mirror of https://github.com/msberends/AMR.git
167 lines
7.7 KiB
R
Executable File
167 lines
7.7 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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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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# CITE AS #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# doi:10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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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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#' Random MIC Values/Disk Zones/RSI Generation
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#'
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#' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial drug, the generated results will reflect reality as much as possible.
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#' @param size desired size of the returned vector. If used in a [data.frame] call or `dplyr` verb, will get the current (group) size if left blank.
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#' @param mo any [character] that can be coerced to a valid microorganism code with [as.mo()]
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#' @param ab any [character] that can be coerced to a valid antimicrobial drug code with [as.ab()]
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#' @param prob_RSI a vector of length 3: the probabilities for "R" (1st value), "S" (2nd value) and "I" (3rd value)
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#' @param ... ignored, only in place to allow future extensions
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#' @details The base \R function [sample()] is used for generating values.
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#'
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#' Generated values are based on the EUCAST `r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))` guideline as implemented in the [rsi_translation] data set. To create specific generated values per bug or drug, set the `mo` and/or `ab` argument.
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#' @return class `mic` for [random_mic()] (see [as.mic()]) and class `disk` for [random_disk()] (see [as.disk()])
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#' @name random
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#' @rdname random
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#' @export
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#' @examples
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#' random_mic(25)
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#' random_disk(25)
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#' random_rsi(25)
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#'
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#' \donttest{
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#' # make the random generation more realistic by setting a bug and/or drug:
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#' random_mic(25, "Klebsiella pneumoniae") # range 0.0625-64
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#' random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
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#' random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
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#'
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#' random_disk(25, "Klebsiella pneumoniae") # range 8-50
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#' random_disk(25, "Klebsiella pneumoniae", "ampicillin") # range 11-17
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#' random_disk(25, "Streptococcus pneumoniae", "ampicillin") # range 12-27
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#' }
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random_mic <- function(size = NULL, mo = NULL, ab = NULL, ...) {
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meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
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meet_criteria(mo, allow_class = "character", has_length = 1, allow_NULL = TRUE)
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meet_criteria(ab, allow_class = "character", has_length = 1, allow_NULL = TRUE)
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if (is.null(size)) {
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size <- NROW(get_current_data(arg_name = "size", call = -3))
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}
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random_exec("MIC", size = size, mo = mo, ab = ab)
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}
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#' @rdname random
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#' @export
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random_disk <- function(size = NULL, mo = NULL, ab = NULL, ...) {
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meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
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meet_criteria(mo, allow_class = "character", has_length = 1, allow_NULL = TRUE)
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meet_criteria(ab, allow_class = "character", has_length = 1, allow_NULL = TRUE)
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if (is.null(size)) {
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size <- NROW(get_current_data(arg_name = "size", call = -3))
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}
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random_exec("DISK", size = size, mo = mo, ab = ab)
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}
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#' @rdname random
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#' @export
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random_rsi <- function(size = NULL, prob_RSI = c(0.33, 0.33, 0.33), ...) {
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meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
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meet_criteria(prob_RSI, allow_class = c("numeric", "integer"), has_length = 3)
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if (is.null(size)) {
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size <- NROW(get_current_data(arg_name = "size", call = -3))
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}
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sample(as.rsi(c("R", "S", "I")), size = size, replace = TRUE, prob = prob_RSI)
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}
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random_exec <- function(type, size, mo = NULL, ab = NULL) {
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df <- rsi_translation %pm>%
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pm_filter(guideline %like% "EUCAST") %pm>%
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pm_arrange(pm_desc(guideline)) %pm>%
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subset(guideline == max(guideline) &
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method == type)
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if (!is.null(mo)) {
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mo_coerced <- as.mo(mo)
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mo_include <- c(
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mo_coerced,
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as.mo(mo_genus(mo_coerced)),
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as.mo(mo_family(mo_coerced)),
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as.mo(mo_order(mo_coerced))
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)
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df_new <- df %pm>%
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subset(mo %in% mo_include)
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if (nrow(df_new) > 0) {
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df <- df_new
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} else {
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warning_("in `random_", tolower(type), "()`: no rows found that match mo '", mo, "', ignoring argument `mo`")
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}
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}
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if (!is.null(ab)) {
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ab_coerced <- as.ab(ab)
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df_new <- df %pm>%
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subset(ab %in% ab_coerced)
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if (nrow(df_new) > 0) {
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df <- df_new
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} else {
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warning_("in `random_", tolower(type), "()`: no rows found that match ab '", ab, "', ignoring argument `ab`")
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}
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}
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if (type == "MIC") {
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# set range
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mic_range <- c(0.001, 0.002, 0.005, 0.010, 0.025, 0.0625, 0.125, 0.250, 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256)
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# get highest/lowest +/- random 1 to 3 higher factors of two
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max_range <- mic_range[min(
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length(mic_range),
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which(mic_range == max(df$breakpoint_R)) + sample(c(1:3), 1)
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)]
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min_range <- mic_range[max(
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1,
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which(mic_range == min(df$breakpoint_S)) - sample(c(1:3), 1)
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)]
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mic_range_new <- mic_range[mic_range <= max_range & mic_range >= min_range]
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if (length(mic_range_new) == 0) {
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mic_range_new <- mic_range
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}
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out <- as.mic(sample(mic_range_new, size = size, replace = TRUE))
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# 50% chance that lowest will get <= and highest will get >=
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if (stats::runif(1) > 0.5) {
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out[out == min(out)] <- paste0("<=", out[out == min(out)])
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}
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if (stats::runif(1) > 0.5) {
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out[out == max(out)] <- paste0(">=", out[out == max(out)])
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}
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return(out)
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} else if (type == "DISK") {
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set_range <- seq(
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from = as.integer(min(df$breakpoint_R) / 1.25),
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to = as.integer(max(df$breakpoint_S) * 1.25),
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by = 1
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)
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out <- sample(set_range, size = size, replace = TRUE)
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out[out < 6] <- sample(c(6:10), length(out[out < 6]), replace = TRUE)
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out[out > 50] <- sample(c(40:50), length(out[out > 50]), replace = TRUE)
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return(as.disk(out))
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}
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}
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