From 976d5292c788d547bafe82e49bdbf3c5ef24babf Mon Sep 17 00:00:00 2001 From: github-actions <41898282+github-actions[bot]@users.noreply.github.com> Date: Fri, 14 Oct 2022 13:52:23 +0000 Subject: [PATCH] Built site for AMR: 1.8.2.9016@85ed7ea --- articles/AMR.html | 498 +++++++++--------- .../AMR_files/figure-html/disk_plots-1.png | Bin 38742 -> 38411 bytes .../figure-html/disk_plots_mo_ab-1.png | Bin 56603 -> 53011 bytes .../AMR_files/figure-html/mic_plots-1.png | Bin 28088 -> 27873 bytes .../AMR_files/figure-html/mic_plots-2.png | Bin 39095 -> 37627 bytes .../figure-html/mic_plots_mo_ab-1.png | Bin 38440 -> 39164 bytes .../figure-html/mic_plots_mo_ab-2.png | Bin 51493 -> 50480 bytes articles/AMR_files/figure-html/plot 1-1.png | Bin 43269 -> 43283 bytes articles/AMR_files/figure-html/plot 3-1.png | Bin 28436 -> 28426 bytes articles/AMR_files/figure-html/plot 4-1.png | Bin 78038 -> 78074 bytes articles/AMR_files/figure-html/plot 5-1.png | Bin 49864 -> 49879 bytes articles/MDR.html | 56 +- articles/datasets.html | 16 +- pkgdown.yml | 2 +- reference/Rplot005.png | Bin 13233 -> 12934 bytes reference/Rplot006.png | Bin 12736 -> 12452 bytes reference/Rplot007.png | Bin 14682 -> 13592 bytes reference/Rplot008.png | Bin 20043 -> 19157 bytes reference/Rplot009.png | Bin 8561 -> 8552 bytes reference/age.html | 20 +- reference/as.rsi.html | 20 +- reference/get_episode.html | 132 +++-- reference/kurtosis.html | 4 +- reference/mean_amr_distance.html | 78 +-- reference/plot-1.png | Bin 25790 -> 26909 bytes reference/plot-2.png | Bin 27062 -> 26433 bytes reference/plot-3.png | Bin 27531 -> 28264 bytes reference/plot-4.png | Bin 37686 -> 38725 bytes reference/plot-5.png | Bin 38699 -> 38018 bytes reference/plot-6.png | Bin 37444 -> 36809 bytes reference/plot-7.png | Bin 38859 -> 38025 bytes reference/plot-8.png | Bin 58693 -> 56834 bytes reference/plot-9.png | Bin 26126 -> 26284 bytes reference/random.html | 33 +- reference/skewness.html | 2 +- search.json | 2 +- 36 files changed, 430 insertions(+), 433 deletions(-) diff --git a/articles/AMR.html b/articles/AMR.html index 3fa02440..a9411b63 100644 --- a/articles/AMR.html +++ b/articles/AMR.html @@ -325,19 +325,41 @@ -2015-11-18 -X2 +2017-11-03 +T2 Hospital A -Klebsiella pneumoniae -S +Escherichia coli +R S S S F -2014-12-18 -I9 +2016-05-27 +Z9 +Hospital B +Staphylococcus aureus +S +S +S +S +F + + +2015-01-19 +N8 +Hospital C +Escherichia coli +I +R +S +S +F + + +2017-03-31 +G7 Hospital C Escherichia coli S @@ -347,48 +369,26 @@ M -2015-09-24 -V4 +2014-04-10 +C6 Hospital A -Staphylococcus aureus +Streptococcus pneumoniae S -R -S -S -F - - -2012-11-26 -E7 -Hospital D -Escherichia coli -R -I -S -S -M - - -2010-08-28 -A7 -Hospital A -Escherichia coli -R S S S M -2016-05-06 -V5 +2011-02-09 +N4 Hospital A Escherichia coli S -I -R S -F +S +S +M @@ -422,16 +422,16 @@ Longest: 1

1 M -10,324 -51.62% -10,324 -51.62% +10,375 +51.88% +10,375 +51.88% 2 F -9,676 -48.38% +9,625 +48.13% 20,000 100.00% @@ -488,9 +488,9 @@ Longest: 1

# Basing inclusion on all antimicrobial results, using a points threshold of # 2 # Including isolates from ICU. -# => Found 10,765 'phenotype-based' first isolates (53.8% of total where a +# => Found 10,660 'phenotype-based' first isolates (53.3% of total where a # microbial ID was available) -

So only 53.8% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:

+

So only 53.3% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:

 data_1st <- data %>%
   filter(first == TRUE)
@@ -499,7 +499,7 @@ Longest: 1

data_1st <- data %>% filter_first_isolate() # Including isolates from ICU. -

So we end up with 10,765 isolates for analysis. Now our data looks like:

+

So we end up with 10,660 isolates for analysis. Now our data looks like:

 head(data_1st)
@@ -537,25 +537,41 @@ Longest: 1

- - - - - - + + + + + + - - - + + + - - - + + + + + + + + + + + + + + + + + + + @@ -568,62 +584,30 @@ Longest: 1

- - - - - - - - - - - - - - - - - - - - - + + + + + + + + - - - - - - + + + - - + + - - - - - - - - - - - - - - - - @@ -632,6 +616,22 @@ Longest: 1

+ + + + + + + + + + + + + + + +
12015-11-18X2Hospital AB_KLBSL_PNMNR22016-05-27Z9Hospital BB_STPHY_AURSS S S S FGram-negativeKlebsiellapneumoniaeGram-positiveStaphylococcusaureus TRUE
22014-12-18I932015-01-19N8Hospital CB_ESCHR_COLIRRSSFGram-negativeEscherichiacoliTRUE
42017-03-31G7 Hospital C B_ESCHR_COLI S coli TRUE
32015-09-24V4Hospital AB_STPHY_AURSRRSSFGram-positiveStaphylococcusaureusTRUE
42012-11-26E7Hospital DB_ESCHR_COLI52014-04-10C6Hospital AB_STRPT_PNMNSSS RISS MGram-negativeEscherichiacoliGram-positiveStreptococcuspneumoniae TRUE
62016-05-06V52011-02-09N4 Hospital A B_ESCHR_COLI S SRSFGram-negativeEscherichiacoliTRUE
72011-03-21N1Hospital DB_ESCHR_COLISS S S M coli TRUE
102010-07-27E4Hospital DB_STPHY_AURSRSRSMGram-positiveStaphylococcusaureusTRUE

Time for the analysis!

@@ -653,8 +653,8 @@ Longest: 1

data_1st %>% freq(genus, species)

Frequency table

Class: character
-Length: 10,765
-Available: 10,765 (100.0%, NA: 0 = 0.0%)
+Length: 10,660
+Available: 10,660 (100.0%, NA: 0 = 0.0%)
Unique: 4

Shortest: 16
Longest: 24

@@ -671,33 +671,33 @@ Longest: 24

1 Escherichia coli -4,655 -43.24% -4,655 -43.24% +4,666 +43.77% +4,666 +43.77% 2 Staphylococcus aureus -2,771 -25.74% -7,426 -68.98% +2,717 +25.49% +7,383 +69.26% 3 Streptococcus pneumoniae -2,156 -20.03% -9,582 -89.01% +2,101 +19.71% +9,484 +88.97% 4 Klebsiella pneumoniae -1,183 -10.99% -10,765 +1,176 +11.03% +10,660 100.00% @@ -716,14 +716,14 @@ Longest: 24

- + - + @@ -744,27 +744,27 @@ Longest: 24

-2016-02-22 -Y6 -Hospital D -B_STPHY_AURS -R -R +2014-04-10 +C6 +Hospital A +B_STRPT_PNMN +S +S S R -F +M Gram-positive -Staphylococcus -aureus +Streptococcus +pneumoniae TRUE -2012-04-22 -E8 +2012-08-04 +I5 Hospital D B_ESCHR_COLI -S -S +R +R R R M @@ -774,12 +774,42 @@ Longest: 24

TRUE -2010-08-20 -F1 +2012-11-28 +Y4 +Hospital A +B_ESCHR_COLI +S +S +R +R +F +Gram-negative +Escherichia +coli +TRUE + + +2010-08-07 +F10 Hospital B B_STRPT_PNMN -I -I +S +S +S +R +M +Gram-positive +Streptococcus +pneumoniae +TRUE + + +2017-09-28 +A7 +Hospital C +B_STRPT_PNMN +R +R S R M @@ -789,24 +819,9 @@ Longest: 24

TRUE -2016-05-20 -X2 -Hospital A -B_STPHY_AURS -R -I -S -R -F -Gram-positive -Staphylococcus -aureus -TRUE - - -2017-02-25 -Y7 -Hospital A +2010-05-24 +W9 +Hospital B B_STRPT_PNMN S S @@ -818,21 +833,6 @@ Longest: 24

pneumoniae TRUE - -2016-01-31 -Y4 -Hospital C -B_ESCHR_COLI -S -S -S -R -F -Gram-negative -Escherichia -coli -TRUE -

If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the bug_drug_combinations() function:

@@ -854,50 +854,50 @@ Longest: 24

E. coli AMX -2244 -103 -2308 -4655 +2239 +144 +2283 +4666 E. coli AMC -3412 -156 -1087 -4655 +3411 +155 +1100 +4666 E. coli CIP -3414 +3389 0 -1241 -4655 +1277 +4666 E. coli GEN -4073 +4077 0 -582 -4655 +589 +4666 K. pneumoniae AMX 0 0 -1183 -1183 +1176 +1176 K. pneumoniae AMC -960 -38 -185 -1183 +910 +50 +216 +1176 @@ -920,34 +920,34 @@ Longest: 24

E. coli GEN -4073 +4077 0 -582 -4655 +589 +4666 K. pneumoniae GEN -1070 +1045 0 -113 -1183 +131 +1176 S. aureus GEN -2442 +2422 0 -329 -2771 +295 +2717 S. pneumoniae GEN 0 0 -2156 -2156 +2101 +2101 @@ -961,7 +961,7 @@ Longest: 24

As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:

 data_1st %>% resistance(AMX)
-# [1] 0.5452856
+# [1] 0.5409006

Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:

 data_1st %>%
@@ -975,19 +975,19 @@ Longest: 24

Hospital A -0.5467334 +0.5371953 Hospital B -0.5472356 +0.5490456 Hospital C -0.5282083 +0.5335844 Hospital D -0.5523125 +0.5374532 @@ -1008,23 +1008,23 @@ Longest: 24

Hospital A -0.5467334 -3199 +0.5371953 +3159 Hospital B -0.5472356 -3726 +0.5490456 +3772 Hospital C -0.5282083 -1613 +0.5335844 +1593 Hospital D -0.5523125 -2227 +0.5374532 +2136 @@ -1047,27 +1047,27 @@ Longest: 24

Escherichia -0.7664876 -0.8749731 -0.9735768 +0.7642520 +0.8737677 +0.9747107 Klebsiella -0.8436179 -0.9044801 -0.9873204 +0.8163265 +0.8886054 +0.9855442 Staphylococcus -0.7971851 -0.8812703 -0.9765428 +0.7935223 +0.8914244 +0.9775488 Streptococcus -0.5343228 +0.5316516 0.0000000 -0.5343228 +0.5316516 @@ -1092,23 +1092,23 @@ Longest: 24

Hospital A -54.7% -27.4% +53.7% +27.5% Hospital B -54.7% -26.0% +54.9% +27.3% Hospital C -52.8% -24.9% +53.4% +26.0% Hospital D -55.2% -26.5% +53.7% +25.7% @@ -1206,17 +1206,17 @@ Longest: 24

mic_values <- random_mic(size = 100) mic_values # Class <mic> -# [1] 32 <=0.001 0.025 <=0.001 4 0.125 0.01 0.002 32 -# [10] 16 64 4 <=0.001 0.01 16 0.01 64 64 -# [19] 256 0.0625 0.002 0.005 0.125 0.025 32 128 0.5 -# [28] 16 0.5 32 0.005 32 0.01 2 0.125 128 -# [37] 64 8 0.002 0.01 2 <=0.001 0.005 64 0.0625 -# [46] <=0.001 8 0.5 64 32 0.5 0.005 0.01 <=0.001 -# [55] 16 0.5 8 0.0625 0.125 128 16 0.5 0.002 -# [64] 16 8 128 128 4 256 0.5 8 4 -# [73] 16 0.025 2 1 16 0.005 0.01 16 1 -# [82] 0.25 0.01 0.002 0.0625 0.005 128 64 128 8 -# [91] 0.002 1 8 8 0.25 0.125 16 2 0.5 +# [1] 64 4 0.25 64 256 4 128 0.125 64 +# [10] 64 4 4 8 16 32 2 0.005 0.002 +# [19] 4 256 0.25 0.5 2 <=0.001 0.025 0.0625 32 +# [28] 1 128 0.005 0.0625 0.25 2 0.002 8 1 +# [37] 16 0.125 8 1 32 16 1 16 8 +# [46] 16 0.005 0.5 128 <=0.001 32 2 0.0625 0.5 +# [55] 2 64 128 4 0.5 16 0.0625 0.0625 0.002 +# [64] 0.25 0.025 4 16 32 0.0625 0.0625 0.5 0.5 +# [73] 0.002 32 16 0.005 1 0.25 256 0.25 0.025 +# [82] 128 8 0.25 0.0625 1 256 16 0.5 0.125 +# [91] 0.005 2 0.002 128 0.5 32 0.0625 0.025 0.125 # [100] 256
 # base R:
@@ -1244,10 +1244,10 @@ Longest: 24

disk_values <- random_disk(size = 100, mo = "E. coli", ab = "cipro") disk_values # Class <disk> -# [1] 29 31 21 24 20 24 29 22 21 20 19 25 17 30 30 28 25 27 23 20 18 22 31 25 24 -# [26] 29 25 21 29 20 22 23 27 30 23 22 30 22 18 20 23 27 25 23 21 21 24 21 29 26 -# [51] 21 26 26 24 30 24 30 25 23 30 20 29 22 17 20 18 21 22 27 29 30 30 30 21 30 -# [76] 26 19 28 26 22 25 19 21 23 29 17 23 23 24 26 28 17 17 30 23 29 31 19 19 31
+# [1] 23 29 23 29 18 26 17 23 28 31 21 24 18 17 25 26 24 18 25 18 28 18 30 20 28 +# [26] 24 28 21 31 25 29 21 19 18 18 20 31 18 28 31 17 24 21 18 31 21 26 22 27 18 +# [51] 29 29 19 31 17 20 30 21 21 26 30 25 31 26 26 29 29 29 20 21 23 18 27 25 28 +# [76] 29 28 20 20 26 17 20 24 21 28 28 28 31 21 20 21 17 28 24 23 29 22 23 30 29
 # base R:
 plot(disk_values, mo = "E. coli", ab = "cipro")
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    q3#krOK^RsYaBzdWuW1x$&3E`yvoz7z6yt)|M|3R|52*LPp=(%zVBr5 z&zYR!*RL+ypJwA(3cCI2q>75lC!a>MLvK;SCeQufoO_4z-S>t52}?n8N=u0zX<%XODCzmv@6k_u(&_j`0(o6ChIjt{K!d*3sFrnY@sjx z{q0KV!{m~h&wObuS`aspUYot4!4nZMK0fXiv9C%|9wjdA;OKbE+1a_g@jm;TCKIDz z@l}8S69)2STZ@g3n8Usm;o;$DuNX>8OP@aa;^OI}yFVMM_LH@=w3L?4)WZtG8O3lt z{b)1#?QL6U{?LVova)`>UhpCF&)}Ml*PNzDE)ex!IyV3Q@V-oTqq+Q^GfXb?ULG4Y zuReX{CSiWKVv`tf^W&eV*cD&5c<(HU_V0WUuryqq?Xe|qlj`sAmY=4ee|)`cgkj#l zsB(MmZR1$T+%e?pa=2(!_bq>Yacm^OV9PMOv2kqiWYPB8NO1jFgh9nUod9a?SGNMF zYk9pE0^SQ+Y3JwXAK$1z-igAdpBFeN3wNcce9p?Y$2g*vR6!~)9)87jD2dSA+>Ctp zNtdVoo@vY($<3aY;nhBy;7C5xI*HBMQ%d41>ObCJF={0pL_VuK>_i%B!~a5%e~ZUB zpY7H{eSNGu@!Z_p+_QFKw6OgrbRdhnQG z?W;4-N_mmbs=_?FeXx~7oinU>uCR}pc{tj>d8(kIyj)ySe5KRhznC=OPGMz$ywpc8 z2IY3FEA!r|8GLrIzkfMt0NY{}Br=`q@9*;S3B{p*UPgE$a^%6$jJsK{{Qa*O)k*V2 z%-Bbjul8~=Gsn7)N8aco;oz2tk^1eoF*cT#0m*Da>{OBRCV68!ME9zn4JLU}-0uB1 z+@dN5#_URN2v6}}&QV};j;dH~>lQ3GKwURr3D;?DYonqf)0`$HWo2rj3`K6E4vlIU9&>`vfzcg$ zL9;$6l;bQKPl{$ovR4vLm9e&?!q{DStZu-!uev)S3%*K$d!6R*0@Arp2j!vJ;p zU_JF~MsX2l=DQf(o_YVmvBn5l=^C%iIU%#n-EufP{YR_LWxLUD(+-DPB*`9eePU}r zpHWfSF1N{2_WqvpBKwx9A6|`of7Mt{TDsq3sj0ln+0n7Kzr_9m^Dvw$L%!#3;dFA` zKUrD2NyLu4=4#!KK~NNb7`U__+FlghEnmuZ~$*zXPR0~x@7h>?Cj^p;o^~?zDI*tVksAEmDQa6cF-)gKtx0&`sD}z?k$$*FcrEm zdO8!Y?oakDvpj!s^9%a$$3wo9O!}VWe5W~W_VdqayWtlw=k5Nw$!xepx`AqqK#EWI zhG@@Va)eC_UDCoxeSM(Nz}Y<0mY{>jsB5Ni86!RiPWp!`NeHtk#g^Tx5`X`DaMOJ| z4^PUN{J~FmZr_Ol(?9q-W2st|-DR$K&|HY$*Zr6M@4iy=ChF+{RCj!9OyF2dAYW65 zb=KHf@yz+OVpy-Kl+d4!tF&ZVykZhB)+ujHjWi<7yy~c{RGq&Hzwds|aSd75sQ*OY z`Y^2rFFbyx(ydIK(W9_nz_{~Ak6$G{MMWWY0Qz;#sZ(%DRew)$niyJXj%LaROZ3QvNGevy0d zZmTqN30&H|j=ThAA@@JuRsa3Y@n6x~|Ib;V{#QUHejFC2g_G5b#nNK0vHlOMJ)e_V zKYS1@3)3sI)&t!1VWr%3DfOCZ@F~kKCKPJ+JA%4q-`sTd-0EABgsCTBUz2m-Ap9^V z3;{2~-jmvY>5Z)j%2qYIzav=QWg?bCMEH{&1a(0QwxWl<-p-q

    A data set with 48,787 rows and 22 columns, containing the following column names:
    mo, fullname, status, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, source, lpsn, lpsn_parent, lpsn_renamed_to, gbif, gbif_parent, gbif_renamed_to, prevalence and snomed.

    This data set is in R available as microorganisms, after you load the AMR package.

    -

    It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here.

    +

    It was last updated on 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.

    Direct download links:

    • Download as original R Data Structure (RDS) file (1.1 MB)
      @@ -449,7 +449,7 @@

      A data set with 464 rows and 14 columns, containing the following column names:
      ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units and loinc.

      This data set is in R available as antibiotics, after you load the AMR package.

      -

      It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here.

      +

      It was last updated on 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.

      Direct download links:

      • Download as original R Data Structure (RDS) file (36 kB)
        @@ -622,7 +622,7 @@

        A data set with 102 rows and 9 columns, containing the following column names:
        atc, cid, name, atc_group, synonyms, oral_ddd, oral_units, iv_ddd and iv_units.

        This data set is in R available as antivirals, after you load the AMR package.

        -

        It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here.

        +

        It was last updated on 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.

        Direct download links:

        • Download as original R Data Structure (RDS) file (4 kB)
          @@ -754,7 +754,7 @@

          A data set with 20,369 rows and 11 columns, containing the following column names:
          guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R and uti.

          This data set is in R available as rsi_translation, after you load the AMR package.

          -

          It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here.

          +

          It was last updated on 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.

          Direct download links:

          • Download as original R Data Structure (RDS) file (49 kB)
            @@ -913,7 +913,7 @@

            A data set with 134,659 rows and 2 columns, containing the following column names:
            mo and ab.

            This data set is in R available as intrinsic_resistant, after you load the AMR package.

            -

            It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here.

            +

            It was last updated on 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.

            Direct download links:

            • Download as original R Data Structure (RDS) file (78 kB)
              @@ -1185,7 +1185,7 @@

              A data set with 169 rows and 9 columns, containing the following column names:
              ab, name, type, dose, dose_times, administration, notes, original_txt and eucast_version.

              This data set is in R available as dosage, after you load the AMR package.

              -

              It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here.

              +

              It was last updated on 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.

              Direct download links:

              • Download as original R Data Structure (RDS) file (3 kB)
                @@ -1302,7 +1302,7 @@

                A data set with 2,000 rows and 46 columns, containing the 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 and RIF.

                This data set is in R available as example_isolates, after you load the AMR package.

                -

                It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here.

                +

                It was last updated on 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.

                Source

                @@ -1707,7 +1707,7 @@

                A data set with 3,000 rows and 8 columns, containing the following column names:
                patient_id, hospital, date, bacteria, AMX, AMC, CIP and GEN.

                This data set is in R available as example_isolates_unclean, after you load the AMR package.

                -

                It was last updated on 14 October 2022 11:03:47 UTC. Find more info about the structure of this data set here.

                +

                It was last updated on 14 October 2022 13:45:54 UTC. Find more info about the structure of this data set here.

                Source

                diff --git a/pkgdown.yml b/pkgdown.yml index cbbbbaec..91e56c2d 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -11,7 +11,7 @@ articles: datasets: datasets.html resistance_predict: resistance_predict.html welcome_to_AMR: welcome_to_AMR.html -last_built: 2022-10-14T11:06Z +last_built: 2022-10-14T13:48Z urls: reference: https://msberends.github.io/AMR/reference article: https://msberends.github.io/AMR/articles diff --git a/reference/Rplot005.png b/reference/Rplot005.png index 4070d81eff37a93ff4d5c33c64e9b63e9cf709fe..50e40f1c5e9ee2a10e15a43692e986a957ada34e 100644 GIT binary patch literal 12934 zcmc(G2T+sSx^@axs?wy3B1jjJCQU)afOHU~29Y8yxG6#i5VlcNs`QR@sR`11ge|D_ z-XVze7NrFOAfwgRfqpW=)l$il_0 z)7Eodd?(w>hSAT}pzVt+3=&l0xJW-kg@QCQqps-jH?b*{O4B$)P0^pWETee{9&mLc zv$t+^m&>%T^_N>;Mz{j5x^3KgUlO%OQA|5nh+Ih4?!ZQE+w;yMKo22uXa;o6l&fHS zqbDdjnn^{G$KSBC`eoTpbQu@S1dP0({v#Q3N^h^E16qNe=05EMnc#zO^Wz-$Qbgcp zW#w%;!{tr+CEo^BJS6-!B$VoD|dA1*U^hBB#&pU|Q`TQzV zkcn*SVJWoHG_ZdewvIN&uzX*_Uwk6((Id<^m 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zuI&4{70;`IBdd$9;z?7Ho3Q0okCfuh4{rO$l^VHa8r$lIJy^sld^brkGWYi|`MjygtOCA#D9OCuKUVBj z%SzAW!Vu9ZOBN2rm%EV@CN_k0&)Br}+b!NwupH?d->${5HVrFSq^{P&m(o;h8y#Ps zRUSJ;M7J*)E$xfPKY4%DeYiLsd)uF3F5|a3sXn=_cOp9(d-v{PMXh+{5@(rj0!<

    9?9J4`%KB6k9W5)4zMfu6;)9xUxS(hCX%!kg< zt|==yn%Oi>Em|TmHbSj$u8_~hO;WsCh$dkQp5hhsoSBfv)8dC=q}Ydg%GsJtnm8!4 uraHqfe~#9}U;gLt;o1ZKw|=A#tr*qcuM%xtetQyoCt`Z~MEUW{5&s2TE(DnX diff --git a/articles/MDR.html b/articles/MDR.html index c07a55ff..751f4ac5 100644 --- a/articles/MDR.html +++ b/articles/MDR.html @@ -314,19 +314,19 @@ Unique: 2

     head(my_TB_data)
     #   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
    -# 1          S         I            I          R            R            S
    -# 2          R         I            R          I            S            R
    -# 3          R         S            R          S            I            S
    -# 4          S         I            I          S            R            I
    -# 5          S         S            I          I            I            R
    -# 6          R         I            R          R            I            I
    +# 1          R         R            I          R            R            S
    +# 2          I         I            I          I            S            R
    +# 3          S         R            R          S            S            I
    +# 4          I         S            I          R            R            S
    +# 5          R         R            R          S            S            S
    +# 6          I         S            S          I            I            I
     #   kanamycin
    -# 1         R
    -# 2         S
    +# 1         S
    +# 2         I
     # 3         I
    -# 4         S
    +# 4         R
     # 5         R
    -# 6         I
    +# 6 R

    We can now add the interpretation of MDR-TB to our data set. You can use:

     mdro(my_TB_data, guideline = "TB")
    @@ -357,40 +357,40 @@ Unique: 5

    1 Mono-resistant -3186 -63.72% -3186 -63.72% +3223 +64.46% +3223 +64.46% 2 Negative -1014 -20.28% -4200 -84.00% +980 +19.60% +4203 +84.06% 3 Multi-drug-resistant -475 -9.50% -4675 -93.50% +435 +8.70% +4638 +92.76% 4 Poly-resistant -233 -4.66% -4908 -98.16% +249 +4.98% +4887 +97.74% 5 Extensively drug-resistant -92 -1.84% +113 +2.26% 5000 100.00% diff --git a/articles/datasets.html b/articles/datasets.html index ea661e49..93650b54 100644 --- a/articles/datasets.html +++ b/articles/datasets.html @@ -177,7 +177,7 @@