mirror of https://github.com/msberends/AMR.git
291 lines
11 KiB
R
Executable File
291 lines
11 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://gitlab.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2020 Berends MS, Luz CF et al. #
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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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# #
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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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# Visit our website for more info: https://msberends.gitlab.io/AMR. #
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# ==================================================================== #
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dots2vars <- function(...) {
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# this function is to give more informative output about
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# variable names in count_* and proportion_* functions
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dots <- substitute(list(...))
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paste(as.character(dots)[2:length(dots)], collapse = ", ")
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}
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rsi_calc <- function(...,
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ab_result,
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minimum = 0,
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as_percent = FALSE,
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only_all_tested = FALSE,
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only_count = FALSE) {
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data_vars <- dots2vars(...)
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if (!is.numeric(minimum)) {
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stop("`minimum` must be numeric", call. = FALSE)
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}
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if (!is.logical(as_percent)) {
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stop("`as_percent` must be logical", call. = FALSE)
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}
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if (!is.logical(only_all_tested)) {
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stop("`only_all_tested` must be logical", call. = FALSE)
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}
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dots_df <- switch(1, ...)
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dots <- base::eval(base::substitute(base::alist(...)))
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if ("also_single_tested" %in% names(dots)) {
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stop("`also_single_tested` was replaced by `only_all_tested`. Please read Details in the help page (`?proportion`) as this may have a considerable impact on your analysis.", call. = FALSE)
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}
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ndots <- length(dots)
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if ("data.frame" %in% class(dots_df)) {
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# data.frame passed with other columns, like: example_isolates %>% proportion_S(AMC, GEN)
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dots <- as.character(dots)
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dots <- dots[dots != "."]
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if (length(dots) == 0 | all(dots == "df")) {
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# for complete data.frames, like example_isolates %>% select(AMC, GEN) %>% proportion_S()
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# and the old rsi function, which has "df" as name of the first parameter
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x <- dots_df
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} else {
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x <- dots_df[, dots[dots %in% colnames(dots_df)]]
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}
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} else if (ndots == 1) {
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# only 1 variable passed (can also be data.frame), like: proportion_S(example_isolates$AMC) and example_isolates$AMC %>% proportion_S()
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x <- dots_df
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} else {
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# multiple variables passed without pipe, like: proportion_S(example_isolates$AMC, example_isolates$GEN)
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x <- NULL
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try(x <- as.data.frame(dots), silent = TRUE)
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if (is.null(x)) {
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# support for example_isolates %>% group_by(hospital_id) %>% summarise(amox = susceptibility(GEN, AMX))
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x <- as.data.frame(list(...))
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}
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}
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if (is.null(x)) {
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warning("argument is NULL (check if columns exist): returning NA", call. = FALSE)
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return(NA)
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}
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print_warning <- FALSE
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ab_result <- as.rsi(ab_result)
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if (is.data.frame(x)) {
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rsi_integrity_check <- character(0)
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for (i in seq_len(ncol(x))) {
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# check integrity of columns: force rsi class
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if (!is.rsi(x %>% pull(i))) {
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rsi_integrity_check <- c(rsi_integrity_check, x %>% pull(i) %>% as.character())
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x[, i] <- suppressWarnings(x %>% pull(i) %>% as.rsi()) # warning will be given later
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print_warning <- TRUE
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}
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}
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if (length(rsi_integrity_check) > 0) {
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# this will give a warning for invalid results, of all input columns (so only 1 warning)
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rsi_integrity_check <- as.rsi(rsi_integrity_check)
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}
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if (only_all_tested == TRUE) {
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# THE NUMBER OF ISOLATES WHERE *ALL* ABx ARE S/I/R
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x <- apply(X = as.data.frame(lapply(x, as.integer), stringsAsFactors = FALSE),
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MARGIN = 1,
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FUN = base::min)
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numerator <- sum(as.integer(x) %in% as.integer(ab_result), na.rm = TRUE)
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denominator <- length(x) - sum(is.na(x))
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} else {
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# THE NUMBER OF ISOLATES WHERE *ANY* ABx IS S/I/R
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other_values <- base::setdiff(c(NA, levels(ab_result)), ab_result)
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other_values_filter <- base::apply(x, 1, function(y) {
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base::all(y %in% other_values) & base::any(is.na(y))
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})
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numerator <- sum(as.logical(by(x, seq_len(nrow(x)), function(row) any(unlist(row) %in% ab_result, na.rm = TRUE))))
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denominator <- nrow(x[!other_values_filter, ])
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}
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} else {
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# x is not a data.frame
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if (!is.rsi(x)) {
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x <- as.rsi(x)
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print_warning <- TRUE
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}
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numerator <- sum(x %in% ab_result, na.rm = TRUE)
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denominator <- sum(x %in% levels(ab_result), na.rm = TRUE)
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}
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if (print_warning == TRUE) {
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warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_if(is.rsi.eligible, as.rsi)",
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call. = FALSE)
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}
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if (only_count == TRUE) {
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return(numerator)
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}
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if (denominator < minimum) {
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if (data_vars != "") {
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data_vars <- paste(" for", data_vars)
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}
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warning("Introducing NA: only ", denominator, " results available", data_vars, " (`minimum` was set to ", minimum, ").", call. = FALSE)
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fraction <- NA
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} else {
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fraction <- numerator / denominator
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}
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if (as_percent == TRUE) {
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percentage(fraction, digits = 1)
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} else {
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fraction
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}
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}
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rsi_calc_df <- function(type, # "proportion", "count" or "both"
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data,
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translate_ab = "name",
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language = get_locale(),
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minimum = 30,
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as_percent = FALSE,
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combine_SI = TRUE,
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combine_IR = FALSE,
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combine_SI_missing = FALSE) {
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check_dataset_integrity()
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if (!"data.frame" %in% class(data)) {
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stop(paste0("`", type, "_df` must be called on a data.frame"), call. = FALSE)
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}
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if (isTRUE(combine_IR) & isTRUE(combine_SI_missing)) {
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combine_SI <- FALSE
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}
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if (isTRUE(combine_SI) & isTRUE(combine_IR)) {
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stop("either `combine_SI` or `combine_IR` can be TRUE, not both", call. = FALSE)
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}
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if (!any(sapply(data, is.rsi), na.rm = TRUE)) {
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stop("No columns with class 'rsi' found. See ?as.rsi.", call. = FALSE)
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}
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if (as.character(translate_ab) %in% c("TRUE", "official")) {
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translate_ab <- "name"
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}
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# select only groups and antibiotics
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if (has_groups(data)) {
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data_has_groups <- TRUE
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groups <- setdiff(names(get_groups(data)), ".rows") # get_groups is from poorman.R
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data <- data[, c(groups, colnames(data)[sapply(data, is.rsi)]), drop = FALSE]
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} else {
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data_has_groups <- FALSE
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data <- data[, colnames(data)[sapply(data, is.rsi)], drop = FALSE]
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}
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data <- as.data.frame(data, stringsAsFactors = FALSE)
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if (isTRUE(combine_SI) | isTRUE(combine_IR)) {
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for (i in seq_len(ncol(data))) {
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if (is.rsi(data[, i, drop = TRUE])) {
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data[, i] <- as.character(data[, i, drop = TRUE])
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if (isTRUE(combine_SI)) {
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data[, i] <- gsub("(I|S)", "SI", data[, i, drop = TRUE])
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} else if (isTRUE(combine_IR)) {
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data[, i] <- gsub("(I|R)", "IR", data[, i, drop = TRUE])
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}
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}
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}
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}
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sum_it <- function(.data) {
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out <- data.frame(antibiotic = character(0),
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interpretation = character(0),
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value = double(0),
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isolates <- integer(0),
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stringsAsFactors = FALSE)
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if (data_has_groups) {
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group_values <- unique(.data[, which(colnames(.data) %in% groups), drop = FALSE])
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rownames(group_values) <- NULL
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.data <- .data[, which(!colnames(.data) %in% groups), drop = FALSE]
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}
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for (i in seq_len(ncol(.data))) {
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col_results <- as.data.frame(as.matrix(table(.data[, i, drop = TRUE])))
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col_results$interpretation <- rownames(col_results)
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col_results$isolates <- col_results[, 1, drop = TRUE]
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if (nrow(col_results) > 0) {
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if (sum(col_results$isolates, na.rm = TRUE) >= minimum) {
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col_results$value <- col_results$isolates / sum(col_results$isolates, na.rm = TRUE)
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} else {
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col_results$value <- rep(NA_real_, NROW(col_results))
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}
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out_new <- data.frame(antibiotic = ifelse(isFALSE(translate_ab),
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colnames(.data)[i],
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ab_property(colnames(.data)[i], property = translate_ab, language = language)),
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interpretation = col_results$interpretation,
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value = col_results$value,
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isolates = col_results$isolates,
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stringsAsFactors = FALSE)
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if (data_has_groups) {
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out_new <- cbind(group_values, out_new)
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}
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out <- rbind(out, out_new)
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}
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}
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out
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}
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# support dplyr groups
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apply_group <- function(.data, fn, groups, ...) {
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grouped <- split(x = .data, f = lapply(groups, function(x, .data) as.factor(.data[, x]), .data))
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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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attr(res, "groups") <- 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(data, "sum_it", groups)
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} else {
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out <- sum_it(data)
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}
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# apply factors for right sorting in interpretation
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if (isTRUE(combine_SI)) {
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out$interpretation <- factor(out$interpretation, levels = c("SI", "R"), ordered = TRUE)
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} else if (isTRUE(combine_IR)) {
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out$interpretation <- factor(out$interpretation, levels = c("S", "IR"), ordered = TRUE)
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} else {
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out$interpretation <- as.rsi(out$interpretation)
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}
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if (data_has_groups) {
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# ordering by the groups and two more: "antibiotic" and "interpretation"
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out <- out[do.call("order", out[, seq_len(length(groups) + 2)]), ]
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} else {
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out <- out[order(out$antibiotic, out$interpretation), ]
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}
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if (type == "proportion") {
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out <- subset(out, select = -c(isolates))
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} else if (type == "count") {
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out$value <- out$isolates
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out <- subset(out, select = -c(isolates))
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}
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rownames(out) <- NULL
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out
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}
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