mirror of https://github.com/msberends/AMR.git
251 lines
7.8 KiB
R
Executable File
251 lines
7.8 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 CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE 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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# https://doi.org/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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#' Transform Input to Disk Diffusion Diameters
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#'
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#' This transforms a vector to a new class [`disk`], which is a disk diffusion growth zone size (around an antibiotic disk) in millimetres between 6 and 50.
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#' @rdname as.disk
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#' @param x vector
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#' @param na.rm a [logical] indicating whether missing values should be removed
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#' @details Interpret disk values as SIR values with [as.sir()]. It supports guidelines from EUCAST and CLSI.
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#'
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#' Disk diffusion growth zone sizes must be between 6 and 50 millimetres. Values higher than 50 but lower than 100 will be maximised to 50. All others input values outside the 6-50 range will return `NA`.
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#' @return An [integer] with additional class [`disk`]
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#' @aliases disk
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#' @export
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#' @seealso [as.sir()]
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#' @examples
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#' # transform existing disk zones to the `disk` class (using base R)
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#' df <- data.frame(
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#' microorganism = "Escherichia coli",
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#' AMP = 20,
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#' CIP = 14,
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#' GEN = 18,
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#' TOB = 16
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#' )
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#' df[, 2:5] <- lapply(df[, 2:5], as.disk)
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#' str(df)
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#'
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#' \donttest{
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#' # transforming is easier with dplyr:
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#' if (require("dplyr")) {
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#' df %>% mutate(across(AMP:TOB, as.disk))
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#' }
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#' }
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#'
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#' # interpret disk values, see ?as.sir
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#' as.sir(
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#' x = as.disk(18),
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#' mo = "Strep pneu", # `mo` will be coerced with as.mo()
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#' ab = "ampicillin", # and `ab` with as.ab()
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#' guideline = "EUCAST"
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#' )
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#'
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#' # interpret whole data set, pretend to be all from urinary tract infections:
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#' as.sir(df, uti = TRUE)
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as.disk <- function(x, na.rm = FALSE) {
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meet_criteria(x, allow_class = c("disk", "character", "numeric", "integer"), allow_NA = TRUE)
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meet_criteria(na.rm, allow_class = "logical", has_length = 1)
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if (!is.disk(x)) {
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x <- unlist(x)
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if (isTRUE(na.rm)) {
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x <- x[!is.na(x)]
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}
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x[trimws2(x) == ""] <- NA
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x.bak <- x
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na_before <- length(x[is.na(x)])
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# heavily based on cleaner::clean_double():
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clean_double2 <- function(x, remove = "[^0-9.,-]", fixed = FALSE) {
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x <- gsub(",", ".", x, fixed = TRUE)
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# remove ending dot/comma
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x <- gsub("[,.]$", "", x)
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# only keep last dot/comma
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reverse <- function(x) vapply(FUN.VALUE = character(1), lapply(strsplit(x, NULL), rev), paste, collapse = "")
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x <- sub("{{dot}}", ".",
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gsub(".", "",
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reverse(sub(".", "}}tod{{",
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reverse(x),
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fixed = TRUE
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)),
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fixed = TRUE
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),
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fixed = TRUE
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)
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x_clean <- gsub(remove, "", x, ignore.case = TRUE, fixed = fixed)
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# remove everything that is not a number or dot
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as.double(gsub("[^0-9.]+", "", x_clean))
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}
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# round up and make it an integer
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x <- as.integer(ceiling(clean_double2(x)))
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# disks can never be less than 6 mm (size of smallest disk) or more than 50 mm
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x[x < 6 | x > 99] <- NA_integer_
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x[x > 50] <- 50L
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na_after <- length(x[is.na(x)])
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if (na_before != na_after) {
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list_missing <- x.bak[is.na(x) & !is.na(x.bak)] %pm>%
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unique() %pm>%
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sort() %pm>%
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vector_and(quotes = TRUE)
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cur_col <- get_current_column()
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warning_("in `as.disk()`: ", na_after - na_before, " result",
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ifelse(na_after - na_before > 1, "s", ""),
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ifelse(is.null(cur_col), "", paste0(" in column '", cur_col, "'")),
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" truncated (",
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round(((na_after - na_before) / length(x)) * 100),
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"%) that were invalid disk zones: ",
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list_missing,
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call = FALSE
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)
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}
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}
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set_clean_class(as.integer(x),
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new_class = c("disk", "integer")
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)
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}
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all_valid_disks <- function(x) {
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if (!inherits(x, c("disk", "character", "numeric", "integer"))) {
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return(FALSE)
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}
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x_disk <- tryCatch(suppressWarnings(as.disk(x[!is.na(x)])),
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error = function(e) NA
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)
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!anyNA(x_disk) && !all(is.na(x))
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}
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#' @rdname as.disk
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#' @details `NA_disk_` is a missing value of the new `disk` class.
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#' @export
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NA_disk_ <- set_clean_class(as.integer(NA_real_),
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new_class = c("disk", "integer")
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)
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#' @rdname as.disk
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#' @export
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is.disk <- function(x) {
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inherits(x, "disk")
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}
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# will be exported using s3_register() in R/zzz.R
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pillar_shaft.disk <- function(x, ...) {
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out <- trimws(format(x))
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out[is.na(x)] <- font_na(NA)
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create_pillar_column(out, align = "right", width = 2)
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}
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# will be exported using s3_register() in R/zzz.R
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type_sum.disk <- function(x, ...) {
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"disk"
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}
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#' @method print disk
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#' @export
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#' @noRd
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print.disk <- function(x, ...) {
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cat("Class 'disk'\n")
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print(as.integer(x), quote = FALSE)
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}
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#' @method [ disk
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#' @export
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#' @noRd
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"[.disk" <- function(x, ...) {
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y <- NextMethod()
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attributes(y) <- attributes(x)
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y
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}
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#' @method [[ disk
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#' @export
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#' @noRd
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"[[.disk" <- function(x, ...) {
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y <- NextMethod()
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attributes(y) <- attributes(x)
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y
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}
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#' @method [<- disk
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#' @export
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#' @noRd
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"[<-.disk" <- function(i, j, ..., value) {
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value <- as.disk(value)
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y <- NextMethod()
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attributes(y) <- attributes(i)
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y
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}
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#' @method [[<- disk
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#' @export
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#' @noRd
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"[[<-.disk" <- function(i, j, ..., value) {
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value <- as.disk(value)
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y <- NextMethod()
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attributes(y) <- attributes(i)
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y
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}
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#' @method c disk
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#' @export
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#' @noRd
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c.disk <- function(...) {
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as.disk(unlist(lapply(list(...), as.character)))
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}
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#' @method unique disk
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#' @export
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#' @noRd
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unique.disk <- function(x, incomparables = FALSE, ...) {
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y <- NextMethod()
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attributes(y) <- attributes(x)
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y
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}
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#' @method rep disk
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#' @export
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#' @noRd
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rep.disk <- function(x, ...) {
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y <- NextMethod()
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attributes(y) <- attributes(x)
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y
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}
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# will be exported using s3_register() in R/zzz.R
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get_skimmers.disk <- function(column) {
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skimr::sfl(
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skim_type = "disk",
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min = ~ min(as.double(.), na.rm = TRUE),
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max = ~ max(as.double(.), na.rm = TRUE),
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median = ~ stats::median(as.double(.), na.rm = TRUE),
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n_unique = ~ length(unique(stats::na.omit(.))),
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hist = ~ skimr::inline_hist(stats::na.omit(as.double(.)))
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)
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}
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