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AMR/R/random.R

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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2021 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Random MIC Values/Disk Zones/RSI Generation
#'
#' 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.
#' @inheritSection lifecycle Stable Lifecycle
#' @param size desired size of the returned vector
#' @param mo any character that can be coerced to a valid microorganism code with [as.mo()]
#' @param ab any character that can be coerced to a valid antimicrobial agent code with [as.ab()]
#' @param prob_RSI a vector of length 3: the probabilities for R (1st value), S (2nd value) and I (3rd value)
#' @param ... extension for future versions, not used at the moment
#' @details The base R function [sample()] is used for generating values.
#'
#' 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.
#' @return class `<mic>` for [random_mic()] (see [as.mic()]) and class `<disk>` for [random_disk()] (see [as.disk()])
#' @name random
#' @rdname random
#' @export
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' random_mic(100)
#' random_disk(100)
#' random_rsi(100)
#'
#' \donttest{
#' # make the random generation more realistic by setting a bug and/or drug:
#' random_mic(100, "Klebsiella pneumoniae") # range 0.0625-64
#' random_mic(100, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
#' random_mic(100, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
#'
#' random_disk(100, "Klebsiella pneumoniae") # range 8-50
#' random_disk(100, "Klebsiella pneumoniae", "ampicillin") # range 11-17
#' random_disk(100, "Streptococcus pneumoniae", "ampicillin") # range 12-27
#' }
random_mic <- function(size, mo = NULL, ab = NULL, ...) {
random_exec("MIC", size = size, mo = mo, ab = ab)
}
#' @rdname random
#' @export
random_disk <- function(size, mo = NULL, ab = NULL, ...) {
random_exec("DISK", size = size, mo = mo, ab = ab)
}
#' @rdname random
#' @export
random_rsi <- function(size, prob_RSI = c(0.33, 0.33, 0.33), ...) {
sample(as.rsi(c("R", "S", "I")), size = size, replace = TRUE, prob = prob_RSI)
}
random_exec <- function(type, size, mo = NULL, ab = NULL) {
df <- rsi_translation %pm>%
pm_filter(guideline %like% "EUCAST") %pm>%
pm_arrange(pm_desc(guideline)) %pm>%
subset(guideline == max(guideline) &
method == type)
if (!is.null(mo)) {
mo_coerced <- as.mo(mo)
mo_include <- c(mo_coerced,
as.mo(mo_genus(mo_coerced)),
as.mo(mo_family(mo_coerced)),
as.mo(mo_order(mo_coerced)))
df_new <- df %pm>%
subset(mo %in% mo_include)
if (nrow(df_new) > 0) {
df <- df_new
} else {
warning_("No rows found that match mo '", mo, "', ignoring argument `mo`", call = FALSE)
}
}
if (!is.null(ab)) {
ab_coerced <- as.ab(ab)
df_new <- df %pm>%
subset(ab %in% ab_coerced)
if (nrow(df_new) > 0) {
df <- df_new
} else {
warning_("No rows found that match ab '", ab, "', ignoring argument `ab`", call = FALSE)
}
}
if (type == "MIC") {
# all valid MIC levels
valid_range <- as.mic(levels(as.mic(1)))
set_range_max <- max(df$breakpoint_R)
if (log(set_range_max, 2) %% 1 == 0) {
# return powers of 2
valid_range <- unique(as.double(valid_range))
# add 1-3 higher MIC levels to set_range_max
set_range_max <- 2 ^ (log(set_range_max, 2) + sample(c(1:3), 1))
set_range <- as.mic(valid_range[log(valid_range, 2) %% 1 == 0 & valid_range <= set_range_max])
} else {
# no power of 2, return factors of 2 to left and right side
valid_mics <- suppressWarnings(as.mic(set_range_max / (2 ^ c(-3:3))))
set_range <- valid_mics[!is.na(valid_mics)]
}
out <- as.mic(sample(set_range, size = size, replace = TRUE))
# 50% chance that lowest will get <= and highest will get >=
if (stats::runif(1) > 0.5) {
out[out == min(out)] <- paste0("<=", out[out == min(out)])
}
if (stats::runif(1) > 0.5) {
out[out == max(out)] <- paste0(">=", out[out == max(out)])
}
return(out)
} else if (type == "DISK") {
set_range <- seq(from = as.integer(min(df$breakpoint_R) / 1.25),
to = as.integer(max(df$breakpoint_S) * 1.25),
by = 1)
out <- sample(set_range, size = size, replace = TRUE)
out[out < 6] <- sample(c(6:10), length(out[out < 6]), replace = TRUE)
out[out > 50] <- sample(c(40:50), length(out[out > 50]), replace = TRUE)
return(as.disk(out))
}
}
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