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234 lines
10 KiB
R
Executable File
234 lines
10 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://github.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.github.io/AMR. #
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# ==================================================================== #
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#' Guess antibiotic column
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#'
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#' This tries to find a column name in a data set based on information from the [antibiotics] data set. Also supports WHONET abbreviations.
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#' @inheritSection lifecycle Maturing lifecycle
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#' @param x a [`data.frame`]
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#' @param search_string a text to search `x` for, will be checked with [as.ab()] if this value is not a column in `x`
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#' @param verbose a logical to indicate whether additional info should be printed
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#' @details You can look for an antibiotic (trade) name or abbreviation and it will search `x` and the [antibiotics] data set for any column containing a name or code of that antibiotic. **Longer columns names take precendence over shorter column names.**
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#' @return A column name of `x`, or `NULL` when no result is found.
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#' @export
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#' @inheritSection AMR Read more on our website!
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#' @examples
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#' df <- data.frame(amox = "S",
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#' tetr = "R")
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#'
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#' guess_ab_col(df, "amoxicillin")
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#' # [1] "amox"
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#' guess_ab_col(df, "J01AA07") # ATC code of tetracycline
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#' # [1] "tetr"
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#'
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#' guess_ab_col(df, "J01AA07", verbose = TRUE)
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#' # NOTE: Using column `tetr` as input for `J01AA07` (tetracycline).
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#' # [1] "tetr"
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#'
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#' # WHONET codes
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#' df <- data.frame(AMP_ND10 = "R",
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#' AMC_ED20 = "S")
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#' guess_ab_col(df, "ampicillin")
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#' # [1] "AMP_ND10"
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#' guess_ab_col(df, "J01CR02")
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#' # [1] "AMC_ED20"
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#' guess_ab_col(df, as.ab("augmentin"))
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#' # [1] "AMC_ED20"
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#'
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#' # Longer names take precendence:
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#' df <- data.frame(AMP_ED2 = "S",
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#' AMP_ED20 = "S")
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#' guess_ab_col(df, "ampicillin")
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#' # [1] "AMP_ED20"
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guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE) {
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if (is.null(x) & is.null(search_string)) {
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return(as.name("guess_ab_col"))
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}
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stop_ifnot(is.data.frame(x), "`x` must be a data.frame")
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if (length(search_string) > 1) {
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warning("argument 'search_string' has length > 1 and only the first element will be used")
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search_string <- search_string[1]
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}
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search_string <- as.character(search_string)
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if (search_string %in% colnames(x)) {
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ab_result <- search_string
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} else {
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search_string.ab <- suppressWarnings(as.ab(search_string))
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if (search_string.ab %in% colnames(x)) {
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ab_result <- colnames(x)[colnames(x) == search_string.ab][1L]
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} else if (any(tolower(colnames(x)) %in% tolower(unlist(ab_property(search_string.ab, "abbreviations", language = NULL))))) {
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ab_result <- colnames(x)[tolower(colnames(x)) %in% tolower(unlist(ab_property(search_string.ab, "abbreviations", language = NULL)))][1L]
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} else {
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# sort colnames on length - longest first
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cols <- colnames(x[, x %>% colnames() %>% nchar() %>% order() %>% rev()])
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df_trans <- data.frame(cols = cols,
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abs = suppressWarnings(as.ab(cols)),
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stringsAsFactors = FALSE)
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ab_result <- df_trans[which(df_trans$abs == search_string.ab), "cols"]
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ab_result <- ab_result[!is.na(ab_result)][1L]
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}
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}
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if (length(ab_result) == 0) {
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if (verbose == TRUE) {
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message(paste0("No column found as input for `", search_string,
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"` (", ab_name(search_string, language = NULL, tolower = TRUE), ")."))
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}
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return(NULL)
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} else {
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if (verbose == TRUE) {
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message(font_blue(paste0("NOTE: Using column `", font_bold(ab_result), "` as input for `", search_string,
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"` (", ab_name(search_string, language = NULL, tolower = TRUE), ").")))
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}
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return(ab_result)
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}
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}
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get_column_abx <- function(x,
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soft_dependencies = NULL,
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hard_dependencies = NULL,
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verbose = FALSE,
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...) {
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message(font_blue("NOTE: Auto-guessing columns suitable for analysis"), appendLF = FALSE)
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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if (NROW(x) > 10000) {
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# only test maximum of 10,000 values per column
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message(font_blue(paste0(" (using only ", font_bold("the first 10,000 rows"), ")...")), appendLF = FALSE)
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x <- x[1:10000, , drop = FALSE]
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} else {
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message(font_blue("..."), appendLF = FALSE)
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}
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x_bak <- x
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# only check columns that are a valid AB code, ATC code, name, abbreviation or synonym,
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# or already have the rsi class (as.rsi)
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# and that have no more than 50% invalid values
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vectr_antibiotics <- unique(toupper(unlist(antibiotics[, c("ab", "atc", "name", "abbreviations", "synonyms")])))
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vectr_antibiotics <- vectr_antibiotics[!is.na(vectr_antibiotics) & nchar(vectr_antibiotics) >= 3]
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x_columns <- sapply(colnames(x), function(col, df = x_bak) {
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if (toupper(col) %in% vectr_antibiotics |
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is.rsi(as.data.frame(df)[, col]) |
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is.rsi.eligible(as.data.frame(df)[, col], threshold = 0.5)) {
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return(col)
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} else {
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return(NA_character_)
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}
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})
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x_columns <- x_columns[!is.na(x_columns)]
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x <- x[, x_columns, drop = FALSE] # without drop = TRUE, x will become a vector when x_columns is length 1
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df_trans <- data.frame(colnames = colnames(x),
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abcode = suppressWarnings(as.ab(colnames(x), info = FALSE)))
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df_trans <- df_trans[!is.na(df_trans$abcode), ]
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x <- as.character(df_trans$colnames)
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names(x) <- df_trans$abcode
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# add from self-defined dots (...):
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# such as get_column_abx(example_isolates %>% rename(thisone = AMX), amox = "thisone")
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dots <- list(...)
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if (length(dots) > 0) {
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newnames <- suppressWarnings(as.ab(names(dots), info = FALSE))
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if (any(is.na(newnames))) {
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warning("Invalid antibiotic reference(s): ", toString(names(dots)[is.na(newnames)]),
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call. = FALSE, immediate. = TRUE)
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}
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# turn all NULLs to NAs
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dots <- unlist(lapply(dots, function(x) if (is.null(x)) NA else x))
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names(dots) <- newnames
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dots <- dots[!is.na(names(dots))]
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# merge, but overwrite automatically determined ones by 'dots'
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x <- c(x[!x %in% dots & !names(x) %in% names(dots)], dots)
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# delete NAs, this will make e.g. eucast_rules(... TMP = NULL) work to prevent TMP from being used
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x <- x[!is.na(x)]
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}
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if (length(x) == 0) {
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message(font_blue("No columns found."))
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return(x)
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}
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# sort on name
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x <- x[order(names(x), x)]
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duplicates <- c(x[base::duplicated(x)], x[base::duplicated(names(x))])
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duplicates <- duplicates[unique(names(duplicates))]
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x <- c(x[!names(x) %in% names(duplicates)], duplicates)
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x <- x[order(names(x), x)]
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# succeeded with auto-guessing
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message(font_blue("OK."))
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for (i in seq_len(length(x))) {
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if (verbose == TRUE & !names(x[i]) %in% names(duplicates)) {
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message(font_blue(paste0("NOTE: Using column `", font_bold(x[i]), "` as input for `", names(x)[i],
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"` (", ab_name(names(x)[i], tolower = TRUE, language = NULL), ").")))
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}
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if (names(x[i]) %in% names(duplicates)) {
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warning(font_red(paste0("Using column `", font_bold(x[i]), "` as input for `", names(x)[i],
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"` (", ab_name(names(x)[i], tolower = TRUE, language = NULL),
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"), although it was matched for multiple antibiotics or columns.")),
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call. = FALSE,
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immediate. = verbose)
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}
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}
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if (!is.null(hard_dependencies)) {
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hard_dependencies <- unique(hard_dependencies)
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if (!all(hard_dependencies %in% names(x))) {
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# missing a hard dependency will return NA and consequently the data will not be analysed
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missing <- hard_dependencies[!hard_dependencies %in% names(x)]
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generate_warning_abs_missing(missing, any = FALSE)
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return(NA)
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}
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}
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if (!is.null(soft_dependencies)) {
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soft_dependencies <- unique(soft_dependencies)
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if (!all(soft_dependencies %in% names(x))) {
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# missing a soft dependency may lower the reliability
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missing <- soft_dependencies[!soft_dependencies %in% names(x)]
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missing_txt <- paste(paste0(ab_name(missing, tolower = TRUE, language = NULL),
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" (", font_bold(missing, collapse = NULL), ")"),
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collapse = ", ")
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message(font_blue("NOTE: Reliability would be improved if these antimicrobial results would be available too:",
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missing_txt))
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}
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}
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x
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}
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generate_warning_abs_missing <- function(missing, any = FALSE) {
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missing <- paste0(missing, " (", ab_name(missing, tolower = TRUE, language = NULL), ")")
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if (any == TRUE) {
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any_txt <- c(" any of", "is")
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} else {
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any_txt <- c("", "are")
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}
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warning(paste0("Introducing NAs since", any_txt[1], " these antimicrobials ", any_txt[2], " required: ",
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paste(missing, collapse = ", ")),
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immediate. = TRUE,
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call. = FALSE)
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}
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