-
Used in more than 100 countries
Since its first public release in early 2018, this package has been downloaded from more than 100 countries. Click the map to enlarge and to also see the names of the countries.
+ Used in more than 120 countries
Since its first public release in early 2018, this package has been downloaded from more than 120 countries. Click the map to enlarge and to also see the names of the countries.
+
@@ -202,7 +202,7 @@
-
-2020-09-30
+2020-10-08
abcd
Escherichia coli
S
S
-2020-09-30
+2020-10-08
abcd
Escherichia coli
S
R
-2020-09-30
+2020-10-08
efgh
Escherichia coli
R
@@ -361,69 +361,69 @@
-2016-08-26
-P9
-Hospital C
-Escherichia coli
-I
-S
-S
-R
-F
-
-
-2016-09-24
-T6
-Hospital B
-Escherichia coli
-S
-I
-S
-S
-F
-
-
-2017-04-02
-X1
-Hospital D
-Klebsiella pneumoniae
-S
-S
-S
-S
-F
-
-
-2016-12-13
-F1
+2014-02-15
+Y9
Hospital A
-Escherichia coli
+Streptococcus pneumoniae
+R
S
S
S
+F
+
+
+2012-05-11
+J4
+Hospital C
+Klebsiella pneumoniae
+R
+S
+S
S
M
-2012-02-05
-O8
+2013-06-10
+J1
Hospital B
Staphylococcus aureus
+R
S
S
+S
+M
+
+
+2013-02-02
+U4
+Hospital B
+Escherichia coli
+S
R
S
+S
+F
+
+
+2015-12-04
+S10
+Hospital A
+Streptococcus pneumoniae
+S
+S
+S
+S
F
-2017-06-17
-K1
+2016-09-17
+G2
Hospital B
-Streptococcus pneumoniae
-R
+Escherichia coli
R
S
S
+S
M
@@ -459,16 +459,16 @@ Longest: 1
1
M
-10,232
-51.16%
-10,232
-51.16%
+10,310
+51.55%
+10,310
+51.55%
2
F
-9,768
-48.84%
+9,690
+48.45%
20,000
100.00%
@@ -485,7 +485,7 @@ Longest: 1
data <- data %>%
mutate_at(vars(AMX:GEN), as.rsi)
-
How to conduct AMR analysis
Matthijs S. Berends
-30 September 2020
+08 October 2020
Source:vignettes/AMR.Rmd
AMR.RmdNote: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 30 September 2020.
+Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 08 October 2020.
Introduction
@@ -233,21 +233,21 @@Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the eucast_rules() function can also apply additional rules, like forcing
Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the eucast_rules() function can also apply additional rules, like forcing
Because the amoxicillin (column AMX) and amoxicillin/clavulanic acid (column AMC) in our data were generated randomly, some rows will undoubtedly contain AMX = S and AMC = R, which is technically impossible. The eucast_rules() fixes this:
data <- eucast_rules(data, col_mo = "bacteria", rules = "all") @@ -506,7 +506,7 @@ Longest: 1First isolates
We also need to know which isolates we can actually use for analysis.
-To conduct an analysis of antimicrobial resistance, you must only include the first isolate of every patient per episode (Hindler et al., Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be selection bias.
+To conduct an analysis of antimicrobial resistance, you must only include the first isolate of every patient per episode (Hindler et al., Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be selection bias.
The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:
(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype). The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.
@@ -533,7 +533,7 @@ Longest: 1
M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4First weighted isolates
-We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient Z5, sorted on date:
+We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient T8, sorted on date:
-
isolate @@ -549,74 +549,74 @@ Longest: 11 -2010-02-11 -Z5 +2010-04-04 +T8 B_ESCHR_COLI S S -S +R S TRUE 2 -2010-04-11 -Z5 +2010-10-03 +T8 B_ESCHR_COLI S S -R +S S FALSE 3 -2010-05-31 -Z5 +2010-10-19 +T8 B_ESCHR_COLI -R -I +S +S S S FALSE 4 -2011-03-29 -Z5 +2011-01-12 +T8 B_ESCHR_COLI -R S -R S -TRUE +S +S +FALSE 5 -2011-04-23 -Z5 +2011-02-03 +T8 B_ESCHR_COLI S S -R +S S FALSE 6 -2011-05-06 -Z5 +2011-04-04 +T8 B_ESCHR_COLI R S -R S -FALSE +S +TRUE 7 -2011-09-05 -Z5 +2011-05-23 +T8 B_ESCHR_COLI R S @@ -626,19 +626,19 @@ Longest: 18 -2011-09-29 -Z5 +2011-06-09 +T8 B_ESCHR_COLI S S -R +S S FALSE 9 -2011-11-16 -Z5 +2011-07-02 +T8 B_ESCHR_COLI S S @@ -648,13 +648,13 @@ Longest: 1@@ -687,133 +687,133 @@ Longest: 1 10 -2011-11-26 -Z5 +2011-12-05 +T8 B_ESCHR_COLI S S S -R +S FALSE 1 -2010-02-11 -Z5 +2010-04-04 +T8 B_ESCHR_COLI S S -S +R S TRUE TRUE 2 -2010-04-11 -Z5 +2010-10-03 +T8 B_ESCHR_COLI S S -R +S S FALSE TRUE 3 -2010-05-31 -Z5 +2010-10-19 +T8 B_ESCHR_COLI -R -I +S +S S S FALSE -TRUE +FALSE 4 -2011-03-29 -Z5 +2011-01-12 +T8 B_ESCHR_COLI -R S -R S -TRUE -TRUE +S +S +FALSE +FALSE 5 -2011-04-23 -Z5 +2011-02-03 +T8 B_ESCHR_COLI S S -R +S S FALSE -TRUE +FALSE 6 -2011-05-06 -Z5 +2011-04-04 +T8 B_ESCHR_COLI R S -R S -FALSE +S +TRUE TRUE 7 -2011-09-05 -Z5 +2011-05-23 +T8 B_ESCHR_COLI R S S S FALSE -TRUE +FALSE 8 -2011-09-29 -Z5 +2011-06-09 +T8 B_ESCHR_COLI S S -R +S S FALSE TRUE 9 -2011-11-16 -Z5 +2011-07-02 +T8 B_ESCHR_COLI S S S S FALSE -TRUE +FALSE 10 -2011-11-26 -Z5 +2011-12-05 +T8 B_ESCHR_COLI S S S -R +S +FALSE FALSE -TRUE Instead of 2, now 10 isolates are flagged. In total, 78.6% of all isolates are marked ‘first weighted’ - 50.1% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
+Instead of 2, now 4 isolates are flagged. In total, 78.7% of all isolates are marked ‘first weighted’ - 50.2% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
As with
filter_first_isolate(), there’s a shortcut for this new algorithm too:-data_1st <- data %>% filter_first_weighted_isolate()So we end up with 15,718 isolates for analysis.
+So we end up with 15,741 isolates for analysis.
We can remove unneeded columns:
data_1st <- data_1st %>% @@ -836,7 +836,7 @@ Longest: 1- + @@ -859,98 +859,98 @@ Longest: 1 - 1 -2016-08-26 -P9 -Hospital C -B_ESCHR_COLI -I -S -S -R -F -Gram-negative -Escherichia -coli -TRUE -- -2 -2016-09-24 -T6 -Hospital B -B_ESCHR_COLI -S -S -S -S -F -Gram-negative -Escherichia -coli -TRUE -- -4 -2016-12-13 -F1 +2014-02-15 +Y9 Hospital A -B_ESCHR_COLI -S -S -S -S -M -Gram-negative -Escherichia -coli -TRUE -- +6 -2017-06-17 -K1 -Hospital B B_STRPT_PNMN R R S R +F +Gram-positive +Streptococcus +pneumoniae +TRUE ++ +2 +2012-05-11 +J4 +Hospital C +B_KLBSL_PNMN +R +S +S +S M +Gram-negative +Klebsiella +pneumoniae +TRUE ++ +4 +2013-02-02 +U4 +Hospital B +B_ESCHR_COLI +R +R +S +S +F +Gram-negative +Escherichia +coli +TRUE ++ 5 +2015-12-04 +S10 +Hospital A +B_STRPT_PNMN +S +S +S +R +F Gram-positive Streptococcus pneumoniae TRUE - 8 -2011-02-20 -V8 -Hospital D +6 +2016-09-17 +G2 +Hospital B B_ESCHR_COLI +R S S S -S -F +M Gram-negative Escherichia coli TRUE - @@ -976,8 +976,8 @@ Longest: 19 -2016-09-22 -D3 +7 +2012-06-30 +V5 Hospital A -B_STPHY_AURS +B_ESCHR_COLI +R R S S -R -M -Gram-positive -Staphylococcus -aureus +F +Gram-negative +Escherichia +coli TRUE Frequency table
Class: character
-Length: 15,718
-Available: 15,718 (100%, NA: 0 = 0%)
+Length: 15,741
+Available: 15,741 (100%, NA: 0 = 0%)
Unique: 4Shortest: 16
@@ -994,33 +994,33 @@ Longest: 24
Longest: 241 Escherichia coli -7,841 -49.89% -7,841 -49.89% +7,868 +49.98% +7,868 +49.98% 2 Staphylococcus aureus -3,956 -25.17% -11,797 -75.05% +3,927 +24.95% +11,795 +74.93% 3 Streptococcus pneumoniae -2,346 -14.93% -14,143 -89.98% +2,394 +15.21% +14,189 +90.14% @@ -1049,50 +1049,50 @@ Longest: 24 4 Klebsiella pneumoniae -1,575 -10.02% -15,718 +1,552 +9.86% +15,741 100.00% E. coli AMX -3793 -242 -3806 -7841 +3823 +237 +3808 +7868 E. coli AMC -6205 -301 -1335 -7841 +6210 +315 +1343 +7868 E. coli CIP -5980 +5984 0 -1861 -7841 +1884 +7868 E. coli GEN -7075 +7078 0 -766 -7841 +790 +7868 K. pneumoniae AMX 0 0 -1575 -1575 +1552 +1552 @@ -1117,34 +1117,34 @@ Longest: 24 K. pneumoniae AMC -1217 -77 -281 -1575 +1198 +63 +291 +1552 E. coli CIP -5980 +5984 0 -1861 -7841 +1884 +7868 K. pneumoniae CIP -1207 +1176 0 -368 -1575 +376 +1552 S. aureus CIP -2971 +3037 0 -985 -3956 +890 +3927 @@ -1157,7 +1157,7 @@ Longest: 24 S. pneumoniae CIP -1790 +1852 0 -556 -2346 +542 +2394 As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (
proportion_R(), equal toresistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal tosusceptibility()). These functions can be used on their own:data_1st %>% resistance(AMX) -# [1] 0.5318107 +# [1] 0.531923Or can be used in conjuction with
group_by()andsummarise(), both from thedplyrpackage:@@ -187,7 +187,7 @@ -@@ -1174,19 +1174,19 @@ Longest: 24Hospital A -0.5401506 +0.5240989 Hospital B -0.5244014 +0.5326633 Hospital C -0.5256917 +0.5554161 @@ -1207,23 +1207,23 @@ Longest: 24 Hospital D -0.5363864 +0.5248050 Hospital A -0.5401506 -4782 +0.5240989 +4772 Hospital B -0.5244014 -5471 +0.5326633 +5373 Hospital C -0.5256917 -2277 +0.5554161 +2391 @@ -1246,27 +1246,27 @@ Longest: 24 Hospital D -0.5363864 -3188 +0.5248050 +3205 Escherichia -0.8297411 -0.9023084 -0.9858436 +0.8293086 +0.8995933 +0.9874174 Klebsiella -0.8215873 -0.9060317 -0.9879365 +0.8125000 +0.9085052 +0.9832474 Staphylococcus -0.8276036 -0.9160768 -0.9848332 +0.8245480 +0.9238605 +0.9870130 diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png index fc3b1d96..1aaf44b5 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 1-1.png and b/docs/articles/AMR_files/figure-html/plot 1-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 3-1.png b/docs/articles/AMR_files/figure-html/plot 3-1.png index de2a805a..a98b90e6 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 3-1.png and b/docs/articles/AMR_files/figure-html/plot 3-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 4-1.png b/docs/articles/AMR_files/figure-html/plot 4-1.png index f94ce7ec..5ef8a6ec 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 4-1.png and b/docs/articles/AMR_files/figure-html/plot 4-1.png differ diff --git a/docs/articles/AMR_files/figure-html/plot 5-1.png b/docs/articles/AMR_files/figure-html/plot 5-1.png index ccbcf09b..c0919c0e 100644 Binary files a/docs/articles/AMR_files/figure-html/plot 5-1.png and b/docs/articles/AMR_files/figure-html/plot 5-1.png differ diff --git a/docs/articles/AMR_files/header-attrs-2.3/header-attrs.js b/docs/articles/AMR_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/AMR_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/EUCAST.html b/docs/articles/EUCAST.html index f2d8dffe..78e08525 100644 --- a/docs/articles/EUCAST.html +++ b/docs/articles/EUCAST.html @@ -39,7 +39,7 @@ Streptococcus -0.5392157 +0.5459482 0.0000000 -0.5392157 +0.5459482 +How to apply EUCAST rules
@@ -203,7 +203,7 @@Introduction
-What are EUCAST rules? The European Committee on Antimicrobial Susceptibility Testing (EUCAST) states on their website:
+What are EUCAST rules? The European Committee on Antimicrobial Susceptibility Testing (EUCAST) states on their website:
diff --git a/docs/articles/EUCAST_files/header-attrs-2.3/header-attrs.js b/docs/articles/EUCAST_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/EUCAST_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/MDR.html b/docs/articles/MDR.html index 27a1f163..07b19c45 100644 --- a/docs/articles/MDR.html +++ b/docs/articles/MDR.html @@ -187,7 +187,7 @@ -EUCAST expert rules are a tabulated collection of expert knowledge on intrinsic resistances, exceptional resistance phenotypes and interpretive rules that may be applied to antimicrobial susceptibility testing in order to reduce errors and make appropriate recommendations for reporting particular resistances.
+How to determine multi-drug resistance (MDR)
@@ -218,7 +218,7 @@-
guideline = "EUCAST"The European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (link)
+The European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (link)
@@ -317,19 +317,19 @@ Unique: 2
guideline = "TB"head(my_TB_data) # rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin -# 1 I R S R R S -# 2 R R S S S R -# 3 S S R S S S -# 4 R R R S R R -# 5 S R S R R S -# 6 S S R R R S +# 1 R S S R S I +# 2 I S S S I R +# 3 R R R R R R +# 4 R I S R S S +# 5 S S I S S R +# 6 S S S R S S # kanamycin -# 1 I -# 2 R +# 1 R +# 2 S # 3 R # 4 R -# 5 R -# 6 S +# 5 S +# 6 RWe can now add the interpretation of MDR-TB to our data set. You can use:
@@ -363,40 +363,40 @@ Unique: 51 Mono-resistant -3235 -64.70% -3235 -64.70% +3225 +64.50% +3225 +64.50% 2 Negative -657 -13.14% -3892 -77.84% +698 +13.96% +3923 +78.46% 3 Multi-drug-resistant -595 -11.90% -4487 -89.74% +581 +11.62% +4504 +90.08% 4 Poly-resistant -306 -6.12% -4793 -95.86% +298 +5.96% +4802 +96.04% diff --git a/docs/articles/MDR_files/header-attrs-2.3/header-attrs.js b/docs/articles/MDR_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/MDR_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/PCA.html b/docs/articles/PCA.html index 89603f74..8e55edd8 100644 --- a/docs/articles/PCA.html +++ b/docs/articles/PCA.html @@ -187,7 +187,7 @@ - 5 Extensively drug-resistant -207 -4.14% +198 +3.96% 5000 100.00% +How to conduct principal component analysis (PCA) for AMR
diff --git a/docs/articles/PCA_files/header-attrs-2.3/header-attrs.js b/docs/articles/PCA_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/PCA_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/SPSS.html b/docs/articles/SPSS.html index b4a68eae..c2a11435 100644 --- a/docs/articles/SPSS.html +++ b/docs/articles/SPSS.html @@ -187,13 +187,13 @@ -+How to import data from SPSS / SAS / Stata
Matthijs S. Berends
-30 September 2020
+08 October 2020
Source:vignettes/SPSS.Rmddiff --git a/docs/articles/SPSS_files/header-attrs-2.3/header-attrs.js b/docs/articles/SPSS_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/SPSS_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/WHONET.html b/docs/articles/WHONET.html index 7fa0ff15..c2a7e97a 100644 --- a/docs/articles/WHONET.html +++ b/docs/articles/WHONET.html @@ -187,7 +187,7 @@ -SPSS.Rmd+diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png index 3db9d40f..3c56ab46 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/docs/articles/benchmarks_files/header-attrs-2.3/header-attrs.js b/docs/articles/benchmarks_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/benchmarks_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/datasets.html b/docs/articles/datasets.html index 53ce4953..43f03b0c 100644 --- a/docs/articles/datasets.html +++ b/docs/articles/datasets.html @@ -187,7 +187,7 @@ -How to work with WHONET data
diff --git a/docs/articles/WHONET_files/header-attrs-2.3/header-attrs.js b/docs/articles/WHONET_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/WHONET_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/benchmarks.html b/docs/articles/benchmarks.html index 77b818f9..7e2ab536 100644 --- a/docs/articles/benchmarks.html +++ b/docs/articles/benchmarks.html @@ -187,7 +187,7 @@ -+@@ -425,14 +411,14 @@ times = 100) print(run_it, unit = "ms", signif = 4) # Unit: milliseconds -# expr min lq mean median uq max neval -# en 13.92 14.62 19.73 15.07 17.06 71.77 100 -# de 16.83 17.63 22.19 18.10 20.41 58.61 100 -# nl 28.71 30.05 35.19 31.64 34.09 69.34 100 -# es 16.78 17.61 22.57 18.56 20.21 57.98 100 -# it 16.61 17.39 21.59 18.32 20.74 56.86 100 -# fr 16.48 17.31 21.96 17.84 20.26 55.84 100 -# pt 16.66 17.45 24.03 18.26 20.85 67.60 100 +# expr min lq mean median uq max neval +# en 14.00 14.36 18.52 14.64 15.18 57.58 100 +# de 17.04 17.33 20.94 17.63 18.49 56.23 100 +# nl 28.21 29.20 32.15 30.06 31.02 72.91 100 +# es 16.85 17.26 19.83 17.44 17.95 152.20 100 +# it 16.58 17.10 19.03 17.43 18.24 54.28 100 +# fr 16.71 17.20 21.24 17.52 19.02 63.25 100 +# pt 16.76 17.29 23.27 17.62 19.14 65.28 100-Benchmarks
@@ -288,34 +288,20 @@ # Result of one value was guessed with uncertainty. Use mo_uncertainties() to review it. print(S.aureus, unit = "ms", signif = 2) # Unit: milliseconds -# expr min lq mean median uq max -# as.mo("sau") 13.0 14.0 15.0 14.0 16.0 17.0 -# as.mo("stau") 220.0 230.0 250.0 260.0 280.0 280.0 -# as.mo("STAU") 220.0 240.0 270.0 250.0 290.0 330.0 -# as.mo("staaur") 13.0 14.0 28.0 15.0 44.0 62.0 -# as.mo("STAAUR") 11.0 14.0 17.0 14.0 15.0 48.0 -# as.mo("S. aureus") 30.0 56.0 56.0 61.0 63.0 66.0 -# as.mo("S aureus") 28.0 33.0 48.0 38.0 66.0 77.0 -# as.mo("Staphylococcus aureus") 1.7 1.9 2.4 2.5 2.6 3.1 -# as.mo("Staphylococcus aureus (MRSA)") 860.0 890.0 920.0 910.0 930.0 1000.0 -# as.mo("Sthafilokkockus aaureuz") 350.0 370.0 380.0 390.0 390.0 410.0 -# as.mo("MRSA") 13.0 15.0 28.0 18.0 42.0 61.0 -# as.mo("VISA") 21.0 22.0 31.0 22.0 43.0 52.0 -# as.mo("VRSA") 23.0 24.0 39.0 45.0 49.0 55.0 -# neval -# 10 -# 10 -# 10 -# 10 -# 10 -# 10 -# 10 -# 10 -# 10 -# 10 -# 10 -# 10 -# 10 +# expr min lq mean median uq max neval +# as.mo("sau") 11.0 12.0 25.0 13.0 14.0 130 10 +# as.mo("stau") 210.0 230.0 240.0 250.0 250.0 260 10 +# as.mo("STAU") 210.0 210.0 240.0 240.0 260.0 270 10 +# as.mo("staaur") 12.0 14.0 22.0 14.0 41.0 43 10 +# as.mo("STAAUR") 12.0 12.0 19.0 13.0 14.0 47 10 +# as.mo("S. aureus") 26.0 28.0 38.0 30.0 58.0 62 10 +# as.mo("S aureus") 28.0 29.0 45.0 52.0 56.0 60 10 +# as.mo("Staphylococcus aureus") 2.1 2.2 7.9 2.4 2.6 32 10 +# as.mo("Staphylococcus aureus (MRSA)") 840.0 880.0 890.0 890.0 890.0 930 10 +# as.mo("Sthafilokkockus aaureuz") 340.0 370.0 390.0 390.0 410.0 490 10 +# as.mo("MRSA") 11.0 12.0 16.0 13.0 14.0 44 10 +# as.mo("VISA") 19.0 20.0 32.0 22.0 50.0 51 10 +# as.mo("VRSA") 18.0 22.0 24.0 22.0 23.0 46 10
In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. It is clear that accepted taxonomic names are extremely fast, but some variations can take up to 500-1000 times as much time.
@@ -345,10 +331,10 @@ times = 10) print(run_it, unit = "ms", signif = 3) # Unit: milliseconds -# expr min lq mean median uq max neval -# mo_name(x) 99.2 127 145 143 145 243 10 +# expr min lq mean median uq max neval +# mo_name(x) 131 161 200 168 187 341 10So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.143 seconds. You only lose time on your unique input values.
+So getting official taxonomic names of 2,000,000 (!!) items consisting of 90 unique values only takes 0.168 seconds. You only lose time on your unique input values.
-@@ -372,11 +358,11 @@ print(run_it, unit = "ms", signif = 3) # Unit: milliseconds # expr min lq mean median uq max neval -# A 7.53 8.50 8.77 8.83 9.14 9.89 10 -# B 23.00 24.20 32.40 26.30 28.80 60.10 10 -# C 1.91 1.98 2.33 2.30 2.66 2.83 10 +# A 6.85 7.05 7.57 7.51 8.05 8.52 10 +# B 21.50 21.70 27.40 23.80 25.00 64.70 10 +# C 1.75 1.86 1.99 1.93 2.08 2.27 10
So going from
+mo_name("Staphylococcus aureus")to"Staphylococcus aureus"takes 0.0023 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:So going from
mo_name("Staphylococcus aureus")to"Staphylococcus aureus"takes 0.0019 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:run_it <- microbenchmark(A = mo_species("aureus"), B = mo_genus("Staphylococcus"), @@ -389,15 +375,15 @@ times = 10) print(run_it, unit = "ms", signif = 3) # Unit: milliseconds -# expr min lq mean median uq max neval -# A 1.55 1.74 1.91 1.98 2.06 2.16 10 -# B 1.52 1.70 1.95 1.88 2.03 2.62 10 -# C 1.61 1.68 6.25 2.04 2.18 44.80 10 -# D 1.65 1.71 1.92 1.95 2.02 2.22 10 -# E 1.56 1.79 1.92 1.99 2.01 2.14 10 -# F 1.62 1.67 1.82 1.78 1.94 2.09 10 -# G 1.61 1.70 1.92 1.97 2.01 2.31 10 -# H 1.62 1.69 1.83 1.87 1.95 2.01 10 +# expr min lq mean median uq max neval +# A 1.38 1.57 1.60 1.61 1.67 1.71 10 +# B 1.55 1.60 1.73 1.63 1.92 2.05 10 +# C 1.39 1.50 1.70 1.62 1.84 2.39 10 +# D 1.32 1.51 1.59 1.57 1.61 2.04 10 +# E 1.34 1.40 1.52 1.53 1.65 1.66 10 +# F 1.28 1.53 1.56 1.60 1.66 1.72 10 +# G 1.30 1.33 1.59 1.61 1.74 1.98 10 +# H 1.31 1.45 1.62 1.55 1.70 2.46 10Of course, when running
mo_phylum("Firmicutes")the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be"Firmicutes"anyway, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
+diff --git a/docs/articles/resistance_predict.html b/docs/articles/resistance_predict.html index 266526e9..204a89e6 100644 --- a/docs/articles/resistance_predict.html +++ b/docs/articles/resistance_predict.html @@ -187,7 +187,7 @@ -Data sets for download / own use
@@ -662,7 +662,7 @@ If you are reading this page from within R, please Antiviral agentsA 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’, ‘iv_units’.This data set is in R available as
-antivirals, after you load theAMRpackage.It was last updated on 29 August 2020 21:53:07 CEST. Find more info about the structure of this data set here.
+It was last updated on 29 August 2020 21:53:07 CEST. Find more info about the structure of this data set here.
Direct download links:
- Download as R file (5 kB)
diff --git a/docs/articles/datasets_files/header-attrs-2.3/header-attrs.js b/docs/articles/datasets_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/datasets_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/index.html b/docs/articles/index.html index 7e90191e..94875c31 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -81,7 +81,7 @@+@@ -258,7 +258,7 @@@@ -313,7 +313,7 @@ This package contains all ~550 antibiotic, antimycotic and antiviral dru -How to predict antimicrobial resistance
diff --git a/docs/articles/resistance_predict_files/header-attrs-2.3/header-attrs.js b/docs/articles/resistance_predict_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/resistance_predict_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/welcome_to_AMR.html b/docs/articles/welcome_to_AMR.html index d71c70c7..afb92eec 100644 --- a/docs/articles/welcome_to_AMR.html +++ b/docs/articles/welcome_to_AMR.html @@ -187,7 +187,7 @@ -+-@@ -236,14 +236,10 @@ Source:@@ -210,7 +210,7 @@ Since you are one of our users, we would like to know how you use the package andiff --git a/docs/countries.png b/docs/countries.png index c65919ce..1f925df6 100644 Binary files a/docs/countries.png and b/docs/countries.png differ diff --git a/docs/countries_large.png b/docs/countries_large.png index b3e95cf1..4528252e 100644 Binary files a/docs/countries_large.png and b/docs/countries_large.png differ diff --git a/docs/extra.css b/docs/extra.css index 5e507034..71d10122 100644 --- a/docs/extra.css +++ b/docs/extra.css @@ -1,23 +1,27 @@ /* # ==================================================================== # # TITLE # -# Antimicrobial Resistance (AMR) Analysis # +# Antimicrobial Resistance (AMR) Analysis for R # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2020 Berends MS, Luz CF et al. # +# Developed at the University of Groningen, the Netherlands, in # +# collaboration with non-profit organisations Certe Medical # +# Diagnostics & Advice, and University Medical Center Groningen. # # # # This R package is free software; you can freely use and distribute # # it for both personal and commercial purposes under the terms of the # # GNU General Public License version 2.0 (GNU GPL-2), as published by # # the Free Software Foundation. # -# # # We created this package for both routine data analysis and academic # # research and it was publicly released in the hope that it will be # # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. # -# Visit our website for more info: https://msberends.github.io/AMR. # +# # +# Visit our website for the full manual and a complete tutorial about # +# how to conduct AMR analysis: https://msberends.github.io/AMR/ # # ==================================================================== # */ diff --git a/docs/extra.js b/docs/extra.js index a7af3e28..b5557041 100644 --- a/docs/extra.js +++ b/docs/extra.js @@ -1,23 +1,27 @@ /* # ==================================================================== # # TITLE # -# Antimicrobial Resistance (AMR) Analysis # +# Antimicrobial Resistance (AMR) Analysis for R # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2020 Berends MS, Luz CF et al. # +# Developed at the University of Groningen, the Netherlands, in # +# collaboration with non-profit organisations Certe Medical # +# Diagnostics & Advice, and University Medical Center Groningen. # # # # This R package is free software; you can freely use and distribute # # it for both personal and commercial purposes under the terms of the # # GNU General Public License version 2.0 (GNU GPL-2), as published by # # the Free Software Foundation. # -# # # We created this package for both routine data analysis and academic # # research and it was publicly released in the hope that it will be # # useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. # -# Visit our website for more info: https://msberends.github.io/AMR. # +# # +# Visit our website for the full manual and a complete tutorial about # +# how to conduct AMR analysis: https://msberends.github.io/AMR/ # # ==================================================================== # */ diff --git a/docs/index.html b/docs/index.html index 95e27d8f..e3188ef1 100644 --- a/docs/index.html +++ b/docs/index.html @@ -43,7 +43,7 @@Welcome to the AMR package
diff --git a/docs/articles/welcome_to_AMR_files/header-attrs-2.3/header-attrs.js b/docs/articles/welcome_to_AMR_files/header-attrs-2.3/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/welcome_to_AMR_files/header-attrs-2.3/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/authors.html b/docs/authors.html index 533d364d..7b9519ae 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -81,7 +81,7 @@This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained and is free software (see Copyright).
@@ -341,7 +341,7 @@ Since you are one of our users, we would like to know how you use the package an
It enhances existing data and adds new data from data sets included in this package.
-
- Use
+eucast_rules()to apply EUCAST expert rules to isolates (not the translation from MIC to R/SI values, useas.rsi()for that).- Use
eucast_rules()to apply EUCAST expert rules to isolates (not the translation from MIC to R/SI values, useas.rsi()for that).- Use
first_isolate()to identify the first isolates of every patient using guidelines from the CLSI (Clinical and Laboratory Standards Institute).
- You can also identify first weighted isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
diff --git a/docs/news/index.html b/docs/news/index.html index 9579a502..bb09c9b6 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -81,7 +81,7 @@NEWS.md-@@ -1048,7 +1043,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/-AMR 1.3.0.9039 Unreleased +
++AMR 1.4.0 2020-10-08
---Last updated: 4 October 2020 -
Note: some changes in this version were suggested by anonymous reviewers from the journal we submitted our manuscipt about this package to. We are those reviewers very grateful for going through our code so thoroughly!
-@@ -344,7 +340,6 @@
Added packages that could be useful for some functions to the Suggestsfield of theDESCRIPTIONfile@@ -442,7 +437,7 @@
Making this package independent of especially the tidyverse (e.g. packages
dplyrandtidyr) tremendously increases sustainability on the long term, since tidyverse functions change quite often. Good for users, but hard for package maintainers. Most of our functions are replaced with versions that only rely on base R, which keeps this package fully functional for many years to come, without requiring a lot of maintenance to keep up with other packages anymore. Another upside it that this package can now be used with all versions of R since R-3.0.0 (April 2013). Our package is being used in settings where the resources are very limited. Fewer dependencies on newer software is helpful for such settings.Negative effects of this change are:
-
- Function
+freq()that was borrowed from thecleanerpackage was removed. Usecleaner::freq(), or runlibrary("cleaner")before you usefreq().- Function
freq()that was borrowed from thecleanerpackage was removed. Usecleaner::freq(), or runlibrary("cleaner")before you usefreq().Printing values of classmoorrsiin a tibble will no longer be in colour and printingrsiin a tibble will show the class<ord>, not<rsi>anymore. This is purely a visual effect.All functions from themo_*family (likemo_name()andmo_gramstain()) are noticeably slower when running on hundreds of thousands of rows.- For developers: classes
@@ -569,7 +564,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/moandabnow both also inherit classcharacter, to support any data transformation. This change invalidates code that checks for class length == 1.New
-
- Support for the newest EUCAST Clinical Breakpoint Tables v.10.0, valid from 1 January 2020. This affects translation of MIC and disk zones using
+as.rsi()and inferred resistance and susceptibility usingeucast_rules().- Support for the newest EUCAST Clinical Breakpoint Tables v.10.0, valid from 1 January 2020. This affects translation of MIC and disk zones using
as.rsi()and inferred resistance and susceptibility usingeucast_rules().- The repository of this package now contains a clean version of the EUCAST and CLSI guidelines from 2011-2020 to translate MIC and disk diffusion values to R/SI: https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt. This allows for machine reading these guidelines, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. This file used to process the EUCAST Clinical Breakpoints Excel file can be found here.
- Support for LOINC and SNOMED codes
@@ -647,7 +642,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Breaking
-
- Adopted Adeolu et al. (2016), PMID 27620848 for the
microorganismsdata set, which means that the new order Enterobacterales now consists of a part of the existing family Enterobacteriaceae, but that this family has been split into other families as well (like Morganellaceae and Yersiniaceae). Although published in 2016, this information is not yet in the Catalogue of Life version of 2019. All MDRO determinations withmdro()will now use the Enterobacterales order for all guidelines before 2016 that were dependent on the Enterobacteriaceae family. +- Adopted Adeolu et al. (2016), PMID 27620848 for the
microorganismsdata set, which means that the new order Enterobacterales now consists of a part of the existing family Enterobacteriaceae, but that this family has been split into other families as well (like Morganellaceae and Yersiniaceae). Although published in 2016, this information is not yet in the Catalogue of Life version of 2019. All MDRO determinations withmdro()will now use the Enterobacterales order for all guidelines before 2016 that were dependent on the Enterobacteriaceae family.
If you were dependent on the old Enterobacteriaceae family e.g. by using in your code:
@@ -779,7 +774,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/This is important, because a value like
-"testvalue"could never be understood by e.g.mo_name(), although the class would suggest a valid microbial code.+ Function
freq()has moved to a new package,clean(CRAN link), since creating frequency tables actually does not fit the scope of this package. Thefreq()function still works, since it is re-exported from thecleanpackage (which will be installed automatically upon updating thisAMRpackage).Function
freq()has moved to a new package,clean(CRAN link), since creating frequency tables actually does not fit the scope of this package. Thefreq()function still works, since it is re-exported from thecleanpackage (which will be installed automatically upon updating thisAMRpackage).Renamed data set
septic_patientstoexample_isolatesThe age()function gained a new parameterexactto determine ages with decimalsRemoved deprecated functions -guess_mo(),guess_atc(),EUCAST_rules(),interpretive_reading(),rsi()Frequency tables ( freq()): +Frequency tables ( @@ -1073,7 +1068,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/freq()):
speed improvement for microbial IDs
- @@ -1057,12 +1052,12 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
fixed factor level names for R Markdown
support for boxplots:
Added ceftazidim intrinsic resistance to Streptococci Changed default settings for -age_groups(), to let groups of fives and tens end with 100+ instead of 120+Fix for freq()for when all values areNA+Fix for freq()for when all values areNAFix for first_isolate()for when dates are missingImproved speed of guess_ab_col()@@ -1141,7 +1136,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/- -Support for data from WHONET and EARS-Net (European Antimicrobial Resistance Surveillance Network):
+Support for data from WHONET and EARS-Net (European Antimicrobial Resistance Surveillance Network):
- Exported files from WHONET can be read and used in this package. For functions like
first_isolate()andeucast_rules(), all parameters will be filled in automatically.- This package now knows all antibiotic abbrevations by EARS-Net (which are also being used by WHONET) - the
@@ -1225,7 +1220,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/antibioticsdata set now contains a columnears_net.
- Function
eucast_rules():-
- Updated EUCAST Clinical breakpoints to version 9.0 of 1 January 2019, the data set
+septic_patientsnow reflects these changes- Updated EUCAST Clinical breakpoints to version 9.0 of 1 January 2019, the data set
septic_patientsnow reflects these changes- Fixed a critical bug where some rules that depend on previous applied rules would not be applied adequately
- Emphasised in manual that penicillin is meant as benzylpenicillin (ATC J01CE01)
- New info is returned when running this function, stating exactly what has been changed or added. Use
@@ -1314,7 +1309,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/eucast_rules(..., verbose = TRUE)to get a data set with all changed per bug and drug combination.Frequency tables ( freq()function): +Frequency tables ( freq()function):
Support for tidyverse quasiquotation! Now you can create frequency tables of function outcomes:
@@ -1323,15 +1318,15 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/ # OLD WAY septic_patients %>% mutate(genus = mo_genus(mo)) %>% - freq(genus) + freq(genus) # NEW WAY septic_patients %>% - freq(mo_genus(mo)) + freq(mo_genus(mo)) # Even supports grouping variables: septic_patients %>% group_by(gender) %>% - freq(mo_genus(mo)) + freq(mo_genus(mo))@@ -1387,7 +1382,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/ Header info is now available as a list, with the
headerfunctionBig changes to the
eucast_rulesfunction:-
- Now also applies rules from the EUCAST ‘Breakpoint tables for bacteria’, version 8.1, 2018, http://www.eucast.org/clinical_breakpoints/ (see Source of the function)
+- Now also applies rules from the EUCAST ‘Breakpoint tables for bacteria’, version 8.1, 2018, https://www.eucast.org/clinical_breakpoints/ (see Source of the function)
- New parameter
rulesto specify which rules should be applied (expert rules, breakpoints, others or all)- New parameter
verbosewhich can be set toTRUEto get very specific messages about which columns and rows were affected- Better error handling when rules cannot be applied (i.e. new values could not be inserted)
@@ -1415,21 +1410,21 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/Using
portion_*functions now throws a warning when total available isolate is below parameterminimumFunctions
as.mo,as.rsi,as.mic,as.atcandfreqwill not set package name as attribute anymore- -
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 2db08a13..f15f8936 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,9 +1,9 @@ -pandoc: 2.7.3 +pandoc: 2.9.2.1 pkgdown: 1.5.1.9000 pkgdown_sha: eae56f08694abebf93cdfc0dd8e9ede06d8c815f articles: [] -last_built: 2020-10-04T19:01Z +last_built: 2020-10-08T09:09Z urls: - reference: https://msberends.github.io/AMR/reference - article: https://msberends.github.io/AMR/articles + reference: https://msberends.github.io/AMR//reference + article: https://msberends.github.io/AMR//articles diff --git a/docs/reference/AMR.html b/docs/reference/AMR.html index 6174add6..d3825341 100644 --- a/docs/reference/AMR.html +++ b/docs/reference/AMR.html @@ -277,7 +277,7 @@ -Frequency tables -
+freq():Frequency tables -
freq():@@ -1696,13 +1691,13 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Support for grouping variables, test with:
- @@ -1448,7 +1443,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/
Support for (un)selecting columns:
Removed diacritics from all authors (columns
microorganisms$refandmicroorganisms.old$ref) to comply with CRAN policy to only allow ASCII charactersFix for
mo_propertynot working properly- -
Fix for
eucast_ruleswhere some Streptococci would become ceftazidime R in EUCAST rule 4.5- +
Support for named vectors of class
mo, useful fortop_freq()Support for named vectors of class
mo, useful fortop_freq()
ggplot_rsiandscale_y_percenthavebreaksparameterAI improvements for
@@ -1615,13 +1610,13 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/as.mo:Support for types (classes) list and matrix for
freqmy_matrix = with(septic_patients, matrix(c(age, gender), ncol = 2)) -freq(my_matrix) +freq(my_matrix)For lists, subsetting is possible:
- A vignette to explain its usage
- Support for
-rsi(antimicrobial resistance) to use as input- Support for
tableto use as input:freq(table(x, y))+- Support for
tableto use as input:freq(table(x, y))- Support for existing functions
histandplotto use a frequency table as input:hist(freq(df$age))- Support for
-as.vector,as.data.frame,as_tibbleandformat- Support for quasiquotation:
freq(mydata, mycolumn)is the same asmydata %>% freq(mycolumn)+- Support for quasiquotation:
freq(mydata, mycolumn)is the same asmydata %>% freq(mycolumn)- Function
top_freqfunction to return the top/below n items as vector- Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR)
@@ -1788,7 +1783,7 @@ This works for all drug combinations, such as ampicillin/sulbactam, ceftazidime/- Function
MDROto determine Multi Drug Resistant Organisms (MDRO) with support for country-specific guidelines.
- -Exceptional resistances defined by EUCAST are also supported instead of countries alone
+Exceptional resistances defined by EUCAST are also supported instead of countries alone- Functions
BRMOandMRGNare wrappers for Dutch and German guidelines, respectivelyOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Contact Us
diff --git a/docs/reference/WHOCC.html b/docs/reference/WHOCC.html index b5043d99..eac07f2b 100644 --- a/docs/reference/WHOCC.html +++ b/docs/reference/WHOCC.html @@ -257,7 +257,7 @@ This package contains all ~550 antibiotic, antimycotic and antiviral dru -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
as.ab("meropenem") diff --git a/docs/reference/WHONET.html b/docs/reference/WHONET.html index 1db8c0c3..428aa291 100644 --- a/docs/reference/WHONET.html +++ b/docs/reference/WHONET.html @@ -285,7 +285,7 @@ -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/as.disk.html b/docs/reference/as.disk.html index 2977f628..3c3c2da9 100644 --- a/docs/reference/as.disk.html +++ b/docs/reference/as.disk.html @@ -276,7 +276,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +@@ -412,7 +412,7 @@On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/as.mic-1.png b/docs/reference/as.mic-1.png new file mode 100644 index 00000000..ea427777 Binary files /dev/null and b/docs/reference/as.mic-1.png differ diff --git a/docs/reference/as.mic.html b/docs/reference/as.mic.html index 0509e098..4467e413 100644 --- a/docs/reference/as.mic.html +++ b/docs/reference/as.mic.html @@ -276,7 +276,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/as.mo.html b/docs/reference/as.mo.html index 178fdbbb..008e5b68 100644 --- a/docs/reference/as.mo.html +++ b/docs/reference/as.mo.html @@ -386,7 +386,7 @@ The lifecycle of this function is stableWith ambiguous user input inas.mo()and all themo_*functions, the returned results are chosen based on their matching score usingmo_matching_score(). This matching score \(m\), is calculated as: -$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$
+$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$
where:
\(x\) is the user input;
- @@ -414,7 +414,7 @@ This package contains the complete taxonomic tree of almost all microorganisms ( -
\(n\) is a taxonomic name (genus, species, and subspecies);
On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
@@ -278,7 +278,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +microorganisms for the data.frame that is being used to determine ID's.
diff --git a/docs/reference/as.rsi-1.png b/docs/reference/as.rsi-1.png new file mode 100644 index 00000000..b711dc87 Binary files /dev/null and b/docs/reference/as.rsi-1.png differ diff --git a/docs/reference/as.rsi-2.png b/docs/reference/as.rsi-2.png new file mode 100644 index 00000000..4f612f69 Binary files /dev/null and b/docs/reference/as.rsi-2.png differ diff --git a/docs/reference/as.rsi.html b/docs/reference/as.rsi.html index 03102f74..eca6e93a 100644 --- a/docs/reference/as.rsi.html +++ b/docs/reference/as.rsi.html @@ -379,7 +379,7 @@ list(version_txt = "v3.2", year = 2020, title = "EUCAST Expert Rules / EUCAST In -In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/).
+
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (https://www.eucast.org/newsiandr/).
R = Resistant
A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.S = Susceptible
+
@@ -405,7 +405,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/atc_online.html b/docs/reference/atc_online.html index 2144c52e..22879c50 100644 --- a/docs/reference/atc_online.html +++ b/docs/reference/atc_online.html @@ -324,7 +324,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
# \donttest{ diff --git a/docs/reference/availability.html b/docs/reference/availability.html index a7ed5b77..5c4d6c12 100644 --- a/docs/reference/availability.html +++ b/docs/reference/availability.html @@ -274,7 +274,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
availability(example_isolates) diff --git a/docs/reference/bug_drug_combinations.html b/docs/reference/bug_drug_combinations.html index 344647e1..2252dff7 100644 --- a/docs/reference/bug_drug_combinations.html +++ b/docs/reference/bug_drug_combinations.html @@ -339,7 +339,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
# \donttest{ diff --git a/docs/reference/catalogue_of_life.html b/docs/reference/catalogue_of_life.html index 9f8e6f57..2b676956 100644 --- a/docs/reference/catalogue_of_life.html +++ b/docs/reference/catalogue_of_life.html @@ -270,7 +270,7 @@ This package contains the complete taxonomic tree of almost all microorganisms ( -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
@@ -345,7 +345,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +Data set microorganisms for the actual data.
diff --git a/docs/reference/catalogue_of_life_version.html b/docs/reference/catalogue_of_life_version.html index fe7e4d6d..f4b2f1ae 100644 --- a/docs/reference/catalogue_of_life_version.html +++ b/docs/reference/catalogue_of_life_version.html @@ -262,7 +262,7 @@ This package contains the complete taxonomic tree of almost all microorganisms ( -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/count.html b/docs/reference/count.html index 03b4191d..d1640739 100644 --- a/docs/reference/count.html +++ b/docs/reference/count.html @@ -323,7 +323,7 @@ The lifecycle of this function is stableIn 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/).+
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (https://www.eucast.org/newsiandr/).
R = Resistant
A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.S = Susceptible
@@ -368,7 +368,7 @@ A microorganism is categorised as Susceptible, Increased exposure when -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html index 45b0a7f1..ce8dd74a 100644 --- a/docs/reference/eucast_rules.html +++ b/docs/reference/eucast_rules.html @@ -83,7 +83,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
proportion_*to calculate microbial resistance and susceptibility.On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
# \donttest{ diff --git a/docs/reference/example_isolates.html b/docs/reference/example_isolates.html index 9ec352bb..ec9390f1 100644 --- a/docs/reference/example_isolates.html +++ b/docs/reference/example_isolates.html @@ -269,7 +269,7 @@ -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
df <- data.frame(amox = "S", diff --git a/docs/reference/index.html b/docs/reference/index.html index d4418bee..d92dea96 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -81,7 +81,7 @@diff --git a/docs/reference/intrinsic_resistant.html b/docs/reference/intrinsic_resistant.html index 488e1be2..5734769c 100644 --- a/docs/reference/intrinsic_resistant.html +++ b/docs/reference/intrinsic_resistant.html @@ -265,7 +265,7 @@ - Preparing data: antimicrobial resistance
-With
+as.mic()andas.disk()you can transform your raw input to valid MIC or disk diffusion values. Useas.rsi()for cleaning raw data to let it only contain “R”, “I” and “S”, or to interpret MIC or disk diffusion values as R/SI based on the lastest EUCAST and CLSI guidelines. Afterwards, you can extend antibiotic interpretations by applying EUCAST rules witheucast_rules().With
as.mic()andas.disk()you can transform your raw input to valid MIC or disk diffusion values. Useas.rsi()for cleaning raw data to let it only contain “R”, “I” and “S”, or to interpret MIC or disk diffusion values as R/SI based on the lastest EUCAST and CLSI guidelines. Afterwards, you can extend antibiotic interpretations by applying EUCAST rules witheucast_rules().On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
if (require("dplyr")) { diff --git a/docs/reference/join.html b/docs/reference/join.html index cb121969..c7e82e2c 100644 --- a/docs/reference/join.html +++ b/docs/reference/join.html @@ -290,7 +290,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
left_join_microorganisms(as.mo("K. pneumoniae")) diff --git a/docs/reference/key_antibiotics.html b/docs/reference/key_antibiotics.html index 627d4f45..7f949749 100644 --- a/docs/reference/key_antibiotics.html +++ b/docs/reference/key_antibiotics.html @@ -386,7 +386,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/kurtosis.html b/docs/reference/kurtosis.html index 05cf8edd..c1bdc5a4 100644 --- a/docs/reference/kurtosis.html +++ b/docs/reference/kurtosis.html @@ -82,7 +82,7 @@x -+ a vector of values, a
matrixor a data.framea vector of values, a matrix or a data.frame
na.rm @@ -281,7 +281,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/like.html b/docs/reference/like.html index ea110a17..aa8ea312 100644 --- a/docs/reference/like.html +++ b/docs/reference/like.html @@ -292,7 +292,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/mdro.html b/docs/reference/mdro.html index 0efabc20..90191543 100644 --- a/docs/reference/mdro.html +++ b/docs/reference/mdro.html @@ -82,7 +82,7 @@ @@ -332,7 +332,7 @@ The international guideline for multi-drug resistant tuberculosis - World Health
guideline = "MRGN"
The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6+The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)" (link)
guideline = "BRMO"
-The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)" (link)Please suggest your own (country-specific) guidelines by letting us know: https://github.com/msberends/AMR/issues/new.
@@ -355,7 +355,7 @@ The lifecycle of this function is stableIn 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/).+
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (https://www.eucast.org/newsiandr/).
R = Resistant
A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.S = Susceptible
@@ -369,7 +369,7 @@ A microorganism is categorised as Susceptible, Increased exposure when -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
Examples
mdro(example_isolates, guideline = "EUCAST") diff --git a/docs/reference/microorganisms.codes.html b/docs/reference/microorganisms.codes.html index 38901f45..c84f3a3c 100644 --- a/docs/reference/microorganisms.codes.html +++ b/docs/reference/microorganisms.codes.html @@ -268,7 +268,7 @@ This package contains the complete taxonomic tree of almost all microorganisms ( -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/microorganisms.html b/docs/reference/microorganisms.html index 7bb6d85c..24e0f879 100644 --- a/docs/reference/microorganisms.html +++ b/docs/reference/microorganisms.html @@ -293,7 +293,7 @@Names of prokaryotes are defined as being validly published by the International Code of Nomenclature of Bacteria. Validly published are all names which are included in the Approved Lists of Bacterial Names and the names subsequently published in the International Journal of Systematic Bacteriology (IJSB) and, from January 2000, in the International Journal of Systematic and Evolutionary Microbiology (IJSEM) as original articles or in the validation lists. -(from https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date/complete-list-readme)
+(from https://www.dsmz.de/services/online-tools/prokaryotic-nomenclature-up-to-date)In February 2020, the DSMZ records were merged with the List of Prokaryotic names with Standing in Nomenclature (LPSN).
Catalogue of Life
@@ -311,7 +311,7 @@ This package contains the complete taxonomic tree of almost all microorganisms ( -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/microorganisms.old.html b/docs/reference/microorganisms.old.html index 2a0125be..4204c57d 100644 --- a/docs/reference/microorganisms.old.html +++ b/docs/reference/microorganisms.old.html @@ -274,7 +274,7 @@ This package contains the complete taxonomic tree of almost all microorganisms ( -On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/mo_matching_score.html b/docs/reference/mo_matching_score.html index a23048b4..afaf3a0d 100644 --- a/docs/reference/mo_matching_score.html +++ b/docs/reference/mo_matching_score.html @@ -262,7 +262,7 @@With ambiguous user input in
-as.mo()and all themo_*functions, the returned results are chosen based on their matching score usingmo_matching_score(). This matching score \(m\), is calculated as:$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$
+$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$
where:
\(x\) is the user input;
- diff --git a/docs/reference/mo_property.html b/docs/reference/mo_property.html index 1a7d473b..1384b61c 100644 --- a/docs/reference/mo_property.html +++ b/docs/reference/mo_property.html @@ -351,7 +351,7 @@ The lifecycle of this function is stableWith ambiguous user input in
\(n\) is a taxonomic name (genus, species, and subspecies);
as.mo()and all themo_*functions, the returned results are chosen based on their matching score usingmo_matching_score(). This matching score \(m\), is calculated as: -$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \operatorname{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$
+$$m_{(x, n)} = \frac{l_{n} - 0.5 \cdot \min \begin{cases}l_{n} \\ \textrm{lev}(x, n)\end{cases}}{l_{n} \cdot p_{n} \cdot k_{n}}$$
where:
\(x\) is the user input;
- @@ -390,7 +390,7 @@ This package contains the complete taxonomic tree of almost all microorganisms ( -
\(n\) is a taxonomic name (genus, species, and subspecies);
On our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
+On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
diff --git a/docs/reference/mo_source.html b/docs/reference/mo_source.html index e99d9ef1..e0fdc23d 100644 --- a/docs/reference/mo_source.html +++ b/docs/reference/mo_source.html @@ -83,7 +83,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p @@ -328,7 +328,7 @@ The lifecycle of this function is stableOn our website https://msberends.github.io/AMR you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey! +On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR analysis, the complete documentation of all functions and an example analysis using WHONET data. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!