthB*ko`+nZ}I{|%`G|8P%%urJtJ!0
zI?)YP<+10#<w1Ffua*GSj|nGdi(T{OOkmGp{6+8DYQZksIiShe-h7q`8T6*RqwGY?
z!T94Zr5=NT$fy5mfw?RQ_a!1ZUF9(hJ=bPq$xfNi>=KATt5TkHYkL-vb1?+^kSPan
zM}5G=R96u}={gpt+aaSnt>A9cxU3GVNF*%F4>#Ct)uJ$PkjVl>c5t9=qY;stiJgaU
z;t^)UsWJ;!PvSf)6k(H?Ji84dw-)S@nZQk;!xpZFn0z60sObz6bEIAVOv`Maq2z+9
z{BVa9@5G~0e&|$NU^(hIlV%1F^t~L64GZ^<H$Q+Hq=ThPX`V~9gMZJPV;(+Ft?qu`
zh+q5P33|W0eGz#D@dcehC7MNyEyTDX8o!{k6X_P-2VWSjh^4-3-2Qv`qi&P(L@g!-
zhxl;3$44?NToSizSa|=>S;SB%>@0=^_Q@Q8eWBD`>cTFWt7!nejHbhVa4|baT}sd0
z9c;fHp88Af`>Y)jbB0S!KR!!e^W<VaF&(T};#&`N4imyiCX{|3r?-Hav5FZ@t#FYC
z5!-@A&X*Icfk9Sc6O$B$yPJi~GmA8R+~z0TD&W(p=4|d?jop~)w{Q!f5>q4cpt85<
zqT(&vK3QK~R|j$lc5N;~K)IS^>CBnGEO76mizO$Yvz2(HgWC_ZsaJ=yxlwwFJuF;P
zP{@m@#U8GsPfjZ?O|e65+)a~!XVMVX9_`Pr^Aq!|Hg&re8!HS|L@4d3dLSn;rAJIe
z)MDt1LD1Y7HECqKcbyJ=q(^(;0_y`bcVQzVqhcbufAW+w!1(e8;z{z+C-zOVT~d-z
zBwZ5GCx%%Ntw-SJ+)X3$Dr_rhg}pA2AZt)i5#gkLxNosM=`gjwXiIW)%vx6%fegSJ
zE_f%PmO{0U5IcTU^C>H6(0MmmdelHm;@l0gYvtq^xwSiZ!6&a*ELLhJRHvzaz~F7y
z&m|mS1Bi<|2@)7oavX}_Og1Ph0Y~Wz%x@VnA&K=LZcpo8q2eo@a={|&{neEuf=v=L
zBWJUpU91Wt2E>jS6_S1M<Jo1ied{v&ATg1|p!xiG6fRb|;VKW;gJjMng@n(gTV}x&
z4tpK1mE_kE220hlG?F+Oh|Ppbe8u_AjVK!YF4Ln#b0x(Rb->uz7zH)^6CBQp4!O{P
ztsBQ7D)UGNgCocrPi5#s!3QxC1CEBcbBL{Q5aHybSyWBh`4+3U`#X>}<-(m_9C9$|
zL!UJle0TPmlCp9HBs{qNY>3q)(Ej5sn$M{nmyr-WBP2*j#~dME7~F?62p^CKB<DNZ
zSp9AEy&KDBQC&KSMY;S?``x?0?GJN)3tNf+sS^%v+Dkrie}rL%OOn)S;>{-p)s2TP
zoy9%H#>J7&)CUTNM0Iv`4L6zpJBrM9lxB}WWsec#FjJjZ`E<g>Bx|cv6Z-PQwWkj~
zp3OdkRz#xK0jp08=DXGTvu0A6I=z-o-*)0MF<5>n;;4A{?1HG+JWQOQRP(2m2^D6F
znnGMQL_N37^dJK^Ng4Vlaume3SIeqQfacRIW)k8uu5PZ)u65~%#OPx-_+Z>-J?77>
zF${s_WKAg|ZTS5)HuzvAXNO38(H;PgE`sv_d_I~|PKJS(Pe{NEV3!$BiY5RNedi9Z
z?0_|omAE>pW%zhJ^5%fK^4?z1fuTzsEi_xJ-gR9!IrP#=+%mqsW^l-n<nyG$Xb2L3
z=?#BI2a2XPI3bfJ_aAa&=4R7-fiU=)W&s{)*>0r^wSYLE(uI!4CnWI9C+_V2c_4lF
zf;`hDMUJz88WAP70=Tpvz$(6Pp<tbPL{gZfbu)}t+5_$GlC$S<!P^Wgf?Lk110M)a
z&b3bD`64ho>Qk)W=cJ?^cIb9E(c~ehQ?FX<sfVnsKxFu8$^?)DGcCL(Zq3ATR@z(<
zdgNNk^ABi9xo?elB#;%h+3_wzGdK90A2*F542ZFlDifKirrxjH)EyPlYV^{?;-Xt&
zF8G|WX;xF*pQ7WDXd>o*rBWW-Agh=)rtp5RZO2H<$La@IxXm>A?K^k$b45r9j76O&
zJiH96$|KA4kzzq<TSaY6q2YT{^U<b6_Rf%VAJ`R1T2@Y`vTEarK-(yuM`k#MF*J*g
ztLRD`Rr&)@;0wqv&)ZIp<Cy!VN}4xqd0tw*L4J2J1mrAj3mBPkr<=w7sXI>GRr~y<
z@rPRF<$tt+aTsogQO!IeUH2Dav6%XT!%nGhZHON|3TU}7pL7v!=Xg?Hi~|%iMbU$Z
zr;JX;{?E6JiR9|6Cm`PCG%^nUi=fhZ4kx>9ZA#wrZnHt*vO*Vo+4W+)*ZlMo&zm*d
zum1jb*mnNK&dcPzv=4t+$t!OidVlfpo5vfJl(u|${C-8oUH&)xrpkw3ZeOCl<VMml
zPM^ku#e*KKO&b0_X*8PojHARhv7Ao;S)HGMtjTe*9Jsn9adUV>zb%tFHnL*XD(i%r
zX$aXnbRQhjFIh(u7J72~@4xqqXUY_ITl+-2(8?79{fb_B3kC8>U+(3;qn?^MED@z|
z6P%O$P{^!NDL!JuFxS8c=aj(~Mg;Y_)8*IiHO%0xB6mpPdX#g3I@0=SF9Vhe=@Rv&
zXJ^Ly>6Yff-r14eK$NO!$ECA?0<@c??r-5tN~r0sP1MfLfm7hXMYgRX)@9=k%r$G4
zFaILFT7T!D#v8b9to#BCO$Fmr89I2MN!7-H=ME(S?T>`?a#gN4;l$cUpXeu29vd>-
zK3uvu&zhr$i`evJm>v6ixR4_MT+h%jJM92Je{)*QnI|`an008?;4G;$P|f*N8nB<q
zW1*YFk<rn6nB(YjcTBV0=2=G>5#htG{qq_^7Nu|B)|Pm4@1)(^EHvJ@w=#)7(Wv7x
zR`+WKoi`DMza&-sYym~zz`&>2;r7R}%Ws{MGvb&L<-~Qoq@K=OQYRCt#Um`-kQ!vS
z{8rBF%=!f#i$Y<PY8xD(YouH}b1|EE6X@X}fKP&El|FUK%erHisXou%y{F8Y(!8lW
znrZQsV_I9+xZhm0@nh-;=G<7=I_TjRc~d8PnGC*^ehe$xCd=413%)fG@bV4~{e5hp
zMU$T5GGm*I1=qI>9`A3;em#JbDwrs;tv|o@;}=#vmm_>X)r4tl#;2RtY{qdqN|8W<
zTgKqcg;MPXb<$^&l9NYr6IW)d|MB(wCgrejWu)xpvPb)m0n{iN8^`h=IG~+;An0^d
zbxlpGS(BhRv<XMNUb{#f4)Gh~4QXbY%5f^P^x6m+cI_PdGb(Y)Y~rKx4LYZ`ITUuO
zBxo4Hew$_8O%D$gxIWR>@Z|nhQRDX4_jJSuakbh_K_WKQfx=eSA6_h>30HJtfUV1S
zb1U;ZAf-^A^$O5_$QaQg6zv<1nJXHb*O+YRVUnN~RTG0r+!&+9CNcY`y^)SP&++?>
zqK}jsr%Kb)%v)l6>f%ghC%;VN-@Jo^g9pd_9zJ}W>omc&fBzW{=b^vY6ZzfKUq5r5
zn@${amAbbnLci1}(_p8ep`poOYi>_{LVypCrpm`n7>L3mWPG!N4YOMi&$cnxv}ue2
zJ877gR8F0`+tmJz0i%@sm7tm4gO;W2zGmyX_3I-ZJowqK%)sx|F8q1J-965xw|*Kh
zO1o&)rh_M+J$rUz+(<A$z+qUArXVeSo7(MX7<@)veqr@c2WtUzc5@nvGvQY|<;|_?
zQ>>?O`0$nU7cQh;Za-nuRe1yj_DiEfQ>sZ+p36)YOTysoZ!KUz6FU#=+I0e~c%I2T
zisX}_p-YUQ{JkwW90_k+WbYvPFT9O@s)ja`{g+=JlS&GLoNLS>EG%rakWDh$vZI7<
zX`mq&BB3oCAgF9_pYhfpdGCfh`18e!7qw5%9g___bOwMRJ6<JD0-3COSWBaIQ_+<x
zlw1Ia#NMu^bjH!6NA>vq*8TQdH0WC6x>#lGi7#}hwDU|M+degYef=2SeAif|7<#Xb
z3#@#Z^?7M(dmZ6>UeSN;>p@{k=nww;&RxYUVqa$KAvSz6FCPcilH#Q;OZE7TUfo{r
z2<>Wx+1kyU<ABXHVEEP%pP=uQ8BW;Qd;it>Dol1!thpJdv1WZUFuxjE)-gXUUR*jV
zhSisrmF2OXT45)&*9o3l>z-Op65cv7&o$d80ogKroG(S0FcZmZu3o!#N4p6SWejfq
zby@jX9ufmKNCo!Bo8K>WO9lcxZ*E+(EJ##!ZhA}+Xm#Djjhx91@$cWCef8>9j73|X
zMv~sG@(@YJ%a?yLRm<=Ex@}t`EO{~|Q@5R3H>+y%9XWDjdUm8D)wW-Q01p)`JwClI
z)kGD>qLZR_14<`P{>`i~d8T&IuzX8@TfQ#zHhFxK-ZQmzn>NLs4if2xRk;qmwT_`H
z$q1QooP<mki^IgmU<3fE-IY>}mDTrdP)^WL*3Po7dh2(<W^(A<jT<){tb?CE-A8Il
z)};?uVcB<Cy;Vf%^5x4b)~xY)b^c6$W2(TV_lI%dW4<~@F)Br`_}Alq{%POMl`C}r
zufP7%NH-6Xm>X5J>2Fj%ckbMZ)vJ{~wuz@WPg$a*s;>Cyr#)xPc6rjE2vxC3&W4hV
zhSfDmZ7(kD2Goehu#3mQ0DXM?Owxe`NrF<qhs%mV2pZQ!%HFaVRq}fK_7t-$2FE;=
z`r7@4ECw_#jA;-PzeO>{?$j<%bck>)!TLr6T&zwUCnL+I7b(BLtughLa<i32Lj8HB
zZ9?zeTemoEb&*u{07JN^g7c{pfByOB_382c0E^=5)kCiiedZ4+>uE@iavbkdzZfu>
zH<S2s4ilLY^rA|j3TYk1TOJFIk#hLPey4F$bm1E)-7YiZdA!RClplQ+6DU`HK$#s2
znV*%y0bVb!WCM%KKGu{=F8i-7SeqymUbHxp+Go$6&D$JsZ<~0dyYiwvtG<<F%I#6u
z-yOyxRVg{{3xW1H_jf!?;Olb%@cBy~l^LWO)tmrrkr{0Nak;MV;?SStzkN8x7e<ha
z09JXti}$2sxp<>$x>H%-uZ==kdi+~6{pWnU{{2^}33`X4VR_&eo8=J-2u59Bir31w
zU%g4VrEBMxF1U;+SDq`gI@?^dk3Nf#6fI`%q;#w}@C}Vn3PQgFlmBbqp1tVHt6hz1
zQ=4sPaa>A@n@at2te46B`<ESXu<jBLI-OqBf(n$Rkw9uqeS0uhoa;T6#W~ys%mpzW
z_ZpFkBwD@Yki2cScw76-#2^#pY77U^7n%i*2-uLbZ6+6z8}yPJR*b5@`^THy+4xlw
zq3jEXG0+5JW>QoCCK2ms1qB5!KR-_Q!pHIPd*{$^u<yY*f%F8V;7pcQ=&60*L+@rX
zE)z$bwdpAaWt&>iZ>GkVEnUim?(yWry=tTeu8w|wA{WJoQLj-y7k2)_x2>V9dev>+
z^H6HrL+VZ{b=^;Pj14G09}3;sv**L=W6a(?eT>e4lZ)1~la~?H8py#FD_7zmVbCaq
zV&7O@T4SzLIwqY2FhzT?ayXD)1+TPMV4zlLLYYAgnuUP#<Rug_T@;hsw{BHoKpAcK
zklZk%anAXhH;=N<3s|Z9&BGn^aQHp#bIFI7m6!YA4d8VM(?~To0v{scI2z5*&!6Ht
z=ZGFGd+XM%ojpu|N(hsfA0EOi8;AT+_w(};ah`k%;yM%srVHn>r|vs|wp*yaS{d@#
z9@P^kZr<M}9t(k23T3Gd8!hIx{p?Tway|V`=`!?92QP-cd2=$PcAx#w+0~l`Pwkcq
z_A2~P*-`2{2K%!#9W+ezN_O6L*$fnF6I>#KL?g%k8CFcI_ou<8bUBpu2MvLeuj90{
z(?G&54mx!Cs9=EX8uw%1Y&4#ozkx=)j)Q}Ng5xme{~_diI-K2;Z8zvcO*O77F%%f>
zZ&F3UlE>djE8emt9*ku)x-LB&XaCsaLQ+x=zA7q`S%6G%)+>GZ@PXg&6h;Z9T*oo5
zM~|YR=<jd4jLNJWBH@C6v{~}wk8kw&ImN`Z#2rT~LBs8&X=Ge_%+C0Yb^BW<q}-$j
z2lLWm6(Vj|7(Y66IjpJ3X|(GPnh2W5vuRKV7E~IJdtdQCX!Oy$5p9Eqc06&}-QgVt
zp4UR0282;cE$X(@2v|{_S+Jr|4o@j$TI$oPGp#zERgNhsc{LT`y!%p`!if_no=rDA
zyWmdk#;hiD%j6`DhCX*E#c|AdtC+pKy1F{uvVPP%9N5n%L+&>ZsYQc0lC02mwI<?w
z@w*cC^7U)!tfUPLKDE6Y(meuBMgBTwH9cxXQyA;1ld*2T!<QF*{rdIoP4-Ubcg?Pq
zIrz2k7s!*6%4xW?_thPC7p;4SmcD*(Ry*Fg(3a;See$IIrAtqJ_;eEy^qJ-<pRGl-
z4>+9JZ-4MwI+aiC+dFJVm`8PWT?9pG;V<cy*Skyqgx6*jhE}EmaT7edub%IkOEp6J
zI)cnu**DQqy*`5_w!(hL_U$>!-=+=uy=bez!}Z`O6K<{DP38w<W}^1v{sRXNjNt^p
zI?=<q!C-^8(W-|#N>dFhwh5TN8}cvG%Ce@>0noZ^C1pZY_U^6vdR3Id?%Ehnc>XzQ
zSzv7h0M@XSl{Wc%P*h-`&&Ac=zJ0s8Izq-p-Td+P9XrM*^5?bbwXw?FE;v?{xH$z|
zxK-HlPrlqyg*=Pubn_Ms`e<Krg>h$jP+~xAkhqfy;=7U@V(oQaKeW@s;H&+ZLdoN|
zXcYt3ZMI3+G8}5h`dr94=1l_VykK5m+axAV;(;m-Tg4rfEON(u9LIV((#oEmE7WR2
z!&e0GPB~Y&Q~<=_;U;K3CV2FLz6#%LsZG%H03V-7jD#}a#}u$ZW2lS->NAsn{Cd}3
zYq5%j?rcoN*eJFm4lG8jMrtfD@A>oR4<dFGw6j%2ZTsYb`WdA>UJa)8kfCCb@FtLv
zZr{I8Fj9g>>Os9XKVwRet&3B&OBM1vrIBoK4|E43BjXSTffnl;Sx4|;-hqMfsFyQC
z#+Py(4qjuToT9k-P?s!Q7UwiEa3f7S*D-aQ{rg4CgAO!}G*iD_ryhmEP}B|n%(LO`
z?O(oL^&Y>!|NaTCr<1rXuByubtUOii^c0Lr+h_W!BklyAHoP_C9S&<I_|W6W(~8&s
z{BtNs{4AfC3=JcbD_`urq9G43dTOFPa02oPDinL;K;`3OJ&ma*d6$G3czlR4j*A6*
zeMMvx%KH0Ki!2lr=q59eSoWl}piO8u;Y3b7rZ=0R_s^X@<ox)LqeumNea&6G<TkPn
zo)7IN)J&VET>=8in*~iDcqRX4ee~#696>dxi&2z<&npc+FIUwjzP!m7o?lI6Q1p&|
zxwyI&(^gj62=8bh6NpZ$NltRkI?n|gMm{)ye$IkcP5vAkY-{tR-!{XD>5s>=JT!y3
zDiWM&U4I^`Z5PO`IylPWK&-`?w`663T(yRlS$zJP*YV<SvHt)S!lLzf1ng)nY3kSm
zDknyD)B*0N`B?tMeh7V4o|e)tyRA#i({+>YiDI|v@C=Mkmkw!p^ta|}qW;#i>pDLM
z#9y*xi9_nbp)$OChCfyT%c<40%d^-l0E07)E@C&(mnIt_?XA_cjBVR!c%Z_@{pYXa
z7FGJjySuw@dwBR3evoJA<km*Xap2v$(Uf2}i(RwjP$-TKa7swzt%`#gbm12p^Zn}O
z%P*T@h-H80HZ`md##ow;llZ68T|TXLNlr=<<BU>z`<X7j1f;27pKW$yx&;FrVb7sM
zYFJAF<Jw9zG)xj+US8oSTi$10eS7N%g-@=e<IxO>{ms7%G0-d<Qd^Micwg<Me~5`S
zds>zRg7LY%o|{SoBhL9$^5DT*fz9^V2xjVnhXZf4o-8delU5VAYU19f^LV(?C9`zX
zhH5h+p1PaNfHn8r-Qksqy~^mhcmKX^6ZTJ#9uD+9kX82$GliZXMm*@`IvNdsDyD}+
zJ<}kgxT$G8wHupTgL!6W$B)k)n2lw<kPj34bA+_Ab-+bLKv<W?iO2vHto7{dQM}q&
z030Wl)UT3o8jq*OU_`|*Vm=e6o*YT-MhY;+>{L`#@;0Y-=s#0iq0M}Eakg6|ddZD*
zVkI1}Ypq!0Tc$O|gWqeEEKRj{m#jO@!`d-8HZz?$G&bc@7LpjEK`D^5UpEAaBo=u&
z4N+-FKv^WpK+EN#tt(cns3xJ@|LfYdEBkj0-m7a&GwY=ZM_9fuSi~I1d__i5FbbzZ
zPSs=E$+$klr<1d%Tl(+b(*s#A(b@p{ZY+IH9|enQTy%e{`2_H%9M+t>Ds^3lhlvp9
z0p(bwFCQ94p7_i<Ph~wwjQ^{jP4ezkp}qYzM$tjl!&js%_;j8>a^&5<{YC25b?ZJA
zN?UsQ?)T1d;>ly#4a&7E=op_n#R&D`cAm?6Gn2VzaON7{q~l(ygl-2HBQ+zt=x560
z0T0Q)+;CjWxFIQGe6Wp=)|jL(lQ&2BrpKiRPEPsq?=cN(P5OD$rk+?Hetrdn+Qy9=
zak~Mi2~i+bj6hi-)a|wc=Tg_t{UKx#+I#8H?N#5ve$F3r4YWMoMOQrRw+!8McgJFC
zlV*wOF#^bhR^rzp3cho!tLn*{cFF0k9ZXxSfjCdpyXG6^0FOQIo;?Ah6Nj?L#mPzA
zeL7J7-X;O6w%<b^yO#HH=v7SHfxee7(7$8|r_tmh)M|erb1LDnq_)&^4QC<vLxb7(
zZEN+JSn8km0;@GBbmq63t73jJ0~XAW%iXekW>Y7}1Ab(U=6HEY)|Ee+X2YO0T@U-5
zSe|@U(TkA8{2bRjE>q6qlp$4}seB80`3JA>ZH|H?t=J&B*i?|+>2nW)xA)$!Ib0$l
z8XyI0LFy5nPPHIlMmZeA<2uJa6z82C&RyB{R(LWA-@43s66>7VC%tp)6^peqE<JZQ
ztYDnwQFc*q|HM&ohfkMXp`7isL2?7%)g5lXu~`g_J^DO6woLNY{;`{*qt?eyoB)yQ
z_=Y)ab}CMVXU^$wA;v4_{J@-%)-dy9_Jn<La_5cxxnVhXV}vHBZ611icTDfP;OLko
zXYyrC?WA*FiL#EQ?vkZTKWqB@NSW2HQamN|$G&|}wf4$ff_QzB4rZg%aj%_h2%e$D
zzDXxg%t3*!gb{#0@#n8~<?!*&ne6wIF=)Fobd%2t18H@}$|@>8_c<gJ(OWYME^DTn
zdsAEBKV?SAFT6(nL(AkbJ6&IS)2INKf!<%QSib5#U|)I-E0l4y5yM2rHuT({R`E`^
zM_`=GZBPeY;7X*2gXS6$QCcwjp{s1qHeY0+q2W)^K6*hnsXgfP);MQv1Un<sW%Xl#
zV?w&*&koDvZH@#DOri($#Y3uqMXT1CGiRpmy8xBx^ZSv>f=4^6Hg(qrsD`5;aJWT9
zHL;7<2~7*8LR`9jN7mk|r<NVkgxBG{w7Ud@KViijA#sjE^^Ub0Y-K*#g0V3{?b%Uo
zmH0g=vS{kS_`2`Epvlic3#r5rdj{x8<L>H+sqvzhFS{V|k2scnT}{fRRDL#lMFoXX
z99Wm3gLe&sgE$sliyltq8$x2_v8GMNx@)A0i;IoHh^hOAV@~10a6_Zf8nbNT)^JF~
zgJY?SK64B?qJYg>2g8+T@u%@g1Z*z^+bUGt*zj;FitcGlQ_4ujIzT4N%bvpDE`!B*
zySNp$9~zx-RN{C}!PGb)*(vg<kTIZtV`!kj$Ca4S6zX@<=$OGC<EcEcP_ZIJ@}#}F
z$QXNb&DNfdl5g)AYA>d7ZDO>$hG{y2@ll2js^iwtCfID$=qbi^TyiB}MGDH56|c~!
zQ0&Rj+h^<a*bHMRb-kLK5Tm($@#4keoZm1rg=?M153RU&a(QG3#Ms=N)?M|)ol5$O
zrAW|{anYdDvOyxs^x9k}yZ`qmOu((PNyt2Ke4r%`^&A!YG=|5i##!j2p1_6hy~IzC
z^_t+g-ZbRPQ9Rg)5YLzdl>m6CnXK<Yks*ky06SO~D6GiI$!S~}`WGC;377~_`OE7*
zW3}oDVgZM{_0Xlmd^tl`v+M>Fp$5t$;A<-+=i@=y%>jNZgG(37`~BO3Meq3`r-}w+
z*cdQRH<pZv;%EaeejFCr5h|b!jz&fUf{3_Gr>HP=5{TLWAmd%rtpw`Q22y4;$}F1H
z@CnXu?<s%Ie&>5{>YZ0%=$bK5ZF<`B^T~h%xOS9)@og@nXVeo^#D@wN6J9pQM*92J
zRElYYJrBaH(~GRGg6}I4_e>tChXn9O$o2Ire%-*(s~DXhV9#J1$lcxqev&5Rv-b>e
z`ueR~6F{gFJ^&&fgo=Pq)*>z23{y4G7#V&>3F^rwAU_ZfNDO1i&~@hGRxPNdp$dwM
z0s!R11sJOsbstX;Io@EJmL#(C>$P$H-PdQ@U@9oQ<^Iv8z3NGSK79Ca)4sD;Bv_Ld
zK*FEGwl?Ow<-<H>lWNhXgT2UxhN1FaEud^yK$*%#N?>WXbynjKdGZE}7*w#8-LS-w
z+Ksj40W}hB+ut~ik-ua(<?9DVzklt)K|t3VJS-6Bv!rRXGX@5cTh_QIB_)j_a4w?y
z5SasySnKPx{7Yd(qoCJX#OCA6<vVxp<`x!Khm4fi7MgOg>L?&%Ep!x{zIXdciFAZ>
z&MP>0Afe_P@TP|YJ{bYl(=C1E)i9-R5;Tp5egsJZRr@>7gNd;=*-(x{b(L3e(IN(D
z3Jm>^$&u4DIO19tPFpKrRQ)H)-R-+~YoUC32L&a7=m&4m3*rUL>^+#<<2MNycX`T5
zWPVn%`+B3HhXPMWZFBha3rztrPt5Y$o7`{SBo8`ZJ~<eA3dl%g%bFIq`8h{C<Z3|z
zQo!daCmYDp6wqWye}wx)#x`4<u0$4d=gw|CkWJOrHVso>1a^T-K%lNr=4;>_K5o}l
z`3IRf=?9^MaHuZ!5=Uww9OU9u5)@EpQ_WjMP{Vtmrir-DX28tHX4a5&ysD}SaOF;`
z^N1|-GLQ-Q6bvI#gwq3ybp*HVFYc69#Qt?kymH%w$e!VZwBqNlE~=yGS3J35hb>!R
zRN??fkTaJ3$r=g9O=$@rjHxt#Q9JX$+A*+`DsPOOj8#eS$K7?(z7@>q_qKzrJT#IE
z9^8u(*>K&TK0y=$1p{qk?b;nQ1PNz~EI!$0wCjxQqkYf6Pe=04Bch^Y=nzppwo6~>
zB`6(|7Cl_oZ7#J^5J=|%mE6snysKBQg3jssmyhHE1gByA2>kw$&7!MpdunND43>ws
zB)WiTLP6?j&P=8n!OIgI_^okfM812cMpLc-IOf%2&S#llW`HyNdp9rw+4gYa!qIGE
z;C~RY?s@=vXM0>-EKphuECSFFRJ61LQD)Il;xH>Z#Tk4Jl!64nK$#MC-ce|@5d%@E
z-t=(j6&o6OF(}fNAZ{pxtQ@+I;Q{(rnrTDW_3J<4BZ1rcn==*R>$qkr7=SUIOvuo-
zbX4N%GzGq;fH|gcIqWe#9CUzhRtTP{aEy-eMoBLlE$QTfM%!bxD0pZON*k^gh8*q#
z2NajB*;0uuN(GsK`Cg8UC&20Jw`}RVr|~u5u53pZ@#y5J(GOn!_@`NGPUf3&6bnbx
z*p*_fP%&xMMB*&nr`3<X<%sgLlSYn}?ABi#@%eH=0Qc{|AI4LL?vU|}R2EE_inzV>
ztZxnKb_516LV{4c;U}zx_=Ms5h^jn*v6sJpEDDh(ViW3=(O(jnBO)=b_W}+Q1=x#D
z4?|U>DF8{)=?k<BO<`i7MHzEsueHr#SL)NRE5FB%0C0FMWp_y3uLUg@rmExc$}Ahj
zwFl4I*)x;@4GlkHgNDHk>;oyt+`|HK4Q=#uUE^Q$p~h9;rrQGDN2hkJ=8(_=!&3vx
zS;97P$7n<}R20J95-u4ma{s$$$G`-e&qQO!TC+_o8bXkKOSYZeRt}VRL~b|f5BS9O
zn>INHG1g>U{Rhh(2#zhgzL7^7G7YjX95z$$9<fD>7WK4br#nszXvenymGt`i<xuwR
z`Rbp?js7#fji%^+({m^lf#&0x3>nJ%9si4@vHf4vjsNA_^Q*tD-Zdw4eaIm4rZrAP
z6*-*w;{Vo-{O^|Sw?=^4gV4Bax~q@>^2;wovPHN>5r~H;-I(|VXJd#1gDnCr+b=jO
z{I((!_GNCQ6Q23gtET>8b}Vz$?jIaE74m&UvSBaWUbQ$rMZ^bU9w4I^$!<u1F{lBX
zBwcfW;QVSI1O@S+)$Vd}3Oi(jR-GT)q8lRPCiPvqI)>Kcg=F1LBU-WR+?cAqp<$_c
zdN1Khz#&sf2P9G+dMsb*l9C_4Yip|vTe55%@$5RSCP)Hb3A~35rYc~|51Kb0B3#af
zJI9H#1~ekhq1vxopQz1^iwCzfowd&X%0Kw-kJch%lkFZdbR3Obf_D6@G7So|BkY(|
z0>ZeT{&=PE!-G0w?>b|i6c47Zn>L^dNDyoBG}tPDnao<Ul<8mwg)*m~X(TMT1k6B@
z?ZFaa3L!)`I9+A%-Y*IZ>z+ulfnQ9hv}!UHOwh^I!1P4)=JgvkL_T`-8zBSnr05HO
zjqi+!J7hA%f{H}yNKb9d696cXJSPFz!pLlv=F(~+j$TM)7`&2bttdLvpaaEYH!k^V
zOT{UCd6KD^R;1}Bw{C{vVZ?{AEU0w^*R1Hvuh&RHkA^mJIx4x~vTAzqoM*b(Kw}aC
zZdhQP>9XKNAqSl`4GOq6%f^Ih`Kzkae<-Xp>&RX1;esvubQGRUXCz?WtcJ(N`G1>G
z$nGp(zC0F%Bn{>BeMYm%9B2@N6o~!~@>UsIO+7=Gf$5HO|NdvS0#APbUK4(kHh*dl
zOI{BSf@K)Y6cijL+W$fecZ#@);4AL7j)fzH9**bF5`f406wJqfBS;$dmIs(ldBKF=
zy}JrNcTIXY{Q4)L%<sK)3LG@y+q-c5HmSiBeH6zBYCwvu!`4hr+M|_JJjrVI6SYeu
z+yxpb0X3lH6Rq%E5~5Ws7<js?ccvGyVYWf&)Sw}CTsmZpDK-}_UV1ptS`Qx7qQl4x
z`(8<|JPr>JM=MJ=*Y3N3*u+!)d<BNCH<*@Bw1vHV^~z{!xPz<{^l^AH4nmU31?5W*
z#}mIc95O((67n}zm5~Tkho;7?UZd+W=1n!iiMnldHmNecyhivTBIjVG6H(l?pFe+|
zXa!f764?+aYol;OWcNQI{)VS#|B6+P1H%~(h`$=Kg2?pzByn*f{PXR*hTrC%e+Wd<
z&pe8Qx<SpW>=IZnb^d(V|I^-=M^nA_eK(&<=X5GXWvEbw;%G1=G;34lWLD-9Lgw*Q
znuyvFMM)%O$~@DAA{0V|vJJ6q61H*g{eG@a-Ouy9Yd!0I*8AVP-n(_Ld!1E#`0d~C
zx~}i=8NRLGwz?~RK^u#X+fp_tCHvVeH#KEbV~Wux+;SkDKv`eeN5KMGiW(2c+_wxi
z?nr@e5gs@-e(rC7c*JTS`qxPliDHsMt+}UYG+^NzG4C?ia5wK<je~W#gWys$;6SY3
zNZE<_{WlBAju+ft8j$XyXZFT*BprnnrLMi$yV%fX@JI)ei|a$yHlCb@WP|#nQ3?pY
z4uZA6w}5|EWax5W)xV(a0IDs}&v=PFmQG!%j!|plr}lPNhI6&?fDzQy)iuYig>Y<9
z|B7aq6}Amt`)=&K&-&8K^_O01J#jJm2Y=dFdv)LJU*RJlmgM@{HE%x@$<oDxtF2wO
zh@63XI&5O;pFJACxBx!wi#53l*WW%XqFa48T!MmRQ3rW9$RpWcV4ygxbdB4AvkRbb
zT7zr$CxaY8zljqiQFrsFUEs>L_3`Yo|2R=W%qg+E5&-sqaV85ek5z6fs;mz<tbC@w
z*N6gPb@QG99cou1^D0u++)x`Ut9)zTrbMlmV-qNRMJUMM8o?w=Mk(<gU;HD}Jl0=e
z=n%k${w9pV`6l_V&3N^VWgQ?p;UCR>lGxa`;z8PiwMmVK&8H|@O1sUFdr&{6Z6YWR
z*z>vX*g`@=A}bXYWSc;b{p|MUJlZ0vKf)3$jGAcqzNRKFsfb%%4^xu}BZZpfF>H>5
z^y#aLh+CU>9JBn_B8kSn(g4@&x-d9ydVs8`>i5tnuNM=uaic>olL`RgD2_M8xDUu<
z+L$|R->sw6!IJw4ITR}m{AEy;k;?G2j8Uv>SGyboK-`v$yTUB=VMvjz9;38H%%*1$
zck)M=sJ#6+>KMH-MTCMCoa{FEsc-S6+Ixyg;ER6+Rpb%5QI?kRNW(T#0KQyNn)$X5
zd43_&#@hN6UU)~w{thCg_Jfk3PD=I4)&B};eWX8wv{M8z^oTYZIF>R|Xiw=rR9$(E
zS9&jUdR%cMR0tp?`EtvczgqYq*+2&=KXSAiZ!D<*u<%B5T5(`(Es=GRaL=glj+J_I
zDq9>GE5eqU-=x*wIiTpj%do1_O)=a-s>%8D36~2r&V)wnejl_h?H>KXzw;{^@URlQ
z4eA&UAJjch@ZAQ0U&d8U26usb?u95Y2KPof(NC-t5Rkn2Mi@s2;#66Axww*&DH_FP
z{b_Q$QvTC%w;nJWs!s_770`kR=tq!k9O5YP48bq%I14^gG3?n0=O`-%A4YO`^h<EH
z*+4h2gSU}?R#jEiMu6f{l#!9qGs-#r9>S7%1cW_5!ddl>n{sLE_g(yoewTKsAB)^X
zL~1-*U--`BHr>&^UyPND19!VW+XwtE^l|A4Q4V#$@lvrXkPUy5aLu>adV9M?`4Vyo
z<Y}XAv>7cF&`y&OU`Ucf=!!%Mug_X}>Cuj3x`Cw`Kf76Tflpi{?E;*85e1<*%ub@C
zbO(Z_zkLZq1OL33WDq%q0LRm7RshsM!JF1hKzdtjY7+KaSqB1Rnx5DpG&Sw=M@hNe
zcj_!iAFP%#RdhD|Z%h}S?d#O47QOr{)t+@+^Vi<^Z~ya?C5$=9AJKhH1;f0b7tJwM
z)_bfiVixgvCHpLYL#Nr{jXEWqspYgUCf1olrRzx79FR~p|DT_y1y@(rM>=n@I&WC?
zGUn8yRh%2+zUv6EpgWrW?GarnEnDucdfM3hSxx7Mlv83g!7VeTOSIZ4hDXD%Sf_y+
zgU&Y9e_mn!%!yq^&zmgnR0)>fk{&Z|Upm76vn2xfMP;pbQJ2~OhF&OrrkhPo{tLWl
zX1M+MlDV_f1Qq<*MNLyr2D0y|)<iXZd3-KgcZ|YM_1`5`;$w1%isbF%X=YHz_359;
zNd)Saw~=0hs94bz2@?jHaq=mZk1dmMH0c&wF}|p%LXEvca?)<N4Hxop<VB>ZmT7sz
zT7sffR%)aDA0Ivbo7wmO(nha5mF<<HJWJHBBqrn+bSk>?*D?8D>zKx$1;t-^eTp>`
zn31CNpG$^ca5}F(8XvwUb7l$t;#AF=#moKcxcK*f{<DVveINdRxDUDkX<XmJ2@L?s
zn+CLE(iFX!BtStUp$WXu6|zNwl{kd9<~hY7KR;^Of$&lf;f~=dtDpNZKrL{I2SC_F
zG;p~+X3*I&EObjjS(t<jRRZy9Rgj3;TI3FeZKjqS^+)wdV?TKl0SYU(enzYWF$c+5
zf4q@H{sHuw(I2U-tgJ)Bcn5+6DFdr+ZP9_+p^Yhx@C<;3vGdpu3q?GmW4f<*c%W=d
zMm!({L6VaH6I#*UgWN)aOe~<rqp9-r-NyoJmq4HXpHFsTnYOAd(gtxco5z8G{?H%&
z`7eXZz7>1{b)gl^W2NMuT$ovlkF9U|Tzm_pDOKu&xVV+emv`n=BeIWzTTD=P9i%6n
zot=dDPYTM7H~YM9{mkv}pWE=cSQV`&P<q!>)?g4uECY0f&n-%>{bAtdKE#DT2Jm@j
zmuE(@9(_mc7{o+?Xe}JZ3Cjnd+2#{tY>+PAHi_hE2Km2XSzT71(Y`SJP10ZgQ8YQ`
z&72p^n_#P4{JuTGSncEeRYTt%wLC8K@A4HCJ-z(xnD!6E^#}Yu!LGj}=p@HSI1_|+
zJ=j*5gwE0YmJ1gz_QQ!<7k7IlINHcLP|gw74m1Vz$ZWT}^c+TxVK(AJTB*<te{qYc
zw$?#btyq#^?C+8Ch&nR1G{93!&5uL~_OgMV@0W(ZEiel`De1q^W3^6gw@Jcl^YjTs
zg>I|EB15J@X`Hs{`n<3RL*9Cc#O}prrIO!EmQHxBRxk?<eASy|+?{<3UAvzlX^7K%
zb|+a9q-BQKT@Q1iPdP5KNitxg0GQB4;<CfJ>kug*QEff}Bx4ZqGbZ1kBlP*GdY*z^
z<7P+%C_s`LF=E2#$?ZTiLJ3Gu$~Zh)(`}#8Fprl+>1k`mBW1eT>G#0l?xE&lPyT!2
z`f7O^{nuw`p2K5X=Ai^f{)T>jsKjvS&tHk!g}$6uM`{91c@s1DtdFOal7%moUOVaG
z(buuBvg$_wrk1YiD6UYurzjQNw-WcE_<2(r7f29D)+q8rOG~=w>DMQG!jpiGV<(Wt
zQq$DjfDFAabK5_i0jkAH1NG@KO6SlLLmyzuws-(&T7TNniq?A8YZh3<Vy^thM!J`-
zL-nk*%i9mxL7K8?*McJUGr{tgYkd?G@@Lj(*&J4kkZo|4<HI$oc&^rYQ`po=_aA3(
zm+XGHs(f_usr(mBX+cFpb4%C{W6~=2c8(lP{cfl>D79_YGf|(?Ll2gA|Lz{O!!7~X
zRXc6Rf~A5=n<<F=qX)12+((*;ixmiq>lC;QEl*FJNB5g_vOM_)?}54b1IEskwf(j0
z9G_g@<}q@y5Xhk(YV!0KZT|r_2LYO>^wj}(FB~)+&{_{sfo7&ACMIr868!mX@r!q!
zAIF{{-z1Fv<EM?@?qYid;H|_Ni|Tyt%Ss77TPP#~a~uIGcG76YMt}W$bDq}vx$Q+@
zhTU-YuJw*1Ox6Dr0!){cz4r4U(=0jqIzSdA1tPqB9Fl?*6&(4;0C2tX#eaT-=<UV%
zznH?a!hZWdrj!4E0l9JigS`9yy&E;*KhWlRmiy=wXIR5VXx`q13W<5^ubx|S`E}m9
zpiY?w_a9t-a4zxwhB=+0i}RKSea$<!aG{@;f#vk$buTVgZrpHLYya;3G6qMQ=Ug~D
zHLg;#<w-%3!rl0Nk7(1bAu7~C-L@y*bfu#jZ3RF1Ve1aw_|<F=;63n<B_eLFIrpMp
zM7+yd!oOq$|9<hT|8h4(_7~1tSVJ4$U$}6Y)}5U58S8la;|5~YgVM4XFYEN~bsjfT
zU0`)3xK7utQroLJSGi?+Lkm4Lr!I?SmA(1l$--Id_=G*w(vd10JZ+#NloS6VsLPF;
z$I9udb>a2+yu6u93sqk((mk0Ndg!v--8G9Ay<aNTaB>(90R|k&W2wg6YSgskhR3>Y
z=0&;%{STAF0(UBgWbpAY+-`)0S+ZIM1Rtz#ab54^YZdk(qpPO}-!kjWhW(tye0xBU
zSZ7|PDn210vXR=VW&F1l<(jH|d|kTPgO8FJ6E*UNHvGDG<Uqdg!#Jf4_>@x~#qG8{
zfA72&fVlgRv`_&muPUQ<N!YvIxnnrWtF8CrD#{FQeJ%5N>@kgNNlk7zDZIbX1Yd0^
zdj>5~?hhIJ3nk%o6rADZ3tHhZ@dcY_?aup}u9_aFBIIr+cW2YO8=pct7jIS@Ro|`p
z((ej0@EqmatpyKb|G9{;i}8xChCcH0@_OfFrKF^;io7}2(w`-_s6Asj?P1+fshW)a
zva9u@S$ppa9MkLHzWhO(WRYoF__wRfBc--Yfv?8Gwe-@`08a|#Y@Q{z%;U|PpQrFE
z(d9oiBD4OCJM@cTy*qfrFL}@`>8t;ka^VgCe(}HW#;kv@jsJG1h_7#%fB7hq%sWcH
z1;D=(2ulv!0wCrOB{A>}#ezy9lzBh?uooQqw(2=B(lN}|G4k~EY#j1qPuj3R44eW;
zMH;G)jq{zgHn0EZ^#P@G6Kq8ZeDM`#D4M3G_XsKe2zc}$KhzNsZ@s$05debkz_^Js
zIsEf${O1aLa1n9Hcdz0Ccpo)DCJK^J6>T^X7#1NM$02nFF;TbBjd9gY>t|ar%n5IM
z-aA5Qw^SmRobPiFlmYmr9Hfqdz7+lLR|9fldi`^wh4|4#K_%RWOLt?{9N;k#rc6O0
zp`oAu^Fn46hpE|nn+x63gdan5MYy~ybyO`P6e!6zpYa4Y`DSge3u`kfWP#8}mqoUi
zsMfn_u9~;cH%t-7e|V0seoLhe-)Xfs_c!0rx>Kocpt^LLsp4(6-<$Pb?@mrk#;o0a
zyU*qRN%+Nm9?P4K0Mino;LyZR-?IV~XqbGQ0?v{eLzqzgt+|fUs`Rtl&DF$<NGx(<
zsE3t_-C^vdc6NRj+#ve<CW~L^FSKXMMVgPwB5vKXW)=Q;DwL88?MK>{xgsqUz9*kV
zWN5GIAC;?gHqL520O7-!qqooHi@#3ZDr!{J+H|Yg&#rtPp1*eQ)ma^W(<uZ7CX7e$
zH=SAPU@SrQEJLY7<01Lgt5^4{(C(d9*Z-NDaBr5C&DC9^KP%wOdyAFl`aW0hDssRH
zKjye?r*tGZRUg-^97w}${j_7$A|~gYuZ~Q!#+}6ajOPuHaZ^VxT|OcZTH&=kaOdy3
z(Ok+?yEb_nRaI5#n3|e~sa@#28WfaS6Ig8O`0nJJKNSXgI~z`Zd^UFU%&YaY(l5?)
zTpy`&1Lf_qQH4uO1#gA<zvnsSwSclmW%gPOrxCMURF#9u#BTp6bcu@!tagWsALZuZ
z*%VXzC^<RVmgl#!u3?X46<U$0l&j$aVes9Ebo~1x0iyK^Q`0Q~9-H#oziRl4ZmO~T
z4?RK%pFG$p1ELWN%fhkh!os*IlNxz}Yu5eUeErUyQ%9OyK!9I<&T2QFW=d2z3O=Ee
zjyJ(wiUi46wea1(!(Was=Yh!LkDXUff^9;L-v&#Y3*kT856?PQENb7IXq=zn%HxPP
zr*;^W_~Z){Qqrbm<Dcu5eGmKdTm|htn2}Nx;@|tM7eBudg_Jm@0NYc;_T#Um&AR@1
zOFUpKErC!KbdMS?3Hl{QNApo8N4nA0(1h(eK{!BlS=mkfSt5zjJ>D3uM^4i>*&EXY
z%xDbEuYSN`XMOy@_B{o$rj|JWKVJ|~x?*|Jfp>{oLe~V9O+l1yA}A3k0*@haAk8B1
z<dY^QCTw|rDt?}I%v#jGfhdzOG%Xf1oBf49JWla(?1ac)+RlIHyR!m#yN4%zN^E)N
z45Gcp!`KiePADdTrGzOv;V~T!%0u_^*gVp``}E1!bC&?nGtc*ue_R!llG1>L?1pf-
zl$j}U<0JcMr*qe3nbXbdC<N1okMZ*E<>8W$IUQgdnHTAx-vt~;oI-GRK<NVHgF*d#
z2+)v1CE^2sHwW_fi>5+%Y4^j-1SCc=oL`k9dz}c;5UwdVgzv(6?P`$4OvH`r*Y7W0
zDZamuch;XW%Zo0r2CXbcFV$F#LWs@ili=H9^HK}?#Tjjd$ADcE(ng#KAGZz;8?;Qs
zD1=H(rtXn_HU?VhSFp&!!u*R$&?3~5Tvqu6U=%r@sKP|!h;CAl^A$~FqQNQ;;g_Ib
zQgg&vn1mf9*9^zBz3}4>6z+p$M-za4obd+kV^H#p0v=VRLR_}LPGFWfZ|>Kho96$%
ztorrH7OJSLhl~wDL*=>ap6IOwH;$*(gXJK4Yu=5|Ba>&rc%|F)Z)bLQcZ<o&9;4zE
z0=<zu&g6{TYy>?Lj>)Ct!QwR(>hk5wxp^w`sQT`B>uoia{;boVrxwqu{Nq2R$a##P
z9LN7jCa5afw-Tdyb%;(^n4pPd9-o?5_``C{mKXyBNa*#d8X8jRR`aKmMg3QZwdUB@
z6NHhd4e+$2zB{WHL~p7DK|2M#M-%u<wV?4#lwCMXNQbll-jyHj@0r__+j#30Q{~ic
z!nP=Z8lhrnmp~}OS^}W<D#UR-k(Rqt9h-k~0l*#}7;=7FRfV8i597>us5sMJ)5oRG
zH_^kxIC{P03()E&#|xas_$oT`>^ZP-{vmq=iG$=4SC4L#4Q%VZu`3}+yCcw~7BFK&
z(lIGY9r4sa2boYiU{BKoIxTU<i2lYdFJS}K<njCnkg*P|DdOcIhW@1XfslzQq3q%V
z(&(uYsyuZRoOse|-*lGse)*M>hTf6KAkI=l#;*+z4~GoRn`U8jJ5f>a^yh_IdWY<f
zmXeTeKdpksc^0H`M^lXr0Q!pmRTI&`j5r&ehBi3$=1o|ltOv?Ebm5gppGR~W`cgH@
z9G@m$sre56lnRer*HvSMn>wTK@%r;gEBQQ(U6TG<pqGnq^x?|0WP19&L#_XBR^3JO
zt&UyU=;5mr1N`0k?sd4b*?>J}LrKlheN$bXol`6vv2q~u!OW5P%|<zgP1BBnwJg#2
z<6*u4y!{wip&Au*X9fB=(vT+7+DBWC5Z5uf{sKVo{)tv<GyBk%diq#_qz!VTmUIi1
zzx^Fc*9BXyc6M<|+4?p{*$i{*ZcmL5IhyDf+LzY$-fW^@J9PUKw_A+L_ee=qUmiC_
z^sc#@-AQ&i{@{H_bCVh0^Ongg%LmEL4z%hllE(D6(j(_&slF!@WA?F1o>uq@x4H;-
z`WRdure;9}>{)C|D9}{m7x4JAoSAm3S)G#~1Re9)_LMS@Og1>Ph2<V%XQtJ5mKUaE
zCUz(;>m7YHLAyNsMdEj>%SShzDm_rB$l(tQA6XS<B68<*8&)t<v@{}qz^WuxCInYm
z)vwNNUXM8=N7Ky@0FsnwIdJ6(;btS=<@)*ItkEs?^95aA6elmyQvzgvM3_!Qxq#01
zgJaDb2uTURyO0=XLq8Y;O=xM``L^dent`rYIh7B!0)UjO9;=C<hfuTd@v7?TjqvIq
zgm7PYwVVP;7peO9%c(0W2uBQyO-tcRZ0Qw&6}>mHe!5;SBYjY)2G%(#9Y(kZiRG3s
zac2Mvfp81ObrbJ{plU!p3o-R6Ja+u$-p6aUyl{%Zk8ICeUI>y~B}}D|-z5>Zn>TVC
zoCFB@_q55{g<}(iPzjQGjkKjpz|C)z-g>G*lxP!PO{}=UyOjH`VohszSC_t%A2hdc
zjZl1lV6NtZaNBd^I41rPLJ<cULfgeM<MGzDNyab+9@Xv)RdY`_B38(Ed9>4Q;OkDt
zvyL)Q6Oy|I(H>6i@aLGoVIh6FAbvE!f{QSA+-ZVA<+DTgsEX)l+%Qcj4p9^cg|~q?
zWhVyW{W!ru84Q`{2&w}SumEgQUAE;s$IS#Tc*MK`wkwa}w5reJFY=LjUTk1zT$?+0
zkK@~6BrdL?=P8(5i4IIbPIe?KQVvh)D1le7TK$9MO2K+0f(;;W!TNO{{G$03+%}t6
zi7(=FpB!*9t&F+)wq0QH)e(`S8X~KEm-5N26H_jSOTxBw<+pT$BcfZ6K2qmz;vM1?
zuXy+uJ(wx4C@rwC`Y@L>)LVU+6)JYawYfEu!+lt^_`x!>fES|a^BfURur$_%R)9IN
zh7b}7(gUsxSJAi3T0s|(tBKeLx+_hDP(Wo*2<-<mQ}4vjtf(e*RYFY$9n2LcW+FIo
zsV>fxNuedjlJ#SW%L=NU%Bre**gvHp?)Tw@`$Nui0_z|6m?-4-nuxcjpY?)Rt_fG1
zu$wpIh>8Z6(r7ck`hAAyh&5S~$d>DXQNgk*l}j>jH!;4=_WdH6^z_B;m?6}Dg!w}(
zhU|oyn2<&oLKlshhy=ov2xI3f^xnXagU3-i5VH%=ovbV^Z6Tc@unK4=$q3e_$Z)-$
zb?`waK}55tX5cMx{{Hi`NCMVuwY&@tVG4eunsZGIA+0o(Ms2eR!7u}U)^rC)4|j}U
zP84mV-9%Uvn9YxK+x2t>VnVN>27#p<98w8k1)Od=807DOiiSr-Jc4Ne(e&@>-9G?l
z0a5|r$BV6BzlYF>z>g${(eJCb?k7@LtZi~$0tbX(Y%Bti4nU)BnyDi3wkiO1Ztkr@
z^RmSR>ckDd8KzvRLM60e<aNYPs0Br6c%DAIh>&&B^$!9UA^VQ7N5Qztq?RegXn^gD
zvXP*tA57nVjSW?1>yykLJC>YHq)Nvo&gxm%3SkJr%An;r+4<@UF$YnH@ErWF-Gm|e
zBa<W?n}fl;vEoOc*uqwT(6vy2$`U0CQO7~ZBIDSeOq|C=L=FIxX@K-0JUrYg8<6u_
zI*N&R2%6!yZ<D%NY||zcRO;Hu6tY2zRw;}@vUfCs9Ee1c16J=oTs`?%I4U$`orJ}x
zDd7fFM?pS@Y@z<W-Z|-tDY)@H5>dM)7g9*fQ6N>DT``S^N*)BNI6^zx6S0L~xxK!w
zF3Wq|fd%p=F+AH**9$`Lt=itqo<Gq`y}xqqVPE3hfU&PP(POaKGLCVKn#lU*Ohw6d
z_~?MfmFp)HdJX<U#OjKQH@4h2DDBHd=&ac_a+rnUN3R34Fki4^0zZU;L%NSJph%u9
z#_9Sr(_4Gk=yQh#ku^aa_wnOLD)(0Q>GmQ67_CHvDT}A##1S_uDMAE&gmng!zk}rY
z6MGy5X0ES1R&XOW3qL(gKmCETvomo$xZY|vl$7h(AsJI^6Ar%mLt>QznVy6g$NaSi
zj_qXDUD%os!$M+y1qbYh2o}WW3K<1FM!Izoq3k4U!IN8XZ5X=rbOhuNFgzot9-&#i
z8#%|C9=C&aj3Meud@IV~N0_&(1CPUn&9w)v4fos_!ZS`n!MTU#tVQzy2xk&u(>pSm
z%zTdwYSKq3NfA|mRG0id+|2FRP{f0W!2UR*u-sA2E+Go>RHt@7zi$BCDO4DsK`~)%
zkp7ZBcIx@L`F9{m!orkHYa;T)52+?b_%VyTOh1>-z-aRnQX1kIf%S=$Ss051r6@@#
ziB=0@KZe|RVfQT~LIhR`u6^_7J!r5A$h7J14$@c)rA_!bLO3VMT*5{ggoEMm&V%OW
zC>+BOw1|a*gqXj2Gin<m4I=&)7Nt>g%4ff7f_ZO8xWafKQ2Jz9RQy3ibfBca1C64;
zc?hR@eSLk38w8{3V9Xi#o<#|gO#4)$T#i%G(8+0X=v4bpITlW@uMjvwQJpb~;^pk9
zL_)~)7~SUXB|&^9!t<g<lM%wt&3$la63-2lXbw)e<gqlfQd4Yol5S*MRjoq$PfYvt
z)>8LYOu&im3E01c!1uwdpv-exVZq_x(k`mnC{S#Yv5@+W_+e})o4~qAl43v+`k|fc
z4sEA?v}|Yt;XLC&zkG#PN}lF(!n`P~gV~g3=4e7A4i+oLb>Cq@zPvh2h$A?>A7U9@
z3?8O#hcE*1E<v|!0(D?tn2x%ZvvUTq6Z<R^#k<3O&=?O1@*ESG-`ykrLLeO+#fEqS
z9xl%EWR^pki%tab%t7#p-e3|#5%Ff2ac?YdZEd-t*Bl+FTz{_=87Db)!1}Di&3=OE
zMpa|eY}e0jzM?A`Yd+ae-bl2_<sGs{U&RX>f$&9|`t}OlxOrv&AM-C?Sg!WX4~fY4
zNuPqws}iWD5>cT(_L=-Xh2S3+9=_7q*=?KuTkQNe*r-%V57mSGmW?QLjZa=1ZJ|0+
z+dxO8AA=*;5U!q$D1=;}&*zc56%`eg1otTkA)%MU)8sJ4F^eMko~c)#2u#>&rjoIg
z;FfuVC{amW3R8}|W4BSfkfRLXjN<6KeIZ?`1~IL)!#Dwng&f4gof9mm7{(BC`;ZLQ
zg9gDrEm}8H%)MQ?1GZeF(3i-~F@g#Jw~(|(={J_kV)*e<$k50hs~8H(XnRh^XdahG
zJ1M8f(nZ1oj|CSJ3$YPlOztsbOM^v$hhqW{yO0i^EKc>6EL<19H$}c5_8EfPpBue%
zBhELrYpL-d@Y;ekA5u%II3V}7_@PA5Y9}vp@(oqwj+~SEGno=pW3#QcZfZcv`T;#+
zJa<K`o=u+>VtYci^PDP&!*&8_+DZ3t^uWSX5-Zq%hnrn>J(|&tVeju@b@&7a2o^hA
z8?9n^QF9FZEn+r(gMnna<Svmukeiwygc~eye0zX~@S0ppR52Cq*kY%uGryWi7UJzj
z95#}aIVFyHAG%4*#SS7oX%cgfnr^E|_cwB~2pPj|t{a}lNo&Zt2g?P-ES@tP6u?Zv
z-H}M9DZ>18>TWTAb3~u=Z|2ake*gYG3x3@J-Kl1!?JaK8Q|GItRt*S$;*6tM%w^k8
z`6+PxUQNJ!XNT9QZG}67N6z6>h@(sWi_#^`30mW^o~|xA$q*5uS-=77`I^Bh1s6GB
z44zn0Uzw0;R_~cUCB_N7fMkTLpqlD3SdG|V;FJ%IVaRv1!?qvy=jNvQgIFBEZeYt|
zPilfPJ+}od4*GVC-42j5i$+c8=q|$FTRr&}NBnw<-khqeM-UubGQQPB{{VOK<(fSZ
zYv&2L_c$V*!trZctXn%8-cx1>BiBT1T<&k~;B<U|O9**%<hb5b#Xaz82+@`r&G=B0
zCB5x#;FV#h>WL1NSY#5V4o**31c-~loZ$hzP_zz7x{fZq1uGTdA8R4Pj(=)=ZbDmB
zln|AOf#(g3Q?HJ|%KA!pB$Ko*RAMYcM;xTv+u6iGx55VbDoLRDS}mEiutDPM7@PEI
ztnN52%|?Efz5EkX&T(|8L-{^?#zHNTmjm{)eEZFH`4yht17Y83S&B7L6UJe@Lmj=B
zR?r@Pc53H^_8Iw>`G)eS?hB{vV*NO1UGDU22hW7od#D`Meua@nI`8|CVbX!+J!ujt
zSBiBUAfHq1z5iVJ31l@yJ`M45GO-zb*JPvp#$>wU$%C%V1D{$lPXecRtw(0a`%RX~
ztBlmR!iKq1s&Yr3@8pf-AIB4l4kTx@<I;sZQ&(N#Tm0uDCz0&W5GJWoUFR^+ZB}^K
z0kN)rl?O_?53&1xJ91|`1^GuE<X)~2bE#IwRE=VUhz`kCPrR6iGEEJ~apt)(<j-@@
zeJ3V!h-YYF5K25rKp~qP>)^1Dp<~~G0I?mbyQ0Hmmw+OMXOR2@@+b!HLy`{@2PhH&
z!;hJ+W|mAM>ly=zOcaSMSr$sL9wFaGJ`>+^7T2f3l8(oSt0TuuT*HZ>rd<aU<_kY0
zqjdAS4j4cYS!DL<k6S-W8VctYB7r5hkJ!UuriLx1a8G@W3nk`L$dc@kgN77reOp$@
zS#<jbRM<!-6LCQA89H<ErSJ`P_^ljG)Qo-Is`aj_lB5holT9jU@&h4D(zka)E;5C*
z>^z%+(-L|PBl-m93FpW-d`g5}%d0ivI!JMdjFSW~6hIF#@?bA2%_iIzqSLGRE?M+#
zZ#4upuqTTYHgNY!g7mn#1Q-E@WKVo!+wn|E1zV*s*>zK-TRF#)UCxVaGOqYlMTJqE
z(QnXhA_DiJ0Ck?JCT$|De1kDB7{K<yirXbCA1O;k#}Wiu@=&p0`LBP|fSVe6`6)pH
zE1pUmeG-pgVuu)x`t}xaP@0HBv0wXsu)4$)B6szjeZoZGL|h%>XO`_sNQHfI%CcH4
zO|HU}$Q8c1v{ZyPYCM_@O-@Cvj(!)si=tQ_I5F&y=aRq_KNDn_sYoo!FaX1rRa_jJ
zhYsHk2!)8@2eI!;d3<=LsRJT$axaM31*=bl0)0Up6!PRqpwuCw7}EDgJ})eGe`x4k
z?D-ElXx<LCgn~PuS}B;)Bj_jq2~LtRe2Jb|ut@8R*2))wfY=Bld5i2Qn0G9ACFpH-
zQu%QasvQ)Oe-ZaEVmx8N!i>SI*~QkJvqSA|?@Q0Z))4-<m!WjDNbv{te>N+@$P7`{
z-p&Lb`Z?`>)An|PttwNuyWJo!TPI0kG<k%;E>8M#knkKs@sd)yE!1GT!ee?$<gt0#
zRfs38pLy;<1xZQ=JYupih~XCc<YE-yN?eymM3&JWQbR|2e9D8p_;6t~pK{^-Ty^k~
zCJw<+6S@YUTIBjEimMbRdd*k)v02{}7!11z=2fOd5e+3pJcK&LWsIma(W%`IH=m~F
z(>67%!93a@#3zEd0w)J$kiI1@Os``YqC)wWNQz{uMUnh?G}xVX!L@JSE?FbN7;A>Y
zkWz;!1hLpcUm@KL4^uw6JF&v<i1KmAae~(pqlzSstnZ~(zfjBQp_c3<Qd6V-q|b6j
z!G^V-Knfg;nYl@jyd;f`3QrENKbi{$8ZsYgMSYpLu8@Ec>1~EuRM(fn*#C%-Zio9m
z3({^<siVkN^@mTtyT7N=X$yrEo0C4@J$8A)g(ihZk<9!!Q8q#L07gY*`Z-Q<bL6Bb
z2=sbUGWOen#Rc$6Q8<{c7sN;VZ3*vvsGSd03eM&qoIL(i&T&W<afg3}wCPTPUY11>
z4)7746(gTIeNd{M1xjGl0WG>`x_J@D^B|U^>e1jB>=L3fhc5NK>I{)ArZ%atr7PQ&
z@tx}Em^P8jNxJb>6dL*SikYdXvu>o;1|Kr}d2a8`?nUu%68mR;6T{!P84@}A$OF^!
zYj31lcn7;%nERl6;EFESP9=#eF4+%yGy@Ka-I#}U<4pwgIJt~A&7vmXq~SLQewdvL
zl@*b2!I`6b*h3dV9?nhskk2Ec=;8cCr&JkgC!pA((46BzWD<iIVB>Ow8PJ&-#D$nb
zf<*eM*fLm!^`yZ?yqzQ^)d~1&5f&p1HqTf5HSkPrPee@?Y{0$Hq9e+9Ft;!GeuGDv
zQJfh0AR-z9fPx1E*!mdSbVwDO0xmROX@rZ_*uX~}U_6Z__9&fPhgk^8dF;FS@BVVN
z-|tmOEGSKYlsp022!<to4f%o~$$~?u!ftWP-P!-c2^0q!nF{Y@^X{)WS>%jn$w7|W
z?!?^}c9Lsd1`*O#3{{H5<bk8mxRJ_<oC<cuY<EXSjpDd5%-&mf`VF{R4(>$bM4Rkp
zi2BdMXE6zmL0+&{2J49DuexV{x$mgv=fL3LWxB6*>7C=G{#FRl5L3>#6F4u3HU;KJ
zY@u(UP>I6~D9(mF3z>!S=q7wX8)^HA7Z(CDFcHMHkutuc$v6%&&$i)%BLtq!P(O=8
zRU8!|_)$0>O&jMIhjoPDewrrT!+R6Z-XVQ`%;5N&TpH9@6}D6$FL0D~4{>v3s-WZ`
zGGKXiCCuc~rbjBUM(a~(LZ5P|ckkMpa6?}#_c?FdCOW&K@M9BardL^HCEFvya*I%d
zlcj@S7Nl;#6twXvM`QQSH*tO=62dbUn8_xlHJFL?o?I_{4{8hntWBS1vLGxA{g6Yf
zX;6L68_*evv?6qAL{XKZeE3Nsph?+%WR9s-ggW+3d}Uzq5YNoU@BScZsLXss6FV+#
zGa`RBn`nM}qg_HUtV<G=OLe;6wl(icN?Fqp1QJGFc@&#Ac??-|QwRgUO2cIOR2=?#
zjgn6uFo_ho4y<Oo;*BRQ4yJyjeMe&eqkU`)T<Z~g$+E{5&1f`3&~4&%H1kL8b{36D
zl3RZ+dKP@o3kcj<;15ZN@&zc_b>j~Hj%pykeDYAaN4>S`)|%F7HJ6ZXe<h2FP?;<{
zaJvx!;)u8mM23Q%J9f!T;rYsgZ|mhr29j+y(7Fr#2J)EETqcDOHlb_vjwXE__q;M|
z1((4lgf0`ek5=d@-a<D>38*OJW2OqqfQ;;9q}cK;`e=`WY{oCmCS@$He-x%vMmlQE
z7DcTy<Lp+;sXr)sig)sDQOFHkZ?y>7S3d}G-D9-6^g%^;+ZD2AU`AZqiH%gbk-22`
z)>ms*Gf@xFqg`s=16O@OOkZ##GNy!c<s5S?a5EZbULub*%Vqg&mXN6G*FeH)e{H!B
z&Qmx~*$_KYy}OZIUMpw}A^O9aogOG-Q(~&OFGZ;c{z=xYdW@`iYVLz>r-1i>bP_9y
zUG-{`fSTHV{!GNaBpBU4F)c0-Dvv4|!gY!|2bY2bkdnuTGdEGrkt{&sK7Y8vgAyX=
z*a_yqBCl)ikoP}9?a}B-w94@CC_&Qzn(777HeoruL&F|tL0JEao0wzTBj?y&6yVmi
zs+Zw38GA{f4roCVo|1xjGUZ}aes5c9u@Qzv#M8G4QJdK5Pvqpegrb>zhjHZcH(g?1
z_4?gme?bUCx}7mUW@9-SToS?%d;A|qZJOu2KKO*Y3R@ACKcb`<*yEp<Dal$}2AKHx
z6uH`zAQY$ahps+i>A$~BI100ewi1V;&yH9CDGIxpDmlRjdqfXUIv51e(+_=xzRhc=
zVWaC%3h$vpHJN$er6FtqaV?<_NfmtEKv}#-^R0!8^}KUV8**EXFQgdfZ~dGJ^p!3d
z+uCdGXEKuW^}*J-M^{G<>SprCVt@>PYQ=6Oaw6TK))}E!vq$_-1U?qhQU1awq3yTf
zA@*QD@+2IQLx&`{!($B_$<?HE^)+IfMxWUJrU?=1(9U+#nI?>L_|hyn{d*i*&&SY^
z%k@*jXyp|DX=Uq;Zwdmk&;W`$Dx={2pfCQWI<+hLQItc$8|l5T2Xwp@7BX!IkYUjb
zBj)qFu&thMqepQ6)sb%Ul-9PZU$>c7{Pa_!V@JZdMl*0$NX|pr(ip_^c_L)>-+Ij@
z9myTSOOV&~8!9LGIs!n$YF(^f+9lvWzzQo~(jzU+Dap7$78TTRW^(*yPcIoQk=pT>
z(?@|`xcQ_6H2_KYxTB`9%*s1hmQIk7*SsBAI0054Yq^BS3uOE)makzuDQQcaicfmj
zIBGbsj$s-{Hp^P_tgJ)D-QrRi*^e`~C;w(t_{z2NiOaMGx(wP-pD?X%@09UPn0!kc
z){oo#=<w|geICK%s0T-vuot%^JSg{^_LH?o7e>g|Oz^RsV~4;t#%`_785FT1-5q@B
zk9m6EZKb8%4*FQ)h+#rwm<*(*%?>-nwEylu{v2IF3s&nZ51j=ZUZY4}$Bq!3{f1WC
zWVz7lGq&(Sx}Ln>`6<(6xpcef0x^&1&@tW)z}H|mthbFujc0no!UulMv5jUe=0z;`
z*iN54thBCs4emk1U&7v)38%3vjyk!l()M7;>wn6_t`6GuT5OH$>Cj=e>}f3iPr=fv
z{l+PY^4#mRHz@s<hCU2Z$<376n|zQxp#S;~gAqqbI|#+jlKkZ3Qd^|mwFg3PHs8K!
zKW&s)9cZ1H?@5plbT)~K$%Qq8?b%2G2;27?D3hUngn+R=_j!y#POI7z)thMQ=ds(3
zn<s0fv1C6P!=W2BRhLKf5%Jp$(x-%!pNpux8-rBiX>Ur7^E@K?B|*mE>#a~Z0v->^
zo6uFXenxv1Xhr3>ZVBi-ts=@?A*nFkR!+tlSd?+x6G8yeJOE0km~2t@wDy25;v9a^
zAu`FPf%tv26?{nd?b-V#SQ6=NlB>_gO`C}OR-^mCm_?%jde+3DSz5~UFXRv*5d$OP
zvu5$>QTFfAt+)<wq%%^*1r6p9m4nrH&0J>NIqt)W>~wkreM0d!2E=RpOtAP?p`hwR
zQki_R&m}((R7A@U-t+1g1CCetEN6ML)CkXjhishVV(%iJMvQDhF?i=bL%Ct97W@HX
zIfA6A5iJM)GupOH_ovJvbYAU*5lLyu`3I_}sxPnP$d_HiMl}+ufLx@nfDK4Zt+F(c
zY<P>wglo5%_B~vqLlWmxlmP|+cQbq!mB41Hp(k!(`W*eLBod)(t-~~*mh4niY}-*2
zb;BF2*fZBgHcH~`ZQI#{=?!)avuXBUl*PGz667`It<$@5v7cO8PCg=|Xi_UK8U=UY
z1}^x6kN2C&w9ed(23;Hj3L3$a!L6TeY5SF?!yxavY<*kEG&<sF->Jdz`3(Oep>8wY
zDZz0juUSL$>E>G2K-JcY?+AR3z7A%$9^(<OwS&KM2rmbuOP#c$?^l62&@T@Yh6IXL
zSBycNe^^lOou}FfTG7dIa$vO-U%3l@z)~;2m1Hm8dl9{2E!*Xfd!9YFi7_#ptA$je
zWf?t4W8E6^i?<Y%dHgT~V0Un){TpRgz7n8Sryma;VX_Y{=BGWJnRKZNmw1GRb3Zmk
z)|8#gVUjGDW^(+HH#L^BI%JPuSV4o`uDQuf2lDUPthlH%oj#i@CICVbyW1)B5&5^*
zTsl>ouvD5-jh7t5@8VJ@-?VX~p+-0?A%%{!9&RZ|_x&yIY!$b74bNEyf3Tt?&!z?Y
zzqcs!5kwVvBmXz==_+AVOKkzQXyE%)TuV4~b$`u<XeRe&hlx^)vLh#xELPCqE!iJ=
zr$T_na%_yyRZkGbzNT)t_qh$dCgM1Q4vpO(M+(7T(IpLE@Kdl8LIWnB4h+QkGv4l5
zJ1|IX9XfKZOsOJ5x!$Q+V*vw6IO#a+k5o=5Z(a3A=D-<K;Z;YT_uS-iRdIxy3JS4Q
zmz4*-3Sk=dGJr>Hf{VHLA{dqi!0iEj-UZTRGoRbV$sJBSD+_%#?m6Kfdo$`xj!@Wf
zN5kBvo2|Osb;J2tOLSB=%3X3vKBNs(d_JLj(9R_z#pp+<se_9?_u1*gtS&TdD;~-B
zRKFJW_Lin^?^u*$;y^z$?TL2Z?6`BEDmUgg={$|-2prj}B>UVn?0CO3swi?1@t-N?
z{?Dhj`X7*Q$5<}zQIDk&kKH*u^?@%(N%40SwjPMpJ!>=$e=kc>oNls9EZFvjuMoX=
zG9n2Tk&CZbFz0)ly6O?{zZk7=tGhdn6{K_|0`H`kndPy9ia+&CENwg%<$3yinP+GP
zZB6eHBQn=1WvJk8S5J3$VDK=Gk<`*$PEAT`b+5H@0smS+LnJf$7WoI$lTtQ$rpD&2
zw0&tl_kaQ$DMU7q9*N;EwTwL3X&3ws;;M9x$MpI1?w6y(&s)-1Lky0Lt1+_00Nxll
zpA&uvQt}Y3UE6GG!ooPOAN!xny+5+bJ9_ESCU&cqX5l!`k6nRdMOEA|T2`8URU-8r
zDjLD8doyOQtQ>>T1ogkmv`1~I^{ndRTj$^F-9P%<B<L+<x-*BS995n&&EBag1tBSv
zdc`HX=uh?N&~DxiH0ogzu51~-v~g2GcJ=Yzlh4fukpCyEybwfJpkm5Gx?C-m=Ch}%
z{dr_9kEAj0x2=2pW6gIXI=x(0<5}3+`+Vxsq09d)=fSbq(oW;^Y9cxnFwr2m39*78
z)}3CJ6|X#UBPzqm!e(vxld??X+Z`SwOKa<bKc36(DeE2S!|l|>juA4McdTcgd_!5t
zqJrlFxp=^LT_g0U7ncgAqCLpdAFw5HUx-yt=WC^9h0ESB+F_ea{6IjgdW@${Xnx{)
z$+jjF-o>UB3%UHQ)juGWI7Q}eF<b1qfjZ`QUG>^jEAaO1-FkrP^L&$^<h8CBOXlh0
zKC5*ia_834rM>!2Mj^2GeF(Fm-%fWRH;M!Ppa>cWF{c&gS5~T4+HRGT%af^Ie`&%o
zVzsMv1Ilk=mJj1|Vkn`AW*-^jvtx}TidkZ;5x96|@{PS{X2C-H7XHqnc;=O<1}PD5
zp_UZmr1~MT=N}`;wNdCj)ybQE8c{MwNLP!1`2bSp6}y;dDraDh1AR*~ppF9cH@oJv
zsyZbs6ua!Xq><sVv;w5G<SsifYDPo-XW}#&9lJ2j_Vb=%%$V4t5)rvfmCg8Qnua2<
z0oyVjocO-?DuS*i9Nbubsl%gQ#pL&8xeu8p`7!M4N@kGIKp$)6g@I5FXkj&VM=|XW
z@=lD0v+21mYftudl!C@=0I!&*e!w?MU~1_=nRGFnj@YDY##q%`J^#95ta<2&wI`gZ
zNPz`Fgmkx%U|ekL2$az6u#&X>6mkvS`6o%Kizf!5BS?P#hFy^@Kd2^NbIC=?qF+eg
z`7dr4<BMKSksK#SW-n00qsfuhd1V!-9Z8Hq@}Eoi{P(LCuc96C`AhaSCJbdGe*wrV
z-_G-;^hkmLOG=;Nq@-_V*YU>+y8#9pP3i_%=5srn?H8|U&3*J&?<;myx=Yd{Qj~$-
z?=@V;Lzsq1_Xh0WZz-Qnt1mg?(8{y9TrDHv<l;@`(~iWOiQvg7&Z_Qonq#zD<L@WD
z1y6*GDfv#>0+OzL{rb#d9V;I7U=2KdJlipZAzN@D!Bjr=cZE964BHA1mm|Z-&rYH>
zw4PRE<jr)90{yKXl^`Om#29)}&v>QyLq|3`*~Qf6XO&H$_eEG0WNa1jq{p1Ier3*N
zsPIX2##61tukdD{`SRlOtAG{V<c6-M4WVSU>yKO^vRppsOp8ul(<QeXd2U<8WIvrb
zPi8Qn&)AP%kkr}W-zu+Ou6d5RBwBVQ!j#6#8>ju5E4q%4+zX#KvVkjCUhem8$L_XT
z{0?4jx`3EJ+AJT6JY9Zl?ZZ2aty7;KA30Ml4jw#lzuk2AES0`UUl>Ku64#-26SR9Y
z;Vr%6O(FNrb!)Ke!h|Ir7lOJ-3`j9qmCLh2cd?Qo9n6!bZ2O+5TjxTJFTZk&jK~iZ
zw|IKtfwGPY^M?NO3wN5{{}sA=$}#4IhEEi&K3HFR(=#U1yzwM6h~sv9*(yS(piOMA
zax1(%h(wJZcggzfP1X=Bf(H+^-%&X;FlK35XEOn)melj{Glf%MvQ|#dyu85#EbyCR
z`^yt)p|DSSs>x86lB(UAirZ~j-8)e3)6;Q}F?OnFy#k7=^Qj$5UgbEjfO@<qAR788
z5+^JjZ!){1q7H%@w<p4}#r<i?)@IsmKw7De`<O#!hdGvhOu;>0%<dicbw%3};V{44
z)5XWs^~2Fd>0`}oN#oe5AG9>><Iz&5gO|F_)~$0=iXK_*GTh54|9XxH?0nsyF58U=
zfX*Mo_lT4EoiD2d8Pi4gt@6tzkU|kl%Blz{;s`tTI)~%qpMYQ(r&z95BgaAFy)uvA
zfl+e*hJ|)lXOXY>Vi$G3t44|+KhobM>o|uFQ(cq{re!8;_kE0~+`KN`p2Y#E%fm}!
z_<O_s_($X3FzMbo#SFea3($848|7{D{#nN5SjJjtTauwI^UFPRnFAd%)wE`7|G=xm
z2}?HCtbHh7{s@9bDaU?16pO{rk6Vd9=3RM(_fPxz*EVjUlvNuX`jY+VOxjY$1}BE0
zHlc^3tgywIz22foOV4HN?)@u@tRD>BRNwgn*#e|s!kGpTB-|adD{xzdo-q6g^|jd-
zSPt%cGcz+&^2-5*^h~@37Z~2!J3YSLC;h6s7-LTCrw|_!L8dh13H~xsq0C+{#;Gp;
zqGZj_MYv5k#OeJ>X>P1t)K>e0ne)6ayxJAAOciZ#(4l^q^P^sG{A3?{aNg|->9)$4
zyLTCpT<>YaUvZS8v;N|xJ$IU~0*Ektl$&RAEnuFV>XLa|Wi@86EF1kJ1xwl5@XDEx
zaLu=uFC8DV?8y%c5sa~pi7a`Q#lvxxftS|t!nb~MU{CW`Jn|gvK@stL;w8{cnkte9
z93!%uZu*|RaXa^esRjoeG`_gY)k<x-jxls4(d6TyJLcO-ShKXhdCm?JZy1*37nEN+
zu<Yr%gAW5wanPCxZ=w^L-`puX&REn6IS%u5V@yOuZ_D^}8i&Ik;v~z+0BZ0>^NtXp
zK5U$6jW2G?D-)dl&S10#a}N(mFzC74gl@CObN!Y3IGG&gB!@3K%{2Q|c>FUtM@eqX
zY&K|;bDTPVJZ|sHzM&T@lZU95*Pi7zxe8kp>3(-=Z@>3xDh&(<x`=R3p~K$nO=3@n
zWauPQn8U7VZ<<#ado*&Vq(k)5RkZ3uDF|~dr{a($IbR!QdKrT{^~|RNt*@c+^Y)io
z+3m1te$BYPfv1<zssoZGr7UzL@G;~*+j$z87QzY&t!wzbVPdy4=M5)?26E++)wJp(
z31`fUd=&<+WP#k@C?2KwW^fe^{oFN;Sz7bORK2Ah%L!KVI;M&dV?}V&u}!C^+RT%S
z?F&q3SkZOEa*htG!kJ)OD&(@(Iz&qy+GAfV*JLAVTJu6QQ8C{{B~WO2(`S(~tO+1^
zH@SIfZDAjxm;CvD9+~nf(p~B!+Q6TNPO_cwCxo+=Z#K*W!O?;LGx#={J-3jQlUWep
zI!l4igb+;fXy+LQn7dgZ`rUP@k#D3WBEM?v>IYkB@zh3De}d}cHzYg5visGV>`35K
zgpGv7w@hu}UC|y%gQsG$8}i$F4y=TOV-w1=lv!@j716bd&qP`dVoGeXN(f_9CHw6G
z2FmD^ARC<Iz@!Wq6$JhusgnqQ%xhn8p&Q3*cG5RfeyH$B*L+XzU#;mFoCfsxMT;X2
zJR-eF(Xx3yr*y41|83#Jh*y}%w-KZfVo!|HC0)RtOXf<^2~+1zGmRb&bN@Nk#P(LJ
zgZB{djuDS?3r;(5Jz|JL_p!l=76B?<NG%zV2;2o=IH5jUm=>ZUYjXPAzS3Gbb}PgF
zZKQMvkMt-8ejoyw?^Z<Ct%kLpDG<7KT9=BN&dMY#%1yAQYIMoA49QP2W@stUt>*8$
zxMY#s;9I3F{R?TDIs=iib&~o|pSiAONJUGt(sjSdz|*+dYmo;0g1L-2$Xw2%&8p_i
zWc5s}-DVveH?%Z|jegiE5Wk4E{q~b}!ess+JQLq1rIkQE1t``e*AWQ<;ZU}W6Pr9B
z;MM6q-ZL<bzZaLbokts8*#-ptF%*mBnGwz=8BAcuN{}QR-L$5)?=<c(_<<*~vFCR-
z4wV)^pE}hyarqNSFYU{EtIK+!lIkAwn%h~l`tNZ#3d)7FzF@Y_m!5vET+=F;IUYK4
zBY0T*4LVx>Crz7aXpl#0aU5XdJ{Hh?qUGk=`c-YFGupT%2rm#MpkZW-B(-sT99%<m
zh6*)Z`8L<6WtdV8|IFoeG!2mltWB&~fKO!#)Q^T@d*c5R1e%f!XoozaZuC1iwfk)3
zcO3sxG_0uY)Lx=kyK5vEK;SOQr2Sdc(S+$i2m+uGG`6&#>-0(VBJ6nnF*vq6TwC0M
zX42S$LRcUZJtvOdi10`(pG={04)PT}ZNo~zk4)$<RjCBCGCBMH0M-R@*(aSVJ4KAN
z-SamvK|i5yz*(;gOQfIdEV?33C6gfR5E96@Tg2S4crP3mv>ZD^aOhC*#d_t7S4Ya>
zVHDKRv0tcMZBllJa32UpPIndR{aK)NEzT?7R?*&^Zt(#?w?w(#6J0UO;0_$V#PA*J
z4x%Ax%s+K9bXX~-cB&0bL4LY>YcH@xrAw}Rq$|VLM|PUVu-aB`vN-`hHVy_>t4rPn
z*WXRG-||hPfHO0welu%X)OmL>bQ<qEZu?D7lw@k;;|V^BZnWI`(A~Sh{%R@lEd-hP
z5irHyd`#hVN(jkrGY;6Gq}D`o$o;LS0h~8AkkgVjYTQdEi8C_wg-LwC^a!aMLpyfa
z`3d_wjLZ(VCOj~ZA@-RbK6t8dEyneMl%)YmO(yV@72hV&?Y)n4U}au3(j!f6_JPa7
z#6P}Ejgwl;G$bRE0sAaIv>8Z}2C`Q7x78bexO=NBFt-0=3Jd4N&ByaMMkZsF8(tyR
z;P%P0_b%}PpKJPu#e{OB{-56bl*tO$rxx8=_3AFdCt2`f+-9jbIh1xu!0Qm^xw#p?
zqfAa$aLmh!4zw9JZ-hKz%a-apkE+=l9+6I3s61BWOX;a{{|i?ZbrRSt<%VB^#y7$A
zGmtXS%P*RMjSaZ@cQVp-b_{gD4TcUU@GbSKe@5o(T+_6Fh+~2*4Nwl(jOIzaou8-`
zyEpK9g*O<;J5biq_a31l<4Yi8M9{({#U=ngkk?nd8&Z9q`=*FxT&a=p+X&!-dCC4b
zGG;SAvR932R+s^eCLYRkeLN<%&9#DYk6p7_9O8)H%E2?-fik(m9WUy?tjwgnYac{o
z-}0JI%=VMiv1#HkOX%!G>U8AZE(1^wat?Y6qQRNYMiX#;@ctQ8jr^@^pQOn+9%O2K
zCnlDJjfK7>nK)8mJA&0?Fx?njiC~;{{l!o&7mf=a(2(a}8zDWztX%5lAEyJ_e%#HQ
z!r-a(EMof>jDLV>@uCS|Jlr|=L~n#>B&`_uy@}|VaP~ezvzBNyv3-`>=oklH9l>$S
zl~kPfgVZUXQ_I%=pb2J{3h{RhsU>do@X9=ZTbY?~>MQaKngX-nY3?-KzqsZ4w^ETD
zND{!2GRb=2E9n32`ES%0K_jvKeK<!KClbB|eL_}tGX@?n|8~nOmzRUPoCu_f42KXZ
z2Oq9>&9Z0|?bw6{F@flFuu7!CBuzTac4A{IhLkk_WiQ-3Z2IR4+K~qn;M-&<NhB8Y
zaQ6Zk(EjSVKW-&W#6YSKdJ@XtBc4+gyEt-4?(D}*a5u7+@0?%e(KkW+>L6`OB{1|q
z^i>baY+2#99C`iV0%X4_{=WrjF}H#$MQ>ZB&DJ>!pL~_{)LPA~=)k*1-ZBoQEI0as
zLnjXOuPKTb-n8krYr_te=IRSBH!n0@5ln^yHch6^+=asGiDv9M=!S-N=4w(7sjAkI
z;P7gNMj{Rwh<J!j0SR(EF^I?bJD0FF`dB}K_k~H{A=NzqB%oxXkjE73I<Qh-ieG}Z
zm+(>1+382)hz!6aJZ+q-n5XFm&c5Z9V&6hxv}4FfLg?U3Uhnsf&uK*pgC0{8+5TAf
zl@QVcOd||F5;?AihJ6H?L7_)?N1Xk63NDijRRPFHn6iY3u-Yb?2y$&P;-b-(o0CCi
z$u}Naz0K<xJ_uWld=SC^yBM~h6Of&0;y2k%8U<+SLE6#p;d$u9M3WZ49`L~NKlC#R
zJCv@!Xl#x^=H^_lt(3Ez8|%R;Yz!oG)S-lvCp79!*TFIgVQvL;jXsC)<8ymn7b1rt
zqzpofz~HFdMt67yCz3)&h(hXB!bS3U``Tw51iu_V8z!A#uP7r7Tb-aNdII`-ND!S!
zS%@P6s&sy<zv8aI)oS^>L9Uk2aukCn?JcxgDk0CB6QZzfsriYX=53*c2v6PXL&tU0
z<qx3YN=SGq_Gq*;9(ek3!ARSK(>Ggp4fQBbPcfv%<!Qm&l1s=`J=_?3{R%U5HH>dE
z>xPkGakv`UYw^gD{8cQoo1ulW<hI*y!cRe`_6bI!0&+^W@WuR;0!B{joSY^Yu;llw
zshC1Vd>VZCRxNjSE4}epD+^PT79RS3JBRes?O2c>5m6Xu0x5lwL<2>SOoGZFcA9Zc
zc;m)`a?hKIU%^4jj!_dYR}&EgK{WWb-ZPHR#^|72<YV(*g`m&-1e}T49o0y04xXN`
zFi7j{Bu5>gW;Lq4I{Z-*=@^p34Fe1p1;4``tOgy8zz?S9$~<m<#T*YULU^y;S2lr<
z(|7D<dh81EI;^3Qd$8b+Qp+oK?`U_s@%h)Thi_Qh+t%{1f~JMFk7WR{@>jV{lB&_N
zW9{I{1bA(YA=x94mouA{m6Vh$gVUh$alL1S=E-}~sQ@0`F8v?{8|XyE1Q}-wHuTxi
z2nQ!N%qNZl9_5!Vfl#Y)`wpaTV$Q+-$R(25Vv35z?^tpE(Xw<u^n_jxIzHg1F?7cB
z%k-xGF|E^oj(Pi*+)tffr|AukEsB#Rqj??gO{8<_g%Dm!L)iqVv5A11^-f>!DyU}r
z{{8qRHc()&7UGqA*mKifZT7V*$O&S<$#eNLnFE3}hU{uC32l@QV*0`EI*ra-1B5+&
z4y-Pi-%Lc1MDdD2q<vGjRQI=oP?v@ft%FAGjc|Q2t_Q^??@j$TUuF&qFvjNkt625r
zO=Dovc8m=sOlB~LT(W{z=o$p8yl+piO;cXAe_B73IW_xLj{cS5-IOa|`HCjIiJlsS
z*W}5kv%*Z?E49ZJ`+XitOy+Y(O5-(}kD5SB@^{UtUvHH(-}UEnd5__7tC*oByFadC
z&=gzfzp+CqUpV!Sd5v+{{@(7VU+!MGU+d4Q^$9UmUx&ON%wBWmOom9+mAPtPV{Fz0
z%u5-a!`xLBojKZGIwdfhT|JmvGVQbO=IW(9fAh==Ffz0&zO_X0nzT&Yf>{$Qw+XZ_
zf{V{5!<6i-#!4qX!JxUb1B|VA8E-cDVJ5fG>L0(K-J*Ci{;Ui8XD-5@s{goPEfo33
z_~*j%e>|`Dl25~**m*zfumUva{1~?VKYqL7tVfrY#R{CT*CC%mIiS8jb?@<W{|i|%
B)`S26

diff --git a/reference/antibiogram.html b/reference/antibiogram.html
index efb72fbc4..c205b9d9e 100644
--- a/reference/antibiogram.html
+++ b/reference/antibiogram.html
@@ -9,7 +9,7 @@ Adhering to previously described approaches (see Source) and especially the Baye
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/antimicrobial_selectors.html b/reference/antimicrobial_selectors.html
index 67989c5af..184381c31 100644
--- a/reference/antimicrobial_selectors.html
+++ b/reference/antimicrobial_selectors.html
@@ -17,7 +17,7 @@ my_data_with_all_these_columns %&amp;gt;%
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html
index 7f873f6ae..d2449188b 100644
--- a/reference/antimicrobials.html
+++ b/reference/antimicrobials.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.ab.html b/reference/as.ab.html
index d9133c2ae..209b72c38 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.av.html b/reference/as.av.html
index e8bbd9e86..d7a9206e8 100644
--- a/reference/as.av.html
+++ b/reference/as.av.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.disk.html b/reference/as.disk.html
index d2728a387..7b5c40a3b 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.mic.html b/reference/as.mic.html
index c2b90fb12..999235d21 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 5c81f38a5..24cb84aba 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.sir.html b/reference/as.sir.html
index 55944a08a..eac826b4f 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -21,7 +21,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -810,16 +810,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 57 × 16</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    datetime            index ab_given mo_given host_given ab   mo              </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span>              <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>      <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>            </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-03-07 <span style="color: #949494;">21:31:43</span>     4 AMX      B_STRPT… human      AMX  <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-03-07 <span style="color: #949494;">21:31:50</span>     4 genta    Escheri… human      GEN  <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2025-03-07 <span style="color: #949494;">21:31:50</span>     4 genta    Escheri… human      GEN  <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-03-07 <span style="color: #949494;">21:31:51</span>     4 genta    Escheri… cattle     GEN  <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-03-07 <span style="color: #949494;">21:31:51</span>     4 genta    Escheri… cattle     GEN  <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-03-07 <span style="color: #949494;">21:31:43</span>     3 AMX      B_STRPT… human      AMX  <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-03-07 <span style="color: #949494;">21:31:50</span>     3 tobra    Escheri… human      TOB  <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-03-07 <span style="color: #949494;">21:31:50</span>     3 tobra    Escheri… human      TOB  <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-03-07 <span style="color: #949494;">21:31:51</span>     3 tobra    Escheri… horses     TOB  <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-03-07 <span style="color: #949494;">21:31:51</span>     3 tobra    Escheri… horses     TOB  <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2025-03-07 <span style="color: #949494;">22:05:49</span>     4 AMX      B_STRPT… human      AMX  <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2025-03-07 <span style="color: #949494;">22:05:56</span>     4 genta    Escheri… human      GEN  <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2025-03-07 <span style="color: #949494;">22:05:56</span>     4 genta    Escheri… human      GEN  <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2025-03-07 <span style="color: #949494;">22:05:57</span>     4 genta    Escheri… cattle     GEN  <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2025-03-07 <span style="color: #949494;">22:05:57</span>     4 genta    Escheri… cattle     GEN  <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2025-03-07 <span style="color: #949494;">22:05:49</span>     3 AMX      B_STRPT… human      AMX  <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2025-03-07 <span style="color: #949494;">22:05:56</span>     3 tobra    Escheri… human      TOB  <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2025-03-07 <span style="color: #949494;">22:05:56</span>     3 tobra    Escheri… human      TOB  <span style="color: #949494;">B_</span>[ORD]<span style="color: #949494;">_</span>ENTRBCTR</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2025-03-07 <span style="color: #949494;">22:05:57</span>     3 tobra    Escheri… horses     TOB  <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2025-03-07 <span style="color: #949494;">22:05:57</span>     3 tobra    Escheri… horses     TOB  <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI    </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 47 more rows</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 9 more variables: host &lt;chr&gt;, method &lt;chr&gt;, input &lt;chr&gt;, outcome &lt;sir&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>
diff --git a/reference/atc_online.html b/reference/atc_online.html
index 368031a69..6f351c25c 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index 91189029f..014f0cf04 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/av_property.html b/reference/av_property.html
index 593564090..1e33ca345 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/availability.html b/reference/availability.html
index 58b03b10a..159c87617 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index e63f9bf8b..8d78792d5 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index 166a9166c..728c9fd9a 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/count.html b/reference/count.html
index 39b04def1..2fc062898 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index 9956931c0..6c1386352 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dosage.html b/reference/dosage.html
index 8e5b588f4..80c93a99c 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index 98b4494cb..7c5f31637 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 6e87ae53e..4da5c027f 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 6d380d800..274325aa8 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
index a7334c711..8ff876f4e 100644
--- a/reference/export_ncbi_biosample.html
+++ b/reference/export_ncbi_biosample.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 243cdbfc3..9a9dcd5e5 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/g.test.html b/reference/g.test.html
index 996f9b394..5712ad4c1 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/get_episode.html b/reference/get_episode.html
index cd5334e28..146f7a567 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index c97490880..71d7ee86b 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index f6f68cdec..55383e732 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index e40f78653..d5076cb19 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index 9a5ae5c52..3b902b245 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index a898795e0..3ab8d951d 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 0134475b9..7dd916a6d 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/join.html b/reference/join.html
index 1cd9338d5..9ce22619c 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 1c8c2c1fc..4af95ccfd 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index 8393c09ba..1a2a424f9 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/like.html b/reference/like.html
index 1cde10aea..368a176c2 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mdro.html b/reference/mdro.html
index b02aec8b3..3df091b68 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index b62869d52..dc096267f 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 8ff7adf26..efa7613dd 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index 808ee116a..2eb515c57 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.groups.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 7fc787a33..6f17d03ca 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 43779ee75..73da94e20 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mo_property.html b/reference/mo_property.html
index d595cd705..066f3a81e 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 707e7dc9a..b3325f20f 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/pca.html b/reference/pca.html
index 5bd42faef..a6a3e0da2 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/plot.html b/reference/plot.html
index c74008533..ae01c1928 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/proportion.html b/reference/proportion.html
index b2de34ad1..9cc80ebe0 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/random.html b/reference/random.html
index a595e16fa..cc3ffc40c 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 38615a6e6..3ac596150 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/skewness.html b/reference/skewness.html
index bf896934a..246d58a13 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html
index bd2ac700e..2927b902d 100644
--- a/reference/top_n_microorganisms.html
+++ b/reference/top_n_microorganisms.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/translate.html b/reference/translate.html
index 7a699db64..f11eabc43 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9187</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9188</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/search.json b/search.json
index 7e3f051b7..05f89c6c8 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # ℹ 2,990 more rows  # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\"  mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\"  mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\"  mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Microorganism translation was uncertain for four microorganisms. Run #>   mo_uncertainties() to review these uncertainties, or use #>   add_custom_microorganisms() to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #> Colour keys:  0.000-0.549  0.550-0.649  0.650-0.749  0.750-1.000  #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #>               (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #>               (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #>               Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #>               (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #>               dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella #>               pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis #>               (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500), #>               Kingella potus (0.400), Kluyveromyces pseudotropicale (0.386), #>               Kluyveromyces pseudotropicalis (0.363), Kosakonia pseudosacchari #>               (0.361), and Kluyveromyces pseudotropicalis pseudotropicalis (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #>               argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #>               Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #>               Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #>               Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #>               Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #>               phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #>               Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #>               (0.533), Staphylococcus pseudintermedius (0.532), Serratia #>               proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #>               pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #>               periodonticum (0.519) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>%   mutate_at(vars(AMX:GEN), as.sir)  # method 2, let the AMR package determine the eligible columns our_data <- our_data %>%   mutate_if(is_sir_eligible, as.sir)  # result: our_data #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> #>  1 J3         A        2012-11-21 B_ESCHR_COLI   R     I     S     S   #>  2 R7         A        2018-04-03 B_KLBSL_PNMN   R     I     S     S   #>  3 P3         A        2014-09-19 B_ESCHR_COLI   R     S     S     S   #>  4 P10        A        2015-12-10 B_ESCHR_COLI   S     I     S     S   #>  5 B7         A        2015-03-02 B_ESCHR_COLI   S     S     S     S   #>  6 W3         A        2018-03-31 B_STPHY_AURS   R     S     R     S   #>  7 J8         A        2016-06-14 B_ESCHR_COLI   R     S     S     S   #>  8 M3         A        2015-10-25 B_ESCHR_COLI   R     S     S     S   #>  9 J3         A        2019-06-19 B_ESCHR_COLI   S     S     S     S   #> 10 G6         A        2015-04-27 B_STPHY_AURS   S     S     S     S   #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 724 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>%   mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a #>    microbial ID was available) our_data_1st <- our_data %>%   filter(first == TRUE) our_data_1st <- our_data %>%   filter_first_isolate() our_data_1st #> # A tibble: 2,724 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J3         A        2012-11-21 B_ESCHR_COLI   R     I     S     S   TRUE  #>  2 R7         A        2018-04-03 B_KLBSL_PNMN   R     I     S     S   TRUE  #>  3 P3         A        2014-09-19 B_ESCHR_COLI   R     S     S     S   TRUE  #>  4 P10        A        2015-12-10 B_ESCHR_COLI   S     I     S     S   TRUE  #>  5 B7         A        2015-03-02 B_ESCHR_COLI   S     S     S     S   TRUE  #>  6 W3         A        2018-03-31 B_STPHY_AURS   R     S     R     S   TRUE  #>  7 M3         A        2015-10-25 B_ESCHR_COLI   R     S     S     S   TRUE  #>  8 J3         A        2019-06-19 B_ESCHR_COLI   S     S     S     S   TRUE  #>  9 G6         A        2015-04-27 B_STPHY_AURS   S     S     S     S   TRUE  #> 10 P4         A        2011-06-21 B_ESCHR_COLI   S     S     S     S   TRUE  #> # ℹ 2,714 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #>   patient_id          hospital              date            #>  Length:2724        Length:2724        Min.   :2011-01-01   #>  Class :character   Class :character   1st Qu.:2013-04-07   #>  Mode  :character   Mode  :character   Median :2015-06-03   #>                                        Mean   :2015-06-09   #>                                        3rd Qu.:2017-08-11   #>                                        Max.   :2019-12-27   #>    bacteria               AMX                    AMC                 #>  Class :mo             Class:sir              Class:sir              #>  <NA>  :0              %S   :41.6% (n=1133)   %S   :52.6% (n=1432)   #>  Unique:4              %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)      #>  #1    :B_ESCHR_COLI   %I   :16.4% (n=446)    %I   :12.2% (n=333)    #>  #2    :B_STPHY_AURS   %R   :42.0% (n=1145)   %R   :35.2% (n=959)    #>  #3    :B_STRPT_PNMN   %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)      #>     CIP                    GEN                  first         #>  Class:sir              Class:sir              Mode:logical   #>  %S   :52.5% (n=1431)   %S   :61.0% (n=1661)   TRUE:2724      #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)                     #>  %I   : 6.5% (n=176)    %I   : 3.0% (n=82)                    #>  %R   :41.0% (n=1117)   %R   :36.0% (n=981)                   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)  glimpse(our_data_1st) #> Rows: 2,724 #> Columns: 9 #> $ patient_id <chr> \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital   <chr> \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date       <date> 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria   <mo> \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX        <sir> R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC        <sir> I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP        <sir> S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN        <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…  # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id   hospital       date   bacteria        AMX        AMC        CIP  #>        260          3       1854          4          3          3          3  #>        GEN      first  #>          3          1"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>%   count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1518 #> 2 Staphylococcus aureus      730 #> 3 Streptococcus pneumoniae   426 #> 4 Klebsiella pneumoniae      326  our_data_1st %>%   count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1321 #> 2 Staphylococcus aureus      682 #> 3 Streptococcus pneumoniae   402 #> 4 Klebsiella pneumoniae      319"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>%   select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,724 × 2 #>    date       GEN   #>    <date>     <sir> #>  1 2012-11-21   S   #>  2 2018-04-03   S   #>  3 2014-09-19   S   #>  4 2015-12-10   S   #>  5 2015-03-02   S   #>  6 2018-03-31   S   #>  7 2015-10-25   S   #>  8 2019-06-19   S   #>  9 2015-04-27   S   #> 10 2011-06-21   S   #> # ℹ 2,714 more rows  our_data_1st %>%   select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 2,724 × 3 #>    bacteria     AMX   AMC   #>    <mo>         <sir> <sir> #>  1 B_ESCHR_COLI   R     I   #>  2 B_KLBSL_PNMN   R     I   #>  3 B_ESCHR_COLI   R     S   #>  4 B_ESCHR_COLI   S     I   #>  5 B_ESCHR_COLI   S     S   #>  6 B_STPHY_AURS   R     S   #>  7 B_ESCHR_COLI   R     S   #>  8 B_ESCHR_COLI   S     S   #>  9 B_STPHY_AURS   S     S   #> 10 B_ESCHR_COLI   S     S   #> # ℹ 2,714 more rows  our_data_1st %>%   select(bacteria, where(is.sir)) #> # A tibble: 2,724 × 5 #>    bacteria     AMX   AMC   CIP   GEN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI   R     I     S     S   #>  2 B_KLBSL_PNMN   R     I     S     S   #>  3 B_ESCHR_COLI   R     S     S     S   #>  4 B_ESCHR_COLI   S     I     S     S   #>  5 B_ESCHR_COLI   S     S     S     S   #>  6 B_STPHY_AURS   R     S     R     S   #>  7 B_ESCHR_COLI   R     S     S     S   #>  8 B_ESCHR_COLI   S     S     S     S   #>  9 B_STPHY_AURS   S     S     S     S   #> 10 B_ESCHR_COLI   S     S     S     S   #> # ℹ 2,714 more rows  # filtering using AB selectors is also possible: our_data_1st %>%   filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 981 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J5         A        2017-12-25 B_STRPT_PNMN   R     S     S     R   TRUE  #>  2 X1         A        2017-07-04 B_STPHY_AURS   R     S     S     R   TRUE  #>  3 B3         A        2016-07-24 B_ESCHR_COLI   S     S     S     R   TRUE  #>  4 V7         A        2012-04-03 B_ESCHR_COLI   S     S     S     R   TRUE  #>  5 C9         A        2017-03-23 B_ESCHR_COLI   S     S     S     R   TRUE  #>  6 R1         A        2018-06-10 B_STPHY_AURS   S     S     S     R   TRUE  #>  7 S2         A        2013-07-19 B_STRPT_PNMN   S     S     S     R   TRUE  #>  8 P5         A        2019-03-09 B_STPHY_AURS   S     S     S     R   TRUE  #>  9 Q8         A        2019-08-10 B_STPHY_AURS   S     S     S     R   TRUE  #> 10 K5         A        2013-03-15 B_STRPT_PNMN   S     S     S     R   TRUE  #> # ℹ 971 more rows  our_data_1st %>%   filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN   R     R     S     S   TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS   R     R     S     S   TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS   R     R     S     S   TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS   R     R     S     S   TRUE  #>  5 B6         A        2016-11-20 B_ESCHR_COLI   R     R     R     R   TRUE  #>  6 I7         A        2015-08-19 B_ESCHR_COLI   R     R     S     S   TRUE  #>  7 N3         A        2014-12-29 B_STRPT_PNMN   R     R     R     S   TRUE  #>  8 Q2         A        2019-09-22 B_ESCHR_COLI   R     R     S     S   TRUE  #>  9 X7         A        2011-03-20 B_ESCHR_COLI   R     R     S     R   TRUE  #> 10 V1         A        2018-08-07 B_STPHY_AURS   R     R     S     S   TRUE  #> # ℹ 452 more rows  # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN   R     R     S     S   TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS   R     R     S     S   TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS   R     R     S     S   TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS   R     R     S     S   TRUE  #>  5 B6         A        2016-11-20 B_ESCHR_COLI   R     R     R     R   TRUE  #>  6 I7         A        2015-08-19 B_ESCHR_COLI   R     R     S     S   TRUE  #>  7 N3         A        2014-12-29 B_STRPT_PNMN   R     R     R     S   TRUE  #>  8 Q2         A        2019-09-22 B_ESCHR_COLI   R     R     S     S   TRUE  #>  9 X7         A        2011-03-20 B_ESCHR_COLI   R     R     S     R   TRUE  #> 10 V1         A        2018-08-07 B_STPHY_AURS   R     R     S     S   TRUE  #> # ℹ 452 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates,             antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates,             mo_transform = \"gramstain\",             antibiotics = aminoglycosides(),             ab_transform = \"name\",             language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates,                            antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),                            ab_transform = NULL) combined_ab"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates,             antibiotics = c(aminoglycosides(), carbapenems()),             syndromic_group = \"ward\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>%   wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),         minimum = 10) # Recommended threshold: ≥30 example_isolates %>%   top_n_microorganisms(n = 10) %>%   group_by(age_group = age_groups(age, c(25, 50, 75)),            gender) %>%   wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function:  calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4203377 our_data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #>   hospital amoxicillin #>   <chr>          <dbl> #> 1 A              0.340 #> 2 B              0.551 #> 3 C              0.370"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"How to conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Klebsiella pneumoniae - assuming body site 'Non-meningitis'.  my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #>          MIC SIR   #>        <mic> <sir> #>  1    16.000   R   #>  2     0.005   S   #>  3     1.000   R   #>  4 >=256.000   R   #>  5     2.000   R   #>  6     0.025   S   #>  7    16.000   R   #>  8     0.025   S   #>  9     0.500   I   #> 10     0.005   S   #> # ℹ 90 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"How to conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories.  plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values:   Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25)   ggplot(my_data,        aes(x = group, y = MIC, colour = SIR)) +   geom_jitter(width = 0.2, size = 2) +   geom_boxplot(fill = NA, colour = \"grey40\") +   scale_y_mic() +   scale_colour_sir() +   labs(title = \"MIC Distribution and SIR Interpretation\",        x = \"Sample Groups\",        y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python Package Index. Python package wrapper round AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"install","dir":"Articles","previous_headings":"","what":"Install","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR  # Sample data data = {     \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],     \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data)  # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug'])  # Display the results print(df)"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd  df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR)          # For AMR data analysis library(tidymodels)   # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) #> ── Attaching packages ────────────────────────────────────── tidymodels 1.3.0 ── #> ✔ broom        1.0.7     ✔ recipes      1.1.1 #> ✔ dials        1.4.0     ✔ rsample      1.2.1 #> ✔ dplyr        1.1.4     ✔ tibble       3.2.1 #> ✔ ggplot2      3.5.1     ✔ tidyr        1.3.1 #> ✔ infer        1.0.7     ✔ tune         1.3.0 #> ✔ modeldata    1.4.0     ✔ workflows    1.2.0 #> ✔ parsnip      1.3.0     ✔ workflowsets 1.1.0 #> ✔ purrr        1.0.4     ✔ yardstick    1.3.2 #> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ── #> ✖ purrr::discard() masks scales::discard() #> ✖ dplyr::filter()  masks stats::filter() #> ✖ dplyr::lag()     masks stats::lag() #> ✖ recipes::step()  masks stats::step()  # Select relevant columns for prediction data <- example_isolates %>%   # select AB results dynamically   select(mo, aminoglycosides(), betalactams()) %>%   # replace NAs with NI (not-interpretable)    mutate(across(where(is.sir),                  ~replace_na(.x, \"NI\")),           # make factors of SIR columns           across(where(is.sir),                  as.integer),           # get Gramstain of microorganisms           mo = as.factor(mo_gramstain(mo))) %>%   # drop NAs - the ones without a Gramstain (fungi, etc.)   drop_na() #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #>   (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #>   (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #>   'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antibiotic selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>%   step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #>  #> ── Recipe ────────────────────────────────────────────────────────────────────── #>  #> ── Inputs #> Number of variables by role #> outcome:    1 #> predictor: 20 #>  #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #>   (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #>   (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #>   'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #>  #> ── Recipe ────────────────────────────────────────────────────────────────────── #>  #> ── Inputs #> Number of variables by role #> outcome:    1 #> predictor: 20 #>  #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #>  #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>%   set_engine(\"glm\") # Use the Generalized Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #>  #> Computational engine: glm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organizes entire modeling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>%   add_recipe(resistance_recipe) %>% # Add the preprocessing recipe   add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #>  #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #>  #> • step_corr() #>  #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #>  #> Computational engine: glm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antibiotic selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram based AMR results 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split)   # Testing set  # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>%   fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>%   predict(testing_data)                # Generate predictions probabilities <- fitted_workflow %>%   predict(testing_data, type = \"prob\") # Generate probabilities  predictions <- predictions %>%   bind_cols(probabilities) %>%   bind_cols(testing_data) # Combine with true labels  predictions #> # A tibble: 394 × 24 #>    .pred_class   `.pred_Gram-negative` `.pred_Gram-positive` mo        GEN   TOB #>    <fct>                         <dbl>                 <dbl> <fct>   <int> <int> #>  1 Gram-positive              1.07e- 1              8.93e- 1 Gram-p…     5     5 #>  2 Gram-positive              3.17e- 8              1.00e+ 0 Gram-p…     5     1 #>  3 Gram-negative              9.99e- 1              1.42e- 3 Gram-n…     5     5 #>  4 Gram-positive              2.22e-16              1   e+ 0 Gram-p…     5     5 #>  5 Gram-negative              9.46e- 1              5.42e- 2 Gram-n…     5     5 #>  6 Gram-positive              1.07e- 1              8.93e- 1 Gram-p…     5     5 #>  7 Gram-positive              2.22e-16              1   e+ 0 Gram-p…     1     5 #>  8 Gram-positive              2.22e-16              1   e+ 0 Gram-p…     4     4 #>  9 Gram-negative              1   e+ 0              2.22e-16 Gram-n…     1     1 #> 10 Gram-positive              6.05e-11              1.00e+ 0 Gram-p…     4     4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK <int>, KAN <int>, PEN <int>, OXA <int>, FLC <int>, #> #   AMX <int>, AMC <int>, AMP <int>, TZP <int>, CZO <int>, FEP <int>, #> #   CXM <int>, FOX <int>, CTX <int>, CAZ <int>, CRO <int>, IPM <int>, MEM <int>  # Evaluate model performance metrics <- predictions %>%   metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics  metrics #> # A tibble: 2 × 3 #>   .metric  .estimator .estimate #>   <chr>    <chr>          <dbl> #> 1 accuracy binary         0.995 #> 2 kap      binary         0.989 predictions %>%   roc_curve(mo, `.pred_Gram-negative`) %>%   autoplot()"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR with tidymodels","text":"post, demonstrated build machine learning pipeline tidymodels framework AMR package. combining selector functions like aminoglycosides() betalactams() tidymodels, efficiently prepared data, trained model, evaluated performance. workflow extensible antibiotic classes resistance patterns, empowering users analyse AMR data systematically reproducibly.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.1, 2016). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antimicrobials: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame(   mo = c(     \"Klebsiella\",     \"Escherichia\"   ),   ampicillin = \"S\" ) oops #>            mo ampicillin #> 1  Klebsiella          S #> 2 Escherichia          S  eucast_rules(oops, info = FALSE) #>            mo ampicillin #> 1  Klebsiella          R #> 2 Escherichia          S mo_is_intrinsic_resistant(   c(\"Klebsiella\", \"Escherichia\"),   \"ampicillin\" ) #> [1]  TRUE FALSE  mo_is_intrinsic_resistant(   \"Klebsiella\",   c(\"ampicillin\", \"kanamycin\") ) #> [1]  TRUE FALSE data <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(   CIP == \"R\" & age > 60 ~ \"Elderly Type A\",   ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #>   1. If CIP is  R  and age is higher than 60 then: Elderly Type A #>   2. If ERY is  R  and age is higher than 60 then: Elderly Type B #>   3. Otherwise: Negative #>  #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #>       Negative Elderly Type A Elderly Type B  #>           1070            198            732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,745 (87.25%, NA: 255 = 12.75%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>%   mdro() %>%   freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(   rifampicin = random_sir(5000),   isoniazid = random_sir(5000),   gatifloxacin = random_sir(5000),   ethambutol = random_sir(5000),   pyrazinamide = random_sir(5000),   moxifloxacin = random_sir(5000),   kanamycin = random_sir(5000) ) my_TB_data <- data.frame(   RIF = random_sir(5000),   INH = random_sir(5000),   GAT = random_sir(5000),   ETH = random_sir(5000),   PZA = random_sir(5000),   MFX = random_sir(5000),   KAN = random_sir(5000) ) head(my_TB_data) #>   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1          I         R            S          S            S            S #> 2          S         S            I          R            R            S #> 3          R         I            I          I            R            I #> 4          I         S            S          S            S            S #> 5          I         I            I          S            I            S #> 6          R         S            R          S            I            I #>   kanamycin #> 1         R #> 2         I #> 3         S #> 4         I #> 5         I #> 6         I mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) #> ℹ No column found as input for col_mo, assuming all rows contain #>   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) #> Rows: 2,000 #> Columns: 46 #> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender  <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward    <chr> \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo      <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>%   group_by(     order = mo_order(mo), # group on anything, like order     genus = mo_genus(mo)   ) %>% #  and genus as we do here   summarise_if(is.sir, resistance) %>% # then get resistance of all drugs   select(     order, genus, AMC, CXM, CTX,     CAZ, GEN, TOB, TMP, SXT   ) # and select only relevant columns  head(resistance_data) #> # A tibble: 6 × 10 #> # Groups:   order [5] #>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA #> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA #> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA #> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA #> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA #> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" #> Importance of components: #>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 #> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 5: S < SDD < < R < NI Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>%   # get microbial ID based on given organism   mutate(mo = as.mo(Organism)) %>%   # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `sir` class   mutate_at(vars(AMP_ND10:CIP_EE), as.sir) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_sir() function:","code":"data %>%   group_by(Country) %>%   select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%   ggplot_sir(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 78 678 rows 26 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, oxygen_tolerance, source, lpsn, lpsn_parent, lpsn_renamed_to, mycobank, mycobank_parent, mycobank_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence, snomed. data set R available microorganisms, load AMR package. last updated 4 October 2024 13:28:44 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.8 MB) Download tab-separated text file (17.7 MB) Download Microsoft Excel workbook (8.7 MB) Download Apache Feather file (8.3 MB) Download Apache Parquet file (3.8 MB) Download IBM SPSS Statistics data file (29 MB) Download Stata DTA file (92.5 MB) NOTE: exported files SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. file structures compression techniques inefficient. Advice? Use R instead. ’s free much better many ways. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy six kingdoms List Prokaryotic names Standing Nomenclature (LPSN), MycoBank, Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antimicrobials-antibiotic-and-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antimicrobials: Antibiotic and Antifungal Drugs","title":"Data sets for download / own use","text":"data set 487 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antimicrobials, load AMR package. last updated 7 March 2025 19:43:26 UTC. Find info structure data set . tab-separated text, Microsoft Excel, SPSS, Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antimicrobials: Antibiotic and Antifungal Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antivirals, load AMR package. last updated 20 October 2023 12:51:48 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (17 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (13 kB) Download IBM SPSS Statistics data file (32 kB) Download Stata DTA file (78 kB) tab-separated text, Microsoft Excel, SPSS, Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir","dir":"Articles","previous_headings":"","what":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","title":"Data sets for download / own use","text":"data set 34 063 rows 14 columns, containing following column names:guideline, type, host, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti, is_SDD. data set R available clinical_breakpoints, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (70 kB) Download tab-separated text file (3.1 MB) Download Microsoft Excel workbook (2 MB) Download Apache Feather file (1.5 MB) Download Apache Parquet file (0.1 MB) Download IBM SPSS Statistics data file (5.6 MB) Download Stata DTA file (9.3 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2011-2024) EUCAST (2011-2024). Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate development WHONET software. CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET AMR package. NOTE: AMR package (WHONET software well) contains internal methods apply guidelines, rather complex. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 301 583 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (0.1 MB) Download tab-separated text file (10.9 MB) Download Microsoft Excel workbook (3 MB) Download Apache Feather file (2.5 MB) Download Apache Parquet file (0.3 MB) Download IBM SPSS Statistics data file (16.2 MB) Download Stata DTA file (25 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 503 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt, eucast_version. data set R available dosage, load AMR package. last updated 22 June 2023 13:10:59 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (43 kB) Download Microsoft Excel workbook (25 kB) Download Apache Feather file (21 kB) Download Apache Parquet file (9 kB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant :  (), ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021), ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2 000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP, RIF. data set R available example_isolates, load AMR package. last updated 15 June 2024 13:33:49 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3 000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP, GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-groups-species-groups-and-microbiological-complexes","dir":"Articles","previous_headings":"","what":"microorganisms.groups: Species Groups and Microbiological Complexes","title":"Data sets for download / own use","text":"data set 521 rows 4 columns, containing following column names:mo_group, mo, mo_group_name, mo_name. data set R available microorganisms.groups, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (49 kB) Download Microsoft Excel workbook (19 kB) Download Apache Feather file (19 kB) Download Apache Parquet file (13 kB) Download IBM SPSS Statistics data file (63 kB) Download Stata DTA file (81 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-8","dir":"Articles","previous_headings":"microorganisms.groups: Species Groups and Microbiological Complexes","what":"Source","title":"Data sets for download / own use","text":"data set contains species groups microbiological complexes, used clinical_breakpoints data set.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-codes-common-laboratory-codes","dir":"Articles","previous_headings":"","what":"microorganisms.codes: Common Laboratory Codes","title":"Data sets for download / own use","text":"data set 4 971 rows 2 columns, containing following column names:code mo. data set R available microorganisms.codes, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (22 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (82 kB) Download Apache Feather file (85 kB) Download Apache Parquet file (56 kB) Download IBM SPSS Statistics data file (0.1 MB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-9","dir":"Articles","previous_headings":"microorganisms.codes: Common Laboratory Codes","what":"Source","title":"Data sets for download / own use","text":"data set contains commonly used codes microorganisms, laboratory systems WHONET.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions:  fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_sir_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\")  # or: example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   )  # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   ) predict_TZP #> # A tibble: 34 × 7 #>     year  value se_min se_max observations observed estimated #>  * <dbl>  <dbl>  <dbl>  <dbl>        <int>    <dbl>     <dbl> #>  1  2002 0.2        NA     NA           15   0.2       0.0562 #>  2  2003 0.0625     NA     NA           32   0.0625    0.0616 #>  3  2004 0.0854     NA     NA           82   0.0854    0.0676 #>  4  2005 0.05       NA     NA           60   0.05      0.0741 #>  5  2006 0.0508     NA     NA           59   0.0508    0.0812 #>  6  2007 0.121      NA     NA           66   0.121     0.0889 #>  7  2008 0.0417     NA     NA           72   0.0417    0.0972 #>  8  2009 0.0164     NA     NA           61   0.0164    0.106  #>  9  2010 0.0566     NA     NA           53   0.0566    0.116  #> 10  2011 0.183      NA     NA           93   0.183     0.127  #> # ℹ 24 more rows plot(predict_TZP) ggplot_sir_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_sir_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous:  Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate:  model also available object, attribute:","code":"example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>%   ggplot_sir_predict() example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>%   ggplot_sir_predict() model <- attributes(predict_TZP)$model  summary(model)$family #>  #> Family: binomial  #> Link function: logit  summary(model)$coefficients #>                  Estimate  Std. Error   z value     Pr(>|z|) #> (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 #> year           0.09883005  0.02295317  4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Andrew P. Norgan. Contributor. Anita Williams. Contributor. Annick Lenglet. Contributor. Anthony Underwood. Contributor. Anton Mymrikov. Contributor. Bart C. Meijer. Contributor. Christian F. Luz. Contributor. Dmytro Mykhailenko. Contributor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Jason Stull. Contributor. Javier Sanchez. Contributor. Jonas Salm. Contributor. Judith M. Fonville. Contributor. Larisse Bolton. Contributor. Matthew Saab. Contributor. Peter Dutey-Magni. Contributor. Rogier P. Schade. Contributor. Sofia Ny. Contributor. Alex W. Friedrich. Thesis advisor. Bhanu N. M. Sinha. Thesis advisor. Casper J. Albers. Thesis advisor. Corinna Glasner. Thesis advisor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Provides --one solution antimicrobial resistance (AMR) data analysis One Health approach Used 175 countries, available 20 languages Generates antibiograms - traditional, combined, syndromic, even WISCA Provides full microbiological taxonomy extensive info antimicrobial drugs Applies recent CLSI EUCAST clinical veterinary breakpoints MICs, disk zones ECOFFs Corrects duplicate isolates, calculates predicts AMR per antimicrobial class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC, SNOMED CT, NCBI 100% free costs dependencies, highly suitable places limited resources Now available Python ! Click read . https://msberends.github.io/AMR https://doi.org/10.18637/jss.v104.i03","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 distinct microbial species (updated December 2022) ~600 antimicrobial antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-over-175-countries-available-in-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, available in 20 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. help contributors corners world, AMR package available  English,  Czech,  Chinese,  Danish,  Dutch,  Finnish,  French,  German,  Greek,  Italian,  Japanese,  Norwegian,  Polish,  Portuguese,  Romanian,  Russian,  Spanish,  Swedish,  Turkish,  Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"One powerful functions package, aside calculating plotting AMR, selecting filtering based antimicrobial columns. can done using -called antimicrobial selectors, work base R, dplyr data.table. defined row filter Gram-negative bacteria intrinsic resistance cefotaxime (mo_is_gram_negative() mo_is_intrinsic_resistant()) column selection two antibiotic groups (aminoglycosides() carbapenems()), reference data microorganisms antimicrobials AMR package make sure get meant:","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr)  example_isolates %>%   mutate(bacteria = mo_fullname()) %>%   # filtering functions for microorganisms:   filter(mo_is_gram_negative(),          mo_is_intrinsic_resistant(ab = \"cefotax\")) %>%   # antimicrobial selectors:   select(bacteria,          aminoglycosides(),          carbapenems())"},{"path":"https://msberends.github.io/AMR/index.html","id":"generating-antibiograms","dir":"","previous_headings":"Practical examples","what":"Generating antibiograms","title":"Antimicrobial Resistance Data Analysis","text":"AMR package supports generating traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). used inside R Markdown Quarto, table printed right output format automatically (markdown, LaTeX, HTML, etc.). combination antibiograms, clear combined antibiotics yield higher empiric coverage: Like many functions package, antibiogram() comes support 20 languages often detected automatically based system language:","code":"antibiogram(example_isolates,             antibiotics = c(aminoglycosides(), carbapenems()),             formatting_type = 14) antibiogram(example_isolates,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),             mo_transform = \"gramstain\",             formatting_type = 14) antibiogram(example_isolates,             antibiotics = c(\"cipro\", \"tobra\", \"genta\"), # any arbitrary name or code will work             mo_transform = \"gramstain\",             ab_transform = \"name\",             formatting_type = 14,             language = \"uk\") # Ukrainian"},{"path":"https://msberends.github.io/AMR/index.html","id":"interpreting-and-plotting-mic-and-sir-values","dir":"","previous_headings":"Practical examples","what":"Interpreting and plotting MIC and SIR values","title":"Antimicrobial Resistance Data Analysis","text":"AMR package allows interpretation MIC disk diffusion values based CLSI EUCAST. Moreover, ggplot2 package extended new scale functions, allow plotting log2-distributed MIC values SIR values.","code":"library(ggplot2) library(AMR)  # generate some random values some_mic_values <- random_mic(size = 100) some_groups <- sample(LETTERS[1:5], 20, replace = TRUE) interpretation <- as.sir(some_mic_values,                          guideline = \"EUCAST 2024\",                          mo = \"E. coli\", # or any code or name resembling a known species                          ab = \"Cipro\")   # or any code or name resembling an antibiotic  # create the plot ggplot(data.frame(mic = some_mic_values,                   group = some_groups,                   sir = interpretation),        aes(x = group, y = mic, colour = sir)) +   theme_minimal() +   geom_boxplot(fill = NA, colour = \"grey\") +   geom_jitter(width = 0.25) +      # NEW scale function: plot MIC values to x, y, colour or fill   scale_y_mic() +      # NEW scale function: write out S/I/R in any of the 20 supported languages   #                     and set colourblind-friendly colours   scale_colour_sir()"},{"path":"https://msberends.github.io/AMR/index.html","id":"calculating-resistance-per-group","dir":"","previous_headings":"Practical examples","what":"Calculating resistance per group","title":"Antimicrobial Resistance Data Analysis","text":"manual approach, can use resistance susceptibility() function: use antimicrobial selectors select series antibiotic columns:","code":"example_isolates %>%   # group by ward:   group_by(ward) %>%   # calculate AMR using resistance() for gentamicin and tobramycin   # and get their 95% confidence intervals using sir_confidence_interval():   summarise(across(c(GEN, TOB),                    list(total_R = resistance,                         conf_int = function(x) sir_confidence_interval(x, collapse = \"-\")))) library(AMR) library(dplyr)  out <- example_isolates %>%   # group by ward:   group_by(ward) %>%   # calculate AMR using resistance(), over all aminoglycosides and polymyxins:   summarise(across(c(aminoglycosides(), polymyxins()),             resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (manual) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antimicrobial (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters SIR (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed rOpenSci R-universe platform:","code":"install.packages(\"AMR\", repos = \"https://msberends.r-universe.dev\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible following non-profit organisations initiatives:","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"objects -called 'Deprecated'. removed future version package. Using give warning name alternative object replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions — AMR-deprecated","text":"","code":"ab_class(...)  ab_selector(...)"},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deprecated Functions — AMR-deprecated","text":"object class tbl_df (inherits tbl, data.frame) 487 rows 14 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Options for the AMR package — AMR-options","title":"Options for the AMR package — AMR-options","text":"overview package-specific options() can set AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"options","dir":"Reference","previous_headings":"","what":"Options","title":"Options for the AMR package — AMR-options","text":"AMR_antibiogram_formatting_type  numeric (1-22) use antibiogram(), indicate formatting type use. AMR_breakpoint_type  character use .sir(), indicate breakpoint type use. must either \"ECOFF\", \"animal\", \"human\". AMR_cleaning_regex  regular expression (case-insensitive) use .mo() mo_* functions, clean user input. default outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". AMR_custom_ab  file location RDS file, use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo  file location RDS file, use custom microorganisms package. explained add_custom_microorganisms(). AMR_eucastrules  character set default types rules eucast_rules() function, must one : \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). AMR_guideline  character set default guideline interpreting MIC values disk diffusion diameters .sir(). Can guideline name (e.g., \"CLSI\") name year (e.g. \"CLSI 2019\"). default latest implemented EUCAST guideline, currently \"EUCAST 2024\". Supported guideline currently EUCAST (2011-2024) CLSI (2011-2024). AMR_ignore_pattern  regular expression ignore (.e., make NA) match given .mo() mo_* functions. AMR_include_PKPD  logical use .sir(), indicate PK/PD clinical breakpoints must applied last resort - default TRUE. AMR_include_screening  logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms  logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_locale  character set language AMR package, can one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). default current system language (supported, English otherwise). AMR_mo_source  file location manual code list used .mo() mo_* functions. explained set_mo_source().","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"saving-settings-between-sessions","dir":"Reference","previous_headings":"","what":"Saving Settings Between Sessions","title":"Options for the AMR package — AMR-options","text":"Settings R saved globally thus lost R exited. can save options .Rprofile file, user-specific file. can edit using:   file, can set options ...   ...add Portuguese language support antimicrobials, allow PK/PD rules interpreting MIC values .sir().","code":"utils::file.edit(\"~/.Rprofile\") options(AMR_locale = \"pt\")  options(AMR_include_PKPD = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"share-options-within-team","dir":"Reference","previous_headings":"","what":"Share Options Within Team","title":"Options for the AMR package — AMR-options","text":"global approach, e.g. within (data) team, save options file remote file location, shared network drive, user read file automatically start-. work way: Save plain text file e.g. \"X:/team_folder/R_options.R\" fill preferred settings. user, open .Rprofile file using utils::file.edit(\"~/.Rprofile\") put :   Reload R/RStudio check settings getOption(), e.g. getOption(\"AMR_locale\") set value. Now team settings configured one place, can maintained .","code":"source(\"X:/team_folder/R_options.R\")"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); doi:10.18637/jss.v104.i03 ) formed basis two PhD theses (doi:10.33612/diss.177417131  doi:10.33612/diss.192486375 ). installing package, R knows ~79 000 microorganisms (updated June 2024) ~610 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences public University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"The AMR Package — AMR","text":"cite AMR publications use: Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). \"AMR: R Package Working Antimicrobial Resistance Data.\" Journal Statistical Software, 104(3), 1-31. doi:10.18637/jss.v104.i03 BibTeX entry LaTeX users :","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"The AMR Package — AMR","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The AMR Package — AMR","text":"Maintainer: Matthijs S. Berends m.s.berends@umcg.nl (ORCID) Authors: Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Andrew P. Norgan (ORCID) [contributor] Anita Williams (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Anthony Underwood (ORCID) [contributor] Anton Mymrikov [contributor] Bart C. Meijer [contributor] Christian F. Luz (ORCID) [contributor] Dmytro Mykhailenko [contributor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Jason Stull (ORCID) [contributor] Javier Sanchez (ORCID) [contributor] Jonas Salm [contributor] Judith M. Fonville [contributor] Larisse Bolton (ORCID) [contributor] Matthew Saab [contributor] Peter Dutey-Magni (ORCID) [contributor] Rogier P. Schade [contributor] Sofia Ny (ORCID) [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Casper J. Albers (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor]","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class 'ab' #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\"  ab_tradenames(\"flucloxacillin\") #>  [1] \"culpen\"               \"floxacillin\"          \"floxacillinsodium\"    #>  [4] \"floxapen\"             \"floxapensodiumsalt\"   \"fluclox\"              #>  [7] \"flucloxacilina\"       \"flucloxacilline\"      \"flucloxacillinum\"     #> [10] \"fluorochloroxacillin\" \"staphylex\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antimcrobial results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"tibble 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 28 different antimicrobials. can lookup abbreviations antimicrobials data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antimcrobial class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> # A tibble: 500 × 53 #>    `Identification number` `Specimen number` Organism Country         Laboratory #>    <chr>                               <int> <chr>    <chr>           <chr>      #>  1 fe41d7bafa                           1748 SPN      Belgium         National … #>  2 91f175ec37                           1767 eco      The Netherlands National … #>  3 cc4015056e                           1343 eco      The Netherlands National … #>  4 e864b692f5                           1894 MAP      Denmark         National … #>  5 3d051fe345                           1739 PVU      Belgium         National … #>  6 c80762a08d                           1846 103      The Netherlands National … #>  7 8022d3727c                           1628 103      Denmark         National … #>  8 f3dc5f553d                           1493 eco      The Netherlands National … #>  9 15add38f6c                           1847 eco      France          National … #> 10 fd41248def                           1458 eco      Germany         National … #> # ℹ 490 more rows #> # ℹ 48 more variables: `Last name` <chr>, `First name` <chr>, Sex <chr>, #> #   Age <dbl>, `Age category` <chr>, `Date of admission` <date>, #> #   `Specimen date` <date>, `Specimen type` <chr>, #> #   `Specimen type (Numeric)` <dbl>, Reason <chr>, `Isolate number` <int>, #> #   `Organism type` <chr>, Serotype <chr>, `Beta-lactamase` <lgl>, ESBL <lgl>, #> #   Carbapenemase <lgl>, `MRSA screening test` <lgl>, …"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text(text, type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL, translate_ab = FALSE, thorough_search = NULL,   info = interactive(), ...)"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antimicrobials data set translate antibiotic abbreviations , using ab_property(). default FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line, # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tid\") #> [[1]] #> Class 'ab' #> [1] AMX #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class 'ab' #> [1] AMX CIP #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\"   #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\"                if (require(\"dplyr\")) {   tibble(clinical_text = c(     \"given 400mg cipro and 500 mg amox\",     \"started on doxy iv today\"   )) %>%     mutate(       abx_codes = ab_from_text(clinical_text),       abx_doses = ab_from_text(clinical_text, type = \"doses\"),       abx_admin = ab_from_text(clinical_text, type = \"admin\"),       abx_coll = ab_from_text(clinical_text, collapse = \"|\"),       abx_coll_names = ab_from_text(clinical_text,         collapse = \"|\",         translate_ab = \"name\"       ),       abx_coll_doses = ab_from_text(clinical_text,         type = \"doses\",         collapse = \"|\"       ),       abx_coll_admin = ab_from_text(clinical_text,         type = \"admin\",         collapse = \"|\"       )     ) } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #>   clinical_text            abx_codes abx_doses abx_admin abx_coll abx_coll_names #>   <chr>                    <list>    <list>    <list>    <chr>    <chr>          #> 1 given 400mg cipro and 5… <ab [2]>  <dbl [2]> <chr [1]> CIP|AMX  Ciprofloxacin… #> 2 started on doxy iv today <ab [1]>  <dbl [1]> <chr [1]> DOX      Doxycycline    #> # ℹ 2 more variables: abx_coll_doses <chr>, abx_coll_admin <chr> # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antimicrobials data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  ab_cid(x, ...)  ab_synonyms(x, ...)  ab_tradenames(x, ...)  ab_group(x, language = get_AMR_locale(), ...)  ab_atc(x, only_first = FALSE, ...)  ab_atc_group1(x, language = get_AMR_locale(), ...)  ab_atc_group2(x, language = get_AMR_locale(), ...)  ab_loinc(x, ...)  ab_ddd(x, administration = \"oral\", ...)  ab_ddd_units(x, administration = \"oral\", ...)  ab_info(x, language = get_AMR_locale(), ...)  ab_url(x, open = FALSE, ...)  ab_property(x, property = \"name\", language = get_AMR_locale(), ...)  set_ab_names(data, ..., property = \"name\", language = get_AMR_locale(),   snake_case = NULL)"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic drug code .ab() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: columns select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antimicrobials data set: vector_or(colnames(antimicrobials), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://atcddd.fhi./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\") #> [1] \"Amoxicillin\" ab_atc(\"AMX\") #> [1] \"J01CA04\" ab_cid(\"AMX\") #> [1] 33613 ab_synonyms(\"AMX\") #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicilline\"       #> [10] \"amoxicillinhydrate\" \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"amoxyke\"            \"anemolin\"           #> [19] \"aspenil\"            \"atoksilin\"          \"biomox\"             #> [22] \"bristamox\"          \"cemoxin\"            \"clamoxyl\"           #> [25] \"damoxy\"             \"delacillin\"         \"demoksil\"           #> [28] \"dispermox\"          \"efpenix\"            \"flemoxin\"           #> [31] \"hiconcil\"           \"histocillin\"        \"hydroxyampicillin\"  #> [34] \"ibiamox\"            \"imacillin\"          \"lamoxy\"             #> [37] \"largopen\"           \"metafarmacapsules\"  \"metifarmacapsules\"  #> [40] \"moksilin\"           \"moxacin\"            \"moxatag\"            #> [43] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [46] \"promoxil\"           \"remoxil\"            \"robamox\"            #> [49] \"sawamoxpm\"          \"tolodina\"           \"topramoxin\"         #> [52] \"unicillin\"          \"utimox\"             \"vetramox\"           ab_tradenames(\"AMX\") #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicilline\"       #> [10] \"amoxicillinhydrate\" \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"amoxyke\"            \"anemolin\"           #> [19] \"aspenil\"            \"atoksilin\"          \"biomox\"             #> [22] \"bristamox\"          \"cemoxin\"            \"clamoxyl\"           #> [25] \"damoxy\"             \"delacillin\"         \"demoksil\"           #> [28] \"dispermox\"          \"efpenix\"            \"flemoxin\"           #> [31] \"hiconcil\"           \"histocillin\"        \"hydroxyampicillin\"  #> [34] \"ibiamox\"            \"imacillin\"          \"lamoxy\"             #> [37] \"largopen\"           \"metafarmacapsules\"  \"metifarmacapsules\"  #> [40] \"moksilin\"           \"moxacin\"            \"moxatag\"            #> [43] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [46] \"promoxil\"           \"remoxil\"            \"robamox\"            #> [49] \"sawamoxpm\"          \"tolodina\"           \"topramoxin\"         #> [52] \"unicillin\"          \"utimox\"             \"vetramox\"           ab_group(\"AMX\") #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\") #>                                                             Amoxicillin  #> \"https://atcddd.fhi.no/atc_ddd_index//?code=J01CA04&showdescription=no\"   # smart lowercase transformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\"                 ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\"                  # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\"  ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #>  #> $cid #> [1] 33613 #>  #> $name #> [1] \"Amoxicillin\" #>  #> $group #> [1] \"Beta-lactams/penicillins\" #>  #> $atc #> [1] \"J01CA04\" #>  #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #>  #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #>  #> $tradenames #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicilline\"       #> [10] \"amoxicillinhydrate\" \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"amoxyke\"            \"anemolin\"           #> [19] \"aspenil\"            \"atoksilin\"          \"biomox\"             #> [22] \"bristamox\"          \"cemoxin\"            \"clamoxyl\"           #> [25] \"damoxy\"             \"delacillin\"         \"demoksil\"           #> [28] \"dispermox\"          \"efpenix\"            \"flemoxin\"           #> [31] \"hiconcil\"           \"histocillin\"        \"hydroxyampicillin\"  #> [34] \"ibiamox\"            \"imacillin\"          \"lamoxy\"             #> [37] \"largopen\"           \"metafarmacapsules\"  \"metifarmacapsules\"  #> [40] \"moksilin\"           \"moxacin\"            \"moxatag\"            #> [43] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [46] \"promoxil\"           \"remoxil\"            \"robamox\"            #> [49] \"sawamoxpm\"          \"tolodina\"           \"topramoxin\"         #> [52] \"unicillin\"          \"utimox\"             \"vetramox\"           #>  #> $loinc #>  [1] \"101498-4\" \"15-8\"     \"16-6\"     \"16365-9\"  \"17-4\"     \"18-2\"     #>  [7] \"18861-5\"  \"18862-3\"  \"19-0\"     \"20-8\"     \"21-6\"     \"22-4\"     #> [13] \"25274-2\"  \"25310-4\"  \"3344-9\"   \"55614-2\"  \"55615-9\"  \"55616-7\"  #> [19] \"6976-5\"   \"6977-3\"   \"80133-2\"  #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #>  [1] \"101477-8\" \"101478-6\" \"18864-9\"  \"18865-6\"  \"20374-5\"  \"21066-6\"  #>  [7] \"23618-2\"  \"27-3\"     \"28-1\"     \"29-9\"     \"30-7\"     \"31-5\"     #> [13] \"32-3\"     \"33-1\"     \"3355-5\"   \"33562-0\"  \"33919-2\"  \"34-9\"     #> [19] \"43883-8\"  \"43884-6\"  \"6979-9\"   \"6980-7\"   \"87604-5\"  ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\"  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\"  # use set_ab_names() for renaming columns colnames(example_isolates) #>  [1] \"date\"    \"patient\" \"age\"     \"gender\"  \"ward\"    \"mo\"      \"PEN\"     #>  [8] \"OXA\"     \"FLC\"     \"AMX\"     \"AMC\"     \"AMP\"     \"TZP\"     \"CZO\"     #> [15] \"FEP\"     \"CXM\"     \"FOX\"     \"CTX\"     \"CAZ\"     \"CRO\"     \"GEN\"     #> [22] \"TOB\"     \"AMK\"     \"KAN\"     \"TMP\"     \"SXT\"     \"NIT\"     \"FOS\"     #> [29] \"LNZ\"     \"CIP\"     \"MFX\"     \"VAN\"     \"TEC\"     \"TCY\"     \"TGC\"     #> [36] \"DOX\"     \"ERY\"     \"CLI\"     \"AZM\"     \"IPM\"     \"MEM\"     \"MTR\"     #> [43] \"CHL\"     \"COL\"     \"MUP\"     \"RIF\"     colnames(set_ab_names(example_isolates)) #>  [1] \"date\"                          \"patient\"                       #>  [3] \"age\"                           \"gender\"                        #>  [5] \"ward\"                          \"mo\"                            #>  [7] \"benzylpenicillin\"              \"oxacillin\"                     #>  [9] \"flucloxacillin\"                \"amoxicillin\"                   #> [11] \"amoxicillin_clavulanic_acid\"   \"ampicillin\"                    #> [13] \"piperacillin_tazobactam\"       \"cefazolin\"                     #> [15] \"cefepime\"                      \"cefuroxime\"                    #> [17] \"cefoxitin\"                     \"cefotaxime\"                    #> [19] \"ceftazidime\"                   \"ceftriaxone\"                   #> [21] \"gentamicin\"                    \"tobramycin\"                    #> [23] \"amikacin\"                      \"kanamycin\"                     #> [25] \"trimethoprim\"                  \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\"                \"fosfomycin\"                    #> [29] \"linezolid\"                     \"ciprofloxacin\"                 #> [31] \"moxifloxacin\"                  \"vancomycin\"                    #> [33] \"teicoplanin\"                   \"tetracycline\"                  #> [35] \"tigecycline\"                   \"doxycycline\"                   #> [37] \"erythromycin\"                  \"clindamycin\"                   #> [39] \"azithromycin\"                  \"imipenem\"                      #> [41] \"meropenem\"                     \"metronidazole\"                 #> [43] \"chloramphenicol\"               \"colistin\"                      #> [45] \"mupirocin\"                     \"rifampicin\"                    colnames(set_ab_names(example_isolates, NIT:VAN)) #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     set_ab_names()    # this does the same:   example_isolates %>%     rename_with(set_ab_names)    # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\")    example_isolates %>%     set_ab_names(where(is.sir)) %>%     colnames()    example_isolates %>%     set_ab_names(NIT:VAN) %>%     colnames() } #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials — add_custom_antimicrobials","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug names codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x)  clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"x data.frame resembling antimicrobials data set, least containing columns \"ab\" \"name\"","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"Important: Due R works, add_custom_antimicrobials() function run every R session - added antimicrobials stored sessions thus lost R exited. two ways circumvent automate process adding antimicrobials: Method 1: Using package option AMR_custom_ab, preferred method. use method: Create data set structure antimicrobials data set (containing least columns \"ab\" \"name\") save saveRDS() location choice, e.g. \"~/my_custom_ab.rds\", remote location. Set file location package option AMR_custom_ab: options(AMR_custom_ab = \"~/my_custom_ab.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file:   Upon package load, file loaded run add_custom_antimicrobials() function. Method 2: Loading antimicrobial additions directly .Rprofile file. Note definitions stored user-specific R file, suboptimal workflow. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_antimicrobials() clear previously added antimicrobials.","code":"# Add custom antimicrobial codes: options(AMR_custom_ab = \"~/my_custom_ab.rds\") # Add custom antibiotic drug codes:  AMR::add_custom_antimicrobials(    data.frame(ab = \"TESTAB\",               name = \"Test Antibiotic\",               group = \"Test Group\")  )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"# \\donttest{ # returns a wildly guessed result: as.ab(\"testab\") #> Class 'ab' #> [1] THA  # now add a custom entry - it will be considered by as.ab() and # all ab_*() functions add_custom_antimicrobials(   data.frame(     ab = \"TESTAB\",     name = \"Test Antibiotic\",     # you can add any property present in the     # 'antimicrobials' data set, such as 'group':     group = \"Test Group\"   ) ) #> ℹ Added one record to the internal antimicrobials data set.  # \"testab\" is now a new antibiotic: as.ab(\"testab\") #> Class 'ab' #> [1] TESTAB ab_name(\"testab\") #> [1] \"Test Antibiotic\" ab_group(\"testab\") #> [1] \"Test Group\"  ab_info(\"testab\") #> $ab #> [1] \"TESTAB\" #>  #> $cid #> [1] NA #>  #> $name #> [1] \"Test Antibiotic\" #>  #> $group #> [1] \"Test Group\" #>  #> $atc #> [1] NA #>  #> $atc_group1 #> [1] NA #>  #> $atc_group2 #> [1] NA #>  #> $tradenames #> [1] NA #>  #> $loinc #> [1] NA #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] NA #>  #> $ddd$oral$units #> [1] NA #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] NA #>  #> $ddd$iv$units #> [1] NA #>  #>  #>    # Add Co-fluampicil, which is one of the many J01CR50 codes, see # https://atcddd.fhi.no/ddd/list_of_ddds_combined_products/ add_custom_antimicrobials(   data.frame(     ab = \"COFLU\",     name = \"Co-fluampicil\",     atc = \"J01CR50\",     group = \"Beta-lactams/penicillins\"   ) ) #> ℹ Added one record to the internal antimicrobials data set. ab_atc(\"Co-fluampicil\") #> [1] \"J01CR50\" ab_name(\"J01CR50\") #> [1] \"Co-fluampicil\"  # even antimicrobial selectors work # see ?amr_selector x <- data.frame(   random_column = \"some value\",   coflu = as.sir(\"S\"),   ampicillin = as.sir(\"R\") ) x #>   random_column coflu ampicillin #> 1    some value     S          R x[, betalactams()] #> ℹ For betalactams() using columns 'coflu' (co-fluampicil) and #>   'ampicillin' #>   coflu ampicillin #> 1     S          R # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Microorganisms — add_custom_microorganisms","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"add_custom_microorganisms() can add custom microorganisms, non-taxonomic outcome laboratory analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"add_custom_microorganisms(x)  clear_custom_microorganisms()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"x data.frame resembling microorganisms data set, least containing column \"genus\" (case-insensitive)","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"function fill missing taxonomy , specific taxonomic columns missing, see Examples. Important: Due R works, add_custom_microorganisms() function run every R session - added microorganisms stored sessions thus lost R exited. two ways circumvent automate process adding microorganisms: Method 1: Using package option AMR_custom_mo, preferred method. use method: Create data set structure microorganisms data set (containing least column \"genus\") save saveRDS() location choice, e.g. \"~/my_custom_mo.rds\", remote location. Set file location package option AMR_custom_mo: options(AMR_custom_mo = \"~/my_custom_mo.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file:   Upon package load, file loaded run add_custom_microorganisms() function. Method 2: Loading microorganism directly .Rprofile file. Note definitions stored user-specific R file, suboptimal workflow. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_microorganisms() clear previously added microorganisms.","code":"# Add custom microorganism codes: options(AMR_custom_mo = \"~/my_custom_mo.rds\") # Add custom antibiotic drug codes:  AMR::add_custom_microorganisms(    data.frame(genus = \"Enterobacter\",               species = \"asburiae/cloacae\")  )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"# \\donttest{ # a combination of species is not formal taxonomy, so # this will result in \"Enterobacter cloacae cloacae\", # since it resembles the input best: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae\"  # now add a custom entry - it will be considered by as.mo() and # all mo_*() functions add_custom_microorganisms(   data.frame(     genus = \"Enterobacter\",     species = \"asburiae/cloacae\"   ) ) #> ℹ Added Enterobacter asburiae/cloacae to the internal microorganisms data #>   set.  # E. asburiae/cloacae is now a new microorganism: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # its code: as.mo(\"Enterobacter asburiae/cloacae\") #> Class 'mo' #> [1] CUSTOM1_ENTRB_ASB/  # all internal algorithms will work as well: mo_name(\"Ent asburia cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # and even the taxonomy was added based on the genus! mo_family(\"E. asburiae/cloacae\") #> [1] \"Enterobacteriaceae\" mo_gramstain(\"Enterobacter asburiae/cloacae\") #> [1] \"Gram-negative\"  mo_info(\"Enterobacter asburiae/cloacae\") #> $mo #> [1] \"CUSTOM1_ENTRB_ASB/\" #>  #> $rank #> [1] \"species\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Enterobacter\" #>  #> $species #> [1] \"asburiae/cloacae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $oxygen_tolerance #> [1] NA #>  #> $url #> [1] \"\" #>  #> $ref #> [1] \"Self-added, 2025\" #>  #> $snomed #> [1] NA #>  #> $lpsn #> [1] NA #>  #> $mycobank #> [1] NA #>  #> $gbif #> [1] NA #>  #> $group_members #> character(0) #>    # the function tries to be forgiving: add_custom_microorganisms(   data.frame(     GENUS = \"BACTEROIDES / PARABACTEROIDES SLASHLINE\",     SPECIES = \"SPECIES\"   ) ) #> ℹ Added Bacteroides/Parabacteroides to the internal microorganisms data #>   set. mo_name(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"Bacteroides/Parabacteroides\" mo_rank(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"genus\"  # taxonomy still works, even though a slashline genus was given as input: mo_family(\"Bacteroides/Parabacteroides\") #> [1] \"Bacteroidaceae\"   # for groups and complexes, set them as species or subspecies: add_custom_microorganisms(   data.frame(     genus = \"Citrobacter\",     species = c(\"freundii\", \"braakii complex\"),     subspecies = c(\"complex\", \"\")   ) ) #> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the #>   internal microorganisms data set. mo_name(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Citrobacter freundii complex\" \"Citrobacter braakii complex\"  mo_species(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"freundii complex\" \"braakii complex\"  mo_gramstain(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Gram-negative\" \"Gram-negative\" # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (default today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000)  # add ages df$age <- age(df$birth_date)  # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE)  # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\")  df #>    birth_date age age_exact age_at_y2k #> 1  1965-12-05  59  59.25205         34 #> 2  1980-03-01  45  45.01644         19 #> 3  1949-11-01  75  75.34521         50 #> 4  1947-02-14  78  78.05753         52 #> 5  1940-02-19  85  85.04384         59 #> 6  1988-01-10  37  37.15342         11 #> 7  1997-08-27  27  27.52603          2 #> 8  1978-01-26  47  47.10959         21 #> 9  1972-06-17  52  52.72055         27 #> 10 1986-08-10  38  38.57260         13"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x - default age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)  # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+  0-49 50+  50+  0-49 0-49 #> Levels: 0-49 < 50+  # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19  0-19  0-19  50+   20-49 50+   50+   20-49 20-49 #> Levels: 0-19 < 20-49 < 50+  # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+  # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+  # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+  # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\") && require(\"ggplot2\")) {   example_isolates %>%     filter_first_isolate() %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(       x = \"age_group\",       minimum = 0,       x.title = \"Age Group\",       title = \"Ciprofloxacin resistance per age group\"     ) } #> Loading required package: ggplot2  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Create detailed antibiograms options traditional, combination, syndromic, Bayesian WISCA methods. Adhering previously described approaches (see Source) especially Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) Bielicki et al., functions provide flexible output formats including plots tables, ideal integration R Markdown Quarto reports.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"","code":"antibiogram(x, antibiotics = where(is.sir), mo_transform = \"shortname\",   ab_transform = \"name\", syndromic_group = NULL, add_total_n = FALSE,   only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),   formatting_type = getOption(\"AMR_antibiogram_formatting_type\",   ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(),   minimum = 30, combine_SI = TRUE, sep = \" + \", wisca = FALSE,   simulations = 1000, conf_interval = 0.95, interval_side = \"two-tailed\",   info = interactive())  wisca(x, antibiotics = where(is.sir), ab_transform = \"name\",   syndromic_group = NULL, add_total_n = FALSE, only_all_tested = FALSE,   digits = 1,   formatting_type = getOption(\"AMR_antibiogram_formatting_type\", 14),   col_mo = NULL, language = get_AMR_locale(), minimum = 30,   combine_SI = TRUE, sep = \" + \", simulations = 1000,   conf_interval = 0.95, interval_side = \"two-tailed\",   info = interactive())  retrieve_wisca_parameters(wisca_model, ...)  # S3 method for class 'antibiogram' plot(x, ...)  # S3 method for class 'antibiogram' autoplot(object, ...)  # S3 method for class 'antibiogram' knit_print(x, italicise = TRUE,   na = getOption(\"knitr.kable.NA\", default = \"\"), ...)"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Bielicki JA et al. (2016). Selecting appropriate empirical antibiotic regimens paediatric bloodstream infections: application Bayesian decision model local pooled antimicrobial resistance surveillance data Journal Antimicrobial Chemotherapy 71(3); doi:10.1093/jac/dkv397 Bielicki JA et al. (2020). Evaluation coverage 3 antibiotic regimens neonatal sepsis hospital setting across Asian countries JAMA Netw Open. 3(2):e1921124; doi:10.1001.jamanetworkopen.2019.21124 Klinker KP et al. (2021). Antimicrobial stewardship antibiograms: importance moving beyond traditional antibiograms. Therapeutic Advances Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373 Barbieri E et al. (2021). Development Weighted-Incidence Syndromic Combination Antibiogram (WISCA) guide choice empiric antibiotic treatment urinary tract infection paediatric patients: Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2 M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"x data.frame containing least column microorganisms columns antimicrobial results (class 'sir', see .sir()) antibiotics vector antimicrobial name code (evaluated .ab(), column name x, (combinations ) antimicrobial selectors aminoglycosides() carbapenems(). combination antibiograms, can also set values separated \"+\", \"TZP+TOB\" \"cipro + genta\", given columns resembling antimicrobials exist x. See Examples. mo_transform character transform microorganism input - must \"name\", \"shortname\" (default), \"gramstain\", one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Can also NULL transform input NA consider microorganisms 'unknown'. ab_transform character transform antimicrobial input - must one column names antimicrobials data set (defaults \"name\"): \"ab\", \"cid\", \"name\", \"group\", \"atc\", \"atc_group1\", \"atc_group2\", \"abbreviations\", \"synonyms\", \"oral_ddd\", \"oral_units\", \"iv_ddd\", \"iv_units\", \"loinc\". Can also NULL transform input. syndromic_group column name x, values calculated split rows x, e.g. using ifelse() case_when(). See Examples. add_total_n logical indicate whether n_tested available numbers per pathogen added table (default TRUE). add lowest highest number available isolates per antimicrobial (e.g, E. coli 200 isolates available ciprofloxacin 150 amoxicillin, returned number \"150-200\"). option unavailable wisca = TRUE; case, use retrieve_wisca_parameters() get parameters used WISCA. only_all_tested (combination antibiograms): logical indicate isolates must tested antimicrobials, see Details digits number digits use rounding antimicrobial coverage, defaults 1 WISCA 0 otherwise formatting_type numeric value (1–22 WISCA, 1-12 non-WISCA) indicating 'cells' antibiogram table formatted. See Details > Formatting Type list options. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). language language translate text, defaults system language (see get_AMR_locale()) minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether susceptibility determined results either S, SDD, , instead S (default TRUE) sep separating character antimicrobial columns combination antibiograms wisca logical indicate whether Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must generated (default FALSE). use Bayesian decision model estimate regimen coverage probabilities using Monte Carlo simulations. Set simulations, conf_interval, interval_side adjust. simulations (WISCA) numerical value set number Monte Carlo simulations conf_interval (WISCA) numerical value set confidence interval (default 0.95) interval_side (WISCA) side confidence interval, either \"two-tailed\" (default), \"left\" \"right\" info logical indicate info printed - default TRUE interactive mode wisca_model outcome wisca() antibiogram(..., wisca = TRUE) ... used R Markdown Quarto: arguments passed knitr::kable() (otherwise, use) object antibiogram() object italicise logical indicate whether microorganism names knitr table made italic, using italicise_taxonomy(). na character use showing NA values","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"functions return table values 0 100 susceptibility, resistance. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine one four available algorithms: isolate-based, patient-based, episode-based, phenotype-based. estimating antimicrobial coverage, especially creating WISCA, outcome might become reliable including top n species encountered data. can filter top n using top_n_microorganisms(). example, use top_n_microorganisms(your_data, n = 10) pre-processing step include top 10 species data. numeric values antibiogram stored long format attribute long_numeric. can retrieve using attributes(x)$long_numeric, x outcome antibiogram() wisca(). ideal e.g. advanced plotting.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"formatting-type","dir":"Reference","previous_headings":"","what":"Formatting Type","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"formatting 'cells' table can set argument formatting_type. examples, 5 indicates antimicrobial coverage (4-6 confidence level), 15 number susceptible isolates, 300 number tested (.e., available) isolates: 5 15 300 15/300 5 (300) 5% (300) 5 (N=300) 5% (N=300) 5 (15/300) 5% (15/300) 5 (N=15/300) 5% (N=15/300) 5 (4-6) 5% (4-6%) - default WISCA 5 (4-6,300) 5% (4-6%,300) 5 (4-6,N=300) 5% (4-6%,N=300) - default non-WISCA 5 (4-6,15/300) 5% (4-6%,15/300) 5 (4-6,N=15/300) 5% (4-6%,N=15/300) default can set globally package option AMR_antibiogram_formatting_type, e.g. options(AMR_antibiogram_formatting_type = 5). note WISCA, total numbers tested susceptible isolates less useful report, since included Bayesian model apparent susceptibility confidence level. Set digits (defaults 0) alter rounding susceptibility percentages.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"antibiogram-types","dir":"Reference","previous_headings":"","what":"Antibiogram Types","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"various antibiogram types, summarised Klinker et al. (2021, doi:10.1177/20499361211011373 ), supported antibiogram(). clinical coverage estimations, use WISCA whenever possible, since provides precise coverage estimates accounting pathogen incidence antimicrobial susceptibility, shown Bielicki et al. (2020, doi:10.1001.jamanetworkopen.2019.21124 ). See section Explaining WISCA page. note WISCA pathogen-agnostic, meaning outcome stratied pathogen, rather syndrome. Traditional Antibiogram Case example: Susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Code example:   Combination Antibiogram Case example: Additional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Code example:   Syndromic Antibiogram Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Code example:   Weighted-Incidence Syndromic Combination Antibiogram (WISCA) WISCA can applied antibiogram, see section Explaining WISCA page information. Code example:   WISCA uses sophisticated Bayesian decision model combine local pooled antimicrobial resistance data. approach evaluates local patterns can also draw multi-centre datasets improve regimen accuracy, even low-incidence infections like paediatric bloodstream infections (BSIs).","code":"antibiogram(your_data,             antibiotics = \"TZP\") antibiogram(your_data,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) antibiogram(your_data,             antibiotics = penicillins(),             syndromic_group = \"ward\") antibiogram(your_data,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),             wisca = TRUE)  # this is equal to: wisca(your_data,       antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"grouped-tibbles","dir":"Reference","previous_headings":"","what":"Grouped tibbles","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"type antibiogram, grouped tibbles can also used calculate susceptibilities various groups. Code example:","code":"library(dplyr) your_data %>%   group_by(has_sepsis, is_neonate, sex) %>%   wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"stepped-approach-for-clinical-insight","dir":"Reference","previous_headings":"","what":"Stepped Approach for Clinical Insight","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"clinical practice, antimicrobial coverage decisions evolve microbiological data becomes available. theoretical stepped approach ensures empirical coverage can continuously assessed improve patient outcomes: Initial Empirical Therapy (Admission / Pre-Culture Data) admission, pathogen information available. Action: broad-spectrum coverage based local resistance patterns syndromic antibiograms. Using pathogen-agnostic yet incidence-weighted WISCA preferred. Code example:   Refinement Gram Stain Results blood culture becomes positive, Gram stain provides initial crucial first stratification (Gram-positive vs. Gram-negative). Action: narrow coverage based Gram stain-specific resistance patterns. Code example:   Definitive Therapy Based Species Identification cultivation pathogen, full pathogen identification allows precise targeting therapy. Action: adjust treatment pathogen-specific antibiograms, minimizing resistance risks. Code example:   structuring antibiograms around stepped approach, clinicians can make data-driven adjustments stage, ensuring optimal empirical targeted therapy reducing unnecessary broad-spectrum antimicrobial use.","code":"antibiogram(your_data,             antibiotics = selected_regimens,             mo_transform = NA) # all pathogens set to `NA`  # preferred: use WISCA wisca(your_data,       antibiotics = selected_regimens) antibiogram(your_data,             antibiotics = selected_regimens,             mo_transform = \"gramstain\") # all pathogens set to Gram-pos/Gram-neg antibiogram(your_data,             antibiotics = selected_regimens,             mo_transform = \"shortname\") # all pathogens set to 'G. species', e.g., E. coli"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"inclusion-in-combination-antibiograms","dir":"Reference","previous_headings":"","what":"Inclusion in Combination Antibiograms","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Note combination antibiograms, important realise susceptibility can calculated two ways, can set only_all_tested argument (default FALSE). See example two antimicrobials, Drug Drug B, antibiogram() works calculate %SI:","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   considered   considered  considered   considered                     susceptible    tested    susceptible    tested --------  --------  -----------  ----------  -----------  ----------  S or I    S or I        X            X           X            X    R       S or I        X            X           X            X   <NA>     S or I        X            X           -            -  S or I      R           X            X           X            X    R         R           -            X           -            X   <NA>       R           -            -           -            -  S or I     <NA>         X            X           -            -    R        <NA>         -            -           -            -   <NA>      <NA>         -            -           -            - --------------------------------------------------------------------"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"plotting","dir":"Reference","previous_headings":"","what":"Plotting","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"types antibiograms listed can plotted (using ggplot2::autoplot() base R's plot() barplot()). mentioned , numeric values antibiogram stored long format attribute long_numeric. can retrieve using attributes(x)$long_numeric, x outcome antibiogram() wisca(). outcome antibiogram() can also used directly R Markdown / Quarto (.e., knitr) reports. case, knitr::kable() applied automatically microorganism names even printed italics default (see argument italicise). can also use functions specific 'table reporting' packages transform output antibiogram() needs, e.g. flextable::as_flextable() gt::gt().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"explaining-wisca","dir":"Reference","previous_headings":"","what":"Explaining WISCA","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"WISCA, outlined Bielicki et al. (doi:10.1093/jac/dkv397 ), stands Weighted-Incidence Syndromic Combination Antibiogram, estimates probability adequate empirical antimicrobial regimen coverage specific infection syndromes. method leverages Bayesian decision model random effects pathogen incidence susceptibility, enabling robust estimates presence sparse data. Bayesian model assumes conjugate priors parameter estimation. example, coverage probability \\(\\theta\\) given antimicrobial regimen modelled using Beta distribution prior: $$\\theta \\sim \\text{Beta}(\\alpha_0, \\beta_0)$$ \\(\\alpha_0\\) \\(\\beta_0\\) represent prior successes failures, respectively, informed expert knowledge weakly informative priors (e.g., \\(\\alpha_0 = 1, \\beta_0 = 1\\)). likelihood function constructed based observed data, number covered cases regimen follows binomial distribution: $$y \\sim \\text{Binomial}(n, \\theta)$$ Posterior parameter estimates obtained combining prior likelihood using Bayes' theorem. posterior distribution \\(\\theta\\) also Beta distribution: $$\\theta | y \\sim \\text{Beta}(\\alpha_0 + y, \\beta_0 + n - y)$$ Pathogen incidence, representing proportion infections caused different pathogens, modelled using Dirichlet distribution, natural conjugate prior multinomial outcomes. Dirichlet distribution parameterised vector concentration parameters \\(\\alpha\\), \\(\\alpha_i\\) corresponds specific pathogen. prior typically chosen uniform (\\(\\alpha_i = 1\\)), reflecting assumption equal prior probability across pathogens. posterior distribution pathogen incidence given : $$\\text{Dirichlet}(\\alpha_1 + n_1, \\alpha_2 + n_2, \\dots, \\alpha_K + n_K)$$ \\(n_i\\) number infections caused pathogen \\(\\) observed data. practical implementation, pathogen incidences sampled posterior using normalised Gamma-distributed random variables: $$x_i \\sim \\text{Gamma}(\\alpha_i + n_i, 1)$$ $$p_i = \\frac{x_i}{\\sum_{j=1}^K x_j}$$ \\(x_i\\) represents unnormalised pathogen counts, \\(p_i\\) normalised proportion pathogen \\(\\). hierarchical modelling, pathogen-level effects (e.g., differences resistance patterns) regimen-level effects modelled using Gaussian priors log-odds. hierarchical structure ensures partial pooling estimates across groups, improving stability strata small sample sizes. model implemented using Hamiltonian Monte Carlo (HMC) sampling. Stratified results can provided based covariates age, sex, clinical complexity (e.g., prior antimicrobial treatments renal/urological comorbidities) using dplyr's group_by() pre-processing step running wisca(). Posterior odds ratios (ORs) derived quantify effect covariates coverage probabilities: $$\\text{}_{\\text{covariate}} = \\frac{\\exp(\\beta_{\\text{covariate}})}{\\exp(\\beta_0)}$$ combining empirical data prior knowledge, WISCA overcomes limitations traditional combination antibiograms, offering disease-specific, patient-stratified estimates robust uncertainty quantification. tool invaluable antimicrobial stewardship programs empirical treatment guideline refinement. Note: WISCA never gives output pathogen/species level, incidences susceptibilities already weighted species.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Implementation: Dr. Larisse Bolton Dr. Matthijs Berends","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # \\donttest{ # Traditional antibiogram ----------------------------------------------  antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()) ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # An Antibiogram (non-WISCA / 95% CI): 10 × 7 #>    Pathogen       Amikacin    Gentamicin Imipenem Kanamycin Meropenem Tobramycin #>    <chr>          <chr>       <chr>      <chr>    <chr>     <chr>     <chr>      #>  1 CoNS           0% (0-8%,N… 86% (82-9… 52% (37… 0% (0-8%… 52% (37-… 22% (12-3… #>  2 E. coli        100% (98-1… 98% (96-9… 100% (9… NA        100% (99… 97% (96-9… #>  3 E. faecalis    0% (0-9%,N… 0% (0-9%,… 100% (9… 0% (0-9%… NA        0% (0-9%,… #>  4 K. pneumoniae  NA          90% (79-9… 100% (9… NA        100% (93… 90% (79-9… #>  5 P. aeruginosa  NA          100% (88-… NA       0% (0-12… NA        100% (88-… #>  6 P. mirabilis   NA          94% (80-9… 94% (79… NA        NA        94% (80-9… #>  7 S. aureus      NA          99% (97-1… NA       NA        NA        98% (92-1… #>  8 S. epidermidis 0% (0-8%,N… 79% (71-8… NA       0% (0-8%… NA        51% (40-6… #>  9 S. hominis     NA          92% (84-9… NA       NA        NA        85% (74-9… #> 10 S. pneumoniae  0% (0-3%,N… 0% (0-3%,… NA       0% (0-3%… NA        0% (0-3%,… #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram  antibiogram(example_isolates,   antibiotics = aminoglycosides(),   ab_transform = \"atc\",   mo_transform = \"gramstain\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 5 #>   Pathogen      J01GB01            J01GB03             J01GB04         J01GB06   #>   <chr>         <chr>              <chr>               <chr>           <chr>     #> 1 Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684)  0% (0-10%,N=35) 98% (96-… #> 2 Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%… #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram  antibiogram(example_isolates,   antibiotics = carbapenems(),   ab_transform = \"name\",   mo_transform = \"name\" ) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # An Antibiogram (non-WISCA / 95% CI): 5 × 3 #>   Pathogen                                 Imipenem             Meropenem        #>   <chr>                                    <chr>                <chr>            #> 1 Coagulase-negative Staphylococcus (CoNS) 52% (37-67%,N=48)    52% (37-67%,N=4… #> 2 Enterococcus faecalis                    100% (91-100%,N=38)  NA               #> 3 Escherichia coli                         100% (99-100%,N=422) 100% (99-100%,N… #> 4 Klebsiella pneumoniae                    100% (93-100%,N=51)  100% (93-100%,N… #> 5 Proteus mirabilis                        94% (79-99%,N=32)    NA               #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram   # Combined antibiogram -------------------------------------------------  # combined antibiotics yield higher empiric coverage antibiogram(example_isolates,   antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),   mo_transform = \"gramstain\" ) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 4 #>   Pathogen  Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³ #>   <chr>     <chr>                  <chr>                  <chr>                  #> 1 Gram-neg… 88% (85-91%,N=641)     99% (97-99%,N=691)     98% (97-99%,N=693)     #> 2 Gram-pos… 86% (82-89%,N=345)     98% (96-98%,N=1044)    95% (93-97%,N=550)     #> # ℹ abbreviated names: ¹​`Piperacillin/tazobactam`, #> #   ²​`Piperacillin/tazobactam + Gentamicin`, #> #   ³​`Piperacillin/tazobactam + Tobramycin` #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram  # names of antibiotics do not need to resemble columns exactly: antibiogram(example_isolates,   antibiotics = c(\"Cipro\", \"cipro + genta\"),   mo_transform = \"gramstain\",   ab_transform = \"name\",   sep = \" & \" ) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 3 #>   Pathogen      Ciprofloxacin      `Ciprofloxacin & Gentamicin` #>   <chr>         <chr>              <chr>                        #> 1 Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694)           #> 2 Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847)           #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram   # Syndromic antibiogram ------------------------------------------------  # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()),   syndromic_group = \"ward\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # An Antibiogram (non-WISCA / 95% CI): 14 × 8 #>    `Syndromic Group` Pathogen   Amikacin Gentamicin Imipenem Kanamycin Meropenem #>    <chr>             <chr>      <chr>    <chr>      <chr>    <chr>     <chr>     #>  1 Clinical          CoNS       NA       89% (84-9… 57% (39… NA        57% (39-… #>  2 ICU               CoNS       NA       79% (68-8… NA       NA        NA        #>  3 Outpatient        CoNS       NA       84% (66-9… NA       NA        NA        #>  4 Clinical          E. coli    100% (9… 98% (96-9… 100% (9… NA        100% (99… #>  5 ICU               E. coli    100% (9… 99% (95-1… 100% (9… NA        100% (97… #>  6 Clinical          K. pneumo… NA       92% (81-9… 100% (9… NA        100% (92… #>  7 Clinical          P. mirabi… NA       100% (88-… NA       NA        NA        #>  8 Clinical          S. aureus  NA       99% (95-1… NA       NA        NA        #>  9 ICU               S. aureus  NA       100% (95-… NA       NA        NA        #> 10 Clinical          S. epider… NA       82% (72-9… NA       NA        NA        #> 11 ICU               S. epider… NA       72% (60-8… NA       NA        NA        #> 12 Clinical          S. hominis NA       96% (85-9… NA       NA        NA        #> 13 Clinical          S. pneumo… 0% (0-5… 0% (0-5%,… NA       0% (0-5%… NA        #> 14 ICU               S. pneumo… 0% (0-1… 0% (0-12%… NA       0% (0-12… NA        #> # ℹ 1 more variable: Tobramycin <chr> #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram  # now define a data set with only E. coli ex1 <- example_isolates[which(mo_genus() == \"Escherichia\"), ] #> ℹ Using column 'mo' as input for mo_genus()  # with a custom language, though this will be determined automatically # (i.e., this table will be in Spanish on Spanish systems) antibiogram(ex1,   antibiotics = aminoglycosides(),   ab_transform = \"name\",   syndromic_group = ifelse(ex1$ward == \"ICU\",     \"UCI\", \"No UCI\"   ),   language = \"es\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 5 #>   `Grupo sindrómico` Patógeno Amikacina            Gentamicina       Tobramicina #>   <chr>              <chr>    <chr>                <chr>             <chr>       #> 1 No UCI             E. coli  100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99… #> 2 UCI                E. coli  100% (93-100%,N=52)  99% (95-100%,N=1… 96% (92-99… #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram   # WISCA antibiogram ----------------------------------------------------  # WISCA are not stratified by species, but rather on syndromes antibiogram(example_isolates,   antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),   syndromic_group = \"ward\",   wisca = TRUE ) #> # An Antibiogram (WISCA / 95% CI): 3 × 4 #>   `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹ #>   <chr>             <chr>                     <chr>                              #> 1 Clinical          76.1% (59.9-88.3%)        94.9% (85.2-99.2%)                 #> 2 ICU               60.3% (42.7-78.9%)        90.9% (77.4-98.1%)                 #> 3 Outpatient        67.2% (39-92%)            90% (68.6-99.4%)                   #> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin` #> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr> #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram   # Print the output for R Markdown / Quarto -----------------------------  ureido <- antibiogram(example_isolates,   antibiotics = ureidopenicillins(),   syndromic_group = \"ward\",   wisca = TRUE ) #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)  # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace(\"knitr\")) {   cat(knitr::knit_print(ureido)) } #>  #>  #> |Syndromic Group |Piperacillin/tazobactam | #> |:---------------|:-----------------------| #> |Clinical        |76% (60-88.7%)          | #> |ICU             |59.8% (40.7-80%)        | #> |Outpatient      |66.2% (38.1-91.3%)      |   # Generate plots with ggplot2 or base R --------------------------------  ab1 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\" ) ab2 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\",   syndromic_group = \"ward\" )  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab1) }  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab2) }   plot(ab1)  plot(ab2)  # }"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antimicrobial Selectors — antimicrobial_selectors","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"functions allow filtering rows selecting columns based antimicrobial test results specific antimicrobial class group, without need define columns antimicrobial abbreviations. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"kefzol\", \"CZO\" \"J01DB04\" picked using:","code":"library(dplyr) my_data_with_all_these_columns %>%   select(cephalosporins())"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"","code":"aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  aminopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)  antifungals(only_sir_columns = FALSE, return_all = TRUE, ...)  antimycobacterials(only_sir_columns = FALSE, return_all = TRUE, ...)  betalactams(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  betalactams_with_inhibitor(only_sir_columns = FALSE, return_all = TRUE,   ...)  carbapenems(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  cephalosporins(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_1st(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_2nd(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_3rd(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_4th(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_5th(only_sir_columns = FALSE, return_all = TRUE, ...)  fluoroquinolones(only_sir_columns = FALSE, return_all = TRUE, ...)  glycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...)  isoxazolylpenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)  lincosamides(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  lipoglycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...)  macrolides(only_sir_columns = FALSE, return_all = TRUE, ...)  monobactams(only_sir_columns = FALSE, return_all = TRUE, ...)  nitrofurans(only_sir_columns = FALSE, return_all = TRUE, ...)  oxazolidinones(only_sir_columns = FALSE, return_all = TRUE, ...)  penicillins(only_sir_columns = FALSE, return_all = TRUE, ...)  phenicols(only_sir_columns = FALSE, return_all = TRUE, ...)  polymyxins(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  quinolones(only_sir_columns = FALSE, return_all = TRUE, ...)  rifamycins(only_sir_columns = FALSE, return_all = TRUE, ...)  streptogramins(only_sir_columns = FALSE, return_all = TRUE, ...)  tetracyclines(only_sir_columns = FALSE, return_all = TRUE, ...)  trimethoprims(only_sir_columns = FALSE, return_all = TRUE, ...)  ureidopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)  amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  administrable_per_os(only_sir_columns = FALSE, return_all = TRUE, ...)  administrable_iv(only_sir_columns = FALSE, return_all = TRUE, ...)  not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL,   version_expertrules = 3.3, ...)"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"only_sir_columns logical indicate whether columns class sir must selected (default FALSE), see .sir() only_treatable logical indicate whether antimicrobial drugs excluded laboratory tests (default TRUE), gentamicin-high (GEH) imipenem/EDTA (IPE) return_all logical indicate whether matched columns must returned (default TRUE). FALSE, first unique antimicrobial returned, e.g. columns \"genta\" \"gentamicin\" exist data, first hit gentamicin returned. ... ignored, place allow future extensions amr_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antimicrobials data set searched (case-insensitive) value. filter expression evaluated antimicrobials data set, name %like% \"trim\" col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can \"3.3\", \"3.2\", \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"used inside selecting filtering, returns character vector column names, additional class \"amr_selector\". used individually, returns 'ab' vector possible antimicrobials function able select filter.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"functions can used data set calls selecting columns filtering rows. work base R, Tidyverse, data.table. heavily inspired Tidyverse selection helpers everything(), limited dplyr verbs. Nonetheless, convenient use dplyr functions select(), filter() summarise(), see Examples. selectors can also used tidymodels packages recipe parsnip. See info tutorial using antimicrobial selectors predictive modelling. columns data functions called searched known antimicrobial names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antimicrobials data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. amr_class() function can used filter/select manually defined antimicrobial class. searches results antimicrobials data set within columns group, atc_group1 atc_group2. administrable_per_os() administrable_iv() functions also rely antimicrobials data set - antimicrobials matched DDD (defined daily dose) resp. oral IV treatment available antimicrobials data set. amr_selector() function can used internally filter antimicrobials data set results, see Examples. allows filtering (part ) certain name, /group name even minimum DDDs oral treatment. function yields highest flexibility, also least user-friendly, since requires hard-coded filter set. not_intrinsic_resistant() function can used select antimicrobials pose intrinsic resistance microorganisms data set. example, data set contains microorganism codes names E. coli K. pneumoniae contains column \"vancomycin\", column removed (rather, unselected) using function. currently applies 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"full-list-of-supported-antimicrobial-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antimicrobial) classes","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"aminoglycosides() can select:  amikacin (AMK), amikacin/fosfomycin (AKF), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB), tobramycin-high (TOH) aminopenicillins() can select:  amoxicillin (AMX) ampicillin (AMP) antifungals() can select:  amorolfine (AMO), amphotericin B (AMB), amphotericin B-high (AMH), anidulafungin (ANI), butoconazole (), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), flucytosine (FCT), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), oteseconazole (OTE), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC), voriconazole (VOR) antimycobacterials() can select:  4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), protionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), simvastatin/fenofibrate (SMF), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone (TAT), thioacetazone/isoniazid (THI1), tiocarlide (TCR), viomycin (VIO) betalactams() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), carumonam (CAR), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nafcillin (NAF), oxacillin (OXA), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC), tigemonam (TMN) betalactams_with_inhibitor() can select:  amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin/sulbactam (SAM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefoperazone/sulbactam (CSL), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefpodoxime/clavulanic acid (CDC), ceftaroline/avibactam (CPA), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), ceftolozane/tazobactam (CZT), ceftriaxone/beta-lactamase inhibitor (CEB), imipenem/relebactam (IMR), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), mezlocillin/sulbactam (MSU), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), ticarcillin/clavulanic acid (TCC) carbapenems() can select:  biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP) cephalosporins() can select:  cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), latamoxef (LTM), loracarbef (LOR) cephalosporins_1st() can select:  cefacetrile (CAC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefroxadine (CRD), ceftezole (CTL), cephradine (CED) cephalosporins_2nd() can select:  cefaclor (CEC), cefamandole (MAN), cefmetazole (CMZ), cefonicid (CID), ceforanide (CND), cefotetan (CTT), cefotiam (CTF), cefoxitin (FOX), cefoxitin screening (FOX1), cefprozil (CPR), cefuroxime (CXM), cefuroxime axetil (CXA), loracarbef (LOR) cephalosporins_3rd() can select:  cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefetamet (CAT), cefetamet pivoxil (CPI), cefixime (CFM), cefmenoxime (CMX), cefodizime (DIZ), cefoperazone (CFP), cefoperazone/sulbactam (CSL), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotiam hexetil (CHE), cefovecin (FOV), cefpimizole (CFZ), cefpiramide (CPM), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefsulodin (CFS), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), latamoxef (LTM) cephalosporins_4th() can select:  cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetecol (CCL), cefoselis (CSE), cefozopran (ZOP), cefpirome (CPO), cefquinome (CEQ) cephalosporins_5th() can select:  ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT) fluoroquinolones() can select:  besifloxacin (BES), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nifuroquine (NIF), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX), trovafloxacin (TVA) glycopeptides() can select:  avoparcin (AVO), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN), vancomycin-macromethod (VAM) isoxazolylpenicillins() can select:  cloxacillin (CLO), dicloxacillin (DIC), flucloxacillin (FLC), meticillin (MET), oxacillin (OXA) lincosamides() can select:  clindamycin (CLI), lincomycin (LIN), pirlimycin (PRL) lipoglycopeptides() can select:  dalbavancin (DAL), oritavancin (ORI), telavancin (TLV) macrolides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), azithromycin (AZM), clarithromycin (CLR), dirithromycin (DIR), erythromycin (ERY), flurithromycin (FLR1), gamithromycin (GAM), josamycin (JOS), kitasamycin (KIT), meleumycin (MEL), midecamycin (MID), miocamycin (MCM), nafithromycin (ZWK), oleandomycin (OLE), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL), tylvalosin (TYL1) monobactams() can select:  aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), carumonam (CAR), tigemonam (TMN) nitrofurans() can select:  furazidin (FUR), furazolidone (FRZ), nifurtoinol (NFR), nitrofurantoin (NIT), nitrofurazone (NIZ) oxazolidinones() can select:  cadazolid (CDZ), cycloserine (CYC), linezolid (LNZ), tedizolid (TZD), thiacetazone (THA) penicillins() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), azidocillin (AZD), azlocillin (AZL), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), carbenicillin (CRB), carindacillin (CRN), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), epicillin (EPC), flucloxacillin (FLC), hetacillin (HET), lenampicillin (LEN), mecillinam (MEC), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nafcillin (NAF), oxacillin (OXA), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), sarmoxicillin (SRX), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC) phenicols() can select:  chloramphenicol (CHL), florfenicol (FLR), thiamphenicol (THI) polymyxins() can select:  colistin (COL), polymyxin B (PLB), polymyxin B/polysorbate 80 (POP) quinolones() can select:  besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), flumequine (FLM), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pipemidic acid (PPA), piromidic acid (PIR), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rosoxacin (ROS), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX), trovafloxacin (TVA) rifamycins() can select:  rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP) streptogramins() can select:  pristinamycin (PRI) quinupristin/dalfopristin (QDA) tetracyclines() can select:  cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), sarecycline (SRC), tetracycline (TCY), tigecycline (TGC) trimethoprims() can select:  brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP), trimethoprim/sulfamethoxazole (SXT) ureidopenicillins() can select:  azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), piperacillin/tazobactam (TZP)","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …   # you can use the selectors separately to retrieve all possible antimicrobials: carbapenems() #> ℹ This 'ab' vector was retrieved using carbapenems(), which should #>   normally be used inside a dplyr verb or data.frame call, e.g.: #>   • your_data %>% select(carbapenems()) #>   • your_data %>% select(column_a, column_b, carbapenems()) #>   • your_data %>% filter(any(carbapenems() == \"R\")) #>   • your_data[, carbapenems()] #>   • your_data[, c(\"column_a\", \"column_b\", carbapenems())] #> Class 'ab' #>  [1] BIA DOR ETP IMR IPE IPM MEM MEV MNC PAN RIA RIT RZM TBP   # Though they are primarily intended to use for selections and filters. # Examples sections below are split into 'dplyr', 'base R', and 'data.table':  # \\donttest{ if (FALSE) { # \\dontrun{ # dplyr -------------------------------------------------------------------  library(dplyr, warn.conflicts = FALSE)  example_isolates %>% select(carbapenems())  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates %>% select(mo, aminoglycosides())  # you can combine selectors like you are used with tidyverse # e.g., for betalactams, but not the ones with an enzyme inhibitor: example_isolates %>% select(betalactams(), -betalactams_with_inhibitor())  # select only antimicrobials with DDDs for oral treatment example_isolates %>% select(administrable_per_os())  # get AMR for all aminoglycosides e.g., per ward: example_isolates %>%   group_by(ward) %>%   summarise(across(aminoglycosides(),                    resistance))  # You can combine selectors with '&' to be more specific: example_isolates %>%   select(penicillins() & administrable_per_os())  # get AMR for only drugs that matter - no intrinsic resistance: example_isolates %>%   filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>%   group_by(ward) %>%   summarise_at(not_intrinsic_resistant(),                resistance)  # get susceptibility for antimicrobials whose name contains \"trim\": example_isolates %>%   filter(first_isolate()) %>%   group_by(ward) %>%   summarise(across(amr_selector(name %like% \"trim\"), susceptibility))  # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem): example_isolates %>%   select(carbapenems())  # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB': example_isolates %>%   select(mo, aminoglycosides())  # any() and all() work in dplyr's filter() too: example_isolates %>%   filter(     any(aminoglycosides() == \"R\"),     all(cephalosporins_2nd() == \"R\")   )  # also works with c(): example_isolates %>%   filter(any(c(carbapenems(), aminoglycosides()) == \"R\"))  # not setting any/all will automatically apply all(): example_isolates %>%   filter(aminoglycosides() == \"R\")  # this will select columns 'mo' and all antimycobacterial drugs ('RIF'): example_isolates %>%   select(mo, amr_class(\"mycobact\"))  # get bug/drug combinations for only glycopeptides in Gram-positives: example_isolates %>%   filter(mo_is_gram_positive()) %>%   select(mo, glycopeptides()) %>%   bug_drug_combinations() %>%   format()  data.frame(   some_column = \"some_value\",   J01CA01 = \"S\" ) %>% # ATC code of ampicillin   select(penicillins()) # only the 'J01CA01' column will be selected  # with recent versions of dplyr, this is all equal: x <- example_isolates[carbapenems() == \"R\", ] y <- example_isolates %>% filter(carbapenems() == \"R\") z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == \"R\")) identical(x, y) && identical(y, z)  } # } # base R ------------------------------------------------------------------  # select columns 'IPM' (imipenem) and 'MEM' (meropenem) example_isolates[, carbapenems()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <sir> <sir> #>  1   NA    NA  #>  2   NA    NA  #>  3   NA    NA  #>  4   NA    NA  #>  5   NA    NA  #>  6   NA    NA  #>  7   NA    NA  #>  8   NA    NA  #>  9   NA    NA  #> 10   NA    NA  #> # ℹ 1,990 more rows  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI   NA    NA    NA    NA  #>  2 B_ESCHR_COLI   NA    NA    NA    NA  #>  3 B_STPHY_EPDR   NA    NA    NA    NA  #>  4 B_STPHY_EPDR   NA    NA    NA    NA  #>  5 B_STPHY_EPDR   NA    NA    NA    NA  #>  6 B_STPHY_EPDR   NA    NA    NA    NA  #>  7 B_STPHY_AURS   NA    S     NA    NA  #>  8 B_STPHY_AURS   NA    S     NA    NA  #>  9 B_STPHY_EPDR   NA    NA    NA    NA  #> 10 B_STPHY_EPDR   NA    NA    NA    NA  #> # ℹ 1,990 more rows  # select only antimicrobials with DDDs for oral treatment example_isolates[, administrable_per_os()] #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 23 #>    OXA   FLC   AMX   AMC   AMP   CXM   KAN   TMP   NIT   FOS   LNZ   CIP   MFX   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1   NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA  #>  2   NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA  #>  3   NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA  #>  4   NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA  #>  5   NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA  #>  6   NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA  #>  7   NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA  #>  8   NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA  #>  9   NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    S     NA  #> 10   NA    S     NA    NA    NA    S     NA    S     NA    NA    NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 10 more variables: VAN <sir>, TCY <sir>, DOX <sir>, ERY <sir>, CLI <sir>, #> #   AZM <sir>, MTR <sir>, CHL <sir>, COL <sir>, RIF <sir>  # filter using any() or all() example_isolates[any(carbapenems() == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP   R     NA    NA    R   #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC        NA    NA    NA    NA  #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC        NA    NA    NA    NA  #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL   R     NA    NA    NA  #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL   R     NA    NA    NA  #> # ℹ 45 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … subset(example_isolates, any(carbapenems() == \"R\")) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP   R     NA    NA    R   #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC        NA    NA    NA    NA  #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC        NA    NA    NA    NA  #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL   R     NA    NA    NA  #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL   R     NA    NA    NA  #> # ℹ 45 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): example_isolates[any(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 962 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R   #>  6 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R   #>  7 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R   #>  8 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN   R     NA    NA    R   #>  9 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN   R     NA    NA    R   #> 10 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI   R     NA    NA    R   #> # ℹ 952 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … example_isolates[all(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 756 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-04-14 F30196     73 M      Outpat… B_STRPT_GRPB    S     NA    S     S   #>  2 2003-04-08 114570     74 M      ICU     B_STRPT_PYGN    S     NA    S     S   #>  3 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA    S     NA    S     S   #>  4 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA    S     NA    S     S   #>  5 2003-08-14 F71508      0 F      Clinic… B_STRPT_GRPB    S     NA    S     S   #>  6 2003-10-16 650870     63 F      ICU     B_ESCHR_COLI    R     NA    NA    R   #>  7 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #>  8 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #>  9 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI    R     NA    NA    NA  #> 10 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI    R     NA    NA    NA  #> # ℹ 746 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter with multiple antimicrobial selectors using c() example_isolates[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 26 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP   R     NA    NA    R   #>  2 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  3 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  4 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  5 2008-12-06 501361     43 F      Clinical B_STNTR_MLTP   R     NA    NA    R   #>  6 2011-05-09 207325     82 F      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  7 2012-03-12 582258     80 M      ICU      B_STPHY_CONS   R     R     R     R   #>  8 2012-05-19 C25552     89 F      Outpati… B_STPHY_CONS   R     R     R     R   #>  9 2012-07-17 F05015     83 M      ICU      B_STPHY_CONS   R     R     R     R   #> 10 2012-07-20 404299     66 F      Clinical B_STPHY_CONS   R     R     R     R   #> # ℹ 16 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter + select in one go: get penicillins in carbapenem-resistant strains example_isolates[any(carbapenems() == \"R\"), penicillins()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #>   (piperacillin/tazobactam) #> # A tibble: 55 × 7 #>    PEN   OXA   FLC   AMX   AMC   AMP   TZP   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1   NA    NA    NA    NA    NA    NA    NA  #>  2   NA    NA    NA    NA    NA    NA    NA  #>  3   R     NA    NA    R     R     R     R   #>  4   NA    NA    NA    NA    NA    NA    R   #>  5   NA    NA    NA    NA    NA    NA    R   #>  6   NA    NA    NA    NA    NA    NA    R   #>  7   NA    NA    NA    NA    NA    NA    R   #>  8   NA    NA    NA    NA    NA    NA    R   #>  9   R     NA    NA    NA    S     NA    S   #> 10   R     NA    NA    NA    S     NA    S   #> # ℹ 45 more rows  # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #>   (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 5 #>    OXA   FLC   AMX   AMC   AMP   #>    <sir> <sir> <sir> <sir> <sir> #>  1   NA    NA    NA    I     NA  #>  2   NA    NA    NA    I     NA  #>  3   NA    R     NA    NA    NA  #>  4   NA    R     NA    NA    NA  #>  5   NA    R     NA    NA    NA  #>  6   NA    R     NA    NA    NA  #>  7   NA    S     R     S     R   #>  8   NA    S     R     S     R   #>  9   NA    R     NA    NA    NA  #> 10   NA    S     NA    NA    NA  #> # ℹ 1,990 more rows  # amr_selector() applies a filter in the `antimicrobials` data set and is thus # very flexible. For instance, to select antimicrobials with an oral DDD # of at least 1 gram: example_isolates[, amr_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For amr_selector(oral_ddd > 1 & oral_units == \"g\") using columns 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'KAN' (kanamycin), 'FOS' #>   (fosfomycin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'ERY' (erythromycin), #>   'CLI' (clindamycin), 'MTR' (metronidazole), and 'CHL' (chloramphenicol) #> # A tibble: 2,000 × 13 #>    OXA   FLC   AMX   AMC   AMP   KAN   FOS   LNZ   VAN   ERY   CLI   MTR   CHL   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1   NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA  #>  2   NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA  #>  3   NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA  #>  4   NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA  #>  5   NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA  #>  6   NA    R     NA    NA    NA    NA    NA    NA    S     R     R     NA    NA  #>  7   NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA  #>  8   NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA  #>  9   NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA  #> 10   NA    S     NA    NA    NA    NA    NA    NA    S     S     NA    NA    NA  #> # ℹ 1,990 more rows   # data.table --------------------------------------------------------------  # data.table is supported as well, just use it in the same way as with # base R, but add `with = FALSE` if using a single AB selector.  if (require(\"data.table\")) {   dt <- as.data.table(example_isolates)    # this does not work, it returns column *names*   dt[, carbapenems()] } #> Loading required package: data.table #>  #> Attaching package: ‘data.table’ #> The following objects are masked from ‘package:dplyr’: #>  #>     between, first, last #> The following objects are masked from ‘package:AMR’: #>  #>     %like%, like #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> Warning: It should never be needed to print an antimicrobial selector class. Are you #> using data.table? Then add the argument with = FALSE, see our examples at #> ?amr_selector. #> Class 'amr_selector' #> [1] IPM MEM if (require(\"data.table\")) {   # so `with = FALSE` is required   dt[, carbapenems(), with = FALSE] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #>         IPM   MEM #>       <sir> <sir> #>    1:  <NA>  <NA> #>    2:  <NA>  <NA> #>    3:  <NA>  <NA> #>    4:  <NA>  <NA> #>    5:  <NA>  <NA> #>   ---             #> 1996:  <NA>  <NA> #> 1997:     S     S #> 1998:     S     S #> 1999:     S     S #> 2000:     S     S  # for multiple selections or AB selectors, `with = FALSE` is not needed: if (require(\"data.table\")) {   dt[, c(\"mo\", aminoglycosides())] } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #>                 mo   GEN   TOB   AMK   KAN #>               <mo> <sir> <sir> <sir> <sir> #>    1: B_ESCHR_COLI  <NA>  <NA>  <NA>  <NA> #>    2: B_ESCHR_COLI  <NA>  <NA>  <NA>  <NA> #>    3: B_STPHY_EPDR  <NA>  <NA>  <NA>  <NA> #>    4: B_STPHY_EPDR  <NA>  <NA>  <NA>  <NA> #>    5: B_STPHY_EPDR  <NA>  <NA>  <NA>  <NA> #>   ---                                      #> 1996: B_STRPT_PNMN     R     R     R     R #> 1997: B_ESCHR_COLI     S     S     S  <NA> #> 1998: B_STPHY_CONS     S  <NA>  <NA>  <NA> #> 1999: B_ESCHR_COLI     S     S  <NA>  <NA> #> 2000: B_KLBSL_PNMN     S     S  <NA>  <NA> if (require(\"data.table\")) {   dt[, c(carbapenems(), aminoglycosides())] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #>         IPM   MEM   GEN   TOB   AMK   KAN #>       <sir> <sir> <sir> <sir> <sir> <sir> #>    1:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>    2:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>    3:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>    4:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>    5:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>   ---                                     #> 1996:  <NA>  <NA>     R     R     R     R #> 1997:     S     S     S     S     S  <NA> #> 1998:     S     S     S  <NA>  <NA>  <NA> #> 1999:     S     S     S     S  <NA>  <NA> #> 2000:     S     S     S     S  <NA>  <NA>  # row filters are also supported: if (require(\"data.table\")) {   dt[any(carbapenems() == \"S\"), ] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #>            date patient   age gender       ward           mo   PEN   OXA   FLC #>          <Date>  <char> <num> <char>     <char>         <mo> <sir> <sir> <sir> #>   1: 2002-01-19  738003    71      M   Clinical B_ESCHR_COLI     R  <NA>  <NA> #>   2: 2002-01-19  738003    71      M   Clinical B_ESCHR_COLI     R  <NA>  <NA> #>   3: 2002-01-22  F35553    50      M        ICU B_PROTS_MRBL     R  <NA>  <NA> #>   4: 2002-01-22  F35553    50      M        ICU B_PROTS_MRBL     R  <NA>  <NA> #>   5: 2002-02-05  067927    45      F        ICU B_SERRT_MRCS     R  <NA>  <NA> #>  ---                                                                           #> 905: 2005-04-12  D71461    70      M        ICU B_ESCHR_COLI     R  <NA>  <NA> #> 906: 2009-11-12  650870    69      F Outpatient B_ESCHR_COLI     R  <NA>  <NA> #> 907: 2012-06-14  8CBCF2    41      F   Clinical B_STPHY_CONS     R     S     S #> 908: 2012-10-11  175532    78      M   Clinical B_ESCHR_COLI     R  <NA>  <NA> #> 909: 2013-11-23  A97263    77      M   Clinical B_KLBSL_PNMN     R  <NA>  <NA> #>        AMX   AMC   AMP   TZP   CZO   FEP   CXM   FOX   CTX   CAZ   CRO   GEN #>      <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>   1:  <NA>     I  <NA>  <NA>  <NA>  <NA>     S  <NA>     S  <NA>     S  <NA> #>   2:  <NA>     I  <NA>  <NA>  <NA>  <NA>     S  <NA>     S  <NA>     S  <NA> #>   3:  <NA>     I  <NA>  <NA>  <NA>  <NA>     S  <NA>     S     S     S  <NA> #>   4:  <NA>     I  <NA>  <NA>  <NA>  <NA>     S  <NA>     S     S     S  <NA> #>   5:     R     R     R  <NA>     R  <NA>     R     R  <NA>  <NA>  <NA>  <NA> #>  ---                                                                         #> 905:     S     S     S     S  <NA>     S     S     S     S     S     S     S #> 906:     S     S     S     S     S     S     S     S     S     S     S     S #> 907:  <NA>     S  <NA>  <NA>     S     S     S     S     S     R     S     S #> 908:     R     S     R     S  <NA>     S     R     R     S     S     S     S #> 909:     R     S     R     S  <NA>     S     S     S     S     S     S     S #>        TOB   AMK   KAN   TMP   SXT   NIT   FOS   LNZ   CIP   MFX   VAN   TEC #>      <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>   1:     S  <NA>  <NA>     S     S  <NA>  <NA>     R  <NA>  <NA>     R     R #>   2:     S  <NA>  <NA>     S     S  <NA>  <NA>     R  <NA>  <NA>     R     R #>   3:  <NA>  <NA>  <NA>     S     S     R  <NA>     R     S  <NA>     R     R #>   4:  <NA>  <NA>  <NA>     S     S     R  <NA>     R     S  <NA>     R     R #>   5:  <NA>  <NA>  <NA>     S     S     R  <NA>     R     S  <NA>     R     R #>  ---                                                                         #> 905:     S     S  <NA>  <NA>     S     S  <NA>     R     S  <NA>     R     R #> 906:     S     S  <NA>     S     S     S  <NA>     R     S  <NA>     R     R #> 907:  <NA>  <NA>  <NA>     S     S  <NA>  <NA>  <NA>     S  <NA>     S  <NA> #> 908:     S  <NA>  <NA>     R     R     R  <NA>     R     R     R     R     R #> 909:     S  <NA>  <NA>     S     S     S  <NA>     R     S  <NA>     R     R #>        TCY   TGC   DOX   ERY   CLI   AZM   IPM   MEM   MTR   CHL   COL   MUP #>      <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>   1:  <NA>  <NA>  <NA>     R     R     R     S  <NA>  <NA>  <NA>  <NA>  <NA> #>   2:  <NA>  <NA>  <NA>     R     R     R     S  <NA>  <NA>  <NA>  <NA>  <NA> #>   3:     R     R     R     R     R     R     S  <NA>  <NA>  <NA>     R  <NA> #>   4:     R     R     R     R     R     R     S  <NA>  <NA>  <NA>     R  <NA> #>   5:     R     R     R     R     R     R     S  <NA>  <NA>  <NA>     R  <NA> #>  ---                                                                         #> 905:  <NA>  <NA>  <NA>     R     R     R     S     S  <NA>  <NA>  <NA>  <NA> #> 906:  <NA>  <NA>  <NA>     R     R     R     S     S  <NA>  <NA>  <NA>  <NA> #> 907:  <NA>  <NA>  <NA>     S     S     S     S     S  <NA>  <NA>     R  <NA> #> 908:  <NA>  <NA>  <NA>     R     R     R     S     S  <NA>  <NA>     S  <NA> #> 909:  <NA>  <NA>  <NA>     R     R     R     S     S  <NA>  <NA>     S  <NA> #>        RIF #>      <sir> #>   1:     R #>   2:     R #>   3:     R #>   4:     R #>   5:     R #>  ---       #> 905:     R #> 906:     R #> 907:  <NA> #> 908:     R #> 909:     R if (require(\"data.table\")) {   dt[any(carbapenems() == \"S\"), penicillins(), with = FALSE] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #>   (piperacillin/tazobactam) #>        PEN   OXA   FLC   AMX   AMC   AMP   TZP #>      <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>   1:     R  <NA>  <NA>  <NA>     I  <NA>  <NA> #>   2:     R  <NA>  <NA>  <NA>     I  <NA>  <NA> #>   3:     R  <NA>  <NA>  <NA>     I  <NA>  <NA> #>   4:     R  <NA>  <NA>  <NA>     I  <NA>  <NA> #>   5:     R  <NA>  <NA>     R     R     R  <NA> #>  ---                                           #> 905:     R  <NA>  <NA>     S     S     S     S #> 906:     R  <NA>  <NA>     S     S     S     S #> 907:     R     S     S  <NA>     S  <NA>  <NA> #> 908:     R  <NA>  <NA>     R     S     R     S #> 909:     R  <NA>  <NA>     R     S     R     S # }"},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"Two data sets containing antimicrobials antivirals. Use .ab() one ab_* functions retrieve values antimicrobials data set. Three identifiers included data set: antimcrobial ID (ab, primarily used package) defined WHONET/EARS-Net, ATC code (atc) defined , Compound ID (cid) found PubChem. properties data set derived one codes. Note drugs multiple ATC codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"","code":"antimicrobials  antivirals"},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"object class tbl_df (inherits tbl, data.frame) 120 rows 11 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"for-the-antimicrobials-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antimicrobials data set: a tibble with 487 observations and 14 variables:","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"ab antimcrobial ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. cid Compound ID found PubChem. unique identifier. name Official name used WHONET/EARS-Net . unique identifier. group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antimcrobial susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 179 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 153 drugs iv_units Units iv_ddd loinc codes associated name antimicrobial drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"for-the-antivirals-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a tibble with 120 observations and 11 variables:","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"av Antiviral ID used package (ACI), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. Combinations codes contain + indicate , ATA+COBI atazanavir/cobicistat. name Official name used WHONET/EARS-Net . unique identifier. atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem. unique identifier. atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc codes associated name antiviral drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use av_loinc() retrieve quickly, see av_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://atcddd.fhi./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Accessed https://loinc.org October 19th, 2023. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived PubChem Compound ID (column cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"Like data sets package, data sets publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"","code":"antimicrobials #> # A tibble: 487 × 14 #>    ab        cid name   group atc   atc_group1 atc_group2 abbreviations synonyms #>    <ab>    <dbl> <chr>  <chr> <lis> <chr>      <chr>      <list>        <named > #>  1 AMA      4649 4-ami… Anti… <chr> Drugs for… Aminosali… <chr [1]>     <chr>    #>  2 ACM   6450012 Acety… Macr… <chr> NA         NA         <chr [1]>     <chr>    #>  3 ASP  49787020 Acety… Macr… <chr> NA         NA         <chr [1]>     <chr>    #>  4 ALS      8954 Aldes… Othe… <chr> Drugs for… Drugs for… <chr [1]>     <chr>    #>  5 AMK     37768 Amika… Amin… <chr> Aminoglyc… Other ami… <chr [5]>     <chr>    #>  6 AKF        NA Amika… Amin… <chr> NA         NA         <chr [1]>     <chr>    #>  7 AMO     54260 Amoro… Anti… <chr> Antifunga… Other ant… <chr [1]>     <chr>    #>  8 AMX     33613 Amoxi… Beta… <chr> Beta-lact… Penicilli… <chr [3]>     <chr>    #>  9 AMC  23665637 Amoxi… Beta… <chr> Beta-lact… Combinati… <chr [5]>     <chr>    #> 10 AXS    465441 Amoxi… Beta… <chr> NA         NA         <chr [1]>     <chr>    #> # ℹ 477 more rows #> # ℹ 5 more variables: oral_ddd <dbl>, oral_units <chr>, iv_ddd <dbl>, #> #   iv_units <chr>, loinc <list> antivirals #> # A tibble: 120 × 11 #>    av       name      atc      cid atc_group synonyms oral_ddd oral_units iv_ddd #>    <av>     <chr>     <chr>  <dbl> <chr>     <list>      <dbl> <chr>       <dbl> #>  1 ABA      Abacavir  J05A… 4.41e5 Nucleosi… <chr>         0.6 g              NA #>  2 ACI      Aciclovir J05A… 1.35e8 Nucleosi… <chr>         4   g               4 #>  3 ADD      Adefovir… J05A… 6.09e4 Nucleosi… <chr>        10   mg             NA #>  4 AME      Amenamev… J05A… 1.14e7 Other an… <chr>         0.4 g              NA #>  5 AMP      Amprenav… J05A… 6.50e4 Protease… <chr>         1.2 g              NA #>  6 ASU      Asunapre… J05A… 1.61e7 Antivira… <chr>         0.2 g              NA #>  7 ATA      Atazanav… J05A… 1.48e5 Protease… <chr>         0.3 g              NA #>  8 ATA+COBI Atazanav… J05A… 8.66e7 Antivira… <chr>        NA   NA             NA #>  9 ATA+RIT  Atazanav… J05A… 2.51e7 Antivira… <chr>         0.3 g              NA #> 10 BAM      Baloxavi… J05A… 1.24e8 Other an… <chr>        40   mg             NA #> # ℹ 110 more rows #> # ℹ 2 more variables: iv_units <chr>, loinc <list>"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antimicrobial drug code one antimicrobials. data set antimicrobials searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, language = get_AMR_locale(),   info = interactive(), ...)  is.ab(x)  ab_reset_session()"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic drug code name can retrieved single input value. language language coerce input values 20 supported languages - default system language supported (see get_AMR_locale()) info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antimicrobials data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems. can add manual codes considered .ab() ab_* functions, see add_custom_antimicrobials().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://atcddd.fhi./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class 'ab' #> [1] ERY as.ab(\"J 01 FA 01\") #> Class 'ab' #> [1] ERY as.ab(\"Erythromycin\") #> Class 'ab' #> [1] ERY as.ab(\"eryt\") #> Class 'ab' #> [1] ERY as.ab(\"ERYT\") #> Class 'ab' #> [1] ERY as.ab(\"ERY\") #> Class 'ab' #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class 'ab' #> [1] ERY as.ab(\"Erythrocin\") # trade name #> Class 'ab' #> [1] ERY as.ab(\"Romycin\") # trade name #> Class 'ab' #> [1] ERY  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\"  # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\") #> [1] \"Erythromycin\" ab_name(\"eryt\") #> [1] \"Erythromycin\"  # \\donttest{ if (require(\"dplyr\")) {   # you can quickly rename 'sir' columns using set_ab_names() with dplyr:   example_isolates %>%     set_ab_names(where(is.sir), property = \"atc\") } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           J01CE01 J01CF04 J01CF05 #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir>   <sir>   <sir>   #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R       NA      NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R       NA      NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R       NA      S     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R       NA      S     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R       NA      S     #> # ℹ 1,990 more rows #> # ℹ 37 more variables: J01CA04 <sir>, J01CR02 <sir>, J01CA01 <sir>, #> #   J01CR05 <sir>, J01DB04 <sir>, J01DE01 <sir>, J01DC02 <sir>, J01DC01 <sir>, #> #   J01DD01 <sir>, J01DD02 <sir>, J01DD04 <sir>, J01GB03 <sir>, J01GB01 <sir>, #> #   J01GB06 <sir>, J01GB04 <sir>, J01EA01 <sir>, J01EE01 <sir>, J01XE01 <sir>, #> #   J01XX01 <sir>, J01XX08 <sir>, J01MA02 <sir>, J01MA14 <sir>, J01XA01 <sir>, #> #   J01XA02 <sir>, J01AA07 <sir>, J01AA12 <sir>, J01AA02 <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antiviral Drug ID — as.av","title":"Transform Input to an Antiviral Drug ID — as.av","text":"Use function determine antiviral drug code one antiviral drugs. data set antivirals searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"as.av(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.av(x)"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antiviral Drug ID — as.av","text":"x character vector determine antiviral drug ID flag_multiple_results logical indicate whether note printed console probably one antiviral drug code name can retrieved single input value. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antiviral Drug ID — as.av","text":"character vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antiviral Drug ID — as.av","text":"entries antivirals data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .av() can correct different forms misspelling: Wrong spelling drug names (\"acyclovir\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"aycclovir\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use av_* functions get properties based returned antiviral drug ID, see Examples. Note: .av() av_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antiviral Drug ID — as.av","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://atcddd.fhi./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antiviral Drug ID — as.av","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antiviral Drug ID — as.av","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"# these examples all return \"ACI\", the ID of aciclovir: as.av(\"J05AB01\") #> Class 'av' #> [1] ACI as.av(\"J 05 AB 01\") #> Class 'av' #> [1] ACI as.av(\"Aciclovir\") #> Class 'av' #> [1] ACI as.av(\"aciclo\") #> Class 'av' #> [1] ACI as.av(\"   aciclo 123\") #> Class 'av' #> [1] ACI as.av(\"ACICL\") #> Class 'av' #> [1] ACI as.av(\"ACI\") #> Class 'av' #> [1] ACI as.av(\"Virorax\") # trade name #> Class 'av' #> [1] ACI as.av(\"Zovirax\") # trade name #> Class 'av' #> [1] ACI  as.av(\"acyklofir\") # severe spelling error, yet works #> Class 'av' #> [1] ACI  # use av_* functions to get a specific properties (see ?av_property); # they use as.av() internally: av_name(\"J05AB01\") #> [1] \"Aciclovir\" av_name(\"acicl\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 0 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE)  NA_disk_  is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values SIR values .sir(). supports guidelines EUCAST CLSI. Disk diffusion growth zone sizes must 0 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 0-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = 20,   CIP = 14,   GEN = 18,   TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of  5 variables: #>  $ microorganism: chr \"Escherichia coli\" #>  $ AMP          : 'disk' int 20 #>  $ CIP          : 'disk' int 14 #>  $ GEN          : 'disk' int 18 #>  $ TOB          : 'disk' int 16  # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) {   df %>% mutate(across(AMP:TOB, as.disk)) } #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli  20  14  18  16 # }  # interpret disk values, see ?as.sir as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2024... #>  OK  #> Class 'sir' #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: column 'AMP' (ampicillin), EUCAST #> 2024... #>  OK  #> Interpreting disk diffusion zones: column 'CIP' (ciprofloxacin), EUCAST #> 2024... #>  OK  #> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST #> 2024... #>  OK  #> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST #> 2024... #>  OK  #>      microorganism AMP  CIP GEN TOB #> 1 Escherichia coli   S <NA>   S   S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE, keep_operators = \"all\")  is.mic(x)  NA_mic_  rescale_mic(x, mic_range, keep_operators = \"edges\", as.mic = TRUE)  mic_p50(x, na.rm = FALSE, ...)  mic_p90(x, na.rm = FALSE, ...)  # S3 method for class 'mic' droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range rescale MIC values, e.g., mic_range = c(0.001, 32). Use NA prevent rescaling one side, e.g., mic_range = c(NA, 32). .mic logical indicate whether mic class kept - default TRUE rescale_mic() FALSE droplevels(). setting FALSE rescale_mic(), output factor levels acknowledge mic_range. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts numeric vector. Bear mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values SIR values, use .sir() MIC values. supports guidelines EUCAST (2011-2024) CLSI (2011-2024). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers:   makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.:   -called group generic functions implemented MIC class (!, !=, <, >=, exp(), log2()). functions stats package also implemented (quantile(), median(), fivenum()). Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. rescale_mic(), existing MIC ranges can limited defined range MIC values. can useful better compare MIC distributions. ggplot2, use one scale_*_mic() functions plot MIC values. allows custom MIC ranges plot intermediate log2 levels missing MIC values. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_. Use mic_p50() mic_p90() get 50th 90th percentile MIC values. return 'normal' numeric values.","code":"x <- random_mic(10) x #> Class 'mic' #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16  is.factor(x) #> [1] TRUE  x[1] * 2 #> [1] 32  median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16    8     8     64    >=128 32    32    16  df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #>        x hospital #> 1     16        A #> 5     64        A #> 6  >=128        A #> 8     32        A #> 9     32        A #> 10    16        A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32    1       1       1       8       <=0.128 8       16      16      is.mic(mic_data) #> [1] TRUE  # this can also coerce combined MIC/SIR values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002  # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1]  0.128  1.000  8.000 16.000 32.000 quantile(mic_data) #>     0%    25%    50%    75%   100%  #>  0.128  1.000  8.000 16.000 32.000  all(mic_data < 512) #> [1] TRUE  # rescale MICs using rescale_mic() rescale_mic(mic_data, mic_range = c(4, 16)) #> Class 'mic' #> [1] >=16 <=4  <=4  <=4  8    <=4  8    >=16 >=16  # interpret MIC values as.sir(   x = as.mic(2),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] R as.sir(   x = as.mic(c(0.01, 2, 4, 8)),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] S R R R  # plot MIC values, see ?plot plot(mic_data)  plot(mic_data, mo = \"E. coli\", ab = \"cipro\")   if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Use function get valid microorganism code (mo) based arbitrary user input. Determination done using intelligent rules complete taxonomic tree kingdoms Animalia, Archaea, Bacteria, Chromista, Protozoa, microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"as.mo(x, Becker = FALSE, Lancefield = FALSE,   minimum_matching_score = NULL,   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   reference_df = get_mo_source(),   ignore_pattern = getOption(\"AMR_ignore_pattern\", NULL),   cleaning_regex = getOption(\"AMR_cleaning_regex\", mo_cleaning_regex()),   only_fungi = getOption(\"AMR_only_fungi\", FALSE),   language = get_AMR_locale(), info = interactive(), ...)  is.mo(x)  mo_uncertainties()  mo_renamed()  mo_failures()  mo_reset_session()  mo_cleaning_regex()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (see Source). Please see Details full list staphylococcal species converted. excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (see Source). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. . Please see Details full list streptococcal species converted. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. minimum_matching_score numeric value set lower limit MO matching score. left blank, determined automatically based character length x, taxonomic kingdom human pathogenicity. keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern Perl-compatible regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set package option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). cleaning_regex Perl-compatible regular expression (case-insensitive) clean input x. Every matched part x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". default can set package option AMR_cleaning_regex. only_fungi logical indicate fungi must found, making sure e.g. misspellings always return records kingdom Fungi. can set globally microorganism functions package option AMR_only_fungi, .e. options(AMR_only_fungi = TRUE). language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate info must printed, e.g. progress bar 25 items coerced, list old taxonomic names. default TRUE interactive mode. ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"character vector additional class mo","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"microorganism (MO) code package (class: mo) human-readable typically looks like examples:   Values coerced considered 'unknown' return MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel scientifically validated (doi:10.18637/jss.v104.i03 ) matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. implicates e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first.","code":"Code               Full name   ---------------    --------------------------------------   B_KLBSL            Klebsiella   B_KLBSL_PNMN       Klebsiella pneumoniae   B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis   |   |    |    |   |   |    |    |   |   |    |    \\---> subspecies, a 3-5 letter acronym   |   |    \\----> species, a 3-6 letter acronym   |   \\----> genus, a 4-8 letter acronym   \\----> kingdom: A (Archaea), AN (Animalia), B (Bacteria),                   C (Chromista), F (Fungi), PL (Plantae),                   P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom, human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user inspect results mo_uncertainties(), returns data.frame specifications. increase quality matching, cleaning_regex argument used clean input. must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value cleaning_regex outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"for-mycologists","dir":"Reference","previous_headings":"","what":"For Mycologists","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"matching score algorithm gives precedence bacteria fungi. analysing fungi, sure use only_fungi = TRUE, better yet, add code run every session:   make sure bacteria 'non-fungi' returned .mo(), mo_* functions.","code":"options(AMR_only_fungi = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coagulase-negative-and-coagulase-positive-staphylococci","dir":"Reference","previous_headings":"","what":"Coagulase-negative and Coagulase-positive Staphylococci","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Becker = TRUE, following staphylococci converted corresponding coagulase group: Coagulase-negative: S. americanisciuri, S. argensis, S. arlettae, S. auricularis, S. borealis, S. brunensis, S. caeli, S. caledonicus, S. canis, S. capitis, S. capitis capitis, S. capitis urealyticus, S. capitis ureolyticus, S. caprae, S. carnosus, S. carnosus carnosus, S. carnosus utilis, S. casei, S. caseolyticus, S. chromogenes, S. cohnii, S. cohnii cohnii, S. cohnii urealyticum, S. cohnii urealyticus, S. condimenti, S. croceilyticus, S. debuckii, S. devriesei, S. durrellii, S. edaphicus, S. epidermidis, S. equorum, S. equorum equorum, S. equorum linens, S. felis, S. fleurettii, S. gallinarum, S. haemolyticus, S. hominis, S. hominis hominis, S. hominis novobiosepticus, S. jettensis, S. kloosii, S. lentus, S. lloydii, S. lugdunensis, S. marylandisciuri, S. massiliensis, S. microti, S. muscae, S. nepalensis, S. pasteuri, S. petrasii, S. petrasii croceilyticus, S. petrasii jettensis, S. petrasii petrasii, S. petrasii pragensis, S. pettenkoferi, S. piscifermentans, S. pragensis, S. pseudoxylosus, S. pulvereri, S. ratti, S. rostri, S. saccharolyticus, S. saprophyticus, S. saprophyticus bovis, S. saprophyticus saprophyticus, S. schleiferi, S. schleiferi schleiferi, S. sciuri, S. sciuri carnaticus, S. sciuri lentus, S. sciuri rodentium, S. sciuri sciuri, S. shinii, S. simulans, S. stepanovicii, S. succinus, S. succinus casei, S. succinus succinus, S. taiwanensis, S. urealyticus, S. ureilyticus, S. veratri, S. vitulinus, S. vitulus, S. warneri, S. xylosus Coagulase-positive: S. agnetis, S. argenteus, S. coagulans, S. cornubiensis, S. delphini, S. hyicus, S. hyicus chromogenes, S. hyicus hyicus, S. intermedius, S. lutrae, S. pseudintermedius, S. roterodami, S. schleiferi coagulans, S. schweitzeri, S. simiae, S. singaporensis based : Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci. Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 newly named staphylococcal species, S. brunensis (2024) S. shinii (2023), looked scientific reference make sure species considered correct coagulase group.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"lancefield-groups-in-streptococci","dir":"Reference","previous_headings":"","what":"Lancefield Groups in Streptococci","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Lancefield = TRUE, following streptococci converted corresponding Lancefield group: Streptococcus Group : S. pyogenes Streptococcus Group B: S. agalactiae Streptococcus Group C: S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis, S. equi, S. equi equi, S. equi ruminatorum, S. equi zooepidemicus Streptococcus Group F: S. anginosus, S. anginosus anginosus, S. anginosus whileyi, S. constellatus, S. constellatus constellatus, S. constellatus pharyngis, S. constellatus viborgensis, S. intermedius Streptococcus Group G: S. canis, S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis Streptococcus Group H: S. sanguinis Streptococcus Group K: S. salivarius, S. salivarius salivarius, S. salivarius thermophilus Streptococcus Group L: S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis based : Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/ Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : $$m_{(x, n)} = \\frac{l_{n} - 0.5 \\cdot \\min \\begin{cases}l_{n} \\\\ \\textrm{lev}(x, n)\\end{cases}}{l_{n} \\cdot p_{n} \\cdot k_{n}}$$ : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.15 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, Genera World Health Organization's () Priority Pathogen List prevalence = 1.0 microorganisms data set; genus present established list also prevalence = 1.15 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.25 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, Zygosaccharomyces; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.381\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c(   \"sau\", # WHONET code   \"stau\",   \"STAU\",   \"staaur\",   \"S. aureus\",   \"S aureus\",   \"Sthafilokkockus aureus\", # handles incorrect spelling   \"Staphylococcus aureus (MRSA)\",   \"MRSA\", # Methicillin Resistant S. aureus   \"VISA\", # Vancomycin Intermediate S. aureus   \"VRSA\", # Vancomycin Resistant S. aureus   115329001 # SNOMED CT code )) #> Class 'mo' #>  [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #>  [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS  # Dyslexia is no problem - these all work: as.mo(c(   \"Ureaplasma urealyticum\",   \"Ureaplasma urealyticus\",   \"Ureaplasmium urealytica\",   \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY  # input will get cleaned up with the input given in the `cleaning_regex` argument, # which defaults to `mo_cleaning_regex()`: cat(mo_cleaning_regex(), \"\\n\") #> ([^A-Za-z- \\(\\)\\[\\]{}]+|([({]|\\[).+([})]|\\])|(^| )( ?[a-z-]+[-](resistant|susceptible) ?|e?spp([^a-z]+|$)|e?ssp([^a-z]+|$)|serogr.?up[a-z]*|e?ss([^a-z]+|$)|e?sp([^a-z]+|$)|var([^a-z]+|$)|serovar[a-z]*|sube?species|biovar[a-z]*|e?species|Ig[ADEGM]|e?subsp|biotype|titer|dummy))   as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA  as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS  as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA  # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #>   'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #>   will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Clean existing SIR values, interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI. .sir() transforms input new class sir, ordered factor containing levels S, SDD, , R, NI. breakpoints currently implemented: clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024; veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024; ECOFFs (Epidemiological Cut-Values): EUCAST 2020-2024 CLSI 2022-2024. breakpoints used interpretation available clinical_breakpoints data set.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...)  NA_sir_  is.sir(x)  is_sir_eligible(x, threshold = 0.05)  # Default S3 method as.sir(x, S = \"^(S|U)+$\", I = \"^(I)+$\", R = \"^(R)+$\",   NI = \"^(N|NI|V)+$\", SDD = \"^(SDD|D|H)+$\", ...)  # S3 method for class 'mic' as.sir(x, mo = NULL, ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL,   conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL,   verbose = FALSE, ...)  # S3 method for class 'disk' as.sir(x, mo = NULL, ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL,   verbose = FALSE, ...)  # S3 method for class 'data.frame' as.sir(x, ..., col_mo = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL,   conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL,   verbose = FALSE)  sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: CLSI M39: Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2011-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. CLSI M100: Performance Standard Antimicrobial Susceptibility Testing, 2011-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. CLSI VET01: Performance Standards Antimicrobial Disk Dilution Susceptibility Tests Bacteria Isolated Animals, 2019-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet01/. CLSI VET09: Understanding Susceptibility Test Data Component Antimicrobial Stewardship Veterinary Settings, 2019-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet09/. EUCAST Breakpoint tables interpretation MICs zone diameters, 2011-2024, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints. WHONET source machine-reading clinical breakpoints (read ), 1989-2024, Collaborating Centre Surveillance Antimicrobial Resistance. https://whonet.org/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples S, , R, NI, SDD case-independent regular expression translate input result. regular expression run non-letters whitespaces removed input. mo vector (column name) characters can coerced valid microorganism codes .mo(), can left empty determine automatically ab vector (column name) characters can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2024 (latest implemented EUCAST guideline clinical_breakpoints data set), can set package option AMR_guideline. Currently supports EUCAST (2011-2024) CLSI (2011-2024), see Details. uti (Urinary Tract Infection) vector (column name) logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_screening logical indicate clinical breakpoints screening allowed - default FALSE. Can also set package option AMR_include_screening. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. host set values veterinary species, automatically set \"animal\". host vector (column name) characters indicate host. useful veterinary breakpoints, requires breakpoint_type = \"animal\". values can text resembling animal species, even 20 supported languages package. foreign languages, sure set language set_AMR_locale() (though automatically guessed based system language). verbose logical indicate notes printed interpretation MIC values disk diffusion values. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Note: clinical breakpoints package validated , imported , WHONET. public use AMR package endorsed CLSI EUCAST. See clinical_breakpoints information. NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function can work four ways: cleaning raw / untransformed data. data cleaned contain valid values, namely: S susceptible, intermediate 'susceptible, increased exposure', R resistant, NI non-interpretable, SDD susceptible dose-dependent. can set using regular expression. Furthermore, .sir() try best clean intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input invalid. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.mic, as.sir, ab = \"column_with_antibiotics\", mo = \"column_with_microorganisms\") your_data %>% mutate_if(is.mic, as.sir, ab = c(\"cipro\", \"ampicillin\", ...), mo = c(\"E. coli\", \"K. pneumoniae\", ...))  # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.mic, as.sir, host = \"column_with_animal_species\", guideline = \"CLSI\") your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir)) your_data %>% mutate_if(is.disk, as.sir, ab = \"column_with_antibiotics\", mo = \"column_with_microorganisms\") your_data %>% mutate_if(is.disk, as.sir, ab = c(\"cipro\", \"ampicillin\", ...), mo = c(\"E. coli\", \"K. pneumoniae\", ...))  # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.disk, as.sir, host = \"column_with_animal_species\", guideline = \"CLSI\")"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024, veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024. Thus, guideline argument must set e.g., \"EUCAST 2024\" \"CLSI 2024\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline package option AMR_guideline (e.g. .Rprofile file), :   veterinary guidelines, might best options:   applying veterinary breakpoints (setting host setting breakpoint_type = \"animal\"), CLSI VET09 guideline applied cope missing animal species-specific breakpoints.","code":"options(AMR_guideline = \"CLSI\")   options(AMR_guideline = \"CLSI 2018\")   options(AMR_guideline = \"EUCAST 2020\")   # or to reset:   options(AMR_guideline = NULL) options(AMR_guideline = \"CLSI\")   options(AMR_breakpoint_type = \"animal\")"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism. determine isolates multi-drug resistant, sure run mdro() (applies MDR/PDR/XDR guideline 2012 default) data set contains S//R values. Read interpreting multidrug-resistant organisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-clinical-breakpoints","dir":"Reference","previous_headings":"","what":"Machine-Readable Clinical Breakpoints","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 34 063 rows 14 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. base R function .double() can used retrieve quantitative values sir object: \"S\" = 1, \"\"/\"SDD\" = 2, \"R\" = 3. values rendered NA . Note: use .integer(), since (R works internally) return factor level indices, aforementioned quantitative values. function is_sir_eligible() returns TRUE column contains 5% invalid antimicrobial interpretations (S //R /NI /SDD), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … summary(example_isolates) # see all SIR results at a glance #>       date              patient               age           gender          #>  Min.   :2002-01-02   Length:2000        Min.   : 0.00   Length:2000        #>  1st Qu.:2005-07-31   Class :character   1st Qu.:63.00   Class :character   #>  Median :2009-07-31   Mode  :character   Median :74.00   Mode  :character   #>  Mean   :2009-11-20                      Mean   :70.69                      #>  3rd Qu.:2014-05-30                      3rd Qu.:82.00                      #>  Max.   :2017-12-28                      Max.   :97.00                      #>      ward                mo                  PEN                 #>  Length:2000        Class :mo             Class:sir              #>  Class :character   <NA>  :0              %S   :25.6% (n=417)    #>  Mode  :character   Unique:90             %SDD : 0.0% (n=0)      #>                     #1    :B_ESCHR_COLI   %I   : 0.7% (n=11)     #>                     #2    :B_STPHY_CONS   %R   :73.7% (n=1201)   #>                     #3    :B_STPHY_AURS   %NI  : 0.0% (n=0)      #>     OXA                   FLC                   AMX                #>  Class:sir             Class:sir             Class:sir             #>  %S   :68.8% (n=251)   %S   :70.5% (n=665)   %S   :40.2% (n=543)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)     %I   : 0.0% (n=0)     %I   : 0.2% (n=3)     #>  %R   :31.2% (n=114)   %R   :29.5% (n=278)   %R   :59.6% (n=804)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     #>     AMC                    AMP                   TZP                #>  Class:sir              Class:sir             Class:sir             #>  %S   :71.4% (n=1342)   %S   :40.2% (n=543)   %S   :86.1% (n=862)   #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 4.8% (n=91)     %I   : 0.2% (n=3)     %I   : 1.3% (n=13)    #>  %R   :23.7% (n=446)    %R   :59.6% (n=804)   %R   :12.6% (n=126)   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     #>     CZO                   FEP                   CXM                 #>  Class:sir             Class:sir             Class:sir              #>  %S   :54.9% (n=245)   %S   :85.6% (n=620)   %S   :72.5% (n=1297)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      #>  %I   : 0.4% (n=2)     %I   : 0.1% (n=1)     %I   : 1.2% (n=22)     #>  %R   :44.6% (n=199)   %R   :14.2% (n=103)   %R   :26.3% (n=470)    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      #>     FOX                   CTX                   CAZ                 #>  Class:sir             Class:sir             Class:sir              #>  %S   :71.6% (n=586)   %S   :84.4% (n=796)   %S   :33.5% (n=607)    #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      #>  %I   : 1.0% (n=8)     %I   : 0.1% (n=1)     %I   : 0.0% (n=0)      #>  %R   :27.4% (n=224)   %R   :15.5% (n=146)   %R   :66.5% (n=1204)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      #>     CRO                   GEN                    TOB                #>  Class:sir             Class:sir              Class:sir             #>  %S   :84.4% (n=796)   %S   :74.0% (n=1372)   %S   :65.1% (n=879)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     #>  %I   : 0.1% (n=1)     %I   : 1.5% (n=27)     %I   : 0.5% (n=7)     #>  %R   :15.5% (n=146)   %R   :24.6% (n=456)    %R   :34.4% (n=465)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     #>     AMK                   KAN                    TMP                #>  Class:sir             Class:sir              Class:sir             #>  %S   :36.3% (n=251)   %S   : 0.0% (n=0)      %S   :61.2% (n=918)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)     %I   : 0.0% (n=0)      %I   : 0.7% (n=10)    #>  %R   :63.7% (n=441)   %R   :100.0% (n=471)   %R   :38.1% (n=571)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     #>     SXT                    NIT                   FOS                #>  Class:sir              Class:sir             Class:sir             #>  %S   :79.1% (n=1392)   %S   :76.0% (n=565)   %S   :57.8% (n=203)   #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 0.3% (n=6)      %I   : 6.9% (n=51)    %I   : 0.0% (n=0)     #>  %R   :20.5% (n=361)    %R   :17.1% (n=127)   %R   :42.2% (n=148)   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     #>     LNZ                   CIP                    MFX                #>  Class:sir             Class:sir              Class:sir             #>  %S   :30.7% (n=314)   %S   :78.9% (n=1112)   %S   :64.5% (n=136)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)     %I   : 4.9% (n=69)     %I   : 1.9% (n=4)     #>  %R   :69.3% (n=709)   %R   :16.2% (n=228)    %R   :33.6% (n=71)    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     #>     VAN                    TEC                   TCY                #>  Class:sir              Class:sir             Class:sir             #>  %S   :61.7% (n=1149)   %S   :24.3% (n=237)   %S   :68.3% (n=820)   #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)      %I   : 0.0% (n=0)     %I   : 1.9% (n=23)    #>  %R   :38.3% (n=712)    %R   :75.7% (n=739)   %R   :29.8% (n=357)   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     #>     TGC                   DOX                   ERY                 #>  Class:sir             Class:sir             Class:sir              #>  %S   :87.3% (n=697)   %S   :71.7% (n=814)   %S   :42.3% (n=801)    #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      #>  %I   : 0.0% (n=0)     %I   : 0.6% (n=7)     %I   : 0.5% (n=9)      #>  %R   :12.7% (n=101)   %R   :27.7% (n=315)   %R   :57.2% (n=1084)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      #>     CLI                   AZM                    IPM                #>  Class:sir             Class:sir              Class:sir             #>  %S   :38.6% (n=586)   %S   :42.3% (n=801)    %S   :92.7% (n=824)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     #>  %I   : 0.3% (n=4)     %I   : 0.5% (n=9)      %I   : 1.1% (n=10)    #>  %R   :61.2% (n=930)   %R   :57.2% (n=1084)   %R   : 6.2% (n=55)    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     #>     MEM                   MTR                  CHL                #>  Class:sir             Class:sir            Class:sir             #>  %S   :94.1% (n=780)   %S   :85.3% (n=29)   %S   :78.6% (n=121)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)    %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)     %I   : 0.0% (n=0)    %I   : 0.0% (n=0)     #>  %R   : 5.9% (n=49)    %R   :14.7% (n=5)    %R   :21.4% (n=33)    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)    %NI  : 0.0% (n=0)     #>     COL                    MUP                   RIF                #>  Class:sir              Class:sir             Class:sir             #>  %S   :18.8% (n=309)    %S   :93.0% (n=251)   %S   :30.2% (n=303)   #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)      %I   : 1.1% (n=3)     %I   : 0.2% (n=2)     #>  %R   :81.2% (n=1331)   %R   : 5.9% (n=16)    %R   :69.6% (n=698)   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      # For INTERPRETING disk diffusion and MIC values -----------------------  # example data sets, with combined MIC values and disk zones df_wide <- data.frame(   microorganism = \"Escherichia coli\",   amoxicillin = as.mic(8),   cipro = as.mic(0.256),   tobra = as.disk(16),   genta = as.disk(18),   ERY = \"R\" ) df_long <- data.frame(   bacteria = rep(\"Escherichia coli\", 4),   antibiotic = c(\"amoxicillin\", \"cipro\", \"tobra\", \"genta\"),   mics = as.mic(c(0.01, 1, 4, 8)),   disks = as.disk(c(6, 10, 14, 18)) )  # \\donttest{ ## Using dplyr ------------------------------------------------- if (require(\"dplyr\")) {   # approaches that all work without additional arguments:   df_wide %>% mutate_if(is.mic, as.sir)   df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)   df_wide %>% mutate(across(where(is.mic), as.sir))   df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)   df_wide %>% mutate(across(amoxicillin:tobra, as.sir))    # approaches that all work with additional arguments:   df_long %>%     # given a certain data type, e.g. MIC values     mutate_if(is.mic, as.sir,       mo = \"bacteria\",       ab = \"antibiotic\",       guideline = \"CLSI\"     )   df_long %>%     mutate(across(       where(is.mic),       function(x) {         as.sir(x,           mo = \"bacteria\",           ab = \"antibiotic\",           guideline = \"CLSI\"         )       }     ))   df_wide %>%     # given certain columns, e.g. from 'cipro' to 'genta'     mutate_at(vars(cipro:genta), as.sir,       mo = \"bacteria\",       guideline = \"CLSI\"     )   df_wide %>%     mutate(across(       cipro:genta,       function(x) {         as.sir(x,           mo = \"bacteria\",           guideline = \"CLSI\"         )       }     ))    # for veterinary breakpoints, add 'host':   df_long$animal_species <- c(\"cats\", \"dogs\", \"horses\", \"cattle\")   df_long %>%     # given a certain data type, e.g. MIC values     mutate_if(is.mic, as.sir,       mo = \"bacteria\",       ab = \"antibiotic\",       host = \"animal_species\",       guideline = \"CLSI\"     )   df_long %>%     mutate(across(       where(is.mic),       function(x) {         as.sir(x,           mo = \"bacteria\",           ab = \"antibiotic\",           host = \"animal_species\",           guideline = \"CLSI\"         )       }     ))   df_wide %>%     mutate_at(vars(cipro:genta), as.sir,       mo = \"bacteria\",       ab = \"antibiotic\",       host = \"animal_species\",       guideline = \"CLSI\"     )   df_wide %>%     mutate(across(       cipro:genta,       function(x) {         as.sir(x,           mo = \"bacteria\",           host = \"animal_species\",           guideline = \"CLSI\"         )       }     ))    # to include information about urinary tract infections (UTI)   data.frame(     mo = \"E. coli\",     nitrofuratoin = c(\"<= 2\", 32),     from_the_bladder = c(TRUE, FALSE)   ) %>%     as.sir(uti = \"from_the_bladder\")    data.frame(     mo = \"E. coli\",     nitrofuratoin = c(\"<= 2\", 32),     specimen = c(\"urine\", \"blood\")   ) %>%     as.sir() # automatically determines urine isolates    df_wide %>%     mutate_at(vars(cipro:genta), as.sir, mo = \"E. coli\", uti = TRUE) } #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin) based on #> column 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #>  #> ℹ Please note that in the absence of specific veterinary breakpoints for #>   certain animal hosts, the CLSI guideline VET09 will be applied where #>   possible. #>  #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #>  #> ℹ Please note that in the absence of specific veterinary breakpoints for #>   certain animal hosts, the CLSI guideline VET09 will be applied where #>   possible. #>  #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #>  #>  #> ℹ Please note that in the absence of specific veterinary breakpoints for #>   certain animal hosts, the CLSI guideline VET09 will be applied where #>   possible. #>  #> Interpreting MIC values: 'antibiotic' (TESTAB, test Antibiotic), CLSI #> 2024... #>  WARNING  #>   • No CLSI 2024 MIC breakpoints available for test Antibiotic (TESTAB). #> Interpreting disk diffusion zones: 'antibiotic' (TESTAB, test Antibiotic), #> CLSI 2024... #>  WARNING  #>   • No CLSI 2024 DISK breakpoints available for test Antibiotic (TESTAB). #> Interpreting disk diffusion zones: 'antibiotic' (TESTAB, test Antibiotic), #> CLSI 2024... #>  WARNING  #>   • No CLSI 2024 DISK breakpoints available for test Antibiotic (TESTAB). #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `cipro = (function (x, ...) ...`. #> Caused by warning: #> ! The following animal host(s) could not be coerced: \"animal_species\" #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #>  #>  #> ℹ Please note that in the absence of specific veterinary breakpoints for #>   certain animal hosts, the CLSI guideline VET09 will be applied where #>   possible. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #>  OK  #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `across(...)`. #> Caused by warning: #> ! The following animal host(s) could not be coerced: \"animal_species\" #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: column 'nitrofuratoin' (NIT, #> nitrofurantoin), EUCAST 2024... #>  OK  #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use as.sir(uti = FALSE) to prevent this. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: column 'nitrofuratoin' (NIT, #> nitrofurantoin), EUCAST 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... #>  OK  #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), EUCAST #> 2024... #>  OK  #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), EUCAST #> 2024... #>  OK  #>      microorganism amoxicillin cipro tobra genta ERY #> 1 Escherichia coli           8  <NA>     S     S   R   ## Using base R ------------------------------------------------  as.sir(df_wide) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: column 'amoxicillin' (AMX), EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: column 'cipro' (CIP, ciprofloxacin), EUCAST #> 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: column 'tobra' (TOB, tobramycin), EUCAST #> 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: column 'genta' (GEN, gentamicin), EUCAST #> 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #>  OK  #>      microorganism amoxicillin cipro tobra genta ERY #> 1 Escherichia coli           S     I     S     S   R  # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 57 × 16 #>    datetime            index ab_given mo_given host_given ab   mo               #>    <dttm>              <int> <chr>    <chr>    <chr>      <ab> <mo>             #>  1 2025-03-07 21:31:43     4 AMX      B_STRPT… human      AMX  B_STRPT_PNMN     #>  2 2025-03-07 21:31:50     4 genta    Escheri… human      GEN  B_[ORD]_ENTRBCTR #>  3 2025-03-07 21:31:50     4 genta    Escheri… human      GEN  B_[ORD]_ENTRBCTR #>  4 2025-03-07 21:31:51     4 genta    Escheri… cattle     GEN  B_ESCHR_COLI     #>  5 2025-03-07 21:31:51     4 genta    Escheri… cattle     GEN  B_ESCHR_COLI     #>  6 2025-03-07 21:31:43     3 AMX      B_STRPT… human      AMX  B_STRPT_PNMN     #>  7 2025-03-07 21:31:50     3 tobra    Escheri… human      TOB  B_[ORD]_ENTRBCTR #>  8 2025-03-07 21:31:50     3 tobra    Escheri… human      TOB  B_[ORD]_ENTRBCTR #>  9 2025-03-07 21:31:51     3 tobra    Escheri… horses     TOB  B_ESCHR_COLI     #> 10 2025-03-07 21:31:51     3 tobra    Escheri… horses     TOB  B_ESCHR_COLI     #> # ℹ 47 more rows #> # ℹ 9 more variables: host <chr>, method <chr>, input <chr>, outcome <sir>, #> #   notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>, #> #   breakpoint_S_R <chr>  # for single values as.sir(   x = as.mic(2),   mo = as.mo(\"S. pneumoniae\"),   ab = \"AMP\",   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ampicillin (AMP) in Streptococcus pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R  as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2024... #>  OK  #> Class 'sir' #> [1] R   # For CLEANING existing SIR values ------------------------------------  as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in index '20' truncated (38%) that were invalid #> antimicrobial interpretations: \"A\", \"B\", and \"C\" as.sir(\"<= 0.002; S\") # will return \"S\" sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages  barplot(sir_data) # for frequencies   # as common in R, you can use as.integer() to return factor indices: as.integer(as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", NA))) #> [1]  1  2  3  4  5 NA # but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R: as.double(as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", NA))) #> [1]  1  2  2  3 NA NA  # the dplyr way if (require(\"dplyr\")) {   example_isolates %>%     mutate_at(vars(PEN:RIF), as.sir)   # same:   example_isolates %>%     as.sir(PEN:RIF)    # fastest way to transform all columns with already valid AMR results to class `sir`:   example_isolates %>%     mutate_if(is_sir_eligible, as.sir)    # since dplyr 1.0.0, this can also be:   # example_isolates %>%   #   mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antimicrobial), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property(atc_code, property, administration = \"O\",   url = \"https://atcddd.fhi.no/atc_ddd_index/?code=%s&showdescription=no\",   url_vet = \"https://atcddd.fhi.no/atcvet/atcvet_index/?code=%s&showdescription=no\")  atc_online_groups(atc_code, ...)  atc_online_ddd(atc_code, ...)  atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://atcddd.fhi./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antimicrobials, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) {   # oral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"O\")   atc_online_ddd(ab_atc(\"amox\"))    # parenteral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"P\")    atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\"         #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\"         #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\"      # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(text, type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL, translate_av = FALSE, thorough_search = NULL,   info = interactive(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). default FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #>  av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  av_cid(x, ...)  av_synonyms(x, ...)  av_tradenames(x, ...)  av_group(x, language = get_AMR_locale(), ...)  av_atc(x, ...)  av_loinc(x, ...)  av_ddd(x, administration = \"oral\", ...)  av_ddd_units(x, administration = \"oral\", ...)  av_info(x, language = get_AMR_locale(), ...)  av_url(x, open = FALSE, ...)  av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text - default system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://atcddd.fhi./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_tradenames(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #>                                                              Aciclovir  #> \"https://atcddd.fhi.no/atc_ddd_index/?code=J05AB01&showdescription=no\"   # lowercase transformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\"    \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\"    \"valaciclovir\"  # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\"  av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #>  #> $cid #> [1] 135398513 #>  #> $name #> [1] \"Aciclovir\" #>  #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #>  #> $atc #> [1] \"J05AB01\" #>  #> $tradenames #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            #>  #> $loinc #> [1] \"\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"30273-7\" \"30287-7\" \"30303-2\" \"78772-1\" \"78773-9\" \"79134-3\" #> [8] \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability - default filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #>         count available     visual_availabilty resistant      visual_resistance #> date     2000    100.0% |####################|                                  #> patient  2000    100.0% |####################|                                  #> age      2000    100.0% |####################|                                  #> gender   2000    100.0% |####################|                                  #> ward     2000    100.0% |####################|                                  #> mo       2000    100.0% |####################|                                  #> PEN      1629     81.5% |################----|     73.7% |##############------| #> OXA       365     18.3% |###-----------------|     31.2% |######--------------| #> FLC       943     47.2% |#########-----------|     29.5% |#####---------------| #> AMX      1350     67.5% |#############-------|     59.6% |###########---------| #> AMC      1879     94.0% |##################--|     23.7% |####----------------| #> AMP      1350     67.5% |#############-------|     59.6% |###########---------| #> TZP      1001     50.0% |##########----------|     12.6% |##------------------| #> CZO       446     22.3% |####----------------|     44.6% |########------------| #> FEP       724     36.2% |#######-------------|     14.2% |##------------------| #> CXM      1789     89.5% |#################---|     26.3% |#####---------------| #> FOX       818     40.9% |########------------|     27.4% |#####---------------| #> CTX       943     47.2% |#########-----------|     15.5% |###-----------------| #> CAZ      1811     90.6% |##################--|     66.5% |#############-------| #> CRO       943     47.2% |#########-----------|     15.5% |###-----------------| #> GEN      1855     92.8% |##################--|     24.6% |####----------------| #> TOB      1351     67.6% |#############-------|     34.4% |######--------------| #> AMK       692     34.6% |######--------------|     63.7% |############--------| #> KAN       471     23.6% |####----------------|    100.0% |####################| #> TMP      1499     75.0% |###############-----|     38.1% |#######-------------| #> SXT      1759     88.0% |#################---|     20.5% |####----------------| #> NIT       743     37.2% |#######-------------|     17.1% |###-----------------| #> FOS       351     17.6% |###-----------------|     42.2% |########------------| #> LNZ      1023     51.2% |##########----------|     69.3% |#############-------| #> CIP      1409     70.5% |#############-------|     16.2% |###-----------------| #> MFX       211     10.6% |##------------------|     33.6% |######--------------| #> VAN      1861     93.1% |##################--|     38.3% |#######-------------| #> TEC       976     48.8% |#########-----------|     75.7% |###############-----| #> TCY      1200     60.0% |###########---------|     29.8% |#####---------------| #> TGC       798     39.9% |########------------|     12.7% |##------------------| #> DOX      1136     56.8% |###########---------|     27.7% |#####---------------| #> ERY      1894     94.7% |##################--|     57.2% |###########---------| #> CLI      1520     76.0% |###############-----|     61.2% |############--------| #> AZM      1894     94.7% |##################--|     57.2% |###########---------| #> IPM       889     44.5% |########------------|      6.2% |#-------------------| #> MEM       829     41.5% |########------------|      5.9% |#-------------------| #> MTR        34      1.7% |--------------------|     14.7% |##------------------| #> CHL       154      7.7% |#-------------------|     21.4% |####----------------| #> COL      1640     82.0% |################----|     81.2% |################----| #> MUP       270     13.5% |##------------------|      5.9% |#-------------------| #> RIF      1003     50.2% |##########----------|     69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     select_if(is.sir) %>%     availability() } #>     count available       visual_availabilty resistant        visual_resistance #> PEN   467    100.0% |######################|    100.0% |######################| #> OXA     0      0.0% |----------------------|                                    #> FLC     0      0.0% |----------------------|                                    #> AMX   392     83.9% |##################----|     50.0% |###########-----------| #> AMC   467    100.0% |######################|     13.1% |##--------------------| #> AMP   392     83.9% |##################----|     50.0% |###########-----------| #> TZP   416     89.1% |###################---|      5.5% |#---------------------| #> CZO    82     17.6% |###-------------------|      2.4% |----------------------| #> FEP   317     67.9% |##############--------|      2.8% |----------------------| #> CXM   465     99.6% |######################|      5.4% |#---------------------| #> FOX   377     80.7% |#################-----|      6.9% |#---------------------| #> CTX   459     98.3% |#####################-|      2.4% |----------------------| #> CAZ   460     98.5% |#####################-|      2.4% |----------------------| #> CRO   459     98.3% |#####################-|      2.4% |----------------------| #> GEN   460     98.5% |#####################-|      2.0% |----------------------| #> TOB   462     98.9% |#####################-|      2.6% |----------------------| #> AMK   171     36.6% |########--------------|      0.0% |----------------------| #> KAN     0      0.0% |----------------------|                                    #> TMP   396     84.8% |##################----|     39.1% |########--------------| #> SXT   465     99.6% |######################|     31.6% |######----------------| #> NIT   458     98.1% |#####################-|      2.8% |----------------------| #> FOS    61     13.1% |##--------------------|      0.0% |----------------------| #> LNZ   467    100.0% |######################|    100.0% |######################| #> CIP   456     97.6% |#####################-|     12.5% |##--------------------| #> MFX    57     12.2% |##--------------------|    100.0% |######################| #> VAN   467    100.0% |######################|    100.0% |######################| #> TEC   467    100.0% |######################|    100.0% |######################| #> TCY     3      0.6% |----------------------|     66.7% |##############--------| #> TGC    68     14.6% |###-------------------|      0.0% |----------------------| #> DOX     0      0.0% |----------------------|                                    #> ERY   467    100.0% |######################|    100.0% |######################| #> CLI   467    100.0% |######################|    100.0% |######################| #> AZM   467    100.0% |######################|    100.0% |######################| #> IPM   422     90.4% |###################---|      0.0% |----------------------| #> MEM   418     89.5% |###################---|      0.0% |----------------------| #> MTR     2      0.4% |----------------------|      0.0% |----------------------| #> CHL     0      0.0% |----------------------|                                    #> COL   240     51.4% |###########-----------|      0.0% |----------------------| #> MUP     0      0.0% |----------------------|                                    #> RIF   467    100.0% |######################|    100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname,   include_n_rows = FALSE, ...)  # S3 method for class 'bug_drug_combinations' format(x, translate_ab = \"name (ab, atc)\",   language = get_AMR_locale(), minimum = 30, combine_SI = TRUE,   add_ab_group = TRUE, remove_intrinsic_resistant = FALSE,   decimal.mark = getOption(\"OutDec\"), big.mark = ifelse(decimal.mark ==   \",\", \".\", \",\"), ...)"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antimicrobials columns, amox, AMX AMC col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes - default mo_shortname() include_n_rows logical indicate total number rows must included output ... arguments passed FUN translate_ab character length 1 containing column names antimicrobials data set language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S, SDD, summed, resistance based R - default TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric     decimal point. big.mark character; empty used mark every     big.interval decimals (hence big)     decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"SDD\", \"\", \"R\", \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination returns table ready reporting/publishing. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S. table can also directly used R Markdown / Quarto without need e.g. knitr::kable().","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 8 #>   mo                ab        S   SDD     I     R    NI total #>   <chr>             <chr> <int> <int> <int> <int> <int> <int> #> 1 (unknown species) AMC      15     0     0     0     0    15 #> 2 (unknown species) AMK       0     0     0     0     0     0 #> 3 (unknown species) AMP      15     0     0     1     0    16 #> 4 (unknown species) AMX      15     0     0     1     0    16 #> 5 (unknown species) AZM       3     0     0     3     0     6 #> 6 (unknown species) CAZ       0     0     0     0     0     0 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #>    Group     Drug  CoNS  `E. coli` `E. faecalis` `K. pneumoniae` `P. aeruginosa` #>    <chr>     <chr> <chr> <chr>     <chr>         <chr>           <chr>           #>  1 \"Aminogl… Amik… \"100… \"  0.0% … \"100.0% (39/… \"\"              \"\"              #>  2 \"\"        Gent… \" 13… \"  2.0% … \"100.0% (39/… \" 10.3% (6/58)\" \"  0.0% (0/30)\" #>  3 \"\"        Kana… \"100… \"\"        \"100.0% (39/… \"\"              \"100.0% (30/30… #>  4 \"\"        Tobr… \" 78… \"  2.6% … \"100.0% (39/… \" 10.3% (6/58)\" \"  0.0% (0/30)\" #>  5 \"Antimyc… Rifa… \"\"    \"100.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  6 \"Beta-la… Amox… \" 93… \" 50.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  7 \"\"        Amox… \" 42… \" 13.1% … \"\"            \" 10.3% (6/58)\" \"100.0% (30/30… #>  8 \"\"        Ampi… \" 93… \" 50.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  9 \"\"        Benz… \" 77… \"100.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #> 10 \"\"        Fluc… \" 42… \"\"        \"\"            \"\"              \"\"              #> # ℹ 29 more rows #> # ℹ 5 more variables: `P. mirabilis` <chr>, `S. aureus` <chr>, #> #   `S. epidermidis` <chr>, `S. hominis` <chr>, `S. pneumoniae` <chr>  # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates,   FUN = mo_gramstain ) #> # A tibble: 80 × 8 #>    mo            ab        S   SDD     I     R    NI total #>    <chr>         <chr> <int> <int> <int> <int> <int> <int> #>  1 Gram-negative AMC     463     0    89   174     0   726 #>  2 Gram-negative AMK     251     0     0     5     0   256 #>  3 Gram-negative AMP     226     0     0   405     0   631 #>  4 Gram-negative AMX     226     0     0   405     0   631 #>  5 Gram-negative AZM       1     0     2   696     0   699 #>  6 Gram-negative CAZ     607     0     0    27     0   634 #>  7 Gram-negative CHL       1     0     0    30     0    31 #>  8 Gram-negative CIP     610     0    11    63     0   684 #>  9 Gram-negative CLI      18     0     1   709     0   728 #> 10 Gram-negative COL     309     0     0    78     0   387 #> # ℹ 70 more rows #> Use 'format()' on this result to get a publishable/printable format.  bug_drug_combinations(example_isolates,   FUN = function(x) {     ifelse(x == as.mo(\"Escherichia coli\"),       \"E. coli\",       \"Others\"     )   } ) #> # A tibble: 80 × 8 #>    mo      ab        S   SDD     I     R    NI total #>    <chr>   <chr> <int> <int> <int> <int> <int> <int> #>  1 E. coli AMC     332     0    74    61     0   467 #>  2 E. coli AMK     171     0     0     0     0   171 #>  3 E. coli AMP     196     0     0   196     0   392 #>  4 E. coli AMX     196     0     0   196     0   392 #>  5 E. coli AZM       0     0     0   467     0   467 #>  6 E. coli CAZ     449     0     0    11     0   460 #>  7 E. coli CHL       0     0     0     0     0     0 #>  8 E. coli CIP     398     0     1    57     0   456 #>  9 E. coli CLI       0     0     0   467     0   467 #> 10 E. coli COL     240     0     0     0     0   240 #> # ℹ 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. dataset contain breakpoints humans, 7 different animal groups, ECOFFs. breakpoints currently implemented: clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024; veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024; ECOFFs (Epidemiological Cut-Values): EUCAST 2020-2024 CLSI 2022-2024. Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 34 063 observations 14 variables: guideline Name guideline type Breakpoint type, either \"ECOFF\", \"animal\", \"human\" host Host infectious agent. mostly useful veterinary breakpoints either \"ECOFF\", \"aquatic\", \"cats\", \"cattle\", \"dogs\", \"horse\", \"human\", \"poultry\", \"swine\" method Testing method, either \"DISK\" \"MIC\" site Body site breakpoint must applied, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antimcrobial code used package, EARS-Net WHONET, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI) is_SDD logical value (TRUE/FALSE) indicate whether intermediate range \"S\" \"R\" interpreted \"SDD\", instead \"\". currently applies 24 breakpoints.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"different-types-of-breakpoints","dir":"Reference","previous_headings":"","what":"Different types of breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Supported types breakpoints ECOFF, animal, human. ECOFF (Epidemiological cut-) values used antimicrobial susceptibility testing differentiate wild-type non-wild-type strains bacteria fungi. default \"human\", can also set package option AMR_breakpoint_type. Use .sir(..., breakpoint_type = ...) interpret raw data using specific breakpoint type, e.g. .sir(..., breakpoint_type = \"ECOFF\") use ECOFFs.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"imported-from-whonet","dir":"Reference","previous_headings":"","what":"Imported from WHONET","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate great development WHONET software.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"response-from-clsi-and-eucast","dir":"Reference","previous_headings":"","what":"Response from CLSI and EUCAST","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET software, developers AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"download","dir":"Reference","previous_headings":"","what":"Download","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI, though initiatives started overcome burdens. NOTE: AMR package (WHONET software well) contains rather complex internal methods apply guidelines. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 34,063 × 14 #>    guideline   type  host  method site    mo            rank_index ab   ref_tbl  #>    <chr>       <chr> <chr> <chr>  <chr>   <mo>               <dbl> <ab> <chr>    #>  1 EUCAST 2024 human human DISK   NA      B_ACHRMB_XYLS          2 MEM  A. xylo… #>  2 EUCAST 2024 human human MIC    NA      B_ACHRMB_XYLS          2 MEM  A. xylo… #>  3 EUCAST 2024 human human DISK   NA      B_ACHRMB_XYLS          2 SXT  A. xylo… #>  4 EUCAST 2024 human human MIC    NA      B_ACHRMB_XYLS          2 SXT  A. xylo… #>  5 EUCAST 2024 human human DISK   NA      B_ACHRMB_XYLS          2 TZP  A. xylo… #>  6 EUCAST 2024 human human MIC    NA      B_ACHRMB_XYLS          2 TZP  A. xylo… #>  7 EUCAST 2024 human human DISK   NA      B_ACNTB                3 AMK  Acineto… #>  8 EUCAST 2024 human human DISK   Uncomp… B_ACNTB                3 AMK  Acineto… #>  9 EUCAST 2024 human human MIC    NA      B_ACNTB                3 AMK  Acineto… #> 10 EUCAST 2024 human human MIC    Uncomp… B_ACNTB                3 AMK  Acineto… #> # ℹ 34,053 more rows #> # ℹ 5 more variables: disk_dose <chr>, breakpoint_S <dbl>, breakpoint_R <dbl>, #> #   uti <lgl>, is_SDD <lgl>"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE)  count_susceptible(..., only_all_tested = FALSE)  count_S(..., only_all_tested = FALSE)  count_SI(..., only_all_tested = FALSE)  count_I(..., only_all_tested = FALSE)  count_IR(..., only_all_tested = FALSE)  count_R(..., only_all_tested = FALSE)  count_all(..., only_all_tested = FALSE)  n_sir(..., only_all_tested = FALSE)  count_df(data, translate_ab = \"name\", language = get_AMR_locale(),   combine_SI = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antimicrobials, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antimicrobials available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antimicrobials, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   considered   considered  considered   considered                     susceptible    tested    susceptible    tested --------  --------  -----------  ----------  -----------  ----------  S or I    S or I        X            X           X            X    R       S or I        X            X           X            X   <NA>     S or I        X            X           -            -  S or I      R           X            X           X            X    R         R           -            X           -            X   <NA>       R           -            -           -            -  S or I     <NA>         X            X           -            -    R        <NA>         -            -           -            -   <NA>      <NA>         -            -           -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350  # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" and \"SDD\" being susceptible. This note will be shown once for #> this session. #> [1] 543 count_SI(example_isolates$AMX) #> Note that count_SI() will also count dose-dependent susceptibility, #> 'SDD'. This note will be shown once for this session. #> [1] 546 count_I(example_isolates$AMX) #> Note that count_I() will also count dose-dependent susceptibility, 'SDD'. #> This note will be shown once for this session. #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" and \"SDD\" being resistant. This note will be shown once for #> this session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804  # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350  # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = count_R(CIP),       I = count_I(CIP),       S = count_S(CIP),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Number of available isolates for a whole antibiotic class   # (i.e., in this data set columns GEN, TOB, AMK, KAN)   example_isolates %>%     group_by(ward) %>%     summarise(across(aminoglycosides(), n_sir))    # Count co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy.   # Please mind that `susceptibility()` calculates percentages right away instead.   example_isolates %>% count_susceptible(AMC) # 1433   example_isolates %>% count_all(AMC) # 1879    example_isolates %>% count_susceptible(GEN) # 1399   example_isolates %>% count_all(GEN) # 1855    example_isolates %>% count_susceptible(AMC, GEN) # 1764   example_isolates %>% count_all(AMC, GEN) # 1936    # Get number of S+I vs. R immediately of selected columns   example_isolates %>%     select(AMX, CIP) %>%     count_df(translate = FALSE)    # It also supports grouping variables   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #>    ward       antibiotic interpretation value #>    <chr>      <chr>      <ord>          <int> #>  1 Clinical   AMX        SI               357 #>  2 Clinical   AMX        R                487 #>  3 Clinical   CIP        SI               741 #>  4 Clinical   CIP        R                128 #>  5 ICU        AMX        SI               158 #>  6 ICU        AMX        R                270 #>  7 ICU        CIP        SI               362 #>  8 ICU        CIP        R                 85 #>  9 Outpatient AMX        SI                31 #> 10 Outpatient AMX        R                 47 #> 11 Outpatient CIP        SI                78 #> 12 Outpatient CIP        R                 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see instructions, Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work:   rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\",                          TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #>   1. If TZP is \"S\" then set to  S : #>      amoxicillin (AMX), ampicillin (AMP) #> #>   2. If TZP is \"R\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"),                  TZP = as.sir(\"R\"),                  ampi = as.sir(\"S\"),                  cipro = as.sir(\"S\")) df #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    S     S  eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    R     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\",                          TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\")  eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-multiple-antimicrobials-and-antimicrobial-group-names","dir":"Reference","previous_headings":"","what":"Usage of multiple antimicrobials and antimicrobial group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"can define antimicrobial groups instead single antimicrobials rule consequence, part tilde (~). examples , antimicrobial group aminopenicillins includes ampicillin amoxicillin. Rules can also applied multiple antimicrobials antimicrobial groups simultaneously. Use c() function combine multiple antimicrobials. instance, following example sets aminopenicillins ureidopenicillins \"R\" column TZP (piperacillin/tazobactam) \"R\":   34 antimicrobial groups allowed rules (case-insensitive) can used combination: aminoglycosides(amikacin, amikacin/fosfomycin, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, tobramycin-high) aminopenicillins(amoxicillin ampicillin) antifungals(amorolfine, amphotericin B, amphotericin B-high, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, voriconazole) antimycobacterials(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, viomycin) betalactams(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, carumonam, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid, tigemonam) betalactams_with_inhibitor(amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin/sulbactam, aztreonam/avibactam, aztreonam/nacubactam, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefoperazone/sulbactam, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefpodoxime/clavulanic acid, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, ceftolozane/tazobactam, ceftriaxone/beta-lactamase inhibitor, imipenem/relebactam, meropenem/nacubactam, meropenem/vaborbactam, mezlocillin/sulbactam, penicillin/novobiocin, penicillin/sulbactam, piperacillin/sulbactam, piperacillin/tazobactam, ticarcillin/clavulanic acid) carbapenems(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil, tebipenem) cephalosporins(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, loracarbef) cephalosporins_1st(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, cephradine) cephalosporins_2nd(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil, loracarbef) cephalosporins_3rd(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, latamoxef) cephalosporins_4th(cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetecol, cefoselis, cefozopran, cefpirome, cefquinome) cephalosporins_5th(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam) cephalosporins_except_caz(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, loracarbef) fluoroquinolones(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) glycopeptides(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, vancomycin-macromethod) glycopeptides_except_lipo(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin, vancomycin-macromethod) isoxazolylpenicillins(cloxacillin, dicloxacillin, flucloxacillin, meticillin, oxacillin) lincosamides(clindamycin, lincomycin, pirlimycin) lipoglycopeptides(dalbavancin, oritavancin, telavancin) macrolides(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin, tylvalosin) monobactams(aztreonam, aztreonam/avibactam, aztreonam/nacubactam, carumonam, tigemonam) nitrofurans(furazidin, furazolidone, nifurtoinol, nitrofurantoin, nitrofurazone) oxazolidinones(cadazolid, cycloserine, linezolid, tedizolid, thiacetazone) penicillins(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, temocillin, ticarcillin, ticarcillin/clavulanic acid) phenicols(chloramphenicol, florfenicol, thiamphenicol) polymyxins(colistin, polymyxin B, polymyxin B/polysorbate 80) quinolones(besifloxacin, cinoxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) rifamycins(rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine) streptogramins(pristinamycin quinupristin/dalfopristin) tetracyclines(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tigecycline) tetracyclines_except_tgc(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline) trimethoprims(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, trimethoprim/sulfamethoxazole) ureidopenicillins(azlocillin, mezlocillin, piperacillin, piperacillin/tazobactam)","code":"x <- custom_eucast_rules(TZP == \"R\" ~ c(aminopenicillins, ureidopenicillins) == \"R\") x #> A set of custom EUCAST rules: #> #>   1. If TZP is \"R\" then set to \"R\": #>      amoxicillin (AMX), ampicillin (AMP), azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), piperacillin/tazobactam (TZP)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules(   AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",   AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #>  #>   1. If AMC is  R  and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is  I  and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP)  # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates,   rules = \"custom\",   custom_rules = x,   info = FALSE,   verbose = TRUE ) #> # A tibble: 8 × 9 #>     row col   mo_fullname     old   new   rule  rule_group rule_name rule_source #>   <int> <chr> <chr>           <ord> <chr> <chr> <chr>      <chr>     <chr>       #> 1    33 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 2    33 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 3    34 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 4    34 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 5   531 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 6   531 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 7  1485 AMP   Klebsiella oxy… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 8  1485 AMX   Klebsiella oxy… R     I     \"rep… Custom EU… Custom E… Object 'x'…  # combine rule sets x2 <- c(   x,   custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #>  #>   1. If AMC is  R  and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is  I  and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   3. If TZP is  R  then set to  R : #>      biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #>      imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #>      meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #>      razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 503 observations 9 variables: ab Antimcrobial ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\", \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\", \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply, either 13, 12, 11","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 503 × 9 #>    ab   name            type  dose  dose_times administration notes original_txt #>    <ab> <chr>           <chr> <chr>      <int> <chr>          <chr> <chr>        #>  1 AMK  Amikacin        stan… 25-3…          1 iv             \"\"    25-30 mg/kg… #>  2 AMX  Amoxicillin     high… 2 g            6 iv             \"\"    2 g x 6 iv   #>  3 AMX  Amoxicillin     stan… 1 g            3 iv             \"\"    1 g x 3-4 iv #>  4 AMX  Amoxicillin     high… 0.75…          3 oral           \"\"    0.75-1 g x … #>  5 AMX  Amoxicillin     stan… 0.5 g          3 oral           \"\"    0.5 g x 3 o… #>  6 AMX  Amoxicillin     unco… 0.5 g          3 oral           \"\"    0.5 g x 3 o… #>  7 AMC  Amoxicillin/cl… high… 2 g …          3 iv             \"\"    (2 g amoxic… #>  8 AMC  Amoxicillin/cl… stan… 1 g …          3 iv             \"\"    (1 g amoxic… #>  9 AMC  Amoxicillin/cl… high… 0.87…          3 oral           \"\"    (0.875 g am… #> 10 AMC  Amoxicillin/cl… stan… 0.5 …          3 oral           \"\"    (0.5 g amox… #> # ℹ 493 more rows #> # ℹ 1 more variable: eucast_version <dbl>"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints notes expected resistant phenotypes defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules(x, col_mo = NULL, info = interactive(),   rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")),   verbose = FALSE, version_breakpoints = 12, version_expertrules = 3.3,   ampc_cephalosporin_resistance = NA, only_sir_columns = FALSE,   custom_rules = NULL, overwrite = TRUE, ...)  eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antimicrobials columns, amox, AMX AMC col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using package option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can \"12.0\", \"11.0\", \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can \"3.3\", \"3.2\", \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants - default NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\".  EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia, Serratia. only_sir_columns logical indicate whether antimicrobial columns must detected transformed class sir (see .sir()) beforehand (default FALSE) custom_rules custom rules apply, created custom_eucast_rules() overwrite logical indicate whether non-NA values must overwritten (defaults TRUE). FALSE, NA values changed. ... column name antimicrobial, see section Antimicrobials ab (vector ) text can coerced valid antimicrobial drug code .ab() administration route administration, either \"im\", \"iv\", \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antimicrobials. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.  Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated June 24th, 2024, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\")  eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set package option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antimicrobials","dir":"Reference","previous_headings":"","what":"Antimicrobials","title":"Apply EUCAST Rules — eucast_rules","text":"define antimicrobials column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antimicrobials eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), apramycin (APR, QA07AA92), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), carumonam (CAR, J01DF02), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefepime/amikacin (CFA, J01RA06), cefetamet (CAT, J01DD10), cefiderocol (FDC, J01DI04), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefovecin (FOV, QJ01DD91), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefquinome (CEQ, QG51AA07), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftiofur (TIO, QJ01DD90), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), ciprofloxacin (CIP, J01MA02), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), danofloxacin (DAN, QJ01MA92), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), difloxacin (DIF, QJ01MA94), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), enrofloxacin (ENR, QJ01MA90), epicillin (EPC, J01CA07), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), gamithromycin (GAM, QJ01FA95), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), kitasamycin (KIT, QJ01FA93), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), marbofloxacin (MAR, QJ01MA93), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), norfloxacin (, J01MA06), novobiocin (NOV, QJ01XX95), ofloxacin (OFX, J01MA01), oleandomycin (OLE, J01FA05), orbifloxacin (ORB, QJ01MA95), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pirlimycin (PRL, QJ51FF90), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pradofloxacin (PRA, QJ01MA97), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, QJ01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sarafloxacin (SAR, QJ01MA98), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tildipirosin (TIP, QJ01FA96), tilmicosin (TIL, QJ01FA91), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), tulathromycin (TUL, QJ01FA94), tylosin (TYL, QJ01FA90), tylvalosin (TYL1, QJ01FA92), vancomycin (VAN, J01XA01)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylpenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE )  head(a) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   -   -   -   S   S #> 2  Enterococcus faecalis   -   -   -   -   -   S   S #> 3       Escherichia coli   -   -   -   -   -   S   S #> 4  Klebsiella pneumoniae   -   -   -   -   -   S   S #> 5 Pseudomonas aeruginosa   -   -   -   -   -   S   S   # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(PEN:FOX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir))  head(b) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   R   R   S   S   S #> 2  Enterococcus faecalis   -   -   R   R   R   S   R #> 3       Escherichia coli   R   -   -   -   -   R   S #> 4  Klebsiella pneumoniae   R   R   -   -   -   R   S #> 5 Pseudomonas aeruginosa   R   R   -   -   R   R   R   # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(PEN:FOX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #>   row col           mo_fullname old new rule   rule_group #> 1   1 CXM Staphylococcus aureus   -   S       Breakpoints #> 2   1 CAZ Staphylococcus aureus   -   R      Expert Rules #> 3   1 COL Staphylococcus aureus   -   R      Expert Rules #> 4   2 CAZ Enterococcus faecalis   -   R      Expert Rules #> 5   2 COL Enterococcus faecalis   -   R      Expert Rules #> 6   2 CXM Enterococcus faecalis   -   R      Expert Rules #>                                                 rule_name #> 1                                          Staphylococcus #> 2 Table 4: Intrinsic resistance in gram-positive bacteria #> 3 Table 4: Intrinsic resistance in gram-positive bacteria #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #>                                                                                 rule_source #> 1                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # }  # Dosage guidelines:  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2 000 Example Isolates — example_isolates","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"data set containing 2 000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"tibble 2 000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\", \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antimicrobials class sir (see .sir()); column names occur antimicrobials data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3 000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"tibble 3 000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antimicrobials transformed .sir()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Unclean Data — example_isolates_unclean","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":null,"dir":"Reference","previous_headings":"","what":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"Export Data Set NCBI BioSample Antibiogram","code":""},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"","code":"export_ncbi_biosample(x, filename = paste0(\"biosample_\", format(Sys.time(),   \"%Y-%m-%d-%H%M%S\"), \".xlsx\"), type = \"pathogen MIC\",   columns = where(is.mic), save_as_xlsx = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"x data set filename character string specifying file name type character string specifying type data set, either \"pathogen MIC\" \"beta-lactamase MIC\", see https://www.ncbi.nlm.nih.gov/biosample/docs/","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate(x = NULL, col_date = NULL, col_patient_id = NULL,   col_mo = NULL, col_testcode = NULL, col_specimen = NULL,   col_icu = NULL, col_keyantimicrobials = NULL, episode_days = 365,   testcodes_exclude = NULL, icu_exclude = FALSE, specimen_group = NULL,   type = \"points\", method = c(\"phenotype-based\", \"episode-based\",   \"patient-based\", \"isolate-based\"), ignore_I = TRUE, points_threshold = 2,   info = interactive(), include_unknown = FALSE,   include_untested_sir = TRUE, ...)  filter_first_isolate(x = NULL, col_date = NULL, col_patient_id = NULL,   col_mo = NULL, episode_days = 365, method = c(\"phenotype-based\",   \"episode-based\", \"patient-based\", \"isolate-based\"), ...)"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology functions strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab) - default first column date class col_patient_id column name unique IDs patients - default first column starts 'patient' 'patid' (case insensitive) col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU). can also logical vector length rows x. col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). default first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed - default TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_sir logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_sir = FALSE always return FALSE rows. checks data set columns class sir consequently requires transforming columns antibiotic results using .sir() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"methodology implemented functions strictly based recommendations outlined CLSI Guideline M39 research overview Hindler et al. (2007, doi:10.1086/511864 ). conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According previously-mentioned sources, different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. method makes sure duplicate isolates selected patient. method preferred e.g. identify first MRSA finding patient determine incidence. Conversely, large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data ICU cases, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  example_isolates[first_isolate(info = TRUE), ] #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a #>    microbial ID was available) #> # A tibble: 1,387 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA  #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R   #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA  #> # ℹ 1,377 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> ℹ Using column 'mo' as input for mo_is_gram_negative() #> # A tibble: 441 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R   #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R   #>  6 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN   R     NA    NA    R   #>  7 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI   R     NA    NA    R   #>  8 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG   R     NA    NA    R   #>  9 2002-04-01 496896     46 F      ICU      B_ESCHR_COLI   R     NA    NA    NA  #> 10 2002-04-23 EE2510     69 F      ICU      B_ESCHR_COLI   R     NA    NA    NA  #> # ℹ 431 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate(info = TRUE)) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a #>    microbial ID was available) #> # A tibble: 1,387 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA  #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R   #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA  #> # ℹ 1,377 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … if (require(\"dplyr\")) {   # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE) } #> # A tibble: 1,387 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA  #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R   #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA  #> # ℹ 1,377 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … if (require(\"dplyr\")) {   # flag the first isolates per group:   example_isolates %>%     group_by(ward) %>%     mutate(first = first_isolate(info = TRUE)) %>%     select(ward, date, patient, mo, first) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #>  #> Group: ward = \"Clinical\" #> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 865 'phenotype-based' first isolates (70.1% of total where a #>    microbial ID was available) #>  #> Group: ward = \"ICU\" #> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 452 'phenotype-based' first isolates (70.0% of total where a #>    microbial ID was available) #>  #> Group: ward = \"Outpatient\" #> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 99 'phenotype-based' first isolates (82.5% of total where a #>    microbial ID was available) #> # A tibble: 2,000 × 5 #> # Groups:   ward [3] #>    ward     date       patient mo           first #>    <chr>    <date>     <chr>   <mo>         <lgl> #>  1 Clinical 2002-01-02 A77334  B_ESCHR_COLI TRUE  #>  2 Clinical 2002-01-03 A77334  B_ESCHR_COLI FALSE #>  3 ICU      2002-01-07 067927  B_STPHY_EPDR TRUE  #>  4 ICU      2002-01-07 067927  B_STPHY_EPDR FALSE #>  5 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  6 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  7 Clinical 2002-01-14 462729  B_STPHY_AURS TRUE  #>  8 Clinical 2002-01-14 462729  B_STPHY_AURS FALSE #>  9 ICU      2002-01-16 067927  B_STPHY_EPDR TRUE  #> 10 ICU      2002-01-17 858515  B_STPHY_EPDR TRUE  #> # ℹ 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also     factors. y numeric vector; ignored x matrix.      x factor, y factor length. p vector probabilities length x.     error given entry p negative. rescale.p logical scalar; TRUE p rescaled     (necessary) sum 1.  rescale.p FALSE,     p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following   components: statistic value chi-squared test statistic. parameter degrees freedom approximate     chi-squared distribution test statistic, NA     p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test     performed, whether Monte Carlo simulation continuity     correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals,     (observed - expected) / sqrt(expected). stdres standardized residuals,     (observed - expected) / sqrt(V), V     residual cell variance (Agresti, 2007, section 2.4.5     case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers.  Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed .  Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R :   df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio.  x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 4.1471, p-value = 0.1257 #>   # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible.   # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills.  x <- c(1752, 1895) g.test(x) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 5.6085, p-value = 0.01787 #>   # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Clinical or Epidemic Episodes — get_episode","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"functions determine items vector can considered (start ) new episode. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns TRUE every new get_episode() index. absolute relative episode determination supported.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"get_episode(x, episode_days = NULL, case_free_days = NULL, ...)  is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days episode length days specify time period new episode begins, can also less day Inf, see Details case_free_days (inter-epidemic) interval length days new episode start, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode(): integer vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"Episodes can determined two ways: absolute relative. Absolute method uses episode_days define episode length days, new episode start. common use case AMR data analysis microbial epidemiology: episodes S. aureus bacteraemia ICU patients example. episode length 30 days, new S. aureus isolates ICU episode 30 days considered different (new) episode. Thus, method counts since start previous episode. Relative method uses case_free_days quantify duration case-free days (inter-epidemic interval), new episode start. common use case infectious disease epidemiology: episodes norovirus outbreaks hospital example. case-free period 14 days, new norovirus cases time considered different (new) episode. Thus, methods counts since last case previous episode. table: ** marks start new episode, 8 January 2023 7 days since start previous episode (1 January 2023).  *** marks start new episode, 21 January 2023 7 days since last case previous episode (8 January 2023). Either episode_days case_free_days must provided function.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"difference-between-get-episode-and-is-new-episode-","dir":"Reference","previous_headings":"","what":"Difference between get_episode() and is_new_episode()","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode() function returns index number episode, cases/patients/isolates first episode number 1, cases/patients/isolates second episode number 2, etc. is_new_episode() function hand, returns TRUE every new get_episode() index. specify, setting episode_days = 365 (using method 1 explained ), two functions differ:","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, episode functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"# difference between absolute and relative determination of episodes: x <- data.frame(dates = as.Date(c(   \"2021-01-01\",   \"2021-01-02\",   \"2021-01-05\",   \"2021-01-08\",   \"2021-02-21\",   \"2021-02-22\",   \"2021-02-23\",   \"2021-02-24\",   \"2021-03-01\",   \"2021-03-01\" ))) x$absolute <- get_episode(x$dates, episode_days = 7) x$relative <- get_episode(x$dates, case_free_days = 7) x #>         dates absolute relative #> 1  2021-01-01        1        1 #> 2  2021-01-02        1        1 #> 3  2021-01-05        1        1 #> 4  2021-01-08        2        1 #> 5  2021-02-21        3        2 #> 6  2021-02-22        3        2 #> 7  2021-02-23        3        2 #> 8  2021-02-24        3        2 #> 9  2021-03-01        4        2 #> 10 2021-03-01        4        2   # `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 100), ]  get_episode(df$date, episode_days = 60) # indices #>   [1] 47 28 47 47 48  1 48 13  7 38 28  5 14 49 13 22 31 18 28 21  6  8  6 39 13 #>  [26]  5 11 15  9 42 48 43 28 18  4 45 26  2 31 43 10  4 23 47 25 35  1 38 24 15 #>  [51] 19  4 16  5 16  1 44 39 41 25  4 11 30  1 17 30  3 48  8 39  6 38  5 20 37 #>  [76] 36 44 31 48 40 42 43 44 33 12  4 10 34 11  8 12 32 10 33 29 46 34 27 22 23 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE #>  [13]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE #>  [25] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE #>  [37]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE #>  [49]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE #>  [61] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE #>  [73] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE #>  [85]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE #>  [97] FALSE  TRUE FALSE FALSE  # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 1 × 46 #>   date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>   <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #> 1 2003-01-31 151041     85 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # the functions also work for less than a day, e.g. to include one per hour: get_episode(   c(     Sys.time(),     Sys.time() + 60 * 60   ),   episode_days = 1 / 24 ) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(       x = c(\"A\", \"B\", \"C\"),       size = 100,       replace = TRUE     )) %>%     group_by(patient, condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient, date, condition, new_episode) %>%     arrange(patient, condition, date) } #> # A tibble: 100 × 4 #> # Groups:   patient, condition [99] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 067927  2002-01-29 A         TRUE        #>  2 069276  2015-06-18 A         TRUE        #>  3 078381  2014-08-17 A         TRUE        #>  4 09B453  2010-03-21 C         TRUE        #>  5 0DBF93  2015-10-12 A         TRUE        #>  6 118928  2004-02-01 B         TRUE        #>  7 119392  2010-11-01 A         TRUE        #>  8 122506  2007-08-10 B         TRUE        #>  9 146120  2011-09-23 B         TRUE        #> 10 151041  2003-01-31 B         TRUE        #> # ℹ 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward, patient) %>%     transmute(date,       patient,       new_index = get_episode(date, 60),       new_logical = is_new_episode(date, 60)     ) %>%     arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups:   ward, patient [94] #>    ward     date       patient new_index new_logical #>    <chr>    <date>     <chr>       <int> <lgl>       #>  1 ICU      2002-01-29 067927          1 TRUE        #>  2 Clinical 2015-06-18 069276          1 TRUE        #>  3 ICU      2014-08-17 078381          1 TRUE        #>  4 Clinical 2010-03-21 09B453          1 TRUE        #>  5 Clinical 2015-10-12 0DBF93          1 TRUE        #>  6 Clinical 2004-02-01 118928          1 TRUE        #>  7 Clinical 2010-11-01 119392          1 TRUE        #>  8 Clinical 2007-08-10 122506          1 TRUE        #>  9 Clinical 2011-09-23 146120          1 TRUE        #> 10 Clinical 2003-01-31 151041          1 TRUE        #> # ℹ 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward) %>%     summarise(       n_patients = n_distinct(patient),       n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),       n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),       n_episodes_30 = sum(is_new_episode(date, episode_days = 30))     ) } #> # A tibble: 3 × 5 #>   ward       n_patients n_episodes_365 n_episodes_60 n_episodes_30 #>   <chr>           <int>          <int>         <int>         <int> #> 1 Clinical           57             14            38            48 #> 2 ICU                31             12            25            28 #> 3 Outpatient          6              5             6             6  # grouping on patients and microorganisms leads to the same # results as first_isolate() when using 'episode-based': if (require(\"dplyr\")) {   x <- df %>%     filter_first_isolate(       include_unknown = TRUE,       method = \"episode-based\"     )    y <- df %>%     group_by(patient, mo) %>%     filter(is_new_episode(date, 365)) %>%     ungroup()    identical(x, y) } #> [1] TRUE  # but is_new_episode() has a lot more flexibility than first_isolate(), # since you can now group on anything that seems relevant: if (require(\"dplyr\")) {   df %>%     group_by(patient, mo, ward) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> # A tibble: 100 × 4 #> # Groups:   patient, mo, ward [98] #>    patient mo           ward     flag_episode #>    <chr>   <mo>         <chr>    <lgl>        #>  1 F5F794  B_STPHY_AURS Clinical TRUE         #>  2 146120  B_STPHY_AURS Clinical TRUE         #>  3 501607  B_STRPT_AGLC ICU      TRUE         #>  4 402950  B_ENTRC_FCLS ICU      TRUE         #>  5 560323  B_ESCHR_COLI Clinical TRUE         #>  6 4FC193  B_ESCHR_COLI Clinical TRUE         #>  7 D80438  B_CRYNB_STRT Clinical TRUE         #>  8 F81262  B_STRPT_PNMN Clinical TRUE         #>  9 619705  B_STRPT_ANGN ICU      TRUE         #> 10 280834  B_STPHY_CONS Clinical TRUE         #> # ℹ 90 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(x, choices = 1:2, scale = 1, pc.biplot = TRUE,   labels = NULL, labels_textsize = 3, labels_text_placement = 1.5,   groups = NULL, ellipse = TRUE, ellipse_prob = 0.68,   ellipse_size = 0.5, ellipse_alpha = 0.5, points_size = 2,   points_alpha = 0.25, arrows = TRUE, arrows_colour = \"darkblue\",   arrows_size = 0.5, arrows_textsize = 3, arrows_textangled = TRUE,   arrows_alpha = 0.75, base_textsize = 10, ...)"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default     biplot strict sense. scale variables scaled lambda ^ scale     observations scaled lambda ^ (1-scale)     lambda singular values computed     princomp. Normally 0 <= scale <= 1, warning     issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component     biplot\", lambda = 1 observations scaled sqrt(n)     variables scaled sqrt(n).  inner products     variables approximate covariances distances observations     approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    summary(pca_result)    # old base R plotting method:   biplot(pca_result, main = \"Base R biplot\")    # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result) +       labs(title = \"ggplot2 biplot\")   }   if (require(\"ggplot2\")) {     # still extendible with any ggplot2 function     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"ggplot2 biplot\")   } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"` `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_sir","title":"AMR Plots with ggplot2 — ggplot_sir","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"ggplot_sir(data, position = NULL, x = \"antibiotic\",   fill = \"interpretation\", facet = NULL, breaks = seq(0, 1, 0.1),   limits = NULL, translate_ab = \"name\", combine_SI = TRUE,   minimum = 30, language = get_AMR_locale(), nrow = NULL, colours = c(S   = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R = \"#ED553B\"),   datalabels = TRUE, datalabels.size = 2.5, datalabels.colour = \"grey15\",   title = NULL, subtitle = NULL, caption = NULL,   x.title = \"Antimicrobial\", y.title = \"Proportion\", ...)  geom_sir(position = NULL, x = c(\"antibiotic\", \"interpretation\"),   fill = \"interpretation\", translate_ab = \"name\", minimum = 30,   language = get_AMR_locale(), combine_SI = TRUE, ...)"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_sir","text":"data data.frame column(s) class sir (see .sir()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_sir_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_sir() , case scale_sir_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_sir","text":"default, names antimicrobials shown plots using ab_name(). can set translate_ab argument. See count_df(). geom_sir() take variable data sir class (created .sir()) using sir_df() plot bars percentage S, , R. default behaviour bars stacked different antimicrobials x axis. Additional functions include: facet_sir() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_sir_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_sir() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"# \\donttest{ if (require(\"ggplot2\") && require(\"dplyr\")) {   # get antimicrobial results for drugs against a UTI:   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +     geom_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # prettify the plot using some additional functions:   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)   ggplot(df) +     geom_sir() +     scale_y_percent() +     scale_sir_colours(aesthetics = \"fill\") +     labels_sir_count() +     theme_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # or better yet, simplify this using the wrapper function - a single command:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # get only proportions and no counts:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(datalabels = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # add other ggplot2 arguments as you like:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(       width = 0.5,       colour = \"black\",       size = 1,       linetype = 2,       alpha = 0.25     ) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # you can alter the colours with colour names:   example_isolates %>%     select(AMX) %>%     ggplot_sir(colours = c(SI = \"yellow\")) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # but you can also use the built-in colour-blind friendly colours for   # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red:   data.frame(     x = c(\"Value1\", \"Value2\", \"Value3\"),     y = c(1, 2, 3),     z = c(\"Value4\", \"Value5\", \"Value6\")   ) %>%     ggplot() +     geom_col(aes(x = x, y = y, fill = z)) +     scale_sir_colours(       aesthetics = \"fill\",       Value4 = \"S\", Value5 = \"I\", Value6 = \"R\"     ) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # resistance of ciprofloxacine per age group   example_isolates %>%     mutate(first_isolate = first_isolate()) %>%     filter(       first_isolate == TRUE,       mo == as.mo(\"Escherichia coli\")     ) %>%     # age_groups() is also a function in this AMR package:     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(x = \"age_group\") } #> Warning: Removed 6 rows containing missing values or values outside the scale range #> (`geom_col()`). #> Warning: Removed 6 rows containing missing values or values outside the scale range #> (`geom_text()`).  if (require(\"ggplot2\") && require(\"dplyr\")) {   # a shorter version which also adjusts data label colours:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(colours = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # it also supports groups (don't forget to use the group var on `x` or `facet`):   example_isolates %>%     filter(mo_is_gram_negative(), ward != \"Outpatient\") %>%     # select only UTI-specific drugs     select(ward, AMX, NIT, FOS, TMP, CIP) %>%     group_by(ward) %>%     ggplot_sir(       x = \"ward\",       facet = \"antibiotic\",       nrow = 1,       title = \"AMR of Anti-UTI Drugs Per Ward\",       x.title = \"Ward\",       datalabels = FALSE     ) } #> ℹ Using column 'mo' as input for mo_is_gram_negative()  # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antimicrobials data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col(x = NULL, search_string = NULL, verbose = FALSE,   only_sir_columns = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (default FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antimicrobials data set column containing name code antibiotic.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame(   amox = \"S\",   tetr = \"R\" )  guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\"  guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #>  OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).  # WHONET codes df <- data.frame(   AMP_ND10 = \"R\",   AMC_ED20 = \"S\" ) guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 301 583 observations 2 variables: mo Microorganism ID ab Antimcrobial ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 301,583 × 2 #>    mo          ab   #>    <mo>        <ab> #>  1 B_GRAMP     ATM  #>  2 B_GRAMP     COL  #>  3 B_GRAMP     NAL  #>  4 B_GRAMP     PLB  #>  5 B_GRAMP     TEM  #>  6 B_ANAER-POS ATM  #>  7 B_ANAER-POS COL  #>  8 B_ANAER-POS NAL  #>  9 B_ANAER-POS PLB  #> 10 B_ANAER-POS TEM  #> # ℹ 301,573 more rows"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\", \"html\"))  italicize_taxonomy(string, type = c(\"markdown\", \"ansi\", \"html\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\", \"html\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, <> <\/> using html. using 'ansi', ANSI colours added using \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\"  cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  semi_join_microorganisms(x, by = NULL, ...)  anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> # A tibble: 1 × 26 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # ℹ 16 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <chr>, source <chr>, lpsn <chr>, lpsn_parent <chr>, #> #   lpsn_renamed_to <chr>, mycobank <chr>, mycobank_parent <chr>, #> #   mycobank_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list> left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 26 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # ℹ 16 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <chr>, source <chr>, lpsn <chr>, lpsn_parent <chr>, #> #   lpsn_renamed_to <chr>, mycobank <chr>, mycobank_parent <chr>, #> #   mycobank_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list>  df <- data.frame(   date = seq(     from = as.Date(\"2018-01-01\"),     to = as.Date(\"2018-01-07\"),     by = 1   ),   bacteria = as.mo(c(     \"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\",     \"E. coli\", \"E. coli\", \"E. coli\"   )),   stringsAsFactors = FALSE ) colnames(df) #> [1] \"date\"     \"bacteria\"  df_joined <- left_join_microorganisms(df, \"bacteria\") colnames(df_joined) #>  [1] \"date\"                \"bacteria\"            \"fullname\"            #>  [4] \"status\"              \"kingdom\"             \"phylum\"              #>  [7] \"class\"               \"order\"               \"family\"              #> [10] \"genus\"               \"species\"             \"subspecies\"          #> [13] \"rank\"                \"ref\"                 \"oxygen_tolerance\"    #> [16] \"source\"              \"lpsn\"                \"lpsn_parent\"         #> [19] \"lpsn_renamed_to\"     \"mycobank\"            \"mycobank_parent\"     #> [22] \"mycobank_renamed_to\" \"gbif\"                \"gbif_parent\"         #> [25] \"gbif_renamed_to\"     \"prevalence\"          \"snomed\"               # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     left_join_microorganisms() %>%     colnames() } #> Joining, by = \"mo\" #>  [1] \"date\"                \"patient\"             \"age\"                 #>  [4] \"gender\"              \"ward\"                \"mo\"                  #>  [7] \"PEN\"                 \"OXA\"                 \"FLC\"                 #> [10] \"AMX\"                 \"AMC\"                 \"AMP\"                 #> [13] \"TZP\"                 \"CZO\"                 \"FEP\"                 #> [16] \"CXM\"                 \"FOX\"                 \"CTX\"                 #> [19] \"CAZ\"                 \"CRO\"                 \"GEN\"                 #> [22] \"TOB\"                 \"AMK\"                 \"KAN\"                 #> [25] \"TMP\"                 \"SXT\"                 \"NIT\"                 #> [28] \"FOS\"                 \"LNZ\"                 \"CIP\"                 #> [31] \"MFX\"                 \"VAN\"                 \"TEC\"                 #> [34] \"TCY\"                 \"TGC\"                 \"DOX\"                 #> [37] \"ERY\"                 \"CLI\"                 \"AZM\"                 #> [40] \"IPM\"                 \"MEM\"                 \"MTR\"                 #> [43] \"CHL\"                 \"COL\"                 \"MUP\"                 #> [46] \"RIF\"                 \"fullname\"            \"status\"              #> [49] \"kingdom\"             \"phylum\"              \"class\"               #> [52] \"order\"               \"family\"              \"genus\"               #> [55] \"species\"             \"subspecies\"          \"rank\"                #> [58] \"ref\"                 \"oxygen_tolerance\"    \"source\"              #> [61] \"lpsn\"                \"lpsn_parent\"         \"lpsn_renamed_to\"     #> [64] \"mycobank\"            \"mycobank_parent\"     \"mycobank_renamed_to\" #> [67] \"gbif\"                \"gbif_parent\"         \"gbif_renamed_to\"     #> [70] \"prevalence\"          \"snomed\"              # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials(x = NULL, col_mo = NULL, universal = c(\"ampicillin\",   \"amoxicillin/clavulanic acid\", \"cefuroxime\", \"piperacillin/tazobactam\",   \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"),   gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\",   \"ceftazidime\", \"meropenem\"), gram_positive = c(\"vancomycin\", \"teicoplanin\",   \"tetracycline\", \"erythromycin\", \"oxacillin\", \"rifampin\"),   antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\",   \"nystatin\", \"voriconazole\"), only_sir_columns = FALSE, ...)  all_antimicrobials(x = NULL, only_sir_columns = FALSE, ...)  antimicrobials_equal(y, z, type = c(\"points\", \"keyantimicrobials\"),   ignore_I = TRUE, points_threshold = 2, ...)"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antimicrobials columns, like AMX amox. Can left blank determine automatically. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial drugs, case-insensitive. Set NULL ignore. See Details default antimicrobial drugs gram_negative names antibiotic drugs Gram-positives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs gram_positive names antibiotic drugs Gram-negatives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs antifungal names antifungal drugs fungi, case-insensitive. Set NULL ignore. See Details default antifungal drugs only_sir_columns logical indicate whether columns must included transformed class sir (see .sir()) beforehand (default FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial drug results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial drugs used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial drugs used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial drugs used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial drugs used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\"  # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values)  antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs  # \\donttest{ if (require(\"dplyr\")) {   # set key antimicrobials to a new variable   my_patients <- example_isolates %>%     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`     mutate(       # now calculate first isolates       first_regular = first_isolate(col_keyantimicrobials = FALSE),       # and first WEIGHTED isolates       first_weighted = first_isolate(col_keyantimicrobials = \"keyab\")     )    # Check the difference in this data set, 'weighted' results in more isolates:   sum(my_patients$first_regular, na.rm = TRUE)   sum(my_patients$first_weighted, na.rm = TRUE) } #> [1] 1383 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE)  # Default S3 method kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for class 'matrix' kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for class 'data.frame' kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 2.971316 kurtosis(rnorm(10000), excess = TRUE) #> [1] -0.05342143"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE)  x %like% pattern  x %unlike% pattern  x %like_case% pattern  x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"# data.table has a more limited version of %like%, so unload it: try(detach(\"package:data.table\", unload = TRUE), silent = TRUE)  a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE  # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(\"case\", \"diff\", \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE  a[1] %like% b #> [1]  TRUE FALSE FALSE a %like% b[1] #> [1]  TRUE FALSE FALSE  # \\donttest{ # get isolates whose name start with 'Entero' (case-insensitive) example_isolates[which(mo_name() %like% \"^entero\"), ] #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM    NA    NA    NA    NA  #>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS    NA    NA    NA    NA  #>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS    NA    NA    NA    NA  #>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS    NA    NA    NA    NA  #>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC         NA    NA    NA    NA  #>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC         NA    NA    NA    NA  #>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS    NA    NA    NA    NA  #>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC         NA    NA    NA    NA  #> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #> # ℹ 96 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM    NA    NA    NA    NA  #>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS    NA    NA    NA    NA  #>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS    NA    NA    NA    NA  #>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS    NA    NA    NA    NA  #>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC         NA    NA    NA    NA  #>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC         NA    NA    NA    NA  #>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS    NA    NA    NA    NA  #>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC         NA    NA    NA    NA  #> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #> # ℹ 96 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national, custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro(x = NULL, guideline = \"CMI2012\", col_mo = NULL, esbl = NA,   carbapenemase = NA, mecA = NA, mecC = NA, vanA = NA, vanB = NA,   info = interactive(), pct_required_classes = 0.5, combine_SI = TRUE,   verbose = FALSE, only_sir_columns = FALSE, ...)  custom_mdro_guideline(..., as_factor = TRUE)  brmo(x = NULL, only_sir_columns = FALSE, ...)  mrgn(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)  mdr_tb(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)  mdr_cmi2012(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)  eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE,   verbose = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antimicrobials columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). esbl logical values, column name containing logical values, indicating presence ESBL gene (production proteins) carbapenemase logical values, column name containing logical values, indicating presence carbapenemase gene (production proteins) mecA logical values, column name containing logical values, indicating presence mecA gene (production proteins) mecC logical values, column name containing logical values, indicating presence mecC gene (production proteins) vanA logical values, column name containing logical values, indicating presence vanA gene (production proteins) vanB logical values, column name containing logical values, indicating presence vanB gene (production proteins) info logical indicate whether progress printed console - default print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_sir_columns logical indicate whether antimicrobial columns must detected transformed class sir (see .sir()) beforehand (default FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antimicrobials . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (doi:10.1111/j.1469-0691.2011.03570.x ) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Samenwerkingverband Richtlijnen Infectiepreventie (SRI) (2024) \"Bijzonder Resistente Micro-Organismen (BRMO)\" (link) Also: guideline = \"BRMO 2017\" former Dutch national guideline - Werkgroep Infectiepreventie (WIP), RIVM, last revision 2017: \"Bijzonder Resistente Micro-Organismen (BRMO)\" Please suggest implement guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours.   outcome function can used guideline argument mdro() function:   Rules can also combined custom rules using c():   rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #>   1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #>   2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #>   3. Otherwise -> Negative #> #> Unmatched rows will return NA. x <- mdro(example_isolates,           guideline = custom) table(x) #>       Negative Elderly Type A Elderly Type B #>           1070            198            732 x <- mdro(example_isolates,           guideline = c(custom,                         custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #>       Negative Elderly Type A Elderly Type B Elderly Type C #>            961            198            732            109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antimicrobials","dir":"Reference","previous_headings":"","what":"Antimicrobials","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antimicrobials column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antimicrobials eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), apramycin (APR, QA07AA92), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), carumonam (CAR, J01DF02), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefepime/amikacin (CFA, J01RA06), cefetamet (CAT, J01DD10), cefiderocol (FDC, J01DI04), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefovecin (FOV, QJ01DD91), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefquinome (CEQ, QG51AA07), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftiofur (TIO, QJ01DD90), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), ciprofloxacin (CIP, J01MA02), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), danofloxacin (DAN, QJ01MA92), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), difloxacin (DIF, QJ01MA94), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), enrofloxacin (ENR, QJ01MA90), epicillin (EPC, J01CA07), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), gamithromycin (GAM, QJ01FA95), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), kitasamycin (KIT, QJ01FA93), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), marbofloxacin (MAR, QJ01MA93), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), norfloxacin (, J01MA06), novobiocin (NOV, QJ01XX95), ofloxacin (OFX, J01MA01), oleandomycin (OLE, J01FA05), orbifloxacin (ORB, QJ01MA95), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pirlimycin (PRL, QJ51FF90), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pradofloxacin (PRA, QJ01MA97), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, QJ01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sarafloxacin (SAR, QJ01MA98), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tildipirosin (TIP, QJ01FA96), tilmicosin (TIL, QJ01FA91), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), tulathromycin (TUL, QJ01FA94), tylosin (TYL, QJ01FA90), tylvalosin (TYL1, QJ01FA92), vancomycin (VAN, J01XA01)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") str(out) #>  Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #>              Negative Positive, unconfirmed              Positive  #>                  1994                     0                     6   out <- mdro(example_isolates,   guideline = custom_mdro_guideline(     AMX == \"R\" ~ \"Custom MDRO 1\",     VAN == \"R\" ~ \"Custom MDRO 2\"   ) ) table(out) #> out #>      Negative Custom MDRO 1 Custom MDRO 2  #>           870           804           326   # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     mdro() %>%     table()    # no need to define `x` when used inside dplyr verbs:   example_isolates %>%     mutate(MDRO = mdro()) %>%     pull(MDRO) %>%     table() } #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `MDRO = mdro()`. #> Caused by warning: #> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #>                         Negative       Multi-drug-resistant (MDR)  #>                             1617                              128  #> Extensively drug-resistant (XDR)          Pandrug-resistant (PDR)  #>                                0                                0  # }"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Mean AMR Distance — mean_amr_distance","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Calculates normalised mean antimicrobial resistance multiple observations, help identify similar isolates without comparing antibiograms hand.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"mean_amr_distance(x, ...)  # S3 method for class 'sir' mean_amr_distance(x, ..., combine_SI = TRUE)  # S3 method for class 'data.frame' mean_amr_distance(x, ..., combine_SI = TRUE)  amr_distance_from_row(amr_distance, row)"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"x vector class sir, mic disk, data.frame containing columns classes ... variables select (supports tidyselect language column1:column4 (.mic), can thus also antimicrobial selectors combine_SI logical indicate whether values S, SDD, must merged one, input consists S+vs. R (susceptible vs. resistant) - default TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"mean AMR distance effectively Z-score; normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. MIC values (see .mic()) transformed log2() first; distance thus calculated (log2(x) - mean(log2(x))) / sd(log2(x)). SIR values (see .sir()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per column, mean per row returned, see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"sir <- random_sir(10) sir #> Class 'sir' #>  [1] S R S S S R R R R R mean_amr_distance(sir) #>  [1] -1.1618950  0.7745967 -1.1618950 -1.1618950 -1.1618950  0.7745967 #>  [7]  0.7745967  0.7745967  0.7745967  0.7745967  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.005 0.25  1     2     >=16  8     1     1     0.025 4     mean_amr_distance(mic) #>  [1] -1.9714967 -0.4147020  0.1369756  0.4128144  1.2403309  0.9644921 #>  [7]  0.1369756  0.1369756 -1.3310188  0.6886533 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -1.9714967 -0.4147020  0.1369756  0.4128144  1.2403309  0.9644921 #>  [7]  0.1369756  0.1369756 -1.3310188  0.6886533  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 15 15 50 41 10 21 44 42 10 41 mean_amr_distance(disk) #>  [1] -0.8693350 -0.8693350  1.3196381  0.7567593 -1.1820455 -0.4940825 #>  [7]  0.9443855  0.8193014 -1.1820455  0.7567593  y <- data.frame(   id = LETTERS[1:10],   amox = random_sir(10, ab = \"amox\", mo = \"Escherichia coli\"),   cipr = random_disk(10, ab = \"cipr\", mo = \"Escherichia coli\"),   gent = random_mic(10, ab = \"gent\", mo = \"Escherichia coli\"),   tobr = random_mic(10, ab = \"tobr\", mo = \"Escherichia coli\") ) y #>    id amox cipr   gent tobr #> 1   A    S   26    >=4 0.25 #> 2   B    S   20      1  >=4 #> 3   C    S   28      2 0.25 #> 4   D    S   26 <=0.25  0.5 #> 5   E    S   23      2 0.25 #> 6   F    I   31    0.5    1 #> 7   G    R   31      2  >=4 #> 8   H    R   29      1    2 #> 9   I    R   27 <=0.25  >=4 #> 10  J    I   22    0.5 0.25 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1] -0.04047504 -0.25789134 -0.08826444 -0.63237868 -0.42488867  0.02453739 #>  [7]  1.18267023  0.72772870  0.36605356 -0.85709171 y$amr_distance <- mean_amr_distance(y, where(is.mic)) #> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] #> Error in order(y$amr_distance): argument 1 is not a vector  if (require(\"dplyr\")) {   y %>%     mutate(       amr_distance = mean_amr_distance(y),       check_id_C = amr_distance_from_row(amr_distance, id == \"C\")     ) %>%     arrange(check_id_C) } #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>    id amox cipr   gent tobr amr_distance check_id_C #> 1   C    S   28      2 0.25  -0.08826444  0.0000000 #> 2   A    S   26    >=4 0.25  -0.04047504  0.0477894 #> 3   F    I   31    0.5    1   0.02453739  0.1128018 #> 4   B    S   20      1  >=4  -0.25789134  0.1696269 #> 5   E    S   23      2 0.25  -0.42488867  0.3366242 #> 6   I    R   27 <=0.25  >=4   0.36605356  0.4543180 #> 7   D    S   26 <=0.25  0.5  -0.63237868  0.5441142 #> 8   J    I   22    0.5 0.25  -0.85709171  0.7688273 #> 9   H    R   29      1    2   0.72772870  0.8159931 #> 10  G    R   31      2  >=4   1.18267023  1.2709347 if (require(\"dplyr\")) {   # support for groups   example_isolates %>%     filter(mo_genus() == \"Enterococcus\" & mo_species() != \"\") %>%     select(mo, TCY, carbapenems()) %>%     group_by(mo) %>%     mutate(dist = mean_amr_distance(.)) %>%     arrange(mo, dist) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ Using column 'mo' as input for mo_species() #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Calculating mean AMR distance based on columns \"TCY\", \"IPM\", and \"MEM\" #> # A tibble: 63 × 5 #> # Groups:   mo [4] #>    mo           TCY   IPM   MEM     dist #>    <mo>         <sir> <sir> <sir>  <dbl> #>  1 B_ENTRC_AVIM   S     S     NA   0     #>  2 B_ENTRC_AVIM   S     S     NA   0     #>  3 B_ENTRC_CSSL   NA    S     NA  NA     #>  4 B_ENTRC_FACM   S     S     NA  -2.66  #>  5 B_ENTRC_FACM   S     R     R   -0.423 #>  6 B_ENTRC_FACM   S     R     R   -0.423 #>  7 B_ENTRC_FACM   NA    R     R    0.224 #>  8 B_ENTRC_FACM   NA    R     R    0.224 #>  9 B_ENTRC_FACM   NA    R     R    0.224 #> 10 B_ENTRC_FACM   NA    R     R    0.224 #> # ℹ 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"tibble 4 971 observations 2 variables: code Commonly used code microorganism. unique identifier. mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 4,971 × 2 #>    code  mo                #>    <chr> <mo>              #>  1 1011  B_GRAMP           #>  2 1012  B_GRAMP           #>  3 1013  B_GRAMN           #>  4 1014  B_GRAMN           #>  5 1015  F_YEAST           #>  6 103   B_ESCHR_COLI      #>  7 104   B_SLMNL_ENTR_ENTR #>  8 1100  B_STRPT           #>  9 1101  B_STRPT_VIRI      #> 10 1102  B_STRPT_HAEM      #> # ℹ 4,961 more rows  # 'ECO' or 'eco' is the WHONET code for E. coli: microorganisms.codes[microorganisms.codes$code == \"ECO\", ] #> # A tibble: 1 × 2 #>   code  mo           #>   <chr> <mo>         #> 1 ECO   B_ESCHR_COLI  # and therefore, 'eco' will be understood as E. coli in this package: mo_info(\"eco\") #> $mo #> [1] \"B_ESCHR_COLI\" #>  #> $rank #> [1] \"species\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Escherichia\" #>  #> $species #> [1] \"coli\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/escherichia-coli\" #>  #> $ref #> [1] \"Castellani et al., 1919\" #>  #> $snomed #>  [1] \"1095001000112106\" \"715307006\"        \"737528008\"        \"416989002\"        #>  [5] \"116397003\"        \"414097009\"        \"414098004\"        \"414099007\"        #>  [9] \"414100004\"        \"116395006\"        \"735270003\"        \"116396007\"        #> [13] \"83285000\"         \"116394005\"        \"112283007\"        \"710886005\"        #> [17] \"710887001\"        \"710888006\"        \"710889003\"        \"414132004\"        #> [21] \"721892009\"        \"416812001\"        \"416740004\"        \"417216001\"        #> [25] \"457541006\"        \"710253004\"        \"416530004\"        \"417189006\"        #> [29] \"409800005\"        \"713925008\"        \"444771000124108\"  \"838549008\"        #>  #> $lpsn #> [1] \"776057\" #>  #> $mycobank #> [1] NA #>  #> $gbif #> [1] \"11286021\" #>  #> $group_members #> character(0) #>   # works for all AMR functions: mo_is_intrinsic_resistant(\"eco\", ab = \"vancomycin\") #> [1] TRUE"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"data set containing species groups microbiological complexes, used clinical breakpoints table.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"tibble 521 observations 4 variables: mo_group ID species group / microbiological complex mo ID microorganism belonging species group / microbiological complex mo_group_name Name species group / microbiological complex, retrieved mo_name() mo_name Name microorganism belonging species group / microbiological complex, retrieved mo_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups #> # A tibble: 521 × 4 #>    mo_group       mo           mo_group_name                   mo_name           #>    <mo>           <mo>         <chr>                           <chr>             #>  1 B_ACNTB_BMNN-C B_ACNTB_BMNN Acinetobacter baumannii complex Acinetobacter ba… #>  2 B_ACNTB_BMNN-C B_ACNTB_CLCC Acinetobacter baumannii complex Acinetobacter ca… #>  3 B_ACNTB_BMNN-C B_ACNTB_LCTC Acinetobacter baumannii complex Acinetobacter di… #>  4 B_ACNTB_BMNN-C B_ACNTB_NSCM Acinetobacter baumannii complex Acinetobacter no… #>  5 B_ACNTB_BMNN-C B_ACNTB_PITT Acinetobacter baumannii complex Acinetobacter pi… #>  6 B_ACNTB_BMNN-C B_ACNTB_SFRT Acinetobacter baumannii complex Acinetobacter se… #>  7 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex    Bacteroides frag… #>  8 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex    Bacteroides ovat… #>  9 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex    Bacteroides thet… #> 10 B_BCTRD_FRGL-C B_PHCCL_VLGT Bacteroides fragilis complex    Bacteroides vulg… #> # ℹ 511 more rows  # these are all species in the Bacteroides fragilis group, as per WHONET: microorganisms.groups[microorganisms.groups$mo_group == \"B_BCTRD_FRGL-C\", ] #> # A tibble: 5 × 4 #>   mo_group       mo           mo_group_name                mo_name               #>   <mo>           <mo>         <chr>                        <chr>                 #> 1 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex Bacteroides fragilis  #> 2 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex Bacteroides ovatus    #> 3 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex Bacteroides thetaiot… #> 4 B_BCTRD_FRGL-C B_PHCCL_VLGT Bacteroides fragilis complex Bacteroides vulgatus  #> 5 B_BCTRD_FRGL-C B_PRBCT_DSTS Bacteroides fragilis complex Parabacteroides dist…"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: June 24th, 2024) six kingdoms. data set backbone AMR package. MO codes can looked using .mo() microorganism properties can looked using mo_* functions. data set carefully crafted, yet made 100% reproducible public authoritative taxonomic sources (using script), namely: List Prokaryotic names Standing Nomenclature (LPSN) bacteria, MycoBank fungi, Global Biodiversity Information Facility (GBIF) others taxons.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"tibble 78 678 observations 26 variables: mo ID microorganism used package. unique identifier. fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. unique identifier. status  Status taxon, either \"accepted\", \"validly published\", \"synonym\", \"unknown\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism. Note fungi, phylum equal taxonomic division. Also, fungi, subkingdom subdivision left since occur bacterial taxonomy. rank Text taxonomic rank microorganism, \"species\" \"genus\" ref Author(s) year related scientific publication. contains first surname year latest authors, e.g. \"Wallis et al. 2006 emend. Smith Jones 2018\" becomes \"Smith et al., 2018\". field directly retrieved source specified column source. Moreover, accents removed comply CRAN allows ASCII characters. oxygen_tolerance  Oxygen tolerance, either \"aerobe\", \"anaerobe\", \"anaerobe/microaerophile\", \"facultative anaerobe\", \"likely facultative anaerobe\", \"microaerophile\". data retrieved BacDive (see Source). Items contain \"likely\" missing BacDive extrapolated species within genus guess oxygen tolerance. Currently 68.3% ~39 000 bacteria data set contain oxygen tolerance. source Either \"GBIF\", \"LPSN\", \"MycoBank\", \"manually added\" (see Source) lpsn Identifier ('Record number') List Prokaryotic names Standing Nomenclature (LPSN). first/highest LPSN identifier keep one identifier per row. example, Acetobacter ascendens LPSN Record number 7864 11011. first available microorganisms data set. unique identifier, though available ~33 000 records. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon mycobank Identifier ('MycoBank #') MycoBank. unique identifier, though available ~18 000 records. mycobank_parent MycoBank identifier parent taxon mycobank_renamed_to MycoBank identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF). unique identifier, though available ~49 000 records. gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon prevalence Prevalence microorganism based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version July 16th, 2024 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"Taxonomic entries imported order importance: List Prokaryotic names Standing Nomenclature (LPSN): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de June 24th, 2024. MycoBank: Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. Global Biodiversity Information Facility (GBIF): GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Furthermore, sources used additional details: BacDive: Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Systematized Nomenclature Medicine - Clinical Terms (SNOMED-CT): Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). Accessed https://www.cdc.gov/phin/php/phinvads/ July 16th, 2024. Grimont et al. (2007). Antigenic Formulae Salmonella Serovars, 9th Edition. Collaborating Centre Reference Research Salmonella (WHOCC-SALM). Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"Please note entries based LPSN, MycoBank, GBIF (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"Included taxonomic data LPSN, MycoBank, GBIF : ~39 000 (sub)species kingdoms Archaea Bacteria ~28 000 species kingdom Fungi. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package. relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histoplasma, Pneumocystis, Saccharomyces Trichophyton). ~8 100 (sub)species kingdom Protozoa ~1 600 (sub)species 39 relevant genera kingdom Animalia (Strongyloides Taenia) ~22 000 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies identifier parent taxons year first author related scientific publication","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"convenience, entries added manually: ~1 500 entries Salmonella, city-like serovars groups H 36 species groups (beta-haemolytic Streptococcus groups K, coagulase-negative Staphylococcus (CoNS), Mycobacterium tuberculosis complex, etc.), group compositions stored microorganisms.groups data set 1 entry Blastocystis (B.  hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 8 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, unknown anaerobic Gram-pos/Gram-neg bacteria) syntax used transform original data cleansed R format, can found .","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 78,678 × 26 #>    mo          fullname   status kingdom phylum class order family genus species #>    <mo>        <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #>  1 B_GRAMN     (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  2 B_GRAMP     (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  3 B_ANAER-NEG (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  4 B_ANAER-POS (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  5 B_ANAER     (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  6 F_FUNGUS    (unknown … unkno… Fungi   (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  7   UNKNOWN   (unknown … unkno… (unkno… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  8 P_PROTOZOAN (unknown … unkno… Protoz… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  9 F_YEAST     (unknown … unkno… Fungi   (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 10 F_AABRN     Aabaarnia  unkno… Fungi   Ascom… Leca… Ostr… \"\"     Aaba… \"\"      #> # ℹ 78,668 more rows #> # ℹ 16 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <chr>, source <chr>, lpsn <chr>, lpsn_parent <chr>, #> #   lpsn_renamed_to <chr>, mycobank <chr>, mycobank_parent <chr>, #> #   mycobank_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list>"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm originally developed 2018 subsequently described : Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 . Later, work Bartlett et al. bacterial pathogens infecting humans (2022, doi:10.1099/mic.0.001269 ) incorporated, optimalisations algorithm made.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : $$m_{(x, n)} = \\frac{l_{n} - 0.5 \\cdot \\min \\begin{cases}l_{n} \\\\ \\textrm{lev}(x, n)\\end{cases}}{l_{n} \\cdot p_{n} \\cdot k_{n}}$$ : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.15 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, Genera World Health Organization's () Priority Pathogen List prevalence = 1.0 microorganisms data set; genus present established list also prevalence = 1.15 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.25 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, Zygosaccharomyces; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.381\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_reset_session() #> ℹ Reset 17 previously matched input values.  as.mo(\"E. coli\") #> Class 'mo' #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #> Colour keys:  0.000-0.549  0.550-0.649  0.650-0.749  0.750-1.000  #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #>               (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #>               (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #>               Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #>               (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #>               dissolvens (0.565) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object.  mo_matching_score(   x = \"E. coli\",   n = c(\"Escherichia coli\", \"Entamoeba coli\") ) #> [1] 0.6875000 0.3809524"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_fullname(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_shortname(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_subspecies(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_species(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_genus(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_family(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_order(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_class(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_phylum(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_kingdom(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_domain(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_type(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_status(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_pathogenicity(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_gramstain(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_gram_negative(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_gram_positive(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_yeast(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_intrinsic_resistant(x, ab, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_oxygen_tolerance(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_anaerobic(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_snomed(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_ref(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_authors(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_year(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_lpsn(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_mycobank(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_gbif(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_rank(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_taxonomy(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_synonyms(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_current(x, language = get_AMR_locale(), ...)  mo_group_members(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_info(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_url(x, open = FALSE, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_property(x, property = \"fullname\", language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() ordered factor case mo_pathogenicity() list case mo_taxonomy(), mo_synonyms(), mo_snomed(), mo_info() logical case mo_is_anaerobic(), mo_is_gram_negative(), mo_is_gram_positive(), mo_is_intrinsic_resistant(), mo_is_yeast() named character case mo_synonyms() mo_url() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions , default, keep old taxonomic properties, synonyms automatically replaced current taxonomy. Take example Enterobacter aerogenes, initially named 1960 renamed Klebsiella aerogenes 2017: mo_genus(\"Enterobacter aerogenes\") return \"Klebsiella\" (note renaming) mo_genus(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Enterobacter\" (-per-session warning name outdated) mo_ref(\"Enterobacter aerogenes\") return \"Tindall et al., 2017\" (note renaming) mo_ref(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Hormaeche et al., 1960\" (-per-session warning name outdated) short name (mo_shortname()) returns first character genus full species, \"E. coli\", species subspecies. Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. result, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Determination human pathogenicity (mo_pathogenicity()) strongly based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ). function returns factor levels Pathogenic, Potentially pathogenic, Non-pathogenic, Unknown. Determination Gram stain (mo_gramstain()) based taxonomic kingdom phylum. Originally, Cavalier-Smith defined -called subkingdoms Negibacteria Posibacteria (2002, PMID 11837318), considered phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, Tenericutes. phyla later renamed Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota (2021, PMID 34694987). Bacteria phyla considered Gram-positive AMR package, except members class Negativicutes (within phylum Bacillota) Gram-negative. bacteria considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (NA input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts (mo_is_yeast()) based taxonomic kingdom class. Budding yeasts yeasts reproduce asexually process called budding, new cell develops small protrusion parent cell. Taxonomically, members phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) Pichiomycetes. True yeasts quite specifically refers yeasts underlying order Saccharomycetales (Saccharomyces cerevisiae). Thus, microorganisms member taxonomic class Saccharomycetes Pichiomycetes, function return TRUE. returns FALSE otherwise (NA input NA MO code UNKNOWN). Determination intrinsic resistance (mo_is_intrinsic_resistant()) based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antimicrobials). Determination bacterial oxygen tolerance (mo_oxygen_tolerance()) based BacDive, see Source. function mo_is_anaerobic() returns TRUE oxygen tolerance \"anaerobe\", indicting obligate anaerobic species genus. always returns FALSE species outside taxonomic kingdom Bacteria. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. MycoBank URL used fungi wherever available , LPSN URL bacteria wherever available, GBIF link otherwise. SNOMED codes (mo_snomed()) last updated July 16th, 2024. See Source microorganisms data set info. Old taxonomic names (-called 'synonyms') can retrieved mo_synonyms() (scientific reference name), current taxonomic name can retrieved mo_current(). functions return full names. output translated possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"function uses .mo() internally, uses advanced algorithm translate arbitrary user input valid taxonomy using -called matching score. can read public algorithm MO matching score page.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/ Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree -----------------------------------------------------------  mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Pseudomonadota\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\"   # full names and short names -----------------------------------------------  mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\"   # other properties ---------------------------------------------------------  mo_pathogenicity(\"Klebsiella pneumoniae\") #> [1] Pathogenic #> Levels: Pathogenic < Potentially pathogenic < Non-pathogenic < Unknown mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #>                                Klebsiella pneumoniae  #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\"  mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1]  TRUE FALSE FALSE  mo_group_members(c(   \"Streptococcus group A\",   \"Streptococcus group C\",   \"Streptococcus group G\",   \"Streptococcus group L\" )) #> $`Streptococcus Group A` #> [1] \"Streptococcus pyogenes\" #>  #> $`Streptococcus Group C` #> [1] \"Streptococcus dysgalactiae\"              #> [2] \"Streptococcus dysgalactiae dysgalactiae\" #> [3] \"Streptococcus dysgalactiae equisimilis\"  #> [4] \"Streptococcus equi\"                      #> [5] \"Streptococcus equi equi\"                 #> [6] \"Streptococcus equi ruminatorum\"          #> [7] \"Streptococcus equi zooepidemicus\"        #>  #> $`Streptococcus Group G` #> [1] \"Streptococcus canis\"                     #> [2] \"Streptococcus dysgalactiae\"              #> [3] \"Streptococcus dysgalactiae dysgalactiae\" #> [4] \"Streptococcus dysgalactiae equisimilis\"  #>  #> $`Streptococcus Group L` #> [1] \"Streptococcus dysgalactiae\"              #> [2] \"Streptococcus dysgalactiae dysgalactiae\" #> [3] \"Streptococcus dysgalactiae equisimilis\"  #>    # scientific reference -----------------------------------------------------  mo_ref(\"Klebsiella aerogenes\") #> [1] \"Tindall et al., 2017\" mo_authors(\"Klebsiella aerogenes\") #> [1] \"Tindall et al.\" mo_year(\"Klebsiella aerogenes\") #> [1] 2017 mo_synonyms(\"Klebsiella aerogenes\") #>   Hormaeche et al., 1960     Bascomb et al., 1971  #> \"Enterobacter aerogenes\"     \"Klebsiella mobilis\"  mo_lpsn(\"Klebsiella aerogenes\") #> [1] \"777146\" mo_gbif(\"Klebsiella aerogenes\") #> [1] \"9281703\" mo_mycobank(\"Candida albicans\") #> [1] \"256187\" mo_mycobank(\"Candida krusei\") #> [1] \"337013\" mo_mycobank(\"Candida krusei\", keep_synonyms = TRUE) #> Warning: Function as.mo() returned one old taxonomic name. Use as.mo(..., #> keep_synonyms = FALSE) to clean the input to currently accepted taxonomic #> names, or set the R option AMR_keep_synonyms to FALSE. This warning #> will be shown once per session. #> [1] \"268707\"   # abbreviations known in the field -----------------------------------------  mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\"  mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EIEC\") #> [1] \"coli\" mo_name(\"UPEC\") #> [1] \"Escherichia coli\"   # known subspecies ---------------------------------------------------------  mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\"  # \\donttest{ # Becker classification, see ?as.mo ----------------------------------------  mo_fullname(\"Staph epidermidis\") #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"Staph epidermidis\") #> [1] \"S. epidermidis\" mo_shortname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"CoNS\"   # Lancefield classification, see ?as.mo ------------------------------------  mo_fullname(\"Strep agalactiae\") #> [1] \"Streptococcus agalactiae\" mo_fullname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"Streptococcus Group B\" mo_shortname(\"Strep agalactiae\") #> [1] \"S. agalactiae\" mo_shortname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"GBS\"   # language support  --------------------------------------------------------  mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") # German #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") # Dutch #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") # Spanish #> [1] \"Gram negativo\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"el\") # Greek #> [1] \"Αρνητικό κατά Gram\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"uk\") # Ukrainian #> [1] \"Грамнегативні\"  # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_kingdom(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, no effect #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, translated #> [1] \"细菌\"  mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"uk\") #> [1] \"Streptococcus Група A\"   # other --------------------------------------------------------------------  # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_gram_positive()) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 18 × 2 #>    `mo_genus()`        n #>    <chr>           <int> #>  1 Staphylococcus    840 #>  2 Streptococcus     275 #>  3 Enterococcus       83 #>  4 Corynebacterium    17 #>  5 Micrococcus         6 #>  6 Gemella             3 #>  7 Aerococcus          2 #>  8 Cutibacterium       1 #>  9 Dermabacter         1 #> 10 Fusibacter          1 #> 11 Globicatella        1 #> 12 Granulicatella      1 #> 13 Lactobacillus       1 #> 14 Leuconostoc         1 #> 15 Listeria            1 #> 16 Paenibacillus       1 #> 17 Rothia              1 #> 18 Schaalia            1 if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_intrinsic_resistant(ab = \"vanco\")) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 20 × 2 #>    `mo_genus()`         n #>    <chr>            <int> #>  1 Escherichia        467 #>  2 Klebsiella          77 #>  3 Proteus             39 #>  4 Pseudomonas         30 #>  5 Serratia            25 #>  6 Enterobacter        23 #>  7 Citrobacter         11 #>  8 Haemophilus          8 #>  9 Acinetobacter        6 #> 10 Morganella           6 #> 11 Pantoea              4 #> 12 Salmonella           3 #> 13 Neisseria            2 #> 14 Stenotrophomonas     2 #> 15 Campylobacter        1 #> 16 Enterococcus         1 #> 17 Hafnia               1 #> 18 Lactobacillus        1 #> 19 Leuconostoc          1 #> 20 Pseudescherichia     1  # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>   # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $mo #> [1] \"B_KLBSL_PNMN\" #>  #> $rank #> [1] \"species\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  #> $lpsn #> [1] \"777151\" #>  #> $mycobank #> [1] NA #>  #> $gbif #> [1] \"3221874\" #>  #> $group_members #> character(0) #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(path, destination = getOption(\"AMR_mo_source\",   \"~/mo_source.rds\"))  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file - default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set package option AMR_mo_source, e.g. options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names:   save \"/Users//Documents/ourcodes.xlsx\". Now set source:   now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions:   edit Excel file , say, adding row 4 like :   ...new usage MO function package update data file:   delete reference data file, just use \"\", NULL FALSE input set_mo_source():   original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"|         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 |                    |                       | set_mo_source(\"/Users/me/Documents/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>       \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\"  # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #>       Use mo_uncertainties() to review it. #> Class 'mo' #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI |         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus   | Staphylococcus aureus | 5 |                    |                       | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>        \"Organisation XYZ\" and \"mo\" #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca(x, ..., retx = TRUE, center = TRUE, scale. = TRUE, tol = NULL,   rank. = NULL)"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables     returned. center logical value indicating whether variables     shifted zero centered. Alternately, vector     length equal number columns x can supplied.     value passed scale. scale. logical value indicating whether variables     scaled unit variance analysis takes     place.  default FALSE consistency S,     general scaling advisable.  Alternatively, vector length     equal number columns x can supplied.      value passed scale. tol value indicating magnitude components     omitted. (Components omitted     standard deviations less equal tol times     standard deviation first component.)  default null     setting, components omitted (unless rank. specified     less min(dim(x)).).  settings tol     tol = 0 tol = sqrt(.Machine$double.eps),     omit essentially constant components. rank. optionally, number specifying maximal rank, .e.,     maximal number principal components used.  Can set     alternative addition tol, useful notably     desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    pca_result   summary(pca_result)    # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"` `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting Helpers for AMR Data Analysis — plot","title":"Plotting Helpers for AMR Data Analysis — plot","text":"Functions plot classes sir, mic disk, support base R ggplot2. Especially scale_*_mic() functions relevant wrappers plot MIC values ggplot2. allows custom MIC ranges plot intermediate log2 levels missing MIC values.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting Helpers for AMR Data Analysis — plot","text":"","code":"scale_x_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_y_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_colour_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_fill_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_x_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\",   \"EUCAST\") == \"EUCAST\", ...)  scale_colour_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\",   \"EUCAST\") == \"EUCAST\", ...)  scale_fill_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\",   \"EUCAST\") == \"EUCAST\", ...)  # S3 method for class 'mic' plot(x, mo = NULL, ab = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   main = deparse(substitute(x)), ylab = translate_AMR(\"Frequency\", language   = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language =   language), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), expand = TRUE,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...)  # S3 method for class 'mic' autoplot(object, mo = NULL, ab = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   title = deparse(substitute(object)), ylab = translate_AMR(\"Frequency\",   language = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language =   language), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), expand = TRUE,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...)  # S3 method for class 'disk' plot(x, main = deparse(substitute(x)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Disk diffusion diameter (mm)\", language = language),   mo = NULL, ab = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), expand = TRUE,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...)  # S3 method for class 'disk' autoplot(object, mo = NULL, ab = NULL,   title = deparse(substitute(object)), ylab = translate_AMR(\"Frequency\",   language = language), xlab = translate_AMR(\"Disk diffusion diameter (mm)\",   language = language), guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), expand = TRUE,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...)  # S3 method for class 'sir' plot(x, ylab = translate_AMR(\"Percentage\", language =   language), xlab = translate_AMR(\"Antimicrobial Interpretation\", language =   language), main = deparse(substitute(x)), language = get_AMR_locale(),   ...)  # S3 method for class 'sir' autoplot(object, title = deparse(substitute(object)),   xlab = translate_AMR(\"Antimicrobial Interpretation\", language = language),   ylab = translate_AMR(\"Frequency\", language = language),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), ...)  facet_sir(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL)  scale_y_percent(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),   limits = c(0, NA))  scale_sir_colours(..., aesthetics, colours_SIR = c(\"#3CAEA3\", \"#F6D55C\",   \"#ED553B\"))  theme_sir()  labels_sir_count(position = NULL, x = \"antibiotic\",   translate_ab = \"name\", minimum = 30, language = get_AMR_locale(),   combine_SI = TRUE, datalabels.size = 3, datalabels.colour = \"grey15\")"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting Helpers for AMR Data Analysis — plot","text":"keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range rescale MIC values (using rescale_mic()), e.g., mic_range = c(0.001, 32). Use NA prevent rescaling one side, e.g., mic_range = c(NA, 32). Note: rescales values filter - use ggplot2 limits argument separately exclude values plot. ... arguments passed methods colours_SIR colours use filling bars, must vector three values (order S, R). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant' - default system language (see get_AMR_locale()) can overwritten setting package option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL prevent translation. eucast_I logical indicate whether '' must interpreted \"Susceptible, increased exposure\". TRUE default AMR interpretation guideline set EUCAST (default). FALSE, interpreted \"Intermediate\". x, object values created .mic(), .disk() .sir() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline interpretation guideline use - default latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. host set values veterinary species, automatically set \"animal\". facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable nrow (using facet) number rows breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum aesthetics aesthetics apply colours - default \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\" position position adjustment bars, either \"fill\", \"stack\" \"dodge\" translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE datalabels.size size datalabels datalabels.colour colour datalabels","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting Helpers for AMR Data Analysis — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"the-scale-mic-functions","dir":"Reference","previous_headings":"","what":"The scale_*_mic() Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"functions scale_x_mic(), scale_y_mic(), scale_colour_mic(), scale_fill_mic() functions allow plot mic class (MIC values) continuous, logarithmic scale. also allow rescale MIC range 'inside' 'outside' range required, retain signs MIC values desired. Missing intermediate log2 levels plotted .","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"the-scale-sir-functions","dir":"Reference","previous_headings":"","what":"The scale_*_sir() Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"functions scale_x_sir(), scale_colour_sir(), scale_fill_sir() functions allow plot sir class right order (S < SDD < < R < NI). default, translate S//R values interpretative text (\"Susceptible\", \"Resistant\", etc.) 20 supported languages (use language = NULL keep S//R). Also, except scale_x_sir(), set colour-blind friendly colours colour fill aesthetics.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"additional-ggplot-functions","dir":"Reference","previous_headings":"","what":"Additional ggplot2 Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"package contains functions extend ggplot2 package, help visualising AMR data results. functions internally used ggplot_sir() . facet_sir() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_sir_colours() allows set colours aesthetic, even shape linetype. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage number isolates, using ggplot2::geom_text(). interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, default guideline EUCAST 2024, unless package option AMR_guideline set. See .sir() information.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting Helpers for AMR Data Analysis — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))  # \\donttest{ # Plotting using ggplot2's autoplot() for MIC, disk, and SIR ----------- if (require(\"ggplot2\")) {   autoplot(some_mic_values) }  if (require(\"ggplot2\")) {   # when providing the microorganism and antibiotic, colours will show interpretations:   autoplot(some_mic_values, mo = \"Escherichia coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   # support for 20 languages, various guidelines, and many options   autoplot(some_disk_values,     mo = \"Escherichia coli\", ab = \"cipro\",     guideline = \"CLSI 2024\", language = \"no\",     title = \"Disk diffusion from the North\"   ) }    # Plotting using scale_x_mic() ----------------------------------------- if (require(\"ggplot2\")) {   mic_plot <- ggplot(     data.frame(       mics = as.mic(c(0.25, \"<=4\", 4, 8, 32, \">=32\")),       counts = c(1, 1, 2, 2, 3, 3)     ),     aes(mics, counts)   ) +     geom_col()   mic_plot +     labs(title = \"without scale_x_mic()\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic() +     labs(title = \"with scale_x_mic()\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(keep_operators = \"all\") +     labs(title = \"with scale_x_mic() keeping all operators\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(mic_range = c(1, 16)) +     labs(title = \"with scale_x_mic() using a manual 'within' range\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(mic_range = c(0.032, 256)) +     labs(title = \"with scale_x_mic() using a manual 'outside' range\") }    # Plotting using scale_y_mic() ----------------------------------------- some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)  if (require(\"ggplot2\")) {   ggplot(     data.frame(       mic = some_mic_values,       group = some_groups     ),     aes(group, mic)   ) +     geom_boxplot() +     geom_violin(linetype = 2, colour = \"grey\", fill = NA) +     scale_y_mic() }  if (require(\"ggplot2\")) {   ggplot(     data.frame(       mic = some_mic_values,       group = some_groups     ),     aes(group, mic)   ) +     geom_boxplot() +     geom_violin(linetype = 2, colour = \"grey\", fill = NA) +     scale_y_mic(mic_range = c(NA, 0.25)) }    # Plotting using scale_x_sir() ----------------------------------------- if (require(\"ggplot2\")) {   ggplot(     data.frame(       x = c(\"I\", \"R\", \"S\"),       y = c(45, 323, 573)     ),     aes(x, y)   ) +     geom_col() +     scale_x_sir() }    # Plotting using scale_y_mic() and scale_colour_sir() ------------------ if (require(\"ggplot2\")) {   plain <- ggplot(     data.frame(       mic = some_mic_values,       group = some_groups,       sir = as.sir(some_mic_values,         mo = \"E. coli\",         ab = \"cipro\"       )     ),     aes(x = group, y = mic, colour = sir)   ) +     theme_minimal() +     geom_boxplot(fill = NA, colour = \"grey\") +     geom_jitter(width = 0.25)    plain } #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'.  if (require(\"ggplot2\")) {   # and now with our MIC and SIR scale functions:   plain +     scale_y_mic() +     scale_colour_sir() }  if (require(\"ggplot2\")) {   plain +     scale_y_mic(mic_range = c(0.005, 32), name = \"Our MICs!\") +     scale_colour_sir(       language = \"pt\",       name = \"Support in 20 languages\"     ) }  # }  # Plotting using base R's plot() ---------------------------------------  plot(some_mic_values)  # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\")   plot(some_disk_values)  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"nl\")   plot(some_sir_values)"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Antimicrobial Resistance — proportion","title":"Calculate Antimicrobial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Antimicrobial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  susceptibility(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  sir_confidence_interval(..., ab_result = \"R\", minimum = 30,   as_percent = FALSE, only_all_tested = FALSE, confidence_level = 0.95,   side = \"both\", collapse = FALSE)  proportion_R(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_IR(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_I(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_SI(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_S(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_df(data, translate_ab = \"name\", language = get_AMR_locale(),   minimum = 30, as_percent = FALSE, combine_SI = TRUE,   confidence_level = 0.95)  sir_df(data, translate_ab = \"name\", language = get_AMR_locale(),   minimum = 30, as_percent = FALSE, combine_SI = TRUE,   confidence_level = 0.95)"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate Antimicrobial Resistance — proportion","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Antimicrobial Resistance — proportion","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. Use multiple columns calculate (lack ) co-resistance: probability one two drugs resistant susceptible result. See Examples. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. as_percent logical indicate whether output must returned hundred fold % sign (character). value 0.123456 returned \"12.3%\". only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antimicrobials, see section Combination Therapy ab_result antibiotic results test , must one values \"S\", \"SDD\", \"\", \"R\" confidence_level confidence level returned confidence interval. calculation, number S SI isolates, R isolates compared total number available isolates R, S, using binom.test(), .e., Clopper-Pearson method. side side confidence interval return. default \"\" length 2 vector, can also (abbreviated ) \"min\"/\"left\"/\"lower\"/\"less\" \"max\"/\"right\"/\"higher\"/\"greater\". collapse logical indicate whether output values 'collapsed', .e. merged together one value, character value use collapsing data data.frame containing columns class sir (see .sir()) translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Antimicrobial Resistance — proportion","text":"double , as_percent = TRUE, character.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Antimicrobial Resistance — proportion","text":"automated comprehensive analysis, consider using antibiogram() wisca(), streamline many aspects susceptibility reporting , importantly, also support WISCA. functions described offer hands-, manual approach greater customisation. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Since AMR v3.0, proportion_SI() proportion_I() include dose-dependent susceptibility ('SDD'). Use sir_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. functions meant count isolates, calculate proportion resistance/susceptibility. Use count_*() functions count isolates. function susceptibility() essentially equal count_susceptible()/count_all(). Low counts can influence outcome - proportion_*() functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data sir class (created .sir()) calculates proportions S, , R. also supports grouped variables. function sir_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Antimicrobial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antimicrobials, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   considered   considered  considered   considered                     susceptible    tested    susceptible    tested --------  --------  -----------  ----------  -----------  ----------  S or I    S or I        X            X           X            X    R       S or I        X            X           X            X   <NA>     S or I        X            X           -            -  S or I      R           X            X           X            X    R         R           -            X           -            X   <NA>       R           -            -           -            -  S or I     <NA>         X            X           -            -    R        <NA>         -            -           -            -   <NA>      <NA>         -            -           -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Calculate Antimicrobial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Antimicrobial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …   # base R ------------------------------------------------------------ # determines %R resistance(example_isolates$AMX) #> [1] 0.5955556 sir_confidence_interval(example_isolates$AMX) #> [1] 0.5688204 0.6218738 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975,   collapse = \", \" ) #> [1] \"0.565, 0.626\"  # determines %S+I: susceptibility(example_isolates$AMX) #> [1] 0.4044444 sir_confidence_interval(example_isolates$AMX,   ab_result = c(\"S\", \"I\") ) #> [1] 0.3781262 0.4311796  # be more specific proportion_S(example_isolates$AMX) #> [1] 0.4022222 proportion_SI(example_isolates$AMX) #> Note that proportion_SI() will also include dose-dependent #> susceptibility, 'SDD'. This note will be shown once for this session. #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> Note that proportion_I() will also include dose-dependent susceptibility, #> 'SDD'. This note will be shown once for this session. #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> Note that proportion_IR() will also include dose-dependent #> susceptibility, 'SDD'. This note will be shown once for this session. #> [1] 0.5977778 proportion_R(example_isolates$AMX) #> [1] 0.5955556  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       r = resistance(CIP),       n = n_sir(CIP)     ) # n_sir works like n_distinct in dplyr, see ?n_sir } #> # A tibble: 3 × 3 #>   ward           r     n #>   <chr>      <dbl> <int> #> 1 Clinical   0.147   869 #> 2 ICU        0.190   447 #> 3 Outpatient 0.161    93 if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       cipro_R = resistance(CIP),       ci_min = sir_confidence_interval(CIP, side = \"min\"),       ci_max = sir_confidence_interval(CIP, side = \"max\"),     ) } #> # A tibble: 3 × 4 #>   ward       cipro_R ci_min ci_max #>   <chr>        <dbl>  <dbl>  <dbl> #> 1 Clinical     0.147 0.124   0.173 #> 2 ICU          0.190 0.155   0.230 #> 3 Outpatient   0.161 0.0932  0.252 if (require(\"dplyr\")) {   # scoped dplyr verbs with antimicrobial selectors   # (you could also use across() of course)   example_isolates %>%     group_by(ward) %>%     summarise_at(       c(aminoglycosides(), carbapenems()),       resistance     ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `KAN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 3: `ward = \"Outpatient\"`. #> Caused by warning: #> ! Introducing NA: only 23 results available for KAN in group: ward = #> \"Outpatient\" (minimum = 30). #> # A tibble: 3 × 7 #>   ward         GEN   TOB   AMK   KAN    IPM    MEM #>   <chr>      <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> #> 1 Clinical   0.229 0.315 0.626     1 0.0498 0.0458 #> 2 ICU        0.290 0.400 0.662     1 0.0862 0.0894 #> 3 Outpatient 0.2   0.368 0.605    NA 0.0541 0.0541 if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = resistance(CIP, as_percent = TRUE),       SI = susceptibility(CIP, as_percent = TRUE),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Calculate co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy:   example_isolates %>% susceptibility(AMC) # %SI = 76.3%   example_isolates %>% count_all(AMC) #   n = 1879    example_isolates %>% susceptibility(GEN) # %SI = 75.4%   example_isolates %>% count_all(GEN) #   n = 1855    example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%   example_isolates %>% count_all(AMC, GEN) #   n = 1939     # See Details on how `only_all_tested` works. Example:   example_isolates %>%     summarise(       numerator = count_susceptible(AMC, GEN),       denominator = count_all(AMC, GEN),       proportion = susceptibility(AMC, GEN)     )    example_isolates %>%     summarise(       numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),       denominator = count_all(AMC, GEN, only_all_tested = TRUE),       proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)     )     example_isolates %>%     group_by(ward) %>%     summarise(       cipro_p = susceptibility(CIP, as_percent = TRUE),       cipro_n = count_all(CIP),       genta_p = susceptibility(GEN, as_percent = TRUE),       genta_n = count_all(GEN),       combination_p = susceptibility(CIP, GEN, as_percent = TRUE),       combination_n = count_all(CIP, GEN)     )    # Get proportions S/I/R immediately of all sir columns   example_isolates %>%     select(AMX, CIP) %>%     proportion_df(translate = FALSE)    # It also supports grouping variables   # (use sir_df to also include the count)   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     sir_df(translate = FALSE) } #> # A tibble: 12 × 7 #>    ward       antibiotic interpretation value ci_min ci_max isolates #>    <chr>      <chr>      <ord>          <dbl>  <dbl>  <dbl>    <int> #>  1 Clinical   AMX        SI             0.423 0.389   0.457      357 #>  2 Clinical   AMX        R              0.577 0.543   0.611      487 #>  3 Clinical   CIP        SI             0.853 0.827   0.876      741 #>  4 Clinical   CIP        R              0.147 0.124   0.173      128 #>  5 ICU        AMX        SI             0.369 0.323   0.417      158 #>  6 ICU        AMX        R              0.631 0.583   0.677      270 #>  7 ICU        CIP        SI             0.810 0.770   0.845      362 #>  8 ICU        CIP        R              0.190 0.155   0.230       85 #>  9 Outpatient AMX        SI             0.397 0.288   0.515       31 #> 10 Outpatient AMX        R              0.603 0.485   0.712       47 #> 11 Outpatient CIP        SI             0.839 0.748   0.907       78 #> 12 Outpatient CIP        R              0.161 0.0932  0.252       15 # }"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":null,"dir":"Reference","previous_headings":"","what":"Random MIC Values/Disk Zones/SIR Generation — random","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"functions can used generating random MIC values disk diffusion diameters, AMR data analysis practice. providing microorganism antimicrobial drug, generated results reflect reality much possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(size = NULL, mo = NULL, ab = NULL, ...)  random_disk(size = NULL, mo = NULL, ab = NULL, ...)  random_sir(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...)"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"size desired size returned vector. used data.frame call dplyr verb, get current (group) size left blank. mo character can coerced valid microorganism code .mo() ab character can coerced valid antimicrobial drug code .ab() ... ignored, place allow future extensions prob_SIR vector length 3: probabilities \"S\" (1st value), \"\" (2nd value) \"R\" (3rd value)","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"class mic random_mic() (see .mic()) class disk random_disk() (see .disk())","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"base R function sample() used generating values. Generated values based EUCAST 2024 guideline implemented clinical_breakpoints data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(25) #> Class 'mic' #>  [1] 0.005  0.125  1      0.002  1      0.125  32     0.01   0.5    16     #> [11] 2      0.001  0.005  0.0625 0.001  0.001  2      256    128    0.005  #> [21] 0.002  1      0.002  32     16     random_disk(25) #> Class 'disk' #>  [1]  8 40 42 43 46 45 40 48 50 23  9 25 36 16  6 43 36 41 50 40 47 25 34 37  9 random_sir(25) #> Class 'sir' #>  [1] I S R R I I I I R R I I I S R S R S S S R R I S R  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] 1     16    2     0.025 8     32    0.001 4     2     128   0.125 4     #> [13] 0.01  >=256 0.25  0.25  1     0.002 0.25  2     0.005 0.001 64    0.001 #> [25] 0.025 random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] <=1  8    4    <=1  >=16 >=16 >=16 2    >=16 2    >=16 4    >=16 >=16 2    #> [16] 2    >=16 <=1  4    8    8    >=16 4    8    4    random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 2      0.125  0.125  0.25   1      1      0.0625 0.5    1      0.5    #> [11] 4      0.125  0.25   0.25   0.0625 4      0.25   0.125  4      1      #> [21] 4      0.5    0.0625 4      0.125   random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 47 49 10 40 23 16 20 34 45  8 42 10 20 11 30 36 40 35 14 24  8 38 10 10 15 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 14 12 14 11 13 15 13 11 17 15 14 13 11 15 17 14 16 17 17 17 11 12 12 12 14 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 27 15 17 23 23 18 18 17 15 18 27 15 25 16 21 15 24 23 22 22 15 23 25 23 24 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict(x, col_ab, col_date = NULL, year_min = NULL,   year_max = NULL, year_every = 1, minimum = 30, model = NULL,   I_as_S = TRUE, preserve_measurements = TRUE, info = interactive(), ...)  sir_predict(x, col_ab, col_date = NULL, year_min = NULL, year_max = NULL,   year_every = 1, minimum = 30, model = NULL, I_as_S = TRUE,   preserve_measurements = TRUE, info = interactive(), ...)  # S3 method for class 'resistance_predict' plot(x, main = paste(\"Resistance Prediction of\",   x_name), ...)  ggplot_sir_predict(x, main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE, ...)  # S3 method for class 'resistance_predict' autoplot(object,   main = paste(\"Resistance Prediction of\", x_name), ribbon = TRUE, ...)"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already - default first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model - default 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates,   col_ab = \"AMX\",   year_min = 2010,   model = \"binomial\" ) plot(x)  # \\donttest{ if (require(\"ggplot2\")) {   ggplot_sir_predict(x) }   # using dplyr: if (require(\"dplyr\")) {   x <- example_isolates %>%     filter_first_isolate() %>%     filter(mo_genus(mo) == \"Staphylococcus\") %>%     resistance_predict(\"PEN\", model = \"binomial\")   print(plot(x))    # get the model from the object   mymodel <- attributes(x)$model   summary(mymodel) }  #> NULL #>  #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 35.76101   72.29172   0.495    0.621 #> year        -0.01720    0.03603  -0.477    0.633 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.3681  on 11  degrees of freedom #> Residual deviance: 5.1408  on 10  degrees of freedom #> AIC: 50.271 #>  #> Number of Fisher Scoring iterations: 4 #>   # create nice plots with ggplot2 yourself if (require(\"dplyr\") && require(\"ggplot2\")) {   data <- example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     resistance_predict(       col_ab = \"AMX\",       col_date = \"date\",       model = \"binomial\",       info = FALSE,       minimum = 15     )   head(data)   autoplot(data) }  # }"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE)  # Default S3 method skewness(x, na.rm = FALSE)  # S3 method for class 'matrix' skewness(x, na.rm = FALSE)  # S3 method for class 'data.frame' skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] -0.0199978"},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Top n Microorganisms — top_n_microorganisms","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"function filters data set include top n microorganisms based specified property, taxonomic family genus. example, can filter data set top 3 species, species top 5 genera, top 3 species top 5 genera.","code":""},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"","code":"top_n_microorganisms(x, n, property = \"fullname\", n_for_each = NULL,   col_mo = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"x data frame containing microbial data n integer specifying maximum number unique values property include output property character string indicating microorganism property use filtering. Must one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". NULL, raw values col_mo used without transformation. n_for_each optional integer specifying maximum number rows retain value selected property. NULL, rows within top n groups included. col_mo character string indicating column x contains microorganism names codes. Defaults first column class mo. Values coerced using .mo(). ... Additional arguments passed mo_property() property NULL.","code":""},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"function useful preprocessing data creating antibiograms analyses require focused subsets microbial data. example, can filter data set include isolates top 10 species.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"","code":"# filter to the top 3 species: top_n_microorganisms(example_isolates,   n = 3 ) #> # A tibble: 1,015 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  4 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  5 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  7 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA  #>  8 2002-02-14 067927     45 F      ICU      B_STPHY_CONS   R     NA    R     NA  #>  9 2002-02-14 067927     45 F      ICU      B_STPHY_CONS   S     NA    S     NA  #> 10 2002-02-21 A56499     64 M      Clinical B_STPHY_CONS   S     NA    S     NA  #> # ℹ 1,005 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter to any species in the top 5 genera: top_n_microorganisms(example_isolates,   n = 5, property = \"genus\" ) #> # A tibble: 1,742 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,732 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter to the top 3 species in each of the top 5 genera: top_n_microorganisms(example_isolates,   n = 5, property = \"genus\", n_for_each = 3 ) #> # A tibble: 1,497 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  2 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS   NA    NA    NA    NA  #>  3 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS   NA    NA    NA    NA  #>  4 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS   NA    NA    NA    NA  #>  5 2003-04-20 6BC362     62 M      ICU      B_ENTRC        NA    NA    NA    NA  #>  6 2003-04-21 6BC362     62 M      ICU      B_ENTRC        NA    NA    NA    NA  #>  7 2003-08-13 F35553     52 M      ICU      B_ENTRC_FCLS   NA    NA    NA    NA  #>  8 2003-09-05 F35553     52 M      ICU      B_ENTRC        NA    NA    NA    NA  #>  9 2003-09-05 F35553     52 M      ICU      B_ENTRC_FCLS   NA    NA    NA    NA  #> 10 2003-09-28 1B0933     80 M      Clinical B_ENTRC        NA    NA    NA    NA  #> # ℹ 1,487 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale()  set_AMR_locale(language)  reset_AMR_locale()  translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 20 supported languages English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set package option AMR_locale .Rprofile file like :   save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\")  # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting package option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, package option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\"  # setting another language set_AMR_locale(\"Dutch\") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacine\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negatieve Staphylococcus (CNS)\"  # setting yet another language set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Koagulase-negative Staphylococcus (KNS)\"  # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> Warning: no non-missing arguments to min; returning Inf #> Warning: no non-missing arguments to min; returning Inf #> [1] \"Amoxicillin/Clavulansäure\"  # reset to system default reset_AMR_locale() #> ℹ Using the English language (English) for the AMR package for this #>   session. ab_name(\"amox/clav\") #> [1] \"Amoxicillin/clavulanic acid\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-2119187","dir":"Changelog","previous_headings":"","what":"AMR 2.1.1.9187","title":"AMR 2.1.1.9187","text":"(beta version eventually become v3.0. ’re happy reach new major milestone soon, new One Health support! Install beta using instructions .)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9187","dir":"Changelog","previous_headings":"","what":"A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)","title":"AMR 2.1.1.9187","text":"package now supports tools AMR data analysis clinical settings, also veterinary environmental microbiology. made possible collaboration University Prince Edward Island’s Atlantic Veterinary College, Canada. celebrate great improvement package, also updated package logo reflect change.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-2-1-1-9187","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 2.1.1.9187","text":"Dataset antibiotics renamed antimicrobials data set contains just antibiotics. Calling antibiotics still work, now returns warning. Removed functions references used deprecated rsi class, replaced sir equivalents two years ago.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-2-1-1-9187","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 2.1.1.9187","text":"Function .sir() now extensive support veterinary breakpoints CLSI. Use breakpoint_type = \"animal\" set host argument variable contains animal species names. CLSI VET09 guideline implemented address cases veterinary breakpoints missing (applies guideline set CLSI) clinical_breakpoints data set contains breakpoints, can downloaded download page. (new) antimicrobials data set contains veterinary antibiotics, pradofloxacin enrofloxacin. WHOCC codes veterinary use added well. ab_atc() now supports ATC codes veterinary antibiotics (start “Q”) ab_url() now supports retrieving WHOCC url ATCvet pages antibiogram() function now supports creating true Weighted-Incidence Syndromic Combination Antibiograms (WISCA), powerful Bayesian method estimating regimen coverage probabilities using pathogen incidence antimicrobial susceptibility data. WISCA offers improved precision syndrome-specific treatment, even datasets sparse data. dedicated wisca() function also available easy usage. MycoBank now integrated primary taxonomic source fungi. microorganisms data set enriched new columns (mycobank, mycobank_parent, mycobank_renamed_to) provide detailed information fungal species. remarkable addition 20,000 new fungal records New function mo_mycobank() retrieve MycoBank record number, analogous existing functions mo_lpsn() mo_gbif(). .mo() function mo_*() functions now include only_fungi argument, allowing users restrict results solely fungal species. ensures fungi prioritised bacteria microorganism identification. can also set globally new AMR_only_fungi option. Also updated kingdoms, welcoming total 2,149 new records 2023 927 2024. EUCAST 2024 CLSI 2024 now supported, adding 4,000 new clinical breakpoints clinical_breakpoints data set usage .sir(). EUCAST 2024 now new default guideline MIC disk diffusion interpretations. .sir() now brings additional factor levels: “NI” non-interpretable “SDD” susceptible dose-dependent. Currently, clinical_breakpoints data set contains 24 breakpoints can return value “SDD” instead “”. New function group scale_*_mic(), namely: scale_x_mic(), scale_y_mic(), scale_colour_mic() scale_fill_mic(). allow easy plotting MIC values. allow manual range definition plotting missing intermediate log2 levels. New function group scale_*_sir(), namely: scale_x_sir(), scale_colour_sir() scale_fill_sir(). allow plot sir class, translates system language default. also set colourblind-safe colours plots. New function rescale_mic(), allows users rescale MIC values manually set range. powerhouse behind scale_*_mic() functions, can used independently , instance, compare equality MIC distributions rescaling range first. using R heavy lifting, ‘AMR’ Python Package developed run AMR R package natively Python. Python package always version number R package, built automatically every code change. antimicrobial selectors (aminoglycosides() betalactams()) now supported tidymodels packages recipe parsnip. See info tutorial using AMR functions predictive modelling. New function top_n_microorganisms() filter data set top n taxonomic property, e.g., filter top 3 species, filter species top 5 genera, filter top 3 species top 5 genera New function mo_group_members() retrieve member microorganisms microorganism group. example, mo_group_members(\"Strep group C\") returns vector microorganisms belong group. New functions mic_p50() mic_p90() retrieve 50th 90th percentile MIC values.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-2-1-1-9187","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 2.1.1.9187","text":"now possible use column names argument ab, mo, uti: .sir(..., ab = \"column1\", mo = \"column2\", uti = \"column3\"). greatly improves flexibility users. Users can now set criteria (using regular expressions) considered S, , R, SDD, NI. get quantitative values, .double() sir object return 1 S, 2 SDD/, 3 R (NI become NA). functions using sir classes (e.g., summary()) updated reflect change contain NI SDD. Fix conserve_capped_values, now works expected: MIC values, <x always S, >x always R New argument formatting_type set 22 options formatting ‘cells’. defaults 10 non-WISCA 14 WISCA, changing output antibiograms cells info. reason, add_total_n now FALSE default since denominators added cells ab_transform argument now defaults \"name\", displaying antibiotic column names instead codes ‘Antibiotic selectors’ now called ‘antimicrobial selectors’ since scope broader just antibiotics. documentation updated, ab_class() ab_selector() replaced amr_class() amr_selector(). old functions now deprecated removed future version. Added selectors isoxazolylpenicillins(), monobactams(), nitrofurans(), phenicols(), rifamycins() using antimicrobial selectors exclude non-treatable drugs (gentamicin-high using aminoglycosides()), function now always returns warning can included using only_treatable = FALSE Added new argument return_all selectors, defaults TRUE include match. FALSE, old behaviour, first hit unique antimicrobial returned. selectors can now run separate command retrieve vector possible antimicrobials selector can select selectors lincosamides() macrolides() overlap anymore - antibiotic now classified either Added “clindamycin inducible screening” CLI1. Since clindamycin lincosamide, antimicrobial selector lincosamides() now contains argument only_treatable = TRUE (similar antibiotic selectors contain non-treatable drugs) Added Amorolfine (AMO, D01AE16), now also part antifungals() selector Added Efflux (EFF), allow mapping AMRFinderPlus Added Tigemonam (TNM), monobactam Added 1,500 trade names Added valid levels: 4096, 6 powers 0.0625, 5 powers 192 (192, 384, 576, 768, 960) Fixed bug .mic() failed translation scientifically formatted numbers Added new argument keep_operators .mic(). can \"\" (default), \"none\", \"edges\". argument also available new rescale_mic() scale_*_mic() functions. Comparisons MIC values now strict. example, >32 higher (never equal ) 32. Thus, .mic(\">32\") == .mic(32) now returns FALSE, .mic(\">32\") > .mic(32) now returns TRUE. Sorting MIC values (using sort()) fixed manner; <0.001 now gets sorted 0.001, >0.001 gets sorted 0.001. Intermediate log2 levels used MIC plotting now common values instead following strict dilution range eucast_rules() now argument overwrite (default: TRUE) indicate whether non-NA values overwritten Disks 0 5 mm now allowed, newly allowed range disk diffusion (.disk()) now 0 50 mm Updated italicise_taxonomy() support HTML output custom_eucast_rules() now supports multiple antibiotics antibiotic groups affected single rule mo_info() now contains extra element rank group_members (contents new mo_group_members() function) Updated ATC codes WHOCC Updated antibiotic DDDs WHOCC Fix using manual value mo_transform antibiogram() Fixed bug antibiogram() returns empty data set Fix mapping ‘high level’ antibiotics .ab() (amphotericin B-high, gentamicin-high, kanamycin-high, streptomycin-high, tobramycin-high) Improved overall algorithm .ab() better performance accuracy, including new function as_reset_session() remove earlier coercions. weight given genus species combinations cases subspecies miswritten, result correct genus species Genera World Health Organization’s () Priority Pathogen List now highest prevalence Fixed bug sir_confidence_interval() isolates available Updated prevalence calculation include genera World Health Organization’s () Priority Pathogen List Improved algorithm first_isolate() using phenotype-based method, prioritise records highest availability SIR values scale_y_percent() can now cope ranges outside 0-100% range Implemented new Dutch national MDRO guideline (SRI-richtlijn BRMO, Nov 2024) Added arguments esbl, carbapenemase, mecA, mecC, vanA, vanB denote column names logical values indicating presence genes (production proteins) Added console colours support sir class Positron","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-2-1-1-9187","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 2.1.1.9187","text":"Added Dr. Larisse Bolton contributor fantastic implementation WISCA mathematically solid way Added Matthew Saab, Dr. Jordan Stull, Prof. Javier Sanchez contributors tremendous input veterinary breakpoints interpretations Greatly improved vctrs integration, Tidyverse package working background many Tidyverse functions. users, means functions dplyr’s bind_rows(), rowwise() c_across() now supported e.g. columns class mic. Despite , AMR package still zero-dependent package, including dplyr vctrs. Greatly updated expanded documentation Stopped support SAS (.xpt) files, since file structure extremely inefficient requires disk space GitHub allows single commit.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"older-versions-2-1-1-9187","dir":"Changelog","previous_headings":"","what":"Older Versions","title":"AMR 2.1.1.9187","text":"changelog contains changes AMR v3.0 (February 2025) later. prior v2 versions, please see v2 archive. prior v1 versions, please see v1 archive.","code":""}]
+[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # ℹ 2,990 more rows  # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"How to conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\"  mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\"  mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\"  mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Microorganism translation was uncertain for four microorganisms. Run #>   mo_uncertainties() to review these uncertainties, or use #>   add_custom_microorganisms() to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #> Colour keys:  0.000-0.549  0.550-0.649  0.650-0.749  0.750-1.000  #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #>               (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #>               (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #>               Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #>               (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #>               dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella #>               pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis #>               (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500), #>               Kingella potus (0.400), Kluyveromyces pseudotropicale (0.386), #>               Kluyveromyces pseudotropicalis (0.363), Kosakonia pseudosacchari #>               (0.361), and Kluyveromyces pseudotropicalis pseudotropicalis (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #>               argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #>               Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #>               Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #>               Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #>               Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #>               phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #>               Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #>               (0.533), Staphylococcus pseudintermedius (0.532), Serratia #>               proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #>               pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #>               periodonticum (0.519) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"How to conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>%   mutate_at(vars(AMX:GEN), as.sir)  # method 2, let the AMR package determine the eligible columns our_data <- our_data %>%   mutate_if(is_sir_eligible, as.sir)  # result: our_data #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> #>  1 J3         A        2012-11-21 B_ESCHR_COLI   R     I     S     S   #>  2 R7         A        2018-04-03 B_KLBSL_PNMN   R     I     S     S   #>  3 P3         A        2014-09-19 B_ESCHR_COLI   R     S     S     S   #>  4 P10        A        2015-12-10 B_ESCHR_COLI   S     I     S     S   #>  5 B7         A        2015-03-02 B_ESCHR_COLI   S     S     S     S   #>  6 W3         A        2018-03-31 B_STPHY_AURS   R     S     R     S   #>  7 J8         A        2016-06-14 B_ESCHR_COLI   R     S     S     S   #>  8 M3         A        2015-10-25 B_ESCHR_COLI   R     S     S     S   #>  9 J3         A        2019-06-19 B_ESCHR_COLI   S     S     S     S   #> 10 G6         A        2015-04-27 B_STPHY_AURS   S     S     S     S   #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"How to conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 724 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>%   mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for col_mo. #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient_id' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a #>    microbial ID was available) our_data_1st <- our_data %>%   filter(first == TRUE) our_data_1st <- our_data %>%   filter_first_isolate() our_data_1st #> # A tibble: 2,724 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J3         A        2012-11-21 B_ESCHR_COLI   R     I     S     S   TRUE  #>  2 R7         A        2018-04-03 B_KLBSL_PNMN   R     I     S     S   TRUE  #>  3 P3         A        2014-09-19 B_ESCHR_COLI   R     S     S     S   TRUE  #>  4 P10        A        2015-12-10 B_ESCHR_COLI   S     I     S     S   TRUE  #>  5 B7         A        2015-03-02 B_ESCHR_COLI   S     S     S     S   TRUE  #>  6 W3         A        2018-03-31 B_STPHY_AURS   R     S     R     S   TRUE  #>  7 M3         A        2015-10-25 B_ESCHR_COLI   R     S     S     S   TRUE  #>  8 J3         A        2019-06-19 B_ESCHR_COLI   S     S     S     S   TRUE  #>  9 G6         A        2015-04-27 B_STPHY_AURS   S     S     S     S   TRUE  #> 10 P4         A        2011-06-21 B_ESCHR_COLI   S     S     S     S   TRUE  #> # ℹ 2,714 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #>   patient_id          hospital              date            #>  Length:2724        Length:2724        Min.   :2011-01-01   #>  Class :character   Class :character   1st Qu.:2013-04-07   #>  Mode  :character   Mode  :character   Median :2015-06-03   #>                                        Mean   :2015-06-09   #>                                        3rd Qu.:2017-08-11   #>                                        Max.   :2019-12-27   #>    bacteria               AMX                    AMC                 #>  Class :mo             Class:sir              Class:sir              #>  <NA>  :0              %S   :41.6% (n=1133)   %S   :52.6% (n=1432)   #>  Unique:4              %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)      #>  #1    :B_ESCHR_COLI   %I   :16.4% (n=446)    %I   :12.2% (n=333)    #>  #2    :B_STPHY_AURS   %R   :42.0% (n=1145)   %R   :35.2% (n=959)    #>  #3    :B_STRPT_PNMN   %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)      #>     CIP                    GEN                  first         #>  Class:sir              Class:sir              Mode:logical   #>  %S   :52.5% (n=1431)   %S   :61.0% (n=1661)   TRUE:2724      #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)                     #>  %I   : 6.5% (n=176)    %I   : 3.0% (n=82)                    #>  %R   :41.0% (n=1117)   %R   :36.0% (n=981)                   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)  glimpse(our_data_1st) #> Rows: 2,724 #> Columns: 9 #> $ patient_id <chr> \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital   <chr> \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date       <date> 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria   <mo> \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX        <sir> R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC        <sir> I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP        <sir> S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN        <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…  # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id   hospital       date   bacteria        AMX        AMC        CIP  #>        260          3       1854          4          3          3          3  #>        GEN      first  #>          3          1"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>%   count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1518 #> 2 Staphylococcus aureus      730 #> 3 Streptococcus pneumoniae   426 #> 4 Klebsiella pneumoniae      326  our_data_1st %>%   count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #>   `mo_name(bacteria)`          n #>   <chr>                    <int> #> 1 Escherichia coli          1321 #> 2 Staphylococcus aureus      682 #> 3 Streptococcus pneumoniae   402 #> 4 Klebsiella pneumoniae      319"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"How to conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>%   select(date, aminoglycosides()) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 2,724 × 2 #>    date       GEN   #>    <date>     <sir> #>  1 2012-11-21   S   #>  2 2018-04-03   S   #>  3 2014-09-19   S   #>  4 2015-12-10   S   #>  5 2015-03-02   S   #>  6 2018-03-31   S   #>  7 2015-10-25   S   #>  8 2019-06-19   S   #>  9 2015-04-27   S   #> 10 2011-06-21   S   #> # ℹ 2,714 more rows  our_data_1st %>%   select(bacteria, betalactams()) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 2,724 × 3 #>    bacteria     AMX   AMC   #>    <mo>         <sir> <sir> #>  1 B_ESCHR_COLI   R     I   #>  2 B_KLBSL_PNMN   R     I   #>  3 B_ESCHR_COLI   R     S   #>  4 B_ESCHR_COLI   S     I   #>  5 B_ESCHR_COLI   S     S   #>  6 B_STPHY_AURS   R     S   #>  7 B_ESCHR_COLI   R     S   #>  8 B_ESCHR_COLI   S     S   #>  9 B_STPHY_AURS   S     S   #> 10 B_ESCHR_COLI   S     S   #> # ℹ 2,714 more rows  our_data_1st %>%   select(bacteria, where(is.sir)) #> # A tibble: 2,724 × 5 #>    bacteria     AMX   AMC   CIP   GEN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI   R     I     S     S   #>  2 B_KLBSL_PNMN   R     I     S     S   #>  3 B_ESCHR_COLI   R     S     S     S   #>  4 B_ESCHR_COLI   S     I     S     S   #>  5 B_ESCHR_COLI   S     S     S     S   #>  6 B_STPHY_AURS   R     S     R     S   #>  7 B_ESCHR_COLI   R     S     S     S   #>  8 B_ESCHR_COLI   S     S     S     S   #>  9 B_STPHY_AURS   S     S     S     S   #> 10 B_ESCHR_COLI   S     S     S     S   #> # ℹ 2,714 more rows  # filtering using AB selectors is also possible: our_data_1st %>%   filter(any(aminoglycosides() == \"R\")) #> ℹ For aminoglycosides() using column 'GEN' (gentamicin) #> # A tibble: 981 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 J5         A        2017-12-25 B_STRPT_PNMN   R     S     S     R   TRUE  #>  2 X1         A        2017-07-04 B_STPHY_AURS   R     S     S     R   TRUE  #>  3 B3         A        2016-07-24 B_ESCHR_COLI   S     S     S     R   TRUE  #>  4 V7         A        2012-04-03 B_ESCHR_COLI   S     S     S     R   TRUE  #>  5 C9         A        2017-03-23 B_ESCHR_COLI   S     S     S     R   TRUE  #>  6 R1         A        2018-06-10 B_STPHY_AURS   S     S     S     R   TRUE  #>  7 S2         A        2013-07-19 B_STRPT_PNMN   S     S     S     R   TRUE  #>  8 P5         A        2019-03-09 B_STPHY_AURS   S     S     S     R   TRUE  #>  9 Q8         A        2019-08-10 B_STPHY_AURS   S     S     S     R   TRUE  #> 10 K5         A        2013-03-15 B_STRPT_PNMN   S     S     S     R   TRUE  #> # ℹ 971 more rows  our_data_1st %>%   filter(all(betalactams() == \"R\")) #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN   R     R     S     S   TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS   R     R     S     S   TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS   R     R     S     S   TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS   R     R     S     S   TRUE  #>  5 B6         A        2016-11-20 B_ESCHR_COLI   R     R     R     R   TRUE  #>  6 I7         A        2015-08-19 B_ESCHR_COLI   R     R     S     S   TRUE  #>  7 N3         A        2014-12-29 B_STRPT_PNMN   R     R     R     S   TRUE  #>  8 Q2         A        2019-09-22 B_ESCHR_COLI   R     R     S     S   TRUE  #>  9 X7         A        2011-03-20 B_ESCHR_COLI   R     R     S     R   TRUE  #> 10 V1         A        2018-08-07 B_STPHY_AURS   R     R     S     S   TRUE  #> # ℹ 452 more rows  # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC' #>   (amoxicillin/clavulanic acid) #> # A tibble: 462 × 9 #>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first #>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl> #>  1 M7         A        2013-07-22 B_STRPT_PNMN   R     R     S     S   TRUE  #>  2 R10        A        2013-12-20 B_STPHY_AURS   R     R     S     S   TRUE  #>  3 R7         A        2015-10-25 B_STPHY_AURS   R     R     S     S   TRUE  #>  4 R8         A        2019-10-25 B_STPHY_AURS   R     R     S     S   TRUE  #>  5 B6         A        2016-11-20 B_ESCHR_COLI   R     R     R     R   TRUE  #>  6 I7         A        2015-08-19 B_ESCHR_COLI   R     R     S     S   TRUE  #>  7 N3         A        2014-12-29 B_STRPT_PNMN   R     R     R     S   TRUE  #>  8 Q2         A        2019-09-22 B_ESCHR_COLI   R     R     S     S   TRUE  #>  9 X7         A        2011-03-20 B_ESCHR_COLI   R     R     S     R   TRUE  #> 10 V1         A        2018-08-07 B_STPHY_AURS   R     R     S     S   TRUE  #> # ℹ 452 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"How to conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"How to conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates,             antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates,             mo_transform = \"gramstain\",             antibiotics = aminoglycosides(),             ab_transform = \"name\",             language = \"es\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"How to conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates,                            antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),                            ab_transform = NULL) combined_ab"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"How to conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates,             antibiotics = c(aminoglycosides(), carbapenems()),             syndromic_group = \"ward\") #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"How to conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>%   wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),         minimum = 10) # Recommended threshold: ≥30 example_isolates %>%   top_n_microorganisms(n = 10) %>%   group_by(age_group = age_groups(age, c(25, 50, 75)),            gender) %>%   wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"How to conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function:  calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4203377 our_data_1st %>%   group_by(hospital) %>%   summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #>   hospital amoxicillin #>   <chr>          <dbl> #> 1 A              0.340 #> 2 B              0.551 #> 3 C              0.370"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"How to conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"set.seed(123) mic_values <- random_mic(100) sir_values <- as.sir(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\") #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Klebsiella pneumoniae - assuming body site 'Non-meningitis'.  my_data <- tibble(MIC = mic_values, SIR = sir_values) my_data #> # A tibble: 100 × 2 #>          MIC SIR   #>        <mic> <sir> #>  1    16.000   R   #>  2     0.005   S   #>  3     1.000   R   #>  4 >=256.000   R   #>  5     2.000   R   #>  6     0.025   S   #>  7    16.000   R   #>  8     0.025   S   #>  9     0.500   I   #> 10     0.005   S   #> # ℹ 90 more rows"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"How to conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories.  plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values:   Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# add a group my_data$group <- rep(c(\"A\", \"B\", \"C\", \"D\"), each = 25)   ggplot(my_data,        aes(x = group, y = MIC, colour = SIR)) +   geom_jitter(width = 0.2, size = 2) +   geom_boxplot(fill = NA, colour = \"grey40\") +   scale_y_mic() +   scale_colour_sir() +   labs(title = \"MIC Distribution and SIR Interpretation\",        x = \"Sample Groups\",        y = \"MIC (mg/L)\") autoplot(mic_values) # by providing `mo` and `ab`, colours will indicate the SIR interpretation: autoplot(mic_values, mo = \"K. pneumoniae\", ab = \"cipro\", guideline = \"EUCAST 2024\")"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python Package Index. Python package wrapper round AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"install","dir":"Articles","previous_headings":"","what":"Install","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR  # Sample data data = {     \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],     \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data)  # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug'])  # Display the results print(df)"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd  df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://msberends.github.io/AMR/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR)          # For AMR data analysis library(tidymodels)   # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) #> ── Attaching packages ────────────────────────────────────── tidymodels 1.3.0 ── #> ✔ broom        1.0.7     ✔ recipes      1.1.1 #> ✔ dials        1.4.0     ✔ rsample      1.2.1 #> ✔ dplyr        1.1.4     ✔ tibble       3.2.1 #> ✔ ggplot2      3.5.1     ✔ tidyr        1.3.1 #> ✔ infer        1.0.7     ✔ tune         1.3.0 #> ✔ modeldata    1.4.0     ✔ workflows    1.2.0 #> ✔ parsnip      1.3.0     ✔ workflowsets 1.1.0 #> ✔ purrr        1.0.4     ✔ yardstick    1.3.2 #> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ── #> ✖ purrr::discard() masks scales::discard() #> ✖ dplyr::filter()  masks stats::filter() #> ✖ dplyr::lag()     masks stats::lag() #> ✖ recipes::step()  masks stats::step()  # Select relevant columns for prediction data <- example_isolates %>%   # select AB results dynamically   select(mo, aminoglycosides(), betalactams()) %>%   # replace NAs with NI (not-interpretable)    mutate(across(where(is.sir),                  ~replace_na(.x, \"NI\")),           # make factors of SIR columns           across(where(is.sir),                  as.integer),           # get Gramstain of microorganisms           mo = as.factor(mo_gramstain(mo))) %>%   # drop NAs - the ones without a Gramstain (fungi, etc.)   drop_na() #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #>   (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #>   (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #>   'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antibiotic selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>%   step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #>  #> ── Recipe ────────────────────────────────────────────────────────────────────── #>  #> ── Inputs #> Number of variables by role #> outcome:    1 #> predictor: 20 #>  #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #>   (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #>   (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #>   'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #>  #> ── Recipe ────────────────────────────────────────────────────────────────────── #>  #> ── Inputs #> Number of variables by role #> outcome:    1 #> predictor: 20 #>  #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #>  #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>%   set_engine(\"glm\") # Use the Generalized Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #>  #> Computational engine: glm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organizes entire modeling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>%   add_recipe(resistance_recipe) %>% # Add the preprocessing recipe   add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #>  #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #>  #> • step_corr() #>  #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #>  #> Computational engine: glm"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antibiotic selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram based AMR results 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split)   # Testing set  # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>%   fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>%   predict(testing_data)                # Generate predictions probabilities <- fitted_workflow %>%   predict(testing_data, type = \"prob\") # Generate probabilities  predictions <- predictions %>%   bind_cols(probabilities) %>%   bind_cols(testing_data) # Combine with true labels  predictions #> # A tibble: 394 × 24 #>    .pred_class   `.pred_Gram-negative` `.pred_Gram-positive` mo        GEN   TOB #>    <fct>                         <dbl>                 <dbl> <fct>   <int> <int> #>  1 Gram-positive              1.07e- 1              8.93e- 1 Gram-p…     5     5 #>  2 Gram-positive              3.17e- 8              1.00e+ 0 Gram-p…     5     1 #>  3 Gram-negative              9.99e- 1              1.42e- 3 Gram-n…     5     5 #>  4 Gram-positive              2.22e-16              1   e+ 0 Gram-p…     5     5 #>  5 Gram-negative              9.46e- 1              5.42e- 2 Gram-n…     5     5 #>  6 Gram-positive              1.07e- 1              8.93e- 1 Gram-p…     5     5 #>  7 Gram-positive              2.22e-16              1   e+ 0 Gram-p…     1     5 #>  8 Gram-positive              2.22e-16              1   e+ 0 Gram-p…     4     4 #>  9 Gram-negative              1   e+ 0              2.22e-16 Gram-n…     1     1 #> 10 Gram-positive              6.05e-11              1.00e+ 0 Gram-p…     4     4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK <int>, KAN <int>, PEN <int>, OXA <int>, FLC <int>, #> #   AMX <int>, AMC <int>, AMP <int>, TZP <int>, CZO <int>, FEP <int>, #> #   CXM <int>, FOX <int>, CTX <int>, CAZ <int>, CRO <int>, IPM <int>, MEM <int>  # Evaluate model performance metrics <- predictions %>%   metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics  metrics #> # A tibble: 2 × 3 #>   .metric  .estimator .estimate #>   <chr>    <chr>          <dbl> #> 1 accuracy binary         0.995 #> 2 kap      binary         0.989 predictions %>%   roc_curve(mo, `.pred_Gram-negative`) %>%   autoplot()"},{"path":"https://msberends.github.io/AMR/articles/AMR_with_tidymodels.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR with tidymodels","text":"post, demonstrated build machine learning pipeline tidymodels framework AMR package. combining selector functions like aminoglycosides() betalactams() tidymodels, efficiently prepared data, trained model, evaluated performance. workflow extensible antibiotic classes resistance patterns, empowering users analyse AMR data systematically reproducibly.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.1, 2016). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antimicrobials: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame(   mo = c(     \"Klebsiella\",     \"Escherichia\"   ),   ampicillin = \"S\" ) oops #>            mo ampicillin #> 1  Klebsiella          S #> 2 Escherichia          S  eucast_rules(oops, info = FALSE) #>            mo ampicillin #> 1  Klebsiella          R #> 2 Escherichia          S mo_is_intrinsic_resistant(   c(\"Klebsiella\", \"Escherichia\"),   \"ampicillin\" ) #> [1]  TRUE FALSE  mo_is_intrinsic_resistant(   \"Klebsiella\",   c(\"ampicillin\", \"kanamycin\") ) #> [1]  TRUE FALSE data <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE ) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(   CIP == \"R\" & age > 60 ~ \"Elderly Type A\",   ERY == \"R\" & age > 60 ~ \"Elderly Type B\" ) custom #> A set of custom MDRO rules: #>   1. If CIP is  R  and age is higher than 60 then: Elderly Type A #>   2. If ERY is  R  and age is higher than 60 then: Elderly Type B #>   3. Otherwise: Negative #>  #> Unmatched rows will return NA. #> Results will be of class 'factor', with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) #> x #>       Negative Elderly Type A Elderly Type B  #>           1070            198            732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,745 (87.25%, NA: 255 = 12.75%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100%, NA: 0 = 0%) Unique: 5","code":"library(dplyr) # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>%   mdro() %>%   freq() # show frequency table of the result #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) # random_sir() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(   rifampicin = random_sir(5000),   isoniazid = random_sir(5000),   gatifloxacin = random_sir(5000),   ethambutol = random_sir(5000),   pyrazinamide = random_sir(5000),   moxifloxacin = random_sir(5000),   kanamycin = random_sir(5000) ) my_TB_data <- data.frame(   RIF = random_sir(5000),   INH = random_sir(5000),   GAT = random_sir(5000),   ETH = random_sir(5000),   PZA = random_sir(5000),   MFX = random_sir(5000),   KAN = random_sir(5000) ) head(my_TB_data) #>   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin #> 1          I         R            S          S            S            S #> 2          S         S            I          R            R            S #> 3          R         I            I          I            R            I #> 4          I         S            S          S            S            S #> 5          I         I            I          S            I            S #> 6          R         S            R          S            I            I #>   kanamycin #> 1         R #> 2         I #> 3         S #> 4         I #> 5         I #> 6         I mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) #> ℹ No column found as input for col_mo, assuming all rows contain #>   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) #> Rows: 2,000 #> Columns: 46 #> $ date    <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2… #> $ patient <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067927\", \"4… #> $ age     <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50… #> $ gender  <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\", \"M\", \"M… #> $ ward    <chr> \"Clinical\", \"Clinical\", \"ICU\", \"ICU\", \"ICU\", \"ICU\", \"Clinical\"… #> $ mo      <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPHY_EPDR\",… #> $ PEN     <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,… #> $ OXA     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ FLC     <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R… #> $ AMX     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ AMC     <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N… #> $ AMP     <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N… #> $ TZP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CZO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ FEP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CXM     <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S… #> $ FOX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,… #> $ CTX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ CAZ     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, … #> $ CRO     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ GEN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TOB     <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA… #> $ AMK     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ KAN     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ TMP     <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, … #> $ SXT     <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N… #> $ NIT     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,… #> $ FOS     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ LNZ     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ CIP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S… #> $ MFX     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ VAN     <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, … #> $ TEC     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… #> $ TCY     <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, … #> $ TGC     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ DOX     <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA… #> $ ERY     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ CLI     <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA… #> $ AZM     <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,… #> $ IPM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S… #> $ MEM     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ MTR     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ CHL     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ COL     <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, … #> $ MUP     <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… #> $ RIF     <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N… resistance_data <- example_isolates %>%   group_by(     order = mo_order(mo), # group on anything, like order     genus = mo_genus(mo)   ) %>% #  and genus as we do here   summarise_if(is.sir, resistance) %>% # then get resistance of all drugs   select(     order, genus, AMC, CXM, CTX,     CAZ, GEN, TOB, TMP, SXT   ) # and select only relevant columns  head(resistance_data) #> # A tibble: 6 × 10 #> # Groups:   order [5] #>   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #>   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA #> 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA #> 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA #> 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA #> 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA #> 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. summary(pca_result) #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" #> Importance of components: #>                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 #> Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16 #> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 #> Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .sir() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100%, NA: 0 = 0%) Unique: 38 Shortest: 11 Longest: 40 (omitted 28 entries, n = 57 [11.4%]) Frequency table Class: factor > ordered > sir (numeric) Length: 500 Levels: 5: S < SDD < < R < NI Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr) # part of tidyverse library(ggplot2) # part of tidyverse library(AMR) # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>%   # get microbial ID based on given organism   mutate(mo = as.mo(Organism)) %>%   # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `sir` class   mutate_at(vars(AMP_ND10:CIP_EE), as.sir) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_sir() function:","code":"data %>%   group_by(Country) %>%   select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%   ggplot_sir(translate_ab = \"ab\", facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-full-microbial-taxonomy","dir":"Articles","previous_headings":"","what":"microorganisms: Full Microbial Taxonomy","title":"Data sets for download / own use","text":"data set 78 678 rows 26 columns, containing following column names:mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, oxygen_tolerance, source, lpsn, lpsn_parent, lpsn_renamed_to, mycobank, mycobank_parent, mycobank_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence, snomed. data set R available microorganisms, load AMR package. last updated 4 October 2024 13:28:44 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (1.8 MB) Download tab-separated text file (17.7 MB) Download Microsoft Excel workbook (8.7 MB) Download Apache Feather file (8.3 MB) Download Apache Parquet file (3.8 MB) Download IBM SPSS Statistics data file (29 MB) Download Stata DTA file (92.5 MB) NOTE: exported files SPSS Stata contain first 50 SNOMED codes per record, file size otherwise exceed 100 MB; file size limit GitHub. file structures compression techniques inefficient. Advice? Use R instead. ’s free much better many ways. tab-separated text file Microsoft Excel workbook contain SNOMED codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Source","title":"Data sets for download / own use","text":"data set contains full microbial taxonomy six kingdoms List Prokaryotic names Standing Nomenclature (LPSN), MycoBank, Global Biodiversity Information Facility (GBIF): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; . Accessed https://lpsn.dsmz.de June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"microorganisms: Full Microbial Taxonomy","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antimicrobials-antibiotic-and-antifungal-drugs","dir":"Articles","previous_headings":"","what":"antimicrobials: Antibiotic and Antifungal Drugs","title":"Data sets for download / own use","text":"data set 487 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antimicrobials, load AMR package. last updated 7 March 2025 19:43:26 UTC. Find info structure data set . tab-separated text, Microsoft Excel, SPSS, Stata files contain ATC codes, common abbreviations, trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"antimicrobials: Antibiotic and Antifungal Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019 LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antivirals-antiviral-drugs","dir":"Articles","previous_headings":"","what":"antivirals: Antiviral Drugs","title":"Data sets for download / own use","text":"data set 120 rows 11 columns, containing following column names:av, name, atc, cid, atc_group, synonyms, oral_ddd, oral_units, iv_ddd, iv_units, loinc. data set R available antivirals, load AMR package. last updated 20 October 2023 12:51:48 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (6 kB) Download tab-separated text file (17 kB) Download Microsoft Excel workbook (16 kB) Download Apache Feather file (16 kB) Download Apache Parquet file (13 kB) Download IBM SPSS Statistics data file (32 kB) Download Stata DTA file (78 kB) tab-separated text, Microsoft Excel, SPSS, Stata files contain trade names LOINC codes comma separated values.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"antivirals: Antiviral Drugs","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine LOINC (Logical Observation Identifiers Names Codes)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir","dir":"Articles","previous_headings":"","what":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","title":"Data sets for download / own use","text":"data set 34 063 rows 14 columns, containing following column names:guideline, type, host, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R, uti, is_SDD. data set R available clinical_breakpoints, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (70 kB) Download tab-separated text file (3.1 MB) Download Microsoft Excel workbook (2 MB) Download Apache Feather file (1.5 MB) Download Apache Parquet file (0.1 MB) Download IBM SPSS Statistics data file (5.6 MB) Download Stata DTA file (9.3 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"clinical_breakpoints: Interpretation from MIC values & disk diameters to SIR","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2011-2024) EUCAST (2011-2024). Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate development WHONET software. CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET AMR package. NOTE: AMR package (WHONET software well) contains internal methods apply guidelines, rather complex. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic_resistant-intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"intrinsic_resistant: Intrinsic Bacterial Resistance","title":"Data sets for download / own use","text":"data set 301 583 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (0.1 MB) Download tab-separated text file (10.9 MB) Download Microsoft Excel workbook (3 MB) Download Apache Feather file (2.5 MB) Download Apache Parquet file (0.3 MB) Download IBM SPSS Statistics data file (16.2 MB) Download Stata DTA file (25 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-4","dir":"Articles","previous_headings":"intrinsic_resistant: Intrinsic Bacterial Resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"dosage: Dosage Guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 503 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt, eucast_version. data set R available dosage, load AMR package. last updated 22 June 2023 13:10:59 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (3 kB) Download tab-separated text file (43 kB) Download Microsoft Excel workbook (25 kB) Download Apache Feather file (21 kB) Download Apache Parquet file (9 kB) Download IBM SPSS Statistics data file (64 kB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"dosage: Dosage Guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant :  (), ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021), ‘EUCAST Clinical Breakpoint Tables’ v12.0 (2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates: Example Data for Practice","title":"Data sets for download / own use","text":"data set 2 000 rows 46 columns, containing following column names:date, patient, age, gender, ward, mo, PEN, OXA, FLC, AMX, AMC, AMP, TZP, CZO, FEP, CXM, FOX, CTX, CAZ, CRO, GEN, TOB, AMK, KAN, TMP, SXT, NIT, FOS, LNZ, CIP, MFX, VAN, TEC, TCY, TGC, DOX, ERY, CLI, AZM, IPM, MEM, MTR, CHL, COL, MUP, RIF. data set R available example_isolates, load AMR package. last updated 15 June 2024 13:33:49 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"example_isolates: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example_isolates_unclean-example-data-for-practice","dir":"Articles","previous_headings":"","what":"example_isolates_unclean: Example Data for Practice","title":"Data sets for download / own use","text":"data set 3 000 rows 8 columns, containing following column names:patient_id, hospital, date, bacteria, AMX, AMC, CIP, GEN. data set R available example_isolates_unclean, load AMR package. last updated 27 August 2022 18:49:37 UTC. Find info structure data set .","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-7","dir":"Articles","previous_headings":"example_isolates_unclean: Example Data for Practice","what":"Source","title":"Data sets for download / own use","text":"data set contains randomised fictitious data, reflects reality can used practise AMR data analysis.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-groups-species-groups-and-microbiological-complexes","dir":"Articles","previous_headings":"","what":"microorganisms.groups: Species Groups and Microbiological Complexes","title":"Data sets for download / own use","text":"data set 521 rows 4 columns, containing following column names:mo_group, mo, mo_group_name, mo_name. data set R available microorganisms.groups, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (5 kB) Download tab-separated text file (49 kB) Download Microsoft Excel workbook (19 kB) Download Apache Feather file (19 kB) Download Apache Parquet file (13 kB) Download IBM SPSS Statistics data file (63 kB) Download Stata DTA file (81 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-8","dir":"Articles","previous_headings":"microorganisms.groups: Species Groups and Microbiological Complexes","what":"Source","title":"Data sets for download / own use","text":"data set contains species groups microbiological complexes, used clinical_breakpoints data set.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-codes-common-laboratory-codes","dir":"Articles","previous_headings":"","what":"microorganisms.codes: Common Laboratory Codes","title":"Data sets for download / own use","text":"data set 4 971 rows 2 columns, containing following column names:code mo. data set R available microorganisms.codes, load AMR package. last updated 29 September 2024 20:17:56 UTC. Find info structure data set . Direct download links: Download original R Data Structure (RDS) file (22 kB) Download tab-separated text file (0.1 MB) Download Microsoft Excel workbook (82 kB) Download Apache Feather file (85 kB) Download Apache Parquet file (56 kB) Download IBM SPSS Statistics data file (0.1 MB) Download Stata DTA file (0.1 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-9","dir":"Articles","previous_headings":"microorganisms.codes: Common Laboratory Codes","what":"Source","title":"Data sets for download / own use","text":"data set contains commonly used codes microorganisms, laboratory systems WHONET.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions:  fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_sir_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\")  # or: example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   )  # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>%   resistance_predict(     col_ab = \"TZP\",     model = \"binomial\"   ) predict_TZP #> # A tibble: 34 × 7 #>     year  value se_min se_max observations observed estimated #>  * <dbl>  <dbl>  <dbl>  <dbl>        <int>    <dbl>     <dbl> #>  1  2002 0.2        NA     NA           15   0.2       0.0562 #>  2  2003 0.0625     NA     NA           32   0.0625    0.0616 #>  3  2004 0.0854     NA     NA           82   0.0854    0.0676 #>  4  2005 0.05       NA     NA           60   0.05      0.0741 #>  5  2006 0.0508     NA     NA           59   0.0508    0.0812 #>  6  2007 0.121      NA     NA           66   0.121     0.0889 #>  7  2008 0.0417     NA     NA           72   0.0417    0.0972 #>  8  2009 0.0164     NA     NA           61   0.0164    0.106  #>  9  2010 0.0566     NA     NA           53   0.0566    0.116  #> 10  2011 0.183      NA     NA           93   0.183     0.127  #> # ℹ 24 more rows plot(predict_TZP) ggplot_sir_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_sir_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous:  Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate:  model also available object, attribute:","code":"example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>%   ggplot_sir_predict() example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>%   ggplot_sir_predict() model <- attributes(predict_TZP)$model  summary(model)$family #>  #> Family: binomial  #> Link function: logit  summary(model)$coefficients #>                  Estimate  Std. Error   z value     Pr(>|z|) #> (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 #> year           0.09883005  0.02295317  4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Andrew P. Norgan. Contributor. Anita Williams. Contributor. Annick Lenglet. Contributor. Anthony Underwood. Contributor. Anton Mymrikov. Contributor. Bart C. Meijer. Contributor. Christian F. Luz. Contributor. Dmytro Mykhailenko. Contributor. Eric H. L. C. M. Hazenberg. Contributor. Gwen Knight. Contributor. Jason Stull. Contributor. Javier Sanchez. Contributor. Jonas Salm. Contributor. Judith M. Fonville. Contributor. Larisse Bolton. Contributor. Matthew Saab. Contributor. Peter Dutey-Magni. Contributor. Rogier P. Schade. Contributor. Sofia Ny. Contributor. Alex W. Friedrich. Thesis advisor. Bhanu N. M. Sinha. Thesis advisor. Casper J. Albers. Thesis advisor. Corinna Glasner. Thesis advisor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). “AMR: R Package Working Antimicrobial Resistance Data.” Journal Statistical Software, 104(3), 1–31. doi:10.18637/jss.v104.i03.","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/index.html","id":"the-amr-package-for-r-","dir":"","previous_headings":"","what":"Antimicrobial Resistance Data Analysis","title":"Antimicrobial Resistance Data Analysis","text":"Provides --one solution antimicrobial resistance (AMR) data analysis One Health approach Used 175 countries, available 20 languages Generates antibiograms - traditional, combined, syndromic, even WISCA Provides full microbiological taxonomy extensive info antimicrobial drugs Applies recent CLSI EUCAST clinical veterinary breakpoints MICs, disk zones ECOFFs Corrects duplicate isolates, calculates predicts AMR per antimicrobial class Integrates WHONET, ATC, EARS-Net, PubChem, LOINC, SNOMED CT, NCBI 100% free costs dependencies, highly suitable places limited resources Now available Python ! Click read . https://msberends.github.io/AMR https://doi.org/10.18637/jss.v104.i03","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); DOI 10.18637/jss.v104.i03) formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375). installing package, R knows ~52,000 distinct microbial species (updated December 2022) ~600 antimicrobial antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-over-175-countries-available-in-20-languages","dir":"","previous_headings":"Introduction","what":"Used in over 175 countries, available in 20 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. help contributors corners world, AMR package available  English,  Czech,  Chinese,  Danish,  Dutch,  Finnish,  French,  German,  Greek,  Italian,  Japanese,  Norwegian,  Polish,  Portuguese,  Romanian,  Russian,  Spanish,  Swedish,  Turkish,  Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"One powerful functions package, aside calculating plotting AMR, selecting filtering based antimicrobial columns. can done using -called antimicrobial selectors, work base R, dplyr data.table. defined row filter Gram-negative bacteria intrinsic resistance cefotaxime (mo_is_gram_negative() mo_is_intrinsic_resistant()) column selection two antibiotic groups (aminoglycosides() carbapenems()), reference data microorganisms antimicrobials AMR package make sure get meant:","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr)  example_isolates %>%   mutate(bacteria = mo_fullname()) %>%   # filtering functions for microorganisms:   filter(mo_is_gram_negative(),          mo_is_intrinsic_resistant(ab = \"cefotax\")) %>%   # antimicrobial selectors:   select(bacteria,          aminoglycosides(),          carbapenems())"},{"path":"https://msberends.github.io/AMR/index.html","id":"generating-antibiograms","dir":"","previous_headings":"Practical examples","what":"Generating antibiograms","title":"Antimicrobial Resistance Data Analysis","text":"AMR package supports generating traditional, combined, syndromic, even weighted-incidence syndromic combination antibiograms (WISCA). used inside R Markdown Quarto, table printed right output format automatically (markdown, LaTeX, HTML, etc.). combination antibiograms, clear combined antibiotics yield higher empiric coverage: Like many functions package, antibiogram() comes support 20 languages often detected automatically based system language:","code":"antibiogram(example_isolates,             antibiotics = c(aminoglycosides(), carbapenems()),             formatting_type = 14) antibiogram(example_isolates,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),             mo_transform = \"gramstain\",             formatting_type = 14) antibiogram(example_isolates,             antibiotics = c(\"cipro\", \"tobra\", \"genta\"), # any arbitrary name or code will work             mo_transform = \"gramstain\",             ab_transform = \"name\",             formatting_type = 14,             language = \"uk\") # Ukrainian"},{"path":"https://msberends.github.io/AMR/index.html","id":"interpreting-and-plotting-mic-and-sir-values","dir":"","previous_headings":"Practical examples","what":"Interpreting and plotting MIC and SIR values","title":"Antimicrobial Resistance Data Analysis","text":"AMR package allows interpretation MIC disk diffusion values based CLSI EUCAST. Moreover, ggplot2 package extended new scale functions, allow plotting log2-distributed MIC values SIR values.","code":"library(ggplot2) library(AMR)  # generate some random values some_mic_values <- random_mic(size = 100) some_groups <- sample(LETTERS[1:5], 20, replace = TRUE) interpretation <- as.sir(some_mic_values,                          guideline = \"EUCAST 2024\",                          mo = \"E. coli\", # or any code or name resembling a known species                          ab = \"Cipro\")   # or any code or name resembling an antibiotic  # create the plot ggplot(data.frame(mic = some_mic_values,                   group = some_groups,                   sir = interpretation),        aes(x = group, y = mic, colour = sir)) +   theme_minimal() +   geom_boxplot(fill = NA, colour = \"grey\") +   geom_jitter(width = 0.25) +      # NEW scale function: plot MIC values to x, y, colour or fill   scale_y_mic() +      # NEW scale function: write out S/I/R in any of the 20 supported languages   #                     and set colourblind-friendly colours   scale_colour_sir()"},{"path":"https://msberends.github.io/AMR/index.html","id":"calculating-resistance-per-group","dir":"","previous_headings":"Practical examples","what":"Calculating resistance per group","title":"Antimicrobial Resistance Data Analysis","text":"manual approach, can use resistance susceptibility() function: use antimicrobial selectors select series antibiotic columns:","code":"example_isolates %>%   # group by ward:   group_by(ward) %>%   # calculate AMR using resistance() for gentamicin and tobramycin   # and get their 95% confidence intervals using sir_confidence_interval():   summarise(across(c(GEN, TOB),                    list(total_R = resistance,                         conf_int = function(x) sir_confidence_interval(x, collapse = \"-\")))) library(AMR) library(dplyr)  out <- example_isolates %>%   # group by ward:   group_by(ward) %>%   # calculate AMR using resistance(), over all aminoglycosides and polymyxins:   summarise(across(c(aminoglycosides(), polymyxins()),             resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species List Prokaryotic names Standing Nomenclature (LPSN) Global Biodiversity Information Facility (GBIF) (manual) Interpreting raw MIC disk diffusion values, based CLSI EUCAST guideline (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antimicrobial (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters SIR (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed rOpenSci R-universe platform:","code":"install.packages(\"AMR\", repos = \"https://msberends.r-universe.dev\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible following non-profit organisations initiatives:","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"objects -called 'Deprecated'. removed future version package. Using give warning name alternative object replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated Functions — AMR-deprecated","text":"","code":"ab_class(...)  ab_selector(...)"},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deprecated Functions — AMR-deprecated","text":"object class tbl_df (inherits tbl, data.frame) 487 rows 14 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Options for the AMR package — AMR-options","title":"Options for the AMR package — AMR-options","text":"overview package-specific options() can set AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"options","dir":"Reference","previous_headings":"","what":"Options","title":"Options for the AMR package — AMR-options","text":"AMR_antibiogram_formatting_type  numeric (1-22) use antibiogram(), indicate formatting type use. AMR_breakpoint_type  character use .sir(), indicate breakpoint type use. must either \"ECOFF\", \"animal\", \"human\". AMR_cleaning_regex  regular expression (case-insensitive) use .mo() mo_* functions, clean user input. default outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". AMR_custom_ab  file location RDS file, use custom antimicrobial drugs package. explained add_custom_antimicrobials(). AMR_custom_mo  file location RDS file, use custom microorganisms package. explained add_custom_microorganisms(). AMR_eucastrules  character set default types rules eucast_rules() function, must one : \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). AMR_guideline  character set default guideline interpreting MIC values disk diffusion diameters .sir(). Can guideline name (e.g., \"CLSI\") name year (e.g. \"CLSI 2019\"). default latest implemented EUCAST guideline, currently \"EUCAST 2024\". Supported guideline currently EUCAST (2011-2024) CLSI (2011-2024). AMR_ignore_pattern  regular expression ignore (.e., make NA) match given .mo() mo_* functions. AMR_include_PKPD  logical use .sir(), indicate PK/PD clinical breakpoints must applied last resort - default TRUE. AMR_include_screening  logical use .sir(), indicate clinical breakpoints screening allowed - default FALSE. AMR_keep_synonyms  logical use .mo() mo_* functions, indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE. AMR_locale  character set language AMR package, can one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). default current system language (supported, English otherwise). AMR_mo_source  file location manual code list used .mo() mo_* functions. explained set_mo_source().","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"saving-settings-between-sessions","dir":"Reference","previous_headings":"","what":"Saving Settings Between Sessions","title":"Options for the AMR package — AMR-options","text":"Settings R saved globally thus lost R exited. can save options .Rprofile file, user-specific file. can edit using:   file, can set options ...   ...add Portuguese language support antimicrobials, allow PK/PD rules interpreting MIC values .sir().","code":"utils::file.edit(\"~/.Rprofile\") options(AMR_locale = \"pt\")  options(AMR_include_PKPD = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/AMR-options.html","id":"share-options-within-team","dir":"Reference","previous_headings":"","what":"Share Options Within Team","title":"Options for the AMR package — AMR-options","text":"global approach, e.g. within (data) team, save options file remote file location, shared network drive, user read file automatically start-. work way: Save plain text file e.g. \"X:/team_folder/R_options.R\" fill preferred settings. user, open .Rprofile file using utils::file.edit(\"~/.Rprofile\") put :   Reload R/RStudio check settings getOption(), e.g. getOption(\"AMR_locale\") set value. Now team settings configured one place, can maintained .","code":"source(\"X:/team_folder/R_options.R\")"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package. AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. Many different researchers around globe continually helping us make successful durable project! work published Journal Statistical Software (Volume 104(3); doi:10.18637/jss.v104.i03 ) formed basis two PhD theses (doi:10.33612/diss.177417131  doi:10.33612/diss.192486375 ). installing package, R knows ~79 000 microorganisms (updated June 2024) ~610 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC SNOMED CT), knows valid SIR MIC values. integral clinical breakpoint guidelines CLSI EUCAST included, even epidemiological cut-(ECOFF) values. supports can read data format, including WHONET data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences public University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. AMR package available English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"The AMR Package — AMR","text":"cite AMR publications use: Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022). \"AMR: R Package Working Antimicrobial Resistance Data.\" Journal Statistical Software, 104(3), 1-31. doi:10.18637/jss.v104.i03 BibTeX entry LaTeX users :","code":"@Article{,   title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},   author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},   journal = {Journal of Statistical Software},   year = {2022},   volume = {104},   number = {3},   pages = {1--31},   doi = {10.18637/jss.v104.i03}, }"},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"The AMR Package — AMR","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"The AMR Package — AMR","text":"Maintainer: Matthijs S. Berends m.s.berends@umcg.nl (ORCID) Authors: Dennis Souverein (ORCID) [contributor] Erwin E. . Hassing [contributor] contributors: Andrew P. Norgan (ORCID) [contributor] Anita Williams (ORCID) [contributor] Annick Lenglet (ORCID) [contributor] Anthony Underwood (ORCID) [contributor] Anton Mymrikov [contributor] Bart C. Meijer [contributor] Christian F. Luz (ORCID) [contributor] Dmytro Mykhailenko [contributor] Eric H. L. C. M. Hazenberg [contributor] Gwen Knight (ORCID) [contributor] Jason Stull (ORCID) [contributor] Javier Sanchez (ORCID) [contributor] Jonas Salm [contributor] Judith M. Fonville [contributor] Larisse Bolton (ORCID) [contributor] Matthew Saab [contributor] Peter Dutey-Magni (ORCID) [contributor] Rogier P. Schade [contributor] Sofia Ny (ORCID) [contributor] Alex W. Friedrich (ORCID) [thesis advisor] Bhanu N. M. Sinha (ORCID) [thesis advisor] Casper J. Albers (ORCID) [thesis advisor] Corinna Glasner (ORCID) [thesis advisor]","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class 'ab' #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\"  ab_tradenames(\"flucloxacillin\") #>  [1] \"culpen\"               \"floxacillin\"          \"floxacillinsodium\"    #>  [4] \"floxapen\"             \"floxapensodiumsalt\"   \"fluclox\"              #>  [7] \"flucloxacilina\"       \"flucloxacilline\"      \"flucloxacillinum\"     #> [10] \"fluorochloroxacillin\" \"staphylex\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antimcrobial results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"tibble 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 28 different antimicrobials. can lookup abbreviations antimicrobials data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antimcrobial class, using .sir().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET #> # A tibble: 500 × 53 #>    `Identification number` `Specimen number` Organism Country         Laboratory #>    <chr>                               <int> <chr>    <chr>           <chr>      #>  1 fe41d7bafa                           1748 SPN      Belgium         National … #>  2 91f175ec37                           1767 eco      The Netherlands National … #>  3 cc4015056e                           1343 eco      The Netherlands National … #>  4 e864b692f5                           1894 MAP      Denmark         National … #>  5 3d051fe345                           1739 PVU      Belgium         National … #>  6 c80762a08d                           1846 103      The Netherlands National … #>  7 8022d3727c                           1628 103      Denmark         National … #>  8 f3dc5f553d                           1493 eco      The Netherlands National … #>  9 15add38f6c                           1847 eco      France          National … #> 10 fd41248def                           1458 eco      Germany         National … #> # ℹ 490 more rows #> # ℹ 48 more variables: `Last name` <chr>, `First name` <chr>, Sex <chr>, #> #   Age <dbl>, `Age category` <chr>, `Date of admission` <date>, #> #   `Specimen date` <date>, `Specimen type` <chr>, #> #   `Specimen type (Numeric)` <dbl>, Reason <chr>, `Isolate number` <int>, #> #   `Organism type` <chr>, Serotype <chr>, `Beta-lactamase` <lgl>, ESBL <lgl>, #> #   Carbapenemase <lgl>, `MRSA screening test` <lgl>, …"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text(text, type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL, translate_ab = FALSE, thorough_search = NULL,   info = interactive(), ...)"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antimicrobials data set translate antibiotic abbreviations , using ab_property(). default FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line, # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tid\") #> [[1]] #> Class 'ab' #> [1] AMX #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class 'ab' #> [1] AMX CIP #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\"   #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\"                if (require(\"dplyr\")) {   tibble(clinical_text = c(     \"given 400mg cipro and 500 mg amox\",     \"started on doxy iv today\"   )) %>%     mutate(       abx_codes = ab_from_text(clinical_text),       abx_doses = ab_from_text(clinical_text, type = \"doses\"),       abx_admin = ab_from_text(clinical_text, type = \"admin\"),       abx_coll = ab_from_text(clinical_text, collapse = \"|\"),       abx_coll_names = ab_from_text(clinical_text,         collapse = \"|\",         translate_ab = \"name\"       ),       abx_coll_doses = ab_from_text(clinical_text,         type = \"doses\",         collapse = \"|\"       ),       abx_coll_admin = ab_from_text(clinical_text,         type = \"admin\",         collapse = \"|\"       )     ) } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #>   clinical_text            abx_codes abx_doses abx_admin abx_coll abx_coll_names #>   <chr>                    <list>    <list>    <list>    <chr>    <chr>          #> 1 given 400mg cipro and 5… <ab [2]>  <dbl [2]> <chr [1]> CIP|AMX  Ciprofloxacin… #> 2 started on doxy iv today <ab [1]>  <dbl [1]> <chr [1]> DOX      Doxycycline    #> # ℹ 2 more variables: abx_coll_doses <chr>, abx_coll_admin <chr> # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antimicrobials data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  ab_cid(x, ...)  ab_synonyms(x, ...)  ab_tradenames(x, ...)  ab_group(x, language = get_AMR_locale(), ...)  ab_atc(x, only_first = FALSE, ...)  ab_atc_group1(x, language = get_AMR_locale(), ...)  ab_atc_group2(x, language = get_AMR_locale(), ...)  ab_loinc(x, ...)  ab_ddd(x, administration = \"oral\", ...)  ab_ddd_units(x, administration = \"oral\", ...)  ab_info(x, language = get_AMR_locale(), ...)  ab_url(x, open = FALSE, ...)  ab_property(x, property = \"name\", language = get_AMR_locale(), ...)  set_ab_names(data, ..., property = \"name\", language = get_AMR_locale(),   snake_case = NULL)"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic drug code .ab() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: columns select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antimicrobials data set: vector_or(colnames(antimicrobials), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://atcddd.fhi./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\") #> [1] \"Amoxicillin\" ab_atc(\"AMX\") #> [1] \"J01CA04\" ab_cid(\"AMX\") #> [1] 33613 ab_synonyms(\"AMX\") #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicilline\"       #> [10] \"amoxicillinhydrate\" \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"amoxyke\"            \"anemolin\"           #> [19] \"aspenil\"            \"atoksilin\"          \"biomox\"             #> [22] \"bristamox\"          \"cemoxin\"            \"clamoxyl\"           #> [25] \"damoxy\"             \"delacillin\"         \"demoksil\"           #> [28] \"dispermox\"          \"efpenix\"            \"flemoxin\"           #> [31] \"hiconcil\"           \"histocillin\"        \"hydroxyampicillin\"  #> [34] \"ibiamox\"            \"imacillin\"          \"lamoxy\"             #> [37] \"largopen\"           \"metafarmacapsules\"  \"metifarmacapsules\"  #> [40] \"moksilin\"           \"moxacin\"            \"moxatag\"            #> [43] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [46] \"promoxil\"           \"remoxil\"            \"robamox\"            #> [49] \"sawamoxpm\"          \"tolodina\"           \"topramoxin\"         #> [52] \"unicillin\"          \"utimox\"             \"vetramox\"           ab_tradenames(\"AMX\") #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicilline\"       #> [10] \"amoxicillinhydrate\" \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"amoxyke\"            \"anemolin\"           #> [19] \"aspenil\"            \"atoksilin\"          \"biomox\"             #> [22] \"bristamox\"          \"cemoxin\"            \"clamoxyl\"           #> [25] \"damoxy\"             \"delacillin\"         \"demoksil\"           #> [28] \"dispermox\"          \"efpenix\"            \"flemoxin\"           #> [31] \"hiconcil\"           \"histocillin\"        \"hydroxyampicillin\"  #> [34] \"ibiamox\"            \"imacillin\"          \"lamoxy\"             #> [37] \"largopen\"           \"metafarmacapsules\"  \"metifarmacapsules\"  #> [40] \"moksilin\"           \"moxacin\"            \"moxatag\"            #> [43] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [46] \"promoxil\"           \"remoxil\"            \"robamox\"            #> [49] \"sawamoxpm\"          \"tolodina\"           \"topramoxin\"         #> [52] \"unicillin\"          \"utimox\"             \"vetramox\"           ab_group(\"AMX\") #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\") #>                                                             Amoxicillin  #> \"https://atcddd.fhi.no/atc_ddd_index//?code=J01CA04&showdescription=no\"   # smart lowercase transformation ab_name(x = c(\"AMC\", \"PLB\")) #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\"                 ab_name(x = c(\"AMC\", \"PLB\"), tolower = TRUE) #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\"                  # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\") #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") #> [1] \"g\" ab_ddd(\"AMX\", \"iv\") #> [1] 3 ab_ddd_units(\"AMX\", \"iv\") #> [1] \"g\"  ab_info(\"AMX\") # all properties as a list #> $ab #> [1] \"AMX\" #>  #> $cid #> [1] 33613 #>  #> $name #> [1] \"Amoxicillin\" #>  #> $group #> [1] \"Beta-lactams/penicillins\" #>  #> $atc #> [1] \"J01CA04\" #>  #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #>  #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #>  #> $tradenames #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicilline\"       #> [10] \"amoxicillinhydrate\" \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"amoxyke\"            \"anemolin\"           #> [19] \"aspenil\"            \"atoksilin\"          \"biomox\"             #> [22] \"bristamox\"          \"cemoxin\"            \"clamoxyl\"           #> [25] \"damoxy\"             \"delacillin\"         \"demoksil\"           #> [28] \"dispermox\"          \"efpenix\"            \"flemoxin\"           #> [31] \"hiconcil\"           \"histocillin\"        \"hydroxyampicillin\"  #> [34] \"ibiamox\"            \"imacillin\"          \"lamoxy\"             #> [37] \"largopen\"           \"metafarmacapsules\"  \"metifarmacapsules\"  #> [40] \"moksilin\"           \"moxacin\"            \"moxatag\"            #> [43] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [46] \"promoxil\"           \"remoxil\"            \"robamox\"            #> [49] \"sawamoxpm\"          \"tolodina\"           \"topramoxin\"         #> [52] \"unicillin\"          \"utimox\"             \"vetramox\"           #>  #> $loinc #>  [1] \"101498-4\" \"15-8\"     \"16-6\"     \"16365-9\"  \"17-4\"     \"18-2\"     #>  [7] \"18861-5\"  \"18862-3\"  \"19-0\"     \"20-8\"     \"21-6\"     \"22-4\"     #> [13] \"25274-2\"  \"25310-4\"  \"3344-9\"   \"55614-2\"  \"55615-9\"  \"55616-7\"  #> [19] \"6976-5\"   \"6977-3\"   \"80133-2\"  #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\") #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\") #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\") #>  [1] \"101477-8\" \"101478-6\" \"18864-9\"  \"18865-6\"  \"20374-5\"  \"21066-6\"  #>  [7] \"23618-2\"  \"27-3\"     \"28-1\"     \"29-9\"     \"30-7\"     \"31-5\"     #> [13] \"32-3\"     \"33-1\"     \"3355-5\"   \"33562-0\"  \"33919-2\"  \"34-9\"     #> [19] \"43883-8\"  \"43884-6\"  \"6979-9\"   \"6980-7\"   \"87604-5\"  ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_name(6249) #> [1] \"Ampicillin\" ab_name(\"J01CA01\") #> [1] \"Ampicillin\"  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\"  # use set_ab_names() for renaming columns colnames(example_isolates) #>  [1] \"date\"    \"patient\" \"age\"     \"gender\"  \"ward\"    \"mo\"      \"PEN\"     #>  [8] \"OXA\"     \"FLC\"     \"AMX\"     \"AMC\"     \"AMP\"     \"TZP\"     \"CZO\"     #> [15] \"FEP\"     \"CXM\"     \"FOX\"     \"CTX\"     \"CAZ\"     \"CRO\"     \"GEN\"     #> [22] \"TOB\"     \"AMK\"     \"KAN\"     \"TMP\"     \"SXT\"     \"NIT\"     \"FOS\"     #> [29] \"LNZ\"     \"CIP\"     \"MFX\"     \"VAN\"     \"TEC\"     \"TCY\"     \"TGC\"     #> [36] \"DOX\"     \"ERY\"     \"CLI\"     \"AZM\"     \"IPM\"     \"MEM\"     \"MTR\"     #> [43] \"CHL\"     \"COL\"     \"MUP\"     \"RIF\"     colnames(set_ab_names(example_isolates)) #>  [1] \"date\"                          \"patient\"                       #>  [3] \"age\"                           \"gender\"                        #>  [5] \"ward\"                          \"mo\"                            #>  [7] \"benzylpenicillin\"              \"oxacillin\"                     #>  [9] \"flucloxacillin\"                \"amoxicillin\"                   #> [11] \"amoxicillin_clavulanic_acid\"   \"ampicillin\"                    #> [13] \"piperacillin_tazobactam\"       \"cefazolin\"                     #> [15] \"cefepime\"                      \"cefuroxime\"                    #> [17] \"cefoxitin\"                     \"cefotaxime\"                    #> [19] \"ceftazidime\"                   \"ceftriaxone\"                   #> [21] \"gentamicin\"                    \"tobramycin\"                    #> [23] \"amikacin\"                      \"kanamycin\"                     #> [25] \"trimethoprim\"                  \"trimethoprim_sulfamethoxazole\" #> [27] \"nitrofurantoin\"                \"fosfomycin\"                    #> [29] \"linezolid\"                     \"ciprofloxacin\"                 #> [31] \"moxifloxacin\"                  \"vancomycin\"                    #> [33] \"teicoplanin\"                   \"tetracycline\"                  #> [35] \"tigecycline\"                   \"doxycycline\"                   #> [37] \"erythromycin\"                  \"clindamycin\"                   #> [39] \"azithromycin\"                  \"imipenem\"                      #> [41] \"meropenem\"                     \"metronidazole\"                 #> [43] \"chloramphenicol\"               \"colistin\"                      #> [45] \"mupirocin\"                     \"rifampicin\"                    colnames(set_ab_names(example_isolates, NIT:VAN)) #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     set_ab_names()    # this does the same:   example_isolates %>%     rename_with(set_ab_names)    # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\")    example_isolates %>%     set_ab_names(where(is.sir)) %>%     colnames()    example_isolates %>%     set_ab_names(NIT:VAN) %>%     colnames() } #>  [1] \"date\"           \"patient\"        \"age\"            \"gender\"         #>  [5] \"ward\"           \"mo\"             \"PEN\"            \"OXA\"            #>  [9] \"FLC\"            \"AMX\"            \"AMC\"            \"AMP\"            #> [13] \"TZP\"            \"CZO\"            \"FEP\"            \"CXM\"            #> [17] \"FOX\"            \"CTX\"            \"CAZ\"            \"CRO\"            #> [21] \"GEN\"            \"TOB\"            \"AMK\"            \"KAN\"            #> [25] \"TMP\"            \"SXT\"            \"nitrofurantoin\" \"fosfomycin\"     #> [29] \"linezolid\"      \"ciprofloxacin\"  \"moxifloxacin\"   \"vancomycin\"     #> [33] \"TEC\"            \"TCY\"            \"TGC\"            \"DOX\"            #> [37] \"ERY\"            \"CLI\"            \"AZM\"            \"IPM\"            #> [41] \"MEM\"            \"MTR\"            \"CHL\"            \"COL\"            #> [45] \"MUP\"            \"RIF\"            # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Antimicrobials — add_custom_antimicrobials","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"add_custom_antimicrobials() can add custom antimicrobial drug names codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"add_custom_antimicrobials(x)  clear_custom_antimicrobials()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"x data.frame resembling antimicrobials data set, least containing columns \"ab\" \"name\"","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"Important: Due R works, add_custom_antimicrobials() function run every R session - added antimicrobials stored sessions thus lost R exited. two ways circumvent automate process adding antimicrobials: Method 1: Using package option AMR_custom_ab, preferred method. use method: Create data set structure antimicrobials data set (containing least columns \"ab\" \"name\") save saveRDS() location choice, e.g. \"~/my_custom_ab.rds\", remote location. Set file location package option AMR_custom_ab: options(AMR_custom_ab = \"~/my_custom_ab.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file:   Upon package load, file loaded run add_custom_antimicrobials() function. Method 2: Loading antimicrobial additions directly .Rprofile file. Note definitions stored user-specific R file, suboptimal workflow. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_antimicrobials() clear previously added antimicrobials.","code":"# Add custom antimicrobial codes: options(AMR_custom_ab = \"~/my_custom_ab.rds\") # Add custom antibiotic drug codes:  AMR::add_custom_antimicrobials(    data.frame(ab = \"TESTAB\",               name = \"Test Antibiotic\",               group = \"Test Group\")  )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Antimicrobials — add_custom_antimicrobials","text":"","code":"# \\donttest{ # returns a wildly guessed result: as.ab(\"testab\") #> Class 'ab' #> [1] THA  # now add a custom entry - it will be considered by as.ab() and # all ab_*() functions add_custom_antimicrobials(   data.frame(     ab = \"TESTAB\",     name = \"Test Antibiotic\",     # you can add any property present in the     # 'antimicrobials' data set, such as 'group':     group = \"Test Group\"   ) ) #> ℹ Added one record to the internal antimicrobials data set.  # \"testab\" is now a new antibiotic: as.ab(\"testab\") #> Class 'ab' #> [1] TESTAB ab_name(\"testab\") #> [1] \"Test Antibiotic\" ab_group(\"testab\") #> [1] \"Test Group\"  ab_info(\"testab\") #> $ab #> [1] \"TESTAB\" #>  #> $cid #> [1] NA #>  #> $name #> [1] \"Test Antibiotic\" #>  #> $group #> [1] \"Test Group\" #>  #> $atc #> [1] NA #>  #> $atc_group1 #> [1] NA #>  #> $atc_group2 #> [1] NA #>  #> $tradenames #> [1] NA #>  #> $loinc #> [1] NA #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] NA #>  #> $ddd$oral$units #> [1] NA #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] NA #>  #> $ddd$iv$units #> [1] NA #>  #>  #>    # Add Co-fluampicil, which is one of the many J01CR50 codes, see # https://atcddd.fhi.no/ddd/list_of_ddds_combined_products/ add_custom_antimicrobials(   data.frame(     ab = \"COFLU\",     name = \"Co-fluampicil\",     atc = \"J01CR50\",     group = \"Beta-lactams/penicillins\"   ) ) #> ℹ Added one record to the internal antimicrobials data set. ab_atc(\"Co-fluampicil\") #> [1] \"J01CR50\" ab_name(\"J01CR50\") #> [1] \"Co-fluampicil\"  # even antimicrobial selectors work # see ?amr_selector x <- data.frame(   random_column = \"some value\",   coflu = as.sir(\"S\"),   ampicillin = as.sir(\"R\") ) x #>   random_column coflu ampicillin #> 1    some value     S          R x[, betalactams()] #> ℹ For betalactams() using columns 'coflu' (co-fluampicil) and #>   'ampicillin' #>   coflu ampicillin #> 1     S          R # }"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Custom Microorganisms — add_custom_microorganisms","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"add_custom_microorganisms() can add custom microorganisms, non-taxonomic outcome laboratory analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"add_custom_microorganisms(x)  clear_custom_microorganisms()"},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"x data.frame resembling microorganisms data set, least containing column \"genus\" (case-insensitive)","code":""},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"function fill missing taxonomy , specific taxonomic columns missing, see Examples. Important: Due R works, add_custom_microorganisms() function run every R session - added microorganisms stored sessions thus lost R exited. two ways circumvent automate process adding microorganisms: Method 1: Using package option AMR_custom_mo, preferred method. use method: Create data set structure microorganisms data set (containing least column \"genus\") save saveRDS() location choice, e.g. \"~/my_custom_mo.rds\", remote location. Set file location package option AMR_custom_mo: options(AMR_custom_mo = \"~/my_custom_mo.rds\"). can even remote file location, https URL. Since options saved R sessions, best save option .Rprofile file loaded start-R. , open .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"), add text save file:   Upon package load, file loaded run add_custom_microorganisms() function. Method 2: Loading microorganism directly .Rprofile file. Note definitions stored user-specific R file, suboptimal workflow. use method: Edit .Rprofile file using e.g. utils::file.edit(\"~/.Rprofile\"). Add text like save file:   Use clear_custom_microorganisms() clear previously added microorganisms.","code":"# Add custom microorganism codes: options(AMR_custom_mo = \"~/my_custom_mo.rds\") # Add custom antibiotic drug codes:  AMR::add_custom_microorganisms(    data.frame(genus = \"Enterobacter\",               species = \"asburiae/cloacae\")  )"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/add_custom_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Custom Microorganisms — add_custom_microorganisms","text":"","code":"# \\donttest{ # a combination of species is not formal taxonomy, so # this will result in \"Enterobacter cloacae cloacae\", # since it resembles the input best: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae\"  # now add a custom entry - it will be considered by as.mo() and # all mo_*() functions add_custom_microorganisms(   data.frame(     genus = \"Enterobacter\",     species = \"asburiae/cloacae\"   ) ) #> ℹ Added Enterobacter asburiae/cloacae to the internal microorganisms data #>   set.  # E. asburiae/cloacae is now a new microorganism: mo_name(\"Enterobacter asburiae/cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # its code: as.mo(\"Enterobacter asburiae/cloacae\") #> Class 'mo' #> [1] CUSTOM1_ENTRB_ASB/  # all internal algorithms will work as well: mo_name(\"Ent asburia cloacae\") #> [1] \"Enterobacter asburiae/cloacae\"  # and even the taxonomy was added based on the genus! mo_family(\"E. asburiae/cloacae\") #> [1] \"Enterobacteriaceae\" mo_gramstain(\"Enterobacter asburiae/cloacae\") #> [1] \"Gram-negative\"  mo_info(\"Enterobacter asburiae/cloacae\") #> $mo #> [1] \"CUSTOM1_ENTRB_ASB/\" #>  #> $rank #> [1] \"species\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Enterobacter\" #>  #> $species #> [1] \"asburiae/cloacae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $oxygen_tolerance #> [1] NA #>  #> $url #> [1] \"\" #>  #> $ref #> [1] \"Self-added, 2025\" #>  #> $snomed #> [1] NA #>  #> $lpsn #> [1] NA #>  #> $mycobank #> [1] NA #>  #> $gbif #> [1] NA #>  #> $group_members #> character(0) #>    # the function tries to be forgiving: add_custom_microorganisms(   data.frame(     GENUS = \"BACTEROIDES / PARABACTEROIDES SLASHLINE\",     SPECIES = \"SPECIES\"   ) ) #> ℹ Added Bacteroides/Parabacteroides to the internal microorganisms data #>   set. mo_name(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"Bacteroides/Parabacteroides\" mo_rank(\"BACTEROIDES / PARABACTEROIDES\") #> [1] \"genus\"  # taxonomy still works, even though a slashline genus was given as input: mo_family(\"Bacteroides/Parabacteroides\") #> [1] \"Bacteroidaceae\"   # for groups and complexes, set them as species or subspecies: add_custom_microorganisms(   data.frame(     genus = \"Citrobacter\",     species = c(\"freundii\", \"braakii complex\"),     subspecies = c(\"complex\", \"\")   ) ) #> ℹ Added Citrobacter braakii complex and Citrobacter freundii complex to the #>   internal microorganisms data set. mo_name(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Citrobacter freundii complex\" \"Citrobacter braakii complex\"  mo_species(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"freundii complex\" \"braakii complex\"  mo_gramstain(c(\"C. freundii complex\", \"C. braakii complex\")) #> [1] \"Gram-negative\" \"Gram-negative\" # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (default today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000)  # add ages df$age <- age(df$birth_date)  # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE)  # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\")  df #>    birth_date age age_exact age_at_y2k #> 1  1965-12-05  59  59.25205         34 #> 2  1980-03-01  45  45.01644         19 #> 3  1949-11-01  75  75.34521         50 #> 4  1947-02-14  78  78.05753         52 #> 5  1940-02-19  85  85.04384         59 #> 6  1988-01-10  37  37.15342         11 #> 7  1997-08-27  27  27.52603          2 #> 8  1978-01-26  47  47.10959         21 #> 9  1972-06-17  52  52.72055         27 #> 10 1986-08-10  38  38.57260         13"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x - default age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)  # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+  0-49 50+  50+  0-49 0-49 #> Levels: 0-49 < 50+  # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19  0-19  0-19  50+   20-49 50+   50+   20-49 20-49 #> Levels: 0-19 < 20-49 < 50+  # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+  # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+  # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+  # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\") && require(\"ggplot2\")) {   example_isolates %>%     filter_first_isolate() %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(       x = \"age_group\",       minimum = 0,       x.title = \"Age Group\",       title = \"Ciprofloxacin resistance per age group\"     ) } #> Loading required package: ggplot2  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Create detailed antibiograms options traditional, combination, syndromic, Bayesian WISCA methods. Adhering previously described approaches (see Source) especially Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) Bielicki et al., functions provide flexible output formats including plots tables, ideal integration R Markdown Quarto reports.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"","code":"antibiogram(x, antibiotics = where(is.sir), mo_transform = \"shortname\",   ab_transform = \"name\", syndromic_group = NULL, add_total_n = FALSE,   only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),   formatting_type = getOption(\"AMR_antibiogram_formatting_type\",   ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(),   minimum = 30, combine_SI = TRUE, sep = \" + \", wisca = FALSE,   simulations = 1000, conf_interval = 0.95, interval_side = \"two-tailed\",   info = interactive())  wisca(x, antibiotics = where(is.sir), ab_transform = \"name\",   syndromic_group = NULL, add_total_n = FALSE, only_all_tested = FALSE,   digits = 1,   formatting_type = getOption(\"AMR_antibiogram_formatting_type\", 14),   col_mo = NULL, language = get_AMR_locale(), minimum = 30,   combine_SI = TRUE, sep = \" + \", simulations = 1000,   conf_interval = 0.95, interval_side = \"two-tailed\",   info = interactive())  retrieve_wisca_parameters(wisca_model, ...)  # S3 method for class 'antibiogram' plot(x, ...)  # S3 method for class 'antibiogram' autoplot(object, ...)  # S3 method for class 'antibiogram' knit_print(x, italicise = TRUE,   na = getOption(\"knitr.kable.NA\", default = \"\"), ...)"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Bielicki JA et al. (2016). Selecting appropriate empirical antibiotic regimens paediatric bloodstream infections: application Bayesian decision model local pooled antimicrobial resistance surveillance data Journal Antimicrobial Chemotherapy 71(3); doi:10.1093/jac/dkv397 Bielicki JA et al. (2020). Evaluation coverage 3 antibiotic regimens neonatal sepsis hospital setting across Asian countries JAMA Netw Open. 3(2):e1921124; doi:10.1001.jamanetworkopen.2019.21124 Klinker KP et al. (2021). Antimicrobial stewardship antibiograms: importance moving beyond traditional antibiograms. Therapeutic Advances Infectious Disease, May 5;8:20499361211011373; doi:10.1177/20499361211011373 Barbieri E et al. (2021). Development Weighted-Incidence Syndromic Combination Antibiogram (WISCA) guide choice empiric antibiotic treatment urinary tract infection paediatric patients: Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; doi:10.1186/s13756-021-00939-2 M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"x data.frame containing least column microorganisms columns antimicrobial results (class 'sir', see .sir()) antibiotics vector antimicrobial name code (evaluated .ab(), column name x, (combinations ) antimicrobial selectors aminoglycosides() carbapenems(). combination antibiograms, can also set values separated \"+\", \"TZP+TOB\" \"cipro + genta\", given columns resembling antimicrobials exist x. See Examples. mo_transform character transform microorganism input - must \"name\", \"shortname\" (default), \"gramstain\", one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Can also NULL transform input NA consider microorganisms 'unknown'. ab_transform character transform antimicrobial input - must one column names antimicrobials data set (defaults \"name\"): \"ab\", \"cid\", \"name\", \"group\", \"atc\", \"atc_group1\", \"atc_group2\", \"abbreviations\", \"synonyms\", \"oral_ddd\", \"oral_units\", \"iv_ddd\", \"iv_units\", \"loinc\". Can also NULL transform input. syndromic_group column name x, values calculated split rows x, e.g. using ifelse() case_when(). See Examples. add_total_n logical indicate whether n_tested available numbers per pathogen added table (default TRUE). add lowest highest number available isolates per antimicrobial (e.g, E. coli 200 isolates available ciprofloxacin 150 amoxicillin, returned number \"150-200\"). option unavailable wisca = TRUE; case, use retrieve_wisca_parameters() get parameters used WISCA. only_all_tested (combination antibiograms): logical indicate isolates must tested antimicrobials, see Details digits number digits use rounding antimicrobial coverage, defaults 1 WISCA 0 otherwise formatting_type numeric value (1–22 WISCA, 1-12 non-WISCA) indicating 'cells' antibiogram table formatted. See Details > Formatting Type list options. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). language language translate text, defaults system language (see get_AMR_locale()) minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether susceptibility determined results either S, SDD, , instead S (default TRUE) sep separating character antimicrobial columns combination antibiograms wisca logical indicate whether Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must generated (default FALSE). use Bayesian decision model estimate regimen coverage probabilities using Monte Carlo simulations. Set simulations, conf_interval, interval_side adjust. simulations (WISCA) numerical value set number Monte Carlo simulations conf_interval (WISCA) numerical value set confidence interval (default 0.95) interval_side (WISCA) side confidence interval, either \"two-tailed\" (default), \"left\" \"right\" info logical indicate info printed - default TRUE interactive mode wisca_model outcome wisca() antibiogram(..., wisca = TRUE) ... used R Markdown Quarto: arguments passed knitr::kable() (otherwise, use) object antibiogram() object italicise logical indicate whether microorganism names knitr table made italic, using italicise_taxonomy(). na character use showing NA values","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"functions return table values 0 100 susceptibility, resistance. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine one four available algorithms: isolate-based, patient-based, episode-based, phenotype-based. estimating antimicrobial coverage, especially creating WISCA, outcome might become reliable including top n species encountered data. can filter top n using top_n_microorganisms(). example, use top_n_microorganisms(your_data, n = 10) pre-processing step include top 10 species data. numeric values antibiogram stored long format attribute long_numeric. can retrieve using attributes(x)$long_numeric, x outcome antibiogram() wisca(). ideal e.g. advanced plotting.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"formatting-type","dir":"Reference","previous_headings":"","what":"Formatting Type","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"formatting 'cells' table can set argument formatting_type. examples, 5 indicates antimicrobial coverage (4-6 confidence level), 15 number susceptible isolates, 300 number tested (.e., available) isolates: 5 15 300 15/300 5 (300) 5% (300) 5 (N=300) 5% (N=300) 5 (15/300) 5% (15/300) 5 (N=15/300) 5% (N=15/300) 5 (4-6) 5% (4-6%) - default WISCA 5 (4-6,300) 5% (4-6%,300) 5 (4-6,N=300) 5% (4-6%,N=300) - default non-WISCA 5 (4-6,15/300) 5% (4-6%,15/300) 5 (4-6,N=15/300) 5% (4-6%,N=15/300) default can set globally package option AMR_antibiogram_formatting_type, e.g. options(AMR_antibiogram_formatting_type = 5). note WISCA, total numbers tested susceptible isolates less useful report, since included Bayesian model apparent susceptibility confidence level. Set digits (defaults 0) alter rounding susceptibility percentages.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"antibiogram-types","dir":"Reference","previous_headings":"","what":"Antibiogram Types","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"various antibiogram types, summarised Klinker et al. (2021, doi:10.1177/20499361211011373 ), supported antibiogram(). clinical coverage estimations, use WISCA whenever possible, since provides precise coverage estimates accounting pathogen incidence antimicrobial susceptibility, shown Bielicki et al. (2020, doi:10.1001.jamanetworkopen.2019.21124 ). See section Explaining WISCA page. note WISCA pathogen-agnostic, meaning outcome stratied pathogen, rather syndrome. Traditional Antibiogram Case example: Susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Code example:   Combination Antibiogram Case example: Additional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Code example:   Syndromic Antibiogram Case example: Susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Code example:   Weighted-Incidence Syndromic Combination Antibiogram (WISCA) WISCA can applied antibiogram, see section Explaining WISCA page information. Code example:   WISCA uses sophisticated Bayesian decision model combine local pooled antimicrobial resistance data. approach evaluates local patterns can also draw multi-centre datasets improve regimen accuracy, even low-incidence infections like paediatric bloodstream infections (BSIs).","code":"antibiogram(your_data,             antibiotics = \"TZP\") antibiogram(your_data,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\")) antibiogram(your_data,             antibiotics = penicillins(),             syndromic_group = \"ward\") antibiogram(your_data,             antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),             wisca = TRUE)  # this is equal to: wisca(your_data,       antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"grouped-tibbles","dir":"Reference","previous_headings":"","what":"Grouped tibbles","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"type antibiogram, grouped tibbles can also used calculate susceptibilities various groups. Code example:","code":"library(dplyr) your_data %>%   group_by(has_sepsis, is_neonate, sex) %>%   wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"))"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"stepped-approach-for-clinical-insight","dir":"Reference","previous_headings":"","what":"Stepped Approach for Clinical Insight","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"clinical practice, antimicrobial coverage decisions evolve microbiological data becomes available. theoretical stepped approach ensures empirical coverage can continuously assessed improve patient outcomes: Initial Empirical Therapy (Admission / Pre-Culture Data) admission, pathogen information available. Action: broad-spectrum coverage based local resistance patterns syndromic antibiograms. Using pathogen-agnostic yet incidence-weighted WISCA preferred. Code example:   Refinement Gram Stain Results blood culture becomes positive, Gram stain provides initial crucial first stratification (Gram-positive vs. Gram-negative). Action: narrow coverage based Gram stain-specific resistance patterns. Code example:   Definitive Therapy Based Species Identification cultivation pathogen, full pathogen identification allows precise targeting therapy. Action: adjust treatment pathogen-specific antibiograms, minimizing resistance risks. Code example:   structuring antibiograms around stepped approach, clinicians can make data-driven adjustments stage, ensuring optimal empirical targeted therapy reducing unnecessary broad-spectrum antimicrobial use.","code":"antibiogram(your_data,             antibiotics = selected_regimens,             mo_transform = NA) # all pathogens set to `NA`  # preferred: use WISCA wisca(your_data,       antibiotics = selected_regimens) antibiogram(your_data,             antibiotics = selected_regimens,             mo_transform = \"gramstain\") # all pathogens set to Gram-pos/Gram-neg antibiogram(your_data,             antibiotics = selected_regimens,             mo_transform = \"shortname\") # all pathogens set to 'G. species', e.g., E. coli"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"inclusion-in-combination-antibiograms","dir":"Reference","previous_headings":"","what":"Inclusion in Combination Antibiograms","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Note combination antibiograms, important realise susceptibility can calculated two ways, can set only_all_tested argument (default FALSE). See example two antimicrobials, Drug Drug B, antibiogram() works calculate %SI:","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   considered   considered  considered   considered                     susceptible    tested    susceptible    tested --------  --------  -----------  ----------  -----------  ----------  S or I    S or I        X            X           X            X    R       S or I        X            X           X            X   <NA>     S or I        X            X           -            -  S or I      R           X            X           X            X    R         R           -            X           -            X   <NA>       R           -            -           -            -  S or I     <NA>         X            X           -            -    R        <NA>         -            -           -            -   <NA>      <NA>         -            -           -            - --------------------------------------------------------------------"},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"plotting","dir":"Reference","previous_headings":"","what":"Plotting","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"types antibiograms listed can plotted (using ggplot2::autoplot() base R's plot() barplot()). mentioned , numeric values antibiogram stored long format attribute long_numeric. can retrieve using attributes(x)$long_numeric, x outcome antibiogram() wisca(). outcome antibiogram() can also used directly R Markdown / Quarto (.e., knitr) reports. case, knitr::kable() applied automatically microorganism names even printed italics default (see argument italicise). can also use functions specific 'table reporting' packages transform output antibiogram() needs, e.g. flextable::as_flextable() gt::gt().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"explaining-wisca","dir":"Reference","previous_headings":"","what":"Explaining WISCA","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"WISCA, outlined Bielicki et al. (doi:10.1093/jac/dkv397 ), stands Weighted-Incidence Syndromic Combination Antibiogram, estimates probability adequate empirical antimicrobial regimen coverage specific infection syndromes. method leverages Bayesian decision model random effects pathogen incidence susceptibility, enabling robust estimates presence sparse data. Bayesian model assumes conjugate priors parameter estimation. example, coverage probability \\(\\theta\\) given antimicrobial regimen modelled using Beta distribution prior: $$\\theta \\sim \\text{Beta}(\\alpha_0, \\beta_0)$$ \\(\\alpha_0\\) \\(\\beta_0\\) represent prior successes failures, respectively, informed expert knowledge weakly informative priors (e.g., \\(\\alpha_0 = 1, \\beta_0 = 1\\)). likelihood function constructed based observed data, number covered cases regimen follows binomial distribution: $$y \\sim \\text{Binomial}(n, \\theta)$$ Posterior parameter estimates obtained combining prior likelihood using Bayes' theorem. posterior distribution \\(\\theta\\) also Beta distribution: $$\\theta | y \\sim \\text{Beta}(\\alpha_0 + y, \\beta_0 + n - y)$$ Pathogen incidence, representing proportion infections caused different pathogens, modelled using Dirichlet distribution, natural conjugate prior multinomial outcomes. Dirichlet distribution parameterised vector concentration parameters \\(\\alpha\\), \\(\\alpha_i\\) corresponds specific pathogen. prior typically chosen uniform (\\(\\alpha_i = 1\\)), reflecting assumption equal prior probability across pathogens. posterior distribution pathogen incidence given : $$\\text{Dirichlet}(\\alpha_1 + n_1, \\alpha_2 + n_2, \\dots, \\alpha_K + n_K)$$ \\(n_i\\) number infections caused pathogen \\(\\) observed data. practical implementation, pathogen incidences sampled posterior using normalised Gamma-distributed random variables: $$x_i \\sim \\text{Gamma}(\\alpha_i + n_i, 1)$$ $$p_i = \\frac{x_i}{\\sum_{j=1}^K x_j}$$ \\(x_i\\) represents unnormalised pathogen counts, \\(p_i\\) normalised proportion pathogen \\(\\). hierarchical modelling, pathogen-level effects (e.g., differences resistance patterns) regimen-level effects modelled using Gaussian priors log-odds. hierarchical structure ensures partial pooling estimates across groups, improving stability strata small sample sizes. model implemented using Hamiltonian Monte Carlo (HMC) sampling. Stratified results can provided based covariates age, sex, clinical complexity (e.g., prior antimicrobial treatments renal/urological comorbidities) using dplyr's group_by() pre-processing step running wisca(). Posterior odds ratios (ORs) derived quantify effect covariates coverage probabilities: $$\\text{}_{\\text{covariate}} = \\frac{\\exp(\\beta_{\\text{covariate}})}{\\exp(\\beta_0)}$$ combining empirical data prior knowledge, WISCA overcomes limitations traditional combination antibiograms, offering disease-specific, patient-stratified estimates robust uncertainty quantification. tool invaluable antimicrobial stewardship programs empirical treatment guideline refinement. Note: WISCA never gives output pathogen/species level, incidences susceptibilities already weighted species.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"Implementation: Dr. Larisse Bolton Dr. Matthijs Berends","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiogram.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Traditional, Combination, Syndromic, or WISCA Antibiograms — antibiogram","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # \\donttest{ # Traditional antibiogram ----------------------------------------------  antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()) ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # An Antibiogram (non-WISCA / 95% CI): 10 × 7 #>    Pathogen       Amikacin    Gentamicin Imipenem Kanamycin Meropenem Tobramycin #>    <chr>          <chr>       <chr>      <chr>    <chr>     <chr>     <chr>      #>  1 CoNS           0% (0-8%,N… 86% (82-9… 52% (37… 0% (0-8%… 52% (37-… 22% (12-3… #>  2 E. coli        100% (98-1… 98% (96-9… 100% (9… NA        100% (99… 97% (96-9… #>  3 E. faecalis    0% (0-9%,N… 0% (0-9%,… 100% (9… 0% (0-9%… NA        0% (0-9%,… #>  4 K. pneumoniae  NA          90% (79-9… 100% (9… NA        100% (93… 90% (79-9… #>  5 P. aeruginosa  NA          100% (88-… NA       0% (0-12… NA        100% (88-… #>  6 P. mirabilis   NA          94% (80-9… 94% (79… NA        NA        94% (80-9… #>  7 S. aureus      NA          99% (97-1… NA       NA        NA        98% (92-1… #>  8 S. epidermidis 0% (0-8%,N… 79% (71-8… NA       0% (0-8%… NA        51% (40-6… #>  9 S. hominis     NA          92% (84-9… NA       NA        NA        85% (74-9… #> 10 S. pneumoniae  0% (0-3%,N… 0% (0-3%,… NA       0% (0-3%… NA        0% (0-3%,… #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram  antibiogram(example_isolates,   antibiotics = aminoglycosides(),   ab_transform = \"atc\",   mo_transform = \"gramstain\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 5 #>   Pathogen      J01GB01            J01GB03             J01GB04         J01GB06   #>   <chr>         <chr>              <chr>               <chr>           <chr>     #> 1 Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684)  0% (0-10%,N=35) 98% (96-… #> 2 Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%… #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram  antibiogram(example_isolates,   antibiotics = carbapenems(),   ab_transform = \"name\",   mo_transform = \"name\" ) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # An Antibiogram (non-WISCA / 95% CI): 5 × 3 #>   Pathogen                                 Imipenem             Meropenem        #>   <chr>                                    <chr>                <chr>            #> 1 Coagulase-negative Staphylococcus (CoNS) 52% (37-67%,N=48)    52% (37-67%,N=4… #> 2 Enterococcus faecalis                    100% (91-100%,N=38)  NA               #> 3 Escherichia coli                         100% (99-100%,N=422) 100% (99-100%,N… #> 4 Klebsiella pneumoniae                    100% (93-100%,N=51)  100% (93-100%,N… #> 5 Proteus mirabilis                        94% (79-99%,N=32)    NA               #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram   # Combined antibiogram -------------------------------------------------  # combined antibiotics yield higher empiric coverage antibiogram(example_isolates,   antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),   mo_transform = \"gramstain\" ) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 4 #>   Pathogen  Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³ #>   <chr>     <chr>                  <chr>                  <chr>                  #> 1 Gram-neg… 88% (85-91%,N=641)     99% (97-99%,N=691)     98% (97-99%,N=693)     #> 2 Gram-pos… 86% (82-89%,N=345)     98% (96-98%,N=1044)    95% (93-97%,N=550)     #> # ℹ abbreviated names: ¹​`Piperacillin/tazobactam`, #> #   ²​`Piperacillin/tazobactam + Gentamicin`, #> #   ³​`Piperacillin/tazobactam + Tobramycin` #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram  # names of antibiotics do not need to resemble columns exactly: antibiogram(example_isolates,   antibiotics = c(\"Cipro\", \"cipro + genta\"),   mo_transform = \"gramstain\",   ab_transform = \"name\",   sep = \" & \" ) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 3 #>   Pathogen      Ciprofloxacin      `Ciprofloxacin & Gentamicin` #>   <chr>         <chr>              <chr>                        #> 1 Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694)           #> 2 Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847)           #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram   # Syndromic antibiogram ------------------------------------------------  # the data set could contain a filter for e.g. respiratory specimens antibiogram(example_isolates,   antibiotics = c(aminoglycosides(), carbapenems()),   syndromic_group = \"ward\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # An Antibiogram (non-WISCA / 95% CI): 14 × 8 #>    `Syndromic Group` Pathogen   Amikacin Gentamicin Imipenem Kanamycin Meropenem #>    <chr>             <chr>      <chr>    <chr>      <chr>    <chr>     <chr>     #>  1 Clinical          CoNS       NA       89% (84-9… 57% (39… NA        57% (39-… #>  2 ICU               CoNS       NA       79% (68-8… NA       NA        NA        #>  3 Outpatient        CoNS       NA       84% (66-9… NA       NA        NA        #>  4 Clinical          E. coli    100% (9… 98% (96-9… 100% (9… NA        100% (99… #>  5 ICU               E. coli    100% (9… 99% (95-1… 100% (9… NA        100% (97… #>  6 Clinical          K. pneumo… NA       92% (81-9… 100% (9… NA        100% (92… #>  7 Clinical          P. mirabi… NA       100% (88-… NA       NA        NA        #>  8 Clinical          S. aureus  NA       99% (95-1… NA       NA        NA        #>  9 ICU               S. aureus  NA       100% (95-… NA       NA        NA        #> 10 Clinical          S. epider… NA       82% (72-9… NA       NA        NA        #> 11 ICU               S. epider… NA       72% (60-8… NA       NA        NA        #> 12 Clinical          S. hominis NA       96% (85-9… NA       NA        NA        #> 13 Clinical          S. pneumo… 0% (0-5… 0% (0-5%,… NA       0% (0-5%… NA        #> 14 ICU               S. pneumo… 0% (0-1… 0% (0-12%… NA       0% (0-12… NA        #> # ℹ 1 more variable: Tobramycin <chr> #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram  # now define a data set with only E. coli ex1 <- example_isolates[which(mo_genus() == \"Escherichia\"), ] #> ℹ Using column 'mo' as input for mo_genus()  # with a custom language, though this will be determined automatically # (i.e., this table will be in Spanish on Spanish systems) antibiogram(ex1,   antibiotics = aminoglycosides(),   ab_transform = \"name\",   syndromic_group = ifelse(ex1$ward == \"ICU\",     \"UCI\", \"No UCI\"   ),   language = \"es\" ) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # An Antibiogram (non-WISCA / 95% CI): 2 × 5 #>   `Grupo sindrómico` Patógeno Amikacina            Gentamicina       Tobramicina #>   <chr>              <chr>    <chr>                <chr>             <chr>       #> 1 No UCI             E. coli  100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99… #> 2 UCI                E. coli  100% (93-100%,N=52)  99% (95-100%,N=1… 96% (92-99… #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram   # WISCA antibiogram ----------------------------------------------------  # WISCA are not stratified by species, but rather on syndromes antibiogram(example_isolates,   antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"),   syndromic_group = \"ward\",   wisca = TRUE ) #> # An Antibiogram (WISCA / 95% CI): 3 × 4 #>   `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹ #>   <chr>             <chr>                     <chr>                              #> 1 Clinical          76.1% (59.9-88.3%)        94.9% (85.2-99.2%)                 #> 2 ICU               60.3% (42.7-78.9%)        90.9% (77.4-98.1%)                 #> 3 Outpatient        67.2% (39-92%)            90% (68.6-99.4%)                   #> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin` #> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr> #> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram, #> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram   # Print the output for R Markdown / Quarto -----------------------------  ureido <- antibiogram(example_isolates,   antibiotics = ureidopenicillins(),   syndromic_group = \"ward\",   wisca = TRUE ) #> ℹ For ureidopenicillins() using column 'TZP' (piperacillin/tazobactam)  # in an Rmd file, you would just need to return `ureido` in a chunk, # but to be explicit here: if (requireNamespace(\"knitr\")) {   cat(knitr::knit_print(ureido)) } #>  #>  #> |Syndromic Group |Piperacillin/tazobactam | #> |:---------------|:-----------------------| #> |Clinical        |76% (60-88.7%)          | #> |ICU             |59.8% (40.7-80%)        | #> |Outpatient      |66.2% (38.1-91.3%)      |   # Generate plots with ggplot2 or base R --------------------------------  ab1 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\" ) ab2 <- antibiogram(example_isolates,   antibiotics = c(\"AMC\", \"CIP\", \"TZP\", \"TZP+TOB\"),   mo_transform = \"gramstain\",   syndromic_group = \"ward\" )  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab1) }  if (requireNamespace(\"ggplot2\")) {   ggplot2::autoplot(ab2) }   plot(ab1)  plot(ab2)  # }"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antimicrobial Selectors — antimicrobial_selectors","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"functions allow filtering rows selecting columns based antimicrobial test results specific antimicrobial class group, without need define columns antimicrobial abbreviations. short, column name resembles antimicrobial drug, picked functions matches pharmaceutical class: \"cefazolin\", \"kefzol\", \"CZO\" \"J01DB04\" picked using:","code":"library(dplyr) my_data_with_all_these_columns %>%   select(cephalosporins())"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"","code":"aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  aminopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)  antifungals(only_sir_columns = FALSE, return_all = TRUE, ...)  antimycobacterials(only_sir_columns = FALSE, return_all = TRUE, ...)  betalactams(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  betalactams_with_inhibitor(only_sir_columns = FALSE, return_all = TRUE,   ...)  carbapenems(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  cephalosporins(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_1st(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_2nd(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_3rd(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_4th(only_sir_columns = FALSE, return_all = TRUE, ...)  cephalosporins_5th(only_sir_columns = FALSE, return_all = TRUE, ...)  fluoroquinolones(only_sir_columns = FALSE, return_all = TRUE, ...)  glycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...)  isoxazolylpenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)  lincosamides(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  lipoglycopeptides(only_sir_columns = FALSE, return_all = TRUE, ...)  macrolides(only_sir_columns = FALSE, return_all = TRUE, ...)  monobactams(only_sir_columns = FALSE, return_all = TRUE, ...)  nitrofurans(only_sir_columns = FALSE, return_all = TRUE, ...)  oxazolidinones(only_sir_columns = FALSE, return_all = TRUE, ...)  penicillins(only_sir_columns = FALSE, return_all = TRUE, ...)  phenicols(only_sir_columns = FALSE, return_all = TRUE, ...)  polymyxins(only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  quinolones(only_sir_columns = FALSE, return_all = TRUE, ...)  rifamycins(only_sir_columns = FALSE, return_all = TRUE, ...)  streptogramins(only_sir_columns = FALSE, return_all = TRUE, ...)  tetracyclines(only_sir_columns = FALSE, return_all = TRUE, ...)  trimethoprims(only_sir_columns = FALSE, return_all = TRUE, ...)  ureidopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)  amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE,   return_all = TRUE, ...)  administrable_per_os(only_sir_columns = FALSE, return_all = TRUE, ...)  administrable_iv(only_sir_columns = FALSE, return_all = TRUE, ...)  not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL,   version_expertrules = 3.3, ...)"},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"only_sir_columns logical indicate whether columns class sir must selected (default FALSE), see .sir() only_treatable logical indicate whether antimicrobial drugs excluded laboratory tests (default TRUE), gentamicin-high (GEH) imipenem/EDTA (IPE) return_all logical indicate whether matched columns must returned (default TRUE). FALSE, first unique antimicrobial returned, e.g. columns \"genta\" \"gentamicin\" exist data, first hit gentamicin returned. ... ignored, place allow future extensions amr_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antimicrobials data set searched (case-insensitive) value. filter expression evaluated antimicrobials data set, name %like% \"trim\" col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can \"3.3\", \"3.2\", \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"used inside selecting filtering, returns character vector column names, additional class \"amr_selector\". used individually, returns 'ab' vector possible antimicrobials function able select filter.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"functions can used data set calls selecting columns filtering rows. work base R, Tidyverse, data.table. heavily inspired Tidyverse selection helpers everything(), limited dplyr verbs. Nonetheless, convenient use dplyr functions select(), filter() summarise(), see Examples. selectors can also used tidymodels packages recipe parsnip. See info tutorial using antimicrobial selectors predictive modelling. columns data functions called searched known antimicrobial names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antimicrobials data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. amr_class() function can used filter/select manually defined antimicrobial class. searches results antimicrobials data set within columns group, atc_group1 atc_group2. administrable_per_os() administrable_iv() functions also rely antimicrobials data set - antimicrobials matched DDD (defined daily dose) resp. oral IV treatment available antimicrobials data set. amr_selector() function can used internally filter antimicrobials data set results, see Examples. allows filtering (part ) certain name, /group name even minimum DDDs oral treatment. function yields highest flexibility, also least user-friendly, since requires hard-coded filter set. not_intrinsic_resistant() function can used select antimicrobials pose intrinsic resistance microorganisms data set. example, data set contains microorganism codes names E. coli K. pneumoniae contains column \"vancomycin\", column removed (rather, unselected) using function. currently applies 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"full-list-of-supported-antimicrobial-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antimicrobial) classes","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"aminoglycosides() can select:  amikacin (AMK), amikacin/fosfomycin (AKF), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB), tobramycin-high (TOH) aminopenicillins() can select:  amoxicillin (AMX) ampicillin (AMP) antifungals() can select:  amorolfine (AMO), amphotericin B (AMB), amphotericin B-high (AMH), anidulafungin (ANI), butoconazole (), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), flucytosine (FCT), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), oteseconazole (OTE), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC), voriconazole (VOR) antimycobacterials() can select:  4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), protionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), simvastatin/fenofibrate (SMF), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone (TAT), thioacetazone/isoniazid (THI1), tiocarlide (TCR), viomycin (VIO) betalactams() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), carumonam (CAR), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nafcillin (NAF), oxacillin (OXA), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC), tigemonam (TMN) betalactams_with_inhibitor() can select:  amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin/sulbactam (SAM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefoperazone/sulbactam (CSL), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefpodoxime/clavulanic acid (CDC), ceftaroline/avibactam (CPA), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), ceftolozane/tazobactam (CZT), ceftriaxone/beta-lactamase inhibitor (CEB), imipenem/relebactam (IMR), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), mezlocillin/sulbactam (MSU), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), ticarcillin/clavulanic acid (TCC) carbapenems() can select:  biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP) cephalosporins() can select:  cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), latamoxef (LTM), loracarbef (LOR) cephalosporins_1st() can select:  cefacetrile (CAC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefroxadine (CRD), ceftezole (CTL), cephradine (CED) cephalosporins_2nd() can select:  cefaclor (CEC), cefamandole (MAN), cefmetazole (CMZ), cefonicid (CID), ceforanide (CND), cefotetan (CTT), cefotiam (CTF), cefoxitin (FOX), cefoxitin screening (FOX1), cefprozil (CPR), cefuroxime (CXM), cefuroxime axetil (CXA), loracarbef (LOR) cephalosporins_3rd() can select:  cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefetamet (CAT), cefetamet pivoxil (CPI), cefixime (CFM), cefmenoxime (CMX), cefodizime (DIZ), cefoperazone (CFP), cefoperazone/sulbactam (CSL), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotiam hexetil (CHE), cefovecin (FOV), cefpimizole (CFZ), cefpiramide (CPM), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefsulodin (CFS), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), latamoxef (LTM) cephalosporins_4th() can select:  cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetecol (CCL), cefoselis (CSE), cefozopran (ZOP), cefpirome (CPO), cefquinome (CEQ) cephalosporins_5th() can select:  ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT) fluoroquinolones() can select:  besifloxacin (BES), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nifuroquine (NIF), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX), trovafloxacin (TVA) glycopeptides() can select:  avoparcin (AVO), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN), vancomycin-macromethod (VAM) isoxazolylpenicillins() can select:  cloxacillin (CLO), dicloxacillin (DIC), flucloxacillin (FLC), meticillin (MET), oxacillin (OXA) lincosamides() can select:  clindamycin (CLI), lincomycin (LIN), pirlimycin (PRL) lipoglycopeptides() can select:  dalbavancin (DAL), oritavancin (ORI), telavancin (TLV) macrolides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), azithromycin (AZM), clarithromycin (CLR), dirithromycin (DIR), erythromycin (ERY), flurithromycin (FLR1), gamithromycin (GAM), josamycin (JOS), kitasamycin (KIT), meleumycin (MEL), midecamycin (MID), miocamycin (MCM), nafithromycin (ZWK), oleandomycin (OLE), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL), tylvalosin (TYL1) monobactams() can select:  aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), carumonam (CAR), tigemonam (TMN) nitrofurans() can select:  furazidin (FUR), furazolidone (FRZ), nifurtoinol (NFR), nitrofurantoin (NIT), nitrofurazone (NIZ) oxazolidinones() can select:  cadazolid (CDZ), cycloserine (CYC), linezolid (LNZ), tedizolid (TZD), thiacetazone (THA) penicillins() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), azidocillin (AZD), azlocillin (AZL), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), carbenicillin (CRB), carindacillin (CRN), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), epicillin (EPC), flucloxacillin (FLC), hetacillin (HET), lenampicillin (LEN), mecillinam (MEC), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nafcillin (NAF), oxacillin (OXA), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), sarmoxicillin (SRX), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC) phenicols() can select:  chloramphenicol (CHL), florfenicol (FLR), thiamphenicol (THI) polymyxins() can select:  colistin (COL), polymyxin B (PLB), polymyxin B/polysorbate 80 (POP) quinolones() can select:  besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), ciprofloxacin/metronidazole (CIM), ciprofloxacin/ornidazole (CIO), ciprofloxacin/tinidazole (CIT), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), flumequine (FLM), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), lascufloxacin (LSC), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nemonoxacin (NEM), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pipemidic acid (PPA), piromidic acid (PIR), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rosoxacin (ROS), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX), trovafloxacin (TVA) rifamycins() can select:  rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP) streptogramins() can select:  pristinamycin (PRI) quinupristin/dalfopristin (QDA) tetracyclines() can select:  cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), sarecycline (SRC), tetracycline (TCY), tigecycline (TGC) trimethoprims() can select:  brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP), trimethoprim/sulfamethoxazole (SXT) ureidopenicillins() can select:  azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), piperacillin/tazobactam (TZP)","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobial_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antimicrobial Selectors — antimicrobial_selectors","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …   # you can use the selectors separately to retrieve all possible antimicrobials: carbapenems() #> ℹ This 'ab' vector was retrieved using carbapenems(), which should #>   normally be used inside a dplyr verb or data.frame call, e.g.: #>   • your_data %>% select(carbapenems()) #>   • your_data %>% select(column_a, column_b, carbapenems()) #>   • your_data %>% filter(any(carbapenems() == \"R\")) #>   • your_data[, carbapenems()] #>   • your_data[, c(\"column_a\", \"column_b\", carbapenems())] #> Class 'ab' #>  [1] BIA DOR ETP IMR IPE IPM MEM MEV MNC PAN RIA RIT RZM TBP   # Though they are primarily intended to use for selections and filters. # Examples sections below are split into 'dplyr', 'base R', and 'data.table':  # \\donttest{ if (FALSE) { # \\dontrun{ # dplyr -------------------------------------------------------------------  library(dplyr, warn.conflicts = FALSE)  example_isolates %>% select(carbapenems())  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates %>% select(mo, aminoglycosides())  # you can combine selectors like you are used with tidyverse # e.g., for betalactams, but not the ones with an enzyme inhibitor: example_isolates %>% select(betalactams(), -betalactams_with_inhibitor())  # select only antimicrobials with DDDs for oral treatment example_isolates %>% select(administrable_per_os())  # get AMR for all aminoglycosides e.g., per ward: example_isolates %>%   group_by(ward) %>%   summarise(across(aminoglycosides(),                    resistance))  # You can combine selectors with '&' to be more specific: example_isolates %>%   select(penicillins() & administrable_per_os())  # get AMR for only drugs that matter - no intrinsic resistance: example_isolates %>%   filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>%   group_by(ward) %>%   summarise_at(not_intrinsic_resistant(),                resistance)  # get susceptibility for antimicrobials whose name contains \"trim\": example_isolates %>%   filter(first_isolate()) %>%   group_by(ward) %>%   summarise(across(amr_selector(name %like% \"trim\"), susceptibility))  # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem): example_isolates %>%   select(carbapenems())  # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB': example_isolates %>%   select(mo, aminoglycosides())  # any() and all() work in dplyr's filter() too: example_isolates %>%   filter(     any(aminoglycosides() == \"R\"),     all(cephalosporins_2nd() == \"R\")   )  # also works with c(): example_isolates %>%   filter(any(c(carbapenems(), aminoglycosides()) == \"R\"))  # not setting any/all will automatically apply all(): example_isolates %>%   filter(aminoglycosides() == \"R\")  # this will select columns 'mo' and all antimycobacterial drugs ('RIF'): example_isolates %>%   select(mo, amr_class(\"mycobact\"))  # get bug/drug combinations for only glycopeptides in Gram-positives: example_isolates %>%   filter(mo_is_gram_positive()) %>%   select(mo, glycopeptides()) %>%   bug_drug_combinations() %>%   format()  data.frame(   some_column = \"some_value\",   J01CA01 = \"S\" ) %>% # ATC code of ampicillin   select(penicillins()) # only the 'J01CA01' column will be selected  # with recent versions of dplyr, this is all equal: x <- example_isolates[carbapenems() == \"R\", ] y <- example_isolates %>% filter(carbapenems() == \"R\") z <- example_isolates %>% filter(if_all(carbapenems(), ~ .x == \"R\")) identical(x, y) && identical(y, z)  } # } # base R ------------------------------------------------------------------  # select columns 'IPM' (imipenem) and 'MEM' (meropenem) example_isolates[, carbapenems()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <sir> <sir> #>  1   NA    NA  #>  2   NA    NA  #>  3   NA    NA  #>  4   NA    NA  #>  5   NA    NA  #>  6   NA    NA  #>  7   NA    NA  #>  8   NA    NA  #>  9   NA    NA  #> 10   NA    NA  #> # ℹ 1,990 more rows  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' example_isolates[, c(\"mo\", aminoglycosides())] #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 2,000 × 5 #>    mo           GEN   TOB   AMK   KAN   #>    <mo>         <sir> <sir> <sir> <sir> #>  1 B_ESCHR_COLI   NA    NA    NA    NA  #>  2 B_ESCHR_COLI   NA    NA    NA    NA  #>  3 B_STPHY_EPDR   NA    NA    NA    NA  #>  4 B_STPHY_EPDR   NA    NA    NA    NA  #>  5 B_STPHY_EPDR   NA    NA    NA    NA  #>  6 B_STPHY_EPDR   NA    NA    NA    NA  #>  7 B_STPHY_AURS   NA    S     NA    NA  #>  8 B_STPHY_AURS   NA    S     NA    NA  #>  9 B_STPHY_EPDR   NA    NA    NA    NA  #> 10 B_STPHY_EPDR   NA    NA    NA    NA  #> # ℹ 1,990 more rows  # select only antimicrobials with DDDs for oral treatment example_isolates[, administrable_per_os()] #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 23 #>    OXA   FLC   AMX   AMC   AMP   CXM   KAN   TMP   NIT   FOS   LNZ   CIP   MFX   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1   NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA  #>  2   NA    NA    NA    I     NA    I     NA    R     NA    NA    R     NA    NA  #>  3   NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA  #>  4   NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    NA    NA  #>  5   NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA  #>  6   NA    R     NA    NA    NA    R     NA    R     NA    NA    NA    NA    NA  #>  7   NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA  #>  8   NA    S     R     S     R     S     NA    R     NA    NA    NA    NA    NA  #>  9   NA    R     NA    NA    NA    R     NA    S     NA    NA    NA    S     NA  #> 10   NA    S     NA    NA    NA    S     NA    S     NA    NA    NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 10 more variables: VAN <sir>, TCY <sir>, DOX <sir>, ERY <sir>, CLI <sir>, #> #   AZM <sir>, MTR <sir>, CHL <sir>, COL <sir>, RIF <sir>  # filter using any() or all() example_isolates[any(carbapenems() == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP   R     NA    NA    R   #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC        NA    NA    NA    NA  #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC        NA    NA    NA    NA  #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL   R     NA    NA    NA  #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL   R     NA    NA    NA  #> # ℹ 45 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … subset(example_isolates, any(carbapenems() == \"R\")) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  2 2004-06-09 529296     69 M      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  3 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP   R     NA    NA    R   #>  4 2005-04-21 452212     82 F      ICU      B_ENTRC        NA    NA    NA    NA  #>  5 2005-04-22 452212     82 F      ICU      B_ENTRC        NA    NA    NA    NA  #>  6 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  7 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  8 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  9 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL   R     NA    NA    NA  #> 10 2008-01-22 1710B8     82 M      Clinical B_PROTS_MRBL   R     NA    NA    NA  #> # ℹ 45 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): example_isolates[any(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 962 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R   #>  6 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R   #>  7 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R   #>  8 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN   R     NA    NA    R   #>  9 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN   R     NA    NA    R   #> 10 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI   R     NA    NA    R   #> # ℹ 952 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … example_isolates[all(carbapenems()), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"S\", \"I\" or \"R\" #> # A tibble: 756 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-04-14 F30196     73 M      Outpat… B_STRPT_GRPB    S     NA    S     S   #>  2 2003-04-08 114570     74 M      ICU     B_STRPT_PYGN    S     NA    S     S   #>  3 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA    S     NA    S     S   #>  4 2003-04-08 114570     74 M      ICU     B_STRPT_GRPA    S     NA    S     S   #>  5 2003-08-14 F71508      0 F      Clinic… B_STRPT_GRPB    S     NA    S     S   #>  6 2003-10-16 650870     63 F      ICU     B_ESCHR_COLI    R     NA    NA    R   #>  7 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #>  8 2003-10-20 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #>  9 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI    R     NA    NA    NA  #> 10 2003-11-04 2FC253     87 F      ICU     B_ESCHR_COLI    R     NA    NA    NA  #> # ℹ 746 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter with multiple antimicrobial selectors using c() example_isolates[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 26 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2004-11-03 D65308     80 F      ICU      B_STNTR_MLTP   R     NA    NA    R   #>  2 2005-04-22 452212     82 F      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  3 2007-02-21 8BBC46     61 F      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  4 2007-12-15 401043     72 M      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  5 2008-12-06 501361     43 F      Clinical B_STNTR_MLTP   R     NA    NA    R   #>  6 2011-05-09 207325     82 F      ICU      B_ENTRC_FACM   NA    NA    NA    NA  #>  7 2012-03-12 582258     80 M      ICU      B_STPHY_CONS   R     R     R     R   #>  8 2012-05-19 C25552     89 F      Outpati… B_STPHY_CONS   R     R     R     R   #>  9 2012-07-17 F05015     83 M      ICU      B_STPHY_CONS   R     R     R     R   #> 10 2012-07-20 404299     66 F      Clinical B_STPHY_CONS   R     R     R     R   #> # ℹ 16 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter + select in one go: get penicillins in carbapenem-resistant strains example_isolates[any(carbapenems() == \"R\"), penicillins()] #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #>   (piperacillin/tazobactam) #> # A tibble: 55 × 7 #>    PEN   OXA   FLC   AMX   AMC   AMP   TZP   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1   NA    NA    NA    NA    NA    NA    NA  #>  2   NA    NA    NA    NA    NA    NA    NA  #>  3   R     NA    NA    R     R     R     R   #>  4   NA    NA    NA    NA    NA    NA    R   #>  5   NA    NA    NA    NA    NA    NA    R   #>  6   NA    NA    NA    NA    NA    NA    R   #>  7   NA    NA    NA    NA    NA    NA    R   #>  8   NA    NA    NA    NA    NA    NA    R   #>  9   R     NA    NA    NA    S     NA    S   #> 10   R     NA    NA    NA    S     NA    S   #> # ℹ 45 more rows  # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: example_isolates[, penicillins() & administrable_per_os()] #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #>   (piperacillin/tazobactam) #> ℹ For administrable_per_os() using columns 'OXA' (oxacillin), 'FLC' #>   (flucloxacillin), 'AMX' (amoxicillin), 'AMC' (amoxicillin/clavulanic acid), #>   'AMP' (ampicillin), 'CXM' (cefuroxime), 'KAN' (kanamycin), 'TMP' #>   (trimethoprim), 'NIT' (nitrofurantoin), 'FOS' (fosfomycin), 'LNZ' #>   (linezolid), 'CIP' (ciprofloxacin), 'MFX' (moxifloxacin), 'VAN' #>   (vancomycin), 'TCY' (tetracycline), 'DOX' (doxycycline), 'ERY' #>   (erythromycin), 'CLI' (clindamycin), 'AZM' (azithromycin), 'MTR' #>   (metronidazole), 'CHL' (chloramphenicol), 'COL' (colistin), and 'RIF' #>   (rifampicin) #> # A tibble: 2,000 × 5 #>    OXA   FLC   AMX   AMC   AMP   #>    <sir> <sir> <sir> <sir> <sir> #>  1   NA    NA    NA    I     NA  #>  2   NA    NA    NA    I     NA  #>  3   NA    R     NA    NA    NA  #>  4   NA    R     NA    NA    NA  #>  5   NA    R     NA    NA    NA  #>  6   NA    R     NA    NA    NA  #>  7   NA    S     R     S     R   #>  8   NA    S     R     S     R   #>  9   NA    R     NA    NA    NA  #> 10   NA    S     NA    NA    NA  #> # ℹ 1,990 more rows  # amr_selector() applies a filter in the `antimicrobials` data set and is thus # very flexible. For instance, to select antimicrobials with an oral DDD # of at least 1 gram: example_isolates[, amr_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For amr_selector(oral_ddd > 1 & oral_units == \"g\") using columns 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'KAN' (kanamycin), 'FOS' #>   (fosfomycin), 'LNZ' (linezolid), 'VAN' (vancomycin), 'ERY' (erythromycin), #>   'CLI' (clindamycin), 'MTR' (metronidazole), and 'CHL' (chloramphenicol) #> # A tibble: 2,000 × 13 #>    OXA   FLC   AMX   AMC   AMP   KAN   FOS   LNZ   VAN   ERY   CLI   MTR   CHL   #>    <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>  1   NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA  #>  2   NA    NA    NA    I     NA    NA    NA    R     R     R     R     NA    NA  #>  3   NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA  #>  4   NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA  #>  5   NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA  #>  6   NA    R     NA    NA    NA    NA    NA    NA    S     R     R     NA    NA  #>  7   NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA  #>  8   NA    S     R     S     R     NA    NA    NA    S     S     NA    NA    NA  #>  9   NA    R     NA    NA    NA    NA    NA    NA    S     R     NA    NA    NA  #> 10   NA    S     NA    NA    NA    NA    NA    NA    S     S     NA    NA    NA  #> # ℹ 1,990 more rows   # data.table --------------------------------------------------------------  # data.table is supported as well, just use it in the same way as with # base R, but add `with = FALSE` if using a single AB selector.  if (require(\"data.table\")) {   dt <- as.data.table(example_isolates)    # this does not work, it returns column *names*   dt[, carbapenems()] } #> Loading required package: data.table #>  #> Attaching package: ‘data.table’ #> The following objects are masked from ‘package:dplyr’: #>  #>     between, first, last #> The following objects are masked from ‘package:AMR’: #>  #>     %like%, like #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> Warning: It should never be needed to print an antimicrobial selector class. Are you #> using data.table? Then add the argument with = FALSE, see our examples at #> ?amr_selector. #> Class 'amr_selector' #> [1] IPM MEM if (require(\"data.table\")) {   # so `with = FALSE` is required   dt[, carbapenems(), with = FALSE] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #>         IPM   MEM #>       <sir> <sir> #>    1:  <NA>  <NA> #>    2:  <NA>  <NA> #>    3:  <NA>  <NA> #>    4:  <NA>  <NA> #>    5:  <NA>  <NA> #>   ---             #> 1996:  <NA>  <NA> #> 1997:     S     S #> 1998:     S     S #> 1999:     S     S #> 2000:     S     S  # for multiple selections or AB selectors, `with = FALSE` is not needed: if (require(\"data.table\")) {   dt[, c(\"mo\", aminoglycosides())] } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #>                 mo   GEN   TOB   AMK   KAN #>               <mo> <sir> <sir> <sir> <sir> #>    1: B_ESCHR_COLI  <NA>  <NA>  <NA>  <NA> #>    2: B_ESCHR_COLI  <NA>  <NA>  <NA>  <NA> #>    3: B_STPHY_EPDR  <NA>  <NA>  <NA>  <NA> #>    4: B_STPHY_EPDR  <NA>  <NA>  <NA>  <NA> #>    5: B_STPHY_EPDR  <NA>  <NA>  <NA>  <NA> #>   ---                                      #> 1996: B_STRPT_PNMN     R     R     R     R #> 1997: B_ESCHR_COLI     S     S     S  <NA> #> 1998: B_STPHY_CONS     S  <NA>  <NA>  <NA> #> 1999: B_ESCHR_COLI     S     S  <NA>  <NA> #> 2000: B_KLBSL_PNMN     S     S  <NA>  <NA> if (require(\"data.table\")) {   dt[, c(carbapenems(), aminoglycosides())] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #>         IPM   MEM   GEN   TOB   AMK   KAN #>       <sir> <sir> <sir> <sir> <sir> <sir> #>    1:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>    2:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>    3:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>    4:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>    5:  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> #>   ---                                     #> 1996:  <NA>  <NA>     R     R     R     R #> 1997:     S     S     S     S     S  <NA> #> 1998:     S     S     S  <NA>  <NA>  <NA> #> 1999:     S     S     S     S  <NA>  <NA> #> 2000:     S     S     S     S  <NA>  <NA>  # row filters are also supported: if (require(\"data.table\")) {   dt[any(carbapenems() == \"S\"), ] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #>            date patient   age gender       ward           mo   PEN   OXA   FLC #>          <Date>  <char> <num> <char>     <char>         <mo> <sir> <sir> <sir> #>   1: 2002-01-19  738003    71      M   Clinical B_ESCHR_COLI     R  <NA>  <NA> #>   2: 2002-01-19  738003    71      M   Clinical B_ESCHR_COLI     R  <NA>  <NA> #>   3: 2002-01-22  F35553    50      M        ICU B_PROTS_MRBL     R  <NA>  <NA> #>   4: 2002-01-22  F35553    50      M        ICU B_PROTS_MRBL     R  <NA>  <NA> #>   5: 2002-02-05  067927    45      F        ICU B_SERRT_MRCS     R  <NA>  <NA> #>  ---                                                                           #> 905: 2005-04-12  D71461    70      M        ICU B_ESCHR_COLI     R  <NA>  <NA> #> 906: 2009-11-12  650870    69      F Outpatient B_ESCHR_COLI     R  <NA>  <NA> #> 907: 2012-06-14  8CBCF2    41      F   Clinical B_STPHY_CONS     R     S     S #> 908: 2012-10-11  175532    78      M   Clinical B_ESCHR_COLI     R  <NA>  <NA> #> 909: 2013-11-23  A97263    77      M   Clinical B_KLBSL_PNMN     R  <NA>  <NA> #>        AMX   AMC   AMP   TZP   CZO   FEP   CXM   FOX   CTX   CAZ   CRO   GEN #>      <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>   1:  <NA>     I  <NA>  <NA>  <NA>  <NA>     S  <NA>     S  <NA>     S  <NA> #>   2:  <NA>     I  <NA>  <NA>  <NA>  <NA>     S  <NA>     S  <NA>     S  <NA> #>   3:  <NA>     I  <NA>  <NA>  <NA>  <NA>     S  <NA>     S     S     S  <NA> #>   4:  <NA>     I  <NA>  <NA>  <NA>  <NA>     S  <NA>     S     S     S  <NA> #>   5:     R     R     R  <NA>     R  <NA>     R     R  <NA>  <NA>  <NA>  <NA> #>  ---                                                                         #> 905:     S     S     S     S  <NA>     S     S     S     S     S     S     S #> 906:     S     S     S     S     S     S     S     S     S     S     S     S #> 907:  <NA>     S  <NA>  <NA>     S     S     S     S     S     R     S     S #> 908:     R     S     R     S  <NA>     S     R     R     S     S     S     S #> 909:     R     S     R     S  <NA>     S     S     S     S     S     S     S #>        TOB   AMK   KAN   TMP   SXT   NIT   FOS   LNZ   CIP   MFX   VAN   TEC #>      <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>   1:     S  <NA>  <NA>     S     S  <NA>  <NA>     R  <NA>  <NA>     R     R #>   2:     S  <NA>  <NA>     S     S  <NA>  <NA>     R  <NA>  <NA>     R     R #>   3:  <NA>  <NA>  <NA>     S     S     R  <NA>     R     S  <NA>     R     R #>   4:  <NA>  <NA>  <NA>     S     S     R  <NA>     R     S  <NA>     R     R #>   5:  <NA>  <NA>  <NA>     S     S     R  <NA>     R     S  <NA>     R     R #>  ---                                                                         #> 905:     S     S  <NA>  <NA>     S     S  <NA>     R     S  <NA>     R     R #> 906:     S     S  <NA>     S     S     S  <NA>     R     S  <NA>     R     R #> 907:  <NA>  <NA>  <NA>     S     S  <NA>  <NA>  <NA>     S  <NA>     S  <NA> #> 908:     S  <NA>  <NA>     R     R     R  <NA>     R     R     R     R     R #> 909:     S  <NA>  <NA>     S     S     S  <NA>     R     S  <NA>     R     R #>        TCY   TGC   DOX   ERY   CLI   AZM   IPM   MEM   MTR   CHL   COL   MUP #>      <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>   1:  <NA>  <NA>  <NA>     R     R     R     S  <NA>  <NA>  <NA>  <NA>  <NA> #>   2:  <NA>  <NA>  <NA>     R     R     R     S  <NA>  <NA>  <NA>  <NA>  <NA> #>   3:     R     R     R     R     R     R     S  <NA>  <NA>  <NA>     R  <NA> #>   4:     R     R     R     R     R     R     S  <NA>  <NA>  <NA>     R  <NA> #>   5:     R     R     R     R     R     R     S  <NA>  <NA>  <NA>     R  <NA> #>  ---                                                                         #> 905:  <NA>  <NA>  <NA>     R     R     R     S     S  <NA>  <NA>  <NA>  <NA> #> 906:  <NA>  <NA>  <NA>     R     R     R     S     S  <NA>  <NA>  <NA>  <NA> #> 907:  <NA>  <NA>  <NA>     S     S     S     S     S  <NA>  <NA>     R  <NA> #> 908:  <NA>  <NA>  <NA>     R     R     R     S     S  <NA>  <NA>     S  <NA> #> 909:  <NA>  <NA>  <NA>     R     R     R     S     S  <NA>  <NA>     S  <NA> #>        RIF #>      <sir> #>   1:     R #>   2:     R #>   3:     R #>   4:     R #>   5:     R #>  ---       #> 905:     R #> 906:     R #> 907:  <NA> #> 908:     R #> 909:     R if (require(\"data.table\")) {   dt[any(carbapenems() == \"S\"), penicillins(), with = FALSE] } #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For penicillins() using columns 'PEN' (benzylpenicillin), 'OXA' #>   (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'AMP' (ampicillin), and 'TZP' #>   (piperacillin/tazobactam) #>        PEN   OXA   FLC   AMX   AMC   AMP   TZP #>      <sir> <sir> <sir> <sir> <sir> <sir> <sir> #>   1:     R  <NA>  <NA>  <NA>     I  <NA>  <NA> #>   2:     R  <NA>  <NA>  <NA>     I  <NA>  <NA> #>   3:     R  <NA>  <NA>  <NA>     I  <NA>  <NA> #>   4:     R  <NA>  <NA>  <NA>     I  <NA>  <NA> #>   5:     R  <NA>  <NA>     R     R     R  <NA> #>  ---                                           #> 905:     R  <NA>  <NA>     S     S     S     S #> 906:     R  <NA>  <NA>     S     S     S     S #> 907:     R     S     S  <NA>     S  <NA>  <NA> #> 908:     R  <NA>  <NA>     R     S     R     S #> 909:     R  <NA>  <NA>     R     S     R     S # }"},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"Two data sets containing antimicrobials antivirals. Use .ab() one ab_* functions retrieve values antimicrobials data set. Three identifiers included data set: antimcrobial ID (ab, primarily used package) defined WHONET/EARS-Net, ATC code (atc) defined , Compound ID (cid) found PubChem. properties data set derived one codes. Note drugs multiple ATC codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"","code":"antimicrobials  antivirals"},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"object class tbl_df (inherits tbl, data.frame) 120 rows 11 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"for-the-antimicrobials-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antimicrobials data set: a tibble with 487 observations and 14 variables:","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"ab antimcrobial ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. cid Compound ID found PubChem. unique identifier. name Official name used WHONET/EARS-Net . unique identifier. group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antimcrobial susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 179 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 153 drugs iv_units Units iv_ddd loinc codes associated name antimicrobial drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"for-the-antivirals-data-set-a-tibble-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a tibble with 120 observations and 11 variables:","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"av Antiviral ID used package (ACI), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available. unique identifier. Combinations codes contain + indicate , ATA+COBI atazanavir/cobicistat. name Official name used WHONET/EARS-Net . unique identifier. atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem. unique identifier. atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd loinc codes associated name antiviral drug Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Use av_loinc() retrieve quickly, see av_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://atcddd.fhi./atc_ddd_index/ Logical Observation Identifiers Names Codes (LOINC), Version 2.76 (18 September, 2023). Accessed https://loinc.org October 19th, 2023. European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived PubChem Compound ID (column cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"Like data sets package, data sets publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 607 Antimicrobial Drugs — antimicrobials","text":"","code":"antimicrobials #> # A tibble: 487 × 14 #>    ab        cid name   group atc   atc_group1 atc_group2 abbreviations synonyms #>    <ab>    <dbl> <chr>  <chr> <lis> <chr>      <chr>      <list>        <named > #>  1 AMA      4649 4-ami… Anti… <chr> Drugs for… Aminosali… <chr [1]>     <chr>    #>  2 ACM   6450012 Acety… Macr… <chr> NA         NA         <chr [1]>     <chr>    #>  3 ASP  49787020 Acety… Macr… <chr> NA         NA         <chr [1]>     <chr>    #>  4 ALS      8954 Aldes… Othe… <chr> Drugs for… Drugs for… <chr [1]>     <chr>    #>  5 AMK     37768 Amika… Amin… <chr> Aminoglyc… Other ami… <chr [5]>     <chr>    #>  6 AKF        NA Amika… Amin… <chr> NA         NA         <chr [1]>     <chr>    #>  7 AMO     54260 Amoro… Anti… <chr> Antifunga… Other ant… <chr [1]>     <chr>    #>  8 AMX     33613 Amoxi… Beta… <chr> Beta-lact… Penicilli… <chr [3]>     <chr>    #>  9 AMC  23665637 Amoxi… Beta… <chr> Beta-lact… Combinati… <chr [5]>     <chr>    #> 10 AXS    465441 Amoxi… Beta… <chr> NA         NA         <chr [1]>     <chr>    #> # ℹ 477 more rows #> # ℹ 5 more variables: oral_ddd <dbl>, oral_units <chr>, iv_ddd <dbl>, #> #   iv_units <chr>, loinc <list> antivirals #> # A tibble: 120 × 11 #>    av       name      atc      cid atc_group synonyms oral_ddd oral_units iv_ddd #>    <av>     <chr>     <chr>  <dbl> <chr>     <list>      <dbl> <chr>       <dbl> #>  1 ABA      Abacavir  J05A… 4.41e5 Nucleosi… <chr>         0.6 g              NA #>  2 ACI      Aciclovir J05A… 1.35e8 Nucleosi… <chr>         4   g               4 #>  3 ADD      Adefovir… J05A… 6.09e4 Nucleosi… <chr>        10   mg             NA #>  4 AME      Amenamev… J05A… 1.14e7 Other an… <chr>         0.4 g              NA #>  5 AMP      Amprenav… J05A… 6.50e4 Protease… <chr>         1.2 g              NA #>  6 ASU      Asunapre… J05A… 1.61e7 Antivira… <chr>         0.2 g              NA #>  7 ATA      Atazanav… J05A… 1.48e5 Protease… <chr>         0.3 g              NA #>  8 ATA+COBI Atazanav… J05A… 8.66e7 Antivira… <chr>        NA   NA             NA #>  9 ATA+RIT  Atazanav… J05A… 2.51e7 Antivira… <chr>         0.3 g              NA #> 10 BAM      Baloxavi… J05A… 1.24e8 Other an… <chr>        40   mg             NA #> # ℹ 110 more rows #> # ℹ 2 more variables: iv_units <chr>, loinc <list>"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antimicrobial drug code one antimicrobials. data set antimicrobials searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, language = get_AMR_locale(),   info = interactive(), ...)  is.ab(x)  ab_reset_session()"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic drug code name can retrieved single input value. language language coerce input values 20 supported languages - default system language supported (see get_AMR_locale()) info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antimicrobials data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems. can add manual codes considered .ab() ab_* functions, see add_custom_antimicrobials().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://atcddd.fhi./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class 'ab' #> [1] ERY as.ab(\"J 01 FA 01\") #> Class 'ab' #> [1] ERY as.ab(\"Erythromycin\") #> Class 'ab' #> [1] ERY as.ab(\"eryt\") #> Class 'ab' #> [1] ERY as.ab(\"ERYT\") #> Class 'ab' #> [1] ERY as.ab(\"ERY\") #> Class 'ab' #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class 'ab' #> [1] ERY as.ab(\"Erythrocin\") # trade name #> Class 'ab' #> [1] ERY as.ab(\"Romycin\") # trade name #> Class 'ab' #> [1] ERY  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\"  # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\") #> [1] \"Erythromycin\" ab_name(\"eryt\") #> [1] \"Erythromycin\"  # \\donttest{ if (require(\"dplyr\")) {   # you can quickly rename 'sir' columns using set_ab_names() with dplyr:   example_isolates %>%     set_ab_names(where(is.sir), property = \"atc\") } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           J01CE01 J01CF04 J01CF05 #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir>   <sir>   <sir>   #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R       NA      NA    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R       NA      NA    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R       NA      S     #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R       NA      S     #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R       NA      R     #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R       NA      S     #> # ℹ 1,990 more rows #> # ℹ 37 more variables: J01CA04 <sir>, J01CR02 <sir>, J01CA01 <sir>, #> #   J01CR05 <sir>, J01DB04 <sir>, J01DE01 <sir>, J01DC02 <sir>, J01DC01 <sir>, #> #   J01DD01 <sir>, J01DD02 <sir>, J01DD04 <sir>, J01GB03 <sir>, J01GB01 <sir>, #> #   J01GB06 <sir>, J01GB04 <sir>, J01EA01 <sir>, J01EE01 <sir>, J01XE01 <sir>, #> #   J01XX01 <sir>, J01XX08 <sir>, J01MA02 <sir>, J01MA14 <sir>, J01XA01 <sir>, #> #   J01XA02 <sir>, J01AA07 <sir>, J01AA12 <sir>, J01AA02 <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antiviral Drug ID — as.av","title":"Transform Input to an Antiviral Drug ID — as.av","text":"Use function determine antiviral drug code one antiviral drugs. data set antivirals searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"as.av(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.av(x)"},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antiviral Drug ID — as.av","text":"x character vector determine antiviral drug ID flag_multiple_results logical indicate whether note printed console probably one antiviral drug code name can retrieved single input value. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antiviral Drug ID — as.av","text":"character vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antiviral Drug ID — as.av","text":"entries antivirals data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .av() can correct different forms misspelling: Wrong spelling drug names (\"acyclovir\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"aycclovir\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use av_* functions get properties based returned antiviral drug ID, see Examples. Note: .av() av_* functions may use long regular expression match brand names antimicrobial drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antiviral Drug ID — as.av","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://atcddd.fhi./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antiviral Drug ID — as.av","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://atcddd.fhi.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://atcddd.fhi./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antiviral Drug ID — as.av","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.av.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antiviral Drug ID — as.av","text":"","code":"# these examples all return \"ACI\", the ID of aciclovir: as.av(\"J05AB01\") #> Class 'av' #> [1] ACI as.av(\"J 05 AB 01\") #> Class 'av' #> [1] ACI as.av(\"Aciclovir\") #> Class 'av' #> [1] ACI as.av(\"aciclo\") #> Class 'av' #> [1] ACI as.av(\"   aciclo 123\") #> Class 'av' #> [1] ACI as.av(\"ACICL\") #> Class 'av' #> [1] ACI as.av(\"ACI\") #> Class 'av' #> [1] ACI as.av(\"Virorax\") # trade name #> Class 'av' #> [1] ACI as.av(\"Zovirax\") # trade name #> Class 'av' #> [1] ACI  as.av(\"acyklofir\") # severe spelling error, yet works #> Class 'av' #> [1] ACI  # use av_* functions to get a specific properties (see ?av_property); # they use as.av() internally: av_name(\"J05AB01\") #> [1] \"Aciclovir\" av_name(\"acicl\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 0 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE)  NA_disk_  is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values SIR values .sir(). supports guidelines EUCAST CLSI. Disk diffusion growth zone sizes must 0 50 millimetres. Values higher 50 lower 100 maximised 50. others input values outside 0-50 range return NA. NA_disk_ missing value new disk class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame(   microorganism = \"Escherichia coli\",   AMP = 20,   CIP = 14,   GEN = 18,   TOB = 16 ) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of  5 variables: #>  $ microorganism: chr \"Escherichia coli\" #>  $ AMP          : 'disk' int 20 #>  $ CIP          : 'disk' int 14 #>  $ GEN          : 'disk' int 18 #>  $ TOB          : 'disk' int 16  # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) {   df %>% mutate(across(AMP:TOB, as.disk)) } #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli  20  14  18  16 # }  # interpret disk values, see ?as.sir as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2024... #>  OK  #> Class 'sir' #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.sir(df, uti = TRUE) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: column 'AMP' (ampicillin), EUCAST #> 2024... #>  OK  #> Interpreting disk diffusion zones: column 'CIP' (ciprofloxacin), EUCAST #> 2024... #>  OK  #> Interpreting disk diffusion zones: column 'GEN' (gentamicin), EUCAST #> 2024... #>  OK  #> Interpreting disk diffusion zones: column 'TOB' (tobramycin), EUCAST #> 2024... #>  OK  #>      microorganism AMP  CIP GEN TOB #> 1 Escherichia coli   S <NA>   S   S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE, keep_operators = \"all\")  is.mic(x)  NA_mic_  rescale_mic(x, mic_range, keep_operators = \"edges\", as.mic = TRUE)  mic_p50(x, na.rm = FALSE, ...)  mic_p90(x, na.rm = FALSE, ...)  # S3 method for class 'mic' droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range rescale MIC values, e.g., mic_range = c(0.001, 32). Use NA prevent rescaling one side, e.g., mic_range = c(NA, 32). .mic logical indicate whether mic class kept - default TRUE rescale_mic() FALSE droplevels(). setting FALSE rescale_mic(), output factor levels acknowledge mic_range. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts numeric vector. Bear mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values SIR values, use .sir() MIC values. supports guidelines EUCAST (2011-2024) CLSI (2011-2024). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers:   makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.:   -called group generic functions implemented MIC class (!, !=, <, >=, exp(), log2()). functions stats package also implemented (quantile(), median(), fivenum()). Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain mic class. rescale_mic(), existing MIC ranges can limited defined range MIC values. can useful better compare MIC distributions. ggplot2, use one scale_*_mic() functions plot MIC values. allows custom MIC ranges plot intermediate log2 levels missing MIC values. NA_mic_ missing value new mic class, analogous e.g. base R's NA_character_. Use mic_p50() mic_p90() get 50th 90th percentile MIC values. return 'normal' numeric values.","code":"x <- random_mic(10) x #> Class 'mic' #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16  is.factor(x) #> [1] TRUE  x[1] * 2 #> [1] 32  median(x) #> [1] 26 x[x > 4] #> Class 'mic' #> [1] 16    8     8     64    >=128 32    32    16  df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #>        x hospital #> 1     16        A #> 5     64        A #> 6  >=128        A #> 8     32        A #> 9     32        A #> 10    16        A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class 'mic' #> [1] >=32    1       1       1       8       <=0.128 8       16      16      is.mic(mic_data) #> [1] TRUE  # this can also coerce combined MIC/SIR values: as.mic(\"<=0.002; S\") #> Class 'mic' #> [1] <=0.002  # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1]  0.128  1.000  8.000 16.000 32.000 quantile(mic_data) #>     0%    25%    50%    75%   100%  #>  0.128  1.000  8.000 16.000 32.000  all(mic_data < 512) #> [1] TRUE  # rescale MICs using rescale_mic() rescale_mic(mic_data, mic_range = c(4, 16)) #> Class 'mic' #> [1] >=16 <=4  <=4  <=4  8    <=4  8    >=16 >=16  # interpret MIC values as.sir(   x = as.mic(2),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] R as.sir(   x = as.mic(c(0.01, 2, 4, 8)),   mo = as.mo(\"Streptococcus pneumoniae\"),   ab = \"AMX\",   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'AMX' (amoxicillin), EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for amoxicillin (AMX) in Streptococcus pneumoniae - assuming body site 'Meningitis'. #> Class 'sir' #> [1] S R R R  # plot MIC values, see ?plot plot(mic_data)  plot(mic_data, mo = \"E. coli\", ab = \"cipro\")   if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch }"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Use function get valid microorganism code (mo) based arbitrary user input. Determination done using intelligent rules complete taxonomic tree kingdoms Animalia, Archaea, Bacteria, Chromista, Protozoa, microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"as.mo(x, Becker = FALSE, Lancefield = FALSE,   minimum_matching_score = NULL,   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE),   reference_df = get_mo_source(),   ignore_pattern = getOption(\"AMR_ignore_pattern\", NULL),   cleaning_regex = getOption(\"AMR_cleaning_regex\", mo_cleaning_regex()),   only_fungi = getOption(\"AMR_only_fungi\", FALSE),   language = get_AMR_locale(), info = interactive(), ...)  is.mo(x)  mo_uncertainties()  mo_renamed()  mo_failures()  mo_reset_session()  mo_cleaning_regex()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (see Source). Please see Details full list staphylococcal species converted. excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (see Source). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. . Please see Details full list streptococcal species converted. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. minimum_matching_score numeric value set lower limit MO matching score. left blank, determined automatically based character length x, taxonomic kingdom human pathogenicity. keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern Perl-compatible regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set package option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). cleaning_regex Perl-compatible regular expression (case-insensitive) clean input x. Every matched part x removed. default, outcome mo_cleaning_regex(), removes texts brackets texts \"species\" \"serovar\". default can set package option AMR_cleaning_regex. only_fungi logical indicate fungi must found, making sure e.g. misspellings always return records kingdom Fungi. can set globally microorganism functions package option AMR_only_fungi, .e. options(AMR_only_fungi = TRUE). language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate info must printed, e.g. progress bar 25 items coerced, list old taxonomic names. default TRUE interactive mode. ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"character vector additional class mo","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"microorganism (MO) code package (class: mo) human-readable typically looks like examples:   Values coerced considered 'unknown' return MO code UNKNOWN warning. Use mo_* functions get properties based returned code, see Examples. .mo() function uses novel scientifically validated (doi:10.18637/jss.v104.i03 ) matching score algorithm (see Matching Score Microorganisms ) match input available microbial taxonomy package. implicates e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first.","code":"Code               Full name   ---------------    --------------------------------------   B_KLBSL            Klebsiella   B_KLBSL_PNMN       Klebsiella pneumoniae   B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis   |   |    |    |   |   |    |    |   |   |    |    \\---> subspecies, a 3-5 letter acronym   |   |    \\----> species, a 3-6 letter acronym   |   \\----> genus, a 4-8 letter acronym   \\----> kingdom: A (Archaea), AN (Animalia), B (Bacteria),                   C (Chromista), F (Fungi), PL (Plantae),                   P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Results non-exact taxonomic input based matching score. lowest allowed score can set minimum_matching_score argument. default determined based character length input, taxonomic kingdom, human pathogenicity taxonomic outcome. values matched uncertainty, message shown suggest user inspect results mo_uncertainties(), returns data.frame specifications. increase quality matching, cleaning_regex argument used clean input. must regular expression matches parts input removed input matched available microbial taxonomy. matched Perl-compatible case-insensitive. default value cleaning_regex outcome helper function mo_cleaning_regex(). three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"for-mycologists","dir":"Reference","previous_headings":"","what":"For Mycologists","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"matching score algorithm gives precedence bacteria fungi. analysing fungi, sure use only_fungi = TRUE, better yet, add code run every session:   make sure bacteria 'non-fungi' returned .mo(), mo_* functions.","code":"options(AMR_only_fungi = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coagulase-negative-and-coagulase-positive-staphylococci","dir":"Reference","previous_headings":"","what":"Coagulase-negative and Coagulase-positive Staphylococci","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Becker = TRUE, following staphylococci converted corresponding coagulase group: Coagulase-negative: S. americanisciuri, S. argensis, S. arlettae, S. auricularis, S. borealis, S. brunensis, S. caeli, S. caledonicus, S. canis, S. capitis, S. capitis capitis, S. capitis urealyticus, S. capitis ureolyticus, S. caprae, S. carnosus, S. carnosus carnosus, S. carnosus utilis, S. casei, S. caseolyticus, S. chromogenes, S. cohnii, S. cohnii cohnii, S. cohnii urealyticum, S. cohnii urealyticus, S. condimenti, S. croceilyticus, S. debuckii, S. devriesei, S. durrellii, S. edaphicus, S. epidermidis, S. equorum, S. equorum equorum, S. equorum linens, S. felis, S. fleurettii, S. gallinarum, S. haemolyticus, S. hominis, S. hominis hominis, S. hominis novobiosepticus, S. jettensis, S. kloosii, S. lentus, S. lloydii, S. lugdunensis, S. marylandisciuri, S. massiliensis, S. microti, S. muscae, S. nepalensis, S. pasteuri, S. petrasii, S. petrasii croceilyticus, S. petrasii jettensis, S. petrasii petrasii, S. petrasii pragensis, S. pettenkoferi, S. piscifermentans, S. pragensis, S. pseudoxylosus, S. pulvereri, S. ratti, S. rostri, S. saccharolyticus, S. saprophyticus, S. saprophyticus bovis, S. saprophyticus saprophyticus, S. schleiferi, S. schleiferi schleiferi, S. sciuri, S. sciuri carnaticus, S. sciuri lentus, S. sciuri rodentium, S. sciuri sciuri, S. shinii, S. simulans, S. stepanovicii, S. succinus, S. succinus casei, S. succinus succinus, S. taiwanensis, S. urealyticus, S. ureilyticus, S. veratri, S. vitulinus, S. vitulus, S. warneri, S. xylosus Coagulase-positive: S. agnetis, S. argenteus, S. coagulans, S. cornubiensis, S. delphini, S. hyicus, S. hyicus chromogenes, S. hyicus hyicus, S. intermedius, S. lutrae, S. pseudintermedius, S. roterodami, S. schleiferi coagulans, S. schweitzeri, S. simiae, S. singaporensis based : Becker K et al. (2014). Coagulase-Negative Staphylococci. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. (2019). Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. (2020). Emergence coagulase-negative staphylococci. Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 newly named staphylococcal species, S. brunensis (2024) S. shinii (2023), looked scientific reference make sure species considered correct coagulase group.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"lancefield-groups-in-streptococci","dir":"Reference","previous_headings":"","what":"Lancefield Groups in Streptococci","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Lancefield = TRUE, following streptococci converted corresponding Lancefield group: Streptococcus Group : S. pyogenes Streptococcus Group B: S. agalactiae Streptococcus Group C: S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis, S. equi, S. equi equi, S. equi ruminatorum, S. equi zooepidemicus Streptococcus Group F: S. anginosus, S. anginosus anginosus, S. anginosus whileyi, S. constellatus, S. constellatus constellatus, S. constellatus pharyngis, S. constellatus viborgensis, S. intermedius Streptococcus Group G: S. canis, S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis Streptococcus Group H: S. sanguinis Streptococcus Group K: S. salivarius, S. salivarius salivarius, S. salivarius thermophilus Streptococcus Group L: S. dysgalactiae, S. dysgalactiae dysgalactiae, S. dysgalactiae equisimilis based : Lancefield RC (1933). serological differentiation human groups hemolytic streptococci. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/ Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : $$m_{(x, n)} = \\frac{l_{n} - 0.5 \\cdot \\min \\begin{cases}l_{n} \\\\ \\textrm{lev}(x, n)\\end{cases}}{l_{n} \\cdot p_{n} \\cdot k_{n}}$$ : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.15 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, Genera World Health Organization's () Priority Pathogen List prevalence = 1.0 microorganisms data set; genus present established list also prevalence = 1.15 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.25 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, Zygosaccharomyces; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.381\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Arbitrary Input to Valid Microbial Taxonomy — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(c(   \"sau\", # WHONET code   \"stau\",   \"STAU\",   \"staaur\",   \"S. aureus\",   \"S aureus\",   \"Sthafilokkockus aureus\", # handles incorrect spelling   \"Staphylococcus aureus (MRSA)\",   \"MRSA\", # Methicillin Resistant S. aureus   \"VISA\", # Vancomycin Intermediate S. aureus   \"VRSA\", # Vancomycin Resistant S. aureus   115329001 # SNOMED CT code )) #> Class 'mo' #>  [1] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #>  [6] B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS B_STPHY_AURS #> [11] B_STPHY_AURS B_STPHY_AURS  # Dyslexia is no problem - these all work: as.mo(c(   \"Ureaplasma urealyticum\",   \"Ureaplasma urealyticus\",   \"Ureaplasmium urealytica\",   \"Ureaplazma urealitycium\" )) #> Class 'mo' #> [1] B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY B_URPLS_URLY  # input will get cleaned up with the input given in the `cleaning_regex` argument, # which defaults to `mo_cleaning_regex()`: cat(mo_cleaning_regex(), \"\\n\") #> ([^A-Za-z- \\(\\)\\[\\]{}]+|([({]|\\[).+([})]|\\])|(^| )( ?[a-z-]+[-](resistant|susceptible) ?|e?spp([^a-z]+|$)|e?ssp([^a-z]+|$)|serogr.?up[a-z]*|e?ss([^a-z]+|$)|e?sp([^a-z]+|$)|var([^a-z]+|$)|serovar[a-z]*|sube?species|biovar[a-z]*|e?species|Ig[ADEGM]|e?subsp|biotype|titer|dummy))   as.mo(\"Streptococcus group A\") #> Class 'mo' #> [1] B_STRPT_GRPA  as.mo(\"S. epidermidis\") # will remain species: B_STPHY_EPDR #> Class 'mo' #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE) # will not remain species: B_STPHY_CONS #> Class 'mo' #> [1] B_STPHY_CONS  as.mo(\"S. pyogenes\") # will remain species: B_STRPT_PYGN #> Class 'mo' #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class 'mo' #> [1] B_STRPT_GRPA  # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\") #> [1] \"Escherichia\" mo_gramstain(\"ESCO\") #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"ESCCOL\", ab = \"vanco\") #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #>   'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #>   will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Clean existing SIR values, interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI. .sir() transforms input new class sir, ordered factor containing levels S, SDD, , R, NI. breakpoints currently implemented: clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024; veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024; ECOFFs (Epidemiological Cut-Values): EUCAST 2020-2024 CLSI 2022-2024. breakpoints used interpretation available clinical_breakpoints data set.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"as.sir(x, ...)  NA_sir_  is.sir(x)  is_sir_eligible(x, threshold = 0.05)  # Default S3 method as.sir(x, S = \"^(S|U)+$\", I = \"^(I)+$\", R = \"^(R)+$\",   NI = \"^(N|NI|V)+$\", SDD = \"^(SDD|D|H)+$\", ...)  # S3 method for class 'mic' as.sir(x, mo = NULL, ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL,   conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL,   verbose = FALSE, ...)  # S3 method for class 'disk' as.sir(x, mo = NULL, ab = deparse(substitute(x)),   guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL,   add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL,   verbose = FALSE, ...)  # S3 method for class 'data.frame' as.sir(x, ..., col_mo = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"), uti = NULL,   conserve_capped_values = FALSE, add_intrinsic_resistance = FALSE,   reference_data = AMR::clinical_breakpoints,   include_screening = getOption(\"AMR_include_screening\", FALSE),   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), host = NULL,   verbose = FALSE)  sir_interpretation_history(clean = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpretations minimum inhibitory concentration (MIC) values disk diffusion diameters: CLSI M39: Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 2011-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. CLSI M100: Performance Standard Antimicrobial Susceptibility Testing, 2011-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/. CLSI VET01: Performance Standards Antimicrobial Disk Dilution Susceptibility Tests Bacteria Isolated Animals, 2019-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet01/. CLSI VET09: Understanding Susceptibility Test Data Component Antimicrobial Stewardship Veterinary Settings, 2019-2024, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet09/. EUCAST Breakpoint tables interpretation MICs zone diameters, 2011-2024, European Committee Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints. WHONET source machine-reading clinical breakpoints (read ), 1989-2024, Collaborating Centre Surveillance Antimicrobial Resistance. https://whonet.org/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .sir() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples S, , R, NI, SDD case-independent regular expression translate input result. regular expression run non-letters whitespaces removed input. mo vector (column name) characters can coerced valid microorganism codes .mo(), can left empty determine automatically ab vector (column name) characters can coerced valid antimicrobial drug code .ab() guideline defaults EUCAST 2024 (latest implemented EUCAST guideline clinical_breakpoints data set), can set package option AMR_guideline. Currently supports EUCAST (2011-2024) CLSI (2011-2024), see Details. uti (Urinary Tract Infection) vector (column name) logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .sir() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults clinical_breakpoints data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure clinical_breakpoints data set (column names column types). Please note guideline argument ignored reference_data manually set. include_screening logical indicate clinical breakpoints screening allowed - default FALSE. Can also set package option AMR_include_screening. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. host set values veterinary species, automatically set \"animal\". host vector (column name) characters indicate host. useful veterinary breakpoints, requires breakpoint_type = \"animal\". values can text resembling animal species, even 20 supported languages package. foreign languages, sure set language set_AMR_locale() (though automatically guessed based system language). verbose logical indicate notes printed interpretation MIC values disk diffusion values. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). clean logical indicate whether previously stored results forgotten returning 'logbook' results","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Ordered factor new class sir","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"Note: clinical breakpoints package validated , imported , WHONET. public use AMR package endorsed CLSI EUCAST. See clinical_breakpoints information. NA_sir_ missing value new sir class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":".sir() function can work four ways: cleaning raw / untransformed data. data cleaned contain valid values, namely: S susceptible, intermediate 'susceptible, increased exposure', R resistant, NI non-interpretable, SDD susceptible dose-dependent. can set using regular expression. Furthermore, .sir() try best clean intelligence. example, mixed values SIR interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input invalid. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, SIR interpretation can done easily either:   interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .sir(your_data). points 2, 3 4: Use sir_interpretation_history() retrieve data.frame (tibble tibble package installed) results last .sir() call.","code":"your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) your_data %>% mutate_if(is.mic, as.sir, ab = \"column_with_antibiotics\", mo = \"column_with_microorganisms\") your_data %>% mutate_if(is.mic, as.sir, ab = c(\"cipro\", \"ampicillin\", ...), mo = c(\"E. coli\", \"K. pneumoniae\", ...))  # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.mic, as.sir, host = \"column_with_animal_species\", guideline = \"CLSI\") your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir)) your_data %>% mutate_if(is.disk, as.sir, ab = \"column_with_antibiotics\", mo = \"column_with_microorganisms\") your_data %>% mutate_if(is.disk, as.sir, ab = c(\"cipro\", \"ampicillin\", ...), mo = c(\"E. coli\", \"K. pneumoniae\", ...))  # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.disk, as.sir, host = \"column_with_animal_species\", guideline = \"CLSI\")"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024, veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024. Thus, guideline argument must set e.g., \"EUCAST 2024\" \"CLSI 2024\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored. can set default guideline package option AMR_guideline (e.g. .Rprofile file), :   veterinary guidelines, might best options:   applying veterinary breakpoints (setting host setting breakpoint_type = \"animal\"), CLSI VET09 guideline applied cope missing animal species-specific breakpoints.","code":"options(AMR_guideline = \"CLSI\")   options(AMR_guideline = \"CLSI 2018\")   options(AMR_guideline = \"EUCAST 2020\")   # or to reset:   options(AMR_guideline = NULL) options(AMR_guideline = \"CLSI\")   options(AMR_breakpoint_type = \"animal\")"},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"using .sir(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism. determine isolates multi-drug resistant, sure run mdro() (applies MDR/PDR/XDR guideline 2012 default) data set contains S//R values. Read interpreting multidrug-resistant organisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"machine-readable-clinical-breakpoints","dir":"Reference","previous_headings":"","what":"Machine-Readable Clinical Breakpoints","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"repository package contains machine-readable version guidelines. CSV file consisting 34 063 rows 14 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial drug microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"function .sir() detects input contains class sir. input data.frame, iterates columns returns logical vector. base R function .double() can used retrieve quantitative values sir object: \"S\" = 1, \"\"/\"SDD\" = 2, \"R\" = 3. values rendered NA . Note: use .integer(), since (R works internally) return factor level indices, aforementioned quantitative values. function is_sir_eligible() returns TRUE column contains 5% invalid antimicrobial interpretations (S //R /NI /SDD), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data — as.sir","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … summary(example_isolates) # see all SIR results at a glance #>       date              patient               age           gender          #>  Min.   :2002-01-02   Length:2000        Min.   : 0.00   Length:2000        #>  1st Qu.:2005-07-31   Class :character   1st Qu.:63.00   Class :character   #>  Median :2009-07-31   Mode  :character   Median :74.00   Mode  :character   #>  Mean   :2009-11-20                      Mean   :70.69                      #>  3rd Qu.:2014-05-30                      3rd Qu.:82.00                      #>  Max.   :2017-12-28                      Max.   :97.00                      #>      ward                mo                  PEN                 #>  Length:2000        Class :mo             Class:sir              #>  Class :character   <NA>  :0              %S   :25.6% (n=417)    #>  Mode  :character   Unique:90             %SDD : 0.0% (n=0)      #>                     #1    :B_ESCHR_COLI   %I   : 0.7% (n=11)     #>                     #2    :B_STPHY_CONS   %R   :73.7% (n=1201)   #>                     #3    :B_STPHY_AURS   %NI  : 0.0% (n=0)      #>     OXA                   FLC                   AMX                #>  Class:sir             Class:sir             Class:sir             #>  %S   :68.8% (n=251)   %S   :70.5% (n=665)   %S   :40.2% (n=543)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)     %I   : 0.0% (n=0)     %I   : 0.2% (n=3)     #>  %R   :31.2% (n=114)   %R   :29.5% (n=278)   %R   :59.6% (n=804)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     #>     AMC                    AMP                   TZP                #>  Class:sir              Class:sir             Class:sir             #>  %S   :71.4% (n=1342)   %S   :40.2% (n=543)   %S   :86.1% (n=862)   #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 4.8% (n=91)     %I   : 0.2% (n=3)     %I   : 1.3% (n=13)    #>  %R   :23.7% (n=446)    %R   :59.6% (n=804)   %R   :12.6% (n=126)   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     #>     CZO                   FEP                   CXM                 #>  Class:sir             Class:sir             Class:sir              #>  %S   :54.9% (n=245)   %S   :85.6% (n=620)   %S   :72.5% (n=1297)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      #>  %I   : 0.4% (n=2)     %I   : 0.1% (n=1)     %I   : 1.2% (n=22)     #>  %R   :44.6% (n=199)   %R   :14.2% (n=103)   %R   :26.3% (n=470)    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      #>     FOX                   CTX                   CAZ                 #>  Class:sir             Class:sir             Class:sir              #>  %S   :71.6% (n=586)   %S   :84.4% (n=796)   %S   :33.5% (n=607)    #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      #>  %I   : 1.0% (n=8)     %I   : 0.1% (n=1)     %I   : 0.0% (n=0)      #>  %R   :27.4% (n=224)   %R   :15.5% (n=146)   %R   :66.5% (n=1204)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      #>     CRO                   GEN                    TOB                #>  Class:sir             Class:sir              Class:sir             #>  %S   :84.4% (n=796)   %S   :74.0% (n=1372)   %S   :65.1% (n=879)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     #>  %I   : 0.1% (n=1)     %I   : 1.5% (n=27)     %I   : 0.5% (n=7)     #>  %R   :15.5% (n=146)   %R   :24.6% (n=456)    %R   :34.4% (n=465)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     #>     AMK                   KAN                    TMP                #>  Class:sir             Class:sir              Class:sir             #>  %S   :36.3% (n=251)   %S   : 0.0% (n=0)      %S   :61.2% (n=918)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)     %I   : 0.0% (n=0)      %I   : 0.7% (n=10)    #>  %R   :63.7% (n=441)   %R   :100.0% (n=471)   %R   :38.1% (n=571)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     #>     SXT                    NIT                   FOS                #>  Class:sir              Class:sir             Class:sir             #>  %S   :79.1% (n=1392)   %S   :76.0% (n=565)   %S   :57.8% (n=203)   #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 0.3% (n=6)      %I   : 6.9% (n=51)    %I   : 0.0% (n=0)     #>  %R   :20.5% (n=361)    %R   :17.1% (n=127)   %R   :42.2% (n=148)   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     #>     LNZ                   CIP                    MFX                #>  Class:sir             Class:sir              Class:sir             #>  %S   :30.7% (n=314)   %S   :78.9% (n=1112)   %S   :64.5% (n=136)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)     %I   : 4.9% (n=69)     %I   : 1.9% (n=4)     #>  %R   :69.3% (n=709)   %R   :16.2% (n=228)    %R   :33.6% (n=71)    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     #>     VAN                    TEC                   TCY                #>  Class:sir              Class:sir             Class:sir             #>  %S   :61.7% (n=1149)   %S   :24.3% (n=237)   %S   :68.3% (n=820)   #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)      %I   : 0.0% (n=0)     %I   : 1.9% (n=23)    #>  %R   :38.3% (n=712)    %R   :75.7% (n=739)   %R   :29.8% (n=357)   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     #>     TGC                   DOX                   ERY                 #>  Class:sir             Class:sir             Class:sir              #>  %S   :87.3% (n=697)   %S   :71.7% (n=814)   %S   :42.3% (n=801)    #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      #>  %I   : 0.0% (n=0)     %I   : 0.6% (n=7)     %I   : 0.5% (n=9)      #>  %R   :12.7% (n=101)   %R   :27.7% (n=315)   %R   :57.2% (n=1084)   #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      #>     CLI                   AZM                    IPM                #>  Class:sir             Class:sir              Class:sir             #>  %S   :38.6% (n=586)   %S   :42.3% (n=801)    %S   :92.7% (n=824)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     #>  %I   : 0.3% (n=4)     %I   : 0.5% (n=9)      %I   : 1.1% (n=10)    #>  %R   :61.2% (n=930)   %R   :57.2% (n=1084)   %R   : 6.2% (n=55)    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     #>     MEM                   MTR                  CHL                #>  Class:sir             Class:sir            Class:sir             #>  %S   :94.1% (n=780)   %S   :85.3% (n=29)   %S   :78.6% (n=121)   #>  %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)    %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)     %I   : 0.0% (n=0)    %I   : 0.0% (n=0)     #>  %R   : 5.9% (n=49)    %R   :14.7% (n=5)    %R   :21.4% (n=33)    #>  %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)    %NI  : 0.0% (n=0)     #>     COL                    MUP                   RIF                #>  Class:sir              Class:sir             Class:sir             #>  %S   :18.8% (n=309)    %S   :93.0% (n=251)   %S   :30.2% (n=303)   #>  %SDD : 0.0% (n=0)      %SDD : 0.0% (n=0)     %SDD : 0.0% (n=0)     #>  %I   : 0.0% (n=0)      %I   : 1.1% (n=3)     %I   : 0.2% (n=2)     #>  %R   :81.2% (n=1331)   %R   : 5.9% (n=16)    %R   :69.6% (n=698)   #>  %NI  : 0.0% (n=0)      %NI  : 0.0% (n=0)     %NI  : 0.0% (n=0)      # For INTERPRETING disk diffusion and MIC values -----------------------  # example data sets, with combined MIC values and disk zones df_wide <- data.frame(   microorganism = \"Escherichia coli\",   amoxicillin = as.mic(8),   cipro = as.mic(0.256),   tobra = as.disk(16),   genta = as.disk(18),   ERY = \"R\" ) df_long <- data.frame(   bacteria = rep(\"Escherichia coli\", 4),   antibiotic = c(\"amoxicillin\", \"cipro\", \"tobra\", \"genta\"),   mics = as.mic(c(0.01, 1, 4, 8)),   disks = as.disk(c(6, 10, 14, 18)) )  # \\donttest{ ## Using dplyr ------------------------------------------------- if (require(\"dplyr\")) {   # approaches that all work without additional arguments:   df_wide %>% mutate_if(is.mic, as.sir)   df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)   df_wide %>% mutate(across(where(is.mic), as.sir))   df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)   df_wide %>% mutate(across(amoxicillin:tobra, as.sir))    # approaches that all work with additional arguments:   df_long %>%     # given a certain data type, e.g. MIC values     mutate_if(is.mic, as.sir,       mo = \"bacteria\",       ab = \"antibiotic\",       guideline = \"CLSI\"     )   df_long %>%     mutate(across(       where(is.mic),       function(x) {         as.sir(x,           mo = \"bacteria\",           ab = \"antibiotic\",           guideline = \"CLSI\"         )       }     ))   df_wide %>%     # given certain columns, e.g. from 'cipro' to 'genta'     mutate_at(vars(cipro:genta), as.sir,       mo = \"bacteria\",       guideline = \"CLSI\"     )   df_wide %>%     mutate(across(       cipro:genta,       function(x) {         as.sir(x,           mo = \"bacteria\",           guideline = \"CLSI\"         )       }     ))    # for veterinary breakpoints, add 'host':   df_long$animal_species <- c(\"cats\", \"dogs\", \"horses\", \"cattle\")   df_long %>%     # given a certain data type, e.g. MIC values     mutate_if(is.mic, as.sir,       mo = \"bacteria\",       ab = \"antibiotic\",       host = \"animal_species\",       guideline = \"CLSI\"     )   df_long %>%     mutate(across(       where(is.mic),       function(x) {         as.sir(x,           mo = \"bacteria\",           ab = \"antibiotic\",           host = \"animal_species\",           guideline = \"CLSI\"         )       }     ))   df_wide %>%     mutate_at(vars(cipro:genta), as.sir,       mo = \"bacteria\",       ab = \"antibiotic\",       host = \"animal_species\",       guideline = \"CLSI\"     )   df_wide %>%     mutate(across(       cipro:genta,       function(x) {         as.sir(x,           mo = \"bacteria\",           host = \"animal_species\",           guideline = \"CLSI\"         )       }     ))    # to include information about urinary tract infections (UTI)   data.frame(     mo = \"E. coli\",     nitrofuratoin = c(\"<= 2\", 32),     from_the_bladder = c(TRUE, FALSE)   ) %>%     as.sir(uti = \"from_the_bladder\")    data.frame(     mo = \"E. coli\",     nitrofuratoin = c(\"<= 2\", 32),     specimen = c(\"urine\", \"blood\")   ) %>%     as.sir() # automatically determines urine isolates    df_wide %>%     mutate_at(vars(cipro:genta), as.sir, mo = \"E. coli\", uti = TRUE) } #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin) based on #> column 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin) based on column #> 'microorganism', EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin) based on #> column 'microorganism', EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #>  #> ℹ Please note that in the absence of specific veterinary breakpoints for #>   certain animal hosts, the CLSI guideline VET09 will be applied where #>   possible. #>  #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #>  #> ℹ Please note that in the absence of specific veterinary breakpoints for #>   certain animal hosts, the CLSI guideline VET09 will be applied where #>   possible. #>  #> Interpreting MIC values: 'amoxicillin' (AMX), 'cipro' (CIP, ciprofloxacin), #> 'tobra' (TOB, tobramycin), and 'genta' (GEN, gentamicin) based on column #> 'bacteria', CLSI 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #>  #>  #> ℹ Please note that in the absence of specific veterinary breakpoints for #>   certain animal hosts, the CLSI guideline VET09 will be applied where #>   possible. #>  #> Interpreting MIC values: 'antibiotic' (TESTAB, test Antibiotic), CLSI #> 2024... #>  WARNING  #>   • No CLSI 2024 MIC breakpoints available for test Antibiotic (TESTAB). #> Interpreting disk diffusion zones: 'antibiotic' (TESTAB, test Antibiotic), #> CLSI 2024... #>  WARNING  #>   • No CLSI 2024 DISK breakpoints available for test Antibiotic (TESTAB). #> Interpreting disk diffusion zones: 'antibiotic' (TESTAB, test Antibiotic), #> CLSI 2024... #>  WARNING  #>   • No CLSI 2024 DISK breakpoints available for test Antibiotic (TESTAB). #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `cipro = (function (x, ...) ...`. #> Caused by warning: #> ! The following animal host(s) could not be coerced: \"animal_species\" #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> ℹ Assuming breakpoint_type = \"animal\", since host is set. #>  #>  #> ℹ Please note that in the absence of specific veterinary breakpoints for #>   certain animal hosts, the CLSI guideline VET09 will be applied where #>   possible. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), CLSI 2024... #>  OK  #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), CLSI 2024... #>  OK  #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `across(...)`. #> Caused by warning: #> ! The following animal host(s) could not be coerced: \"animal_species\" #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: column 'nitrofuratoin' (NIT, #> nitrofurantoin), EUCAST 2024... #>  OK  #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use as.sir(uti = FALSE) to prevent this. #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: column 'nitrofuratoin' (NIT, #> nitrofurantoin), EUCAST 2024... #>  OK  #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... #>  OK  #> Interpreting disk diffusion zones: 'tobra' (TOB, tobramycin), EUCAST #> 2024... #>  OK  #> Interpreting disk diffusion zones: 'genta' (GEN, gentamicin), EUCAST #> 2024... #>  OK  #>      microorganism amoxicillin cipro tobra genta ERY #> 1 Escherichia coli           8  <NA>     S     S   R   ## Using base R ------------------------------------------------  as.sir(df_wide) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: column 'amoxicillin' (AMX), EUCAST 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for amoxicillin (AMX) in Escherichia coli - assuming body site 'Intravenous'. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting MIC values: column 'cipro' (CIP, ciprofloxacin), EUCAST #> 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'. #> Interpreting disk diffusion zones: column 'tobra' (TOB, tobramycin), EUCAST #> 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Interpreting disk diffusion zones: column 'genta' (GEN, gentamicin), EUCAST #> 2024... #>  NOTE  #>   • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in Escherichia coli - assuming an unspecified body site. Use argument uti to set which isolates are from urine. See ?as.sir. #> Assigning class 'sir' to already clean column 'ERY' (erythromycin)... #>  OK  #>      microorganism amoxicillin cipro tobra genta ERY #> 1 Escherichia coli           S     I     S     S   R  # return a 'logbook' about the results: sir_interpretation_history() #> # A tibble: 57 × 16 #>    datetime            index ab_given mo_given host_given ab   mo               #>    <dttm>              <int> <chr>    <chr>    <chr>      <ab> <mo>             #>  1 2025-03-07 22:05:49     4 AMX      B_STRPT… human      AMX  B_STRPT_PNMN     #>  2 2025-03-07 22:05:56     4 genta    Escheri… human      GEN  B_[ORD]_ENTRBCTR #>  3 2025-03-07 22:05:56     4 genta    Escheri… human      GEN  B_[ORD]_ENTRBCTR #>  4 2025-03-07 22:05:57     4 genta    Escheri… cattle     GEN  B_ESCHR_COLI     #>  5 2025-03-07 22:05:57     4 genta    Escheri… cattle     GEN  B_ESCHR_COLI     #>  6 2025-03-07 22:05:49     3 AMX      B_STRPT… human      AMX  B_STRPT_PNMN     #>  7 2025-03-07 22:05:56     3 tobra    Escheri… human      TOB  B_[ORD]_ENTRBCTR #>  8 2025-03-07 22:05:56     3 tobra    Escheri… human      TOB  B_[ORD]_ENTRBCTR #>  9 2025-03-07 22:05:57     3 tobra    Escheri… horses     TOB  B_ESCHR_COLI     #> 10 2025-03-07 22:05:57     3 tobra    Escheri… horses     TOB  B_ESCHR_COLI     #> # ℹ 47 more rows #> # ℹ 9 more variables: host <chr>, method <chr>, input <chr>, outcome <sir>, #> #   notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>, #> #   breakpoint_S_R <chr>  # for single values as.sir(   x = as.mic(2),   mo = as.mo(\"S. pneumoniae\"),   ab = \"AMP\",   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'AMP' (ampicillin), EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ampicillin (AMP) in Streptococcus pneumoniae - assuming body site 'Non-meningitis'. #> Class 'sir' #> [1] R  as.sir(   x = as.disk(18),   mo = \"Strep pneu\", # `mo` will be coerced with as.mo()   ab = \"ampicillin\", # and `ab` with as.ab()   guideline = \"EUCAST\" ) #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting disk diffusion zones: 'ampicillin' (AMP), EUCAST 2024... #>  OK  #> Class 'sir' #> [1] R   # For CLEANING existing SIR values ------------------------------------  as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", \"A\", \"B\", \"C\")) #> Warning: in as.sir(): 3 results in index '20' truncated (38%) that were invalid #> antimicrobial interpretations: \"A\", \"B\", and \"C\" as.sir(\"<= 0.002; S\") # will return \"S\" sir_data <- as.sir(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.sir(sir_data) #> [1] TRUE plot(sir_data) # for percentages  barplot(sir_data) # for frequencies   # as common in R, you can use as.integer() to return factor indices: as.integer(as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", NA))) #> [1]  1  2  3  4  5 NA # but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R: as.double(as.sir(c(\"S\", \"SDD\", \"I\", \"R\", \"NI\", NA))) #> [1]  1  2  2  3 NA NA  # the dplyr way if (require(\"dplyr\")) {   example_isolates %>%     mutate_at(vars(PEN:RIF), as.sir)   # same:   example_isolates %>%     as.sir(PEN:RIF)    # fastest way to transform all columns with already valid AMR results to class `sir`:   example_isolates %>%     mutate_if(is_sir_eligible, as.sir)    # since dplyr 1.0.0, this can also be:   # example_isolates %>%   #   mutate(across(where(is_sir_eligible), as.sir)) } #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antimicrobial), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property(atc_code, property, administration = \"O\",   url = \"https://atcddd.fhi.no/atc_ddd_index/?code=%s&showdescription=no\",   url_vet = \"https://atcddd.fhi.no/atcvet/atcvet_index/?code=%s&showdescription=no\")  atc_online_groups(atc_code, ...)  atc_online_ddd(atc_code, ...)  atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://atcddd.fhi./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antimicrobials, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) {   # oral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"O\")   atc_online_ddd(ab_atc(\"amox\"))    # parenteral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"P\")    atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\"         #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\"         #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\"      # }"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Use function e.g. clinical texts health care records. returns list antiviral drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(text, type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL, translate_av = FALSE, thorough_search = NULL,   info = interactive(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_av type = \"drug\": column name antivirals data set translate antibiotic abbreviations , using av_property(). default FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed - default TRUE interactive mode ... arguments passed .av()","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"function also internally used .av(), although searches first drug name throw note drug names returned. Note: .av() function may use long regular expression match brand names antiviral drugs. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"default, function search antiviral drug names. text elements searched official names, ATC codes brand names. uses .av() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(avx = av_from_text(clinical_text)) returned AV codes can transformed official names, groups, etc. av_* functions av_name() av_group(), using translate_av argument. using collapse, function return character:df %>% mutate(avx = av_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/av_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antiviral Drug Names and Doses from Clinical Text — av_from_text","text":"","code":"av_from_text(\"28/03/2020 valaciclovir po tid\") #> [[1]] #> Class 'av' #> [1] VALA #>  av_from_text(\"28/03/2020 valaciclovir po tid\", type = \"admin\") #> [[1]] #> [1] \"oral\" #>"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antiviral Drug — av_property","title":"Get Properties of an Antiviral Drug — av_property","text":"Use functions return specific property antiviral drug antivirals data set. input values evaluated internally .av().","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"av_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  av_cid(x, ...)  av_synonyms(x, ...)  av_tradenames(x, ...)  av_group(x, language = get_AMR_locale(), ...)  av_atc(x, ...)  av_loinc(x, ...)  av_ddd(x, administration = \"oral\", ...)  av_ddd_units(x, administration = \"oral\", ...)  av_info(x, language = get_AMR_locale(), ...)  av_url(x, open = FALSE, ...)  av_property(x, property = \"name\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antiviral Drug — av_property","text":"x (vector ) text can coerced valid antiviral drug code .av() language language returned text - default system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. ... arguments passed .av() administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antivirals data set: vector_or(colnames(antivirals), sort = FALSE).","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antiviral Drug — av_property","text":"integer case av_cid() named list case av_info() multiple av_atc()/av_synonyms()/av_tradenames() double case av_ddd() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antiviral Drug — av_property","text":"output translated possible. function av_url() return direct URL official website. warning returned required ATC code available.","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antiviral Drug — av_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://atcddd.fhi./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antiviral Drug — av_property","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/av_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antiviral Drug — av_property","text":"","code":"# all properties: av_name(\"ACI\") #> [1] \"Aciclovir\" av_atc(\"ACI\") #> [1] \"J05AB01\" av_cid(\"ACI\") #> [1] 135398513 av_synonyms(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_tradenames(\"ACI\") #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            av_group(\"ACI\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_url(\"ACI\") #>                                                              Aciclovir  #> \"https://atcddd.fhi.no/atc_ddd_index/?code=J05AB01&showdescription=no\"   # lowercase transformation av_name(x = c(\"ACI\", \"VALA\")) #> [1] \"Aciclovir\"    \"Valaciclovir\" av_name(x = c(\"ACI\", \"VALA\"), tolower = TRUE) #> [1] \"aciclovir\"    \"valaciclovir\"  # defined daily doses (DDD) av_ddd(\"ACI\", \"oral\") #> [1] 4 av_ddd_units(\"ACI\", \"oral\") #> [1] \"g\" av_ddd(\"ACI\", \"iv\") #> [1] 4 av_ddd_units(\"ACI\", \"iv\") #> [1] \"g\"  av_info(\"ACI\") # all properties as a list #> $av #> [1] \"ACI\" #>  #> $cid #> [1] 135398513 #>  #> $name #> [1] \"Aciclovir\" #>  #> $group #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" #>  #> $atc #> [1] \"J05AB01\" #>  #> $tradenames #>  [1] \"acicloftal\"        \"aciclovier\"        \"aciclovirum\"       #>  [4] \"activir\"           \"acyclofoam\"        \"acycloguanosine\"   #>  [7] \"acyclovir\"         \"acyclovir lauriad\" \"avaclyr\"           #> [10] \"cargosil\"          \"cyclovir\"          \"genvir\"            #> [13] \"gerpevir\"          \"hascovir\"          \"maynar\"            #> [16] \"novirus\"           \"poviral\"           \"sitavig\"           #> [19] \"sitavir\"           \"vipral\"            \"viropump\"          #> [22] \"virorax\"           \"zovirax\"           \"zyclir\"            #>  #> $loinc #> [1] \"\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 4 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 4 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all av_* functions use as.av() internally, so you can go from 'any' to 'any': av_atc(\"ACI\") #> [1] \"J05AB01\" av_group(\"J05AB01\") #> [1] \"Nucleosides and nucleotides excl. reverse transcriptase inhibitors\" av_loinc(\"abacavir\") #> [1] \"29113-8\" \"30273-7\" \"30287-7\" \"30303-2\" \"78772-1\" \"78773-9\" \"79134-3\" #> [8] \"80118-3\" av_name(\"29113-8\") #> [1] \"Abacavir\" av_name(135398513) #> [1] \"Aciclovir\" av_name(\"J05AB01\") #> [1] \"Aciclovir\""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability - default filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #>         count available     visual_availabilty resistant      visual_resistance #> date     2000    100.0% |####################|                                  #> patient  2000    100.0% |####################|                                  #> age      2000    100.0% |####################|                                  #> gender   2000    100.0% |####################|                                  #> ward     2000    100.0% |####################|                                  #> mo       2000    100.0% |####################|                                  #> PEN      1629     81.5% |################----|     73.7% |##############------| #> OXA       365     18.3% |###-----------------|     31.2% |######--------------| #> FLC       943     47.2% |#########-----------|     29.5% |#####---------------| #> AMX      1350     67.5% |#############-------|     59.6% |###########---------| #> AMC      1879     94.0% |##################--|     23.7% |####----------------| #> AMP      1350     67.5% |#############-------|     59.6% |###########---------| #> TZP      1001     50.0% |##########----------|     12.6% |##------------------| #> CZO       446     22.3% |####----------------|     44.6% |########------------| #> FEP       724     36.2% |#######-------------|     14.2% |##------------------| #> CXM      1789     89.5% |#################---|     26.3% |#####---------------| #> FOX       818     40.9% |########------------|     27.4% |#####---------------| #> CTX       943     47.2% |#########-----------|     15.5% |###-----------------| #> CAZ      1811     90.6% |##################--|     66.5% |#############-------| #> CRO       943     47.2% |#########-----------|     15.5% |###-----------------| #> GEN      1855     92.8% |##################--|     24.6% |####----------------| #> TOB      1351     67.6% |#############-------|     34.4% |######--------------| #> AMK       692     34.6% |######--------------|     63.7% |############--------| #> KAN       471     23.6% |####----------------|    100.0% |####################| #> TMP      1499     75.0% |###############-----|     38.1% |#######-------------| #> SXT      1759     88.0% |#################---|     20.5% |####----------------| #> NIT       743     37.2% |#######-------------|     17.1% |###-----------------| #> FOS       351     17.6% |###-----------------|     42.2% |########------------| #> LNZ      1023     51.2% |##########----------|     69.3% |#############-------| #> CIP      1409     70.5% |#############-------|     16.2% |###-----------------| #> MFX       211     10.6% |##------------------|     33.6% |######--------------| #> VAN      1861     93.1% |##################--|     38.3% |#######-------------| #> TEC       976     48.8% |#########-----------|     75.7% |###############-----| #> TCY      1200     60.0% |###########---------|     29.8% |#####---------------| #> TGC       798     39.9% |########------------|     12.7% |##------------------| #> DOX      1136     56.8% |###########---------|     27.7% |#####---------------| #> ERY      1894     94.7% |##################--|     57.2% |###########---------| #> CLI      1520     76.0% |###############-----|     61.2% |############--------| #> AZM      1894     94.7% |##################--|     57.2% |###########---------| #> IPM       889     44.5% |########------------|      6.2% |#-------------------| #> MEM       829     41.5% |########------------|      5.9% |#-------------------| #> MTR        34      1.7% |--------------------|     14.7% |##------------------| #> CHL       154      7.7% |#-------------------|     21.4% |####----------------| #> COL      1640     82.0% |################----|     81.2% |################----| #> MUP       270     13.5% |##------------------|      5.9% |#-------------------| #> RIF      1003     50.2% |##########----------|     69.6% |#############-------| # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     filter(mo == as.mo(\"Escherichia coli\")) %>%     select_if(is.sir) %>%     availability() } #>     count available       visual_availabilty resistant        visual_resistance #> PEN   467    100.0% |######################|    100.0% |######################| #> OXA     0      0.0% |----------------------|                                    #> FLC     0      0.0% |----------------------|                                    #> AMX   392     83.9% |##################----|     50.0% |###########-----------| #> AMC   467    100.0% |######################|     13.1% |##--------------------| #> AMP   392     83.9% |##################----|     50.0% |###########-----------| #> TZP   416     89.1% |###################---|      5.5% |#---------------------| #> CZO    82     17.6% |###-------------------|      2.4% |----------------------| #> FEP   317     67.9% |##############--------|      2.8% |----------------------| #> CXM   465     99.6% |######################|      5.4% |#---------------------| #> FOX   377     80.7% |#################-----|      6.9% |#---------------------| #> CTX   459     98.3% |#####################-|      2.4% |----------------------| #> CAZ   460     98.5% |#####################-|      2.4% |----------------------| #> CRO   459     98.3% |#####################-|      2.4% |----------------------| #> GEN   460     98.5% |#####################-|      2.0% |----------------------| #> TOB   462     98.9% |#####################-|      2.6% |----------------------| #> AMK   171     36.6% |########--------------|      0.0% |----------------------| #> KAN     0      0.0% |----------------------|                                    #> TMP   396     84.8% |##################----|     39.1% |########--------------| #> SXT   465     99.6% |######################|     31.6% |######----------------| #> NIT   458     98.1% |#####################-|      2.8% |----------------------| #> FOS    61     13.1% |##--------------------|      0.0% |----------------------| #> LNZ   467    100.0% |######################|    100.0% |######################| #> CIP   456     97.6% |#####################-|     12.5% |##--------------------| #> MFX    57     12.2% |##--------------------|    100.0% |######################| #> VAN   467    100.0% |######################|    100.0% |######################| #> TEC   467    100.0% |######################|    100.0% |######################| #> TCY     3      0.6% |----------------------|     66.7% |##############--------| #> TGC    68     14.6% |###-------------------|      0.0% |----------------------| #> DOX     0      0.0% |----------------------|                                    #> ERY   467    100.0% |######################|    100.0% |######################| #> CLI   467    100.0% |######################|    100.0% |######################| #> AZM   467    100.0% |######################|    100.0% |######################| #> IPM   422     90.4% |###################---|      0.0% |----------------------| #> MEM   418     89.5% |###################---|      0.0% |----------------------| #> MTR     2      0.4% |----------------------|      0.0% |----------------------| #> CHL     0      0.0% |----------------------|                                    #> COL   240     51.4% |###########-----------|      0.0% |----------------------| #> MUP     0      0.0% |----------------------|                                    #> RIF   467    100.0% |######################|    100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname,   include_n_rows = FALSE, ...)  # S3 method for class 'bug_drug_combinations' format(x, translate_ab = \"name (ab, atc)\",   language = get_AMR_locale(), minimum = 30, combine_SI = TRUE,   add_ab_group = TRUE, remove_intrinsic_resistant = FALSE,   decimal.mark = getOption(\"OutDec\"), big.mark = ifelse(decimal.mark ==   \",\", \".\", \",\"), ...)"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data set antimicrobials columns, amox, AMX AMC col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes - default mo_shortname() include_n_rows logical indicate total number rows must included output ... arguments passed FUN translate_ab character length 1 containing column names antimicrobials data set language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S, SDD, summed, resistance based R - default TRUE add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric     decimal point. big.mark character; empty used mark every     big.interval decimals (hence big)     decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"SDD\", \"\", \"R\", \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination returns table ready reporting/publishing. Use combine_SI = TRUE (default) test R vs. S+combine_SI = FALSE test R+vs. S. table can also directly used R Markdown / Quarto without need e.g. knitr::kable().","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # \\donttest{ x <- bug_drug_combinations(example_isolates) head(x) #> # A tibble: 6 × 8 #>   mo                ab        S   SDD     I     R    NI total #>   <chr>             <chr> <int> <int> <int> <int> <int> <int> #> 1 (unknown species) AMC      15     0     0     0     0    15 #> 2 (unknown species) AMK       0     0     0     0     0     0 #> 3 (unknown species) AMP      15     0     0     1     0    16 #> 4 (unknown species) AMX      15     0     0     1     0    16 #> 5 (unknown species) AZM       3     0     0     3     0     6 #> 6 (unknown species) CAZ       0     0     0     0     0     0 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #> # A tibble: 39 × 12 #>    Group     Drug  CoNS  `E. coli` `E. faecalis` `K. pneumoniae` `P. aeruginosa` #>    <chr>     <chr> <chr> <chr>     <chr>         <chr>           <chr>           #>  1 \"Aminogl… Amik… \"100… \"  0.0% … \"100.0% (39/… \"\"              \"\"              #>  2 \"\"        Gent… \" 13… \"  2.0% … \"100.0% (39/… \" 10.3% (6/58)\" \"  0.0% (0/30)\" #>  3 \"\"        Kana… \"100… \"\"        \"100.0% (39/… \"\"              \"100.0% (30/30… #>  4 \"\"        Tobr… \" 78… \"  2.6% … \"100.0% (39/… \" 10.3% (6/58)\" \"  0.0% (0/30)\" #>  5 \"Antimyc… Rifa… \"\"    \"100.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  6 \"Beta-la… Amox… \" 93… \" 50.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  7 \"\"        Amox… \" 42… \" 13.1% … \"\"            \" 10.3% (6/58)\" \"100.0% (30/30… #>  8 \"\"        Ampi… \" 93… \" 50.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #>  9 \"\"        Benz… \" 77… \"100.0% … \"\"            \"100.0% (58/58… \"100.0% (30/30… #> 10 \"\"        Fluc… \" 42… \"\"        \"\"            \"\"              \"\"              #> # ℹ 29 more rows #> # ℹ 5 more variables: `P. mirabilis` <chr>, `S. aureus` <chr>, #> #   `S. epidermidis` <chr>, `S. hominis` <chr>, `S. pneumoniae` <chr>  # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates,   FUN = mo_gramstain ) #> # A tibble: 80 × 8 #>    mo            ab        S   SDD     I     R    NI total #>    <chr>         <chr> <int> <int> <int> <int> <int> <int> #>  1 Gram-negative AMC     463     0    89   174     0   726 #>  2 Gram-negative AMK     251     0     0     5     0   256 #>  3 Gram-negative AMP     226     0     0   405     0   631 #>  4 Gram-negative AMX     226     0     0   405     0   631 #>  5 Gram-negative AZM       1     0     2   696     0   699 #>  6 Gram-negative CAZ     607     0     0    27     0   634 #>  7 Gram-negative CHL       1     0     0    30     0    31 #>  8 Gram-negative CIP     610     0    11    63     0   684 #>  9 Gram-negative CLI      18     0     1   709     0   728 #> 10 Gram-negative COL     309     0     0    78     0   387 #> # ℹ 70 more rows #> Use 'format()' on this result to get a publishable/printable format.  bug_drug_combinations(example_isolates,   FUN = function(x) {     ifelse(x == as.mo(\"Escherichia coli\"),       \"E. coli\",       \"Others\"     )   } ) #> # A tibble: 80 × 8 #>    mo      ab        S   SDD     I     R    NI total #>    <chr>   <chr> <int> <int> <int> <int> <int> <int> #>  1 E. coli AMC     332     0    74    61     0   467 #>  2 E. coli AMK     171     0     0     0     0   171 #>  3 E. coli AMP     196     0     0   196     0   392 #>  4 E. coli AMX     196     0     0   196     0   392 #>  5 E. coli AZM       0     0     0   467     0   467 #>  6 E. coli CAZ     449     0     0    11     0   460 #>  7 E. coli CHL       0     0     0     0     0     0 #>  8 E. coli CIP     398     0     1    57     0   456 #>  9 E. coli CLI       0     0     0   467     0   467 #> 10 E. coli COL     240     0     0     0     0   240 #> # ℹ 70 more rows #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Data set containing clinical breakpoints interpret MIC disk diffusion SIR values, according international guidelines. dataset contain breakpoints humans, 7 different animal groups, ECOFFs. breakpoints currently implemented: clinical microbiology: EUCAST 2011-2024 CLSI 2011-2024; veterinary microbiology: EUCAST 2021-2024 CLSI 2019-2024; ECOFFs (Epidemiological Cut-Values): EUCAST 2020-2024 CLSI 2022-2024. Use .sir() transform MICs disks measurements SIR values.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints"},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"tibble 34 063 observations 14 variables: guideline Name guideline type Breakpoint type, either \"ECOFF\", \"animal\", \"human\" host Host infectious agent. mostly useful veterinary breakpoints either \"ECOFF\", \"aquatic\", \"cats\", \"cattle\", \"dogs\", \"horse\", \"human\", \"poultry\", \"swine\" method Testing method, either \"DISK\" \"MIC\" site Body site breakpoint must applied, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antimcrobial code used package, EARS-Net WHONET, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI) is_SDD logical value (TRUE/FALSE) indicate whether intermediate range \"S\" \"R\" interpreted \"SDD\", instead \"\". currently applies 24 breakpoints.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"different-types-of-breakpoints","dir":"Reference","previous_headings":"","what":"Different types of breakpoints","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Supported types breakpoints ECOFF, animal, human. ECOFF (Epidemiological cut-) values used antimicrobial susceptibility testing differentiate wild-type non-wild-type strains bacteria fungi. default \"human\", can also set package option AMR_breakpoint_type. Use .sir(..., breakpoint_type = ...) interpret raw data using specific breakpoint type, e.g. .sir(..., breakpoint_type = \"ECOFF\") use ECOFFs.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"imported-from-whonet","dir":"Reference","previous_headings":"","what":"Imported from WHONET","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Clinical breakpoints package validated imported WHONET, free desktop Windows application developed supported Collaborating Centre Surveillance Antimicrobial Resistance. can read website. developers WHONET AMR package contact sharing work. highly appreciate great development WHONET software.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"response-from-clsi-and-eucast","dir":"Reference","previous_headings":"","what":"Response from CLSI and EUCAST","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"CEO CLSI chairman EUCAST endorsed work public use AMR package (consequently use breakpoints) June 2023, future development distributing clinical breakpoints discussed meeting CLSI, EUCAST, , developers WHONET software, developers AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"download","dir":"Reference","previous_headings":"","what":"Download","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI, though initiatives started overcome burdens. NOTE: AMR package (WHONET software well) contains rather complex internal methods apply guidelines. example, breakpoints must applied certain species groups (case package available microorganisms.groups data set). important considered using breakpoints use.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/clinical_breakpoints.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Clinical Breakpoints for SIR Interpretation — clinical_breakpoints","text":"","code":"clinical_breakpoints #> # A tibble: 34,063 × 14 #>    guideline   type  host  method site    mo            rank_index ab   ref_tbl  #>    <chr>       <chr> <chr> <chr>  <chr>   <mo>               <dbl> <ab> <chr>    #>  1 EUCAST 2024 human human DISK   NA      B_ACHRMB_XYLS          2 MEM  A. xylo… #>  2 EUCAST 2024 human human MIC    NA      B_ACHRMB_XYLS          2 MEM  A. xylo… #>  3 EUCAST 2024 human human DISK   NA      B_ACHRMB_XYLS          2 SXT  A. xylo… #>  4 EUCAST 2024 human human MIC    NA      B_ACHRMB_XYLS          2 SXT  A. xylo… #>  5 EUCAST 2024 human human DISK   NA      B_ACHRMB_XYLS          2 TZP  A. xylo… #>  6 EUCAST 2024 human human MIC    NA      B_ACHRMB_XYLS          2 TZP  A. xylo… #>  7 EUCAST 2024 human human DISK   NA      B_ACNTB                3 AMK  Acineto… #>  8 EUCAST 2024 human human DISK   Uncomp… B_ACNTB                3 AMK  Acineto… #>  9 EUCAST 2024 human human MIC    NA      B_ACNTB                3 AMK  Acineto… #> 10 EUCAST 2024 human human MIC    Uncomp… B_ACNTB                3 AMK  Acineto… #> # ℹ 34,053 more rows #> # ℹ 5 more variables: disk_dose <chr>, breakpoint_S <dbl>, breakpoint_R <dbl>, #> #   uti <lgl>, is_SDD <lgl>"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE)  count_susceptible(..., only_all_tested = FALSE)  count_S(..., only_all_tested = FALSE)  count_SI(..., only_all_tested = FALSE)  count_I(..., only_all_tested = FALSE)  count_IR(..., only_all_tested = FALSE)  count_R(..., only_all_tested = FALSE)  count_all(..., only_all_tested = FALSE)  n_sir(..., only_all_tested = FALSE)  count_df(data, translate_ab = \"name\", language = get_AMR_locale(),   combine_SI = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antimicrobials, see section Combination Therapy data data.frame containing columns class sir (see .sir()) translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_sir() alias count_all(). can used count available isolates, .e. input antimicrobials available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data sir class (created .sir()) counts number S's, 's R's. also supports grouped variables. function sir_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antimicrobials, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   considered   considered  considered   considered                     susceptible    tested    susceptible    tested --------  --------  -----------  ----------  -----------  ----------  S or I    S or I        X            X           X            X    R       S or I        X            X           X            X   <NA>     S or I        X            X           -            -  S or I      R           X            X           X            X    R         R           -            X           -            X   <NA>       R           -            -           -            -  S or I     <NA>         X            X           -            -    R        <NA>         -            -           -            -   <NA>      <NA>         -            -           -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX) # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX) # counts \"S\", \"I\" and \"R\" #> [1] 1350  # be more specific count_S(example_isolates$AMX) #> Using count_S() is discouraged; use count_susceptible() instead to also #> consider \"I\" and \"SDD\" being susceptible. This note will be shown once for #> this session. #> [1] 543 count_SI(example_isolates$AMX) #> Note that count_SI() will also count dose-dependent susceptibility, #> 'SDD'. This note will be shown once for this session. #> [1] 546 count_I(example_isolates$AMX) #> Note that count_I() will also count dose-dependent susceptibility, 'SDD'. #> This note will be shown once for this session. #> [1] 3 count_IR(example_isolates$AMX) #> Using count_IR() is discouraged; use count_resistant() instead to not #> consider \"I\" and \"SDD\" being resistant. This note will be shown once for #> this session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804  # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_sir(example_isolates$AMX) #> [1] 1350  # n_sir() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_sir(example_isolates$AMX) #> [1] 546  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = count_R(CIP),       I = count_I(CIP),       S = count_S(CIP),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Number of available isolates for a whole antibiotic class   # (i.e., in this data set columns GEN, TOB, AMK, KAN)   example_isolates %>%     group_by(ward) %>%     summarise(across(aminoglycosides(), n_sir))    # Count co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy.   # Please mind that `susceptibility()` calculates percentages right away instead.   example_isolates %>% count_susceptible(AMC) # 1433   example_isolates %>% count_all(AMC) # 1879    example_isolates %>% count_susceptible(GEN) # 1399   example_isolates %>% count_all(GEN) # 1855    example_isolates %>% count_susceptible(AMC, GEN) # 1764   example_isolates %>% count_all(AMC, GEN) # 1936    # Get number of S+I vs. R immediately of selected columns   example_isolates %>%     select(AMX, CIP) %>%     count_df(translate = FALSE)    # It also supports grouping variables   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     count_df(translate = FALSE) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> # A tibble: 12 × 4 #>    ward       antibiotic interpretation value #>    <chr>      <chr>      <ord>          <int> #>  1 Clinical   AMX        SI               357 #>  2 Clinical   AMX        R                487 #>  3 Clinical   CIP        SI               741 #>  4 Clinical   CIP        R                128 #>  5 ICU        AMX        SI               158 #>  6 ICU        AMX        R                270 #>  7 ICU        CIP        SI               362 #>  8 ICU        CIP        R                 85 #>  9 Outpatient AMX        SI                31 #> 10 Outpatient AMX        R                 47 #> 11 Outpatient CIP        SI                78 #> 12 Outpatient CIP        R                 15 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see instructions, Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work:   rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\",                          TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #> #>   1. If TZP is \"S\" then set to  S : #>      amoxicillin (AMX), ampicillin (AMP) #> #>   2. If TZP is \"R\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"),                  TZP = as.sir(\"R\"),                  ampi = as.sir(\"S\"),                  cipro = as.sir(\"S\")) df #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    S     S  eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    R     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception columns used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\",                          TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\")  eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-multiple-antimicrobials-and-antimicrobial-group-names","dir":"Reference","previous_headings":"","what":"Usage of multiple antimicrobials and antimicrobial group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"can define antimicrobial groups instead single antimicrobials rule consequence, part tilde (~). examples , antimicrobial group aminopenicillins includes ampicillin amoxicillin. Rules can also applied multiple antimicrobials antimicrobial groups simultaneously. Use c() function combine multiple antimicrobials. instance, following example sets aminopenicillins ureidopenicillins \"R\" column TZP (piperacillin/tazobactam) \"R\":   34 antimicrobial groups allowed rules (case-insensitive) can used combination: aminoglycosides(amikacin, amikacin/fosfomycin, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, tobramycin-high) aminopenicillins(amoxicillin ampicillin) antifungals(amorolfine, amphotericin B, amphotericin B-high, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, voriconazole) antimycobacterials(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, viomycin) betalactams(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, carumonam, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid, tigemonam) betalactams_with_inhibitor(amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin/sulbactam, aztreonam/avibactam, aztreonam/nacubactam, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefoperazone/sulbactam, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefpodoxime/clavulanic acid, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, ceftolozane/tazobactam, ceftriaxone/beta-lactamase inhibitor, imipenem/relebactam, meropenem/nacubactam, meropenem/vaborbactam, mezlocillin/sulbactam, penicillin/novobiocin, penicillin/sulbactam, piperacillin/sulbactam, piperacillin/tazobactam, ticarcillin/clavulanic acid) carbapenems(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil, tebipenem) cephalosporins(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, loracarbef) cephalosporins_1st(cefacetrile, cefadroxil, cefalexin, cefaloridine, cefalotin, cefapirin, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, cephradine) cephalosporins_2nd(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil, loracarbef) cephalosporins_3rd(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, latamoxef) cephalosporins_4th(cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetecol, cefoselis, cefozopran, cefpirome, cefquinome) cephalosporins_5th(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam) cephalosporins_except_caz(cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, latamoxef, loracarbef) fluoroquinolones(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) glycopeptides(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin, vancomycin-macromethod) glycopeptides_except_lipo(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin, vancomycin-macromethod) isoxazolylpenicillins(cloxacillin, dicloxacillin, flucloxacillin, meticillin, oxacillin) lincosamides(clindamycin, lincomycin, pirlimycin) lipoglycopeptides(dalbavancin, oritavancin, telavancin) macrolides(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin, tylvalosin) monobactams(aztreonam, aztreonam/avibactam, aztreonam/nacubactam, carumonam, tigemonam) nitrofurans(furazidin, furazolidone, nifurtoinol, nitrofurantoin, nitrofurazone) oxazolidinones(cadazolid, cycloserine, linezolid, tedizolid, thiacetazone) penicillins(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, temocillin, ticarcillin, ticarcillin/clavulanic acid) phenicols(chloramphenicol, florfenicol, thiamphenicol) polymyxins(colistin, polymyxin B, polymyxin B/polysorbate 80) quinolones(besifloxacin, cinoxacin, ciprofloxacin, ciprofloxacin/metronidazole, ciprofloxacin/ornidazole, ciprofloxacin/tinidazole, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, lascufloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nemonoxacin, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin, trovafloxacin) rifamycins(rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine) streptogramins(pristinamycin quinupristin/dalfopristin) tetracyclines(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tigecycline) tetracyclines_except_tgc(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline) trimethoprims(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, trimethoprim/sulfamethoxazole) ureidopenicillins(azlocillin, mezlocillin, piperacillin, piperacillin/tazobactam)","code":"x <- custom_eucast_rules(TZP == \"R\" ~ c(aminopenicillins, ureidopenicillins) == \"R\") x #> A set of custom EUCAST rules: #> #>   1. If TZP is \"R\" then set to \"R\": #>      amoxicillin (AMX), ampicillin (AMP), azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), piperacillin/tazobactam (TZP)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules(   AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",   AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\" ) x #> A set of custom EUCAST rules: #>  #>   1. If AMC is  R  and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is  I  and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP)  # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates,   rules = \"custom\",   custom_rules = x,   info = FALSE,   verbose = TRUE ) #> # A tibble: 8 × 9 #>     row col   mo_fullname     old   new   rule  rule_group rule_name rule_source #>   <int> <chr> <chr>           <ord> <chr> <chr> <chr>      <chr>     <chr>       #> 1    33 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 2    33 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 3    34 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 4    34 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 5   531 AMP   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 6   531 AMX   Klebsiella pne… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 7  1485 AMP   Klebsiella oxy… R     I     \"rep… Custom EU… Custom E… Object 'x'… #> 8  1485 AMX   Klebsiella oxy… R     I     \"rep… Custom EU… Custom E… Object 'x'…  # combine rule sets x2 <- c(   x,   custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\") ) x2 #> A set of custom EUCAST rules: #>  #>   1. If AMC is  R  and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is  I  and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   3. If TZP is  R  then set to  R : #>      biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #>      imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #>      meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #>      razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"tibble 503 observations 9 variables: ab Antimcrobial ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial drug used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\", \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\", \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply, either 13, 12, 11","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage #> # A tibble: 503 × 9 #>    ab   name            type  dose  dose_times administration notes original_txt #>    <ab> <chr>           <chr> <chr>      <int> <chr>          <chr> <chr>        #>  1 AMK  Amikacin        stan… 25-3…          1 iv             \"\"    25-30 mg/kg… #>  2 AMX  Amoxicillin     high… 2 g            6 iv             \"\"    2 g x 6 iv   #>  3 AMX  Amoxicillin     stan… 1 g            3 iv             \"\"    1 g x 3-4 iv #>  4 AMX  Amoxicillin     high… 0.75…          3 oral           \"\"    0.75-1 g x … #>  5 AMX  Amoxicillin     stan… 0.5 g          3 oral           \"\"    0.5 g x 3 o… #>  6 AMX  Amoxicillin     unco… 0.5 g          3 oral           \"\"    0.5 g x 3 o… #>  7 AMC  Amoxicillin/cl… high… 2 g …          3 iv             \"\"    (2 g amoxic… #>  8 AMC  Amoxicillin/cl… stan… 1 g …          3 iv             \"\"    (1 g amoxic… #>  9 AMC  Amoxicillin/cl… high… 0.87…          3 oral           \"\"    (0.875 g am… #> 10 AMC  Amoxicillin/cl… stan… 0.5 …          3 oral           \"\"    (0.5 g amox… #> # ℹ 493 more rows #> # ℹ 1 more variable: eucast_version <dbl>"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints notes expected resistant phenotypes defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://www.eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules(x, col_mo = NULL, info = interactive(),   rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")),   verbose = FALSE, version_breakpoints = 12, version_expertrules = 3.3,   ampc_cephalosporin_resistance = NA, only_sir_columns = FALSE,   custom_rules = NULL, overwrite = TRUE, ...)  eucast_dosage(ab, administration = \"iv\", version_breakpoints = 12)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data set antimicrobials columns, amox, AMX AMC col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console - default print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value using package option AMR_eucastrules: options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can \"12.0\", \"11.0\", \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can \"3.3\", \"3.2\", \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants - default NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three drugs. value NA (default) argument remove results three drugs, e.g. value \"R\" make results drugs resistant. Use NULL FALSE alter results three drugs AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\".  EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia, Serratia. only_sir_columns logical indicate whether antimicrobial columns must detected transformed class sir (see .sir()) beforehand (default FALSE) custom_rules custom rules apply, created custom_eucast_rules() overwrite logical indicate whether non-NA values must overwritten (defaults TRUE). FALSE, NA values changed. ... column name antimicrobial, see section Antimicrobials ab (vector ) text can coerced valid antimicrobial drug code .ab() administration route administration, either \"im\", \"iv\", \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antimicrobials. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values SIR values. Use .sir() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.  Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. AMR package contains full microbial taxonomy updated June 24th, 2024, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\")  eucast_rules(example_isolates, rules = \"custom\", custom_rules = x)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set package option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antimicrobials","dir":"Reference","previous_headings":"","what":"Antimicrobials","title":"Apply EUCAST Rules — eucast_rules","text":"define antimicrobials column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antimicrobials eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), apramycin (APR, QA07AA92), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), carumonam (CAR, J01DF02), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefepime/amikacin (CFA, J01RA06), cefetamet (CAT, J01DD10), cefiderocol (FDC, J01DI04), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefovecin (FOV, QJ01DD91), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefquinome (CEQ, QG51AA07), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftiofur (TIO, QJ01DD90), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), ciprofloxacin (CIP, J01MA02), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), danofloxacin (DAN, QJ01MA92), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), difloxacin (DIF, QJ01MA94), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), enrofloxacin (ENR, QJ01MA90), epicillin (EPC, J01CA07), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), gamithromycin (GAM, QJ01FA95), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), kitasamycin (KIT, QJ01FA93), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), marbofloxacin (MAR, QJ01MA93), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), norfloxacin (, J01MA06), novobiocin (NOV, QJ01XX95), ofloxacin (OFX, J01MA01), oleandomycin (OLE, J01FA05), orbifloxacin (ORB, QJ01MA95), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pirlimycin (PRL, QJ51FF90), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pradofloxacin (PRA, QJ01MA97), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, QJ01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sarafloxacin (SAR, QJ01MA98), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tildipirosin (TIP, QJ01FA96), tilmicosin (TIL, QJ01FA91), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), tulathromycin (TUL, QJ01FA94), tylosin (TYL, QJ01FA90), tylvalosin (TYL1, QJ01FA92), vancomycin (VAN, J01XA01)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame(   mo = c(     \"Staphylococcus aureus\",     \"Enterococcus faecalis\",     \"Escherichia coli\",     \"Klebsiella pneumoniae\",     \"Pseudomonas aeruginosa\"   ),   VAN = \"-\", # Vancomycin   AMX = \"-\", # Amoxicillin   COL = \"-\", # Colistin   CAZ = \"-\", # Ceftazidime   CXM = \"-\", # Cefuroxime   PEN = \"S\", # Benzylpenicillin   FOX = \"S\", # Cefoxitin   stringsAsFactors = FALSE )  head(a) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   -   -   -   S   S #> 2  Enterococcus faecalis   -   -   -   -   -   S   S #> 3       Escherichia coli   -   -   -   -   -   S   S #> 4  Klebsiella pneumoniae   -   -   -   -   -   S   S #> 5 Pseudomonas aeruginosa   -   -   -   -   -   S   S   # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(PEN:FOX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir))  head(b) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   R   R   S   S   S #> 2  Enterococcus faecalis   -   -   R   R   R   S   R #> 3       Escherichia coli   R   -   -   -   -   R   S #> 4  Klebsiella pneumoniae   R   R   -   -   -   R   S #> 5 Pseudomonas aeruginosa   R   R   -   -   R   R   R   # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in eucast_rules(): not all columns with antimicrobial results are of #> class 'sir'. Transform them on beforehand, with e.g.: #>   - a %>% as.sir(PEN:FOX) #>   - a %>% mutate_if(is_sir_eligible, as.sir) #>   - a %>% mutate(across(where(is_sir_eligible), as.sir)) head(c) #>   row col           mo_fullname old new rule   rule_group #> 1   1 CXM Staphylococcus aureus   -   S       Breakpoints #> 2   1 CAZ Staphylococcus aureus   -   R      Expert Rules #> 3   1 COL Staphylococcus aureus   -   R      Expert Rules #> 4   2 CAZ Enterococcus faecalis   -   R      Expert Rules #> 5   2 COL Enterococcus faecalis   -   R      Expert Rules #> 6   2 CXM Enterococcus faecalis   -   R      Expert Rules #>                                                 rule_name #> 1                                          Staphylococcus #> 2 Table 4: Intrinsic resistance in gram-positive bacteria #> 3 Table 4: Intrinsic resistance in gram-positive bacteria #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #>                                                                                 rule_source #> 1                                           'EUCAST Clinical Breakpoint Tables' v12.0, 2022 #> 2 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # }  # Dosage guidelines:  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v12.0 (2022). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v10.0 (2020). This note will be shown once per session. #> # A tibble: 3 × 4 #>   ab   name          standard_dosage  high_dosage  #>   <ab> <chr>         <chr>            <chr>        #> 1 TOB  Tobramycin    6-7 mg/kg x 1 iv NA           #> 2 GEN  Gentamicin    6-7 mg/kg x 1 iv NA           #> 3 CIP  Ciprofloxacin 0.4 g x 2 iv     0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2 000 Example Isolates — example_isolates","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"data set containing 2 000 microbial isolates full antibiograms. data set contains randomised fictitious data, reflects reality can used practise AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"tibble 2 000 observations 46 variables: date Date receipt laboratory patient ID patient age Age patient gender Gender patient, either \"F\" \"M\" ward Ward type patient admitted, either \"Clinical\", \"ICU\", \"Outpatient\" mo ID microorganism created .mo(), see also microorganisms data set PEN:RIF 40 different antimicrobials class sir (see .sir()); column names occur antimicrobials data set can translated set_ab_names() ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2 000 Example Isolates — example_isolates","text":"","code":"example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3 000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"tibble 3 000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antimicrobials transformed .sir()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Unclean Data — example_isolates_unclean","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean #> # A tibble: 3,000 × 8 #>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN   #>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr> #>  1 J3         A        2012-11-21 E. coli       R     I     S     S     #>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S     #>  3 P3         A        2014-09-19 E. coli       R     S     S     S     #>  4 P10        A        2015-12-10 E. coli       S     I     S     S     #>  5 B7         A        2015-03-02 E. coli       S     S     S     S     #>  6 W3         A        2018-03-31 S. aureus     R     S     R     S     #>  7 J8         A        2016-06-14 E. coli       R     S     S     S     #>  8 M3         A        2015-10-25 E. coli       R     S     S     S     #>  9 J3         A        2019-06-19 E. coli       S     S     S     S     #> 10 G6         A        2015-04-27 S. aureus     S     S     S     S     #> # ℹ 2,990 more rows"},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":null,"dir":"Reference","previous_headings":"","what":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"Export Data Set NCBI BioSample Antibiogram","code":""},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"","code":"export_ncbi_biosample(x, filename = paste0(\"biosample_\", format(Sys.time(),   \"%Y-%m-%d-%H%M%S\"), \".xlsx\"), type = \"pathogen MIC\",   columns = where(is.mic), save_as_xlsx = TRUE)"},{"path":"https://msberends.github.io/AMR/reference/export_ncbi_biosample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export Data Set as NCBI BioSample Antibiogram — export_ncbi_biosample","text":"x data set filename character string specifying file name type character string specifying type data set, either \"pathogen MIC\" \"beta-lactamase MIC\", see https://www.ncbi.nlm.nih.gov/biosample/docs/","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate(x = NULL, col_date = NULL, col_patient_id = NULL,   col_mo = NULL, col_testcode = NULL, col_specimen = NULL,   col_icu = NULL, col_keyantimicrobials = NULL, episode_days = 365,   testcodes_exclude = NULL, icu_exclude = FALSE, specimen_group = NULL,   type = \"points\", method = c(\"phenotype-based\", \"episode-based\",   \"patient-based\", \"isolate-based\"), ignore_I = TRUE, points_threshold = 2,   info = interactive(), include_unknown = FALSE,   include_untested_sir = TRUE, ...)  filter_first_isolate(x = NULL, col_date = NULL, col_patient_id = NULL,   col_mo = NULL, episode_days = 365, method = c(\"phenotype-based\",   \"episode-based\", \"patient-based\", \"isolate-based\"), ...)"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology functions strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab) - default first column date class col_patient_id column name unique IDs patients - default first column starts 'patient' 'patid' (case insensitive) col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU). can also logical vector length rows x. col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). default first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed - default TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_sir logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_sir = FALSE always return FALSE rows. checks data set columns class sir consequently requires transforming columns antibiotic results using .sir() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"methodology implemented functions strictly based recommendations outlined CLSI Guideline M39 research overview Hindler et al. (2007, doi:10.1086/511864 ). conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According previously-mentioned sources, different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. method makes sure duplicate isolates selected patient. method preferred e.g. identify first MRSA finding patient determine incidence. Conversely, large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data ICU cases, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial drugs available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial drugs, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  example_isolates[first_isolate(info = TRUE), ] #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'date' as input for col_date. #> ℹ Using column 'patient' as input for col_patient_id. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a #>    microbial ID was available) #> # A tibble: 1,387 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA  #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R   #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA  #> # ℹ 1,377 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> ℹ Using column 'mo' as input for mo_is_gram_negative() #> # A tibble: 441 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R   #>  4 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #>  5 2002-02-05 067927     45 F      ICU      B_SERRT_MRCS   R     NA    NA    R   #>  6 2002-02-27 066895     85 F      Clinical B_KLBSL_PNMN   R     NA    NA    R   #>  7 2002-03-08 4FC193     69 M      Clinical B_ESCHR_COLI   R     NA    NA    R   #>  8 2002-03-16 4FC193     69 M      Clinical B_PSDMN_AERG   R     NA    NA    R   #>  9 2002-04-01 496896     46 F      ICU      B_ESCHR_COLI   R     NA    NA    NA  #> 10 2002-04-23 EE2510     69 F      ICU      B_ESCHR_COLI   R     NA    NA    NA  #> # ℹ 431 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate(info = TRUE)) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #> ℹ Excluding 16 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 1,387 'phenotype-based' first isolates (69.4% of total where a #>    microbial ID was available) #> # A tibble: 1,387 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA  #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R   #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA  #> # ℹ 1,377 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … if (require(\"dplyr\")) {   # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE) } #> # A tibble: 1,387 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  4 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #>  6 2002-01-17 495616     67 M      Clinical B_STPHY_EPDR   R     NA    S     NA  #>  7 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  8 2002-01-21 462081     75 F      Clinical B_CTRBC_FRND   R     NA    NA    R   #>  9 2002-01-22 F35553     50 M      ICU      B_PROTS_MRBL   R     NA    NA    NA  #> 10 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA  #> # ℹ 1,377 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … if (require(\"dplyr\")) {   # flag the first isolates per group:   example_isolates %>%     group_by(ward) %>%     mutate(first = first_isolate(info = TRUE)) %>%     select(ward, date, patient, mo, first) } #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #>   of 2 #>  #> Group: ward = \"Clinical\" #> ℹ Excluding 9 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 865 'phenotype-based' first isolates (70.1% of total where a #>    microbial ID was available) #>  #> Group: ward = \"ICU\" #> ℹ Excluding 6 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 452 'phenotype-based' first isolates (70.0% of total where a #>    microbial ID was available) #>  #> Group: ward = \"Outpatient\" #> ℹ Excluding 1 isolates with a microbial ID 'UNKNOWN' (in column 'mo') #> => Found 99 'phenotype-based' first isolates (82.5% of total where a #>    microbial ID was available) #> # A tibble: 2,000 × 5 #> # Groups:   ward [3] #>    ward     date       patient mo           first #>    <chr>    <date>     <chr>   <mo>         <lgl> #>  1 Clinical 2002-01-02 A77334  B_ESCHR_COLI TRUE  #>  2 Clinical 2002-01-03 A77334  B_ESCHR_COLI FALSE #>  3 ICU      2002-01-07 067927  B_STPHY_EPDR TRUE  #>  4 ICU      2002-01-07 067927  B_STPHY_EPDR FALSE #>  5 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  6 ICU      2002-01-13 067927  B_STPHY_EPDR FALSE #>  7 Clinical 2002-01-14 462729  B_STPHY_AURS TRUE  #>  8 Clinical 2002-01-14 462729  B_STPHY_AURS FALSE #>  9 ICU      2002-01-16 067927  B_STPHY_EPDR TRUE  #> 10 ICU      2002-01-17 858515  B_STPHY_EPDR TRUE  #> # ℹ 1,990 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also     factors. y numeric vector; ignored x matrix.      x factor, y factor length. p vector probabilities length x.     error given entry p negative. rescale.p logical scalar; TRUE p rescaled     (necessary) sum 1.  rescale.p FALSE,     p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following   components: statistic value chi-squared test statistic. parameter degrees freedom approximate     chi-squared distribution test statistic, NA     p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test     performed, whether Monte Carlo simulation continuity     correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals,     (observed - expected) / sqrt(expected). stdres standardized residuals,     (observed - expected) / sqrt(V), V     residual cell variance (Agresti, 2007, section 2.4.5     case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers.  Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed .  Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R :   df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio.  x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 4.1471, p-value = 0.1257 #>   # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible.   # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills.  x <- c(1752, 1895) g.test(x) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 5.6085, p-value = 0.01787 #>   # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Clinical or Epidemic Episodes — get_episode","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"functions determine items vector can considered (start ) new episode. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns TRUE every new get_episode() index. absolute relative episode determination supported.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"get_episode(x, episode_days = NULL, case_free_days = NULL, ...)  is_new_episode(x, episode_days = NULL, case_free_days = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days episode length days specify time period new episode begins, can also less day Inf, see Details case_free_days (inter-epidemic) interval length days new episode start, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode(): integer vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"Episodes can determined two ways: absolute relative. Absolute method uses episode_days define episode length days, new episode start. common use case AMR data analysis microbial epidemiology: episodes S. aureus bacteraemia ICU patients example. episode length 30 days, new S. aureus isolates ICU episode 30 days considered different (new) episode. Thus, method counts since start previous episode. Relative method uses case_free_days quantify duration case-free days (inter-epidemic interval), new episode start. common use case infectious disease epidemiology: episodes norovirus outbreaks hospital example. case-free period 14 days, new norovirus cases time considered different (new) episode. Thus, methods counts since last case previous episode. table: ** marks start new episode, 8 January 2023 7 days since start previous episode (1 January 2023).  *** marks start new episode, 21 January 2023 7 days since last case previous episode (8 January 2023). Either episode_days case_free_days must provided function.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"difference-between-get-episode-and-is-new-episode-","dir":"Reference","previous_headings":"","what":"Difference between get_episode() and is_new_episode()","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"get_episode() function returns index number episode, cases/patients/isolates first episode number 1, cases/patients/isolates second episode number 2, etc. is_new_episode() function hand, returns TRUE every new get_episode() index. specify, setting episode_days = 365 (using method 1 explained ), two functions differ:","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, episode functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Clinical or Epidemic Episodes — get_episode","text":"","code":"# difference between absolute and relative determination of episodes: x <- data.frame(dates = as.Date(c(   \"2021-01-01\",   \"2021-01-02\",   \"2021-01-05\",   \"2021-01-08\",   \"2021-02-21\",   \"2021-02-22\",   \"2021-02-23\",   \"2021-02-24\",   \"2021-03-01\",   \"2021-03-01\" ))) x$absolute <- get_episode(x$dates, episode_days = 7) x$relative <- get_episode(x$dates, case_free_days = 7) x #>         dates absolute relative #> 1  2021-01-01        1        1 #> 2  2021-01-02        1        1 #> 3  2021-01-05        1        1 #> 4  2021-01-08        2        1 #> 5  2021-02-21        3        2 #> 6  2021-02-22        3        2 #> 7  2021-02-23        3        2 #> 8  2021-02-24        3        2 #> 9  2021-03-01        4        2 #> 10 2021-03-01        4        2   # `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 100), ]  get_episode(df$date, episode_days = 60) # indices #>   [1] 47 28 47 47 48  1 48 13  7 38 28  5 14 49 13 22 31 18 28 21  6  8  6 39 13 #>  [26]  5 11 15  9 42 48 43 28 18  4 45 26  2 31 43 10  4 23 47 25 35  1 38 24 15 #>  [51] 19  4 16  5 16  1 44 39 41 25  4 11 30  1 17 30  3 48  8 39  6 38  5 20 37 #>  [76] 36 44 31 48 40 42 43 44 33 12  4 10 34 11  8 12 32 10 33 29 46 34 27 22 23 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1]  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE #>  [13]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE #>  [25] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE #>  [37]  TRUE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE #>  [49]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE #>  [61] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE #>  [73] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE #>  [85]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE #>  [97] FALSE  TRUE FALSE FALSE  # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 1 × 46 #>   date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>   <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #> 1 2003-01-31 151041     85 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, RIF <sir>  # the functions also work for less than a day, e.g. to include one per hour: get_episode(   c(     Sys.time(),     Sys.time() + 60 * 60   ),   episode_days = 1 / 24 ) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(       x = c(\"A\", \"B\", \"C\"),       size = 100,       replace = TRUE     )) %>%     group_by(patient, condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient, date, condition, new_episode) %>%     arrange(patient, condition, date) } #> # A tibble: 100 × 4 #> # Groups:   patient, condition [99] #>    patient date       condition new_episode #>    <chr>   <date>     <chr>     <lgl>       #>  1 067927  2002-01-29 A         TRUE        #>  2 069276  2015-06-18 A         TRUE        #>  3 078381  2014-08-17 A         TRUE        #>  4 09B453  2010-03-21 C         TRUE        #>  5 0DBF93  2015-10-12 A         TRUE        #>  6 118928  2004-02-01 B         TRUE        #>  7 119392  2010-11-01 A         TRUE        #>  8 122506  2007-08-10 B         TRUE        #>  9 146120  2011-09-23 B         TRUE        #> 10 151041  2003-01-31 B         TRUE        #> # ℹ 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward, patient) %>%     transmute(date,       patient,       new_index = get_episode(date, 60),       new_logical = is_new_episode(date, 60)     ) %>%     arrange(patient, ward, date) } #> # A tibble: 100 × 5 #> # Groups:   ward, patient [94] #>    ward     date       patient new_index new_logical #>    <chr>    <date>     <chr>       <int> <lgl>       #>  1 ICU      2002-01-29 067927          1 TRUE        #>  2 Clinical 2015-06-18 069276          1 TRUE        #>  3 ICU      2014-08-17 078381          1 TRUE        #>  4 Clinical 2010-03-21 09B453          1 TRUE        #>  5 Clinical 2015-10-12 0DBF93          1 TRUE        #>  6 Clinical 2004-02-01 118928          1 TRUE        #>  7 Clinical 2010-11-01 119392          1 TRUE        #>  8 Clinical 2007-08-10 122506          1 TRUE        #>  9 Clinical 2011-09-23 146120          1 TRUE        #> 10 Clinical 2003-01-31 151041          1 TRUE        #> # ℹ 90 more rows  if (require(\"dplyr\")) {   df %>%     group_by(ward) %>%     summarise(       n_patients = n_distinct(patient),       n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),       n_episodes_60 = sum(is_new_episode(date, episode_days = 60)),       n_episodes_30 = sum(is_new_episode(date, episode_days = 30))     ) } #> # A tibble: 3 × 5 #>   ward       n_patients n_episodes_365 n_episodes_60 n_episodes_30 #>   <chr>           <int>          <int>         <int>         <int> #> 1 Clinical           57             14            38            48 #> 2 ICU                31             12            25            28 #> 3 Outpatient          6              5             6             6  # grouping on patients and microorganisms leads to the same # results as first_isolate() when using 'episode-based': if (require(\"dplyr\")) {   x <- df %>%     filter_first_isolate(       include_unknown = TRUE,       method = \"episode-based\"     )    y <- df %>%     group_by(patient, mo) %>%     filter(is_new_episode(date, 365)) %>%     ungroup()    identical(x, y) } #> [1] TRUE  # but is_new_episode() has a lot more flexibility than first_isolate(), # since you can now group on anything that seems relevant: if (require(\"dplyr\")) {   df %>%     group_by(patient, mo, ward) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> # A tibble: 100 × 4 #> # Groups:   patient, mo, ward [98] #>    patient mo           ward     flag_episode #>    <chr>   <mo>         <chr>    <lgl>        #>  1 F5F794  B_STPHY_AURS Clinical TRUE         #>  2 146120  B_STPHY_AURS Clinical TRUE         #>  3 501607  B_STRPT_AGLC ICU      TRUE         #>  4 402950  B_ENTRC_FCLS ICU      TRUE         #>  5 560323  B_ESCHR_COLI Clinical TRUE         #>  6 4FC193  B_ESCHR_COLI Clinical TRUE         #>  7 D80438  B_CRYNB_STRT Clinical TRUE         #>  8 F81262  B_STRPT_PNMN Clinical TRUE         #>  9 619705  B_STRPT_ANGN ICU      TRUE         #> 10 280834  B_STPHY_CONS Clinical TRUE         #> # ℹ 90 more rows # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(x, choices = 1:2, scale = 1, pc.biplot = TRUE,   labels = NULL, labels_textsize = 3, labels_text_placement = 1.5,   groups = NULL, ellipse = TRUE, ellipse_prob = 0.68,   ellipse_size = 0.5, ellipse_alpha = 0.5, points_size = 2,   points_alpha = 0.25, arrows = TRUE, arrows_colour = \"darkblue\",   arrows_size = 0.5, arrows_textsize = 3, arrows_textangled = TRUE,   arrows_alpha = 0.75, base_textsize = 10, ...)"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default     biplot strict sense. scale variables scaled lambda ^ scale     observations scaled lambda ^ (1-scale)     lambda singular values computed     princomp. Normally 0 <= scale <= 1, warning     issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component     biplot\", lambda = 1 observations scaled sqrt(n)     variables scaled sqrt(n).  inner products     variables approximate covariances distances observations     approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    summary(pca_result)    # old base R plotting method:   biplot(pca_result, main = \"Base R biplot\")    # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result) +       labs(title = \"ggplot2 biplot\")   }   if (require(\"ggplot2\")) {     # still extendible with any ggplot2 function     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"ggplot2 biplot\")   } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"` `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_sir","title":"AMR Plots with ggplot2 — ggplot_sir","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"ggplot_sir(data, position = NULL, x = \"antibiotic\",   fill = \"interpretation\", facet = NULL, breaks = seq(0, 1, 0.1),   limits = NULL, translate_ab = \"name\", combine_SI = TRUE,   minimum = 30, language = get_AMR_locale(), nrow = NULL, colours = c(S   = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R = \"#ED553B\"),   datalabels = TRUE, datalabels.size = 2.5, datalabels.colour = \"grey15\",   title = NULL, subtitle = NULL, caption = NULL,   x.title = \"Antimicrobial\", y.title = \"Proportion\", ...)  geom_sir(position = NULL, x = c(\"antibiotic\", \"interpretation\"),   fill = \"interpretation\", translate_ab = \"name\", minimum = 30,   language = get_AMR_locale(), combine_SI = TRUE, ...)"},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_sir","text":"data data.frame column(s) class sir (see .sir()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_sir_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_sir() , case scale_sir_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_sir","text":"default, names antimicrobials shown plots using ab_name(). can set translate_ab argument. See count_df(). geom_sir() take variable data sir class (created .sir()) using sir_df() plot bars percentage S, , R. default behaviour bars stacked different antimicrobials x axis. Additional functions include: facet_sir() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_sir_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_sir() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_sir","text":"","code":"# \\donttest{ if (require(\"ggplot2\") && require(\"dplyr\")) {   # get antimicrobial results for drugs against a UTI:   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +     geom_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # prettify the plot using some additional functions:   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)   ggplot(df) +     geom_sir() +     scale_y_percent() +     scale_sir_colours(aesthetics = \"fill\") +     labels_sir_count() +     theme_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # or better yet, simplify this using the wrapper function - a single command:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir() }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # get only proportions and no counts:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(datalabels = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # add other ggplot2 arguments as you like:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(       width = 0.5,       colour = \"black\",       size = 1,       linetype = 2,       alpha = 0.25     ) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # you can alter the colours with colour names:   example_isolates %>%     select(AMX) %>%     ggplot_sir(colours = c(SI = \"yellow\")) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # but you can also use the built-in colour-blind friendly colours for   # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red:   data.frame(     x = c(\"Value1\", \"Value2\", \"Value3\"),     y = c(1, 2, 3),     z = c(\"Value4\", \"Value5\", \"Value6\")   ) %>%     ggplot() +     geom_col(aes(x = x, y = y, fill = z)) +     scale_sir_colours(       aesthetics = \"fill\",       Value4 = \"S\", Value5 = \"I\", Value6 = \"R\"     ) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # resistance of ciprofloxacine per age group   example_isolates %>%     mutate(first_isolate = first_isolate()) %>%     filter(       first_isolate == TRUE,       mo == as.mo(\"Escherichia coli\")     ) %>%     # age_groups() is also a function in this AMR package:     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_sir(x = \"age_group\") } #> Warning: Removed 6 rows containing missing values or values outside the scale range #> (`geom_col()`). #> Warning: Removed 6 rows containing missing values or values outside the scale range #> (`geom_text()`).  if (require(\"ggplot2\") && require(\"dplyr\")) {   # a shorter version which also adjusts data label colours:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_sir(colours = FALSE) }  if (require(\"ggplot2\") && require(\"dplyr\")) {   # it also supports groups (don't forget to use the group var on `x` or `facet`):   example_isolates %>%     filter(mo_is_gram_negative(), ward != \"Outpatient\") %>%     # select only UTI-specific drugs     select(ward, AMX, NIT, FOS, TMP, CIP) %>%     group_by(ward) %>%     ggplot_sir(       x = \"ward\",       facet = \"antibiotic\",       nrow = 1,       title = \"AMR of Anti-UTI Drugs Per Ward\",       x.title = \"Ward\",       datalabels = FALSE     ) } #> ℹ Using column 'mo' as input for mo_is_gram_negative()  # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antimicrobials data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col(x = NULL, search_string = NULL, verbose = FALSE,   only_sir_columns = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_sir_columns logical indicate whether antibiotic columns must detected transformed class sir (see .sir()) beforehand (default FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antimicrobials data set column containing name code antibiotic.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame(   amox = \"S\",   tetr = \"R\" )  guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\"  guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #>  OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline).  # WHONET codes df <- data.frame(   AMP_ND10 = \"R\",   AMC_ED20 = \"S\" ) guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"tibble 301 583 observations 2 variables: mo Microorganism ID ab Antimcrobial ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository. allow machine reading EUCAST CLSI guidelines, almost impossible MS Excel PDF files distributed EUCAST CLSI.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant #> # A tibble: 301,583 × 2 #>    mo          ab   #>    <mo>        <ab> #>  1 B_GRAMP     ATM  #>  2 B_GRAMP     COL  #>  3 B_GRAMP     NAL  #>  4 B_GRAMP     PLB  #>  5 B_GRAMP     TEM  #>  6 B_ANAER-POS ATM  #>  7 B_ANAER-POS COL  #>  8 B_ANAER-POS NAL  #>  9 B_ANAER-POS PLB  #> 10 B_ANAER-POS TEM  #> # ℹ 301,573 more rows"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\", \"html\"))  italicize_taxonomy(string, type = c(\"markdown\", \"ansi\", \"html\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\", \"html\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, <> <\/> using html. using 'ansi', ANSI colours added using \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\"  cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  semi_join_microorganisms(x, by = NULL, ...)  anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> # A tibble: 1 × 26 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # ℹ 16 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <chr>, source <chr>, lpsn <chr>, lpsn_parent <chr>, #> #   lpsn_renamed_to <chr>, mycobank <chr>, mycobank_parent <chr>, #> #   mycobank_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list> left_join_microorganisms(\"B_KLBSL_PNMN\") #> # A tibble: 1 × 26 #>   mo           fullname   status kingdom phylum class order family genus species #>   <mo>         <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #> 1 B_KLBSL_PNMN Klebsiell… accep… Bacter… Pseud… Gamm… Ente… Enter… Kleb… pneumo… #> # ℹ 16 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <chr>, source <chr>, lpsn <chr>, lpsn_parent <chr>, #> #   lpsn_renamed_to <chr>, mycobank <chr>, mycobank_parent <chr>, #> #   mycobank_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list>  df <- data.frame(   date = seq(     from = as.Date(\"2018-01-01\"),     to = as.Date(\"2018-01-07\"),     by = 1   ),   bacteria = as.mo(c(     \"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\",     \"E. coli\", \"E. coli\", \"E. coli\"   )),   stringsAsFactors = FALSE ) colnames(df) #> [1] \"date\"     \"bacteria\"  df_joined <- left_join_microorganisms(df, \"bacteria\") colnames(df_joined) #>  [1] \"date\"                \"bacteria\"            \"fullname\"            #>  [4] \"status\"              \"kingdom\"             \"phylum\"              #>  [7] \"class\"               \"order\"               \"family\"              #> [10] \"genus\"               \"species\"             \"subspecies\"          #> [13] \"rank\"                \"ref\"                 \"oxygen_tolerance\"    #> [16] \"source\"              \"lpsn\"                \"lpsn_parent\"         #> [19] \"lpsn_renamed_to\"     \"mycobank\"            \"mycobank_parent\"     #> [22] \"mycobank_renamed_to\" \"gbif\"                \"gbif_parent\"         #> [25] \"gbif_renamed_to\"     \"prevalence\"          \"snomed\"               # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     left_join_microorganisms() %>%     colnames() } #> Joining, by = \"mo\" #>  [1] \"date\"                \"patient\"             \"age\"                 #>  [4] \"gender\"              \"ward\"                \"mo\"                  #>  [7] \"PEN\"                 \"OXA\"                 \"FLC\"                 #> [10] \"AMX\"                 \"AMC\"                 \"AMP\"                 #> [13] \"TZP\"                 \"CZO\"                 \"FEP\"                 #> [16] \"CXM\"                 \"FOX\"                 \"CTX\"                 #> [19] \"CAZ\"                 \"CRO\"                 \"GEN\"                 #> [22] \"TOB\"                 \"AMK\"                 \"KAN\"                 #> [25] \"TMP\"                 \"SXT\"                 \"NIT\"                 #> [28] \"FOS\"                 \"LNZ\"                 \"CIP\"                 #> [31] \"MFX\"                 \"VAN\"                 \"TEC\"                 #> [34] \"TCY\"                 \"TGC\"                 \"DOX\"                 #> [37] \"ERY\"                 \"CLI\"                 \"AZM\"                 #> [40] \"IPM\"                 \"MEM\"                 \"MTR\"                 #> [43] \"CHL\"                 \"COL\"                 \"MUP\"                 #> [46] \"RIF\"                 \"fullname\"            \"status\"              #> [49] \"kingdom\"             \"phylum\"              \"class\"               #> [52] \"order\"               \"family\"              \"genus\"               #> [55] \"species\"             \"subspecies\"          \"rank\"                #> [58] \"ref\"                 \"oxygen_tolerance\"    \"source\"              #> [61] \"lpsn\"                \"lpsn_parent\"         \"lpsn_renamed_to\"     #> [64] \"mycobank\"            \"mycobank_parent\"     \"mycobank_renamed_to\" #> [67] \"gbif\"                \"gbif_parent\"         \"gbif_renamed_to\"     #> [70] \"prevalence\"          \"snomed\"              # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials(x = NULL, col_mo = NULL, universal = c(\"ampicillin\",   \"amoxicillin/clavulanic acid\", \"cefuroxime\", \"piperacillin/tazobactam\",   \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"),   gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\",   \"ceftazidime\", \"meropenem\"), gram_positive = c(\"vancomycin\", \"teicoplanin\",   \"tetracycline\", \"erythromycin\", \"oxacillin\", \"rifampin\"),   antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\",   \"nystatin\", \"voriconazole\"), only_sir_columns = FALSE, ...)  all_antimicrobials(x = NULL, only_sir_columns = FALSE, ...)  antimicrobials_equal(y, z, type = c(\"points\", \"keyantimicrobials\"),   ignore_I = TRUE, points_threshold = 2, ...)"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antimicrobials columns, like AMX amox. Can left blank determine automatically. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial drugs, case-insensitive. Set NULL ignore. See Details default antimicrobial drugs gram_negative names antibiotic drugs Gram-positives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs gram_positive names antibiotic drugs Gram-negatives, case-insensitive. Set NULL ignore. See Details default antibiotic drugs antifungal names antifungal drugs fungi, case-insensitive. Set NULL ignore. See Details default antifungal drugs only_sir_columns logical indicate whether columns must included transformed class sir (see .sir()) beforehand (default FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial drug results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial drugs used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial drugs used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial drugs used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial drugs used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\"  # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values)  antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs  # \\donttest{ if (require(\"dplyr\")) {   # set key antimicrobials to a new variable   my_patients <- example_isolates %>%     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`     mutate(       # now calculate first isolates       first_regular = first_isolate(col_keyantimicrobials = FALSE),       # and first WEIGHTED isolates       first_weighted = first_isolate(col_keyantimicrobials = \"keyab\")     )    # Check the difference in this data set, 'weighted' results in more isolates:   sum(my_patients$first_regular, na.rm = TRUE)   sum(my_patients$first_weighted, na.rm = TRUE) } #> [1] 1383 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE)  # Default S3 method kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for class 'matrix' kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for class 'data.frame' kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 2.971316 kurtosis(rnorm(10000), excess = TRUE) #> [1] -0.05342143"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE)  x %like% pattern  x %unlike% pattern  x %like_case% pattern  x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"# data.table has a more limited version of %like%, so unload it: try(detach(\"package:data.table\", unload = TRUE), silent = TRUE)  a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE  # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(\"case\", \"diff\", \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE  a[1] %like% b #> [1]  TRUE FALSE FALSE a %like% b[1] #> [1]  TRUE FALSE FALSE  # \\donttest{ # get isolates whose name start with 'Entero' (case-insensitive) example_isolates[which(mo_name() %like% \"^entero\"), ] #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM    NA    NA    NA    NA  #>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS    NA    NA    NA    NA  #>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS    NA    NA    NA    NA  #>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS    NA    NA    NA    NA  #>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC         NA    NA    NA    NA  #>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC         NA    NA    NA    NA  #>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS    NA    NA    NA    NA  #>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC         NA    NA    NA    NA  #> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #> # ℹ 96 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for mo_name() #> # A tibble: 106 × 46 #>    date       patient   age gender ward    mo            PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>   <mo>          <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinic… B_ENTRC_FACM    NA    NA    NA    NA  #>  2 2002-04-08 130252     78 M      ICU     B_ENTRC_FCLS    NA    NA    NA    NA  #>  3 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS    NA    NA    NA    NA  #>  4 2002-06-23 798871     82 M      Clinic… B_ENTRC_FCLS    NA    NA    NA    NA  #>  5 2003-04-20 6BC362     62 M      ICU     B_ENTRC         NA    NA    NA    NA  #>  6 2003-04-21 6BC362     62 M      ICU     B_ENTRC         NA    NA    NA    NA  #>  7 2003-08-13 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #>  8 2003-08-13 F35553     52 M      ICU     B_ENTRC_FCLS    NA    NA    NA    NA  #>  9 2003-09-05 F35553     52 M      ICU     B_ENTRC         NA    NA    NA    NA  #> 10 2003-09-05 F35553     52 M      ICU     B_ENTRBC_CLOC   R     NA    NA    R   #> # ℹ 96 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, … # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national, custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro(x = NULL, guideline = \"CMI2012\", col_mo = NULL, esbl = NA,   carbapenemase = NA, mecA = NA, mecC = NA, vanA = NA, vanB = NA,   info = interactive(), pct_required_classes = 0.5, combine_SI = TRUE,   verbose = FALSE, only_sir_columns = FALSE, ...)  custom_mdro_guideline(..., as_factor = TRUE)  brmo(x = NULL, only_sir_columns = FALSE, ...)  mrgn(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)  mdr_tb(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)  mdr_cmi2012(x = NULL, only_sir_columns = FALSE, verbose = FALSE, ...)  eucast_exceptional_phenotypes(x = NULL, only_sir_columns = FALSE,   verbose = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antimicrobials columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name names codes microorganisms (see .mo()) - default first column class mo. Values coerced using .mo(). esbl logical values, column name containing logical values, indicating presence ESBL gene (production proteins) carbapenemase logical values, column name containing logical values, indicating presence carbapenemase gene (production proteins) mecA logical values, column name containing logical values, indicating presence mecA gene (production proteins) mecC logical values, column name containing logical values, indicating presence mecC gene (production proteins) vanA logical values, column name containing logical values, indicating presence vanA gene (production proteins) vanB logical values, column name containing logical values, indicating presence vanB gene (production proteins) info logical indicate whether progress printed console - default print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_sir_columns logical indicate whether antimicrobial columns must detected transformed class sir (see .sir()) beforehand (default FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antimicrobials . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (doi:10.1111/j.1469-0691.2011.03570.x ) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Samenwerkingverband Richtlijnen Infectiepreventie (SRI) (2024) \"Bijzonder Resistente Micro-Organismen (BRMO)\" (link) Also: guideline = \"BRMO 2017\" former Dutch national guideline - Werkgroep Infectiepreventie (WIP), RIVM, last revision 2017: \"Bijzonder Resistente Micro-Organismen (BRMO)\" Please suggest implement guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours.   outcome function can used guideline argument mdro() function:   Rules can also combined custom rules using c():   rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #>   1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #>   2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #>   3. Otherwise -> Negative #> #> Unmatched rows will return NA. x <- mdro(example_isolates,           guideline = custom) table(x) #>       Negative Elderly Type A Elderly Type B #>           1070            198            732 x <- mdro(example_isolates,           guideline = c(custom,                         custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #>       Negative Elderly Type A Elderly Type B Elderly Type C #>            961            198            732            109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antimicrobials","dir":"Reference","previous_headings":"","what":"Antimicrobials","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antimicrobials column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antimicrobials eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, J01GB06), amoxicillin (AMX, J01CA04), amoxicillin/clavulanic acid (AMC, J01CR02), ampicillin (AMP, J01CA01), ampicillin/sulbactam (SAM, J01CR01), apramycin (APR, QA07AA92), arbekacin (ARB, J01GB12), aspoxicillin (APX, J01CA19), azidocillin (AZD, J01CE04), azithromycin (AZM, J01FA10), azlocillin (AZL, J01CA09), aztreonam (ATM, J01DF01), bacampicillin (BAM, J01CA06), bekanamycin (BEK, J01GB13), benzathine benzylpenicillin (BNB, J01CE08), benzathine phenoxymethylpenicillin (BNP, J01CE10), benzylpenicillin (PEN, J01CE01), besifloxacin (BES, S01AE08), biapenem (BIA, J01DH05), carbenicillin (CRB, J01CA03), carindacillin (CRN, J01CA05), carumonam (CAR, J01DF02), cefacetrile (CAC, J01DB10), cefaclor (CEC, J01DC04), cefadroxil (CFR, J01DB05), cefalexin (LEX, J01DB01), cefaloridine (RID, J01DB02), cefalotin (CEP, J01DB03), cefamandole (MAN, J01DC03), cefapirin (HAP, J01DB08), cefatrizine (CTZ, J01DB07), cefazedone (CZD, J01DB06), cefazolin (CZO, J01DB04), cefcapene (CCP, J01DD17), cefdinir (CDR, J01DD15), cefditoren (DIT, J01DD16), cefepime (FEP, J01DE01), cefepime/amikacin (CFA, J01RA06), cefetamet (CAT, J01DD10), cefiderocol (FDC, J01DI04), cefixime (CFM, J01DD08), cefmenoxime (CMX, J01DD05), cefmetazole (CMZ, J01DC09), cefodizime (DIZ, J01DD09), cefonicid (CID, J01DC06), cefoperazone (CFP, J01DD12), cefoperazone/sulbactam (CSL, J01DD62), ceforanide (CND, J01DC11), cefotaxime (CTX, J01DD01), cefotaxime/clavulanic acid (CTC, J01DD51), cefotetan (CTT, J01DC05), cefotiam (CTF, J01DC07), cefovecin (FOV, QJ01DD91), cefoxitin (FOX, J01DC01), cefozopran (ZOP, J01DE03), cefpiramide (CPM, J01DD11), cefpirome (CPO, J01DE02), cefpodoxime (CPD, J01DD13), cefprozil (CPR, J01DC10), cefquinome (CEQ, QG51AA07), cefroxadine (CRD, J01DB11), cefsulodin (CFS, J01DD03), ceftaroline (CPT, J01DI02), ceftazidime (CAZ, J01DD02), ceftazidime/clavulanic acid (CCV, J01DD52), cefteram (CEM, J01DD18), ceftezole (CTL, J01DB12), ceftibuten (CTB, J01DD14), ceftiofur (TIO, QJ01DD90), ceftizoxime (CZX, J01DD07), ceftobiprole medocaril (CFM1, J01DI01), ceftolozane/tazobactam (CZT, J01DI54), ceftriaxone (CRO, J01DD04), ceftriaxone/beta-lactamase inhibitor (CEB, J01DD63), cefuroxime (CXM, J01DC02), cephradine (CED, J01DB09), chloramphenicol (CHL, J01BA01), ciprofloxacin (CIP, J01MA02), clarithromycin (CLR, J01FA09), clindamycin (CLI, J01FF01), clometocillin (CLM, J01CE07), cloxacillin (CLO, J01CF02), colistin (COL, J01XB01), cycloserine (CYC, J04AB01), dalbavancin (DAL, J01XA04), danofloxacin (DAN, QJ01MA92), daptomycin (DAP, J01XX09), delafloxacin (DFX, J01MA23), dibekacin (DKB, J01GB09), dicloxacillin (DIC, J01CF01), difloxacin (DIF, QJ01MA94), dirithromycin (DIR, J01FA13), doripenem (DOR, J01DH04), doxycycline (DOX, J01AA02), enoxacin (ENX, J01MA04), enrofloxacin (ENR, QJ01MA90), epicillin (EPC, J01CA07), ertapenem (ETP, J01DH03), erythromycin (ERY, J01FA01), fleroxacin (FLE, J01MA08), flucloxacillin (FLC, J01CF05), flurithromycin (FLR1, J01FA14), fosfomycin (FOS, J01XX01), framycetin (FRM, D09AA01), fusidic acid (FUS, J01XC01), gamithromycin (GAM, QJ01FA95), garenoxacin (GRN, J01MA19), gatifloxacin (GAT, J01MA16), gemifloxacin (GEM, J01MA15), gentamicin (GEN, J01GB03), grepafloxacin (GRX, J01MA11), hetacillin (HET, J01CA18), imipenem (IPM, J01DH51), imipenem/relebactam (IMR, J01DH56), isepamicin (ISE, J01GB11), josamycin (JOS, J01FA07), kanamycin (KAN, J01GB04), kitasamycin (KIT, QJ01FA93), lascufloxacin (LSC, J01MA25), latamoxef (LTM, J01DD06), levofloxacin (LVX, J01MA12), levonadifloxacin (LND, J01MA24), lincomycin (LIN, J01FF02), linezolid (LNZ, J01XX08), lomefloxacin (LOM, J01MA07), loracarbef (LOR, J01DC08), marbofloxacin (MAR, QJ01MA93), mecillinam (MEC, J01CA11), meropenem (MEM, J01DH02), meropenem/vaborbactam (MEV, J01DH52), metampicillin (MTM, J01CA14), meticillin (MET, J01CF03), mezlocillin (MEZ, J01CA10), micronomicin (MCR, S01AA22), midecamycin (MID, J01FA03), minocycline (MNO, J01AA08), miocamycin (MCM, J01FA11), moxifloxacin (MFX, J01MA14), nadifloxacin (NAD, D10AF05), nafcillin (NAF, J01CF06), nalidixic acid (NAL, J01MB02), neomycin (NEO, J01GB05), netilmicin (NET, J01GB07), nitrofurantoin (NIT, J01XE01), norfloxacin (, J01MA06), novobiocin (NOV, QJ01XX95), ofloxacin (OFX, J01MA01), oleandomycin (OLE, J01FA05), orbifloxacin (ORB, QJ01MA95), oritavancin (ORI, J01XA05), oxacillin (OXA, J01CF04), panipenem (PAN, J01DH55), pazufloxacin (PAZ, J01MA18), pefloxacin (PEF, J01MA03), penamecillin (PNM, J01CE06), pheneticillin (PHE, J01CE05), phenoxymethylpenicillin (PHN, J01CE02), piperacillin (PIP, J01CA12), piperacillin/tazobactam (TZP, J01CR05), pirlimycin (PRL, QJ51FF90), pivampicillin (PVM, J01CA02), pivmecillinam (PME, J01CA08), plazomicin (PLZ, J01GB14), polymyxin B (PLB, J01XB02), pradofloxacin (PRA, QJ01MA97), pristinamycin (PRI, J01FG01), procaine benzylpenicillin (PRB, J01CE09), propicillin (PRP, J01CE03), prulifloxacin (PRU, J01MA17), quinupristin/dalfopristin (QDA, QJ01FG02), ribostamycin (RST, J01GB10), rifampicin (RIF, J04AB02), rokitamycin (ROK, J01FA12), roxithromycin (RXT, J01FA06), rufloxacin (RFL, J01MA10), sarafloxacin (SAR, QJ01MA98), sisomicin (SIS, J01GB08), sitafloxacin (SIT, J01MA21), solithromycin (SOL, J01FA16), sparfloxacin (SPX, J01MA09), spiramycin (SPI, J01FA02), streptoduocin (STR, J01GA02), streptomycin (STR1, J01GA01), sulbenicillin (SBC, J01CA16), sulfadiazine (SDI, J01EC02), sulfadiazine/trimethoprim (SLT1, J01EE02), sulfadimethoxine (SUD, J01ED01), sulfadimidine (SDM, J01EB03), sulfadimidine/trimethoprim (SLT2, J01EE05), sulfafurazole (SLF, J01EB05), sulfaisodimidine (SLF1, J01EB01), sulfalene (SLF2, J01ED02), sulfamazone (SZO, J01ED09), sulfamerazine (SLF3, J01ED07), sulfamerazine/trimethoprim (SLT3, J01EE07), sulfamethizole (SLF4, J01EB02), sulfamethoxazole (SMX, J01EC01), sulfamethoxypyridazine (SLF5, J01ED05), sulfametomidine (SLF6, J01ED03), sulfametoxydiazine (SLF7, J01ED04), sulfametrole/trimethoprim (SLT4, J01EE03), sulfamoxole (SLF8, J01EC03), sulfamoxole/trimethoprim (SLT5, J01EE04), sulfanilamide (SLF9, J01EB06), sulfaperin (SLF10, J01ED06), sulfaphenazole (SLF11, J01ED08), sulfapyridine (SLF12, J01EB04), sulfathiazole (SUT, J01EB07), sulfathiourea (SLF13, J01EB08), sultamicillin (SLT6, J01CR04), talampicillin (TAL, J01CA15), tebipenem (TBP, J01DH06), tedizolid (TZD, J01XX11), teicoplanin (TEC, J01XA02), telavancin (TLV, J01XA03), telithromycin (TLT, J01FA15), temafloxacin (TMX, J01MA05), temocillin (TEM, J01CA17), tetracycline (TCY, J01AA07), ticarcillin (TIC, J01CA13), ticarcillin/clavulanic acid (TCC, J01CR03), tigecycline (TGC, J01AA12), tilbroquinol (TBQ, P01AA05), tildipirosin (TIP, QJ01FA96), tilmicosin (TIL, QJ01FA91), tobramycin (TOB, J01GB01), tosufloxacin (TFX, J01MA22), trimethoprim (TMP, J01EA01), trimethoprim/sulfamethoxazole (SXT, J01EE01), troleandomycin (TRL, J01FA08), trovafloxacin (TVA, J01MA13), tulathromycin (TUL, QJ01FA94), tylosin (TYL, QJ01FA90), tylvalosin (TYL1, QJ01FA92), vancomycin (VAN, J01XA01)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") str(out) #>  Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #>              Negative Positive, unconfirmed              Positive  #>                  1994                     0                     6   out <- mdro(example_isolates,   guideline = custom_mdro_guideline(     AMX == \"R\" ~ \"Custom MDRO 1\",     VAN == \"R\" ~ \"Custom MDRO 2\"   ) ) table(out) #> out #>      Negative Custom MDRO 1 Custom MDRO 2  #>           870           804           326   # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     mdro() %>%     table()    # no need to define `x` when used inside dplyr verbs:   example_isolates %>%     mutate(MDRO = mdro()) %>%     pull(MDRO) %>%     table() } #> Warning: in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `MDRO = mdro()`. #> Caused by warning: #> ! in mdro(): NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with pct_required_classes) #> . #>                         Negative       Multi-drug-resistant (MDR)  #>                             1617                              128  #> Extensively drug-resistant (XDR)          Pandrug-resistant (PDR)  #>                                0                                0  # }"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Mean AMR Distance — mean_amr_distance","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Calculates normalised mean antimicrobial resistance multiple observations, help identify similar isolates without comparing antibiograms hand.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"mean_amr_distance(x, ...)  # S3 method for class 'sir' mean_amr_distance(x, ..., combine_SI = TRUE)  # S3 method for class 'data.frame' mean_amr_distance(x, ..., combine_SI = TRUE)  amr_distance_from_row(amr_distance, row)"},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"x vector class sir, mic disk, data.frame containing columns classes ... variables select (supports tidyselect language column1:column4 (.mic), can thus also antimicrobial selectors combine_SI logical indicate whether values S, SDD, must merged one, input consists S+vs. R (susceptible vs. resistant) - default TRUE amr_distance outcome mean_amr_distance() row index, row number","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"mean AMR distance effectively Z-score; normalised numeric value compare AMR test results can help identify similar isolates, without comparing antibiograms hand. MIC values (see .mic()) transformed log2() first; distance thus calculated (log2(x) - mean(log2(x))) / sd(log2(x)). SIR values (see .sir()) transformed using \"S\" = 1, \"\" = 2, \"R\" = 3. combine_SI TRUE (default), \"\" considered 1. data sets, mean AMR distance calculated per column, mean per row returned, see Examples. Use amr_distance_from_row() subtract distances distance one row, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"interpretation","dir":"Reference","previous_headings":"","what":"Interpretation","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"Isolates distances less 0.01 difference considered similar. Differences lower 0.025 considered suspicious.","code":""},{"path":"https://msberends.github.io/AMR/reference/mean_amr_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Mean AMR Distance — mean_amr_distance","text":"","code":"sir <- random_sir(10) sir #> Class 'sir' #>  [1] S R S S S R R R R R mean_amr_distance(sir) #>  [1] -1.1618950  0.7745967 -1.1618950 -1.1618950 -1.1618950  0.7745967 #>  [7]  0.7745967  0.7745967  0.7745967  0.7745967  mic <- random_mic(10) mic #> Class 'mic' #>  [1] 0.005 0.25  1     2     >=16  8     1     1     0.025 4     mean_amr_distance(mic) #>  [1] -1.9714967 -0.4147020  0.1369756  0.4128144  1.2403309  0.9644921 #>  [7]  0.1369756  0.1369756 -1.3310188  0.6886533 # equal to the Z-score of their log2: (log2(mic) - mean(log2(mic))) / sd(log2(mic)) #>  [1] -1.9714967 -0.4147020  0.1369756  0.4128144  1.2403309  0.9644921 #>  [7]  0.1369756  0.1369756 -1.3310188  0.6886533  disk <- random_disk(10) disk #> Class 'disk' #>  [1] 15 15 50 41 10 21 44 42 10 41 mean_amr_distance(disk) #>  [1] -0.8693350 -0.8693350  1.3196381  0.7567593 -1.1820455 -0.4940825 #>  [7]  0.9443855  0.8193014 -1.1820455  0.7567593  y <- data.frame(   id = LETTERS[1:10],   amox = random_sir(10, ab = \"amox\", mo = \"Escherichia coli\"),   cipr = random_disk(10, ab = \"cipr\", mo = \"Escherichia coli\"),   gent = random_mic(10, ab = \"gent\", mo = \"Escherichia coli\"),   tobr = random_mic(10, ab = \"tobr\", mo = \"Escherichia coli\") ) y #>    id amox cipr   gent tobr #> 1   A    S   26    >=4 0.25 #> 2   B    S   20      1  >=4 #> 3   C    S   28      2 0.25 #> 4   D    S   26 <=0.25  0.5 #> 5   E    S   23      2 0.25 #> 6   F    I   31    0.5    1 #> 7   G    R   31      2  >=4 #> 8   H    R   29      1    2 #> 9   I    R   27 <=0.25  >=4 #> 10  J    I   22    0.5 0.25 mean_amr_distance(y) #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>  [1] -0.04047504 -0.25789134 -0.08826444 -0.63237868 -0.42488867  0.02453739 #>  [7]  1.18267023  0.72772870  0.36605356 -0.85709171 y$amr_distance <- mean_amr_distance(y, where(is.mic)) #> Error in .subset(x, j): invalid subscript type 'list' y[order(y$amr_distance), ] #> Error in order(y$amr_distance): argument 1 is not a vector  if (require(\"dplyr\")) {   y %>%     mutate(       amr_distance = mean_amr_distance(y),       check_id_C = amr_distance_from_row(amr_distance, id == \"C\")     ) %>%     arrange(check_id_C) } #> ℹ Calculating mean AMR distance based on columns \"amox\", \"cipr\", \"gent\", #>   and \"tobr\" #>    id amox cipr   gent tobr amr_distance check_id_C #> 1   C    S   28      2 0.25  -0.08826444  0.0000000 #> 2   A    S   26    >=4 0.25  -0.04047504  0.0477894 #> 3   F    I   31    0.5    1   0.02453739  0.1128018 #> 4   B    S   20      1  >=4  -0.25789134  0.1696269 #> 5   E    S   23      2 0.25  -0.42488867  0.3366242 #> 6   I    R   27 <=0.25  >=4   0.36605356  0.4543180 #> 7   D    S   26 <=0.25  0.5  -0.63237868  0.5441142 #> 8   J    I   22    0.5 0.25  -0.85709171  0.7688273 #> 9   H    R   29      1    2   0.72772870  0.8159931 #> 10  G    R   31      2  >=4   1.18267023  1.2709347 if (require(\"dplyr\")) {   # support for groups   example_isolates %>%     filter(mo_genus() == \"Enterococcus\" & mo_species() != \"\") %>%     select(mo, TCY, carbapenems()) %>%     group_by(mo) %>%     mutate(dist = mean_amr_distance(.)) %>%     arrange(mo, dist) } #> ℹ Using column 'mo' as input for mo_genus() #> ℹ Using column 'mo' as input for mo_species() #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Calculating mean AMR distance based on columns \"TCY\", \"IPM\", and \"MEM\" #> # A tibble: 63 × 5 #> # Groups:   mo [4] #>    mo           TCY   IPM   MEM     dist #>    <mo>         <sir> <sir> <sir>  <dbl> #>  1 B_ENTRC_AVIM   S     S     NA   0     #>  2 B_ENTRC_AVIM   S     S     NA   0     #>  3 B_ENTRC_CSSL   NA    S     NA  NA     #>  4 B_ENTRC_FACM   S     S     NA  -2.66  #>  5 B_ENTRC_FACM   S     R     R   -0.423 #>  6 B_ENTRC_FACM   S     R     R   -0.423 #>  7 B_ENTRC_FACM   NA    R     R    0.224 #>  8 B_ENTRC_FACM   NA    R     R    0.224 #>  9 B_ENTRC_FACM   NA    R     R    0.224 #> 10 B_ENTRC_FACM   NA    R     R    0.224 #> # ℹ 53 more rows"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"tibble 4 971 observations 2 variables: code Commonly used code microorganism. unique identifier. mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 4 971 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes #> # A tibble: 4,971 × 2 #>    code  mo                #>    <chr> <mo>              #>  1 1011  B_GRAMP           #>  2 1012  B_GRAMP           #>  3 1013  B_GRAMN           #>  4 1014  B_GRAMN           #>  5 1015  F_YEAST           #>  6 103   B_ESCHR_COLI      #>  7 104   B_SLMNL_ENTR_ENTR #>  8 1100  B_STRPT           #>  9 1101  B_STRPT_VIRI      #> 10 1102  B_STRPT_HAEM      #> # ℹ 4,961 more rows  # 'ECO' or 'eco' is the WHONET code for E. coli: microorganisms.codes[microorganisms.codes$code == \"ECO\", ] #> # A tibble: 1 × 2 #>   code  mo           #>   <chr> <mo>         #> 1 ECO   B_ESCHR_COLI  # and therefore, 'eco' will be understood as E. coli in this package: mo_info(\"eco\") #> $mo #> [1] \"B_ESCHR_COLI\" #>  #> $rank #> [1] \"species\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Escherichia\" #>  #> $species #> [1] \"coli\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/escherichia-coli\" #>  #> $ref #> [1] \"Castellani et al., 1919\" #>  #> $snomed #>  [1] \"1095001000112106\" \"715307006\"        \"737528008\"        \"416989002\"        #>  [5] \"116397003\"        \"414097009\"        \"414098004\"        \"414099007\"        #>  [9] \"414100004\"        \"116395006\"        \"735270003\"        \"116396007\"        #> [13] \"83285000\"         \"116394005\"        \"112283007\"        \"710886005\"        #> [17] \"710887001\"        \"710888006\"        \"710889003\"        \"414132004\"        #> [21] \"721892009\"        \"416812001\"        \"416740004\"        \"417216001\"        #> [25] \"457541006\"        \"710253004\"        \"416530004\"        \"417189006\"        #> [29] \"409800005\"        \"713925008\"        \"444771000124108\"  \"838549008\"        #>  #> $lpsn #> [1] \"776057\" #>  #> $mycobank #> [1] NA #>  #> $gbif #> [1] \"11286021\" #>  #> $group_members #> character(0) #>   # works for all AMR functions: mo_is_intrinsic_resistant(\"eco\", ab = \"vancomycin\") #> [1] TRUE"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"data set containing species groups microbiological complexes, used clinical breakpoints table.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"tibble 521 observations 4 variables: mo_group ID species group / microbiological complex mo ID microorganism belonging species group / microbiological complex mo_group_name Name species group / microbiological complex, retrieved mo_name() mo_name Name microorganism belonging species group / microbiological complex, retrieved mo_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 521 Microorganisms In Species Groups — microorganisms.groups","text":"","code":"microorganisms.groups #> # A tibble: 521 × 4 #>    mo_group       mo           mo_group_name                   mo_name           #>    <mo>           <mo>         <chr>                           <chr>             #>  1 B_ACNTB_BMNN-C B_ACNTB_BMNN Acinetobacter baumannii complex Acinetobacter ba… #>  2 B_ACNTB_BMNN-C B_ACNTB_CLCC Acinetobacter baumannii complex Acinetobacter ca… #>  3 B_ACNTB_BMNN-C B_ACNTB_LCTC Acinetobacter baumannii complex Acinetobacter di… #>  4 B_ACNTB_BMNN-C B_ACNTB_NSCM Acinetobacter baumannii complex Acinetobacter no… #>  5 B_ACNTB_BMNN-C B_ACNTB_PITT Acinetobacter baumannii complex Acinetobacter pi… #>  6 B_ACNTB_BMNN-C B_ACNTB_SFRT Acinetobacter baumannii complex Acinetobacter se… #>  7 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex    Bacteroides frag… #>  8 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex    Bacteroides ovat… #>  9 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex    Bacteroides thet… #> 10 B_BCTRD_FRGL-C B_PHCCL_VLGT Bacteroides fragilis complex    Bacteroides vulg… #> # ℹ 511 more rows  # these are all species in the Bacteroides fragilis group, as per WHONET: microorganisms.groups[microorganisms.groups$mo_group == \"B_BCTRD_FRGL-C\", ] #> # A tibble: 5 × 4 #>   mo_group       mo           mo_group_name                mo_name               #>   <mo>           <mo>         <chr>                        <chr>                 #> 1 B_BCTRD_FRGL-C B_BCTRD_FRGL Bacteroides fragilis complex Bacteroides fragilis  #> 2 B_BCTRD_FRGL-C B_BCTRD_OVTS Bacteroides fragilis complex Bacteroides ovatus    #> 3 B_BCTRD_FRGL-C B_BCTRD_THTT Bacteroides fragilis complex Bacteroides thetaiot… #> 4 B_BCTRD_FRGL-C B_PHCCL_VLGT Bacteroides fragilis complex Bacteroides vulgatus  #> 5 B_BCTRD_FRGL-C B_PRBCT_DSTS Bacteroides fragilis complex Parabacteroides dist…"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: June 24th, 2024) six kingdoms. data set backbone AMR package. MO codes can looked using .mo() microorganism properties can looked using mo_* functions. data set carefully crafted, yet made 100% reproducible public authoritative taxonomic sources (using script), namely: List Prokaryotic names Standing Nomenclature (LPSN) bacteria, MycoBank fungi, Global Biodiversity Information Facility (GBIF) others taxons.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"tibble 78 678 observations 26 variables: mo ID microorganism used package. unique identifier. fullname Full name, like \"Escherichia coli\". taxonomic ranks genus, species subspecies, 'pasted' text genus, species, subspecies. taxonomic ranks higher genus, name taxon. unique identifier. status  Status taxon, either \"accepted\", \"validly published\", \"synonym\", \"unknown\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism. Note fungi, phylum equal taxonomic division. Also, fungi, subkingdom subdivision left since occur bacterial taxonomy. rank Text taxonomic rank microorganism, \"species\" \"genus\" ref Author(s) year related scientific publication. contains first surname year latest authors, e.g. \"Wallis et al. 2006 emend. Smith Jones 2018\" becomes \"Smith et al., 2018\". field directly retrieved source specified column source. Moreover, accents removed comply CRAN allows ASCII characters. oxygen_tolerance  Oxygen tolerance, either \"aerobe\", \"anaerobe\", \"anaerobe/microaerophile\", \"facultative anaerobe\", \"likely facultative anaerobe\", \"microaerophile\". data retrieved BacDive (see Source). Items contain \"likely\" missing BacDive extrapolated species within genus guess oxygen tolerance. Currently 68.3% ~39 000 bacteria data set contain oxygen tolerance. source Either \"GBIF\", \"LPSN\", \"MycoBank\", \"manually added\" (see Source) lpsn Identifier ('Record number') List Prokaryotic names Standing Nomenclature (LPSN). first/highest LPSN identifier keep one identifier per row. example, Acetobacter ascendens LPSN Record number 7864 11011. first available microorganisms data set. unique identifier, though available ~33 000 records. lpsn_parent LPSN identifier parent taxon lpsn_renamed_to LPSN identifier currently valid taxon mycobank Identifier ('MycoBank #') MycoBank. unique identifier, though available ~18 000 records. mycobank_parent MycoBank identifier parent taxon mycobank_renamed_to MycoBank identifier currently valid taxon gbif Identifier ('taxonID') Global Biodiversity Information Facility (GBIF). unique identifier, though available ~49 000 records. gbif_parent GBIF identifier parent taxon gbif_renamed_to GBIF identifier currently valid taxon prevalence Prevalence microorganism based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ), see mo_matching_score() full explanation snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, version July 16th, 2024 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"Taxonomic entries imported order importance: List Prokaryotic names Standing Nomenclature (LPSN): Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de June 24th, 2024. MycoBank: Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. Global Biodiversity Information Facility (GBIF): GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Furthermore, sources used additional details: BacDive: Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Systematized Nomenclature Medicine - Clinical Terms (SNOMED-CT): Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). Accessed https://www.cdc.gov/phin/php/phinvads/ July 16th, 2024. Grimont et al. (2007). Antigenic Formulae Salmonella Serovars, 9th Edition. Collaborating Centre Reference Research Salmonella (WHOCC-SALM). Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"Please note entries based LPSN, MycoBank, GBIF (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"Included taxonomic data LPSN, MycoBank, GBIF : ~39 000 (sub)species kingdoms Archaea Bacteria ~28 000 species kingdom Fungi. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package. relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histoplasma, Pneumocystis, Saccharomyces Trichophyton). ~8 100 (sub)species kingdom Protozoa ~1 600 (sub)species 39 relevant genera kingdom Animalia (Strongyloides Taenia) ~22 000 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies identifier parent taxons year first author related scientific publication","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"convenience, entries added manually: ~1 500 entries Salmonella, city-like serovars groups H 36 species groups (beta-haemolytic Streptococcus groups K, coagulase-negative Staphylococcus (CoNS), Mycobacterium tuberculosis complex, etc.), group compositions stored microorganisms.groups data set 1 entry Blastocystis (B.  hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 8 'undefined' entries (unknown, unknown Gram-negatives, unknown Gram-positives, unknown yeast, unknown fungus, unknown anaerobic Gram-pos/Gram-neg bacteria) syntax used transform original data cleansed R format, can found .","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"Like data sets package, data set publicly available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 78 678 Taxonomic Records of Microorganisms — microorganisms","text":"","code":"microorganisms #> # A tibble: 78,678 × 26 #>    mo          fullname   status kingdom phylum class order family genus species #>    <mo>        <chr>      <chr>  <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   #>  1 B_GRAMN     (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  2 B_GRAMP     (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  3 B_ANAER-NEG (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  4 B_ANAER-POS (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  5 B_ANAER     (unknown … unkno… Bacter… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  6 F_FUNGUS    (unknown … unkno… Fungi   (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  7   UNKNOWN   (unknown … unkno… (unkno… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  8 P_PROTOZOAN (unknown … unkno… Protoz… (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #>  9 F_YEAST     (unknown … unkno… Fungi   (unkn… (unk… (unk… \"(unk… (unk… \"(unkn… #> 10 F_AABRN     Aabaarnia  unkno… Fungi   Ascom… Leca… Ostr… \"\"     Aaba… \"\"      #> # ℹ 78,668 more rows #> # ℹ 16 more variables: subspecies <chr>, rank <chr>, ref <chr>, #> #   oxygen_tolerance <chr>, source <chr>, lpsn <chr>, lpsn_parent <chr>, #> #   lpsn_renamed_to <chr>, mycobank <chr>, mycobank_parent <chr>, #> #   mycobank_renamed_to <chr>, gbif <chr>, gbif_parent <chr>, #> #   gbif_renamed_to <chr>, prevalence <dbl>, snomed <list>"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm originally developed 2018 subsequently described : Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 . Later, work Bartlett et al. bacterial pathogens infecting humans (2022, doi:10.1099/mic.0.001269 ) incorporated, optimalisations algorithm made.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated : $$m_{(x, n)} = \\frac{l_{n} - 0.5 \\cdot \\min \\begin{cases}l_{n} \\\\ \\textrm{lev}(x, n)\\end{cases}}{l_{n} \\cdot p_{n} \\cdot k_{n}}$$ : \\(x\\) user input; \\(n\\) taxonomic name (genus, species, subspecies); \\(l_n\\) length \\(n\\); \\(lev\\) Levenshtein distance function (counting insertion 1, deletion substitution 2) needed change \\(x\\) \\(n\\); \\(p_n\\) human pathogenic prevalence group \\(n\\), described ; \\(k_n\\) taxonomic kingdom \\(n\\), set Bacteria = 1, Fungi = 1.25, Protozoa = 1.5, Chromista = 1.75, Archaea = 2, others = 3. grouping human pathogenic prevalence \\(p\\) based recent work Bartlett et al. (2022, doi:10.1099/mic.0.001269 ) extensively studied medical-scientific literature categorise bacterial species groups: Established, taxonomic species infected least three persons three references. records prevalence = 1.15 microorganisms data set; Putative, taxonomic species fewer three known cases. records prevalence = 1.25 microorganisms data set. Furthermore, Genera World Health Organization's () Priority Pathogen List prevalence = 1.0 microorganisms data set; genus present established list also prevalence = 1.15 microorganisms data set; genus present putative list prevalence = 1.25 microorganisms data set; species subspecies genus present two aforementioned groups, prevalence = 1.5 microorganisms data set; non-bacterial genus, species subspecies genus present following list, prevalence = 1.25 microorganisms data set: Absidia, Acanthamoeba, Acremonium, Actinomucor, Aedes, Alternaria, Amoeba, Ancylostoma, Angiostrongylus, Anisakis, Anopheles, Apophysomyces, Arthroderma, Aspergillus, Aureobasidium, Basidiobolus, Beauveria, Bipolaris, Blastobotrys, Blastocystis, Blastomyces, Candida, Capillaria, Chaetomium, Chilomastix, Chrysonilia, Chrysosporium, Cladophialophora, Cladosporium, Clavispora, Coccidioides, Cokeromyces, Conidiobolus, Coniochaeta, Contracaecum, Cordylobia, Cryptococcus, Cryptosporidium, Cunninghamella, Curvularia, Cyberlindnera, Debaryozyma, Demodex, Dermatobia, Dientamoeba, Diphyllobothrium, Dirofilaria, Echinostoma, Entamoeba, Enterobius, Epidermophyton, Exidia, Exophiala, Exserohilum, Fasciola, Fonsecaea, Fusarium, Geotrichum, Giardia, Graphium, Haloarcula, Halobacterium, Halococcus, Hansenula, Hendersonula, Heterophyes, Histomonas, Histoplasma, Hortaea, Hymenolepis, Hypomyces, Hysterothylacium, Kloeckera, Kluyveromyces, Kodamaea, Lacazia, Leishmania, Lichtheimia, Lodderomyces, Lomentospora, Madurella, Malassezia, Malbranchea, Metagonimus, Meyerozyma, Microsporidium, Microsporum, Millerozyma, Mortierella, Mucor, Mycocentrospora, Nannizzia, Necator, Nectria, Ochroconis, Oesophagostomum, Oidiodendron, Opisthorchis, Paecilomyces, Paracoccidioides, Pediculus, Penicillium, Phaeoacremonium, Phaeomoniella, Phialophora, Phlebotomus, Phoma, Pichia, Piedraia, Pithomyces, Pityrosporum, Pneumocystis, Pseudallescheria, Pseudoscopulariopsis, Pseudoterranova, Pulex, Purpureocillium, Quambalaria, Rhinocladiella, Rhizomucor, Rhizopus, Rhodotorula, Saccharomyces, Saksenaea, Saprochaete, Sarcoptes, Scedosporium, Schistosoma, Schizosaccharomyces, Scolecobasidium, Scopulariopsis, Scytalidium, Spirometra, Sporobolomyces, Sporopachydermia, Sporothrix, Sporotrichum, Stachybotrys, Strongyloides, Syncephalastrum, Syngamus, Taenia, Talaromyces, Teleomorph, Toxocara, Trichinella, Trichobilharzia, Trichoderma, Trichomonas, Trichophyton, Trichosporon, Trichostrongylus, Trichuris, Tritirachium, Trombicula, Trypanosoma, Tunga, Ulocladium, Ustilago, Verticillium, Wallemia, Wangiella, Wickerhamomyces, Wuchereria, Yarrowia, Zygosaccharomyces; records prevalence = 2.0 microorganisms data set. calculating matching score, characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.381\\), less prevalent microorganism humans), although latter alphabetically come first.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_reset_session() #> ℹ Reset 17 previously matched input values.  as.mo(\"E. coli\") #> Class 'mo' #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score. #> Colour keys:  0.000-0.549  0.550-0.649  0.650-0.749  0.750-1.000  #>  #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #>               (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #>               (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #>               Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #>               (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #>               dissolvens (0.565) #>  #> Only the first 10 other matches of each record are shown. Run #> print(mo_uncertainties(), n = ...) to view more entries, or save #> mo_uncertainties() to an object.  mo_matching_score(   x = \"E. coli\",   n = c(\"Escherichia coli\", \"Entamoeba coli\") ) #> [1] 0.6875000 0.3809524"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_fullname(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_shortname(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_subspecies(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_species(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_genus(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_family(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_order(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_class(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_phylum(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_kingdom(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_domain(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_type(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_status(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_pathogenicity(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_gramstain(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_gram_negative(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_gram_positive(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_yeast(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_intrinsic_resistant(x, ab, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_oxygen_tolerance(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_is_anaerobic(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_snomed(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_ref(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_authors(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_year(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_lpsn(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_mycobank(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_gbif(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_rank(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_taxonomy(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_synonyms(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_current(x, language = get_AMR_locale(), ...)  mo_group_members(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_info(x, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_url(x, open = FALSE, language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)  mo_property(x, property = \"fullname\", language = get_AMR_locale(),   keep_synonyms = getOption(\"AMR_keep_synonyms\", FALSE), ...)"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language translate text like \"growth\", defaults system language (see get_AMR_locale()) keep_synonyms logical indicate old, previously valid taxonomic names must preserved corrected currently accepted names. default FALSE, return note old taxonomic names processed. default can set package option AMR_keep_synonyms, .e. options(AMR_keep_synonyms = TRUE) options(AMR_keep_synonyms = FALSE). ... arguments passed .mo(), 'minimum_matching_score', 'ignore_pattern', 'remove_from_input' ab (vector ) text can coerced valid antibiotic drug code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() ordered factor case mo_pathogenicity() list case mo_taxonomy(), mo_synonyms(), mo_snomed(), mo_info() logical case mo_is_anaerobic(), mo_is_gram_negative(), mo_is_gram_positive(), mo_is_intrinsic_resistant(), mo_is_yeast() named character case mo_synonyms() mo_url() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions , default, keep old taxonomic properties, synonyms automatically replaced current taxonomy. Take example Enterobacter aerogenes, initially named 1960 renamed Klebsiella aerogenes 2017: mo_genus(\"Enterobacter aerogenes\") return \"Klebsiella\" (note renaming) mo_genus(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Enterobacter\" (-per-session warning name outdated) mo_ref(\"Enterobacter aerogenes\") return \"Tindall et al., 2017\" (note renaming) mo_ref(\"Enterobacter aerogenes\", keep_synonyms = TRUE) return \"Hormaeche et al., 1960\" (-per-session warning name outdated) short name (mo_shortname()) returns first character genus full species, \"E. coli\", species subspecies. Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. result, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Determination human pathogenicity (mo_pathogenicity()) strongly based Bartlett et al. (2022, doi:10.1099/mic.0.001269 ). function returns factor levels Pathogenic, Potentially pathogenic, Non-pathogenic, Unknown. Determination Gram stain (mo_gramstain()) based taxonomic kingdom phylum. Originally, Cavalier-Smith defined -called subkingdoms Negibacteria Posibacteria (2002, PMID 11837318), considered phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, Tenericutes. phyla later renamed Actinomycetota, Chloroflexota, Bacillota, Mycoplasmatota (2021, PMID 34694987). Bacteria phyla considered Gram-positive AMR package, except members class Negativicutes (within phylum Bacillota) Gram-negative. bacteria considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (NA input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts (mo_is_yeast()) based taxonomic kingdom class. Budding yeasts yeasts reproduce asexually process called budding, new cell develops small protrusion parent cell. Taxonomically, members phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) Pichiomycetes. True yeasts quite specifically refers yeasts underlying order Saccharomycetales (Saccharomyces cerevisiae). Thus, microorganisms member taxonomic class Saccharomycetes Pichiomycetes, function return TRUE. returns FALSE otherwise (NA input NA MO code UNKNOWN). Determination intrinsic resistance (mo_is_intrinsic_resistant()) based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() function can vectorised argument x (input microorganisms) ab (input antimicrobials). Determination bacterial oxygen tolerance (mo_oxygen_tolerance()) based BacDive, see Source. function mo_is_anaerobic() returns TRUE oxygen tolerance \"anaerobe\", indicting obligate anaerobic species genus. always returns FALSE species outside taxonomic kingdom Bacteria. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. MycoBank URL used fungi wherever available , LPSN URL bacteria wherever available, GBIF link otherwise. SNOMED codes (mo_snomed()) last updated July 16th, 2024. See Source microorganisms data set info. Old taxonomic names (-called 'synonyms') can retrieved mo_synonyms() (scientific reference name), current taxonomic name can retrieved mo_current(). functions return full names. output translated possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"function uses .mo() internally, uses advanced algorithm translate arbitrary user input valid taxonomy using -called matching score. can read public algorithm MO matching score page.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Berends MS et al. (2022). AMR: R Package Working Antimicrobial Resistance Data. Journal Statistical Software, 104(3), 1-31; doi:10.18637/jss.v104.i03 Parte, AC et al. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 . Accessed https://lpsn.dsmz.de June 24th, 2024. Vincent, R et al (2013). MycoBank gearing new horizons. IMA Fungus, 4(2), 371-9; doi:10.5598/imafungus.2013.04.02.16 . Accessed https://www.mycobank.org June 24th, 2024. GBIF Secretariat (2023). GBIF Backbone Taxonomy. Checklist dataset doi:10.15468/39omei . Accessed https://www.gbif.org June 24th, 2024. Reimer, LC et al. (2022). BacDive 2022: knowledge base standardized bacterial archaeal data. Nucleic Acids Res., 50(D1):D741-D74; doi:10.1093/nar/gkab961 . Accessed https://bacdive.dsmz.de July 16th, 2024. Public Health Information Network Vocabulary Access Distribution System (PHIN VADS). US Edition SNOMED CT 1 September 2020. Value Set Name 'Microorganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: https://www.cdc.gov/phin/php/phinvads/ Bartlett et al. (2022). comprehensive list bacterial pathogens infecting humans Microbiology 168:001269; doi:10.1099/mic.0.001269","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"data sets AMR package (microorganisms, antimicrobials, SIR interpretation, EUCAST rules, etc.) publicly freely available download following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, Stata. also provide tab-separated plain text files machine-readable suitable input software program, laboratory information systems. Please visit website download links. actual files course available GitHub repository.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree -----------------------------------------------------------  mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Pseudomonadota\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\"   # full names and short names -----------------------------------------------  mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\"   # other properties ---------------------------------------------------------  mo_pathogenicity(\"Klebsiella pneumoniae\") #> [1] Pathogenic #> Levels: Pathogenic < Potentially pathogenic < Non-pathogenic < Unknown mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #>                                Klebsiella pneumoniae  #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\"  mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1]  TRUE FALSE FALSE  mo_group_members(c(   \"Streptococcus group A\",   \"Streptococcus group C\",   \"Streptococcus group G\",   \"Streptococcus group L\" )) #> $`Streptococcus Group A` #> [1] \"Streptococcus pyogenes\" #>  #> $`Streptococcus Group C` #> [1] \"Streptococcus dysgalactiae\"              #> [2] \"Streptococcus dysgalactiae dysgalactiae\" #> [3] \"Streptococcus dysgalactiae equisimilis\"  #> [4] \"Streptococcus equi\"                      #> [5] \"Streptococcus equi equi\"                 #> [6] \"Streptococcus equi ruminatorum\"          #> [7] \"Streptococcus equi zooepidemicus\"        #>  #> $`Streptococcus Group G` #> [1] \"Streptococcus canis\"                     #> [2] \"Streptococcus dysgalactiae\"              #> [3] \"Streptococcus dysgalactiae dysgalactiae\" #> [4] \"Streptococcus dysgalactiae equisimilis\"  #>  #> $`Streptococcus Group L` #> [1] \"Streptococcus dysgalactiae\"              #> [2] \"Streptococcus dysgalactiae dysgalactiae\" #> [3] \"Streptococcus dysgalactiae equisimilis\"  #>    # scientific reference -----------------------------------------------------  mo_ref(\"Klebsiella aerogenes\") #> [1] \"Tindall et al., 2017\" mo_authors(\"Klebsiella aerogenes\") #> [1] \"Tindall et al.\" mo_year(\"Klebsiella aerogenes\") #> [1] 2017 mo_synonyms(\"Klebsiella aerogenes\") #>   Hormaeche et al., 1960     Bascomb et al., 1971  #> \"Enterobacter aerogenes\"     \"Klebsiella mobilis\"  mo_lpsn(\"Klebsiella aerogenes\") #> [1] \"777146\" mo_gbif(\"Klebsiella aerogenes\") #> [1] \"9281703\" mo_mycobank(\"Candida albicans\") #> [1] \"256187\" mo_mycobank(\"Candida krusei\") #> [1] \"337013\" mo_mycobank(\"Candida krusei\", keep_synonyms = TRUE) #> Warning: Function as.mo() returned one old taxonomic name. Use as.mo(..., #> keep_synonyms = FALSE) to clean the input to currently accepted taxonomic #> names, or set the R option AMR_keep_synonyms to FALSE. This warning #> will be shown once per session. #> [1] \"268707\"   # abbreviations known in the field -----------------------------------------  mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\"  mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EIEC\") #> [1] \"coli\" mo_name(\"UPEC\") #> [1] \"Escherichia coli\"   # known subspecies ---------------------------------------------------------  mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\"  # \\donttest{ # Becker classification, see ?as.mo ----------------------------------------  mo_fullname(\"Staph epidermidis\") #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"Staph epidermidis\") #> [1] \"S. epidermidis\" mo_shortname(\"Staph epidermidis\", Becker = TRUE) #> [1] \"CoNS\"   # Lancefield classification, see ?as.mo ------------------------------------  mo_fullname(\"Strep agalactiae\") #> [1] \"Streptococcus agalactiae\" mo_fullname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"Streptococcus Group B\" mo_shortname(\"Strep agalactiae\") #> [1] \"S. agalactiae\" mo_shortname(\"Strep agalactiae\", Lancefield = TRUE) #> [1] \"GBS\"   # language support  --------------------------------------------------------  mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") # German #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") # Dutch #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") # Spanish #> [1] \"Gram negativo\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"el\") # Greek #> [1] \"Αρνητικό κατά Gram\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"uk\") # Ukrainian #> [1] \"Грамнегативні\"  # mo_type is equal to mo_kingdom, but mo_kingdom will remain untranslated mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_kingdom(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, no effect #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\", language = \"zh\") # Chinese, translated #> [1] \"细菌\"  mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\", Lancefield = TRUE, language = \"uk\") #> [1] \"Streptococcus Група A\"   # other --------------------------------------------------------------------  # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_gram_positive()) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_gram_positive() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 18 × 2 #>    `mo_genus()`        n #>    <chr>           <int> #>  1 Staphylococcus    840 #>  2 Streptococcus     275 #>  3 Enterococcus       83 #>  4 Corynebacterium    17 #>  5 Micrococcus         6 #>  6 Gemella             3 #>  7 Aerococcus          2 #>  8 Cutibacterium       1 #>  9 Dermabacter         1 #> 10 Fusibacter          1 #> 11 Globicatella        1 #> 12 Granulicatella      1 #> 13 Lactobacillus       1 #> 14 Leuconostoc         1 #> 15 Listeria            1 #> 16 Paenibacillus       1 #> 17 Rothia              1 #> 18 Schaalia            1 if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_intrinsic_resistant(ab = \"vanco\")) %>%     count(mo_genus(), sort = TRUE) } #> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant() #> ℹ Using column 'mo' as input for mo_genus() #> # A tibble: 20 × 2 #>    `mo_genus()`         n #>    <chr>            <int> #>  1 Escherichia        467 #>  2 Klebsiella          77 #>  3 Proteus             39 #>  4 Pseudomonas         30 #>  5 Serratia            25 #>  6 Enterobacter        23 #>  7 Citrobacter         11 #>  8 Haemophilus          8 #>  9 Acinetobacter        6 #> 10 Morganella           6 #> 11 Pantoea              4 #> 12 Salmonella           3 #> 13 Neisseria            2 #> 14 Stenotrophomonas     2 #> 15 Campylobacter        1 #> 16 Enterococcus         1 #> 17 Hafnia               1 #> 18 Lactobacillus        1 #> 19 Leuconostoc          1 #> 20 Pseudescherichia     1  # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>   # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $mo #> [1] \"B_KLBSL_PNMN\" #>  #> $rank #> [1] \"species\" #>  #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Pseudomonadota\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>  #> $status #> [1] \"accepted\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $oxygen_tolerance #> [1] \"facultative anaerobe\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] \"1098101000112102\" \"446870005\"        \"1098201000112108\" \"409801009\"        #> [5] \"56415008\"         \"714315002\"        \"713926009\"        #>  #> $lpsn #> [1] \"777151\" #>  #> $mycobank #> [1] NA #>  #> $gbif #> [1] \"3221874\" #>  #> $group_members #> character(0) #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(path, destination = getOption(\"AMR_mo_source\",   \"~/mo_source.rds\"))  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file - default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set package option AMR_mo_source, e.g. options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names:   save \"/Users//Documents/ourcodes.xlsx\". Now set source:   now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions:   edit Excel file , say, adding row 4 like :   ...new usage MO function package update data file:   delete reference data file, just use \"\", NULL FALSE input set_mo_source():   original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"|         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 |                    |                       | set_mo_source(\"/Users/me/Documents/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>       \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\"  # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #>       Use mo_uncertainties() to review it. #> Class 'mo' #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI |         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus   | Staphylococcus aureus | 5 |                    |                       | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>        \"Organisation XYZ\" and \"mo\" #> Class 'mo' #> [1] B_ESCHR_COLI  mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca(x, ..., retx = TRUE, center = TRUE, scale. = TRUE, tol = NULL,   rank. = NULL)"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables     returned. center logical value indicating whether variables     shifted zero centered. Alternately, vector     length equal number columns x can supplied.     value passed scale. scale. logical value indicating whether variables     scaled unit variance analysis takes     place.  default FALSE consistency S,     general scaling advisable.  Alternatively, vector length     equal number columns x can supplied.      value passed scale. tol value indicating magnitude components     omitted. (Components omitted     standard deviations less equal tol times     standard deviation first component.)  default null     setting, components omitted (unless rank. specified     less min(dim(x)).).  settings tol     tol = 0 tol = sqrt(.Machine$double.eps),     omit essentially constant components. rank. optionally, number specifying maximal rank, .e.,     maximal number principal components used.  Can set     alternative addition tol, useful notably     desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first   resistance_data <- example_isolates %>%     group_by(       order = mo_order(mo), # group on anything, like order       genus = mo_genus(mo)     ) %>% #   and genus as we do here;     filter(n() >= 30) %>% # filter on only 30 results per group     summarise_if(is.sir, resistance) # then get resistance of all drugs    # now conduct PCA for certain antimicrobial drugs   pca_result <- resistance_data %>%     pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)    pca_result   summary(pca_result)    # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   if (require(\"ggplot2\")) {     ggplot_pca(pca_result)      ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: There were 73 warnings in `summarise()`. #> The first warning was: #> ℹ In argument: `PEN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 5: `order = \"Lactobacillales\"` `genus = \"Enterococcus\"`. #> Caused by warning: #> ! Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (minimum = 30). #> ℹ Run `dplyr::last_dplyr_warnings()` to see the 72 remaining warnings. #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", #>   \"TMP\", and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting Helpers for AMR Data Analysis — plot","title":"Plotting Helpers for AMR Data Analysis — plot","text":"Functions plot classes sir, mic disk, support base R ggplot2. Especially scale_*_mic() functions relevant wrappers plot MIC values ggplot2. allows custom MIC ranges plot intermediate log2 levels missing MIC values.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting Helpers for AMR Data Analysis — plot","text":"","code":"scale_x_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_y_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_colour_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_fill_mic(keep_operators = \"edges\", mic_range = NULL, ...)  scale_x_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\",   \"EUCAST\") == \"EUCAST\", ...)  scale_colour_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\",   \"EUCAST\") == \"EUCAST\", ...)  scale_fill_sir(colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), eucast_I = getOption(\"AMR_guideline\",   \"EUCAST\") == \"EUCAST\", ...)  # S3 method for class 'mic' plot(x, mo = NULL, ab = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   main = deparse(substitute(x)), ylab = translate_AMR(\"Frequency\", language   = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language =   language), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), expand = TRUE,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...)  # S3 method for class 'mic' autoplot(object, mo = NULL, ab = NULL,   guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   title = deparse(substitute(object)), ylab = translate_AMR(\"Frequency\",   language = language),   xlab = translate_AMR(\"Minimum Inhibitory Concentration (mg/L)\", language =   language), colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), expand = TRUE,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...)  # S3 method for class 'disk' plot(x, main = deparse(substitute(x)),   ylab = translate_AMR(\"Frequency\", language = language),   xlab = translate_AMR(\"Disk diffusion diameter (mm)\", language = language),   mo = NULL, ab = NULL, guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), expand = TRUE,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...)  # S3 method for class 'disk' autoplot(object, mo = NULL, ab = NULL,   title = deparse(substitute(object)), ylab = translate_AMR(\"Frequency\",   language = language), xlab = translate_AMR(\"Disk diffusion diameter (mm)\",   language = language), guideline = getOption(\"AMR_guideline\", \"EUCAST\"),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), expand = TRUE,   include_PKPD = getOption(\"AMR_include_PKPD\", TRUE),   breakpoint_type = getOption(\"AMR_breakpoint_type\", \"human\"), ...)  # S3 method for class 'sir' plot(x, ylab = translate_AMR(\"Percentage\", language =   language), xlab = translate_AMR(\"Antimicrobial Interpretation\", language =   language), main = deparse(substitute(x)), language = get_AMR_locale(),   ...)  # S3 method for class 'sir' autoplot(object, title = deparse(substitute(object)),   xlab = translate_AMR(\"Antimicrobial Interpretation\", language = language),   ylab = translate_AMR(\"Frequency\", language = language),   colours_SIR = c(\"#3CAEA3\", \"#F6D55C\", \"#ED553B\"),   language = get_AMR_locale(), ...)  facet_sir(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL)  scale_y_percent(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),   limits = c(0, NA))  scale_sir_colours(..., aesthetics, colours_SIR = c(\"#3CAEA3\", \"#F6D55C\",   \"#ED553B\"))  theme_sir()  labels_sir_count(position = NULL, x = \"antibiotic\",   translate_ab = \"name\", minimum = 30, language = get_AMR_locale(),   combine_SI = TRUE, datalabels.size = 3, datalabels.colour = \"grey15\")"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting Helpers for AMR Data Analysis — plot","text":"keep_operators character specifying handle operators (> <=) input. Accepts one three values: \"\" (TRUE) keep operators, \"none\" (FALSE) remove operators, \"edges\" keep operators ends range. mic_range manual range rescale MIC values (using rescale_mic()), e.g., mic_range = c(0.001, 32). Use NA prevent rescaling one side, e.g., mic_range = c(NA, 32). Note: rescales values filter - use ggplot2 limits argument separately exclude values plot. ... arguments passed methods colours_SIR colours use filling bars, must vector three values (order S, R). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant' - default system language (see get_AMR_locale()) can overwritten setting package option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL prevent translation. eucast_I logical indicate whether '' must interpreted \"Susceptible, increased exposure\". TRUE default AMR interpretation guideline set EUCAST (default). FALSE, interpreted \"Intermediate\". x, object values created .mic(), .disk() .sir() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial drug code .ab() guideline interpretation guideline use - default latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. include_PKPD logical indicate PK/PD clinical breakpoints must applied last resort - default TRUE. Can also set package option AMR_include_PKPD. breakpoint_type type breakpoints use, either \"ECOFF\", \"animal\", \"human\". ECOFF stands Epidemiological Cut-values. default \"human\", can also set package option AMR_breakpoint_type. host set values veterinary species, automatically set \"animal\". facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable nrow (using facet) number rows breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum aesthetics aesthetics apply colours - default \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\" position position adjustment bars, either \"fill\", \"stack\" \"dodge\" translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE datalabels.size size datalabels datalabels.colour colour datalabels","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting Helpers for AMR Data Analysis — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"the-scale-mic-functions","dir":"Reference","previous_headings":"","what":"The scale_*_mic() Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"functions scale_x_mic(), scale_y_mic(), scale_colour_mic(), scale_fill_mic() functions allow plot mic class (MIC values) continuous, logarithmic scale. also allow rescale MIC range 'inside' 'outside' range required, retain signs MIC values desired. Missing intermediate log2 levels plotted .","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"the-scale-sir-functions","dir":"Reference","previous_headings":"","what":"The scale_*_sir() Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"functions scale_x_sir(), scale_colour_sir(), scale_fill_sir() functions allow plot sir class right order (S < SDD < < R < NI). default, translate S//R values interpretative text (\"Susceptible\", \"Resistant\", etc.) 20 supported languages (use language = NULL keep S//R). Also, except scale_x_sir(), set colour-blind friendly colours colour fill aesthetics.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"additional-ggplot-functions","dir":"Reference","previous_headings":"","what":"Additional ggplot2 Functions","title":"Plotting Helpers for AMR Data Analysis — plot","text":"package contains functions extend ggplot2 package, help visualising AMR data results. functions internally used ggplot_sir() . facet_sir() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_sir_colours() allows set colours aesthetic, even shape linetype. theme_sir() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_sir_count() print datalabels bars percentage number isolates, using ggplot2::geom_text(). interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, default guideline EUCAST 2024, unless package option AMR_guideline set. See .sir() information.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting Helpers for AMR Data Analysis — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))  # \\donttest{ # Plotting using ggplot2's autoplot() for MIC, disk, and SIR ----------- if (require(\"ggplot2\")) {   autoplot(some_mic_values) }  if (require(\"ggplot2\")) {   # when providing the microorganism and antibiotic, colours will show interpretations:   autoplot(some_mic_values, mo = \"Escherichia coli\", ab = \"cipro\") }  if (require(\"ggplot2\")) {   # support for 20 languages, various guidelines, and many options   autoplot(some_disk_values,     mo = \"Escherichia coli\", ab = \"cipro\",     guideline = \"CLSI 2024\", language = \"no\",     title = \"Disk diffusion from the North\"   ) }    # Plotting using scale_x_mic() ----------------------------------------- if (require(\"ggplot2\")) {   mic_plot <- ggplot(     data.frame(       mics = as.mic(c(0.25, \"<=4\", 4, 8, 32, \">=32\")),       counts = c(1, 1, 2, 2, 3, 3)     ),     aes(mics, counts)   ) +     geom_col()   mic_plot +     labs(title = \"without scale_x_mic()\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic() +     labs(title = \"with scale_x_mic()\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(keep_operators = \"all\") +     labs(title = \"with scale_x_mic() keeping all operators\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(mic_range = c(1, 16)) +     labs(title = \"with scale_x_mic() using a manual 'within' range\") }  if (require(\"ggplot2\")) {   mic_plot +     scale_x_mic(mic_range = c(0.032, 256)) +     labs(title = \"with scale_x_mic() using a manual 'outside' range\") }    # Plotting using scale_y_mic() ----------------------------------------- some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)  if (require(\"ggplot2\")) {   ggplot(     data.frame(       mic = some_mic_values,       group = some_groups     ),     aes(group, mic)   ) +     geom_boxplot() +     geom_violin(linetype = 2, colour = \"grey\", fill = NA) +     scale_y_mic() }  if (require(\"ggplot2\")) {   ggplot(     data.frame(       mic = some_mic_values,       group = some_groups     ),     aes(group, mic)   ) +     geom_boxplot() +     geom_violin(linetype = 2, colour = \"grey\", fill = NA) +     scale_y_mic(mic_range = c(NA, 0.25)) }    # Plotting using scale_x_sir() ----------------------------------------- if (require(\"ggplot2\")) {   ggplot(     data.frame(       x = c(\"I\", \"R\", \"S\"),       y = c(45, 323, 573)     ),     aes(x, y)   ) +     geom_col() +     scale_x_sir() }    # Plotting using scale_y_mic() and scale_colour_sir() ------------------ if (require(\"ggplot2\")) {   plain <- ggplot(     data.frame(       mic = some_mic_values,       group = some_groups,       sir = as.sir(some_mic_values,         mo = \"E. coli\",         ab = \"cipro\"       )     ),     aes(x = group, y = mic, colour = sir)   ) +     theme_minimal() +     geom_boxplot(fill = NA, colour = \"grey\") +     geom_jitter(width = 0.25)    plain } #>  #> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with #>   all the details of the breakpoint interpretations. #>  #> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... #>  NOTE  #>   • Multiple breakpoints available for ciprofloxacin (CIP) in Escherichia coli - assuming body site 'Non-meningitis'.  if (require(\"ggplot2\")) {   # and now with our MIC and SIR scale functions:   plain +     scale_y_mic() +     scale_colour_sir() }  if (require(\"ggplot2\")) {   plain +     scale_y_mic(mic_range = c(0.005, 32), name = \"Our MICs!\") +     scale_colour_sir(       language = \"pt\",       name = \"Support in 20 languages\"     ) }  # }  # Plotting using base R's plot() ---------------------------------------  plot(some_mic_values)  # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\")   plot(some_disk_values)  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"nl\")   plot(some_sir_values)"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Antimicrobial Resistance — proportion","title":"Calculate Antimicrobial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Antimicrobial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  susceptibility(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  sir_confidence_interval(..., ab_result = \"R\", minimum = 30,   as_percent = FALSE, only_all_tested = FALSE, confidence_level = 0.95,   side = \"both\", collapse = FALSE)  proportion_R(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_IR(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_I(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_SI(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_S(..., minimum = 30, as_percent = FALSE,   only_all_tested = FALSE)  proportion_df(data, translate_ab = \"name\", language = get_AMR_locale(),   minimum = 30, as_percent = FALSE, combine_SI = TRUE,   confidence_level = 0.95)  sir_df(data, translate_ab = \"name\", language = get_AMR_locale(),   minimum = 30, as_percent = FALSE, combine_SI = TRUE,   confidence_level = 0.95)"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate Antimicrobial Resistance — proportion","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Antimicrobial Resistance — proportion","text":"... one vectors (columns) antibiotic interpretations. transformed internally .sir() needed. Use multiple columns calculate (lack ) co-resistance: probability one two drugs resistant susceptible result. See Examples. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. as_percent logical indicate whether output must returned hundred fold % sign (character). value 0.123456 returned \"12.3%\". only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antimicrobials, see section Combination Therapy ab_result antibiotic results test , must one values \"S\", \"SDD\", \"\", \"R\" confidence_level confidence level returned confidence interval. calculation, number S SI isolates, R isolates compared total number available isolates R, S, using binom.test(), .e., Clopper-Pearson method. side side confidence interval return. default \"\" length 2 vector, can also (abbreviated ) \"min\"/\"left\"/\"lower\"/\"less\" \"max\"/\"right\"/\"higher\"/\"greater\". collapse logical indicate whether output values 'collapsed', .e. merged together one value, character value use collapsing data data.frame containing columns class sir (see .sir()) translate_ab column name antimicrobials data set translate antibiotic abbreviations , using ab_property() language language returned text - default current system language (see get_AMR_locale()) can also set package option AMR_locale. Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S, SDD, must merged one, output consists S+SDD+vs. R (susceptible vs. resistant) - default TRUE","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Antimicrobial Resistance — proportion","text":"double , as_percent = TRUE, character.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Antimicrobial Resistance — proportion","text":"automated comprehensive analysis, consider using antibiogram() wisca(), streamline many aspects susceptibility reporting , importantly, also support WISCA. functions described offer hands-, manual approach greater customisation. Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set one four available algorithms. function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Since AMR v3.0, proportion_SI() proportion_I() include dose-dependent susceptibility ('SDD'). Use sir_confidence_interval() calculate confidence interval, relies binom.test(), .e., Clopper-Pearson method. function returns vector length 2 default antimicrobial resistance. Change side argument \"left\"/\"min\" \"right\"/\"max\" return single value, change ab_result argument e.g. c(\"S\", \"\") test antimicrobial susceptibility, see Examples. functions meant count isolates, calculate proportion resistance/susceptibility. Use count_*() functions count isolates. function susceptibility() essentially equal count_susceptible()/count_all(). Low counts can influence outcome - proportion_*() functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data sir class (created .sir()) calculates proportions S, , R. also supports grouped variables. function sir_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Antimicrobial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antimicrobials, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   considered   considered  considered   considered                     susceptible    tested    susceptible    tested --------  --------  -----------  ----------  -----------  ----------  S or I    S or I        X            X           X            X    R       S or I        X            X           X            X   <NA>     S or I        X            X           -            -  S or I      R           X            X           X            X    R         R           -            X           -            X   <NA>       R           -            -           -            -  S or I     <NA>         X            X           -            -    R        <NA>         -            -           -            -   <NA>      <NA>         -            -           -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Calculate Antimicrobial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Antimicrobial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info. example_isolates #> # A tibble: 2,000 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …   # base R ------------------------------------------------------------ # determines %R resistance(example_isolates$AMX) #> [1] 0.5955556 sir_confidence_interval(example_isolates$AMX) #> [1] 0.5688204 0.6218738 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975 ) #> [1] 0.5650148 0.6255670 sir_confidence_interval(example_isolates$AMX,   confidence_level = 0.975,   collapse = \", \" ) #> [1] \"0.565, 0.626\"  # determines %S+I: susceptibility(example_isolates$AMX) #> [1] 0.4044444 sir_confidence_interval(example_isolates$AMX,   ab_result = c(\"S\", \"I\") ) #> [1] 0.3781262 0.4311796  # be more specific proportion_S(example_isolates$AMX) #> [1] 0.4022222 proportion_SI(example_isolates$AMX) #> Note that proportion_SI() will also include dose-dependent #> susceptibility, 'SDD'. This note will be shown once for this session. #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> Note that proportion_I() will also include dose-dependent susceptibility, #> 'SDD'. This note will be shown once for this session. #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> Note that proportion_IR() will also include dose-dependent #> susceptibility, 'SDD'. This note will be shown once for this session. #> [1] 0.5977778 proportion_R(example_isolates$AMX) #> [1] 0.5955556  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       r = resistance(CIP),       n = n_sir(CIP)     ) # n_sir works like n_distinct in dplyr, see ?n_sir } #> # A tibble: 3 × 3 #>   ward           r     n #>   <chr>      <dbl> <int> #> 1 Clinical   0.147   869 #> 2 ICU        0.190   447 #> 3 Outpatient 0.161    93 if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       cipro_R = resistance(CIP),       ci_min = sir_confidence_interval(CIP, side = \"min\"),       ci_max = sir_confidence_interval(CIP, side = \"max\"),     ) } #> # A tibble: 3 × 4 #>   ward       cipro_R ci_min ci_max #>   <chr>        <dbl>  <dbl>  <dbl> #> 1 Clinical     0.147 0.124   0.173 #> 2 ICU          0.190 0.155   0.230 #> 3 Outpatient   0.161 0.0932  0.252 if (require(\"dplyr\")) {   # scoped dplyr verbs with antimicrobial selectors   # (you could also use across() of course)   example_isolates %>%     group_by(ward) %>%     summarise_at(       c(aminoglycosides(), carbapenems()),       resistance     ) } #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `KAN = (function (..., minimum = 30, as_percent = FALSE, #>   only_all_tested = FALSE) ...`. #> ℹ In group 3: `ward = \"Outpatient\"`. #> Caused by warning: #> ! Introducing NA: only 23 results available for KAN in group: ward = #> \"Outpatient\" (minimum = 30). #> # A tibble: 3 × 7 #>   ward         GEN   TOB   AMK   KAN    IPM    MEM #>   <chr>      <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> #> 1 Clinical   0.229 0.315 0.626     1 0.0498 0.0458 #> 2 ICU        0.290 0.400 0.662     1 0.0862 0.0894 #> 3 Outpatient 0.2   0.368 0.605    NA 0.0541 0.0541 if (require(\"dplyr\")) {   example_isolates %>%     group_by(ward) %>%     summarise(       R = resistance(CIP, as_percent = TRUE),       SI = susceptibility(CIP, as_percent = TRUE),       n1 = count_all(CIP), # the actual total; sum of all three       n2 = n_sir(CIP), # same - analogous to n_distinct       total = n()     ) # NOT the number of tested isolates!    # Calculate co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy:   example_isolates %>% susceptibility(AMC) # %SI = 76.3%   example_isolates %>% count_all(AMC) #   n = 1879    example_isolates %>% susceptibility(GEN) # %SI = 75.4%   example_isolates %>% count_all(GEN) #   n = 1855    example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%   example_isolates %>% count_all(AMC, GEN) #   n = 1939     # See Details on how `only_all_tested` works. Example:   example_isolates %>%     summarise(       numerator = count_susceptible(AMC, GEN),       denominator = count_all(AMC, GEN),       proportion = susceptibility(AMC, GEN)     )    example_isolates %>%     summarise(       numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),       denominator = count_all(AMC, GEN, only_all_tested = TRUE),       proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)     )     example_isolates %>%     group_by(ward) %>%     summarise(       cipro_p = susceptibility(CIP, as_percent = TRUE),       cipro_n = count_all(CIP),       genta_p = susceptibility(GEN, as_percent = TRUE),       genta_n = count_all(GEN),       combination_p = susceptibility(CIP, GEN, as_percent = TRUE),       combination_n = count_all(CIP, GEN)     )    # Get proportions S/I/R immediately of all sir columns   example_isolates %>%     select(AMX, CIP) %>%     proportion_df(translate = FALSE)    # It also supports grouping variables   # (use sir_df to also include the count)   example_isolates %>%     select(ward, AMX, CIP) %>%     group_by(ward) %>%     sir_df(translate = FALSE) } #> # A tibble: 12 × 7 #>    ward       antibiotic interpretation value ci_min ci_max isolates #>    <chr>      <chr>      <ord>          <dbl>  <dbl>  <dbl>    <int> #>  1 Clinical   AMX        SI             0.423 0.389   0.457      357 #>  2 Clinical   AMX        R              0.577 0.543   0.611      487 #>  3 Clinical   CIP        SI             0.853 0.827   0.876      741 #>  4 Clinical   CIP        R              0.147 0.124   0.173      128 #>  5 ICU        AMX        SI             0.369 0.323   0.417      158 #>  6 ICU        AMX        R              0.631 0.583   0.677      270 #>  7 ICU        CIP        SI             0.810 0.770   0.845      362 #>  8 ICU        CIP        R              0.190 0.155   0.230       85 #>  9 Outpatient AMX        SI             0.397 0.288   0.515       31 #> 10 Outpatient AMX        R              0.603 0.485   0.712       47 #> 11 Outpatient CIP        SI             0.839 0.748   0.907       78 #> 12 Outpatient CIP        R              0.161 0.0932  0.252       15 # }"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":null,"dir":"Reference","previous_headings":"","what":"Random MIC Values/Disk Zones/SIR Generation — random","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"functions can used generating random MIC values disk diffusion diameters, AMR data analysis practice. providing microorganism antimicrobial drug, generated results reflect reality much possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(size = NULL, mo = NULL, ab = NULL, ...)  random_disk(size = NULL, mo = NULL, ab = NULL, ...)  random_sir(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...)"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"size desired size returned vector. used data.frame call dplyr verb, get current (group) size left blank. mo character can coerced valid microorganism code .mo() ab character can coerced valid antimicrobial drug code .ab() ... ignored, place allow future extensions prob_SIR vector length 3: probabilities \"S\" (1st value), \"\" (2nd value) \"R\" (3rd value)","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"class mic random_mic() (see .mic()) class disk random_disk() (see .disk())","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"base R function sample() used generating values. Generated values based EUCAST 2024 guideline implemented clinical_breakpoints data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/SIR Generation — random","text":"","code":"random_mic(25) #> Class 'mic' #>  [1] 0.005  0.125  1      0.002  1      0.125  32     0.01   0.5    16     #> [11] 2      0.001  0.005  0.0625 0.001  0.001  2      256    128    0.005  #> [21] 0.002  1      0.002  32     16     random_disk(25) #> Class 'disk' #>  [1]  8 40 42 43 46 45 40 48 50 23  9 25 36 16  6 43 36 41 50 40 47 25 34 37  9 random_sir(25) #> Class 'sir' #>  [1] I S R R I I I I R R I I I S R S R S S S R R I S R  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\") # range 0.0625-64 #> Class 'mic' #>  [1] 1     16    2     0.025 8     32    0.001 4     2     128   0.125 4     #> [13] 0.01  >=256 0.25  0.25  1     0.002 0.25  2     0.005 0.001 64    0.001 #> [25] 0.025 random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\") # range 0.0625-16 #> Class 'mic' #>  [1] <=1  8    4    <=1  >=16 >=16 >=16 2    >=16 2    >=16 4    >=16 >=16 2    #> [16] 2    >=16 <=1  4    8    8    >=16 4    8    4    random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class 'mic' #>  [1] 2      0.125  0.125  0.25   1      1      0.0625 0.5    1      0.5    #> [11] 4      0.125  0.25   0.25   0.0625 4      0.25   0.125  4      1      #> [21] 4      0.5    0.0625 4      0.125   random_disk(25, \"Klebsiella pneumoniae\") # range 8-50 #> Class 'disk' #>  [1] 47 49 10 40 23 16 20 34 45  8 42 10 20 11 30 36 40 35 14 24  8 38 10 10 15 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\") # range 11-17 #> Class 'disk' #>  [1] 14 12 14 11 13 15 13 11 17 15 14 13 11 15 17 14 16 17 17 17 11 12 12 12 14 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class 'disk' #>  [1] 27 15 17 23 23 18 18 17 15 18 27 15 25 16 21 15 24 23 22 22 15 23 25 23 24 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict(x, col_ab, col_date = NULL, year_min = NULL,   year_max = NULL, year_every = 1, minimum = 30, model = NULL,   I_as_S = TRUE, preserve_measurements = TRUE, info = interactive(), ...)  sir_predict(x, col_ab, col_date = NULL, year_min = NULL, year_max = NULL,   year_every = 1, minimum = 30, model = NULL, I_as_S = TRUE,   preserve_measurements = TRUE, info = interactive(), ...)  # S3 method for class 'resistance_predict' plot(x, main = paste(\"Resistance Prediction of\",   x_name), ...)  ggplot_sir_predict(x, main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE, ...)  # S3 method for class 'resistance_predict' autoplot(object,   main = paste(\"Resistance Prediction of\", x_name), ribbon = TRUE, ...)"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already - default first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model - default 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-sir","dir":"Reference","previous_headings":"","what":"Interpretation of SIR","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories S, , R shown (https://www.eucast.org/newsiandr): S - Susceptible, standard dosing regimen microorganism categorised \"Susceptible, standard dosing regimen\", high likelihood therapeutic success using standard dosing regimen agent. - Susceptible, increased exposure  microorganism categorised \"Susceptible, Increased exposure\" high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. R = Resistant microorganism categorised \"Resistant\" high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. AMR package honours insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates,   col_ab = \"AMX\",   year_min = 2010,   model = \"binomial\" ) plot(x)  # \\donttest{ if (require(\"ggplot2\")) {   ggplot_sir_predict(x) }   # using dplyr: if (require(\"dplyr\")) {   x <- example_isolates %>%     filter_first_isolate() %>%     filter(mo_genus(mo) == \"Staphylococcus\") %>%     resistance_predict(\"PEN\", model = \"binomial\")   print(plot(x))    # get the model from the object   mymodel <- attributes(x)$model   summary(mymodel) }  #> NULL #>  #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 35.76101   72.29172   0.495    0.621 #> year        -0.01720    0.03603  -0.477    0.633 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.3681  on 11  degrees of freedom #> Residual deviance: 5.1408  on 10  degrees of freedom #> AIC: 50.271 #>  #> Number of Fisher Scoring iterations: 4 #>   # create nice plots with ggplot2 yourself if (require(\"dplyr\") && require(\"ggplot2\")) {   data <- example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     resistance_predict(       col_ab = \"AMX\",       col_date = \"date\",       model = \"binomial\",       info = FALSE,       minimum = 15     )   head(data)   autoplot(data) }  # }"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE)  # Default S3 method skewness(x, na.rm = FALSE)  # S3 method for class 'matrix' skewness(x, na.rm = FALSE)  # S3 method for class 'data.frame' skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] -0.0199978"},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Top n Microorganisms — top_n_microorganisms","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"function filters data set include top n microorganisms based specified property, taxonomic family genus. example, can filter data set top 3 species, species top 5 genera, top 3 species top 5 genera.","code":""},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"","code":"top_n_microorganisms(x, n, property = \"fullname\", n_for_each = NULL,   col_mo = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"x data frame containing microbial data n integer specifying maximum number unique values property include output property character string indicating microorganism property use filtering. Must one column names microorganisms data set: \"mo\", \"fullname\", \"status\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"oxygen_tolerance\", \"source\", \"lpsn\", \"lpsn_parent\", \"lpsn_renamed_to\", \"mycobank\", \"mycobank_parent\", \"mycobank_renamed_to\", \"gbif\", \"gbif_parent\", \"gbif_renamed_to\", \"prevalence\", \"snomed\". NULL, raw values col_mo used without transformation. n_for_each optional integer specifying maximum number rows retain value selected property. NULL, rows within top n groups included. col_mo character string indicating column x contains microorganism names codes. Defaults first column class mo. Values coerced using .mo(). ... Additional arguments passed mo_property() property NULL.","code":""},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"function useful preprocessing data creating antibiograms analyses require focused subsets microbial data. example, can filter data set include isolates top 10 species.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/top_n_microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter Top n Microorganisms — top_n_microorganisms","text":"","code":"# filter to the top 3 species: top_n_microorganisms(example_isolates,   n = 3 ) #> # A tibble: 1,015 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  4 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  5 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  6 2002-01-19 738003     71 M      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  7 2002-02-03 481442     76 M      ICU      B_STPHY_CONS   R     NA    S     NA  #>  8 2002-02-14 067927     45 F      ICU      B_STPHY_CONS   R     NA    R     NA  #>  9 2002-02-14 067927     45 F      ICU      B_STPHY_CONS   S     NA    S     NA  #> 10 2002-02-21 A56499     64 M      Clinical B_STPHY_CONS   S     NA    S     NA  #> # ℹ 1,005 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter to any species in the top 5 genera: top_n_microorganisms(example_isolates,   n = 5, property = \"genus\" ) #> # A tibble: 1,742 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA  #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R   #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA  #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA  #> # ℹ 1,732 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …  # filter to the top 3 species in each of the top 5 genera: top_n_microorganisms(example_isolates,   n = 5, property = \"genus\", n_for_each = 3 ) #> # A tibble: 1,497 × 46 #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX   #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir> #>  1 2002-02-21 4FC193     69 M      Clinical B_ENTRC_FACM   NA    NA    NA    NA  #>  2 2002-04-08 130252     78 M      ICU      B_ENTRC_FCLS   NA    NA    NA    NA  #>  3 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS   NA    NA    NA    NA  #>  4 2002-06-23 798871     82 M      Clinical B_ENTRC_FCLS   NA    NA    NA    NA  #>  5 2003-04-20 6BC362     62 M      ICU      B_ENTRC        NA    NA    NA    NA  #>  6 2003-04-21 6BC362     62 M      ICU      B_ENTRC        NA    NA    NA    NA  #>  7 2003-08-13 F35553     52 M      ICU      B_ENTRC_FCLS   NA    NA    NA    NA  #>  8 2003-09-05 F35553     52 M      ICU      B_ENTRC        NA    NA    NA    NA  #>  9 2003-09-05 F35553     52 M      ICU      B_ENTRC_FCLS   NA    NA    NA    NA  #> 10 2003-09-28 1B0933     80 M      Clinical B_ENTRC        NA    NA    NA    NA  #> # ℹ 1,487 more rows #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>, #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>, #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>, #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>, #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>, #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale()  set_AMR_locale(language)  reset_AMR_locale()  translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"language language choose. Use one supported language names ISO-639-1 codes: English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 20 supported languages English (en), Chinese (zh), Czech (cs), Danish (da), Dutch (nl), Finnish (fi), French (fr), German (de), Greek (el), Italian (), Japanese (ja), Norwegian (), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Spanish (es), Swedish (sv), Turkish (tr), Ukrainian (uk). languages translations available antimicrobial drugs colloquial microorganism names. permanently silence -per-session language note non-English operating system, can set package option AMR_locale .Rprofile file like :   save file. Please read adding updating language Wiki.","code":"# Open .Rprofile file utils::file.edit(\"~/.Rprofile\")  # Then add e.g. Italian support to that file using: options(AMR_locale = \"Italian\")"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting package option AMR_locale, either using e.g. set_AMR_locale(\"German\") running e.g. options(AMR_locale = \"German\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Run utils::file.edit(\"~/.Rprofile\") edit .Rprofile file. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, package option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\"  # setting another language set_AMR_locale(\"Dutch\") #> ℹ Using Dutch (Nederlands) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacine\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Coagulase-negatieve Staphylococcus (CNS)\"  # setting yet another language set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus (CoNS)\") #> [1] \"Koagulase-negative Staphylococcus (KNS)\"  # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using German (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using German (Deutsch) for the AMR package for this session. ab_name(\"amox/clav\") #> Warning: no non-missing arguments to min; returning Inf #> Warning: no non-missing arguments to min; returning Inf #> [1] \"Amoxicillin/Clavulansäure\"  # reset to system default reset_AMR_locale() #> ℹ Using the English language (English) for the AMR package for this #>   session. ab_name(\"amox/clav\") #> [1] \"Amoxicillin/clavulanic acid\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-2119188","dir":"Changelog","previous_headings":"","what":"AMR 2.1.1.9188","title":"AMR 2.1.1.9188","text":"(beta version eventually become v3.0. ’re happy reach new major milestone soon, new One Health support! Install beta using instructions .)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9188","dir":"Changelog","previous_headings":"","what":"A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)","title":"AMR 2.1.1.9188","text":"package now supports tools AMR data analysis clinical settings, also veterinary environmental microbiology. made possible collaboration University Prince Edward Island’s Atlantic Veterinary College, Canada. celebrate great improvement package, also updated package logo reflect change.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-2-1-1-9188","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 2.1.1.9188","text":"Dataset antibiotics renamed antimicrobials data set contains just antibiotics. Calling antibiotics still work, now returns warning. Removed functions references used deprecated rsi class, replaced sir equivalents two years ago.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-2-1-1-9188","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 2.1.1.9188","text":"Function .sir() now extensive support veterinary breakpoints CLSI. Use breakpoint_type = \"animal\" set host argument variable contains animal species names. CLSI VET09 guideline implemented address cases veterinary breakpoints missing (applies guideline set CLSI) clinical_breakpoints data set contains breakpoints, can downloaded download page. (new) antimicrobials data set contains veterinary antibiotics, pradofloxacin enrofloxacin. WHOCC codes veterinary use added well. ab_atc() now supports ATC codes veterinary antibiotics (start “Q”) ab_url() now supports retrieving WHOCC url ATCvet pages antibiogram() function now supports creating true Weighted-Incidence Syndromic Combination Antibiograms (WISCA), powerful Bayesian method estimating regimen coverage probabilities using pathogen incidence antimicrobial susceptibility data. WISCA offers improved precision syndrome-specific treatment, even datasets sparse data. dedicated wisca() function also available easy usage. MycoBank now integrated primary taxonomic source fungi. microorganisms data set enriched new columns (mycobank, mycobank_parent, mycobank_renamed_to) provide detailed information fungal species. remarkable addition 20,000 new fungal records New function mo_mycobank() retrieve MycoBank record number, analogous existing functions mo_lpsn() mo_gbif(). .mo() function mo_*() functions now include only_fungi argument, allowing users restrict results solely fungal species. ensures fungi prioritised bacteria microorganism identification. can also set globally new AMR_only_fungi option. Also updated kingdoms, welcoming total 2,149 new records 2023 927 2024. EUCAST 2024 CLSI 2024 now supported, adding 4,000 new clinical breakpoints clinical_breakpoints data set usage .sir(). EUCAST 2024 now new default guideline MIC disk diffusion interpretations. .sir() now brings additional factor levels: “NI” non-interpretable “SDD” susceptible dose-dependent. Currently, clinical_breakpoints data set contains 24 breakpoints can return value “SDD” instead “”. New function group scale_*_mic(), namely: scale_x_mic(), scale_y_mic(), scale_colour_mic() scale_fill_mic(). allow easy plotting MIC values. allow manual range definition plotting missing intermediate log2 levels. New function group scale_*_sir(), namely: scale_x_sir(), scale_colour_sir() scale_fill_sir(). allow plot sir class, translates system language default. also set colourblind-safe colours plots. New function rescale_mic(), allows users rescale MIC values manually set range. powerhouse behind scale_*_mic() functions, can used independently , instance, compare equality MIC distributions rescaling range first. using R heavy lifting, ‘AMR’ Python Package developed run AMR R package natively Python. Python package always version number R package, built automatically every code change. antimicrobial selectors (aminoglycosides() betalactams()) now supported tidymodels packages recipe parsnip. See info tutorial using AMR functions predictive modelling. New function top_n_microorganisms() filter data set top n taxonomic property, e.g., filter top 3 species, filter species top 5 genera, filter top 3 species top 5 genera New function mo_group_members() retrieve member microorganisms microorganism group. example, mo_group_members(\"Strep group C\") returns vector microorganisms belong group. New functions mic_p50() mic_p90() retrieve 50th 90th percentile MIC values.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-2-1-1-9188","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 2.1.1.9188","text":"now possible use column names argument ab, mo, uti: .sir(..., ab = \"column1\", mo = \"column2\", uti = \"column3\"). greatly improves flexibility users. Users can now set criteria (using regular expressions) considered S, , R, SDD, NI. get quantitative values, .double() sir object return 1 S, 2 SDD/, 3 R (NI become NA). functions using sir classes (e.g., summary()) updated reflect change contain NI SDD. Fix conserve_capped_values, now works expected: MIC values, <x always S, >x always R New argument formatting_type set 22 options formatting ‘cells’. defaults 10 non-WISCA 14 WISCA, changing output antibiograms cells info. reason, add_total_n now FALSE default since denominators added cells ab_transform argument now defaults \"name\", displaying antibiotic column names instead codes ‘Antibiotic selectors’ now called ‘antimicrobial selectors’ since scope broader just antibiotics. documentation updated, ab_class() ab_selector() replaced amr_class() amr_selector(). old functions now deprecated removed future version. Added selectors isoxazolylpenicillins(), monobactams(), nitrofurans(), phenicols(), rifamycins() using antimicrobial selectors exclude non-treatable drugs (gentamicin-high using aminoglycosides()), function now always returns warning can included using only_treatable = FALSE Added new argument return_all selectors, defaults TRUE include match. FALSE, old behaviour, first hit unique antimicrobial returned. selectors can now run separate command retrieve vector possible antimicrobials selector can select selectors lincosamides() macrolides() overlap anymore - antibiotic now classified either Added “clindamycin inducible screening” CLI1. Since clindamycin lincosamide, antimicrobial selector lincosamides() now contains argument only_treatable = TRUE (similar antibiotic selectors contain non-treatable drugs) Added Amorolfine (AMO, D01AE16), now also part antifungals() selector Added Efflux (EFF), allow mapping AMRFinderPlus Added Tigemonam (TNM), monobactam Added 1,500 trade names Added valid levels: 4096, 6 powers 0.0625, 5 powers 192 (192, 384, 576, 768, 960) Fixed bug .mic() failed translation scientifically formatted numbers Added new argument keep_operators .mic(). can \"\" (default), \"none\", \"edges\". argument also available new rescale_mic() scale_*_mic() functions. Comparisons MIC values now strict. example, >32 higher (never equal ) 32. Thus, .mic(\">32\") == .mic(32) now returns FALSE, .mic(\">32\") > .mic(32) now returns TRUE. Sorting MIC values (using sort()) fixed manner; <0.001 now gets sorted 0.001, >0.001 gets sorted 0.001. Intermediate log2 levels used MIC plotting now common values instead following strict dilution range eucast_rules() now argument overwrite (default: TRUE) indicate whether non-NA values overwritten Disks 0 5 mm now allowed, newly allowed range disk diffusion (.disk()) now 0 50 mm Updated italicise_taxonomy() support HTML output custom_eucast_rules() now supports multiple antibiotics antibiotic groups affected single rule mo_info() now contains extra element rank group_members (contents new mo_group_members() function) Updated ATC codes WHOCC Updated antibiotic DDDs WHOCC Fix using manual value mo_transform antibiogram() Fixed bug antibiogram() returns empty data set Fix mapping ‘high level’ antibiotics .ab() (amphotericin B-high, gentamicin-high, kanamycin-high, streptomycin-high, tobramycin-high) Improved overall algorithm .ab() better performance accuracy, including new function as_reset_session() remove earlier coercions. weight given genus species combinations cases subspecies miswritten, result correct genus species Genera World Health Organization’s () Priority Pathogen List now highest prevalence Fixed bug sir_confidence_interval() isolates available Updated prevalence calculation include genera World Health Organization’s () Priority Pathogen List Improved algorithm first_isolate() using phenotype-based method, prioritise records highest availability SIR values scale_y_percent() can now cope ranges outside 0-100% range Implemented new Dutch national MDRO guideline (SRI-richtlijn BRMO, Nov 2024) Added arguments esbl, carbapenemase, mecA, mecC, vanA, vanB denote column names logical values indicating presence genes (production proteins) Added console colours support sir class Positron","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-2-1-1-9188","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 2.1.1.9188","text":"Added Dr. Larisse Bolton contributor fantastic implementation WISCA mathematically solid way Added Matthew Saab, Dr. Jordan Stull, Prof. Javier Sanchez contributors tremendous input veterinary breakpoints interpretations Greatly improved vctrs integration, Tidyverse package working background many Tidyverse functions. users, means functions dplyr’s bind_rows(), rowwise() c_across() now supported e.g. columns class mic. Despite , AMR package still zero-dependent package, including dplyr vctrs. Greatly updated expanded documentation Stopped support SAS (.xpt) files, since file structure extremely inefficient requires disk space GitHub allows single commit.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"older-versions-2-1-1-9188","dir":"Changelog","previous_headings":"","what":"Older Versions","title":"AMR 2.1.1.9188","text":"changelog contains changes AMR v3.0 (February 2025) later. prior v2 versions, please see v2 archive. prior v1 versions, please see v1 archive.","code":""}]