# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis # # # # SOURCE # # https://gitlab.com/msberends/AMR # # # # LICENCE # # (c) 2018-2020 Berends MS, Luz CF et al. # # # # 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 more info: https://msberends.gitlab.io/AMR. # # ==================================================================== # #' Determine bug-drug combinations #' #' 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 publicable/printable format, see Examples. #' @inheritSection lifecycle Stable lifecycle #' @inheritParams eucast_rules #' @param combine_IR logical to indicate whether values R and I should be summed #' @param add_ab_group logical to indicate where the group of the antimicrobials must be included as a first column #' @param remove_intrinsic_resistant logical to indicate that rows with 100% resistance for all tested antimicrobials must be removed from the table #' @param FUN the function to call on the `mo` column to transform the microorganism IDs, defaults to [mo_shortname()] #' @param translate_ab a character of length 1 containing column names of the [antibiotics] data set #' @param ... arguments passed on to `FUN` #' @inheritParams rsi_df #' @inheritParams base::formatC #' @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. #' #' The language of the output can be overwritten with `options(AMR_locale)`, please see [translate]. #' @export #' @rdname bug_drug_combinations #' @return The function [bug_drug_combinations()] returns a [`data.frame`] with columns "mo", "ab", "S", "I", "R" and "total". #' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. . #' @inheritSection AMR Read more on our website! #' @examples #' \donttest{ #' x <- bug_drug_combinations(example_isolates) #' x #' format(x, translate_ab = "name (atc)") #' #' # Use FUN to change to transformation of microorganism codes #' x <- bug_drug_combinations(example_isolates, #' FUN = mo_gramstain) #' #' x <- bug_drug_combinations(example_isolates, #' FUN = function(x) ifelse(x == "B_ESCHR_COLI", #' "E. coli", #' "Others")) #' } bug_drug_combinations <- function(x, col_mo = NULL, FUN = mo_shortname, ...) { stop_ifnot(is.data.frame(x), "`x` must be a data frame") stop_ifnot(any(sapply(x, is.rsi), na.rm = TRUE), "No columns with class found. See ?as.rsi.") # try to find columns based on type # -- mo if (is.null(col_mo)) { col_mo <- search_type_in_df(x = x, type = "mo") } stop_if(is.null(col_mo), "`col_mo` must be set") x_class <- class(x) x <- as.data.frame(x, stringsAsFactors = FALSE) x[, col_mo] <- FUN(x[, col_mo, drop = TRUE]) x <- x[, c(col_mo, names(which(sapply(x, is.rsi)))), drop = FALSE] unique_mo <- sort(unique(x[, col_mo, drop = TRUE])) out <- data.frame( mo = character(0), ab = character(0), S = integer(0), I = integer(0), R = integer(0), total = integer(0)) for (i in seq_len(length(unique_mo))) { # filter on MO group and only select R/SI columns x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(sapply(x, is.rsi))), drop = FALSE] # turn and merge everything pivot <- lapply(x_mo_filter, function(x) { m <- as.matrix(table(x)) data.frame(S = m["S", ], I = m["I", ], R = m["R", ], stringsAsFactors = FALSE) }) merged <- do.call(rbind, pivot) out_group <- data.frame(mo = unique_mo[i], ab = rownames(merged), S = merged$S, I = merged$I, R = merged$R, total = merged$S + merged$I + merged$R) out <- rbind(out, out_group) } structure(.Data = out, class = c("bug_drug_combinations", x_class)) } #' @method format bug_drug_combinations #' @export #' @rdname bug_drug_combinations format.bug_drug_combinations <- function(x, translate_ab = "name (ab, atc)", language = get_locale(), minimum = 30, combine_SI = TRUE, combine_IR = FALSE, add_ab_group = TRUE, remove_intrinsic_resistant = FALSE, decimal.mark = getOption("OutDec"), big.mark = ifelse(decimal.mark == ",", ".", ","), ...) { x <- as.data.frame(x, stringsAsFactors = FALSE) x <- subset(x, total >= minimum) if (remove_intrinsic_resistant == TRUE) { x <- subset(x, R != total) } if (combine_SI == TRUE | combine_IR == FALSE) { x$isolates <- x$R } else { x$isolates <- x$R + x$I } give_ab_name <- function(ab, format, language) { format <- tolower(format) ab_txt <- rep(format, length(ab)) for (i in seq_len(length(ab_txt))) { ab_txt[i] <- gsub("ab", ab[i], ab_txt[i]) ab_txt[i] <- gsub("cid", ab_cid(ab[i]), ab_txt[i]) ab_txt[i] <- gsub("group", ab_group(ab[i], language = language), ab_txt[i]) ab_txt[i] <- gsub("atc_group1", ab_atc_group1(ab[i], language = language), ab_txt[i]) ab_txt[i] <- gsub("atc_group2", ab_atc_group2(ab[i], language = language), ab_txt[i]) ab_txt[i] <- gsub("atc", ab_atc(ab[i]), ab_txt[i]) ab_txt[i] <- gsub("name", ab_name(ab[i], language = language), ab_txt[i]) ab_txt[i] } ab_txt } remove_NAs <- function(.data) { cols <- colnames(.data) .data <- as.data.frame(sapply(.data, function(x) ifelse(is.na(x), "", x), simplify = FALSE)) colnames(.data) <- cols .data } create_var <- function(.data, ...) { dots <- list(...) for (i in seq_len(length(dots))) { .data[, names(dots)[i]] <- dots[[i]] } .data } y <- x %>% create_var(ab = as.ab(x$ab), ab_txt = give_ab_name(ab = x$ab, format = translate_ab, language = language)) %>% group_by(ab, ab_txt, mo) %>% summarise(isolates = sum(isolates, na.rm = TRUE), total = sum(total, na.rm = TRUE)) %>% ungroup() y <- y %>% create_var(txt = paste0(percentage(y$isolates / y$total, decimal.mark = decimal.mark, big.mark = big.mark), " (", trimws(format(y$isolates, big.mark = big.mark)), "/", trimws(format(y$total, big.mark = big.mark)), ")")) %>% select(ab, ab_txt, mo, txt) %>% arrange(mo) # replace tidyr::pivot_wider() from here for (i in unique(y$mo)) { mo_group <- y[which(y$mo == i), c("ab", "txt")] colnames(mo_group) <- c("ab", i) rownames(mo_group) <- NULL y <- y %>% left_join(mo_group, by = "ab") } y <- y %>% distinct(ab, .keep_all = TRUE) %>% select(-mo, -txt) %>% # replace tidyr::pivot_wider() until here remove_NAs() select_ab_vars <- function(.data) { .data[, c("ab_group", "ab_txt", colnames(.data)[!colnames(.data) %in% c("ab_group", "ab_txt", "ab")])] } y <- y %>% create_var(ab_group = ab_group(y$ab, language = language)) %>% select_ab_vars() %>% arrange(ab_group, ab_txt) y <- y %>% create_var(ab_group = ifelse(y$ab_group != lag(y$ab_group) | is.na(lag(y$ab_group)), y$ab_group, "")) if (add_ab_group == FALSE) { y <- y %>% select(-ab_group) %>% rename("Drug" = ab_txt) colnames(y)[1] <- translate_AMR(colnames(y)[1], language = get_locale(), only_unknown = FALSE) } else { y <- y %>% rename("Group" = ab_group, "Drug" = ab_txt) colnames(y)[1:2] <- translate_AMR(colnames(y)[1:2], language = get_locale(), only_unknown = FALSE) } rownames(y) <- NULL y } #' @method print bug_drug_combinations #' @export print.bug_drug_combinations <- function(x, ...) { x_class <- class(x) print(structure(x, class = x_class[x_class != "bug_drug_combinations"]), ...) message(font_blue("NOTE: Use 'format()' on this result to get a publishable/printable format.")) }