mirror of https://github.com/msberends/AMR.git
103 lines
4.5 KiB
R
Executable File
103 lines
4.5 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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#' Check Availability of Columns
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#'
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#' Easy check for data availability of all columns in a data set. This makes it easy to get an idea of which antimicrobial combinations can be used for calculation with e.g. [susceptibility()] and [resistance()].
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#' @param tbl a [data.frame] or [list]
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#' @param width number of characters to present the visual availability - the default is filling the width of the console
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#' @details The function returns a [data.frame] with columns `"resistant"` and `"visual_resistance"`. The values in that columns are calculated with [resistance()].
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#' @return [data.frame] with column names of `tbl` as row names
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#' @export
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#' @examples
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#' availability(example_isolates)
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#' \donttest{
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#' if (require("dplyr")) {
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#' example_isolates %>%
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#' filter(mo == as.mo("Escherichia coli")) %>%
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#' select_if(is.sir) %>%
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#' availability()
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#' }
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#' }
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availability <- function(tbl, width = NULL) {
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meet_criteria(tbl, allow_class = "data.frame")
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meet_criteria(width, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
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tbl <- as.data.frame(tbl, stringsAsFactors = FALSE)
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x <- vapply(FUN.VALUE = double(1), tbl, function(x) {
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1 - sum(is.na(x)) / length(x)
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})
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n <- vapply(FUN.VALUE = double(1), tbl, function(x) length(x[!is.na(x)]))
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R <- vapply(FUN.VALUE = double(1), tbl, function(x) ifelse(is.sir(x), resistance(x, minimum = 0), NA_real_))
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R_print <- character(length(R))
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R_print[!is.na(R)] <- percentage(R[!is.na(R)])
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R_print[is.na(R)] <- ""
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if (is.null(width)) {
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width <- getOption("width", 100) -
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(max(nchar(colnames(tbl))) +
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# count col
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8 +
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# available % column
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10 +
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# resistant % column
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10 +
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# extra margin
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5)
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width <- width / 2
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}
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if (length(R[is.na(R)]) == ncol(tbl)) {
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width <- width * 2 + 10
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}
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x_chars_R <- strrep("#", round(width * R, digits = 2))
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x_chars_SI <- strrep("-", width - nchar(x_chars_R))
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vis_resistance <- paste0("|", x_chars_R, x_chars_SI, "|")
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vis_resistance[is.na(R)] <- ""
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x_chars <- strrep("#", round(x, digits = 2) / (1 / width))
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x_chars_empty <- strrep("-", width - nchar(x_chars))
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df <- data.frame(
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count = n,
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available = percentage(x),
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visual_availabilty = paste0("|", x_chars, x_chars_empty, "|"),
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resistant = R_print,
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visual_resistance = vis_resistance,
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stringsAsFactors = FALSE
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)
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if (length(R[is.na(R)]) == ncol(tbl)) {
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df[, 1:3, drop = FALSE]
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} else {
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df
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}
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}
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