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(v1.2.0.9007) Tidyverse selections
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R/ab_class_selectors.R
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R/ab_class_selectors.R
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# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://gitlab.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2020 Berends MS, Luz CF et al. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.gitlab.io/AMR. #
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# ==================================================================== #
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#' Antibiotic class selectors
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#'
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#' Use these selection helpers inside any function that allows [Tidyverse selections](https://tidyselect.r-lib.org/reference/language.html), like `dplyr::select()` or `tidyr::pivot_longer()`. They help to select the columns of antibiotics that are of a specific antibiotic class, without the need to define the columns or antibiotic abbreviations.
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#' @details All columns will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.). This means that a selector like e.g. [aminoglycosides()] will pick up column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
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#'
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#' These functions only work if the `tidyselect` package is installed, that comes with the `dplyr` package. An error will be thrown if `tidyselect` package is not installed, or if the functions are used outside a function that allows Tidyverse selections like `select()` or `pivot_longer()`.
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#' @rdname antibiotic_class_selectors
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#' @seealso [filter_ab_class()] for the `filter()` equivalent.
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#' @name antibiotic_class_selectors
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#' @export
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#' @examples
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#' if (require("dplyr")) {
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#'
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#' # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):
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#' example_isolates %>%
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#' select(carbapenems())
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#'
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#'
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#' # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':
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#' example_isolates %>%
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#' select(mo, aminoglycosides())
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#'
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#'
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#' data.frame(irrelevant = "value",
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#' J01CA01 = "S") %>% # ATC code of ampicillin
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#' select(penicillins()) # so the 'J01CA01' column is selected
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#'
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#' }
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aminoglycosides <- function() {
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ab_selector("aminoglycoside")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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carbapenems <- function() {
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ab_selector("carbapenem")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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cephalosporins <- function() {
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ab_selector("cephalosporin")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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cephalosporins_1st <- function() {
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ab_selector("cephalosporins.*1")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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cephalosporins_2nd <- function() {
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ab_selector("cephalosporins.*2")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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cephalosporins_3rd <- function() {
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ab_selector("cephalosporins.*3")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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cephalosporins_4th <- function() {
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ab_selector("cephalosporins.*4")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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cephalosporins_5th <- function() {
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ab_selector("cephalosporins.*5")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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fluoroquinolones <- function() {
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ab_selector("fluoroquinolone")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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glycopeptides <- function() {
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ab_selector("glycopeptide")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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macrolides <- function() {
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ab_selector("macrolide")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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penicillins <- function() {
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ab_selector("penicillin")
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}
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#' @rdname antibiotic_class_selectors
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#' @export
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tetracyclines <- function() {
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ab_selector("tetracycline")
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}
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ab_selector <- function(ab_class, vars = NULL) {
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stopifnot_installed_package("tidyselect")
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peek_vars_tidyselect <- get("peek_vars", envir = asNamespace("tidyselect"))
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vars_vct <- peek_vars_tidyselect(fn = ab_class)
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vars_df <- data.frame(as.list(vars_vct))[0, , drop = FALSE]
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colnames(vars_df) <- vars_vct
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ab_in_data <- suppressMessages(get_column_abx(vars_df))
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if (length(ab_in_data) == 0) {
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message(font_blue("NOTE: no antimicrobial agents found."))
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return(NULL)
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}
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ab_reference <- subset(antibiotics,
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group %like% ab_class |
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atc_group1 %like% ab_class |
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atc_group2 %like% ab_class)
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ab_group <- find_ab_group(ab_class)
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# get the columns with a group names in the chosen ab class
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agents <- ab_in_data[names(ab_in_data) %in% ab_reference$ab]
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if (length(agents) == 0) {
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message(font_blue(paste0("NOTE: no antimicrobial agents of class ", ab_group,
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" found (such as ", find_ab_names(ab_class, 2),
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").")))
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} else {
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message(font_blue(paste0("Selecting ", ab_group, ": ",
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paste(paste0("`", font_bold(agents, collapse = NULL),
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"` (", ab_name(names(agents), tolower = TRUE, language = NULL), ")"),
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collapse = ", "))))
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}
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unname(agents)
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}
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#' Filter isolates on results in specific antimicrobial classes. This makes it easy to filter on isolates that were tested for e.g. any aminoglycoside, or to filter on carbapenem-resistant isolates without the need to specify the drugs.
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#' @inheritSection lifecycle Stable lifecycle
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#' @param x a data set
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#' @param ab_class an antimicrobial class, like `"carbapenems"`, as can be found in [`antibiotics$group`][antibiotics]
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#' @param ab_class an antimicrobial class, like `"carbapenems"`. The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched (case-insensitive) for this value.
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#' @param result an antibiotic result: S, I or R (or a combination of more of them)
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#' @param scope the scope to check which variables to check, can be `"any"` (default) or `"all"`
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#' @param ... parameters passed on to `filter_at` from the `dplyr` package
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#' @details The columns `group`, `atc_group1` and `atc_group2` of the [antibiotics] data set will be searched for the input given in `ab_class` (case-insensitive). Next, `x` will be checked for column names with a value in any abbreviation, code or official name found in the [antibiotics] data set.
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#' @details All columns of `x` will be searched for known antibiotic names, abbreviations, brand names and codes (ATC, EARS-Net, WHO, etc.). This means that a filter function like e.g. [filter_aminoglycosides()] will include column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc.
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#' @rdname filter_ab_class
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#' @seealso [antibiotic_class_selectors()] for the `select()` equivalent.
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#' @export
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#' @examples
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#' \dontrun{
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@ -329,6 +330,7 @@ filter_tetracyclines <- function(x,
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}
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find_ab_group <- function(ab_class) {
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ab_class <- gsub("[^a-zA-Z0-9]", ".*", ab_class)
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ifelse(ab_class %in% c("aminoglycoside",
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"carbapenem",
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"cephalosporin",
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@ -344,12 +346,14 @@ find_ab_group <- function(ab_class) {
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pull(group) %>%
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unique() %>%
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tolower() %>%
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sort() %>%
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paste(collapse = "/")
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)
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}
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find_ab_names <- function(ab_group, n = 3) {
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drugs <- antibiotics[which(antibiotics$group %like% ab_group), "name"]
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ab_group <- gsub("[^a-zA-Z0-9]", ".*", ab_group)
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drugs <- antibiotics[which(antibiotics$group %like% ab_group & !antibiotics$ab %like% "[0-9]$"), ]$name
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paste0(sort(ab_name(sample(drugs, size = min(n, length(drugs)), replace = FALSE),
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tolower = TRUE, language = NULL)),
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collapse = ", ")
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} else if (isTRUE(combine_IR)) {
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out$interpretation <- factor(out$interpretation, levels = c("S", "IR"), ordered = TRUE)
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} else {
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out$interpretation <- as.rsi(out$interpretation)
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# don't use as.rsi() here, as it would add the class <rsi> and we would like
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# the same data structure as output, regardless of input
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out$interpretation <- factor(out$interpretation, levels = c("S", "I", "R"), ordered = TRUE)
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}
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if (data_has_groups) {
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