mirror of https://github.com/msberends/AMR.git
171 lines
7.9 KiB
R
Executable File
171 lines
7.9 KiB
R
Executable File
# ==================================================================== #
|
|
# TITLE #
|
|
# AMR: An R Package for Working with Antimicrobial Resistance Data #
|
|
# #
|
|
# SOURCE #
|
|
# https://github.com/msberends/AMR #
|
|
# #
|
|
# CITE AS #
|
|
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
|
|
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
|
|
# Data. Journal of Statistical Software, 104(3), 1-31. #
|
|
# doi:10.18637/jss.v104.i03 #
|
|
# #
|
|
# Developed at the University of Groningen and the University Medical #
|
|
# Center Groningen in The Netherlands, in collaboration with many #
|
|
# colleagues from around the world, see our website. #
|
|
# #
|
|
# 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/SIR 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 drug, the generated results will reflect reality as much as possible.
|
|
#' @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.
|
|
#' @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 drug code with [as.ab()]
|
|
#' @param prob_SIR a vector of length 3: the probabilities for "S" (1st value), "I" (2nd value) and "R" (3rd value)
|
|
#' @param ... ignored, only in place to allow future extensions
|
|
#' @details The base \R function [sample()] is used for generating values.
|
|
#'
|
|
#' Generated values are based on the EUCAST `r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))` guideline as implemented in the [clinical_breakpoints] 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
|
|
#' @examples
|
|
#' random_mic(25)
|
|
#' random_disk(25)
|
|
#' random_sir(25)
|
|
#'
|
|
#' \donttest{
|
|
#' # make the random generation more realistic by setting a bug and/or drug:
|
|
#' random_mic(25, "Klebsiella pneumoniae") # range 0.0625-64
|
|
#' random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
|
|
#' random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
|
|
#'
|
|
#' random_disk(25, "Klebsiella pneumoniae") # range 8-50
|
|
#' random_disk(25, "Klebsiella pneumoniae", "ampicillin") # range 11-17
|
|
#' random_disk(25, "Streptococcus pneumoniae", "ampicillin") # range 12-27
|
|
#' }
|
|
random_mic <- function(size = NULL, mo = NULL, ab = NULL, ...) {
|
|
meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
|
|
meet_criteria(mo, allow_class = "character", has_length = 1, allow_NULL = TRUE)
|
|
meet_criteria(ab, allow_class = "character", has_length = 1, allow_NULL = TRUE)
|
|
if (is.null(size)) {
|
|
size <- NROW(get_current_data(arg_name = "size", call = -3))
|
|
}
|
|
random_exec("MIC", size = size, mo = mo, ab = ab)
|
|
}
|
|
|
|
#' @rdname random
|
|
#' @export
|
|
random_disk <- function(size = NULL, mo = NULL, ab = NULL, ...) {
|
|
meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
|
|
meet_criteria(mo, allow_class = "character", has_length = 1, allow_NULL = TRUE)
|
|
meet_criteria(ab, allow_class = "character", has_length = 1, allow_NULL = TRUE)
|
|
if (is.null(size)) {
|
|
size <- NROW(get_current_data(arg_name = "size", call = -3))
|
|
}
|
|
random_exec("DISK", size = size, mo = mo, ab = ab)
|
|
}
|
|
|
|
#' @rdname random
|
|
#' @export
|
|
random_sir <- function(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...) {
|
|
meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
|
|
if ("prob_RSI" %in% names(list(...))) {
|
|
deprecation_warning("prob_RSI", "prob_SIR", is_function = FALSE)
|
|
prob_SIR <- list(...)$prob_RSI
|
|
}
|
|
meet_criteria(prob_SIR, allow_class = c("numeric", "integer"), has_length = 3)
|
|
if (is.null(size)) {
|
|
size <- NROW(get_current_data(arg_name = "size", call = -3))
|
|
}
|
|
sample(as.sir(c("S", "I", "R")), size = size, replace = TRUE, prob = prob_SIR)
|
|
}
|
|
|
|
random_exec <- function(type, size, mo = NULL, ab = NULL) {
|
|
df <- AMR::clinical_breakpoints %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_("in `random_", tolower(type), "()`: no rows found that match mo '", mo, "', ignoring argument `mo`")
|
|
}
|
|
}
|
|
|
|
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_("in `random_", tolower(type), "()`: no rows found that match ab '", ab, "', ignoring argument `ab`")
|
|
}
|
|
}
|
|
|
|
if (type == "MIC") {
|
|
# set range
|
|
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)
|
|
|
|
# get highest/lowest +/- random 1 to 3 higher factors of two
|
|
max_range <- mic_range[min(
|
|
length(mic_range),
|
|
which(mic_range == max(df$breakpoint_R)) + sample(c(1:3), 1)
|
|
)]
|
|
min_range <- mic_range[max(
|
|
1,
|
|
which(mic_range == min(df$breakpoint_S)) - sample(c(1:3), 1)
|
|
)]
|
|
|
|
mic_range_new <- mic_range[mic_range <= max_range & mic_range >= min_range]
|
|
if (length(mic_range_new) == 0) {
|
|
mic_range_new <- mic_range
|
|
}
|
|
out <- as.mic(sample(mic_range_new, 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))
|
|
}
|
|
}
|