# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) 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 analysis: https://msberends.github.io/AMR/ # # ==================================================================== # #' Transform Input to Disk Diffusion Diameters #' #' 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. #' @inheritSection lifecycle Stable Lifecycle #' @rdname as.disk #' @param x vector #' @param na.rm a logical indicating whether missing values should be removed #' @details Interpret disk values as RSI values with [as.rsi()]. It supports guidelines from EUCAST and CLSI. #' @return An [integer] with additional class [`disk`] #' @aliases disk #' @export #' @seealso [as.rsi()] #' @inheritSection AMR Read more on Our Website! #' @examples #' \donttest{ #' # transform existing disk zones to the `disk` class #' df <- data.frame(microorganism = "E. coli", #' AMP = 20, #' CIP = 14, #' GEN = 18, #' TOB = 16) #' df[, 2:5] <- lapply(df[, 2:5], as.disk) #' # same with dplyr: #' # df %>% mutate(across(AMP:TOB, as.disk)) #' #' # interpret disk values, see ?as.rsi #' as.rsi(x = as.disk(18), #' mo = "Strep pneu", # `mo` will be coerced with as.mo() #' ab = "ampicillin", # and `ab` with as.ab() #' guideline = "EUCAST") #' #' as.rsi(df) #' } as.disk <- function(x, na.rm = FALSE) { meet_criteria(x, allow_class = c("disk", "character", "numeric", "integer"), allow_NA = TRUE) meet_criteria(na.rm, allow_class = "logical", has_length = 1) if (!is.disk(x)) { x <- unlist(x) if (na.rm == TRUE) { x <- x[!is.na(x)] } x.bak <- x na_before <- length(x[is.na(x)]) # heavily based on cleaner::clean_double(): clean_double2 <- function(x, remove = "[^0-9.,-]", fixed = FALSE) { x <- gsub(",", ".", x) # remove ending dot/comma x <- gsub("[,.]$", "", x) # only keep last dot/comma reverse <- function(x) vapply(FUN.VALUE = character(1), lapply(strsplit(x, NULL), rev), paste, collapse = "") x <- sub("{{dot}}", ".", gsub(".", "", reverse(sub(".", "}}tod{{", reverse(x), fixed = TRUE)), fixed = TRUE), fixed = TRUE) x_clean <- gsub(remove, "", x, ignore.case = TRUE, fixed = fixed) # remove everything that is not a number or dot as.numeric(gsub("[^0-9.]+", "", x_clean)) } # round up and make it an integer x <- as.integer(ceiling(clean_double2(x))) # disks can never be less than 6 mm (size of smallest disk) or more than 50 mm x[x < 6 | x > 50] <- NA_integer_ na_after <- length(x[is.na(x)]) if (na_before != na_after) { list_missing <- x.bak[is.na(x) & !is.na(x.bak)] %pm>% unique() %pm>% sort() list_missing <- paste0('"', list_missing, '"', collapse = ", ") warning_(na_after - na_before, " results truncated (", round(((na_after - na_before) / length(x)) * 100), "%) that were invalid disk zones: ", list_missing, call = FALSE) } } set_clean_class(as.integer(x), new_class = c("disk", "integer")) } all_valid_disks <- function(x) { if (!inherits(x, c("disk", "character", "numeric", "integer"))) { return(FALSE) } x_disk <- tryCatch(suppressWarnings(as.disk(x[!is.na(x)])), error = function(e) NA) !any(is.na(x_disk)) && !all(is.na(x)) } #' @rdname as.disk #' @export is.disk <- function(x) { inherits(x, "disk") } # will be exported using s3_register() in R/zzz.R pillar_shaft.disk <- function(x, ...) { out <- trimws(format(x)) out[is.na(x)] <- font_na(NA) create_pillar_column(out, align = "right", width = 2) } # will be exported using s3_register() in R/zzz.R type_sum.disk <- function(x, ...) { "disk" } #' @method print disk #' @export #' @noRd print.disk <- function(x, ...) { cat("Class \n") print(as.integer(x), quote = FALSE) } #' @method plot disk #' @export #' @importFrom graphics barplot axis #' @rdname plot plot.disk <- function(x, main = paste("Disk zones values of", deparse(substitute(x))), ylab = "Frequency", xlab = "Disk diffusion (mm)", axes = FALSE, ...) { meet_criteria(main, allow_class = "character", has_length = 1) meet_criteria(ylab, allow_class = "character", has_length = 1) meet_criteria(xlab, allow_class = "character", has_length = 1) meet_criteria(axes, allow_class = "logical", has_length = 1) barplot(table(x), ylab = ylab, xlab = xlab, axes = axes, main = main, ...) axis(2, seq(0, max(table(x)))) } #' @method [ disk #' @export #' @noRd "[.disk" <- function(x, ...) { y <- NextMethod() attributes(y) <- attributes(x) y } #' @method [[ disk #' @export #' @noRd "[[.disk" <- function(x, ...) { y <- NextMethod() attributes(y) <- attributes(x) y } #' @method [<- disk #' @export #' @noRd "[<-.disk" <- function(i, j, ..., value) { value <- as.disk(value) y <- NextMethod() attributes(y) <- attributes(i) y } #' @method [[<- disk #' @export #' @noRd "[[<-.disk" <- function(i, j, ..., value) { value <- as.disk(value) y <- NextMethod() attributes(y) <- attributes(i) y } #' @method c disk #' @export #' @noRd c.disk <- function(x, ...) { y <- NextMethod() y <- as.disk(y) attributes(y) <- attributes(x) y } #' @method unique disk #' @export #' @noRd unique.disk <- function(x, incomparables = FALSE, ...) { y <- NextMethod() attributes(y) <- attributes(x) y } # will be exported using s3_register() in R/zzz.R get_skimmers.disk <- function(column) { skimr::sfl( skim_type = "disk", min = ~min(as.double(.), na.rm = TRUE), max = ~max(as.double(.), na.rm = TRUE), median = ~stats::median(as.double(.), na.rm = TRUE), n_unique = ~pm_n_distinct(., na.rm = TRUE), hist = ~skimr::inline_hist(stats::na.omit(as.double(.))) ) }