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318 lines
14 KiB
R
318 lines
14 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) 2018-2022 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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#' Determine Bug-Drug Combinations
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#'
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#' Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use [format()] on the result to prettify it to a publishable/printable format, see *Examples*.
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#' @inheritSection lifecycle Stable Lifecycle
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#' @inheritParams eucast_rules
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#' @param combine_IR a [logical] to indicate whether values R and I should be summed
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#' @param add_ab_group a [logical] to indicate where the group of the antimicrobials must be included as a first column
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#' @param remove_intrinsic_resistant [logical] to indicate that rows and columns with 100% resistance for all tested antimicrobials must be removed from the table
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#' @param FUN the function to call on the `mo` column to transform the microorganism codes, defaults to [mo_shortname()]
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#' @param translate_ab a [character] of length 1 containing column names of the [antibiotics] data set
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#' @param ... arguments passed on to `FUN`
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#' @inheritParams rsi_df
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#' @inheritParams base::formatC
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#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_IR = FALSE` (default) to test R vs. S+I and `combine_IR = TRUE` to test R+I vs. S.
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#' @export
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#' @rdname bug_drug_combinations
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#' @return The function [bug_drug_combinations()] returns a [data.frame] with columns "mo", "ab", "S", "I", "R" and "total".
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#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
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#' @inheritSection AMR Read more on Our Website!
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#' @examples
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#' \donttest{
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#' x <- bug_drug_combinations(example_isolates)
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#' x
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#' format(x, translate_ab = "name (atc)")
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#'
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#' # Use FUN to change to transformation of microorganism codes
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#' bug_drug_combinations(example_isolates,
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#' FUN = mo_gramstain)
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#'
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#' bug_drug_combinations(example_isolates,
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#' FUN = function(x) ifelse(x == as.mo("E. coli"),
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#' "E. coli",
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#' "Others"))
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#' }
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bug_drug_combinations <- function(x,
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col_mo = NULL,
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FUN = mo_shortname,
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...) {
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meet_criteria(x, allow_class = "data.frame", contains_column_class = "rsi")
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meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), has_length = 1, allow_NULL = TRUE)
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meet_criteria(FUN, allow_class = "function", has_length = 1)
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# try to find columns based on type
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# -- mo
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if (is.null(col_mo)) {
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col_mo <- search_type_in_df(x = x, type = "mo")
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stop_if(is.null(col_mo), "`col_mo` must be set")
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} else {
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stop_ifnot(col_mo %in% colnames(x), "column '", col_mo, "' (`col_mo`) not found")
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}
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x.bak <- x
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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x[, col_mo] <- FUN(x[, col_mo, drop = TRUE], ...)
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unique_mo <- sort(unique(x[, col_mo, drop = TRUE]))
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# select only groups and antibiotics
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if (is_null_or_grouped_tbl(x.bak)) {
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data_has_groups <- TRUE
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groups <- setdiff(names(attributes(x.bak)$groups), ".rows")
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x <- x[, c(groups, col_mo, colnames(x)[vapply(FUN.VALUE = logical(1), x, is.rsi)]), drop = FALSE]
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} else {
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data_has_groups <- FALSE
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x <- x[, c(col_mo, names(which(vapply(FUN.VALUE = logical(1), x, is.rsi)))), drop = FALSE]
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}
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run_it <- function(x) {
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out <- data.frame(mo = character(0),
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ab = character(0),
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S = integer(0),
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I = integer(0),
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R = integer(0),
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total = integer(0),
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stringsAsFactors = FALSE)
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if (data_has_groups) {
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group_values <- unique(x[, which(colnames(x) %in% groups), drop = FALSE])
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rownames(group_values) <- NULL
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x <- x[, which(!colnames(x) %in% groups), drop = FALSE]
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}
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for (i in seq_len(length(unique_mo))) {
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# filter on MO group and only select R/SI columns
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x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(vapply(FUN.VALUE = logical(1), x, is.rsi))), drop = FALSE]
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# turn and merge everything
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pivot <- lapply(x_mo_filter, function(x) {
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m <- as.matrix(table(x))
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data.frame(S = m["S", ], I = m["I", ], R = m["R", ], stringsAsFactors = FALSE)
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})
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merged <- do.call(rbind, pivot)
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out_group <- data.frame(mo = rep(unique_mo[i], NROW(merged)),
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ab = rownames(merged),
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S = merged$S,
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I = merged$I,
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R = merged$R,
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total = merged$S + merged$I + merged$R,
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stringsAsFactors = FALSE)
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if (data_has_groups) {
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if (nrow(group_values) < nrow(out_group)) {
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# repeat group_values for the number of rows in out_group
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repeated <- rep(seq_len(nrow(group_values)),
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each = nrow(out_group) / nrow(group_values))
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group_values <- group_values[repeated, , drop = FALSE]
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}
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out_group <- cbind(group_values, out_group)
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}
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out <- rbind(out, out_group, stringsAsFactors = FALSE)
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}
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out
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}
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# based on pm_apply_grouped_function
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apply_group <- function(.data, fn, groups, drop = FALSE, ...) {
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grouped <- pm_split_into_groups(.data, groups, drop)
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res <- do.call(rbind, unname(lapply(grouped, fn, ...)))
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if (any(groups %in% colnames(res))) {
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class(res) <- c("grouped_data", class(res))
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res <- pm_set_groups(res, groups[groups %in% colnames(res)])
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}
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res
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}
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if (data_has_groups) {
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out <- apply_group(x, "run_it", groups)
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rownames(out) <- NULL
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set_clean_class(out,
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new_class = c("grouped", "bug_drug_combinations", "data.frame"))
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} else {
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out <- run_it(x)
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rownames(out) <- NULL
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set_clean_class(out,
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new_class = c("bug_drug_combinations", "data.frame"))
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}
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}
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#' @method format bug_drug_combinations
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#' @export
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#' @rdname bug_drug_combinations
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format.bug_drug_combinations <- function(x,
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translate_ab = "name (ab, atc)",
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language = get_AMR_locale(),
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minimum = 30,
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combine_SI = TRUE,
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combine_IR = FALSE,
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add_ab_group = TRUE,
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remove_intrinsic_resistant = FALSE,
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decimal.mark = getOption("OutDec"),
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big.mark = ifelse(decimal.mark == ",", ".", ","),
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...) {
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meet_criteria(x, allow_class = "data.frame")
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meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
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meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
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meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
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meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
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meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
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meet_criteria(add_ab_group, allow_class = "logical", has_length = 1)
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meet_criteria(remove_intrinsic_resistant, allow_class = "logical", has_length = 1)
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meet_criteria(decimal.mark, allow_class = "character", has_length = 1)
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meet_criteria(big.mark, allow_class = "character", has_length = 1)
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if (inherits(x, "grouped")) {
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# bug_drug_combinations() has been run on groups, so de-group here
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warning_("formatting the output of `bug_drug_combinations()` does not support grouped variables, they are ignored", call = FALSE)
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idx <- split(seq_len(nrow(x)), paste0(x$mo, "%%", x$ab))
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x <- data.frame(mo = gsub("(.*)%%(.*)", "\\1", names(idx)),
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ab = gsub("(.*)%%(.*)", "\\2", names(idx)),
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S = sapply(idx, function(i) sum(y$S[i], na.rm = TRUE)),
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I = sapply(idx, function(i) sum(y$I[i], na.rm = TRUE)),
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R = sapply(idx, function(i) sum(y$R[i], na.rm = TRUE)),
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total = sapply(idx, function(i) sum(y$S[i], na.rm = TRUE) +
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sum(y$I[i], na.rm = TRUE) +
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sum(y$R[i], na.rm = TRUE)),
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stringsAsFactors = FALSE)
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}
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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x <- subset(x, total >= minimum)
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if (remove_intrinsic_resistant == TRUE) {
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x <- subset(x, R != total)
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}
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if (combine_SI == TRUE | combine_IR == FALSE) {
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x$isolates <- x$R
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} else {
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x$isolates <- x$R + x$I
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}
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give_ab_name <- function(ab, format, language) {
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format <- tolower(format)
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ab_txt <- rep(format, length(ab))
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for (i in seq_len(length(ab_txt))) {
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ab_txt[i] <- gsub("ab", as.character(as.ab(ab[i])), ab_txt[i])
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ab_txt[i] <- gsub("cid", ab_cid(ab[i]), ab_txt[i])
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ab_txt[i] <- gsub("group", ab_group(ab[i], language = language), ab_txt[i])
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ab_txt[i] <- gsub("atc_group1", ab_atc_group1(ab[i], language = language), ab_txt[i])
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ab_txt[i] <- gsub("atc_group2", ab_atc_group2(ab[i], language = language), ab_txt[i])
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ab_txt[i] <- gsub("atc", ab_atc(ab[i], only_first = TRUE), ab_txt[i])
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ab_txt[i] <- gsub("name", ab_name(ab[i], language = language), ab_txt[i])
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ab_txt[i]
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}
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ab_txt
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}
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remove_NAs <- function(.data) {
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cols <- colnames(.data)
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.data <- as.data.frame(lapply(.data, function(x) ifelse(is.na(x), "", x)),
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stringsAsFactors = FALSE)
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colnames(.data) <- cols
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.data
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}
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create_var <- function(.data, ...) {
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dots <- list(...)
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for (i in seq_len(length(dots))) {
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.data[, names(dots)[i]] <- dots[[i]]
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}
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.data
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}
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y <- x %pm>%
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create_var(ab = as.ab(x$ab),
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ab_txt = give_ab_name(ab = x$ab, format = translate_ab, language = language)) %pm>%
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pm_group_by(ab, ab_txt, mo) %pm>%
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pm_summarise(isolates = sum(isolates, na.rm = TRUE),
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total = sum(total, na.rm = TRUE)) %pm>%
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pm_ungroup()
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y <- y %pm>%
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create_var(txt = paste0(percentage(y$isolates / y$total, decimal.mark = decimal.mark, big.mark = big.mark),
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" (", trimws(format(y$isolates, big.mark = big.mark)), "/",
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trimws(format(y$total, big.mark = big.mark)), ")")) %pm>%
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pm_select(ab, ab_txt, mo, txt) %pm>%
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pm_arrange(mo)
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# replace tidyr::pivot_wider() from here
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for (i in unique(y$mo)) {
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mo_group <- y[which(y$mo == i), c("ab", "txt")]
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colnames(mo_group) <- c("ab", i)
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rownames(mo_group) <- NULL
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y <- y %pm>%
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pm_left_join(mo_group, by = "ab")
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}
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y <- y %pm>%
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pm_distinct(ab, .keep_all = TRUE) %pm>%
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pm_select(-mo, -txt) %pm>%
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# replace tidyr::pivot_wider() until here
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remove_NAs()
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select_ab_vars <- function(.data) {
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.data[, c("ab_group", "ab_txt", colnames(.data)[!colnames(.data) %in% c("ab_group", "ab_txt", "ab")])]
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}
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y <- y %pm>%
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create_var(ab_group = ab_group(y$ab, language = language)) %pm>%
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select_ab_vars() %pm>%
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pm_arrange(ab_group, ab_txt)
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y <- y %pm>%
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create_var(ab_group = ifelse(y$ab_group != pm_lag(y$ab_group) | is.na(pm_lag(y$ab_group)), y$ab_group, ""))
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if (add_ab_group == FALSE) {
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y <- y %pm>%
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pm_select(-ab_group) %pm>%
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pm_rename("Drug" = ab_txt)
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colnames(y)[1] <- translate_AMR(colnames(y)[1], language, only_unknown = FALSE)
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} else {
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y <- y %pm>%
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pm_rename("Group" = ab_group,
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"Drug" = ab_txt)
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}
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if (!is.null(language)) {
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colnames(y) <- translate_AMR(colnames(y), language, only_unknown = FALSE)
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}
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if (remove_intrinsic_resistant == TRUE) {
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y <- y[, !vapply(FUN.VALUE = logical(1), y, function(col) all(col %like% "100", na.rm = TRUE) & !any(is.na(col))), drop = FALSE]
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}
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rownames(y) <- NULL
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y
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}
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#' @method print bug_drug_combinations
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#' @export
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print.bug_drug_combinations <- function(x, ...) {
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x_class <- class(x)
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print(set_clean_class(x,
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new_class = x_class[!x_class %in% c("bug_drug_combinations", "grouped")]),
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...)
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message_("Use 'format()' on this result to get a publishable/printable format.",
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ifelse(inherits(x, "grouped"), " Note: The grouping variable(s) will be ignored.", ""),
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as_note = FALSE)
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}
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