Update CNAME

2026-09-16 11:19:38 +02:00 · 2025-04-08 12:48:48 +01:00 · 2025-04-08 12:42:39 +01:00 · 2025-04-08 12:31:39 +01:00 · 2025-04-08 12:09:46 +01:00 · 2025-04-08 11:40:30 +01:00
629 changed files with 57109 additions and 3902 deletions
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 www.amr-for-r.org
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 Package: AMR
 Version: 0.1.1
 Date: 2018-03-13
 Title: Antimicrobial Resistance Analysis
 Authors@R: c(
    person(
        given = c("Matthijs", "S."),
        family = "Berends",
        email = "m.s.berends@umcg.nl",
        role = c("aut", "cre")),
    person(
        given = c("Christian", "F."),
        family = "Luz",
        email = "c.f.luz@umcg.nl",
        role = c("aut", "ctb")),
    person(
        given = c("Erwin", "E.A."),
        family = "Hassing",
        email = "e.hassing@certe.nl",
        role = "ctb"))
 Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR)
    of microbial isolates, by using new S3 classes and applying EUCAST expert rules
    on antibiograms according to Leclercq (2013)
    <doi:10.1111/j.1469-0691.2011.03703.x>.
 Depends: R (>= 3.0)
 Imports: dplyr (>= 0.7.0), reshape2 (>= 1.4.0), xml2, rvest
 URL: https://github.com/msberends/AMR
 BugReports: https://github.com/msberends/AMR/issues
 License: GPL-2 | file LICENSE
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 6.0.1.9000
@@ -1,339 +0,0 @@
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 You should also get your employer (if you work as a programmer) or your
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 This General Public License does not permit incorporating your program into
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@@ -0,0 +1,319 @@
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      <img src="logo.svg" class="logo" alt=""><h1>License</h1>
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 <pre>GNU GENERAL PUBLIC LICENSE
 Version 2, June 1991
 Copyright (C) 1989, 1991 Free Software Foundation, Inc., &lt;http://fsf.org/&gt;
  51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
 Everyone is permitted to copy and distribute verbatim copies
 of this license document, but changing it is not allowed.
 A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
 This R package, with package name 'AMR':
 - May be used for commercial purposes
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  - Modifications MUST be released under the same license when distributing the package
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  - Source code MUST be made available when the package is distributed
  - A copy of the license and copyright notice MUST be included with the package.
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 - Comes with NO warranty
 END OF THE SUMMARY
 GNU GENERAL PUBLIC LICENSE
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@@ -1,75 +0,0 @@
 # Generated by roxygen2: do not edit by hand
 S3method(as.double,mic)
 S3method(as.integer,mic)
 S3method(as.numeric,mic)
 S3method(barplot,mic)
 S3method(barplot,rsi)
 S3method(plot,mic)
 S3method(plot,rsi)
 S3method(print,mic)
 S3method(print,rsi)
 S3method(summary,mic)
 S3method(summary,rsi)
 export(EUCAST_rules)
 export(abname)
 export(anti_join_bactlist)
 export(as.mic)
 export(as.rsi)
 export(atc_property)
 export(first_isolate)
 export(full_join_bactlist)
 export(inner_join_bactlist)
 export(interpretive_reading)
 export(is.mic)
 export(is.rsi)
 export(key_antibiotics)
 export(left_join_bactlist)
 export(mo_property)
 export(right_join_bactlist)
 export(rsi)
 export(rsi_df)
 export(rsi_predict)
 export(semi_join_bactlist)
 exportMethods(as.double.mic)
 exportMethods(as.integer.mic)
 exportMethods(as.numeric.mic)
 exportMethods(barplot.mic)
 exportMethods(barplot.rsi)
 exportMethods(plot.mic)
 exportMethods(plot.rsi)
 exportMethods(print.mic)
 exportMethods(print.rsi)
 exportMethods(summary.mic)
 exportMethods(summary.rsi)
 importFrom(dplyr,"%>%")
 importFrom(dplyr,all_vars)
 importFrom(dplyr,any_vars)
 importFrom(dplyr,arrange)
 importFrom(dplyr,arrange_at)
 importFrom(dplyr,between)
 importFrom(dplyr,filter)
 importFrom(dplyr,filter_at)
 importFrom(dplyr,group_by)
 importFrom(dplyr,group_by_at)
 importFrom(dplyr,if_else)
 importFrom(dplyr,lag)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,n_distinct)
 importFrom(dplyr,progress_estimated)
 importFrom(dplyr,pull)
 importFrom(dplyr,row_number)
 importFrom(dplyr,select)
 importFrom(dplyr,slice)
 importFrom(dplyr,summarise)
 importFrom(dplyr,tibble)
 importFrom(dplyr,vars)
 importFrom(graphics,axis)
 importFrom(graphics,barplot)
 importFrom(graphics,plot)
 importFrom(graphics,text)
 importFrom(reshape2,dcast)
 importFrom(rvest,html_nodes)
 importFrom(rvest,html_table)
 importFrom(xml2,read_html)
@@ -1,9 +0,0 @@
 ## 0.1.1
 - `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
 - Edited column names to comply with GLIMS, the laboratory information system
 - Added more valid MIC values
 - Renamed 'Daily Defined Dose' to 'Defined Daily Dose'
 - Added barplots for `rsi` and `mic` classes
 ## 0.1.0
 - First submission to CRAN.
@@ -1,660 +0,0 @@
 # ==================================================================== #
 # TITLE                                                                #
 # Antimicrobial Resistance (AMR) Analysis                              #
 #                                                                      #
 # AUTHORS                                                              #
 # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
 #                                                                      #
 # LICENCE                                                              #
 # This program is free software; you can redistribute it and/or modify #
 # it under the terms of the GNU General Public License version 2.0,    #
 # as published by the Free Software Foundation.                        #
 #                                                                      #
 # This program is distributed in the hope that it will be useful,      #
 # but WITHOUT ANY WARRANTY; without even the implied warranty of       #
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
 # GNU General Public License for more details.                         #
 # ==================================================================== #
 #' EUCAST expert rules
 #'
 #' Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
 #' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
 #' @param col_bactcode column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{bactlist$bactid}, see \code{\link{bactlist}}
 #' @param info print progress
 #' @param amcl,amik,amox,ampi,azit,aztr,cefa,cfra,cfep,cfot,cfox,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,mino,moxi,nali,neom,neti,nitr,novo,norf,oflo,peni,pita,poly,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing column will be skipped.
 #' @param ... parameters that are passed on to \code{EUCAST_rules}
 #' @rdname EUCAST
 #' @export
 #' @importFrom dplyr %>% left_join select
 #' @return table with edited variables of antibiotics.
 #' @source
 #'   EUCAST Expert Rules Version 2.0: \cr
 #'   Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
 #'   \url{https://doi.org/10.1111/j.1469-0691.2011.03703.x} \cr
 #'   \cr
 #'   EUCAST Expert Rules Version 3.1: \cr
 #'   \url{http://www.eucast.org/expert_rules_and_intrinsic_resistance}
 #' @examples
 #' a <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
 #'                            "ENCFAE",  # Enterococcus faecalis
 #'                            "ESCCOL",  # Escherichia coli
 #'                            "KLEPNE",  # Klebsiella pneumoniae
 #'                            "PSEAER"), # Pseudomonas aeruginosa
 #'                 vanc = "-",           # Vancomycin
 #'                 amox = "-",           # Amoxicillin
 #'                 coli = "-",           # Colistin
 #'                 cfta = "-",           # Ceftazidime
 #'                 cfur = "-",           # Cefuroxime
 #'                 stringsAsFactors = FALSE)
 #' a
 #' 
 #' b <- EUCAST_rules(a)
 #' b
 EUCAST_rules <- function(tbl,
                         col_bactcode = 'bactid',
                         info = TRUE,
                         amcl = 'amcl',
                         amik = 'amik',
                         amox = 'amox',
                         ampi = 'ampi',
                         azit = 'azit',
                         aztr = 'aztr',
                         cefa = 'cefa',
                         cfra = 'cfra',
                         cfep = 'cfep',
                         cfot = 'cfot',
                         cfox = 'cfox',
                         cfta = 'cfta',
                         cftr = 'cftr',
                         cfur = 'cfur',
                         chlo = 'chlo',
                         cipr = 'cipr',
                         clar = 'clar',
                         clin = 'clin',
                         clox = 'clox',
                         coli = 'coli',
                         czol = 'czol',
                         dapt = 'dapt',
                         doxy = 'doxy',
                         erta = 'erta',
                         eryt = 'eryt',
                         fosf = 'fosf',
                         fusi = 'fusi',
                         gent = 'gent',
                         imip = 'imip',
                         kana = 'kana',
                         levo = 'levo',
                         linc = 'linc',
                         line = 'line',
                         mero = 'mero',
                         mino = 'mino',
                         moxi = 'moxi',
                         nali = 'nali',
                         neom = 'neom',
                         neti = 'neti',
                         nitr = 'nitr',
                         novo = 'novo',
                         norf = 'norf',
                         oflo = 'oflo',
                         peni = 'peni',
                         pita = 'pita',
                         poly = 'poly',
                         qida = 'qida',
                         rifa = 'rifa',
                         roxi = 'roxi',
                         siso = 'siso',
                         teic = 'teic',
                         tetr = 'tetr',
                         tica = 'tica',
                         tige = 'tige',
                         tobr = 'tobr',
                         trim = 'trim',
                         trsu = 'trsu',
                         vanc = 'vanc') {
  if (!col_bactcode %in% colnames(tbl)) {
    stop('Column ', col_bactcode, ' not found.')
  }
  # kolommen controleren
  col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep,
                cfot, cfox, cfta, cftr, cfur, cipr, clar, clin, clox, coli, czol,
                dapt, doxy, erta, eryt, fusi, gent, imip, kana, levo, linc, line,
                mero, mino, moxi, nali, neom, neti, nitr, novo, norf, oflo, peni,
                pita, poly, qida, rifa, roxi, siso, teic, tetr, tica, tige, tobr,
                trim, trsu, vanc)
  col.list <- col.list[!is.na(col.list)]
  if (!all(col.list %in% colnames(tbl))) {
    if (info == TRUE) {
      cat('\n')
    }
    if (info == TRUE) {
      warning('These columns do not exist and will be ignored:\n',
              col.list[!(col.list %in% colnames(tbl))] %>% toString(),
              immediate. = TRUE,
              call. = FALSE)
    }
    if (!amcl %in% colnames(tbl)) { amcl <- NA }
    if (!amik %in% colnames(tbl)) { amik <- NA }
    if (!amox %in% colnames(tbl)) { amox <- NA }
    if (!ampi %in% colnames(tbl)) { ampi <- NA }
    if (!azit %in% colnames(tbl)) { azit <- NA }
    if (!aztr %in% colnames(tbl)) { aztr <- NA }
    if (!cefa %in% colnames(tbl)) { cefa <- NA }
    if (!cfra %in% colnames(tbl)) { cfra <- NA }
    if (!cfep %in% colnames(tbl)) { cfep <- NA }
    if (!cfot %in% colnames(tbl)) { cfot <- NA }
    if (!cfox %in% colnames(tbl)) { cfox <- NA }
    if (!cfta %in% colnames(tbl)) { cfta <- NA }
    if (!cftr %in% colnames(tbl)) { cftr <- NA }
    if (!cfur %in% colnames(tbl)) { cfur <- NA }
    if (!chlo %in% colnames(tbl)) { chlo <- NA }
    if (!cipr %in% colnames(tbl)) { cipr <- NA }
    if (!clar %in% colnames(tbl)) { clar <- NA }
    if (!clin %in% colnames(tbl)) { clin <- NA }
    if (!clox %in% colnames(tbl)) { clox <- NA }
    if (!coli %in% colnames(tbl)) { coli <- NA }
    if (!czol %in% colnames(tbl)) { czol <- NA }
    if (!dapt %in% colnames(tbl)) { dapt <- NA }
    if (!doxy %in% colnames(tbl)) { doxy <- NA }
    if (!erta %in% colnames(tbl)) { erta <- NA }
    if (!eryt %in% colnames(tbl)) { eryt <- NA }
    if (!fosf %in% colnames(tbl)) { fosf <- NA }
    if (!fusi %in% colnames(tbl)) { fusi <- NA }
    if (!gent %in% colnames(tbl)) { gent <- NA }
    if (!imip %in% colnames(tbl)) { imip <- NA }
    if (!kana %in% colnames(tbl)) { kana <- NA }
    if (!levo %in% colnames(tbl)) { levo <- NA }
    if (!linc %in% colnames(tbl)) { linc <- NA }
    if (!line %in% colnames(tbl)) { line <- NA }
    if (!mero %in% colnames(tbl)) { mero <- NA }
    if (!mino %in% colnames(tbl)) { mino <- NA }
    if (!moxi %in% colnames(tbl)) { moxi <- NA }
    if (!nali %in% colnames(tbl)) { nali <- NA }
    if (!neom %in% colnames(tbl)) { neom <- NA }
    if (!neti %in% colnames(tbl)) { neti <- NA }
    if (!nitr %in% colnames(tbl)) { nitr <- NA }
    if (!novo %in% colnames(tbl)) { novo <- NA }
    if (!norf %in% colnames(tbl)) { norf <- NA }
    if (!oflo %in% colnames(tbl)) { oflo <- NA }
    if (!peni %in% colnames(tbl)) { peni <- NA }
    if (!pita %in% colnames(tbl)) { pita <- NA }
    if (!poly %in% colnames(tbl)) { poly <- NA }
    if (!qida %in% colnames(tbl)) { qida <- NA }
    if (!rifa %in% colnames(tbl)) { rifa <- NA }
    if (!roxi %in% colnames(tbl)) { roxi <- NA }
    if (!siso %in% colnames(tbl)) { siso <- NA }
    if (!teic %in% colnames(tbl)) { teic <- NA }
    if (!tetr %in% colnames(tbl)) { tetr <- NA }
    if (!tica %in% colnames(tbl)) { tica <- NA }
    if (!tige %in% colnames(tbl)) { tige <- NA }
    if (!tobr %in% colnames(tbl)) { tobr <- NA }
    if (!trim %in% colnames(tbl)) { trim <- NA }
    if (!trsu %in% colnames(tbl)) { trsu <- NA }
    if (!vanc %in% colnames(tbl)) { vanc <- NA }
  }
  total <- 0
  total_rows <- integer(0)
  # functie voor uitvoeren
  edit_rsi <- function(to, rows, cols) {
    #voortgang$tick()$print()
    cols <- cols[!is.na(cols)]
    if (length(rows) > 0 & length(cols) > 0) {
      tbl[rows, cols] <<- to
      total <<- total + (length(rows) * length(cols))
      total_rows <<- c(total_rows, rows)
    }
  }
  # bactlist aan vastknopen (bestaande kolommen krijgen extra suffix)
  joinby <- colnames(AMR::bactlist)[1]
  names(joinby) <- col_bactcode
  tbl <- tbl %>% left_join(y = AMR::bactlist, by = joinby, suffix = c("_tempbactlist", ""))
  # antibioticagroepen
  aminoglycosiden <- c(tobr, gent, kana, neom, neti, siso)
  tetracyclines <- c(doxy, mino, tetr) # sinds EUCAST v3.1 is tige(cycline) apart
  polymyxines <- c(poly, coli)
  macroliden <- c(eryt, azit, roxi, clar) # sinds EUCAST v3.1 is clinda apart
  glycopeptiden <- c(vanc, teic)
  streptogramines <- qida # eigenlijk pristinamycine en quinupristine/dalfopristine
  cefalosporines <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
  carbapenems <- c(erta, imip, mero)
  aminopenicillines <- c(ampi, amox)
  ureidopenicillines <- pita # eigenlijk ook azlo en mezlo
  fluorochinolonen <- c(oflo, cipr, norf, levo, moxi)
  if (info == TRUE) {
    cat('\nApplying rules to',
        tbl[!is.na(tbl$genus),] %>% nrow() %>% format(big.mark = ","),
        'rows according to "EUCAST Expert Rules Version 3.1"\n\n')
  }
  # Table 1: Intrinsic resistance in Enterobacteriaceae ----
  if (info == TRUE) {
    cat('...Table 1: Intrinsic resistance in Enterobacteriaceae\n')
  }
  #voortgang <- progress_estimated(17)
  # Intrisiek R voor groep
  edit_rsi(to = 'R',
           rows = which(tbl$family == 'Enterobacteriaceae'),
           cols = c(peni, glycopeptiden, fusi, macroliden, linc, streptogramines, rifa, dapt, line))
  # Citrobacter
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)'),
           cols = c(aminopenicillines, tica))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)'),
           cols = c(aminopenicillines, amcl, czol, cfox))
  # Enterobacter
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Enterobacter cloacae'),
           cols = c(aminopenicillines, amcl, czol, cfox))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Enterobacter aerogenes'),
           cols = c(aminopenicillines, amcl, czol, cfox))
  # Escherichia
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Escherichia hermanni'),
           cols = c(aminopenicillines, tica))
  # Hafnia
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Hafnia alvei'),
           cols = c(aminopenicillines, amcl, czol, cfox))
  # Klebsiella
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Klebsiella'),
           cols = c(aminopenicillines, tica))
  # Morganella / Proteus
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Morganella morganii'),
           cols = c(aminopenicillines, amcl, czol, tetracyclines, polymyxines, nitr))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Proteus mirabilis'),
           cols = c(tetracyclines, tige, polymyxines, nitr))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Proteus penneri'),
           cols = c(aminopenicillines, czol, cfur, tetracyclines, tige, polymyxines, nitr))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Proteus vulgaris'),
           cols = c(aminopenicillines, czol, cfur, tetracyclines, tige, polymyxines, nitr))
  # Providencia
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Providencia rettgeri'),
           cols = c(aminopenicillines, amcl, czol, cfur, tetracyclines, tige, polymyxines, nitr))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Providencia stuartii'),
           cols = c(aminopenicillines, amcl, czol, cfur, tetracyclines, tige, polymyxines, nitr))
  # Raoultella
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Raoultella'),
           cols = c(aminopenicillines, tica))
  # Serratia
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Serratia marcescens'),
           cols = c(aminopenicillines, amcl, czol, cfox, cfur, tetracyclines[tetracyclines != 'mino'], polymyxines, nitr))
  # Yersinia
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Yersinia enterocolitica'),
           cols = c(aminopenicillines, amcl, tica, czol, cfox))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Yersinia pseudotuberculosis'),
           cols = c(poly, coli))
  # Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria ----
  if (info == TRUE) {
    cat('...Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria\n')
  }
  #voortgang <- progress_estimated(8)
  # Intrisiek R voor groep
  edit_rsi(to = 'R',
           rows = which(tbl$genus %in% c('Achromobacter',
                                         'Acinetobacter',
                                         'Alcaligenes',
                                         'Bordatella',
                                         'Burkholderia',
                                         'Elizabethkingia',
                                         'Flavobacterium',
                                         'Ochrobactrum',
                                         'Pseudomonas',
                                         'Stenotrophomonas')),
           cols = c(peni, cfox, cfur, glycopeptiden, fusi, macroliden, linc, streptogramines, rifa, dapt, line))
  # Acinetobacter
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Acinetobacter (baumannii|pittii|nosocomialis|calcoaceticus)'),
           cols = c(aminopenicillines, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, tetracyclines[tetracyclines != 'mino']))
  # Achromobacter
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Achromobacter (xylosoxydans|xylosoxidans)'),
           cols = c(aminopenicillines, czol, cfot, cftr, erta))
  # Burkholderia
  edit_rsi(to = 'R',
           # onder 'Burkholderia cepacia complex' vallen deze species allemaal: PMID 16217180.
           rows = which(tbl$fullname %like% '^Burkholderia (cepacia|multivorans|cenocepacia|stabilis|vietnamiensis|dolosa|ambifaria|anthina|pyrrocinia|ubonensis)'),
           cols = c(aminopenicillines, amcl, tica, pita, czol, cfot, cftr, aztr, erta, cipr, chlo, aminoglycosiden, trim, fosf, polymyxines))
  # Elizabethkingia
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Elizabethkingia meningoseptic(a|um)'),
           cols = c(aminopenicillines, amcl, tica, czol, cfot, cftr, cfta, cfep, aztr, erta, imip, mero, polymyxines))
  # Ochrobactrum
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Ochrobactrum anthropi'),
           cols = c(aminopenicillines, amcl, tica, pita, czol, cfot, cftr, cfta, cfep, aztr, erta))
  # Pseudomonas
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Pseudomonas aeruginosa'),
           cols = c(aminopenicillines, amcl, czol, cfot, cftr, erta, chlo, kana, neom, trim, trsu, tetracyclines, tige))
  # Stenotrophomonas
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Stenotrophomonas maltophilia'),
           cols = c(aminopenicillines, amcl, tica, pita, czol, cfot, cftr, cfta, aztr, erta, imip, mero, aminoglycosiden, trim, fosf, tetr))
  # Table 3: Intrinsic resistance in other Gram-negative bacteria ----
  if (info == TRUE) {
    cat('...Table 3: Intrinsic resistance in other Gram-negative bacteria\n')
  }
  #voortgang <- progress_estimated(7)
  # Intrisiek R voor groep
  edit_rsi(to = 'R',
           rows = which(tbl$genus %in% c('Haemophilus',
                                         'Moraxella',
                                         'Neisseria',
                                         'Campylobacter')),
           cols = c(glycopeptiden, linc, dapt, line))
  # Haemophilus
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Haemophilus influenzae'),
           cols = c(fusi, streptogramines))
  # Moraxella
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Moraxella catarrhalis'),
           cols = trim)
  # Neisseria
  edit_rsi(to = 'R',
           rows = which(tbl$genus == 'Neisseria'),
           cols = trim)
  # Campylobacter
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Campylobacter fetus'),
           cols = c(fusi, streptogramines, trim, nali))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Campylobacter (jejuni|coli)'),
           cols = c(fusi, streptogramines, trim))
  # Table 4: Intrinsic resistance in Gram-positive bacteria ----
  if (info == TRUE) {
    cat('...Table 4: Intrinsic resistance in Gram-positive bacteria\n')
  }
  #voortgang <- progress_estimated(14)
  # Intrisiek R voor groep
  edit_rsi(to = 'R',
           rows = which(tbl$gramstain %like% 'Positi(e|)(v|f)'),
           cols = c(aztr, polymyxines, nali))
  # Staphylococcus
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Staphylococcus saprophyticus'),
           cols = c(fusi, cfta, fosf, novo))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Staphylococcus (cohnii|xylosus)'),
           cols = c(cfta, novo))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Staphylococcus capitis'),
           cols = c(cfta, fosf))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
           cols = cfta)
  # Streptococcus
  edit_rsi(to = 'R',
           rows = which(tbl$genus == 'Streptococcus'),
           cols = c(fusi, cfta, aminoglycosiden))
  # Enterococcus
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Enterococcus faecalis'),
           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, clin, qida, trim, trsu))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Enterococcus (gallinarum|casseliflavus)'),
           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, clin, qida, vanc, trim, trsu))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Enterococcus faecium'),
           cols = c(fusi, cfta, cefalosporines[cefalosporines != cfta], aminoglycosiden, macroliden, trim, trsu))
  # Corynebacterium
  edit_rsi(to = 'R',
           rows = which(tbl$genus == 'Corynebacterium'),
           cols = fosf)
  # Listeria
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Listeria monocytogenes'),
           cols = c(cfta, cefalosporines[cefalosporines != cfta]))
  # overig
  edit_rsi(to = 'R',
           rows = which(tbl$genus %in% c('Leuconostoc', 'Pediococcus')),
           cols = c(vanc, teic))
  edit_rsi(to = 'R',
           rows = which(tbl$genus == 'Lactobacillus'),
           cols = c(vanc, teic))
  edit_rsi(to = 'R',
           rows = which(tbl$fullname %like% '^Clostridium (ramosum|innocuum)'),
           cols = vanc)
  # Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci ----
  if (info == TRUE) {
    cat('...Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci\n')
  }
  #voortgang <- progress_estimated(2)
  # regel 8.3
  if (!is.na(peni)) {
    edit_rsi(to = 'S',
             rows = which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|dysgalactiae|groep A|groep B|groep C|groep G)'
                          & tbl[, peni] == 'S'),
             cols = c(aminopenicillines, cefalosporines, carbapenems))
  }
  # regel 8.6
  if (!is.na(ampi)) {
    edit_rsi(to = 'R',
             rows = which(tbl$genus == 'Enterococcus'
                          & tbl[, ampi] == 'R'),
             cols = c(ureidopenicillines, carbapenems))
  }
  if (!is.na(amox)) {
    edit_rsi(to = 'R',
             rows = which(tbl$genus == 'Enterococcus'
                          & tbl[, amox] == 'R'),
             cols = c(ureidopenicillines, carbapenems))
  }
  # Table 9: Interpretive rules for B-lactam agents and Gram-negative rods ----
  if (info == TRUE) {
    cat('...Table 9: Interpretive rules for B-lactam agents and Gram-negative rods\n')
  }
  #voortgang <- progress_estimated(1)
  # regel 9.3
  if (!is.na(tica) & !is.na(pita)) {
    edit_rsi(to = 'R',
             rows = which(tbl$family == 'Enterobacteriaceae'
                          & tbl[, tica] == 'R'
                          & tbl[, pita] == 'S'),
             cols = pita)
  }
  # Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria ----
  if (info == TRUE) {
    cat('...Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria\n')
  }
  #voortgang <- progress_estimated(1)
  # regel 10.2
  if (!is.na(ampi)) {
    # hiervoor moeten we eerst weten of ze B-lactamase-positief zijn
    # edit_rsi(to = 'R',
    #          rows = which(tbl$fullname %like% '^Haemophilus influenza'
    #                       & tbl[, ampi] == 'R'),
    #          cols = c(ampi, amox, amcl, pita, cfur))
  }
  # Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins ----
  if (info == TRUE) {
    cat('...Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins\n')
  }
  # regel 11.1
  if (!is.na(eryt)) {
    if (!is.na(azit)) {
      tbl[, azit] <- tbl[, eryt]
    }
    if (!is.na(clar)) {
      tbl[, clar] <- tbl[, eryt]
    }
  }
  # Table 12: Interpretive rules for aminoglycosides ----
  if (info == TRUE) {
    cat('...Table 12: Interpretive rules for aminoglycosides\n')
  }
  #voortgang <- progress_estimated(4)
  # regel 12.2
  if (!is.na(tobr)) {
    edit_rsi(to = 'R',
             rows = which(tbl$genus == 'Staphylococcus'
                          & tbl[, tobr] == 'R'),
             cols = c(kana, amik))
  }
  # regel 12.3
  if (!is.na(gent)) {
    edit_rsi(to = 'R',
             rows = which(tbl$genus == 'Staphylococcus'
                          & tbl[, gent] == 'R'),
             cols = aminoglycosiden)
  }
  # regel 12.8
  if (!is.na(gent) & !is.na(tobr)) {
    edit_rsi(to = 'R',
             rows = which(tbl$family == 'Enterobacteriaceae'
                          & tbl[, gent] == 'I'
                          & tbl[, tobr] == 'S'),
             cols = gent)
  }
  # regel 12.9
  if (!is.na(gent) & !is.na(tobr)) {
    edit_rsi(to = 'R',
             rows = which(tbl$family == 'Enterobacteriaceae'
                          & tbl[, tobr] == 'I'
                          & tbl[, gent] == 'R'),
             cols = tobr)
  }
  # Table 13: Interpretive rules for quinolones ----
  if (info == TRUE) {
    cat('...Table 13: Interpretive rules for quinolones\n')
  }
  #voortgang <- progress_estimated(4)
  # regel 13.2
  if (!is.na(moxi)) {
    edit_rsi(to = 'R',
             rows = which(tbl$genus == 'Staphylococcus'
                          & tbl[, moxi] == 'R'),
             cols = fluorochinolonen)
  }
  # regel 13.4
  if (!is.na(moxi)) {
    edit_rsi(to = 'R',
             rows = which(tbl$fullname %like% '^Streptococcus pneumoniae'
                          & tbl[, moxi] == 'R'),
             cols = fluorochinolonen)
  }
  # regel 13.5
  if (!is.na(cipr)) {
    edit_rsi(to = 'R',
             rows = which(tbl$family == 'Enterobacteriaceae'
                          & tbl[, cipr] == 'R'),
             cols = fluorochinolonen)
  }
  # regel 13.8
  if (!is.na(cipr)) {
    edit_rsi(to = 'R',
             rows = which(tbl$fullname %like% '^Neisseria gonorrhoeae'
                          & tbl[, cipr] == 'R'),
             cols = fluorochinolonen)
  }
  # Other ----
  if (info == TRUE) {
    cat('...Other\n')
  }
  #voortgang <- progress_estimated(2)
  if (!is.na(amcl)) {
    edit_rsi(to = 'R',
             rows = which(tbl[, amcl] == 'R'),
             cols = ampi)
  }
  if (!is.na(trsu)) {
    edit_rsi(to = 'R',
             rows = which(tbl[, trsu] == 'R'),
             cols = trim)
  }
  if (!is.na(ampi) & !is.na(amox)) {
    tbl[, amox] <- tbl %>% pull(ampi)
  }
  # Toegevoegde kolommen weer verwijderen
  bactlist.ncol <- ncol(AMR::bactlist) - 2
  tbl.ncol <- ncol(tbl)
  tbl <- tbl %>% select(-c((tbl.ncol - bactlist.ncol):tbl.ncol))
  # en eventueel toegevoegde suffix aan bestaande kolommen weer verwijderen
  colnames(tbl) <- gsub("_tempbactlist", "", colnames(tbl))
  if (info == TRUE) {
    cat('\nDone.\nEUCAST Expert rules applied to',
        total_rows %>% unique() %>% length() %>% format(big.mark = ","),
        'different rows, to a total of',
        total %>% format(big.mark = ","), 'test results.\n\n')
  }
  tbl
 }
 #' @rdname EUCAST
 #' @export
 interpretive_reading <- function(...) {
  EUCAST_rules(...)
 }
 #' Poperties of a microorganism
 #'
 #' @param bactcode ID of a microorganisme, like \code{"STAAUR} and \code{"ESCCOL}
 #' @param property One of the values \code{bactid}, \code{bactsys}, \code{family}, \code{genus}, \code{species}, \code{subspecies}, \code{fullname}, \code{type}, \code{gramstain}, \code{aerobic}
 #' @export
 #' @importFrom dplyr %>% filter select
 #' @seealso \code{\link{bactlist}}
 mo_property <- function(bactcode, property = 'fullname') {
  mocode <- as.character(bactcode)
  for (i in 1:length(mocode)) {
    bug <- mocode[i]
    if (!is.na(bug)) {
      result = tryCatch({
        mocode[i] <-
          AMR::bactlist %>%
          filter(bactid == bactcode) %>%
          select(property) %>%
          unlist() %>%
          as.character()
      }, error = function(error_condition) {
        warning('Code ', bug, ' not found in bacteria list.')
      }, finally = {
        if (mocode[i] == bug & !property %in% c('bactid', 'bactsys')) {
          mocode[i] <- NA
        }
      })
    }
  }
  mocode
 }
@@ -1,226 +0,0 @@
 # ==================================================================== #
 # TITLE                                                                #
 # Antimicrobial Resistance (AMR) Analysis                              #
 #                                                                      #
 # AUTHORS                                                              #
 # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
 #                                                                      #
 # LICENCE                                                              #
 # This program is free software; you can redistribute it and/or modify #
 # it under the terms of the GNU General Public License version 2.0,    #
 # as published by the Free Software Foundation.                        #
 #                                                                      #
 # This program is distributed in the hope that it will be useful,      #
 # but WITHOUT ANY WARRANTY; without even the implied warranty of       #
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
 # GNU General Public License for more details.                         #
 # ==================================================================== #
 #' Properties of an ATC code
 #'
 #' Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \strong{This function requires an internet connection.}
 #' @param atc_code a character or character vector with ATC code(s) of antibiotic(s)
 #' @param property property of an ATC code. Valid values are \code{"ATC code"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"} en \code{"Note"}.
 #' @param administration type of administration, see \emph{Details}
 #' @param url url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.
 #' @details
 #' Abbreviations for the property \code{"Adm.R"} (parameter \code{administration}):
 #' \itemize{
 #'   \item{\code{"Implant"}}{ = Implant}
 #'   \item{\code{"Inhal"}}{ = Inhalation}
 #'   \item{\code{"Instill"}}{ = Instillation}
 #'   \item{\code{"N"}}{ = nasal}
 #'   \item{\code{"O"}}{ = oral}
 #'   \item{\code{"P"}}{ = parenteral}
 #'   \item{\code{"R"}}{ = rectal}
 #'   \item{\code{"SL"}}{ = sublingual/buccal}
 #'   \item{\code{"TD"}}{ = transdermal}
 #'   \item{\code{"V"}}{ = vaginal}
 #' }
 #'
 #' Abbreviations for the property \code{"U"} (unit):
 #' \itemize{
 #'   \item{\code{"g"}}{ = gram}
 #'   \item{\code{"mg"}}{ = milligram}
 #'   \item{\code{"mcg"}}{ = microgram}
 #'   \item{\code{"U"}}{ = unit}
 #'   \item{\code{"TU"}}{ = thousand units}
 #'   \item{\code{"MU"}}{ = million units}
 #'   \item{\code{"mmol"}}{ = millimole}
 #'   \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
 #' }
 #' @export
 #' @importFrom dplyr %>% progress_estimated
 #' @importFrom xml2 read_html
 #' @importFrom rvest html_nodes html_table
 #' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
 #' @examples 
 #' \donttest{
 #' atc_property("J01CA04", "DDD", "O") # oral DDD (Defined Daily Dose) of amoxicillin
 #' atc_property("J01CA04", "DDD", "P") # parenteral DDD (Defined Daily Dose) of amoxicillin
 #' }
 atc_property <- function(atc_code,
                         property,
                         administration = 'O',
                         url = 'https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no') {
  # property <- property %>% tolower()
  #
  if (property %like% 'unit') {
    property <- 'U'
  }
  # validation of properties
  valid_properties.bak <- c("ATC code", "Name", "DDD", "U", "Adm.R", "Note")
  valid_properties <- valid_properties.bak #%>% tolower()
  if (!property %in% valid_properties) {
    stop('Invalid `property`, use one of ', paste(valid_properties, collapse = ", "), '.')
  }
  returnvalue <- rep(NA_character_, length(atc_code))
  if (property == 'DDD') {
    returnvalue <- rep(NA_real_, length(atc_code))
  }
  progress <- progress_estimated(n = length(atc_code))
  for (i in 1:length(atc_code)) {
    progress$tick()$print()
    atc_url <- sub('%s', atc_code[i], url, fixed = TRUE)
    tbl <- xml2::read_html(atc_url) %>%
      rvest::html_nodes('table') %>%
      rvest::html_table(header = TRUE)
    if (length(tbl) == 0) {
      warning('ATC not found: ', atc_code[i], '. Please check ', atc_url, '.', call. = FALSE)
      returnvalue[i] <- NA
      next
    }
    tbl <- tbl[[1]]
    if (property == 'Name') {
      returnvalue[i] <- tbl[1, 2]
    } else {
      names(returnvalue)[i] <- tbl[1, 2] %>% as.character()
      if (!'Adm.R' %in% colnames(tbl) | is.na(tbl[1, 'Adm.R'])) {
        returnvalue[i] <- NA
        next
      } else {
        for (j in 1:nrow(tbl)) {
          if (tbl[j, 'Adm.R'] == administration) {
            returnvalue[i] <- tbl[j, property]
          }
        }
      }
    }
  }
  cat('\n')
  returnvalue
 }
 #' Name of an antibiotic
 #'
 #' Convert antibiotic codes (from a laboratory information system like MOLIS or GLIMS) to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{ablist}}.
 #' @param abcode a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}
 #' @param from,to type to transform from and to. See \code{\link{ablist}} for its column names.
 #' @param textbetween text to put between multiple returned texts
 #' @param tolower return output as lower case with function \code{\link{tolower}}.
 #' @keywords ab antibiotics
 #' @source \code{\link{ablist}}
 #' @export
 #' @importFrom dplyr %>% filter select slice 
 #' @examples
 #' abname("AMCL")
 #' # "amoxicillin and enzyme inhibitor"
 #'
 #' abname("AMCL+GENT")
 #' # "amoxicillin and enzyme inhibitor + gentamicin"
 #'
 #' abname(c("AMCL", "GENT"))
 #' # "amoxicillin and enzyme inhibitor" "gentamicin" 
 #'
 #' abname("AMCL", to = "trivial_nl")
 #' # "Amoxicilline/clavulaanzuur"
 #'
 #' abname("AMCL", to = "atc")
 #' # "J01CR02"
 #'
 #' abname("J01CR02", from = "atc", to = "umcg")
 #' # "AMCL"
 abname <- function(abcode, from = 'umcg', to = 'official', textbetween = ' + ', tolower = FALSE) {
  ablist <- AMR::ablist
  colnames(ablist) <- colnames(ablist) %>% tolower()
  from <- from %>% tolower()
  to <- to %>% tolower()
  if (!from %in% colnames(ablist) |
      !to %in% colnames(ablist)) {
    stop(paste0('Invalid `from` or `to`. Choose one of ', 
                colnames(ablist) %>% paste(collapse = ","), '.'), call. = FALSE)
  }
  abcode <- as.character(abcode)
  for (i in 1:length(abcode)) {
    drug <- abcode[i]
    if (!grepl('+', drug, fixed = TRUE) & !grepl(' en ', drug, fixed = TRUE)) {
      # bestaat maar uit 1 middel
      if (any(ablist[, from] == drug)) {
        abcode[i] <-
          ablist %>%
          filter(.[, from] == drug) %>%
          select(to) %>%
          slice(1) %>%
          as.character()
      } else {
        # niet gevonden
        warning('Code "', drug, '" not found in antibiotics list.', call. = FALSE)
        abcode[i] <- NA
      }
    } else {
      # meerdere middelen
      if (grepl('+', drug, fixed = TRUE)) {
        drug.group <-
          strsplit(drug, '+', fixed = TRUE) %>%
          unlist() %>%
          trimws('both')
      } else if (grepl(' en ', drug, fixed = TRUE)) {
        drug.group <-
          strsplit(drug, ' en ', fixed = TRUE) %>%
          unlist() %>%
          trimws('both')
      } else {
        warning('Invalid concat.')
        abcode[i] <- NA
        next
      }
      for (j in 1:length(drug.group)) {
        drug.group[j] <-
          ablist %>%
          filter(.[, from] == drug.group[j]) %>%
          select(to) %>%
          slice(1) %>%
          as.character()
        if (j > 1 & to %in% c('official', 'trivial_nl')) {
          drug.group[j] <- drug.group[j] %>% tolower()
        }
      }
      abcode[i] <- paste(drug.group, collapse = textbetween)
    }
  }
  if (tolower == TRUE) {
    abcode <- abcode %>% tolower()
  }
  abcode
 }
@@ -1,408 +0,0 @@
 # ==================================================================== #
 # TITLE                                                                #
 # Antimicrobial Resistance (AMR) Analysis                              #
 #                                                                      #
 # AUTHORS                                                              #
 # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
 #                                                                      #
 # LICENCE                                                              #
 # This program is free software; you can redistribute it and/or modify #
 # it under the terms of the GNU General Public License version 2.0,    #
 # as published by the Free Software Foundation.                        #
 #                                                                      #
 # This program is distributed in the hope that it will be useful,      #
 # but WITHOUT ANY WARRANTY; without even the implied warranty of       #
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
 # GNU General Public License for more details.                         #
 # ==================================================================== #
 #' Class 'rsi'
 #'
 #' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
 #' @rdname as.rsi
 #' @param x vector
 #' @return New class \code{rsi}
 #' @export
 #' @importFrom dplyr %>%
 #' @examples
 #' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
 #' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
 #' is.rsi(rsi_data)
 #' 
 #' plot(rsi_data)    # for percentages
 #' barplot(rsi_data) # for frequencies
 as.rsi <- function(x) {
  if (is.rsi(x)) {
    x
  } else {
    x <- x %>% unlist()
    x.bak <- x
    na_before <- x[is.na(x) | x == ''] %>% length()
    x <- gsub('[^RSI]+', '', x %>% toupper())
    # needed for UMCG in cases of "S;S" but also "S;I"; the latter will be NA:
    x <- gsub('^S+$', 'S', x)
    x <- gsub('^I+$', 'I', x)
    x <- gsub('^R+$', 'R', x)
    x[!x %in% c('S', 'I', 'R')] <- NA
    na_after <- x[is.na(x) | x == ''] %>% length()
    if (na_before != na_after) {
      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
        unique() %>%
        sort()
      list_missing <- paste0('"', list_missing , '"', collapse = ", ")
      warning(na_after - na_before, ' results truncated (',
              round(((na_after - na_before) / length(x)) / 100),
              '%) that were invalid antimicrobial interpretations: ',
              list_missing, call. = FALSE)
    }
    x <- x %>% toupper() %>% factor(levels = c("S", "I", "R"), ordered = TRUE)
    class(x) <- c('rsi', 'ordered', 'factor')
    x
  }
 }
 #' @rdname as.rsi
 #' @export
 #' @importFrom dplyr %>%
 is.rsi <- function(x) {
  class(x) %>% identical(c('rsi', 'ordered', 'factor'))
 }
 #' @exportMethod print.rsi
 #' @export
 #' @importFrom dplyr %>%
 #' @noRd
 print.rsi <- function(x, ...) {
  n_total <- x %>% length()
  x <- x[!is.na(x)]
  n <- x %>% length()
  S <- x[x == 'S'] %>% length()
  I <- x[x == 'I'] %>% length()
  R <- x[x == 'R'] %>% length()
  IR <- x[x %in% c('I', 'R')] %>% length()
  cat("Class 'rsi': ", n, " isolates\n", sep = '')
  cat('\n')
  cat('<NA>:      ', n_total - n, '\n')
  cat('Sum of S:  ', S, '\n')
  cat('Sum of IR: ', IR, '\n')
  cat('- Sum of R:', R, '\n')
  cat('- Sum of I:', I, '\n')
  cat('\n')
  print(c(
    `%S` = round((S / n) * 100, 1),
    `%IR` = round((IR / n) * 100, 1),
    `%I` = round((I / n) * 100, 1),
    `%R` = round((R / n) * 100, 1)
  ))
 }
 #' @exportMethod summary.rsi
 #' @export
 #' @importFrom dplyr %>%
 #' @noRd
 summary.rsi <- function(object, ...) {
  x <- object
  n_total <- x %>% length()
  x <- x[!is.na(x)]
  n <- x %>% length()
  S <- x[x == 'S'] %>% length()
  I <- x[x == 'I'] %>% length()
  R <- x[x == 'R'] %>% length()
  IR <- x[x %in% c('I', 'R')] %>% length()
  lst <- c('rsi', n_total - n, S, IR, R, I)
  names(lst) <- c("Mode", "<NA>", "Sum S", "Sum IR", "Sum R", "Sum I")
  lst
 }
 #' @exportMethod plot.rsi
 #' @export
 #' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
 #' @importFrom graphics plot text
 #' @noRd
 plot.rsi <- function(x, ...) {
  x_name <- deparse(substitute(x))
  data <- data.frame(x = x,
                     y = 1,
                     stringsAsFactors = TRUE) %>%
    group_by(x) %>%
    summarise(n = sum(y)) %>%
    filter(!is.na(x)) %>%
    mutate(s = round((n / sum(n)) * 100, 1))
  data$x <- factor(data$x, levels = c('S', 'I', 'R'), ordered = TRUE)
  ymax <- if_else(max(data$s) > 95, 105, 100)
  plot(x = data$x,
       y = data$s,
       lwd = 2,
       col = c('green', 'orange', 'red'),
       ylim = c(0, ymax),
       ylab = 'Percentage',
       xlab = 'Antimicrobial Interpretation',
       main = paste('Susceptibilty Analysis of', x_name),
       axes = FALSE,
       ...)
  # x axis
  axis(side = 1, at = 1:n_distinct(data$x), labels = levels(data$x), lwd = 0)
  # y axis, 0-100%
  axis(side = 2, at = seq(0, 100, 5))
  text(x = data$x,
       y = data$s + 4,
       labels = paste0(data$s, '% (n = ', data$n, ')'))
 }
 #' @exportMethod barplot.rsi
 #' @export
 #' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
 #' @importFrom graphics barplot axis
 #' @noRd
 barplot.rsi <- function(height, ...) {
  x <- height
  x_name <- deparse(substitute(height))
  data <- data.frame(rsi = x, cnt = 1) %>%
    group_by(rsi) %>%
    summarise(cnt = sum(cnt)) %>%
    droplevels()
  barplot(table(x),
          col = c('green3', 'orange2', 'red3'),
          xlab = 'Antimicrobial Interpretation',
          main = paste('Susceptibilty Analysis of', x_name),
          ylab = 'Frequency',
          axes = FALSE,
          ...)
  # y axis, 0-100%
  axis(side = 2, at = seq(0, max(data$cnt) + max(data$cnt) * 1.1, by = 25))
 }
 #' Class 'mic'
 #'
 #' This transforms a vector to a new class\code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
 #' @rdname as.mic
 #' @param x vector
 #' @param na.rm a logical indicating whether missing values should be removed
 #' @return New class \code{mic}
 #' @export
 #' @importFrom dplyr %>%
 #' @examples
 #' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
 #' is.mic(mic_data)
 #' 
 #' plot(mic_data)
 #' barplot(mic_data)
 as.mic <- function(x, na.rm = FALSE) {
  if (is.mic(x)) {
    x
  } else {
    x <- x %>% unlist()
    if (na.rm == TRUE) {
      x <- x[!is.na(x)]
    }
    x.bak <- x
    # comma to dot
    x <- gsub(',', '.', x, fixed = TRUE)
    # starting dots must start with 0
    x <- gsub('^[.]', '0.', x)
    # <=0.2560.512 should be 0.512
    x <- gsub('.*[.].*[.]', '0.', x)
    # remove ending .0
    x <- gsub('[.]0$', '', x)
    # remove all after last digit
    x <- gsub('[^0-9]$', '', x)
    # remove last zeroes
    x <- gsub('[.]?0+$', '', x)
    lvls <- c("<0.002", "<=0.002", "0.002", ">=0.002", ">0.002",
              "<0.003", "<=0.003", "0.003", ">=0.003", ">0.003",
              "<0.004", "<=0.004", "0.004", ">=0.004", ">0.004",
              "<0.006", "<=0.006", "0.006", ">=0.006", ">0.006",
              "<0.008", "<=0.008", "0.008", ">=0.008", ">0.008",
              "<0.012", "<=0.012", "0.012", ">=0.012", ">0.012",
              "<0.016", "<=0.016", "0.016", ">=0.016", ">0.016",
              "<0.023", "<=0.023", "0.023", ">=0.023", ">0.023",
              "<0.025", "<=0.025", "0.025", ">=0.025", ">0.025",
              "<0.03", "<=0.03", "0.03", ">=0.03", ">0.03",
              "<0.032", "<=0.032", "0.032", ">=0.032", ">0.032",
              "<0.047", "<=0.047", "0.047", ">=0.047", ">0.047",
              "<0.05", "<=0.05", "0.05", ">=0.05", ">0.05",
              "<0.06", "<=0.06", "0.06", ">=0.06", ">0.06",
              "<0.0625", "<=0.0625", "0.0625", ">=0.0625", ">0.0625",
              "<0.063", "<=0.063", "0.063", ">=0.063", ">0.063",
              "<0.064", "<=0.064", "0.064", ">=0.064", ">0.064",
              "<0.09", "<=0.09", "0.09", ">=0.09", ">0.09",
              "<0.094", "<=0.094", "0.094", ">=0.094", ">0.094",
              "<0.12", "<=0.12", "0.12", ">=0.12", ">0.12",
              "<0.125", "<=0.125", "0.125", ">=0.125", ">0.125",
              "<0.128", "<=0.128", "0.128", ">=0.128", ">0.128",
              "<0.16", "<=0.16", "0.16", ">=0.16", ">0.16",
              "<0.19", "<=0.19", "0.19", ">=0.19", ">0.19",
              "<0.25", "<=0.25", "0.25", ">=0.25", ">0.25",
              "<0.256", "<=0.256", "0.256", ">=0.256", ">0.256",
              "<0.32", "<=0.32", "0.32", ">=0.32", ">0.32",
              "<0.38", "<=0.38", "0.38", ">=0.38", ">0.38",
              "<0.5", "<=0.5", "0.5", ">=0.5", ">0.5",
              "<0.512", "<=0.512", "0.512", ">=0.512", ">0.512",
              "<0.64", "<=0.64", "0.64", ">=0.64", ">0.64",
              "<0.75", "<=0.75", "0.75", ">=0.75", ">0.75",
              "<1", "<=1", "1", ">=1", ">1",
              "<1.5", "<=1.5", "1.5", ">=1.5", ">1.5",
              "<2", "<=2", "2", ">=2", ">2",
              "<3", "<=3", "3", ">=3", ">3",
              "<4", "<=4", "4", ">=4", ">4",
              "<6", "<=6", "6", ">=6", ">6",
              "<8", "<=8", "8", ">=8", ">8",
              "<10", "<=10", "10", ">=10", ">10",
              "<12", "<=12", "12", ">=12", ">12",
              "<16", "<=16", "16", ">=16", ">16",
              "<20", "<=20", "20", ">=20", ">20",
              "<24", "<=24", "24", ">=24", ">24",
              "<32", "<=32", "32", ">=32", ">32",
              "<40", "<=40", "40", ">=40", ">40",
              "<48", "<=48", "48", ">=48", ">48",
              "<64", "<=64", "64", ">=64", ">64",
              "<80", "<=80", "80", ">=80", ">80",
              "<96", "<=96", "96", ">=96", ">96",
              "<128", "<=128", "128", ">=128", ">128",
              "<160", "<=160", "160", ">=160", ">160",
              "<256", "<=256", "256", ">=256", ">256",
              "<320", "<=320", "320", ">=320", ">320",
              "<512", "<=512", "512", ">=512", ">512",
              "<1024", "<=1024", "1024", ">=1024", ">1024")
    x <- x %>% as.character()
    na_before <- x[is.na(x) | x == ''] %>% length()
    x[!x %in% lvls] <- NA
    na_after <- x[is.na(x) | x == ''] %>% length()
    if (na_before != na_after) {
      list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
        unique() %>%
        sort()
      list_missing <- paste0('"', list_missing , '"', collapse = ", ")
      warning(na_after - na_before, ' results truncated (',
              round(((na_after - na_before) / length(x)) / 100),
              '%) that were invalid MICs: ',
              list_missing, call. = FALSE)
    }
    x <- factor(x = x,
                levels = lvls,
                ordered = TRUE)
    class(x) <- c('mic', 'ordered', 'factor')
    x
  }
 }
 #' @rdname as.mic
 #' @export
 #' @importFrom dplyr %>%
 is.mic <- function(x) {
  class(x) %>% identical(c('mic', 'ordered', 'factor'))
 }
 #' @exportMethod as.double.mic
 #' @export
 #' @noRd
 as.double.mic <- function(x, ...) {
  as.double(gsub('(<|=|>)+', '', as.character(x)))
 }
 #' @exportMethod as.integer.mic
 #' @export
 #' @noRd
 as.integer.mic <- function(x, ...) {
  as.integer(gsub('(<|=|>)+', '', as.character(x)))
 }
 #' @exportMethod as.numeric.mic
 #' @export
 #' @noRd
 as.numeric.mic <- function(x, ...) {
  as.numeric(gsub('(<|=|>)+', '', as.character(x)))
 }
 #' @exportMethod print.mic
 #' @export
 #' @importFrom dplyr %>% tibble group_by summarise pull
 #' @noRd
 print.mic <- function(x, ...) {
  n_total <- x %>% length()
  x <- x[!is.na(x)]
  n <- x %>% length()
  cat("Class 'mic': ", n, " isolates\n", sep = '')
  cat('\n')
  cat('<NA> ', n_total - n, '\n')
  cat('\n')
  tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
  cnt <- tbl %>% pull(y)
  names(cnt) <- tbl %>% pull(x)
  print(cnt)
 }
 #' @exportMethod summary.mic
 #' @export
 #' @importFrom dplyr %>% tibble group_by summarise pull
 #' @noRd
 summary.mic <- function(object, ...) {
  x <- object
  n_total <- x %>% length()
  x <- x[!is.na(x)]
  n <- x %>% length()
  return(c("Mode" = 'mic',
           "NA" = n_total - n,
           "Min." = sort(x)[1] %>% as.character(),
           "Max." = sort(x)[n] %>% as.character()
  ))
  cat("Class 'mic': ", n, " isolates\n", sep = '')
  cat('\n')
  cat('<NA> ', n_total - n, '\n')
  cat('\n')
  tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
  cnt <- tbl %>% pull(y)
  names(cnt) <- tbl %>% pull(x)
  print(cnt)
 }
 #' @exportMethod plot.mic
 #' @export
 #' @importFrom dplyr %>% group_by summarise
 #' @importFrom graphics plot text
 #' @noRd
 plot.mic <- function(x, ...) {
  x_name <- deparse(substitute(x))
  create_barplot_mic(x, x_name, ...)
 }
 #' @exportMethod barplot.mic
 #' @export
 #' @importFrom dplyr %>% group_by summarise
 #' @importFrom graphics barplot axis
 #' @noRd
 barplot.mic <- function(height, ...) {
  x_name <- deparse(substitute(height))
  create_barplot_mic(height, x_name, ...)
 }
 #' @importFrom graphics barplot axis
 create_barplot_mic <- function(x, x_name, ...) {
  data <- data.frame(mic = x, cnt = 1) %>%
    group_by(mic) %>%
    summarise(cnt = sum(cnt)) %>%
    droplevels()
  barplot(table(droplevels(x)),
          ylab = 'Frequency',
          xlab = 'MIC value',
          main = paste('MIC values of', x_name), 
          axes = FALSE,
          ...)
  axis(2, seq(0, max(data$cnt)))
 }
@@ -1,96 +0,0 @@
 # ==================================================================== #
 # TITLE                                                                #
 # Antimicrobial Resistance (AMR) Analysis                              #
 #                                                                      #
 # AUTHORS                                                              #
 # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
 #                                                                      #
 # LICENCE                                                              #
 # This program is free software; you can redistribute it and/or modify #
 # it under the terms of the GNU General Public License version 2.0,    #
 # as published by the Free Software Foundation.                        #
 #                                                                      #
 # This program is distributed in the hope that it will be useful,      #
 # but WITHOUT ANY WARRANTY; without even the implied warranty of       #
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
 # GNU General Public License for more details.                         #
 # ==================================================================== #
 #' Dataset with 420 antibiotics
 #'
 #' A dataset containing all antibiotics with a J0 code, with their DDD's.
 #' @format A data.frame with 420 observations and 12 variables:
 #' \describe{
 #'   \item{\code{atc}}{ATC code, like \code{J01CR02}}
 #'   \item{\code{molis}}{MOLIS code, like \code{amcl}}
 #'   \item{\code{umcg}}{UMCG code, like \code{AMCL}}
 #'   \item{\code{official}}{Official name by the WHO, like \code{"amoxicillin and enzyme inhibitor"}}
 #'   \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
 #'   \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
 #'   \item{\code{oral_ddd}}{Defined Daily Dose (DDD) according to the WHO, oral treatment}
 #'   \item{\code{oral_units}}{Units of \code{ddd_units}}
 #'   \item{\code{iv_ddd}}{Defined Daily Dose (DDD) according to the WHO, parenteral treatment}
 #'   \item{\code{iv_units}}{Units of \code{iv_ddd}}
 #'   \item{\code{atc_group1}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
 #'   \item{\code{atc_group2}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
 #' }
 #' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl} \cr \cr World Health Organization - \url{https://www.whocc.no/atc_ddd_index/}
 #' @seealso \code{\link{bactlist}}
 # todo:
 # ablist <- ablist %>% mutate(useful_gramnegative = if_else(atc_group2 == 'Tetracyclines', FALSE, TRUE))
 # ablist <- ablist %>% mutate(useful_gramnegative = if_else(atc_group2 %like% 'Glycopept', FALSE, useful_gramnegative))
 # Tbl1 Enterobacteriaceae are also intrinsically resistant to benzylpenicillin, glycopeptides, fusidic acid, macrolides (with some exceptions1), lincosamides, streptogramins, rifampicin, daptomycin and linezolid.
 # Tbl2 Non-fermentative Gram-negative bacteria are also generally intrinsically resistant to benzylpenicillin, first and second generation cephalosporins, glycopeptides, fusidic acid, macrolides, lincosamides, streptogramins, rifampicin, daptomycin and linezolid
 # Tbl3 Gram-negative bacteria other than Enterobacteriaceae and non-fermentative Gram-negative bacteria listed are also intrinsically resistant to glycopeptides, lincosamides, daptomycin and linezolid.
 "ablist"
 #' Dataset with ~2500 microorganisms
 #'
 #' A dataset containing all microorganisms of MOLIS. MO codes of the UMCG can be looked up using \code{\link{bactlist.umcg}}.
 #' @format A data.frame with 2507 observations and 10 variables:
 #' \describe{
 #'   \item{\code{bactid}}{ID of microorganism}
 #'   \item{\code{bactsys}}{Bactsyscode of microorganism}
 #'   \item{\code{family}}{Family name of microorganism}
 #'   \item{\code{genus}}{Genus name of microorganism, like \code{"Echerichia"}}
 #'   \item{\code{species}}{Species name of microorganism, like \code{"coli"}}
 #'   \item{\code{subspecies}}{Subspecies name of bio-/serovar of microorganism, like \code{"EHEC"}}
 #'   \item{\code{fullname}}{Full name, like \code{"Echerichia coli (EHEC)"}}
 #'   \item{\code{type}}{Type of microorganism in Dutch, like \code{"Bacterie"} and \code{"Schimmel/gist"}}
 #'   \item{\code{gramstain}}{Gram of microorganism in Dutch, like \code{"Negatieve staven"}}
 #'   \item{\code{aerobic}}{Type aerobe/anaerobe of bacteria}
 #' }
 #' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
 #' @seealso \code{\link{ablist}} \code{\link{bactlist.umcg}}
 "bactlist"
 #' Translation table for UMCG with ~1100 microorganisms
 #'
 #' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{bactlist}$bactid}, using \code{\link{left_join_bactlist}}.
 #' @format A data.frame with 1090 observations and 2 variables:
 #' \describe{
 #'   \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
 #'   \item{\code{bactid}}{Code of microorganism in \code{\link{bactlist}}}
 #' }
 #' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
 #' @seealso \code{\link{bactlist}}
 "bactlist.umcg"
 #' Dataset with 2000 blood culture isolates of septic patients
 #'
 #' An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis e.g. with \code{\link{rsi}} or \code{\link{rsi_predict}}, or it can be used to practice other statistics.
 #' @format A data.frame with 2000 observations and 47 variables:
 #' \describe{
 #'   \item{\code{date}}{date of receipt at the laboratory}
 #'   \item{\code{hospital_id}}{ID of the hospital}
 #'   \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
 #'   \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
 #'   \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
 #'   \item{\code{age}}{age of the patient}
 #'   \item{\code{sex}}{sex of the patient}
 #'   \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
 #'   \item{\code{bactid}}{ID of microorganism, see \code{\link{bactlist}}}
 #'   \item{\code{peni:mupi}}{38 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}), these column names occur in \code{\link{ablist}} and can be translated with \code{\link{abname}}}
 #' }
 #' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
 "septic_patients"
@@ -1,504 +0,0 @@
 # ==================================================================== #
 # TITLE                                                                #
 # Antimicrobial Resistance (AMR) Analysis                              #
 #                                                                      #
 # AUTHORS                                                              #
 # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
 #                                                                      #
 # LICENCE                                                              #
 # This program is free software; you can redistribute it and/or modify #
 # it under the terms of the GNU General Public License version 2.0,    #
 # as published by the Free Software Foundation.                        #
 #                                                                      #
 # This program is distributed in the hope that it will be useful,      #
 # but WITHOUT ANY WARRANTY; without even the implied warranty of       #
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
 # GNU General Public License for more details.                         #
 # ==================================================================== #
 #' Determine first (weighted) isolates
 #'
 #' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
 #' @param tbl a \code{data.frame} containing isolates.
 #' @param col_date column name of the result date (or date that is was received on the lab)
 #' @param col_patient_id column name of the unique IDs of the patients
 #' @param col_genus column name of the genus of the microorganisms
 #' @param col_species column name of the species of the microorganisms
 #' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.
 #' @param col_specimen column name of the specimen type or group
 #' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
 #' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}.
 #' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
 #' @param testcodes_exclude character vector with test codes that should be excluded (caseINsensitive)
 #' @param icu_exclude logical whether ICU isolates should be excluded
 #' @param filter_specimen specimen group or type that should be excluded
 #' @param output_logical return output as \code{logical} (will else the values \code{0} or \code{1})
 #' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate, see Details
 #' @param info print progress
 #' @details \strong{Why this is so important} \cr
 #'     To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. 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 week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
 #'
 #'     \strong{Using parameter \code{points_threshold}} \cr
 #'     To compare key antibiotics, the difference between antimicrobial interpretations will be measured. A difference from I to S|R (or vice versa) means 0.5 points. A difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
 #' @keywords isolate isolates first
 #' @export
 #' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
 #' @return A vector to add to table, see Examples.
 #' @examples
 #' \dontrun{
 #'
 #' # set key antibiotics to a new variable
 #' tbl$keyab <- key_antibiotics(tbl)
 #'
 #' tbl$first_isolate <-
 #'   first_isolate(tbl)
 #'
 #' tbl$first_isolate_weighed <-
 #'   first_isolate(tbl,
 #'                 col_keyantibiotics = 'keyab')
 #'
 #' tbl$first_blood_isolate <-
 #'   first_isolate(tbl,
 #'                 filter_specimen = 'Blood')
 #'
 #' tbl$first_blood_isolate_weighed <-
 #'   first_isolate(tbl,
 #'                 filter_specimen = 'Blood',
 #'                 col_keyantibiotics = 'keyab')
 #'
 #' tbl$first_urine_isolate <-
 #'   first_isolate(tbl,
 #'                 filter_specimen = 'Urine')
 #'
 #' tbl$first_urine_isolate_weighed <-
 #'   first_isolate(tbl,
 #'                 filter_specimen = 'Urine',
 #'                 col_keyantibiotics = 'keyab')
 #'
 #' tbl$first_resp_isolate <-
 #'   first_isolate(tbl,
 #'                 filter_specimen = 'Respiratory')
 #'
 #' tbl$first_resp_isolate_weighed <-
 #'   first_isolate(tbl,
 #'                 filter_specimen = 'Respiratory',
 #'                 col_keyantibiotics = 'keyab')
 #' }
 first_isolate <- function(tbl,
                          col_date,
                          col_patient_id,
                          col_genus,
                          col_species,
                          col_testcode = NA,
                          col_specimen,
                          col_icu,
                          col_keyantibiotics = NA,
                          episode_days = 365,
                          testcodes_exclude = '',
                          icu_exclude = FALSE,
                          filter_specimen = NA,
                          output_logical = TRUE,
                          points_threshold = 2,
                          info = TRUE) {
  # controleren of kolommen wel bestaan
  check_columns_existance <- function(column, tblname = tbl) {
    if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
      stop('Please check tbl for existance.')
    }
    if (!is.na(column)) {
      if (!(column %in% colnames(tblname))) {
        stop('Column ', column, ' not found.')
      }
    }
  }
  check_columns_existance(col_date)
  check_columns_existance(col_patient_id)
  check_columns_existance(col_genus)
  check_columns_existance(col_species)
  check_columns_existance(col_testcode)
  check_columns_existance(col_icu)
  check_columns_existance(col_keyantibiotics)
  if (is.na(col_testcode)) {
    testcodes_exclude <- NA
  }
  # testcodes verwijderen die ingevuld zijn
  if (!is.na(testcodes_exclude[1]) & testcodes_exclude[1] != '' & info == TRUE) {
    cat('Isolates from these test codes will be ignored:\n', toString(testcodes_exclude), '\n')
  }
  if (is.na(col_icu)) {
    icu_exclude <- FALSE
  } else {
    tbl <- tbl %>%
      mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
  }
  specgroup.notice <- ''
  weighted.notice <- ''
  # filteren op materiaalgroep en sleutelantibiotica gebruiken wanneer deze ingevuld zijn
  if (!is.na(filter_specimen) & filter_specimen != '') {
    check_columns_existance(col_specimen, tbl)
    if (info == TRUE) {
      cat('Isolates other than of specimen group \'', filter_specimen, '\' will be ignored. ', sep = '')
    }
  } else {
    filter_specimen <- ''
  }
  if (col_keyantibiotics %in% c(NA, '')) {
    col_keyantibiotics <- ''
  } else {
    tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics))
  }
  if (is.na(testcodes_exclude[1])) {
    testcodes_exclude <- ''
  }
  # nieuwe dataframe maken met de oorspronkelijke rij-index, 0-bepaling en juiste sortering
  #cat('Sorting table...')
  tbl <- tbl %>%
    mutate(first_isolate_row_index = 1:nrow(tbl),
           eersteisolaatbepaling = 0,
           date_lab = tbl %>% pull(col_date),
           patient_id = tbl %>% pull(col_patient_id),
           species = tbl %>% pull(col_species),
           genus = tbl %>% pull(col_genus)) %>%
    mutate(species = if_else(is.na(species), '', species),
           genus = if_else(is.na(genus), '', genus))
  if (filter_specimen == '') {
    if (icu_exclude == FALSE) {
      if (info == TRUE) {
        cat('Isolates from ICU will *NOT* be ignored.\n')
      }
      tbl <- tbl %>%
        arrange_at(c(col_patient_id,
                     col_genus,
                     col_species,
                     col_date))
      row.start <- 1
      row.end <- nrow(tbl)
    } else {
      if (info == TRUE) {
        cat('Isolates from ICU will be ignored.\n')
      }
      tbl <- tbl %>%
        arrange_at(c(col_icu,
                     col_patient_id,
                     col_genus,
                     col_species,
                     col_date))
      suppressWarnings(
        row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
      )
      suppressWarnings(
        row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
      )
    }
  } else {
    # sorteren op materiaal en alleen die rijen analyseren om tijd te besparen
    if (icu_exclude == FALSE) {
      if (info == TRUE) {
        cat('Isolates from ICU will *NOT* be ignored.\n')
      }
      tbl <- tbl %>%
        arrange_at(c(col_specimen,
                     col_patient_id,
                     col_genus,
                     col_species,
                     col_date))
      suppressWarnings(
        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% min(na.rm = TRUE)
      )
      suppressWarnings(
        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% max(na.rm = TRUE)
      )
    } else {
      if (info == TRUE) {
        cat('Isolates from ICU will be ignored.\n')
      }
      tbl <- tbl %>%
        arrange_at(c(col_icu,
                     col_specimen,
                     col_patient_id,
                     col_genus,
                     col_species,
                     col_date))
      suppressWarnings(
        row.start <- which(tbl %>% pull(col_specimen) == filter_specimen
                           & tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
      )
      suppressWarnings(
        row.end <- which(tbl %>% pull(col_specimen) == filter_specimen
                         & tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
      )
    }
  }
  if (abs(row.start) == Inf | abs(row.end) == Inf) {
    if (info == TRUE) {
      cat('No isolates found.\n')
    }
    # NA's maken waar genus niet beschikbaar is
    tbl <- tbl %>%
      mutate(real_first_isolate = if_else(genus == '', NA, FALSE))
    if (output_logical == FALSE) {
      tbl$real_first_isolate <- tbl %>% pull(real_first_isolate) %>% as.integer()
    }
    return(tbl %>% pull(real_first_isolate))
  }
  scope.size <- tbl %>%
    filter(row_number() %>%
             between(row.start,
                     row.end),
           genus != '') %>%
    nrow()
  # Analyse van eerste isolaat ----
  all_first <- tbl %>%
    mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
                                     & genus == lag(genus)
                                     & species == lag(species),
                                     FALSE,
                                     TRUE),
           days_diff = 0) %>%
    mutate(days_diff = if_else(other_pat_or_mo == FALSE,
                               (date_lab - lag(date_lab)) + lag(days_diff),
                               0))
  if (col_keyantibiotics != '') {
    if (info == TRUE) {
      cat(paste0('Comparing key antibiotics for first weighted isolates (using points threshold of '
                 , points_threshold, ')...\n'))
    }
    all_first <- all_first %>%
      mutate(key_ab_lag = lag(key_ab)) %>%
      mutate(key_ab_other = !key_antibiotics_equal(x = key_ab,
                                                   y = key_ab_lag,
                                                   points_threshold = points_threshold,
                                                   info = info)) %>%
      mutate(
        real_first_isolate =
          if_else(
            between(row_number(), row.start, row.end)
            & genus != ''
            & (other_pat_or_mo
               | days_diff >= episode_days
               | key_ab_other),
            TRUE,
            FALSE))
    if (info == TRUE) {
      cat('\n')
    }
  } else {
    all_first <- all_first %>%
      mutate(
        real_first_isolate =
          if_else(
            between(row_number(), row.start, row.end)
            & genus != ''
            & (other_pat_or_mo
               | days_diff >= episode_days),
            TRUE,
            FALSE))
  }
  # allereerst isolaat als TRUE
  all_first[row.start, 'real_first_isolate'] <- TRUE
  # geen testen die uitgesloten moeten worden, of ICU
  if (!is.na(col_testcode)) {
    all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE
  }
  if (icu_exclude == TRUE) {
    all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
  }
  # NA's maken waar genus niet beschikbaar is
  all_first <- all_first %>%
    mutate(real_first_isolate = if_else(genus == '', NA, real_first_isolate))
  all_first <- all_first %>%
    arrange(first_isolate_row_index) %>%
    pull(real_first_isolate)
  if (info == TRUE) {
    cat(paste0('\nFound ',
               all_first %>% sum(na.rm = TRUE),
               ' first ', weighted.notice, 'isolates (',
               (all_first %>% sum(na.rm = TRUE) / scope.size) %>% percent(),
               ' of isolates in scope [where genus was not empty] and ',
               (all_first %>% sum(na.rm = TRUE) / tbl %>% nrow()) %>% percent(),
               ' of total)\n'))
  }
  if (output_logical == FALSE) {
    all_first <- all_first %>% as.integer()
  }
  all_first
 }
 #' Key antibiotics based on bacteria ID
 #'
 #' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
 #' @param col_bactcode column of bacteria IDs in \code{tbl}; these should occur in \code{bactlist$bactid}, see \code{\link{bactlist}}
 #' @param info print warnings
 #' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics.
 #' @export
 #' @importFrom dplyr %>% mutate if_else 
 #' @return Character of length 1.
 #' @seealso \code{\link{mo_property}} \code{\link{ablist}}
 #' @examples 
 #' \donttest{
 #' #' # set key antibiotics to a new variable
 #' tbl$keyab <- key_antibiotics(tbl)
 #' }
 key_antibiotics <- function(tbl,
                            col_bactcode = 'bactid',
                            info = TRUE,
                            amcl = 'amcl',
                            amox = 'amox',
                            cfot = 'cfot',
                            cfta = 'cfta',
                            cftr = 'cftr',
                            cfur = 'cfur',
                            cipr = 'cipr',
                            clar = 'clar',
                            clin = 'clin',
                            clox = 'clox',
                            doxy = 'doxy',
                            gent = 'gent',
                            line = 'line',
                            mero = 'mero',
                            peni = 'peni',
                            pita = 'pita',
                            rifa = 'rifa',
                            teic = 'teic',
                            trsu = 'trsu',
                            vanc = 'vanc') {
  keylist <- character(length = nrow(tbl))
  # check columns
  col.list <- c(amox, cfot, cfta, cftr, cfur, cipr, clar,
                clin, clox, doxy, gent, line, mero, peni,
                pita, rifa, teic, trsu, vanc)
  col.list <- col.list[!is.na(col.list)]
  if (!all(col.list %in% colnames(tbl))) {
    if (info == TRUE) {
      warning('These columns do not exist and will be ignored:\n',
              col.list[!(col.list %in% colnames(tbl))] %>% toString(),
              immediate. = TRUE,
              call. = FALSE)
    }
  }
  # bactlist aan vastknopen
  tbl <- tbl %>% left_join_bactlist(col_bactcode)
  tbl$key_ab <- NA_character_
  # Staphylococcus
  list_ab <- c(clox, trsu, teic, vanc, doxy, line, clar, rifa)
  list_ab <- list_ab[list_ab %in% colnames(tbl)]
  tbl <- tbl %>% mutate(key_ab =
                          if_else(genus == 'Staphylococcus',
                                  apply(X = tbl[, list_ab],
                                        MARGIN = 1,
                                        FUN = function(x) paste(x, collapse = "")),
                                  key_ab))
  # Rest of Gram +
  list_ab <- c(peni, amox, teic, vanc, clin, line, clar, trsu)
  list_ab <- list_ab[list_ab %in% colnames(tbl)]
  tbl <- tbl %>% mutate(key_ab =
                          if_else(gramstain %like% '^Positi[e]?ve',
                                  apply(X = tbl[, list_ab],
                                        MARGIN = 1,
                                        FUN = function(x) paste(x, collapse = "")),
                                  key_ab))
  # Gram -
  list_ab <- c(amox, amcl, pita, cfur, cfot, cfta, cftr, mero, cipr, trsu, gent)
  list_ab <- list_ab[list_ab %in% colnames(tbl)]
  tbl <- tbl %>% mutate(key_ab =
                          if_else(gramstain %like% '^Negati[e]?ve',
                                  apply(X = tbl[, list_ab],
                                        MARGIN = 1,
                                        FUN = function(x) paste(x, collapse = "")),
                                  key_ab))
  # format
  tbl <- tbl %>%
    mutate(key_ab = gsub('(NA|NULL)', '-', key_ab) %>% toupper())
  tbl$key_ab
 }
 #' @importFrom dplyr progress_estimated %>%
 #' @noRd
 key_antibiotics_equal <- function(x, y, points_threshold = 2, info = FALSE) {
  # x is active row, y is lag
  if (length(x) != length(y)) {
    stop('Length of `x` and `y` must be equal.')
  }
  result <- logical(length(x))
  if (info == TRUE) {
    p <- dplyr::progress_estimated(length(x))
  }
  for (i in 1:length(x)) {
    if (info == TRUE) {
      p$tick()$print()
    }
    if (is.na(x[i])) {
      x[i] <- ''
    }
    if (is.na(y[i])) {
      y[i] <- ''
    }
    if (nchar(x[i]) != nchar(y[i])) {
      result[i] <- FALSE
    } else if (x[i] == '' & y[i] == '') {
      result[i] <- TRUE
    } else {
      # count points for every single character:
      # - no change is 0 points
      # - I <-> S|R is 0.5 point
      # - S|R <-> R|S is 1 point
      # use the levels of as.rsi (S = 1, I = 2, R = 3)
      x2 <- strsplit(x[i], "")[[1]] %>% as.rsi() %>% as.double()
      y2 <- strsplit(y[i], "")[[1]] %>% as.rsi() %>% as.double()
      points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
      result[i] <- ((points / 2) >= points_threshold)
    }
  }
  if (info == TRUE) {
    cat('\n')
  }
  result
 }
@@ -1,37 +0,0 @@
 # ==================================================================== #
 # TITLE                                                                #
 # Antimicrobial Resistance (AMR) Analysis                              #
 #                                                                      #
 # AUTHORS                                                              #
 # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
 #                                                                      #
 # LICENCE                                                              #
 # This program is free software; you can redistribute it and/or modify #
 # it under the terms of the GNU General Public License version 2.0,    #
 # as published by the Free Software Foundation.                        #
 #                                                                      #
 # This program is distributed in the hope that it will be useful,      #
 # but WITHOUT ANY WARRANTY; without even the implied warranty of       #
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
 # GNU General Public License for more details.                         #
 # ==================================================================== #
 globalVariables(c('.',
                  'abname',
                  'bactid',
                  'cnt',
                  'date_lab',
                  'days_diff',
                  'first_isolate_row_index',
                  'genus',
                  'gramstain',
                  'key_ab',
                  'key_ab_lag',
                  'key_ab_other',
                  'mic',
                  'n',
                  'other_pat_or_mo',
                  'patient_id',
                  'real_first_isolate',
                  'species',
                  'y'))
@@ -1,109 +0,0 @@
 #' Join a table with \code{bactlist}
 #'
 #' Join the list of microorganisms \code{\link{bactlist}} easily to an existing table.
 #' @rdname join
 #' @name join
 #' @aliases join inner_join
 #' @param x existing table to join
 #' @param by a variable to join by - could be a column name of \code{x} with values that exist in \code{bactlist$bactid} (like \code{by = "bacteria_id"}), or another column in \code{\link{bactlist}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
 #' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.
 #' @details As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
 #' @export
 #' @examples 
 #' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
 #'                             to = as.Date("2018-01-07"),
 #'                             by = 1),
 #'                  bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
 #'                                  "ESCCOL", "ESCCOL", "ESCCOL"),
 #'                  stringsAsFactors = FALSE)
 #'                  
 #' colnames(df)
 #' df2 <- left_join_bactlist(df, "bacteria_id")
 #' colnames(df2)
 inner_join_bactlist <- function(x, by = 'bactid', ...) {
  # no name set to `by` parameter
  if (is.null(names(by))) {
    joinby <- colnames(AMR::bactlist)[1]
    names(joinby) <- by
  } else {
    joinby <- by
  }
  join <- dplyr::inner_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
  if (nrow(join) > nrow(x)) {
    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
  }
  join
 }
 #' @rdname join
 #' @export
 left_join_bactlist <- function(x, by = 'bactid', ...) {
  # no name set to `by` parameter
  if (is.null(names(by))) {
    joinby <- colnames(AMR::bactlist)[1]
    names(joinby) <- by
  } else {
    joinby <- by
  }
  join <- dplyr::left_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
  if (nrow(join) > nrow(x)) {
    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
  }
  join
 }
 #' @rdname join
 #' @export
 right_join_bactlist <- function(x, by = 'bactid', ...) {
  # no name set to `by` parameter
  if (is.null(names(by))) {
    joinby <- colnames(AMR::bactlist)[1]
    names(joinby) <- by
  } else {
    joinby <- by
  }
  join <- dplyr::right_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
  if (nrow(join) > nrow(x)) {
    warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
  }
  join
 }
 #' @rdname join
 #' @export
 full_join_bactlist <- function(x, by = 'bactid', ...) {
  # no name set to `by` parameter
  if (is.null(names(by))) {
    joinby <- colnames(AMR::bactlist)[1]
    names(joinby) <- by
  } else {
    joinby <- by
  }
  dplyr::full_join(x = x, y = AMR::bactlist, by = joinby, suffix = c("2", ""), ...)
 }
 #' @rdname join
 #' @export
 semi_join_bactlist <- function(x, by = 'bactid', ...) {
  # no name set to `by` parameter
  if (is.null(names(by))) {
    joinby <- colnames(AMR::bactlist)[1]
    names(joinby) <- by
  } else {
    joinby <- by
  }
  dplyr::semi_join(x = x, y = AMR::bactlist, by = joinby, ...)
 }
 #' @rdname join
 #' @export
 anti_join_bactlist <- function(x, by = 'bactid', ...) {
  # no name set to `by` parameter
  if (is.null(names(by))) {
    joinby <- colnames(AMR::bactlist)[1]
    names(joinby) <- by
  } else {
    joinby <- by
  }
  dplyr::anti_join(x = x, y = AMR::bactlist, by = joinby, ...)
 }
@@ -1,49 +0,0 @@
 # ==================================================================== #
 # TITLE                                                                #
 # Antimicrobial Resistance (AMR) Analysis                              #
 #                                                                      #
 # AUTHORS                                                              #
 # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
 #                                                                      #
 # LICENCE                                                              #
 # This program is free software; you can redistribute it and/or modify #
 # it under the terms of the GNU General Public License version 2.0,    #
 # as published by the Free Software Foundation.                        #
 #                                                                      #
 # This program is distributed in the hope that it will be useful,      #
 # but WITHOUT ANY WARRANTY; without even the implied warranty of       #
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
 # GNU General Public License for more details.                         #
 # ==================================================================== #
 # No export, no Rd
 "%like%" <- function(vector, pattern) {
  # Source: https://github.com/Rdatatable/data.table/blob/master/R/like.R
  if (is.factor(vector)) {
    as.integer(vector) %in% grep(pattern, levels(vector))
  } else {
    grepl(pattern, vector)
  }
 }
 percent <- function(x, round = 1, ...) {
  base::paste0(base::round(x * 100, digits = round), "%")
 }
 quasiquotate <- function(deparsed, parsed) {
  # when text: remove first and last "
  if (any(deparsed %like% '^".+"$' | deparsed %like% "^'.+'$")) {
    deparsed <- deparsed %>% substr(2, nchar(.) - 1)
  }
  # apply if needed
  if (any(!deparsed %like% '[[$:()]'
      & !deparsed %in% c('""', "''", "", # empty text
                         ".", ".data", # dplyr references
                         "TRUE", "FALSE", # logicals
                         "NA", "NaN", "NULL", # empty values
                         ls(.GlobalEnv)))) {
    deparsed
  } else {
    parsed
  }
 }
@@ -1,426 +0,0 @@
 # ==================================================================== #
 # TITLE                                                                #
 # Antimicrobial Resistance (AMR) Analysis                              #
 #                                                                      #
 # AUTHORS                                                              #
 # Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
 #                                                                      #
 # LICENCE                                                              #
 # This program is free software; you can redistribute it and/or modify #
 # it under the terms of the GNU General Public License version 2.0,    #
 # as published by the Free Software Foundation.                        #
 #                                                                      #
 # This program is distributed in the hope that it will be useful,      #
 # but WITHOUT ANY WARRANTY; without even the implied warranty of       #
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
 # GNU General Public License for more details.                         #
 # ==================================================================== #
 #' Resistance of isolates in data.frame
 #'
 #' \strong{NOTE: use \code{\link{rsi}} in dplyr functions like \code{\link[dplyr]{summarise}}.} \cr Calculate the percentage of S, SI, I, IR or R of a \code{data.frame} containing isolates.
 #' @param tbl \code{data.frame} containing columns with antibiotic interpretations.
 #' @param antibiotics character vector with 1, 2 or 3 antibiotics that occur as column names in \code{tbl}, like \code{antibiotics = c("amox", "amcl")}
 #' @param interpretation antimicrobial interpretation of which the portion must be calculated. Valid values are \code{"S"}, \code{"SI"}, \code{"I"}, \code{"IR"} or \code{"R"}.
 #' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA} with a warning (when \code{warning = TRUE}).
 #' @param percent return output as percent (text), will else (at default) be a double
 #' @param info calculate the amount of available isolates and print it, like \code{n = 423}
 #' @param warning show a warning when the available amount of isolates is below \code{minimum}
 #' @details Remember that you should filter your table to let it contain \strong{only first isolates}!
 #' @keywords rsi antibiotics isolate isolates
 #' @return Double or, when \code{percent = TRUE}, a character.
 #' @export
 #' @importFrom dplyr %>% n_distinct filter filter_at pull vars all_vars any_vars
 #' @seealso \code{\link{rsi}} for the function that can be used with \code{\link[dplyr]{summarise}} directly.
 #' @examples
 #' \dontrun{
 #' rsi_df(tbl_with_bloodcultures, 'amcl')
 #'
 #' rsi_df(tbl_with_bloodcultures, c('amcl', 'gent'), interpretation = 'IR')
 #'
 #' library(dplyr)
 #' # calculate current empiric therapy of Helicobacter gastritis:
 #' my_table %>%
 #'   filter(first_isolate == TRUE, 
 #'          genus == "Helicobacter") %>%
 #'   rsi_df(antibiotics = c("amox", "metr"))
 #' }
 rsi_df <- function(tbl,
                   antibiotics,
                   interpretation = 'IR',
                   minimum = 30,
                   percent = FALSE,
                   info = TRUE,
                   warning = TRUE) {
  # we willen niet dat tbl$interpretation toevallig ook bestaat, dus:
  te_testen_uitslag_ab <- interpretation
  # validatie:
  if (min(grepl('^[a-z]{3,4}$', antibiotics)) == 0 &
      min(grepl('^rsi[1-2]$', antibiotics)) == 0) {
    for (i in 1:length(antibiotics)) {
      antibiotics[i] <- paste0('rsi', i)
    }
  }
  if (!grepl('^(S|SI|IS|I|IR|RI|R){1}$', te_testen_uitslag_ab)) {
    stop('Invalid `interpretation`; must be "S", "SI", "I", "IR", or "R".')
  }
  if ('is_ic' %in% colnames(tbl)) {
    if (n_distinct(tbl$is_ic) > 1) {
      warning('Dataset contains isolates from the Intensive Care. Exclude them from proper epidemiological analysis.')
    }
  }
  # transformeren wanneer gezocht wordt op verschillende uitslagen
  if (te_testen_uitslag_ab %in% c('SI', 'IS')) {
    for (i in 1:length(antibiotics)) {
      lijst <- tbl[, antibiotics[i]]
      if ('I' %in% lijst) {
        tbl[which(tbl[antibiotics[i]] == 'I'), ][antibiotics[i]] <- 'S'
      }
    }
    te_testen_uitslag_ab <- 'S'
  }
  if (te_testen_uitslag_ab %in% c('RI', 'IR')) {
    for (i in 1:length(antibiotics)) {
      lijst <- tbl[, antibiotics[i]]
      if ('I' %in% lijst) {
        tbl[which(tbl[antibiotics[i]] == 'I'), ][antibiotics[i]] <- 'R'
      }
    }
    te_testen_uitslag_ab <- 'R'
  }
  # breuk samenstellen
  if (length(antibiotics) == 1) {
    numerator <- tbl %>%
      filter(pull(., antibiotics[1]) == te_testen_uitslag_ab) %>%
      nrow()
    denominator <- tbl %>%
      filter(pull(., antibiotics[1]) %in% c("S", "I", "R")) %>%
      nrow()
  } else if (length(antibiotics) == 2) {
    numerator <- tbl %>%
      filter_at(vars(antibiotics[1], antibiotics[2]),
                any_vars(. == te_testen_uitslag_ab)) %>%
      filter_at(vars(antibiotics[1], antibiotics[2]),
                all_vars(. %in% c("S", "R", "I"))) %>%
      nrow()
    denominator <- tbl %>%
      filter_at(vars(antibiotics[1], antibiotics[2]),
                all_vars(. %in% c("S", "R", "I"))) %>%
      nrow()
  } else if (length(antibiotics) == 3) {
    numerator <- tbl %>%
      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
                any_vars(. == te_testen_uitslag_ab)) %>%
      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
                all_vars(. %in% c("S", "R", "I"))) %>%
      nrow()
    denominator <- tbl %>%
      filter_at(vars(antibiotics[1], antibiotics[2], antibiotics[3]),
                all_vars(. %in% c("S", "R", "I"))) %>%
      nrow()
  } else {
    stop('Maximum of 3 drugs allowed.')
  }
  # tekstdeel opbouwen
  if (info == TRUE) {
    cat('n =', denominator)
    info.txt1 <- percent(denominator / nrow(tbl))
    if (denominator == 0) {
      info.txt1 <- 'none'
    }
    info.txt2 <- gsub(',', ' and',
                      antibiotics %>%
                        abname(to = 'trivial',
                               tolower = TRUE) %>%
                        toString(), fixed = TRUE)
    info.txt2 <- gsub('rsi1 and rsi2', 'these two drugs', info.txt2, fixed = TRUE)
    info.txt2 <- gsub('rsi1', 'this drug', info.txt2, fixed = TRUE)
    cat(paste0(' (of ', nrow(tbl), ' in total; ', info.txt1, ' tested on ', info.txt2, ')\n'))
  }
  # rekenen en opmaken
  y <- numerator / denominator
  if (percent == TRUE) {
    y <- percent(y)
  }
  if (denominator < minimum) {
    if (warning == TRUE) {
      warning(paste0('TOO FEW ISOLATES OF ', toString(antibiotics), ' (n = ', denominator, ', n < ', minimum, '); NO RESULT.'))
    }
    y <- NA
  }
  # output
  y
 }
 #' Resistance of isolates
 #'
 #' This function can be used in \code{dplyr}s \code{\link[dplyr]{summarise}}, see \emph{Examples}. Calculate the percentage S, SI, I, IR or R of a vector of isolates.
 #' @param ab1,ab2 list with interpretations of an antibiotic
 #' @inheritParams rsi_df
 #' @details This function uses the \code{\link{rsi_df}} function internally.
 #' @keywords rsi antibiotics isolate isolates
 #' @return Double or, when \code{percent = TRUE}, a character.
 #' @export
 #' @examples
 #' \dontrun{
 #' tbl %>%
 #'   group_by(hospital) %>%
 #'   summarise(cipr = rsi(cipr))
 #'   
 #' tbl %>%
 #'   group_by(year, hospital) %>%
 #'   summarise(
 #'     isolates = n(),
 #'     cipro = rsi(cipr %>% as.rsi(), percent = TRUE),
 #'     amoxi = rsi(amox %>% as.rsi(), percent = TRUE))
 #'     
 #' rsi(as.rsi(isolates$amox))
 #'
 #' rsi(as.rsi(isolates$amcl), interpretation = "S")
 #' }
 rsi <- function(ab1, ab2 = NA, interpretation = 'IR', minimum = 30, percent = FALSE, info = FALSE, warning = FALSE) {
  function_text <- as.character(match.call())
  # param 1 = functienaam
  # param 2 = ab1
  # param 3 = ab2
  ab1.naam <- function_text[2]
  if (!grepl('^[a-z]{3,4}$', ab1.naam)) {
    ab1.naam <- 'rsi1'
  }
  ab2.naam <- function_text[3]
  if (!grepl('^[a-z]{3,4}$', ab2.naam)) {
    ab2.naam <- 'rsi2'
  }
  tbl <- tibble(rsi1 = ab1, rsi2 = ab2)
  colnames(tbl) <- c(ab1.naam, ab2.naam)
  if (length(ab2) == 1) {
    return(rsi_df(tbl = tbl,
                  antibiotics = ab1.naam,
                  interpretation = interpretation,
                  minimum = minimum,
                  percent = percent,
                  info = info,
                  warning = warning))
  } else {
    if (length(ab1) != length(ab2)) {
      stop('`ab1` (n = ', length(ab1), ') and `ab2` (n = ', length(ab2), ') must be of same length.', call. = FALSE)
    }
    if (interpretation != 'S') {
      warning('`interpretation` is not set to S, albeit analysing a combination therapy.')
    }
    return(rsi_df(tbl = tbl,
                  antibiotics = c(ab1.naam, ab2.naam),
                  interpretation = interpretation,
                  minimum = minimum,
                  percent = percent,
                  info = info,
                  warning = warning))
  }
 }
 #' Predict antimicrobial resistance
 #'
 #' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
 #' @param tbl table that contains columns \code{col_ab} and \code{col_date}
 #' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation
 #' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, supports tidyverse-like quotation
 #' @param year_max highest year to use in the prediction model, deafults to 15 years after today
 #' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
 #' @param model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
 #' @param I_as_R treat \code{I} as \code{R}
 #' @param preserve_measurements overwrite predictions of years that are actually available in the data, with the original data. The standard errors of those years will be \code{NA}.
 #' @param info print textual analysis with the name and \code{\link{summary}} of the model.
 #' @return \code{data.frame} with columns \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
 #' @seealso \code{\link{lm}} \cr \code{\link{glm}}
 #' @export
 #' @importFrom dplyr %>% pull mutate group_by_at summarise filter
 #' @importFrom reshape2 dcast
 #' @examples
 #' \dontrun{
 #' # use it directly:
 #' rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
 #'             col_ab = "amcl", col_date = "date")
 #'   
 #' # or with dplyr so you can actually read it:
 #' library(dplyr)
 #' tbl %>%
 #'   filter(first_isolate == TRUE,
 #'          genus == "Haemophilus") %>%
 #'   rsi_predict(amcl, date)
 #' }
 #'
 #'
 #' # real live example:
 #' library(dplyr)
 #' septic_patients %>%
 #'   # get bacteria properties like genus and species
 #'   left_join_bactlist("bactid") %>% 
 #'   # calculate first isolates
 #'   mutate(first_isolate = 
 #'            first_isolate(.,
 #'                          "date",
 #'                          "patient_id",
 #'                          "genus",
 #'                          "species",
 #'                          col_specimen = NA,
 #'                          col_icu = NA)) %>% 
 #'   # filter on first E. coli isolates
 #'   filter(genus == "Escherichia", 
 #'          species == "coli", 
 #'          first_isolate == TRUE) %>%
 #'   # predict resistance of cefotaxime for next years
 #'   rsi_predict(col_ab = cfot,
 #'               col_date = date,
 #'               year_max = 2025,
 #'               preserve_measurements = FALSE)
 #'
 rsi_predict <- function(tbl,
                        col_ab,
                        col_date,
                        year_max = as.integer(format(as.Date(Sys.Date()), '%Y')) + 15,
                        year_every = 1,
                        model = 'binomial',
                        I_as_R = TRUE,
                        preserve_measurements = TRUE,
                        info = TRUE) {
  col_ab <- quasiquotate(deparse(substitute(col_ab)), col_ab)
  if (!col_ab %in% colnames(tbl)) {
    stop('Column ', col_ab, ' not found.')
  }
  col_date <- quasiquotate(deparse(substitute(col_date)), col_date)
  if (!col_date %in% colnames(tbl)) {
    stop('Column ', col_date, ' not found.')
  }
  if ('grouped_df' %in% class(tbl)) {
    # no grouped tibbles please, mutate will throw errors
    tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
  }
  if (I_as_R == TRUE) {
    tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
  }
  if (!all(tbl %>% pull(col_ab) %>% as.rsi() %in% c(NA, 'S', 'I', 'R'))) {
    stop('Column ', col_ab, ' must contain antimicrobial interpretations (S, I, R).')
  }
  year <- function(x) {
    if (all(grepl('^[0-9]{4}$', x))) {
      x
    } else {
      as.integer(format(as.Date(x), '%Y'))
    }
  }
  years_predict <- seq(from = min(year(tbl %>% pull(col_date))), to = year_max, by = year_every)
  df <- tbl %>%
    mutate(year = year(tbl %>% pull(col_date))) %>%
    group_by_at(c('year', col_ab)) %>%
    summarise(n())
  colnames(df) <- c('year', 'antibiotic', 'count')
  df <- df %>%
    reshape2::dcast(year ~ antibiotic, value.var = 'count')
  if (model %in% c('binomial', 'binom', 'logit')) {
    logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
    if (info == TRUE) {
      cat('\nLogistic regression model (logit) with binomial distribution')
      cat('\n------------------------------------------------------------\n')
      print(summary(logitmodel))
    }
    predictmodel <- stats::predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
    prediction <- predictmodel$fit
    se <- predictmodel$se.fit
  } else if (model == 'loglin') {
    loglinmodel <- with(df, glm(R ~ year, family = poisson))
    if (info == TRUE) {
      cat('\nLog-linear regression model (loglin) with poisson distribution')
      cat('\n--------------------------------------------------------------\n')
      print(summary(loglinmodel))
    }
    predictmodel <- stats::predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
    prediction <- predictmodel$fit
    se <- predictmodel$se.fit
  } else if (model %in% c('lin', 'linear')) {
    linmodel <- with(df, lm((R / (R + S)) ~ year))
    if (info == TRUE) {
      cat('\nLinear regression model')
      cat('\n-----------------------\n')
      print(summary(linmodel))
    }
    predictmodel <- stats::predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
    prediction <- predictmodel$fit
    se <- predictmodel$se.fit
  } else {
    stop('No valid model selected.')
  }
  # prepare the output dataframe
  prediction <- data.frame(year = years_predict, probR = prediction, stringsAsFactors = FALSE)
  prediction$se_min <- prediction$probR - se
  prediction$se_max <- prediction$probR + se
  if (model == 'loglin') {
    prediction$probR <- prediction$probR %>%
      format(scientific = FALSE) %>%
      as.integer()
    prediction$se_min <- prediction$se_min %>% as.integer()
    prediction$se_max <- prediction$se_max %>% as.integer()
    colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
  } else {
    prediction$se_max[which(prediction$se_max > 1)] <- 1
  }
  prediction$se_min[which(prediction$se_min < 0)] <- 0
  total <- prediction
  if (preserve_measurements == TRUE) {
    # geschatte data vervangen door gemeten data
    if (I_as_R == TRUE) {
      if (!'I' %in% colnames(df)) {
        df$I <- 0
      }
      df$probR <- df$R / rowSums(df[, c('R', 'S', 'I')])
    } else {
      df$probR <- df$R / rowSums(df[, c('R', 'S')])
    }
    measurements <- data.frame(year = df$year,
                           probR = df$probR,
                           se_min = NA,
                           se_max = NA,
                           stringsAsFactors = FALSE)
    colnames(measurements) <- colnames(prediction)
    prediction <- prediction %>% filter(!year %in% df$year)
    total <- rbind(measurements, prediction)
  }
  total
 }
@@ -1,182 +0,0 @@
 # `AMR`
 This is an [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR).
 ![logo_uni](man/figures/logo_en.png)![logo_umcg](man/figures/logo_umcg.png)
 This R package was created for academic research by PhD students of the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl/) and the Medical Microbiology & Infection Prevention department of the University Medical Center Groningen (UMCG). They also maintain this package, see [Authors](#authors).
 ## Why this package?
 This R package contains functions to make microbiological, epidemiological data analysis easier. It allows the use of some new S3 classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).
 AMR can also be predicted for the forthcoming years with the `rsi_predict` function. For use with the `dplyr` package, the `rsi` function can be used in conjunction with `summarise` to calculate the resistance percentages of different antibiotic columns of a table.
 It also contains functions to translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`) and vice versa.
 ## How to get it?
 This package is available on CRAN and also here on GitHub.
 ### From CRAN (recommended, latest stable version)
 [![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
 [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
 - RStudio:
  - Click on `Tools` and then `Install Packages...`
  -  Type in `AMR` and press <kbd>Install</kbd>
 - R console:
  - `install.packages("AMR")`
 ### From GitHub (latest development version)
 [![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
 [![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/releases)
 [![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)
 ```r
 install.packages("devtools")
 devtools::install_github("msberends/AMR")
 ```
 ## How to use it?
 ```r
 # Call it with:
 library(AMR)
 # For a list of functions:
 help(package = "AMR")
 ```
 ### Overwrite/force resistance based on EUCAST rules
 This is also called *interpretive reading*.
 ```r
 before <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
                                "ENCFAE",  # Enterococcus faecalis
                                "ESCCOL",  # Escherichia coli
                                "KLEPNE",  # Klebsiella pneumoniae
                                "PSEAER"), # Pseudomonas aeruginosa
                     vanc = "-",           # Vancomycin
                     amox = "-",           # Amoxicillin
                     coli = "-",           # Colistin
                     cfta = "-",           # Ceftazidime
                     cfur = "-",           # Cefuroxime
                     stringsAsFactors = FALSE)
 before
 #   bactid vanc amox coli cfta cfur
 # 1 STAAUR    -    -    -    -    -
 # 2 ENCFAE    -    -    -    -    -
 # 3 ESCCOL    -    -    -    -    -
 # 4 KLEPNE    -    -    -    -    -
 # 5 PSEAER    -    -    -    -    -
 # Now apply those rules; just need a column with bacteria ID's and antibiotic results:
 after <- EUCAST_rules(before)
 after
 #   bactid vanc amox coli cfta cfur
 # 1 STAAUR    -    -    R    R    -
 # 2 ENCFAE    -    -    R    R    R
 # 3 ESCCOL    R    -    -    -    -
 # 4 KLEPNE    R    R    -    -    -
 # 5 PSEAER    R    R    -    -    R
 ```
 ### New classes
 This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`). 
 Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
 ```r
 # Transform values to new classes
 mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
 rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
 ```
 These functions also try to coerce valid values.
 Quick overviews when just printing objects:
 ```r
 mic_data
 # Class 'mic': 7 isolates
 # 
 # <NA>  0
 # 
 # <=0.128       1       8      16    >=32
 #       1       1       2       2       1
 rsi_data
 # Class 'rsi': 880 isolates
 # 
 # <NA>:       0 
 # Sum of S:   474 
 # Sum of IR:  406 
 # - Sum of R: 370 
 # - Sum of I: 36 
 # 
 #   %S  %IR   %I   %R 
 # 53.9 46.1  4.1 42.0 
 ```
 A plot of `rsi_data`:
 ```r
 plot(rsi_data)
 ```
 ![example](man/figures/rsi_example.png)
 Other epidemiological functions:
 ```r
 # Determine key antibiotic based on bacteria ID
 key_antibiotics(...)
 # Selection of first isolates of any patient
 first_isolate(...)
 # Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
 rsi(...)
 # Predict resistance levels of antibiotics
 rsi_predict(...)
 # Get name of antibiotic by ATC code
 abname(...)
 abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
 ```
 ### Databases included in package
 Datasets to work with antibiotics and bacteria properties.
 ```r
 # Dataset with ATC antibiotics codes, official names and DDD's (oral and parenteral)
 ablist        # A tibble: 420 x 12
 # Dataset with bacteria codes and properties like gram stain and aerobic/anaerobic
 bactlist      # A tibble: 2,507 x 10
 ```
 ## Authors
  - [Berends MS](https://github.com/msberends)<sup>1,2</sup>, PhD Student
  - [Luz CF](https://github.com/ceefluz)<sup>1</sup>, PhD Student
  - [Hassing EEA](https://github.com/erwinhassing)<sup>2</sup>, Data Analyst (contributor)
 <sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
 <sup>2</sup> Department of Medical, Market and Innovation (MMI), Certe Medische diagnostiek & advies, Groningen, the Netherlands
 ## Copyright
 [![License](https://img.shields.io/github/license/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/blob/master/LICENSE)
 This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:
 - May be used for commercial purposes
 - May be used for private purposes
 - May be modified, although:
  - Modifications **must** be released under the same license when distributing the package
  - Changes made to the code **must** be documented
 - May be distributed, although:
  - Source code **must** be made available when the package is distributed
  - A copy of the license and copyright notice **must** be included with the package.
 - Comes with a LIMITATION of liability
 - Comes with NO warranty
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      <img src="../logo.svg" class="logo" alt=""><h1>AMR for Python</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class="external-link"><code>vignettes/AMR_for_Python.Rmd</code></a></small>
      <div class="d-none name"><code>AMR_for_Python.Rmd</code></div>
    </div>
 <div class="section level2">
 <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
 <p>The <code>AMR</code> package for R is a powerful tool for
 antimicrobial resistance (AMR) analysis. It provides extensive features
 for handling microbial and antimicrobial data. However, for those who
 work primarily in Python, we now have a more intuitive option available:
 the <a href="https://pypi.org/project/AMR/" class="external-link"><code>AMR</code> Python
 package</a>.</p>
 <p>This Python package is a wrapper around the <code>AMR</code> R
 package. It uses the <code>rpy2</code> package internally. Despite the
 need to have R installed, Python users can now easily work with AMR data
 directly through Python code.</p>
 </div>
 <div class="section level2">
 <h2 id="prerequisites">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
 </h2>
 <p>This package was only tested with a <a href="https://docs.python.org/3/library/venv.html" class="external-link">virtual environment
 (venv)</a>. You can set up such an environment by running:</p>
 <div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="co"># linux and macOS:</span></span>
 <span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>python <span class="op">-</span>m venv <span class="op">/</span>path<span class="op">/</span>to<span class="op">/</span>new<span class="op">/</span>virtual<span class="op">/</span>environment</span>
 <span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
 <span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="co"># Windows:</span></span>
 <span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>python <span class="op">-</span>m venv C:\path\to\new\virtual\environment</span></code></pre></div>
 <p>Then you can <a href="https://docs.python.org/3/library/venv.html#how-venvs-work" class="external-link">activate
 the environment</a>, after which the venv is ready to work with.</p>
 </div>
 <div class="section level2">
 <h2 id="install-amr">Install AMR<a class="anchor" aria-label="anchor" href="#install-amr"></a>
 </h2>
 <ol style="list-style-type: decimal">
 <li>
 <p>Since the Python package is available on the official <a href="https://pypi.org/project/AMR/" class="external-link">Python Package Index</a>, you can
 just run:</p>
 <div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
 </li>
 <li>
 <p>Make sure you have R installed. There is <strong>no need to
 install the <code>AMR</code> R package</strong>, as it will be installed
 automatically.</p>
 <p>For Linux:</p>
 <div class="sourceCode" id="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
 <span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
 <span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
 <span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
 <span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
 <span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a><span class="fu">sudo</span> yum install R</span></code></pre></div>
 <p>For macOS (using <a href="https://brew.sh" class="external-link">Homebrew</a>):</p>
 <div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="ex">brew</span> install r</span></code></pre></div>
 <p>For Windows, visit the <a href="https://cran.r-project.org" class="external-link">CRAN
 download page</a> to download and install R.</p>
 </li>
 </ol>
 </div>
 <div class="section level2">
 <h2 id="examples-of-usage">Examples of Usage<a class="anchor" aria-label="anchor" href="#examples-of-usage"></a>
 </h2>
 <div class="section level3">
 <h3 id="cleaning-taxonomy">Cleaning Taxonomy<a class="anchor" aria-label="anchor" href="#cleaning-taxonomy"></a>
 </h3>
 <p>Here’s an example that demonstrates how to clean microorganism and
 drug names using the <code>AMR</code> Python package:</p>
 <div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
 <span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
 <span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
 <span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
 <span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a>    <span class="st">"MOs"</span>: [<span class="st">'E. coli'</span>, <span class="st">'ESCCOL'</span>, <span class="st">'esco'</span>, <span class="st">'Esche coli'</span>],</span>
 <span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>    <span class="st">"Drug"</span>: [<span class="st">'Cipro'</span>, <span class="st">'CIP'</span>, <span class="st">'J01MA02'</span>, <span class="st">'Ciproxin'</span>]</span>
 <span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>}</span>
 <span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
 <span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a></span>
 <span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
 <span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a>df[<span class="st">'MO_clean'</span>] <span class="op">=</span> AMR.mo_name(df[<span class="st">'MOs'</span>])</span>
 <span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>df[<span class="st">'Drug_clean'</span>] <span class="op">=</span> AMR.ab_name(df[<span class="st">'Drug'</span>])</span>
 <span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a></span>
 <span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
 <span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th>MOs</th>
 <th>Drug</th>
 <th>MO_clean</th>
 <th>Drug_clean</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>E. coli</td>
 <td>Cipro</td>
 <td>Escherichia coli</td>
 <td>Ciprofloxacin</td>
 </tr>
 <tr class="even">
 <td>ESCCOL</td>
 <td>CIP</td>
 <td>Escherichia coli</td>
 <td>Ciprofloxacin</td>
 </tr>
 <tr class="odd">
 <td>esco</td>
 <td>J01MA02</td>
 <td>Escherichia coli</td>
 <td>Ciprofloxacin</td>
 </tr>
 <tr class="even">
 <td>Esche coli</td>
 <td>Ciproxin</td>
 <td>Escherichia coli</td>
 <td>Ciprofloxacin</td>
 </tr>
 </tbody>
 </table>
 <div class="section level4">
 <h4 id="explanation">Explanation<a class="anchor" aria-label="anchor" href="#explanation"></a>
 </h4>
 <ul>
 <li><p><strong>mo_name:</strong> This function standardises
 microorganism names. Here, different variations of <em>Escherichia
 coli</em> (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are
 all converted into the correct, standardised form, “Escherichia
 coli”.</p></li>
 <li><p><strong>ab_name</strong>: Similarly, this function standardises
 antimicrobial names. The different representations of ciprofloxacin
 (e.g., “Cipro”, “CIP”, “J01MA02”, and “Ciproxin”) are all converted to
 the standard name, “Ciprofloxacin”.</p></li>
 </ul>
 </div>
 </div>
 <div class="section level3">
 <h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
 </h3>
 <div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
 <span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
 <span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a></span>
 <span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
 <span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
 <span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
 <pre><code>[0.59555556]</code></pre>
 </div>
 <div class="section level3">
 <h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
 </h3>
 <p>One of the core functions of the <code>AMR</code> package is
 generating an antibiogram, a table that summarises the antimicrobial
 susceptibility of bacterial isolates. Here’s how you can generate an
 antibiogram from Python:</p>
 <div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>result2a <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]])</span>
 <span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="22%">
 <col width="22%">
 <col width="22%">
 <col width="33%">
 </colgroup>
 <thead><tr class="header">
 <th>Pathogen</th>
 <th>Amoxicillin</th>
 <th>Ciprofloxacin</th>
 <th>Piperacillin/tazobactam</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>CoNS</td>
 <td>7% (10/142)</td>
 <td>73% (183/252)</td>
 <td>30% (10/33)</td>
 </tr>
 <tr class="even">
 <td>E. coli</td>
 <td>50% (196/392)</td>
 <td>88% (399/456)</td>
 <td>94% (393/416)</td>
 </tr>
 <tr class="odd">
 <td>K. pneumoniae</td>
 <td>0% (0/58)</td>
 <td>96% (53/55)</td>
 <td>89% (47/53)</td>
 </tr>
 <tr class="even">
 <td>P. aeruginosa</td>
 <td>0% (0/30)</td>
 <td>100% (30/30)</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>P. mirabilis</td>
 <td>None</td>
 <td>94% (34/36)</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>S. aureus</td>
 <td>6% (8/131)</td>
 <td>90% (171/191)</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>S. epidermidis</td>
 <td>1% (1/91)</td>
 <td>64% (87/136)</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>S. hominis</td>
 <td>None</td>
 <td>80% (56/70)</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>S. pneumoniae</td>
 <td>100% (112/112)</td>
 <td>None</td>
 <td>100% (112/112)</td>
 </tr>
 </tbody>
 </table>
 <div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>result2b <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]], mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>)</span>
 <span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="20%">
 <col width="22%">
 <col width="23%">
 <col width="33%">
 </colgroup>
 <thead><tr class="header">
 <th>Pathogen</th>
 <th>Amoxicillin</th>
 <th>Ciprofloxacin</th>
 <th>Piperacillin/tazobactam</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>Gram-negative</td>
 <td>36% (226/631)</td>
 <td>91% (621/684)</td>
 <td>88% (565/641)</td>
 </tr>
 <tr class="even">
 <td>Gram-positive</td>
 <td>43% (305/703)</td>
 <td>77% (560/724)</td>
 <td>86% (296/345)</td>
 </tr>
 </tbody>
 </table>
 <p>In this example, we generate an antibiogram by selecting various
 antibiotics.</p>
 </div>
 <div class="section level3">
 <h3 id="taxonomic-data-sets-now-in-python">Taxonomic Data Sets Now in Python!<a class="anchor" aria-label="anchor" href="#taxonomic-data-sets-now-in-python"></a>
 </h3>
 <p>As a Python user, you might like that the most important data sets of
 the <code>AMR</code> R package, <code>microorganisms</code>,
 <code>antimicrobials</code>, <code>clinical_breakpoints</code>, and
 <code>example_isolates</code>, are now available as regular Python data
 frames:</p>
 <div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
 <table class="table">
 <colgroup>
 <col width="11%">
 <col width="29%">
 <col width="8%">
 <col width="8%">
 <col width="8%">
 <col width="10%">
 <col width="13%">
 <col width="9%">
 </colgroup>
 <thead><tr class="header">
 <th>mo</th>
 <th>fullname</th>
 <th>status</th>
 <th>kingdom</th>
 <th>gbif</th>
 <th>gbif_parent</th>
 <th>gbif_renamed_to</th>
 <th>prevalence</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>B_GRAMN</td>
 <td>(unknown Gram-negatives)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>B_GRAMP</td>
 <td>(unknown Gram-positives)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="odd">
 <td>B_ANAER-NEG</td>
 <td>(unknown anaerobic Gram-negatives)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>B_ANAER-POS</td>
 <td>(unknown anaerobic Gram-positives)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="odd">
 <td>B_ANAER</td>
 <td>(unknown anaerobic bacteria)</td>
 <td>unknown</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 </tr>
 <tr class="odd">
 <td>B_ZYMMN_POMC</td>
 <td>Zymomonas pomaceae</td>
 <td>accepted</td>
 <td>Bacteria</td>
 <td>10744418</td>
 <td>3221412</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>B_ZYMPH</td>
 <td>Zymophilus</td>
 <td>synonym</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>9475166</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="odd">
 <td>B_ZYMPH_PCVR</td>
 <td>Zymophilus paucivorans</td>
 <td>synonym</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="even">
 <td>B_ZYMPH_RFFN</td>
 <td>Zymophilus raffinosivorans</td>
 <td>synonym</td>
 <td>Bacteria</td>
 <td>None</td>
 <td>None</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 <tr class="odd">
 <td>F_ZYZYG</td>
 <td>Zyzygomyces</td>
 <td>unknown</td>
 <td>Fungi</td>
 <td>None</td>
 <td>7581</td>
 <td>None</td>
 <td>2.0</td>
 </tr>
 </tbody>
 </table>
 <div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>AMR.antimicrobials</span></code></pre></div>
 <table style="width:100%;" class="table">
 <colgroup>
 <col width="4%">
 <col width="12%">
 <col width="20%">
 <col width="25%">
 <col width="9%">
 <col width="11%">
 <col width="7%">
 <col width="9%">
 </colgroup>
 <thead><tr class="header">
 <th>ab</th>
 <th>cid</th>
 <th>name</th>
 <th>group</th>
 <th>oral_ddd</th>
 <th>oral_units</th>
 <th>iv_ddd</th>
 <th>iv_units</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td>AMA</td>
 <td>4649.0</td>
 <td>4-aminosalicylic acid</td>
 <td>Antimycobacterials</td>
 <td>12.00</td>
 <td>g</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>ACM</td>
 <td>6450012.0</td>
 <td>Acetylmidecamycin</td>
 <td>Macrolides/lincosamides</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>ASP</td>
 <td>49787020.0</td>
 <td>Acetylspiramycin</td>
 <td>Macrolides/lincosamides</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>ALS</td>
 <td>8954.0</td>
 <td>Aldesulfone sodium</td>
 <td>Other antibacterials</td>
 <td>0.33</td>
 <td>g</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>AMK</td>
 <td>37768.0</td>
 <td>Amikacin</td>
 <td>Aminoglycosides</td>
 <td>NaN</td>
 <td>None</td>
 <td>1.0</td>
 <td>g</td>
 </tr>
 <tr class="even">
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 <td>…</td>
 </tr>
 <tr class="odd">
 <td>VIR</td>
 <td>11979535.0</td>
 <td>Virginiamycine</td>
 <td>Other antibacterials</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>VOR</td>
 <td>71616.0</td>
 <td>Voriconazole</td>
 <td>Antifungals/antimycotics</td>
 <td>0.40</td>
 <td>g</td>
 <td>0.4</td>
 <td>g</td>
 </tr>
 <tr class="odd">
 <td>XBR</td>
 <td>72144.0</td>
 <td>Xibornol</td>
 <td>Other antibacterials</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="even">
 <td>ZID</td>
 <td>77846445.0</td>
 <td>Zidebactam</td>
 <td>Other antibacterials</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 <tr class="odd">
 <td>ZFD</td>
 <td>NaN</td>
 <td>Zoliflodacin</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 <td>NaN</td>
 <td>None</td>
 </tr>
 </tbody>
 </table>
 </div>
 </div>
 <div class="section level2">
 <h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
 </h2>
 <p>With the <code>AMR</code> Python package, Python users can now
 effortlessly call R functions from the <code>AMR</code> R package. This
 eliminates the need for complex <code>rpy2</code> configurations and
 provides a clean, easy-to-use interface for antimicrobial resistance
 analysis. The examples provided above demonstrate how this can be
 applied to typical workflows, such as standardising microorganism and
 antimicrobial names or calculating resistance.</p>
 <p>By just running <code>import AMR</code>, users can seamlessly
 integrate the robust features of the R <code>AMR</code> package into
 Python workflows.</p>
 <p>Whether you’re cleaning data or analysing resistance patterns, the
 <code>AMR</code> Python package makes it easy to work with AMR data in
 Python.</p>
 </div>
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  <main id="main" class="col-md-9"><div class="page-header">
      <img src="../logo.svg" class="logo" alt=""><h1>AMR with tidymodels</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_with_tidymodels.Rmd" class="external-link"><code>vignettes/AMR_with_tidymodels.Rmd</code></a></small>
      <div class="d-none name"><code>AMR_with_tidymodels.Rmd</code></div>
    </div>
 <blockquote>
 <p>This page was entirely written by our <a href="https://chatgpt.com/g/g-M4UNLwFi5-amr-for-r-assistant" class="external-link">AMR for R
 Assistant</a>, a ChatGPT manually-trained model able to answer any
 question about the AMR package.</p>
 </blockquote>
 <p>Antimicrobial resistance (AMR) is a global health crisis, and
 understanding resistance patterns is crucial for managing effective
 treatments. The <code>AMR</code> R package provides robust tools for
 analysing AMR data, including convenient antimicrobial selector
 functions like <code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and
 <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>.</p>
 <p>In this post, we will explore how to use the <code>tidymodels</code>
 framework to predict resistance patterns in the
 <code>example_isolates</code> dataset in two examples.</p>
 <div class="section level2">
 <h2 id="example-1-using-antimicrobial-selectors">Example 1: Using Antimicrobial Selectors<a class="anchor" aria-label="anchor" href="#example-1-using-antimicrobial-selectors"></a>
 </h2>
 <p>By leveraging the power of <code>tidymodels</code> and the
 <code>AMR</code> package, we’ll build a reproducible machine learning
 workflow to predict the Gramstain of the microorganism to two important
 antibiotic classes: aminoglycosides and beta-lactams.</p>
 <div class="section level3">
 <h3 id="objective">
 <strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective"></a>
 </h3>
 <p>Our goal is to build a predictive model using the
 <code>tidymodels</code> framework to determine the Gramstain of the
 microorganism based on microbial data. We will:</p>
 <ol style="list-style-type: decimal">
 <li>Preprocess data using the selector functions
 <code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>.</li>
 <li>Define a logistic regression model for prediction.</li>
 <li>Use a structured <code>tidymodels</code> workflow to preprocess,
 train, and evaluate the model.</li>
 </ol>
 </div>
 <div class="section level3">
 <h3 id="data-preparation">
 <strong>Data Preparation</strong><a class="anchor" aria-label="anchor" href="#data-preparation"></a>
 </h3>
 <p>We begin by loading the required libraries and preparing the
 <code>example_isolates</code> dataset from the <code>AMR</code>
 package.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Load required libraries</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span>          <span class="co"># For AMR data analysis</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span>   <span class="co"># For machine learning workflows, and data manipulation (dplyr, tidyr, ...)</span></span></code></pre></div>
 <p>Prepare the data:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Your data could look like this:</span></span>
 <span><span class="va">example_isolates</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
 <span><span class="co">#&gt;    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  </span></span>
 <span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>         <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334     65 F      Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span>  <span style="color: #B2B2B2;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729     78 M      Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #080808; background-color: #FFAFAF;">  R  </span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927     45 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2002-01-17 858515     79 F      ICU      <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FFAFAF;">  R  </span> <span style="color: #B2B2B2;">  NA</span>  <span style="color: #080808; background-color: #5FD7AF;">  S  </span> <span style="color: #B2B2B2;">  NA</span> </span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 36 more variables: AMC &lt;sir&gt;, AMP &lt;sir&gt;, TZP &lt;sir&gt;, CZO &lt;sir&gt;, FEP &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;sir&gt;, FOX &lt;sir&gt;, CTX &lt;sir&gt;, CAZ &lt;sir&gt;, CRO &lt;sir&gt;, GEN &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   TOB &lt;sir&gt;, AMK &lt;sir&gt;, KAN &lt;sir&gt;, TMP &lt;sir&gt;, SXT &lt;sir&gt;, NIT &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   FOS &lt;sir&gt;, LNZ &lt;sir&gt;, CIP &lt;sir&gt;, MFX &lt;sir&gt;, VAN &lt;sir&gt;, TEC &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   TCY &lt;sir&gt;, TGC &lt;sir&gt;, DOX &lt;sir&gt;, ERY &lt;sir&gt;, CLI &lt;sir&gt;, AZM &lt;sir&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   IPM &lt;sir&gt;, MEM &lt;sir&gt;, MTR &lt;sir&gt;, CHL &lt;sir&gt;, COL &lt;sir&gt;, MUP &lt;sir&gt;, …</span></span></span>
 <span></span>
 <span><span class="co"># Select relevant columns for prediction</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># select AB results dynamically</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">mo</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># replace NAs with NI (not-interpretable)</span></span>
 <span>   <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
 <span>                 <span class="op">~</span><span class="fu">replace_na</span><span class="op">(</span><span class="va">.x</span>, <span class="st">"NI"</span><span class="op">)</span><span class="op">)</span>,</span>
 <span>          <span class="co"># make factors of SIR columns</span></span>
 <span>          <span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
 <span>                 <span class="va">as.integer</span><span class="op">)</span>,</span>
 <span>          <span class="co"># get Gramstain of microorganisms</span></span>
 <span>          mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># drop NAs - the ones without a Gramstain (fungi, etc.)</span></span>
 <span>  <span class="fu">drop_na</span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
 <span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">betalactams()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code>
 dynamically select columns for antimicrobials in these classes.</li>
 <li>
 <code>drop_na()</code> ensures the model receives complete cases for
 training.</li>
 </ul>
 </div>
 <div class="section level3">
 <h3 id="defining-the-workflow">
 <strong>Defining the Workflow</strong><a class="anchor" aria-label="anchor" href="#defining-the-workflow"></a>
 </h3>
 <p>We now define the <code>tidymodels</code> workflow, which consists of
 three steps: preprocessing, model specification, and fitting.</p>
 <div class="section level4">
 <h4 id="preprocessing-with-a-recipe">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe"></a>
 </h4>
 <p>We create a recipe to preprocess the data for modelling.</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Define the recipe for data preprocessing</span></span>
 <span><span class="va">resistance_recipe</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">mo</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">data</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">step_corr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, threshold <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span>
 <span><span class="va">resistance_recipe</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Inputs</span></span>
 <span><span class="co">#&gt; Number of variables by role</span></span>
 <span><span class="co">#&gt; outcome:    1</span></span>
 <span><span class="co">#&gt; predictor: 20</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Operations</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">c(aminoglycosides(), betalactams())</span></span></span></code></pre></div>
 <p>For a recipe that includes at least one preprocessing operation, like
 we have with <code>step_corr()</code>, the necessary parameters can be
 estimated from a training set using <code>prep()</code>:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">prep</span><span class="op">(</span><span class="va">resistance_recipe</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">aminoglycosides()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
 <span><span class="co">#&gt; <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">betalactams()</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Inputs</span></span>
 <span><span class="co">#&gt; Number of variables by role</span></span>
 <span><span class="co">#&gt; outcome:    1</span></span>
 <span><span class="co">#&gt; predictor: 20</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Training information</span></span>
 <span><span class="co">#&gt; Training data contained 1968 data points and no incomplete rows.</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Operations</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">AMX</span> <span style="color: #0000BB;">CTX</span> | <span style="font-style: italic;">Trained</span></span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code>recipe(mo ~ ., data = data)</code> will take the
 <code>mo</code> column as outcome and all other columns as
 predictors.</li>
 <li>
 <code>step_corr()</code> removes predictors (i.e., antibiotic
 columns) that have a higher correlation than 90%.</li>
 </ul>
 <p>Notice how the recipe contains just the antimicrobial selector
 functions - no need to define the columns specifically. In the
 preparation (retrieved with <code>prep()</code>) we can see that the
 columns or variables ‘AMX’ and ‘CTX’ were removed as they correlate too
 much with existing, other variables.</p>
 </div>
 <div class="section level4">
 <h4 id="specifying-the-model">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model"></a>
 </h4>
 <p>We define a logistic regression model since resistance prediction is
 a binary classification task.</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Specify a logistic regression model</span></span>
 <span><span class="va">logistic_model</span> <span class="op">&lt;-</span> <span class="fu">logistic_reg</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">set_engine</span><span class="op">(</span><span class="st">"glm"</span><span class="op">)</span> <span class="co"># Use the Generalised Linear Model engine</span></span>
 <span><span class="va">logistic_model</span></span>
 <span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code>logistic_reg()</code> sets up a logistic regression
 model.</li>
 <li>
 <code>set_engine("glm")</code> specifies the use of R’s built-in GLM
 engine.</li>
 </ul>
 </div>
 <div class="section level4">
 <h4 id="building-the-workflow">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow"></a>
 </h4>
 <p>We bundle the recipe and model together into a <code>workflow</code>,
 which organises the entire modeling process.</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Combine the recipe and model into a workflow</span></span>
 <span><span class="va">resistance_workflow</span> <span class="op">&lt;-</span> <span class="fu">workflow</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Add the preprocessing recipe</span></span>
 <span>  <span class="fu">add_model</span><span class="op">(</span><span class="va">logistic_model</span><span class="op">)</span> <span class="co"># Add the logistic regression model</span></span>
 <span><span class="va">resistance_workflow</span></span>
 <span><span class="co">#&gt; ══ Workflow ════════════════════════════════════════════════════════════════════</span></span>
 <span><span class="co">#&gt; <span style="font-style: italic;">Preprocessor:</span> Recipe</span></span>
 <span><span class="co">#&gt; <span style="font-style: italic;">Model:</span> logistic_reg()</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Preprocessor ────────────────────────────────────────────────────────────────</span></span>
 <span><span class="co">#&gt; 1 Recipe Step</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; • step_corr()</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Model ───────────────────────────────────────────────────────────────────────</span></span>
 <span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
 </div>
 </div>
 <div class="section level3">
 <h3 id="training-and-evaluating-the-model">
 <strong>Training and Evaluating the Model</strong><a class="anchor" aria-label="anchor" href="#training-and-evaluating-the-model"></a>
 </h3>
 <p>To train the model, we split the data into training and testing sets.
 Then, we fit the workflow on the training set and evaluate its
 performance.</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Split data into training and testing sets</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span> <span class="co"># For reproducibility</span></span>
 <span><span class="va">data_split</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span> <span class="co"># 80% training, 20% testing</span></span>
 <span><span class="va">training_data</span> <span class="op">&lt;-</span> <span class="fu">training</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span> <span class="co"># Training set</span></span>
 <span><span class="va">testing_data</span> <span class="op">&lt;-</span> <span class="fu">testing</span><span class="op">(</span><span class="va">data_split</span><span class="op">)</span>   <span class="co"># Testing set</span></span>
 <span></span>
 <span><span class="co"># Fit the workflow to the training data</span></span>
 <span><span class="va">fitted_workflow</span> <span class="op">&lt;-</span> <span class="va">resistance_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">fit</span><span class="op">(</span><span class="va">training_data</span><span class="op">)</span> <span class="co"># Train the model</span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code>initial_split()</code> splits the data into training and
 testing sets.</li>
 <li>
 <code>fit()</code> trains the workflow on the training set.</li>
 </ul>
 <p>Notice how in <code>fit()</code>, the antimicrobial selector
 functions are internally called again. For training, these functions are
 called since they are stored in the recipe.</p>
 <p>Next, we evaluate the model on the testing data.</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Make predictions on the testing set</span></span>
 <span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span>                <span class="co"># Generate predictions</span></span>
 <span><span class="va">probabilities</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">testing_data</span>, type <span class="op">=</span> <span class="st">"prob"</span><span class="op">)</span> <span class="co"># Generate probabilities</span></span>
 <span></span>
 <span><span class="va">predictions</span> <span class="op">&lt;-</span> <span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">probabilities</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span> <span class="co"># Combine with true labels</span></span>
 <span></span>
 <span><span class="va">predictions</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 394 × 24</span></span></span>
 <span><span class="co">#&gt;    .pred_class   `.pred_Gram-negative` `.pred_Gram-positive` mo        GEN   TOB</span></span>
 <span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span>                         <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> Gram-positive              1.07<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              8.93<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span> Gram-p…     5     5</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> Gram-positive              3.17<span style="color: #949494;">e</span><span style="color: #BB0000;">- 8</span>              1.00<span style="color: #949494;">e</span>+ 0 Gram-p…     5     1</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> Gram-negative              9.99<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              1.42<span style="color: #949494;">e</span><span style="color: #BB0000;">- 3</span> Gram-n…     5     5</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     5     5</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> Gram-negative              9.46<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              5.42<span style="color: #949494;">e</span><span style="color: #BB0000;">- 2</span> Gram-n…     5     5</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> Gram-positive              1.07<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span>              8.93<span style="color: #949494;">e</span><span style="color: #BB0000;">- 1</span> Gram-p…     5     5</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     1     5</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> Gram-positive              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span>              1   <span style="color: #949494;">e</span>+ 0 Gram-p…     4     4</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> Gram-negative              1   <span style="color: #949494;">e</span>+ 0              2.22<span style="color: #949494;">e</span><span style="color: #BB0000;">-16</span> Gram-n…     1     1</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> Gram-positive              6.05<span style="color: #949494;">e</span><span style="color: #BB0000;">-11</span>              1.00<span style="color: #949494;">e</span>+ 0 Gram-p…     4     4</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 384 more rows</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 18 more variables: AMK &lt;int&gt;, KAN &lt;int&gt;, PEN &lt;int&gt;, OXA &lt;int&gt;, FLC &lt;int&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   AMX &lt;int&gt;, AMC &lt;int&gt;, AMP &lt;int&gt;, TZP &lt;int&gt;, CZO &lt;int&gt;, FEP &lt;int&gt;,</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;">#   CXM &lt;int&gt;, FOX &lt;int&gt;, CTX &lt;int&gt;, CAZ &lt;int&gt;, CRO &lt;int&gt;, IPM &lt;int&gt;, MEM &lt;int&gt;</span></span></span>
 <span></span>
 <span><span class="co"># Evaluate model performance</span></span>
 <span><span class="va">metrics</span> <span class="op">&lt;-</span> <span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">metrics</span><span class="op">(</span>truth <span class="op">=</span> <span class="va">mo</span>, estimate <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span> <span class="co"># Calculate performance metrics</span></span>
 <span></span>
 <span><span class="va">metrics</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 3</span></span></span>
 <span><span class="co">#&gt;   .metric  .estimator .estimate</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy binary         0.995</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> kap      binary         0.989</span></span>
 <span></span>
 <span></span>
 <span><span class="co"># To assess some other model properties, you can make our own `metrics()` function</span></span>
 <span><span class="va">our_metrics</span> <span class="op">&lt;-</span> <span class="fu">metric_set</span><span class="op">(</span><span class="va">accuracy</span>, <span class="va">kap</span>, <span class="va">ppv</span>, <span class="va">npv</span><span class="op">)</span> <span class="co"># add Positive Predictive Value and Negative Predictive Value</span></span>
 <span><span class="va">metrics2</span> <span class="op">&lt;-</span> <span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">our_metrics</span><span class="op">(</span>truth <span class="op">=</span> <span class="va">mo</span>, estimate <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span> <span class="co"># run again on our `our_metrics()` function</span></span>
 <span></span>
 <span><span class="va">metrics2</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 4 × 3</span></span></span>
 <span><span class="co">#&gt;   .metric  .estimator .estimate</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy binary         0.995</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> kap      binary         0.989</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> ppv      binary         0.987</span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> npv      binary         1</span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> generates predictions on the testing
 set.</li>
 <li>
 <code>metrics()</code> computes evaluation metrics like accuracy and
 kappa.</li>
 </ul>
 <p>It appears we can predict the Gram stain with a 99.5% accuracy based
 on AMR results of only aminoglycosides and beta-lactam antibiotics. The
 ROC curve looks like this:</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">roc_curve</span><span class="op">(</span><span class="va">mo</span>, <span class="va">`.pred_Gram-negative`</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" width="720"></p>
 </div>
 <div class="section level3">
 <h3 id="conclusion">
 <strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion"></a>
 </h3>
 <p>In this post, we demonstrated how to build a machine learning
 pipeline with the <code>tidymodels</code> framework and the
 <code>AMR</code> package. By combining selector functions like
 <code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code> with
 <code>tidymodels</code>, we efficiently prepared data, trained a model,
 and evaluated its performance.</p>
 <p>This workflow is extensible to other antimicrobial classes and
 resistance patterns, empowering users to analyse AMR data systematically
 and reproducibly.</p>
 <hr>
 </div>
 </div>
 <div class="section level2">
 <h2 id="example-2-predicting-amr-over-time">Example 2: Predicting AMR Over Time<a class="anchor" aria-label="anchor" href="#example-2-predicting-amr-over-time"></a>
 </h2>
 <p>In this second example, we aim to predict antimicrobial resistance
 (AMR) trends over time using <code>tidymodels</code>. We will model
 resistance to three antibiotics (amoxicillin <code>AMX</code>,
 amoxicillin-clavulanic acid <code>AMC</code>, and ciprofloxacin
 <code>CIP</code>), based on historical data grouped by year and hospital
 ward.</p>
 <div class="section level3">
 <h3 id="objective-1">
 <strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective-1"></a>
 </h3>
 <p>Our goal is to:</p>
 <ol style="list-style-type: decimal">
 <li>Prepare the dataset by aggregating resistance data over time.</li>
 <li>Define a regression model to predict AMR trends.</li>
 <li>Use <code>tidymodels</code> to preprocess, train, and evaluate the
 model.</li>
 </ol>
 </div>
 <div class="section level3">
 <h3 id="data-preparation-1">
 <strong>Data Preparation</strong><a class="anchor" aria-label="anchor" href="#data-preparation-1"></a>
 </h3>
 <p>We start by transforming the <code>example_isolates</code> dataset
 into a structured time-series format.</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Load required libraries</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Transform dataset</span></span>
 <span><span class="va">data_time</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span>n <span class="op">=</span> <span class="fl">10</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Filter on the top #10 species</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>year <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/format.html" class="external-link">format</a></span><span class="op">(</span><span class="va">date</span>, <span class="st">"%Y"</span><span class="op">)</span><span class="op">)</span>,  <span class="co"># Extract year from date</span></span>
 <span>         gramstain <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Get taxonomic names</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">year</span>, <span class="va">gramstain</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">AMX</span>, <span class="va">AMC</span>, <span class="va">CIP</span><span class="op">)</span>, </span>
 <span>                   <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">x</span>, minimum <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span>
 <span>                   .names <span class="op">=</span> <span class="st">"res_{.col}"</span><span class="op">)</span>, </span>
 <span>            .groups <span class="op">=</span> <span class="st">"drop"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_AMX</span><span class="op">)</span> <span class="op">&amp;</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_AMC</span><span class="op">)</span> <span class="op">&amp;</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_CIP</span><span class="op">)</span><span class="op">)</span> <span class="co"># Drop missing values</span></span>
 <span><span class="co">#&gt; <span style="color: #0000BB;">ℹ Using column '</span><span style="color: #0000BB; font-weight: bold;">mo</span><span style="color: #0000BB;">' as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">col_mo</span><span style="color: #0000BB;">.</span></span></span>
 <span></span>
 <span><span class="va">data_time</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 32 × 5</span></span></span>
 <span><span class="co">#&gt;     year gramstain     res_AMX res_AMC res_CIP</span></span>
 <span><span class="co">#&gt;    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>           <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span>  <span style="text-decoration: underline;">2</span>002 Gram-negative   1      0.105   0.060<span style="text-decoration: underline;">6</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span>  <span style="text-decoration: underline;">2</span>002 Gram-positive   0.838  0.182   0.162 </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span>  <span style="text-decoration: underline;">2</span>003 Gram-negative   1      0.071<span style="text-decoration: underline;">4</span>  0     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span>  <span style="text-decoration: underline;">2</span>003 Gram-positive   0.714  0.244   0.154 </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span>  <span style="text-decoration: underline;">2</span>004 Gram-negative   0.464  0.093<span style="text-decoration: underline;">8</span>  0     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span>  <span style="text-decoration: underline;">2</span>004 Gram-positive   0.849  0.299   0.244 </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span>  <span style="text-decoration: underline;">2</span>005 Gram-negative   0.412  0.132   0.058<span style="text-decoration: underline;">8</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span>  <span style="text-decoration: underline;">2</span>005 Gram-positive   0.882  0.382   0.154 </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span>  <span style="text-decoration: underline;">2</span>006 Gram-negative   0.379  0       0.1   </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span>  <span style="text-decoration: underline;">2</span>006 Gram-positive   0.778  0.333   0.353 </span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># ℹ 22 more rows</span></span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code>mo_name(mo)</code>: Converts microbial codes into proper
 species names.</li>
 <li>
 <code><a href="../reference/proportion.html">resistance()</a></code>: Converts AMR results into numeric values
 (proportion of resistant isolates).</li>
 <li>
 <code>group_by(year, ward, species)</code>: Aggregates resistance
 rates by year and ward.</li>
 </ul>
 </div>
 <div class="section level3">
 <h3 id="defining-the-workflow-1">
 <strong>Defining the Workflow</strong><a class="anchor" aria-label="anchor" href="#defining-the-workflow-1"></a>
 </h3>
 <p>We now define the modeling workflow, which consists of a
 preprocessing step, a model specification, and the fitting process.</p>
 <div class="section level4">
 <h4 id="preprocessing-with-a-recipe-1">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe-1"></a>
 </h4>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Define the recipe</span></span>
 <span><span class="va">resistance_recipe_time</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">res_AMX</span> <span class="op">~</span> <span class="va">year</span> <span class="op">+</span> <span class="va">gramstain</span>, data <span class="op">=</span> <span class="va">data_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">step_dummy</span><span class="op">(</span><span class="va">gramstain</span>, one_hot <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>  <span class="co"># Convert categorical to numerical</span></span>
 <span>  <span class="fu">step_normalize</span><span class="op">(</span><span class="va">year</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span>  <span class="co"># Normalise year for better model performance</span></span>
 <span>  <span class="fu">step_nzv</span><span class="op">(</span><span class="fu">all_predictors</span><span class="op">(</span><span class="op">)</span><span class="op">)</span>  <span class="co"># Remove near-zero variance predictors</span></span>
 <span></span>
 <span><span class="va">resistance_recipe_time</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Inputs</span></span>
 <span><span class="co">#&gt; Number of variables by role</span></span>
 <span><span class="co">#&gt; outcome:   1</span></span>
 <span><span class="co">#&gt; predictor: 2</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Operations</span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Dummy variables from: <span style="color: #0000BB;">gramstain</span></span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Centering and scaling for: <span style="color: #0000BB;">year</span></span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Sparse, unbalanced variable filter on: <span style="color: #0000BB;">all_predictors()</span></span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code>step_dummy()</code>: Encodes categorical variables
 (<code>ward</code>, <code>species</code>) as numerical indicators.</li>
 <li>
 <code>step_normalize()</code>: Normalises the <code>year</code>
 variable.</li>
 <li>
 <code>step_nzv()</code>: Removes near-zero variance predictors.</li>
 </ul>
 </div>
 <div class="section level4">
 <h4 id="specifying-the-model-1">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model-1"></a>
 </h4>
 <p>We use a linear regression model to predict resistance trends.</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Define the linear regression model</span></span>
 <span><span class="va">lm_model</span> <span class="op">&lt;-</span> <span class="fu">linear_reg</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">set_engine</span><span class="op">(</span><span class="st">"lm"</span><span class="op">)</span> <span class="co"># Use linear regression</span></span>
 <span></span>
 <span><span class="va">lm_model</span></span>
 <span><span class="co">#&gt; Linear Regression Model Specification (regression)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Computational engine: lm</span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code>linear_reg()</code>: Defines a linear regression model.</li>
 <li>
 <code>set_engine("lm")</code>: Uses R’s built-in linear regression
 engine.</li>
 </ul>
 </div>
 <div class="section level4">
 <h4 id="building-the-workflow-1">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow-1"></a>
 </h4>
 <p>We combine the preprocessing recipe and model into a workflow.</p>
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Create workflow</span></span>
 <span><span class="va">resistance_workflow_time</span> <span class="op">&lt;-</span> <span class="fu">workflow</span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">add_model</span><span class="op">(</span><span class="va">lm_model</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">resistance_workflow_time</span></span>
 <span><span class="co">#&gt; ══ Workflow ════════════════════════════════════════════════════════════════════</span></span>
 <span><span class="co">#&gt; <span style="font-style: italic;">Preprocessor:</span> Recipe</span></span>
 <span><span class="co">#&gt; <span style="font-style: italic;">Model:</span> linear_reg()</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Preprocessor ────────────────────────────────────────────────────────────────</span></span>
 <span><span class="co">#&gt; 3 Recipe Steps</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; • step_dummy()</span></span>
 <span><span class="co">#&gt; • step_normalize()</span></span>
 <span><span class="co">#&gt; • step_nzv()</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; ── Model ───────────────────────────────────────────────────────────────────────</span></span>
 <span><span class="co">#&gt; Linear Regression Model Specification (regression)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Computational engine: lm</span></span></code></pre></div>
 </div>
 </div>
 <div class="section level3">
 <h3 id="training-and-evaluating-the-model-1">
 <strong>Training and Evaluating the Model</strong><a class="anchor" aria-label="anchor" href="#training-and-evaluating-the-model-1"></a>
 </h3>
 <p>We split the data into training and testing sets, fit the model, and
 evaluate performance.</p>
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Split the data</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
 <span><span class="va">data_split_time</span> <span class="op">&lt;-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data_time</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span></span>
 <span><span class="va">train_time</span> <span class="op">&lt;-</span> <span class="fu">training</span><span class="op">(</span><span class="va">data_split_time</span><span class="op">)</span></span>
 <span><span class="va">test_time</span> <span class="op">&lt;-</span> <span class="fu">testing</span><span class="op">(</span><span class="va">data_split_time</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Train the model</span></span>
 <span><span class="va">fitted_workflow_time</span> <span class="op">&lt;-</span> <span class="va">resistance_workflow_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">fit</span><span class="op">(</span><span class="va">train_time</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Make predictions</span></span>
 <span><span class="va">predictions_time</span> <span class="op">&lt;-</span> <span class="va">fitted_workflow_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span> </span>
 <span></span>
 <span><span class="co"># Evaluate model</span></span>
 <span><span class="va">metrics_time</span> <span class="op">&lt;-</span> <span class="va">predictions_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu">metrics</span><span class="op">(</span>truth <span class="op">=</span> <span class="va">res_AMX</span>, estimate <span class="op">=</span> <span class="va">.pred</span><span class="op">)</span></span>
 <span></span>
 <span><span class="va">metrics_time</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3 × 3</span></span></span>
 <span><span class="co">#&gt;   .metric .estimator .estimate</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>          <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> rmse    standard      0.077<span style="text-decoration: underline;">4</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> rsq     standard      0.711 </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> mae     standard      0.070<span style="text-decoration: underline;">4</span></span></span></code></pre></div>
 <p><strong>Explanation:</strong></p>
 <ul>
 <li>
 <code>initial_split()</code>: Splits data into training and testing
 sets.</li>
 <li>
 <code>fit()</code>: Trains the workflow.</li>
 <li>
 <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code>: Generates resistance predictions.</li>
 <li>
 <code>metrics()</code>: Evaluates model performance.</li>
 </ul>
 </div>
 <div class="section level3">
 <h3 id="visualising-predictions">
 <strong>Visualising Predictions</strong><a class="anchor" aria-label="anchor" href="#visualising-predictions"></a>
 </h3>
 <p>We plot resistance trends over time for amoxicillin.</p>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Plot actual vs predicted resistance over time</span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">predictions_time</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">year</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>y <span class="op">=</span> <span class="va">res_AMX</span>, color <span class="op">=</span> <span class="st">"Actual"</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_path.html" class="external-link">geom_line</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>y <span class="op">=</span> <span class="va">.pred</span>, color <span class="op">=</span> <span class="st">"Predicted"</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"Predicted vs Actual AMX Resistance Over Time"</span>,</span>
 <span>       x <span class="op">=</span> <span class="st">"Year"</span>,</span>
 <span>       y <span class="op">=</span> <span class="st">"Resistance Proportion"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" width="720"></p>
 <p>Additionally, we can visualise resistance trends in
 <code>ggplot2</code> and directly add linear models there:</p>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">data_time</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">year</span>, y <span class="op">=</span> <span class="va">res_AMX</span>, color <span class="op">=</span> <span class="va">gramstain</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_path.html" class="external-link">geom_line</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"AMX Resistance Trends"</span>,</span>
 <span>       x <span class="op">=</span> <span class="st">"Year"</span>,</span>
 <span>       y <span class="op">=</span> <span class="st">"Resistance Proportion"</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="co"># add a linear model directly in ggplot2:</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_smooth.html" class="external-link">geom_smooth</a></span><span class="op">(</span>method <span class="op">=</span> <span class="st">"lm"</span>,</span>
 <span>              formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span>
 <span>              alpha <span class="op">=</span> <span class="fl">0.25</span><span class="op">)</span> <span class="op">+</span></span>
 <span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png" width="720"></p>
 </div>
 <div class="section level3">
 <h3 id="conclusion-1">
 <strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion-1"></a>
 </h3>
 <p>In this example, we demonstrated how to analyze AMR trends over time
 using <code>tidymodels</code>. By aggregating resistance rates by year
 and hospital ward, we built a predictive model to track changes in
 resistance to amoxicillin (<code>AMX</code>), amoxicillin-clavulanic
 acid (<code>AMC</code>), and ciprofloxacin (<code>CIP</code>).</p>
 <p>This method can be extended to other antibiotics and resistance
 patterns, providing valuable insights into AMR dynamics in healthcare
 settings.</p>
 </div>
 </div>
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      <img src="../logo.svg" class="logo" alt=""><h1>How to apply EUCAST rules</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/EUCAST.Rmd" class="external-link"><code>vignettes/EUCAST.Rmd</code></a></small>
      <div class="d-none name"><code>EUCAST.Rmd</code></div>
    </div>
 <div class="section level2">
 <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
 <p>What are EUCAST rules? The European Committee on Antimicrobial
 Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes" class="external-link">on
 their website</a>:</p>
 <blockquote>
 <p><em>EUCAST expert rules (see below) are a tabulated collection of
 expert knowledge on interpretive rules, expected resistant phenotypes
 and expected susceptible phenotypes which should be applied to
 antimicrobial susceptibility testing in order to reduce testing, reduce
 errors and make appropriate recommendations for reporting particular
 resistances.</em></p>
 </blockquote>
 <p>In Europe, a lot of medical microbiological laboratories already
 apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown
 <em>et al.</em>, 2015</a>). Our package features their latest insights
 on expected resistant phenotypes (v1.2, 2023).</p>
 </div>
 <div class="section level2">
 <h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
 </h2>
 <p>These rules can be used to discard improbable bug-drug combinations
 in your data. For example, <em>Klebsiella</em> produces beta-lactamase
 that prevents ampicillin (or amoxicillin) from working against it. In
 other words, practically every strain of <em>Klebsiella</em> is
 resistant to ampicillin.</p>
 <p>Sometimes, laboratory data can still contain such strains with
 <em>Klebsiella</em> being susceptible to ampicillin. This could be
 because an antibiogram is available before an identification is
 available, and the antibiogram is then not re-interpreted based on the
 identification. The <code><a href="../reference/eucast_rules.html">eucast_rules()</a></code> function resolves this,
 by applying the latest ‘EUCAST Expected Resistant Phenotypes’
 guideline:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">oops</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
 <span>  mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
 <span>    <span class="st">"Klebsiella pneumoniae"</span>,</span>
 <span>    <span class="st">"Escherichia coli"</span></span>
 <span>  <span class="op">)</span>,</span>
 <span>  ampicillin <span class="op">=</span> <span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="st">"S"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span><span class="va">oops</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
 <span><span class="co">#&gt;   mo                    ampicillin</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #5FD7AF;">  S  </span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span>
 <span></span>
 <span><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
 <span><span class="co">#&gt;   mo                    ampicillin</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>                 <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #FFAFAF;">  R  </span>     </span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Escherichia coli      <span style="color: #080808; background-color: #5FD7AF;">  S  </span></span></span></code></pre></div>
 <p>A more convenient function is
 <code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline,
 but allows to check for one or more specific microorganisms or
 antimicrobials:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"Escherichia coli"</span><span class="op">)</span>,</span>
 <span>  <span class="st">"ampicillin"</span></span>
 <span><span class="op">)</span></span>
 <span><span class="co">#&gt; [1]  TRUE FALSE</span></span>
 <span></span>
 <span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
 <span>  <span class="st">"Klebsiella pneumoniae"</span>,</span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span><span class="co">#&gt; [1]  TRUE FALSE</span></span></code></pre></div>
 <p>EUCAST rules can not only be used for correction, they can also be
 used for filling in known resistance and susceptibility based on results
 of other antimicrobials drugs. This process is called <em>interpretive
 reading</em>, and is basically a form of imputation:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
 <span>  mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
 <span>    <span class="st">"Staphylococcus aureus"</span>,</span>
 <span>    <span class="st">"Enterococcus faecalis"</span>,</span>
 <span>    <span class="st">"Escherichia coli"</span>,</span>
 <span>    <span class="st">"Klebsiella pneumoniae"</span>,</span>
 <span>    <span class="st">"Pseudomonas aeruginosa"</span></span>
 <span>  <span class="op">)</span>,</span>
 <span>  VAN <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Vancomycin</span></span>
 <span>  AMX <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Amoxicillin</span></span>
 <span>  COL <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Colistin</span></span>
 <span>  CAZ <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Ceftazidime</span></span>
 <span>  CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span></span>
 <span>  PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span></span>
 <span>  FOX <span class="op">=</span> <span class="st">"S"</span>  <span class="co"># Cefoxitin</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">mo</th>
 <th align="center">VAN</th>
 <th align="center">AMX</th>
 <th align="center">COL</th>
 <th align="center">CAZ</th>
 <th align="center">CXM</th>
 <th align="center">PEN</th>
 <th align="center">FOX</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">Staphylococcus aureus</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
 <td align="left">Enterococcus faecalis</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="odd">
 <td align="left">Escherichia coli</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
 <td align="left">Klebsiella pneumoniae</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="odd">
 <td align="left">Pseudomonas aeruginosa</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 </tbody>
 </table>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/eucast_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <table class="table">
 <thead><tr class="header">
 <th align="left">mo</th>
 <th align="center">VAN</th>
 <th align="center">AMX</th>
 <th align="center">COL</th>
 <th align="center">CAZ</th>
 <th align="center">CXM</th>
 <th align="center">PEN</th>
 <th align="center">FOX</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">Staphylococcus aureus</td>
 <td align="center">-</td>
 <td align="center">S</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">S</td>
 <td align="center">S</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
 <td align="left">Enterococcus faecalis</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">S</td>
 <td align="center">R</td>
 </tr>
 <tr class="odd">
 <td align="left">Escherichia coli</td>
 <td align="center">R</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">R</td>
 <td align="center">S</td>
 </tr>
 <tr class="even">
 <td align="left">Klebsiella pneumoniae</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">R</td>
 <td align="center">S</td>
 </tr>
 <tr class="odd">
 <td align="left">Pseudomonas aeruginosa</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">-</td>
 <td align="center">-</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">R</td>
 </tr>
 </tbody>
 </table>
 </div>
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      <img src="../logo.svg" class="logo" alt=""><h1>How to determine multi-drug resistance (MDR)</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/MDR.Rmd" class="external-link"><code>vignettes/MDR.Rmd</code></a></small>
      <div class="d-none name"><code>MDR.Rmd</code></div>
    </div>
 <p>With the function <code><a href="../reference/mdro.html">mdro()</a></code>, you can determine which
 micro-organisms are multi-drug resistant organisms (MDRO).</p>
 <div class="section level3">
 <h3 id="type-of-input">Type of input<a class="anchor" aria-label="anchor" href="#type-of-input"></a>
 </h3>
 <p>The <code><a href="../reference/mdro.html">mdro()</a></code> function takes a data set as input, such as a
 regular <code>data.frame</code>. It tries to automatically determine the
 right columns for info about your isolates, such as the name of the
 species and all columns with results of antimicrobial agents. See the
 help page for more info about how to set the right settings for your
 data with the command <code><a href="../reference/mdro.html">?mdro</a></code>.</p>
 <p>For WHONET data (and most other data), all settings are automatically
 set correctly.</p>
 </div>
 <div class="section level3">
 <h3 id="guidelines">Guidelines<a class="anchor" aria-label="anchor" href="#guidelines"></a>
 </h3>
 <p>The <code><a href="../reference/mdro.html">mdro()</a></code> function support multiple guidelines. You can
 select a guideline with the <code>guideline</code> parameter. Currently
 supported guidelines are (case-insensitive):</p>
 <ul>
 <li>
 <p><code>guideline = "CMI2012"</code> (default)</p>
 <p>Magiorakos AP, Srinivasan A <em>et al.</em> “Multidrug-resistant,
 extensively drug-resistant and pandrug-resistant bacteria: an
 international expert proposal for interim standard definitions for
 acquired resistance.” Clinical Microbiology and Infection (2012) (<a href="https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(14)61632-3/fulltext" class="external-link">link</a>)</p>
 </li>
 <li>
 <p><code>guideline = "EUCAST3.2"</code> (or simply
 <code>guideline = "EUCAST"</code>)</p>
 <p>The European international guideline - EUCAST Expert Rules Version
 3.2 “Intrinsic Resistance and Unusual Phenotypes” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/2020/Intrinsic_Resistance_and_Unusual_Phenotypes_Tables_v3.2_20200225.pdf" class="external-link">link</a>)</p>
 </li>
 <li>
 <p><code>guideline = "EUCAST3.1"</code></p>
 <p>The European international guideline - EUCAST Expert Rules Version
 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (<a href="https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf" class="external-link">link</a>)</p>
 </li>
 <li>
 <p><code>guideline = "TB"</code></p>
 <p>The international guideline for multi-drug resistant tuberculosis -
 World Health Organization “Companion handbook to the WHO guidelines for
 the programmatic management of drug-resistant tuberculosis” (<a href="https://www.who.int/tb/publications/pmdt_companionhandbook/en/" class="external-link">link</a>)</p>
 </li>
 <li>
 <p><code>guideline = "MRGN"</code></p>
 <p>The German national guideline - Mueller <em>et al.</em> (2015)
 Antimicrobial Resistance and Infection Control 4:7. DOI:
 10.1186/s13756-015-0047-6</p>
 </li>
 <li>
 <p><code>guideline = "BRMO"</code></p>
 <p>The Dutch national guideline - Rijksinstituut voor Volksgezondheid en
 Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen)
 (ZKH)” (<a href="https://www.rivm.nl/wip-richtlijn-brmo-bijzonder-resistente-micro-organismen-zkh" class="external-link">link</a>)</p>
 </li>
 </ul>
 <p>Please suggest your own (country-specific) guidelines by letting us
 know: <a href="https://github.com/msberends/AMR/issues/new" class="external-link uri">https://github.com/msberends/AMR/issues/new</a>.</p>
 <div class="section level4">
 <h4 id="custom-guidelines">Custom Guidelines<a class="anchor" aria-label="anchor" href="#custom-guidelines"></a>
 </h4>
 <p>You can also use your own custom guideline. Custom guidelines can be
 set with the <code><a href="../reference/mdro.html">custom_mdro_guideline()</a></code> function. This is of
 great importance if you have custom rules to determine MDROs in your
 hospital, e.g., rules that are dependent on ward, state of contact
 isolation or other variables in your data.</p>
 <p>If you are familiar with <code><a href="https://dplyr.tidyverse.org/reference/case_when.html" class="external-link">case_when()</a></code> of the
 <code>dplyr</code> package, you will recognise the input method to set
 your own rules. Rules must be set using what R considers to be the
 ‘formula notation’:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">custom</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">custom_mdro_guideline</a></span><span class="op">(</span></span>
 <span>  <span class="va">CIP</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type A"</span>,</span>
 <span>  <span class="va">ERY</span> <span class="op">==</span> <span class="st">"R"</span> <span class="op">&amp;</span> <span class="va">age</span> <span class="op">&gt;</span> <span class="fl">60</span> <span class="op">~</span> <span class="st">"Elderly Type B"</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <p>If a row/an isolate matches the first rule, the value after the first
 <code>~</code> (in this case <em>‘Elderly Type A’</em>) will be set as
 MDRO value. Otherwise, the second rule will be tried and so on. The
 maximum number of rules is unlimited.</p>
 <p>You can print the rules set in the console for an overview. Colours
 will help reading it if your console supports colours.</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">custom</span></span>
 <span><span class="co">#&gt; A set of custom MDRO rules:</span></span>
 <span><span class="co">#&gt;   1. <span style="font-weight: bold;">If </span><span style="color: #0000BB;">CIP</span><span style="color: #080808;"> is </span><span style="color: #080808; background-color: #FFAFAF;"> R </span><span style="color: #080808; font-weight: bold;"> and </span><span style="color: #0000BB;">age</span><span style="color: #080808;"> is higher than </span><span style="color: #0000BB;">60</span><span style="font-weight: bold;"> then: </span><span style="color: #BB0000;">Elderly Type A</span></span></span>
 <span><span class="co">#&gt;   2. <span style="font-weight: bold;">If </span><span style="color: #0000BB;">ERY</span><span style="color: #080808;"> is </span><span style="color: #080808; background-color: #FFAFAF;"> R </span><span style="color: #080808; font-weight: bold;"> and </span><span style="color: #0000BB;">age</span><span style="color: #080808;"> is higher than </span><span style="color: #0000BB;">60</span><span style="font-weight: bold;"> then: </span><span style="color: #BB0000;">Elderly Type B</span></span></span>
 <span><span class="co">#&gt;   3. <span style="font-weight: bold;">Otherwise: </span><span style="color: #BB0000;">Negative</span></span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; Unmatched rows will return <span style="color: #BB0000;">NA</span>.</span></span>
 <span><span class="co">#&gt; Results will be of class 'factor', with ordered levels: Negative &lt; Elderly Type A &lt; Elderly Type B</span></span></code></pre></div>
 <p>The outcome of the function can be used for the
 <code>guideline</code> argument in the <code><a href="../reference/mdro.html">mdro()</a></code> function:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">example_isolates</span>, guideline <span class="op">=</span> <span class="va">custom</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/table.html" class="external-link">table</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span></span>
 <span><span class="co">#&gt; x</span></span>
 <span><span class="co">#&gt;       Negative Elderly Type A Elderly Type B </span></span>
 <span><span class="co">#&gt;           1070            198            732</span></span></code></pre></div>
 <p>The rules set (the <code>custom</code> object in this case) could be
 exported to a shared file location using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS()</a></code> if you
 collaborate with multiple users. The custom rules set could then be
 imported using <code><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">readRDS()</a></code>.</p>
 </div>
 </div>
 <div class="section level3">
 <h3 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
 </h3>
 <p>The <code><a href="../reference/mdro.html">mdro()</a></code> function always returns an ordered
 <code>factor</code> for predefined guidelines. For example, the output
 of the default guideline by Magiorakos <em>et al.</em> returns a
 <code>factor</code> with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in
 that order.</p>
 <p>The next example uses the <code>example_isolates</code> data set.
 This is a data set included with this package and contains full
 antibiograms of 2,000 microbial isolates. It reflects reality and can be
 used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline
 on this data set, we get:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># to support pipes: %&gt;%</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="op">)</span> <span class="co"># show frequency table of the result</span></span>
 <span><span class="co">#&gt; Warning: in <span style="background-color: #EEEEEE;">mdro()</span>: NA introduced for isolates where the available percentage of</span></span>
 <span><span class="co">#&gt; antimicrobial classes was below 50% (set with <span style="background-color: #EEEEEE;">pct_required_classes</span>)</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: factor &gt; ordered (numeric)<br>
 Length: 2,000<br>
 Levels: 4: Negative &lt; Multi-drug-resistant (MDR) &lt; Extensively
 drug-resistant …<br>
 Available: 1,745 (87.25%, NA: 255 = 12.75%)<br>
 Unique: 2</p>
 <table style="width:100%;" class="table">
 <colgroup>
 <col width="4%">
 <col width="38%">
 <col width="9%">
 <col width="12%">
 <col width="16%">
 <col width="19%">
 </colgroup>
 <thead><tr class="header">
 <th align="left"></th>
 <th align="left">Item</th>
 <th align="right">Count</th>
 <th align="right">Percent</th>
 <th align="right">Cum. Count</th>
 <th align="right">Cum. Percent</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Negative</td>
 <td align="right">1617</td>
 <td align="right">92.66%</td>
 <td align="right">1617</td>
 <td align="right">92.66%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Multi-drug-resistant (MDR)</td>
 <td align="right">128</td>
 <td align="right">7.34%</td>
 <td align="right">1745</td>
 <td align="right">100.00%</td>
 </tr>
 </tbody>
 </table>
 <p>For another example, I will create a data set to determine multi-drug
 resistant TB:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># random_sir() is a helper function to generate</span></span>
 <span><span class="co"># a random vector with values S, I and R</span></span>
 <span><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
 <span>  rifampicin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  isoniazid <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  gatifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  ethambutol <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  pyrazinamide <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  moxifloxacin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  kanamycin <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <p>Because all column names are automatically verified for valid drug
 names or codes, this would have worked exactly the same way:</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">my_TB_data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
 <span>  RIF <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  INH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  GAT <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  ETH <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  PZA <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  MFX <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span>,</span>
 <span>  KAN <span class="op">=</span> <span class="fu"><a href="../reference/random.html">random_sir</a></span><span class="op">(</span><span class="fl">5000</span><span class="op">)</span></span>
 <span><span class="op">)</span></span></code></pre></div>
 <p>The data set now looks like this:</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span></span>
 <span><span class="co">#&gt;   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></span>
 <span><span class="co">#&gt; 1          I         R            S          S            S            S</span></span>
 <span><span class="co">#&gt; 2          S         S            I          R            R            S</span></span>
 <span><span class="co">#&gt; 3          R         I            I          I            R            I</span></span>
 <span><span class="co">#&gt; 4          I         S            S          S            S            S</span></span>
 <span><span class="co">#&gt; 5          I         I            I          S            I            S</span></span>
 <span><span class="co">#&gt; 6          R         S            R          S            I            I</span></span>
 <span><span class="co">#&gt;   kanamycin</span></span>
 <span><span class="co">#&gt; 1         R</span></span>
 <span><span class="co">#&gt; 2         I</span></span>
 <span><span class="co">#&gt; 3         S</span></span>
 <span><span class="co">#&gt; 4         I</span></span>
 <span><span class="co">#&gt; 5         I</span></span>
 <span><span class="co">#&gt; 6         I</span></span></code></pre></div>
 <p>We can now add the interpretation of MDR-TB to our data set. You can
 use:</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/mdro.html">mdro</a></span><span class="op">(</span><span class="va">my_TB_data</span>, guideline <span class="op">=</span> <span class="st">"TB"</span><span class="op">)</span></span></code></pre></div>
 <p>or its shortcut <code><a href="../reference/mdro.html">mdr_tb()</a></code>:</p>
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/mdro.html">mdr_tb</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #0000BB;">ℹ No column found as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">col_mo</span><span style="color: #0000BB;">, </span><span style="color: #0000BB; font-weight: bold;">assuming all rows contain</span></span></span>
 <span><span class="co"><span style="color: #0000BB; font-weight: bold;">#&gt;   </span><span style="color: #0000BB; font-weight: bold; font-style: italic;">Mycobacterium tuberculosis</span><span style="color: #0000BB; font-weight: bold;">.</span></span></span></code></pre></div>
 <p>Create a frequency table of the results:</p>
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">$</span><span class="va">mdr</span><span class="op">)</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: factor &gt; ordered (numeric)<br>
 Length: 5,000<br>
 Levels: 5: Negative &lt; Mono-resistant &lt; Poly-resistant &lt;
 Multi-drug-resistant &lt;…<br>
 Available: 5,000 (100%, NA: 0 = 0%)<br>
 Unique: 5</p>
 <table style="width:100%;" class="table">
 <colgroup>
 <col width="4%">
 <col width="38%">
 <col width="9%">
 <col width="12%">
 <col width="16%">
 <col width="19%">
 </colgroup>
 <thead><tr class="header">
 <th align="left"></th>
 <th align="left">Item</th>
 <th align="right">Count</th>
 <th align="right">Percent</th>
 <th align="right">Cum. Count</th>
 <th align="right">Cum. Percent</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Mono-resistant</td>
 <td align="right">3223</td>
 <td align="right">64.46%</td>
 <td align="right">3223</td>
 <td align="right">64.46%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Negative</td>
 <td align="right">967</td>
 <td align="right">19.34%</td>
 <td align="right">4190</td>
 <td align="right">83.80%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Multi-drug-resistant</td>
 <td align="right">454</td>
 <td align="right">9.08%</td>
 <td align="right">4644</td>
 <td align="right">92.88%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Poly-resistant</td>
 <td align="right">245</td>
 <td align="right">4.90%</td>
 <td align="right">4889</td>
 <td align="right">97.78%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Extensively drug-resistant</td>
 <td align="right">111</td>
 <td align="right">2.22%</td>
 <td align="right">5000</td>
 <td align="right">100.00%</td>
 </tr>
 </tbody>
 </table>
 </div>
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      <img src="../logo.svg" class="logo" alt=""><h1>How to conduct principal component analysis (PCA) for AMR</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
      <div class="d-none name"><code>PCA.Rmd</code></div>
    </div>
 <p><strong>NOTE: This page will be updated soon, as the pca() function
 is currently being developed.</strong></p>
 <div class="section level2">
 <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
 </div>
 <div class="section level2">
 <h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
 </h2>
 <p>For PCA, we need to transform our AMR data first. This is what the
 <code>example_isolates</code> data set in this package looks like:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Rows: 2,000</span></span>
 <span><span class="co">#&gt; Columns: 46</span></span>
 <span><span class="co">#&gt; $ date    <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2002-01-02<span style="color: #949494;">, </span>2002-01-03<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-13<span style="color: #949494;">, </span>2…</span></span>
 <span><span class="co">#&gt; $ patient <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A77334"<span style="color: #949494;">, </span>"A77334"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"4…</span></span>
 <span><span class="co">#&gt; $ age     <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> 65<span style="color: #949494;">, </span>65<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>79<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>75<span style="color: #949494;">, </span>50…</span></span>
 <span><span class="co">#&gt; $ gender  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M…</span></span>
 <span><span class="co">#&gt; $ ward    <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "Clinical"<span style="color: #949494;">, </span>"Clinical"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"Clinical"…</span></span>
 <span><span class="co">#&gt; $ mo      <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ PEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ OXA     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ FLC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R…</span></span>
 <span><span class="co">#&gt; $ AMX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ AMC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ AMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ TZP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ CZO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ FEP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ CXM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ FOX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ CTX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ CAZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ CRO     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ GEN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ TOB     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ AMK     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ KAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ TMP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ SXT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ NIT     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ FOS     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ LNZ     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ CIP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ MFX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ VAN     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ TEC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
 <span><span class="co">#&gt; $ TCY     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ TGC     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ DOX     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ ERY     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ CLI     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ AZM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
 <span><span class="co">#&gt; $ IPM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
 <span><span class="co">#&gt; $ MEM     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ MTR     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ CHL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ COL     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
 <span><span class="co">#&gt; $ MUP     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
 <span><span class="co">#&gt; $ RIF     <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span></code></pre></div>
 <p>Now to transform this to a data set with only resistance percentages
 per taxonomic order and genus:</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">resistance_data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span>
 <span>    order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span></span>
 <span>    genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span>
 <span>  <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co">#  and genus as we do here</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.sir</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># then get resistance of all drugs</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span></span>
 <span>    <span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,</span>
 <span>    <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span></span>
 <span>  <span class="op">)</span> <span class="co"># and select only relevant columns</span></span>
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 6 × 10</span></span></span>
 <span><span class="co">#&gt; <span style="color: #949494;"># Groups:   order [5]</span></span></span>
 <span><span class="co">#&gt;   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT</span></span>
 <span><span class="co">#&gt;   <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>             <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>        <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> (unknown order)   (unknown ge…    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Actinomycetales   Schaalia        <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Bacteroidales     Bacteroides     <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Campylobacterales Campylobact…    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> Caryophanales     Gemella         <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span>
 <span><span class="co">#&gt; <span style="color: #BCBCBC;">6</span> Caryophanales     Listeria        <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span>    <span style="color: #BB0000;">NA</span></span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
 </h2>
 <p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows
 that contain numeric values in all selected variables, so we now only
 need to do:</p>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
 <span><span class="co">#&gt; <span style="color: #0000BB;">ℹ Columns selected for PCA: "</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">SXT</span><span style="color: #0000BB;">",</span></span></span>
 <span><span class="co"><span style="color: #0000BB;">#&gt;   "</span><span style="color: #0000BB; font-weight: bold;">TMP</span><span style="color: #0000BB;">", and "</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">". Total observations available: 7.</span></span></span></code></pre></div>
 <p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
 function:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
 <span><span class="co">#&gt; [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"</span></span>
 <span><span class="co">#&gt; Importance of components:</span></span>
 <span><span class="co">#&gt;                           PC1    PC2    PC3     PC4     PC5     PC6       PC7</span></span>
 <span><span class="co">#&gt; Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16</span></span>
 <span><span class="co">#&gt; Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span></span>
 <span><span class="co">#&gt; Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></span></code></pre></div>
 <pre><code><span><span class="co">#&gt; Groups (n=4, named as 'order'):</span></span>
 <span><span class="co">#&gt; [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"</span></span></code></pre>
 <p>Good news. The first two components explain a total of 93.3% of the
 variance (see the PC1 and PC2 values of the <em>Proportion of
 Variance</em>. We can create a so-called biplot with the base R
 <code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance
 per drug explain the difference per microorganism.</p>
 </div>
 <div class="section level2">
 <h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
 </h2>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
 <p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
 <p>But we can’t see the explanation of the points. Perhaps this works
 better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that
 automatically adds the right labels and even groups:</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
 <p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
 <p>You can also print an ellipse per group, and edit the appearance:</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span></span>
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      <img src="../logo.svg" class="logo" alt=""><h1>How to work with WHONET data</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
      <div class="d-none name"><code>WHONET.Rmd</code></div>
    </div>
 <div class="section level3">
 <h3 id="import-of-data">Import of data<a class="anchor" aria-label="anchor" href="#import-of-data"></a>
 </h3>
 <p>This tutorial assumes you already imported the WHONET data with
 e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code>
 package</a>. In RStudio, this can be done using the menu button ‘Import
 Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
 select your exported file. Make sure date fields are imported
 correctly.</p>
 <p>An example syntax could look like this:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></span></code></pre></div>
 <p>This package comes with an <a href="https://msberends.github.io/AMR/reference/WHONET.html">example
 data set <code>WHONET</code></a>. We will use it for this analysis.</p>
 </div>
 <div class="section level3">
 <h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
 </h3>
 <p>First, load the relevant packages if you did not yet did this. I use
 the tidyverse for all of my analyses. All of them. If you don’t know it
 yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
 <p>We will have to transform some variables to simplify and automate the
 analysis:</p>
 <ul>
 <li>Microorganisms should be transformed to our own microorganism codes
 (called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our
 Catalogue of Life reference data set</a>, which contains all ~70,000
 microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
 We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
 recognises almost all WHONET abbreviations of microorganisms.</li>
 <li>Antimicrobial results or interpretations have to be clean and valid.
 In other words, they should only contain values <code>"S"</code>,
 <code>"I"</code> or <code>"R"</code>. That is exactly where the
 <code><a href="../reference/as.sir.html">as.sir()</a></code> function is for.</li>
 </ul>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># transform variables</span></span>
 <span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">WHONET</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># get microbial ID based on given organism</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span></span></code></pre></div>
 <p>No errors or warnings, so all values are transformed succesfully.</p>
 <p>We also created a package dedicated to data cleaning and checking,
 called the <code>cleaner</code> package. Its <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code>
 function can be used to create frequency tables.</p>
 <p>So let’s check our data, with a couple of frequency tables:</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># our newly created `mo` variable, put in the mo_name() function</span></span>
 <span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: character<br>
 Length: 500<br>
 Available: 500 (100%, NA: 0 = 0%)<br>
 Unique: 38</p>
 <p>Shortest: 11<br>
 Longest: 40</p>
 <table class="table">
 <colgroup>
 <col width="4%">
 <col width="47%">
 <col width="7%">
 <col width="10%">
 <col width="13%">
 <col width="15%">
 </colgroup>
 <thead><tr class="header">
 <th align="left"></th>
 <th align="left">Item</th>
 <th align="right">Count</th>
 <th align="right">Percent</th>
 <th align="right">Cum. Count</th>
 <th align="right">Cum. Percent</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Escherichia coli</td>
 <td align="right">245</td>
 <td align="right">49.0%</td>
 <td align="right">245</td>
 <td align="right">49.0%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Coagulase-negative Staphylococcus (CoNS)</td>
 <td align="right">74</td>
 <td align="right">14.8%</td>
 <td align="right">319</td>
 <td align="right">63.8%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Staphylococcus epidermidis</td>
 <td align="right">38</td>
 <td align="right">7.6%</td>
 <td align="right">357</td>
 <td align="right">71.4%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Streptococcus pneumoniae</td>
 <td align="right">31</td>
 <td align="right">6.2%</td>
 <td align="right">388</td>
 <td align="right">77.6%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Staphylococcus hominis</td>
 <td align="right">21</td>
 <td align="right">4.2%</td>
 <td align="right">409</td>
 <td align="right">81.8%</td>
 </tr>
 <tr class="even">
 <td align="left">6</td>
 <td align="left">Proteus mirabilis</td>
 <td align="right">9</td>
 <td align="right">1.8%</td>
 <td align="right">418</td>
 <td align="right">83.6%</td>
 </tr>
 <tr class="odd">
 <td align="left">7</td>
 <td align="left">Enterococcus faecium</td>
 <td align="right">8</td>
 <td align="right">1.6%</td>
 <td align="right">426</td>
 <td align="right">85.2%</td>
 </tr>
 <tr class="even">
 <td align="left">8</td>
 <td align="left">Staphylococcus capitis urealyticus</td>
 <td align="right">8</td>
 <td align="right">1.6%</td>
 <td align="right">434</td>
 <td align="right">86.8%</td>
 </tr>
 <tr class="odd">
 <td align="left">9</td>
 <td align="left">Enterobacter cloacae</td>
 <td align="right">5</td>
 <td align="right">1.0%</td>
 <td align="right">439</td>
 <td align="right">87.8%</td>
 </tr>
 <tr class="even">
 <td align="left">10</td>
 <td align="left">Enterococcus columbae</td>
 <td align="right">4</td>
 <td align="right">0.8%</td>
 <td align="right">443</td>
 <td align="right">88.6%</td>
 </tr>
 </tbody>
 </table>
 <p>(omitted 28 entries, n = 57 [11.4%])</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># our transformed antibiotic columns</span></span>
 <span><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></span>
 <span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: factor &gt; ordered &gt; sir (numeric)<br>
 Length: 500<br>
 Levels: 5: S &lt; SDD &lt; I &lt; R &lt; NI<br>
 Available: 481 (96.2%, NA: 19 = 3.8%)<br>
 Unique: 3</p>
 <p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02)<br>
 Drug group: Beta-lactams/penicillins<br>
 %SI: 78.59%</p>
 <table class="table">
 <thead><tr class="header">
 <th align="left"></th>
 <th align="left">Item</th>
 <th align="right">Count</th>
 <th align="right">Percent</th>
 <th align="right">Cum. Count</th>
 <th align="right">Cum. Percent</th>
 </tr></thead>
 <tbody>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">S</td>
 <td align="right">356</td>
 <td align="right">74.01%</td>
 <td align="right">356</td>
 <td align="right">74.01%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">R</td>
 <td align="right">103</td>
 <td align="right">21.41%</td>
 <td align="right">459</td>
 <td align="right">95.43%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">I</td>
 <td align="right">22</td>
 <td align="right">4.57%</td>
 <td align="right">481</td>
 <td align="right">100.00%</td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="section level3">
 <h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
 </h3>
 <p>An easy <code>ggplot</code> will already give a lot of information,
 using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code> function:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/ggplot_sir.html">ggplot_sir</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">"ab"</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
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      <img src="../logo.svg" class="logo" alt=""><h1>Welcome to the `AMR` package</h1>
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/welcome_to_AMR.Rmd" class="external-link"><code>vignettes/welcome_to_AMR.Rmd</code></a></small>
      <div class="d-none name"><code>welcome_to_AMR.Rmd</code></div>
    </div>
 <p>Note: to keep the package size as small as possible, we only include
 this vignette on CRAN. You can read more vignettes on our website about
 how to conduct AMR data analysis, determine MDROs, find explanation of
 EUCAST and CLSI breakpoints, and much more: <a href="https://msberends.github.io/AMR/articles/" class="uri">https://msberends.github.io/AMR/articles/</a>.</p>
 <hr>
 <p>The <code>AMR</code> package is a <a href="https://msberends.github.io/AMR/#copyright">free and
 open-source</a> R package with <a href="https://en.wikipedia.org/wiki/Dependency_hell" class="external-link">zero
 dependencies</a> to simplify the analysis and prediction of
 Antimicrobial Resistance (AMR) and to work with microbial and
 antimicrobial data and properties, by using evidence-based methods.
 <strong>Our aim is to provide a standard</strong> for clean and
 reproducible AMR data analysis, that can therefore empower
 epidemiological analyses to continuously enable surveillance and
 treatment evaluation in any setting. <a href="https://msberends.github.io/AMR/authors.html">Many different
 researchers</a> from around the globe are continually helping us to make
 this a successful and durable project!</p>
 <p>This work was published in the Journal of Statistical Software
 (Volume 104(3); <a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">DOI
 10.18637/jss.v104.i03</a>) and formed the basis of two PhD theses (<a href="https://doi.org/10.33612/diss.177417131" class="external-link">DOI
 10.33612/diss.177417131</a> and <a href="https://doi.org/10.33612/diss.192486375" class="external-link">DOI
 10.33612/diss.192486375</a>).</p>
 <p>After installing this package, R knows ~79 000 distinct microbial
 species and all ~620 antibiotic, antimycotic and antiviral drugs by name
 and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED
 CT), and knows all about valid SIR and MIC values. The integral
 breakpoint guidelines from CLSI and EUCAST are included from the last 10
 years. It supports and can read any data format, including WHONET
 data.</p>
 <p>With the help of contributors from all corners of the world, the
 <code>AMR</code> package is available in English, Czech, Chinese,
 Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese,
 Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish,
 Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial
 microorganism names are provided in these languages.</p>
 <p>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 (April
 2013). <strong>It was designed to work in any setting, including those
 with very limited resources</strong>. Since its first public release in
 early 2018, this package has been downloaded from more than 175
 countries.</p>
 <p>This package can be used for:</p>
 <ul>
 <li>Reference for the taxonomy of microorganisms, since the package
 contains all microbial (sub)species from the List of Prokaryotic names
 with Standing in Nomenclature (LPSN) and the Global Biodiversity
 Information Facility (GBIF)</li>
 <li>Interpreting raw MIC and disk diffusion values, based on the latest
 CLSI or EUCAST guidelines</li>
 <li>Retrieving antimicrobial drug names, doses and forms of
 administration from clinical health care records</li>
 <li>Determining first isolates to be used for AMR data analysis</li>
 <li>Calculating antimicrobial resistance</li>
 <li>Determining multi-drug resistance (MDR) / multi-drug resistant
 organisms (MDRO)</li>
 <li>Calculating (empirical) susceptibility of both mono therapy and
 combination therapies</li>
 <li>Predicting future antimicrobial resistance using regression
 models</li>
 <li>Getting properties for any microorganism (like Gram stain, species,
 genus or family)</li>
 <li>Getting properties for any antibiotic (like name, code of
 EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name)</li>
 <li>Plotting antimicrobial resistance</li>
 <li>Applying EUCAST expert rules</li>
 <li>Getting SNOMED codes of a microorganism, or getting properties of a
 microorganism based on a SNOMED code</li>
 <li>Getting LOINC codes of an antibiotic, or getting properties of an
 antibiotic based on a LOINC code</li>
 <li>Machine reading the EUCAST and CLSI guidelines from 2011-2020 to
 translate MIC values and disk diffusion diameters to SIR</li>
 <li>Principal component analysis for AMR</li>
 </ul>
 <p>All reference data sets (about microorganisms, antimicrobials, SIR
 interpretation, EUCAST rules, etc.) in this <code>AMR</code> package are
 publicly and freely available. We continually export our data sets to
 formats for use in R, SPSS, Stata and Excel. We also supply flat files
 that are machine-readable and suitable for input in any software
 program, such as laboratory information systems. Please find <a href="https://msberends.github.io/AMR/articles/datasets.html">all
 download links on our website</a>, which is automatically updated with
 every code change.</p>
 <p>This R package was created for both routine data analysis and
 academic research at the Faculty of Medical Sciences of the <a href="https://www.rug.nl" class="external-link">University of Groningen</a>, in collaboration
 with non-profit organisations <a href="https://www.certe.nl" class="external-link">Certe
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  <main id="main" class="col-md-9"><div class="page-header">
      <img src="logo.svg" class="logo" alt=""><h1>Authors and Citation</h1>
    </div>
    <div class="section level2">
      <h2>Authors</h2>
      <ul class="list-unstyled"><li>
          <p><strong>Matthijs S. Berends</strong>. Author, maintainer. <a href="https://orcid.org/0000-0001-7620-1800" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Dennis Souverein</strong>. Author, contributor. <a href="https://orcid.org/0000-0003-0455-0336" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Erwin E. A. Hassing</strong>. Author, contributor.
          </p>
        </li>
        <li>
          <p><strong>Aislinn Cook</strong>. Contributor. <a href="https://orcid.org/0000-0002-9189-7815" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Andrew P. Norgan</strong>. Contributor. <a href="https://orcid.org/0000-0002-2955-2066" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Anita Williams</strong>. Contributor. <a href="https://orcid.org/0000-0002-5295-8451" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Annick Lenglet</strong>. Contributor. <a href="https://orcid.org/0000-0003-2013-8405" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Anthony Underwood</strong>. Contributor. <a href="https://orcid.org/0000-0002-8547-4277" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Anton Mymrikov</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Bart C. Meijer</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Christian F. Luz</strong>. Contributor. <a href="https://orcid.org/0000-0001-5809-5995" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Dmytro Mykhailenko</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Eric H. L. C. M. Hazenberg</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Gwen Knight</strong>. Contributor. <a href="https://orcid.org/0000-0002-7263-9896" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Jane Hawkey</strong>. Contributor. <a href="https://orcid.org/0000-0001-9661-5293" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Jason Stull</strong>. Contributor. <a href="https://orcid.org/0000-0002-9028-8153" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Javier Sanchez</strong>. Contributor. <a href="https://orcid.org/0000-0003-2605-8094" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Jonas Salm</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Judith M. Fonville</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Kathryn Holt</strong>. Contributor. <a href="https://orcid.org/0000-0003-3949-2471" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Larisse Bolton</strong>. Contributor. <a href="https://orcid.org/0000-0001-7879-2173" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Matthew Saab</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Natacha Couto</strong>. Contributor. <a href="https://orcid.org/0000-0002-9152-5464" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Peter Dutey-Magni</strong>. Contributor. <a href="https://orcid.org/0000-0002-8942-9836" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Rogier P. Schade</strong>. Contributor.
          </p>
        </li>
        <li>
          <p><strong>Sofia Ny</strong>. Contributor. <a href="https://orcid.org/0000-0002-2017-1363" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Alex W. Friedrich</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-4881-038X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Bhanu N. M. Sinha</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1634-0010" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Casper J. Albers</strong>. Thesis advisor. <a href="https://orcid.org/0000-0002-9213-6743" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
        <li>
          <p><strong>Corinna Glasner</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1241-1328" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
          </p>
        </li>
      </ul></div>
    <div class="section level2">
      <h2 id="citation">Citation</h2>
      <p><small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
      <p>Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022).
 “AMR: An R Package for Working with Antimicrobial Resistance Data.”
 <em>Journal of Statistical Software</em>, <b>104</b>(3), 1–31.
 <a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">doi:10.18637/jss.v104.i03</a>.
 </p>
      <pre>@Article{,
  title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
  author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
  journal = {Journal of Statistical Software},
  year = {2022},
  volume = {104},
  number = {3},
  pages = {1--31},
  doi = {10.18637/jss.v104.i03},
 }</pre>
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@font-face {
  font-family: 'Lato';
  font-style: italic;
  font-weight: 400;
  font-display: swap;
  src: url(fonts/S6u8w4BMUTPHjxsAXC-q.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
 }
 /* latin-ext */
@font-face {
  font-family: 'Lato';
  font-style: normal;
  font-weight: 400;
  font-display: swap;
  src: url(fonts/S6uyw4BMUTPHjxAwXjeu.woff2) format('woff2');
  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
 }
 /* latin */
@font-face {
  font-family: 'Lato';
  font-style: normal;
  font-weight: 400;
  font-display: swap;
  src: url(fonts/S6uyw4BMUTPHjx4wXg.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
 }
 /* latin-ext */
@font-face {
  font-family: 'Lato';
  font-style: normal;
  font-weight: 700;
  font-display: swap;
  src: url(fonts/S6u9w4BMUTPHh6UVSwaPGR_p.woff2) format('woff2');
  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
 }
 /* latin */
@font-face {
  font-family: 'Lato';
  font-style: normal;
  font-weight: 700;
  font-display: swap;
  src: url(fonts/S6u9w4BMUTPHh6UVSwiPGQ.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
 }
--- a/Show More
+++ b/Show More