mirror of https://github.com/msberends/AMR.git
326 lines
15 KiB
R
Executable File
326 lines
15 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2021 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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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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# 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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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' (Key) Antimicrobials for First Weighted Isolates
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#'
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#' These functions can be used to determine first weighted isolates by considering the phenotype for isolate selection (see [first_isolate()]). Using a phenotype-based method to determine first isolates is more reliable than methods that disregard phenotypes.
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#' @inheritSection lifecycle Stable Lifecycle
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#' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank to determine automatically
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#' @param y,z [character] vectors to compare
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#' @inheritParams first_isolate
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#' @param universal names of **broad-spectrum** antimicrobial agents, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents.
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#' @param gram_negative names of antibiotic agents for **Gram-positives**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents.
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#' @param gram_positive names of antibiotic agents for **Gram-negatives**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents.
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#' @param antifungal names of antifungal agents for **fungi**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents.
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#' @param only_rsi_columns a [logical] to indicate whether only columns must be included that were transformed to class `<rsi>` (see [as.rsi()]) on beforehand (defaults to `FALSE`)
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#' @param ... ignored, only in place to allow future extensions
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#' @details
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#' The [key_antimicrobials()] and [all_antimicrobials()] functions are context-aware. This means that the `x` argument can be left blank if used inside a [data.frame] call, see *Examples*.
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#'
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#' The function [key_antimicrobials()] returns a [character] vector with 12 antimicrobial results for every isolate. The function [all_antimicrobials()] returns a [character] vector with all antimicrobial results for every isolate. These vectors can then be compared using [antimicrobials_equal()], to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (`"."`) by [key_antimicrobials()] and ignored by [antimicrobials_equal()].
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#'
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#' Please see the [first_isolate()] function how these important functions enable the 'phenotype-based' method for determination of first isolates.
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#'
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#' The default antimicrobial agents used for **all rows** (set in `universal`) are:
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#'
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#' - Ampicillin
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#' - Amoxicillin/clavulanic acid
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#' - Cefuroxime
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#' - Ciprofloxacin
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#' - Piperacillin/tazobactam
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#' - Trimethoprim/sulfamethoxazole
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#'
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#' The default antimicrobial agents used for **Gram-negative bacteria** (set in `gram_negative`) are:
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#'
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#' - Cefotaxime
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#' - Ceftazidime
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#' - Colistin
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#' - Gentamicin
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#' - Meropenem
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#' - Tobramycin
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#'
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#' The default antimicrobial agents used for **Gram-positive bacteria** (set in `gram_positive`) are:
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#'
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#' - Erythromycin
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#' - Oxacillin
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#' - Rifampin
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#' - Teicoplanin
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#' - Tetracycline
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#' - Vancomycin
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#'
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#'
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#' The default antimicrobial agents used for **fungi** (set in `antifungal`) are:
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#'
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#' - Anidulafungin
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#' - Caspofungin
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#' - Fluconazole
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#' - Miconazole
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#' - Nystatin
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#' - Voriconazole
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#' @rdname key_antimicrobials
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#' @export
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#' @seealso [first_isolate()]
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#' @inheritSection AMR Read more on Our Website!
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#' @examples
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#' # `example_isolates` is a data set available in the AMR package.
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#' # See ?example_isolates.
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#'
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#' # output of the `key_antimicrobials()` function could be like this:
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#' strainA <- "SSSRR.S.R..S"
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#' strainB <- "SSSIRSSSRSSS"
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#'
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#' # those strings can be compared with:
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#' antimicrobials_equal(strainA, strainB, type = "keyantimicrobials")
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#' # TRUE, because I is ignored (as well as missing values)
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#'
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#' antimicrobials_equal(strainA, strainB, type = "keyantimicrobials", ignore_I = FALSE)
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#' # FALSE, because I is not ignored and so the 4th [character] differs
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#'
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#' \donttest{
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#' if (require("dplyr")) {
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#' # set key antibiotics to a new variable
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#' my_patients <- example_isolates %>%
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#' mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`
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#' mutate(
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#' # now calculate first isolates
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#' first_regular = first_isolate(col_keyantimicrobials = FALSE),
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#' # and first WEIGHTED isolates
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#' first_weighted = first_isolate(col_keyantimicrobials = "keyab")
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#' )
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#'
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#' # Check the difference, in this data set it results in more isolates:
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#' sum(my_patients$first_regular, na.rm = TRUE)
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#' sum(my_patients$first_weighted, na.rm = TRUE)
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#' }
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#' }
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key_antimicrobials <- function(x = NULL,
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col_mo = NULL,
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universal = c("ampicillin", "amoxicillin/clavulanic acid", "cefuroxime",
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"piperacillin/tazobactam", "ciprofloxacin", "trimethoprim/sulfamethoxazole"),
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gram_negative = c("gentamicin", "tobramycin", "colistin",
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"cefotaxime", "ceftazidime", "meropenem"),
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gram_positive = c("vancomycin", "teicoplanin", "tetracycline",
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"erythromycin", "oxacillin", "rifampin"),
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antifungal = c("anidulafungin", "caspofungin", "fluconazole",
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"miconazole", "nystatin", "voriconazole"),
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only_rsi_columns = FALSE,
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...) {
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if (is_null_or_grouped_tbl(x)) {
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# when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
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# is also fix for using a grouped df as input (a dot as first argument)
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x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x)
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}
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meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
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meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE, is_in = colnames(x))
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meet_criteria(universal, allow_class = "character", allow_NULL = TRUE)
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meet_criteria(gram_negative, allow_class = "character", allow_NULL = TRUE)
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meet_criteria(gram_positive, allow_class = "character", allow_NULL = TRUE)
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meet_criteria(antifungal, allow_class = "character", allow_NULL = TRUE)
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meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
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# force regular [data.frame], not a tibble or data.table
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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cols <- get_column_abx(x, info = FALSE, only_rsi_columns = only_rsi_columns)
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# try to find columns based on type
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# -- mo
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if (is.null(col_mo)) {
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col_mo <- search_type_in_df(x = x, type = "mo", info = FALSE)
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}
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if (is.null(col_mo)) {
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warning_("No column found for `col_mo`, ignoring antibiotics set in `gram_negative` and `gram_positive`, and antimycotics set in `antifungal`", call = FALSE)
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gramstain <- NA_character_
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kingdom <- NA_character_
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} else {
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x.mo <- as.mo(x[, col_mo, drop = TRUE])
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gramstain <- mo_gramstain(x.mo, language = NULL)
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kingdom <- mo_kingdom(x.mo, language = NULL)
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}
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AMR_string <- function(x, values, name, filter, cols = cols) {
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if (is.null(values)) {
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return(rep(NA_character_, length(which(filter))))
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}
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values_old_length <- length(values)
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values <- as.ab(values, flag_multiple_results = FALSE, info = FALSE)
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values <- cols[names(cols) %in% values]
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values_new_length <- length(values)
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if (values_new_length < values_old_length &
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any(filter, na.rm = TRUE) &
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message_not_thrown_before(paste0("key_antimicrobials.", name))) {
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warning_(ifelse(values_new_length == 0,
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"No columns available ",
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paste0("Only using ", values_new_length, " out of ", values_old_length, " defined columns ")),
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"as key antimicrobials for ", name, "s. See ?key_antimicrobials.",
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call = FALSE)
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remember_thrown_message(paste0("key_antimicrobials.", name))
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}
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generate_antimcrobials_string(x[which(filter), c(universal, values), drop = FALSE])
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}
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if (is.null(universal)) {
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universal <- character(0)
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} else {
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universal <- as.ab(universal, flag_multiple_results = FALSE, info = FALSE)
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universal <- cols[names(cols) %in% universal]
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}
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key_ab <- rep(NA_character_, nrow(x))
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key_ab[which(gramstain == "Gram-negative")] <- AMR_string(x = x,
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values = gram_negative,
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name = "Gram-negative",
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filter = gramstain == "Gram-negative",
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cols = cols)
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key_ab[which(gramstain == "Gram-positive")] <- AMR_string(x = x,
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values = gram_positive,
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name = "Gram-positive",
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filter = gramstain == "Gram-positive",
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cols = cols)
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key_ab[which(kingdom == "Fungi")] <- AMR_string(x = x,
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values = antifungal,
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name = "antifungal",
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filter = kingdom == "Fungi",
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cols = cols)
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# back-up - only use `universal`
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key_ab[which(is.na(key_ab))] <- AMR_string(x = x,
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values = character(0),
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name = "",
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filter = is.na(key_ab),
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cols = cols)
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if (length(unique(key_ab)) == 1) {
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warning_("No distinct key antibiotics determined.", call = FALSE)
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}
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key_ab
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}
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#' @rdname key_antimicrobials
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#' @export
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all_antimicrobials <- function(x = NULL,
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only_rsi_columns = FALSE,
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...) {
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if (is_null_or_grouped_tbl(x)) {
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# when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
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# is also fix for using a grouped df as input (a dot as first argument)
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x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x)
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}
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meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
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meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
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# force regular [data.frame], not a tibble or data.table
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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cols <- get_column_abx(x, only_rsi_columns = only_rsi_columns, info = FALSE, sort = FALSE)
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generate_antimcrobials_string(x[ , cols, drop = FALSE])
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}
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generate_antimcrobials_string <- function(df) {
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if (NCOL(df) == 0) {
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return(rep("", NROW(df)))
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}
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if (NROW(df) == 0) {
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return(character(0))
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}
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out <- tryCatch(
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do.call(paste0,
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lapply(as.list(df),
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function(x) {
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x <- toupper(as.character(x))
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x[!x %in% c("R", "S", "I")] <- "."
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paste(x)
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})),
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error = function(e) rep(strrep(".", NCOL(df)), NROW(df)))
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out
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}
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#' @rdname key_antimicrobials
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#' @export
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antimicrobials_equal <- function(y,
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z,
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type = c("points", "keyantimicrobials"),
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ignore_I = TRUE,
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points_threshold = 2,
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...) {
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meet_criteria(y, allow_class = "character")
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meet_criteria(z, allow_class = "character")
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stop_if(missing(type), "argument \"type\" is missing, with no default")
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meet_criteria(type, allow_class = "character", has_length = 1, is_in = c("points", "keyantimicrobials"))
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meet_criteria(ignore_I, allow_class = "logical", has_length = 1)
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meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
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stop_ifnot(length(y) == length(z), "length of `y` and `z` must be equal")
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key2rsi <- function(val) {
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as.double(as.rsi(gsub(".", NA_character_, unlist(strsplit(val, "")), fixed = TRUE)))
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}
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y <- lapply(y, key2rsi)
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z <- lapply(z, key2rsi)
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determine_equality <- function(a, b, type, points_threshold, ignore_I) {
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if (length(a) != length(b)) {
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# incomparable, so not equal
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return(FALSE)
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}
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# ignore NAs on both sides
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NA_ind <- which(is.na(a) | is.na(b))
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a[NA_ind] <- NA_real_
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b[NA_ind] <- NA_real_
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if (type == "points") {
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# count points for every single character:
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# - no change is 0 points
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# - I <-> S|R is 0.5 point
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# - S|R <-> R|S is 1 point
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# use the levels of as.rsi (S = 1, I = 2, R = 3)
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# and divide by 2 (S = 0.5, I = 1, R = 1.5)
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(sum(abs(a - b), na.rm = TRUE) / 2) < points_threshold
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} else {
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if (ignore_I == TRUE) {
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ind <- which(a == 2 | b == 2) # since as.double(as.rsi("I")) == 2
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a[ind] <- NA_real_
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b[ind] <- NA_real_
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}
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all(a == b, na.rm = TRUE)
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}
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}
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out <- unlist(mapply(FUN = determine_equality,
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y,
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z,
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MoreArgs = list(type = type,
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points_threshold = points_threshold,
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ignore_I = ignore_I),
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SIMPLIFY = FALSE,
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USE.NAMES = FALSE))
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out[is.na(y) | is.na(z)] <- NA
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out
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}
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