mirror of https://github.com/msberends/AMR.git
185 lines
7.5 KiB
R
Executable File
185 lines
7.5 KiB
R
Executable File
# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
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# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
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# Data. Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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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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# 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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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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#' Calculate the Mean AMR Distance
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#'
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#' Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
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#' @param x a vector of class [sir][as.sir()], [mic][as.mic()] or [disk][as.disk()], or a [data.frame] containing columns of any of these classes
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#' @param ... variables to select (supports [tidyselect language][tidyselect::language] such as `column1:column4` and `where(is.mic)`, and can thus also be [antibiotic selectors][ab_selector()]
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#' @param combine_SI a [logical] to indicate whether all values of S and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant) - the default is `TRUE`
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#' @details The mean AMR distance is effectively [the Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
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#'
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#' MIC values (see [as.mic()]) are transformed with [log2()] first; their distance is thus calculated as `(log2(x) - mean(log2(x))) / sd(log2(x))`.
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#'
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#' SIR values (see [as.sir()]) are transformed using `"S"` = 1, `"I"` = 2, and `"R"` = 3. If `combine_SI` is `TRUE` (default), the `"I"` will be considered to be 1.
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#'
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#' For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, see *Examples*.
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#'
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#' Use [amr_distance_from_row()] to subtract distances from the distance of one row, see *Examples*.
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#' @section Interpretation:
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#' Isolates with distances less than 0.01 difference from each other should be considered similar. Differences lower than 0.025 should be considered suspicious.
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#' @export
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#' @examples
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#' sir <- random_sir(10)
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#' sir
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#' mean_amr_distance(sir)
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#'
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#' mic <- random_mic(10)
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#' mic
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#' mean_amr_distance(mic)
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#' # equal to the Z-score of their log2:
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#' (log2(mic) - mean(log2(mic))) / sd(log2(mic))
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#'
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#' disk <- random_disk(10)
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#' disk
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#' mean_amr_distance(disk)
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#'
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#' y <- data.frame(
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#' id = LETTERS[1:10],
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#' amox = random_sir(10, ab = "amox", mo = "Escherichia coli"),
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#' cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
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#' gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
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#' tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
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#' )
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#' y
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#' mean_amr_distance(y)
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#' y$amr_distance <- mean_amr_distance(y, where(is.mic))
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#' y[order(y$amr_distance), ]
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#'
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#' if (require("dplyr")) {
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#' y %>%
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#' mutate(
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#' amr_distance = mean_amr_distance(y),
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#' check_id_C = amr_distance_from_row(amr_distance, id == "C")
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#' ) %>%
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#' arrange(check_id_C)
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#' }
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#' if (require("dplyr")) {
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#' # support for groups
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#' example_isolates %>%
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#' filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%
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#' select(mo, TCY, carbapenems()) %>%
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#' group_by(mo) %>%
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#' mutate(dist = mean_amr_distance(.)) %>%
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#' arrange(mo, dist)
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#' }
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mean_amr_distance <- function(x, ...) {
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UseMethod("mean_amr_distance")
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}
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#' @noRd
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#' @export
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mean_amr_distance.default <- function(x, ...) {
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x <- as.double(x)
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# calculate z-score
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(x - mean(x, na.rm = TRUE)) / stats::sd(x, na.rm = TRUE)
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}
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#' @noRd
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#' @export
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mean_amr_distance.mic <- function(x, ...) {
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mean_amr_distance(log2(x))
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}
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#' @noRd
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#' @export
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mean_amr_distance.disk <- function(x, ...) {
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mean_amr_distance(as.double(x))
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}
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#' @rdname mean_amr_distance
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#' @export
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mean_amr_distance.sir <- function(x, ..., combine_SI = TRUE) {
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meet_criteria(combine_SI, allow_class = "logical", has_length = 1, .call_depth = -1)
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if (isTRUE(combine_SI)) {
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x[x == "I"] <- "S"
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}
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mean_amr_distance(as.double(x))
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}
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#' @rdname mean_amr_distance
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#' @export
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mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
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meet_criteria(combine_SI, allow_class = "logical", has_length = 1, .call_depth = -1)
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df <- x
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if (is_null_or_grouped_tbl(df)) {
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df <- get_current_data("x", -2)
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}
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df <- as.data.frame(df, stringsAsFactors = FALSE)
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if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
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out <- tryCatch(suppressWarnings(c(...)), error = function(e) NULL)
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if (!is.null(out)) {
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df <- df[, out, drop = FALSE]
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} else {
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df <- pm_select(df, ...)
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}
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}
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df_classes <- colnames(df)[vapply(FUN.VALUE = logical(1), df, function(x) is.disk(x) | is.mic(x) | is.disk(x), USE.NAMES = FALSE)]
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df_antibiotics <- unname(get_column_abx(df, info = FALSE))
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df <- df[, colnames(df)[colnames(df) %in% union(df_classes, df_antibiotics)], drop = FALSE]
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stop_if(ncol(df) < 2,
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"data set must contain at least two variables",
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call = -2
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)
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if (message_not_thrown_before("mean_amr_distance", "groups")) {
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message_("Calculating mean AMR distance based on columns ", vector_and(colnames(df), sort = FALSE))
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}
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res <- vapply(
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FUN.VALUE = double(nrow(df)),
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df,
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mean_amr_distance,
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combine_SI = combine_SI
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)
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if (is.null(dim(res))) {
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if (all(is.na(res))) {
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return(NA_real_)
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} else {
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return(mean(res, na.rm = TRUE))
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}
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}
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res <- rowMeans(res, na.rm = TRUE)
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res[is.infinite(res) | is.nan(res)] <- 0
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res
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}
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#' @rdname mean_amr_distance
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#' @param amr_distance the outcome of [mean_amr_distance()]
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#' @param row an index, such as a row number
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#' @export
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amr_distance_from_row <- function(amr_distance, row) {
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meet_criteria(amr_distance, allow_class = "numeric", is_finite = TRUE)
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meet_criteria(row, allow_class = c("logical", "numeric", "integer"))
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if (is.logical(row)) {
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row <- which(row)
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}
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abs(amr_distance[row] - amr_distance)
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}
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