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(v1.5.0.9014) only_rsi_columns, is.rsi.eligible improvement
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@ -1,6 +1,6 @@
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# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis for R #
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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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@ -20,7 +20,7 @@
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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 analysis: https://msberends.github.io/AMR/ #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' Guess Antibiotic Column
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@ -30,6 +30,7 @@
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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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#' @param only_rsi_columns a logical to indicate whether only antibiotic columns must be detected that were [transformed to class `<rsi>`]([rsi]) on beforehand. Defaults to `TRUE` if any column of `x` is of class `<rsi>`.
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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 precedence 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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@ -62,35 +63,20 @@
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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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guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE, only_rsi_columns = any(is.rsi(x))) {
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meet_criteria(x, allow_class = "data.frame", allow_NULL = TRUE)
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meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = TRUE)
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meet_criteria(verbose, allow_class = "logical", has_length = 1)
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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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} else {
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meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = FALSE)
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}
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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 %pm>% colnames() %pm>% nchar() %pm>% order() %pm>% 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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all_found <- get_column_abx(x, info = verbose, only_rsi_columns = only_rsi_columns, verbose = verbose)
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search_string.ab <- suppressWarnings(as.ab(search_string))
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ab_result <- unname(all_found[names(all_found) == search_string.ab])
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if (length(ab_result) == 0) {
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if (verbose == TRUE) {
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@ -114,18 +100,24 @@ get_column_abx <- function(x,
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hard_dependencies = NULL,
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verbose = FALSE,
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info = TRUE,
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only_rsi_columns = FALSE,
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...) {
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meet_criteria(x, allow_class = "data.frame")
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meet_criteria(soft_dependencies, allow_class = "character", allow_NULL = TRUE)
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meet_criteria(hard_dependencies, allow_class = "character", allow_NULL = TRUE)
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meet_criteria(verbose, allow_class = "logical", has_length = 1)
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meet_criteria(info, allow_class = "logical", has_length = 1)
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meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
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if (info == TRUE) {
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message_("Auto-guessing columns suitable for analysis", appendLF = FALSE, as_note = FALSE)
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}
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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if (only_rsi_columns == TRUE) {
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x <- x[, which(is.rsi(x)), drop = FALSE]
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}
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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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if (info == TRUE) {
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@ -141,21 +133,23 @@ get_column_abx <- function(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 they 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 <- unlist(AB_lookup$generalised_all)
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vectr_antibiotics <- vectr_antibiotics[!is.na(vectr_antibiotics) & nchar(vectr_antibiotics) >= 3]
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x_columns <- vapply(FUN.VALUE = character(1), colnames(x), function(col, df = x) {
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if (toupper(col) %in% vectr_antibiotics ||
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is.rsi(x[, col, drop = TRUE]) ||
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is.rsi.eligible(x[, col, drop = TRUE], threshold = 0.5)
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) {
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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 <- vapply(FUN.VALUE = character(1),
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colnames(x),
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function(col, df = x) {
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if (generalise_antibiotic_name(col) %in% vectr_antibiotics ||
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is.rsi(x[, col, drop = TRUE]) ||
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is.rsi.eligible(x[, col, drop = TRUE], threshold = 0.5)
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) {
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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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x <- x[, x_columns, drop = FALSE] # without drop = FALSE, 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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stringsAsFactors = FALSE)
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@ -164,7 +158,7 @@ get_column_abx <- function(x,
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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 %pm>% rename(thisone = AMX), amox = "thisone")
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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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