mirror of https://github.com/msberends/AMR.git
133 lines
6.1 KiB
R
133 lines
6.1 KiB
R
# ==================================================================== #
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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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#' 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 agent, the generated results will reflect reality as much as possible.
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#' @inheritSection lifecycle Maturing Lifecycle
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#' @param size desired size of the returned vector
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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 agent 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 ... extension for future versions, not used at the moment
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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 latest EUCAST guideline 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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#' @inheritSection AMR Read more on Our Website!
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#' @examples
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#' random_mic(100)
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#' random_disk(100)
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#' random_rsi(100)
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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(100, "Klebsiella pneumoniae") # range 0.0625-64
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#' random_mic(100, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
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#' random_mic(100, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
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#'
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#' random_disk(100, "Klebsiella pneumoniae") # range 8-50
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#' random_disk(100, "Klebsiella pneumoniae", "ampicillin") # range 11-17
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#' random_disk(100, "Streptococcus pneumoniae", "ampicillin") # range 12-27
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#' }
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random_mic <- function(size, mo = NULL, ab = NULL, ...) {
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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, mo = NULL, ab = NULL, ...) {
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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, prob_RSI = c(0.33, 0.33, 0.33), ...) {
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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(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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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_("No rows found that match mo '", mo, "', ignoring argument `mo`", call = FALSE)
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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_("No rows found that match ab '", ab, "', ignoring argument `ab`", call = FALSE)
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}
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}
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if (type == "MIC") {
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# all valid MIC levels
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valid_range <- as.mic(levels(as.mic(1)))
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set_range_max <- max(df$breakpoint_R)
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if (log(set_range_max, 2) %% 1 == 0) {
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# return powers of 2
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valid_range <- unique(as.double(valid_range))
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# add 1-3 higher MIC levels to set_range_max
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set_range_max <- 2 ^ (log(set_range_max, 2) + sample(c(1:3), 1))
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set_range <- as.mic(valid_range[log(valid_range, 2) %% 1 == 0 & valid_range <= set_range_max])
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} else {
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# no power of 2, return factors of 2 to left and right side
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valid_mics <- suppressWarnings(as.mic(set_range_max / (2 ^ c(-3:3))))
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set_range <- valid_mics[!is.na(valid_mics)]
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}
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return(as.mic(sample(set_range, size = size, replace = TRUE)))
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} else if (type == "DISK") {
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set_range <- seq(from = as.integer(min(df$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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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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