# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Data 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 data analysis: https://msberends.github.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 publishable/printable format, see *Examples*. #' @inheritSection lifecycle Stable Lifecycle #' @inheritParams eucast_rules #' @param combine_IR a [logical] to indicate whether values R and I should be summed #' @param add_ab_group a [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 and columns 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 codes, 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. #' @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 #' bug_drug_combinations(example_isolates, #' FUN = mo_gramstain) #' #' bug_drug_combinations(example_isolates, #' FUN = function(x) ifelse(x == as.mo("E. coli"), #' "E. coli", #' "Others")) #' } bug_drug_combinations <- function(x, col_mo = NULL, FUN = mo_shortname, ...) { meet_criteria(x, allow_class = "data.frame", contains_column_class = "rsi") meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), has_length = 1, allow_NULL = TRUE) meet_criteria(FUN, allow_class = "function", has_length = 1) # 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") } else { stop_ifnot(col_mo %in% colnames(x), "column '", col_mo, "' (`col_mo`) not found") } x.bak <- x x <- as.data.frame(x, stringsAsFactors = FALSE) x[, col_mo] <- FUN(x[, col_mo, drop = TRUE], ...) unique_mo <- sort(unique(x[, col_mo, drop = TRUE])) # select only groups and antibiotics if (is_null_or_grouped_tbl(x.bak)) { data_has_groups <- TRUE groups <- setdiff(names(attributes(x.bak)$groups), ".rows") x <- x[, c(groups, col_mo, colnames(x)[vapply(FUN.VALUE = logical(1), x, is.rsi)]), drop = FALSE] } else { data_has_groups <- FALSE x <- x[, c(col_mo, names(which(vapply(FUN.VALUE = logical(1), x, is.rsi)))), drop = FALSE] } run_it <- function(x) { out <- data.frame(mo = character(0), ab = character(0), S = integer(0), I = integer(0), R = integer(0), total = integer(0), stringsAsFactors = FALSE) if (data_has_groups) { group_values <- unique(x[, which(colnames(x) %in% groups), drop = FALSE]) rownames(group_values) <- NULL x <- x[, which(!colnames(x) %in% groups), drop = FALSE] } for (i in seq_len(length(unique_mo))) { # filter on MO group and only select R/SI columns x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(vapply(FUN.VALUE = logical(1), x, is.rsi))), drop = FALSE] # turn and merge everything pivot <- lapply(x_mo_filter, function(x) { m <- as.matrix(table(x)) data.frame(S = m["S", ], I = m["I", ], R = m["R", ], stringsAsFactors = FALSE) }) merged <- do.call(rbind, pivot) out_group <- data.frame(mo = rep(unique_mo[i], NROW(merged)), ab = rownames(merged), S = merged$S, I = merged$I, R = merged$R, total = merged$S + merged$I + merged$R, stringsAsFactors = FALSE) if (data_has_groups) { if (nrow(group_values) < nrow(out_group)) { # repeat group_values for the number of rows in out_group repeated <- rep(seq_len(nrow(group_values)), each = nrow(out_group) / nrow(group_values)) group_values <- group_values[repeated, , drop = FALSE] } out_group <- cbind(group_values, out_group) } out <- rbind(out, out_group, stringsAsFactors = FALSE) } out } # based on pm_apply_grouped_function apply_group <- function(.data, fn, groups, drop = FALSE, ...) { grouped <- pm_split_into_groups(.data, groups, drop) res <- do.call(rbind, unname(lapply(grouped, fn, ...))) if (any(groups %in% colnames(res))) { class(res) <- c("grouped_data", class(res)) res <- pm_set_groups(res, groups[groups %in% colnames(res)]) } res } if (data_has_groups) { out <- apply_group(x, "run_it", groups) rownames(out) <- NULL set_clean_class(out, new_class = c("grouped", "bug_drug_combinations", "data.frame")) } else { out <- run_it(x) rownames(out) <- NULL set_clean_class(out, new_class = c("bug_drug_combinations", "data.frame")) } } #' @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 == ",", ".", ","), ...) { meet_criteria(x, allow_class = "data.frame") meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE) meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE) meet_criteria(combine_SI, allow_class = "logical", has_length = 1) meet_criteria(combine_IR, allow_class = "logical", has_length = 1) meet_criteria(add_ab_group, allow_class = "logical", has_length = 1) meet_criteria(remove_intrinsic_resistant, allow_class = "logical", has_length = 1) meet_criteria(decimal.mark, allow_class = "character", has_length = 1) meet_criteria(big.mark, allow_class = "character", has_length = 1) if (inherits(x, "grouped")) { # bug_drug_combinations() has been run on groups, so de-group here warning_("formatting the output of `bug_drug_combinations()` does not support grouped variables, they are ignored", call = FALSE) idx <- split(seq_len(nrow(x)), paste0(x$mo, "%%", x$ab)) x <- data.frame(mo = gsub("(.*)%%(.*)", "\\1", names(idx)), ab = gsub("(.*)%%(.*)", "\\2", names(idx)), S = sapply(idx, function(i) sum(y$S[i], na.rm = TRUE)), I = sapply(idx, function(i) sum(y$I[i], na.rm = TRUE)), R = sapply(idx, function(i) sum(y$R[i], na.rm = TRUE)), total = sapply(idx, function(i) sum(y$S[i], na.rm = TRUE) + sum(y$I[i], na.rm = TRUE) + sum(y$R[i], na.rm = TRUE)), stringsAsFactors = FALSE) } x <- as.data.frame(x, stringsAsFactors = FALSE) x <- subset(x, total >= minimum) if (remove_intrinsic_resistant == TRUE) { x <- subset(x, R != total) } if (combine_SI == TRUE | combine_IR == FALSE) { x$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", as.character(as.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(lapply(.data, function(x) ifelse(is.na(x), "", x)), stringsAsFactors = 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 %pm>% create_var(ab = as.ab(x$ab), ab_txt = give_ab_name(ab = x$ab, format = translate_ab, language = language)) %pm>% pm_group_by(ab, ab_txt, mo) %pm>% pm_summarise(isolates = sum(isolates, na.rm = TRUE), total = sum(total, na.rm = TRUE)) %pm>% pm_ungroup() y <- y %pm>% 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)), ")")) %pm>% pm_select(ab, ab_txt, mo, txt) %pm>% pm_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 %pm>% pm_left_join(mo_group, by = "ab") } y <- y %pm>% pm_distinct(ab, .keep_all = TRUE) %pm>% pm_select(-mo, -txt) %pm>% # 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 %pm>% create_var(ab_group = ab_group(y$ab, language = language)) %pm>% select_ab_vars() %pm>% pm_arrange(ab_group, ab_txt) y <- y %pm>% create_var(ab_group = ifelse(y$ab_group != pm_lag(y$ab_group) | is.na(pm_lag(y$ab_group)), y$ab_group, "")) if (add_ab_group == FALSE) { y <- y %pm>% pm_select(-ab_group) %pm>% pm_rename("Drug" = ab_txt) colnames(y)[1] <- translate_AMR(colnames(y)[1], language, only_unknown = FALSE) } else { y <- y %pm>% pm_rename("Group" = ab_group, "Drug" = ab_txt) } if (!is.null(language)) { colnames(y) <- translate_AMR(colnames(y), language, only_unknown = FALSE) } if (remove_intrinsic_resistant == TRUE) { y <- y[, !vapply(FUN.VALUE = logical(1), y, function(col) all(col %like% "100", na.rm = TRUE) & !any(is.na(col))), drop = FALSE] } rownames(y) <- NULL y } #' @method print bug_drug_combinations #' @export print.bug_drug_combinations <- function(x, ...) { x_class <- class(x) print(set_clean_class(x, new_class = x_class[!x_class %in% c("bug_drug_combinations", "grouped")]), ...) message_("Use 'format()' on this result to get a publishable/printable format.", ifelse(inherits(x, "grouped"), " Note: The grouping variable(s) will be ignored.", ""), as_note = FALSE) }