mirror of https://github.com/msberends/AMR.git
243 lines
9.9 KiB
R
243 lines
9.9 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# AUTHORS #
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# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# LICENCE #
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# This program is free software; you can redistribute it and/or modify #
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# it under the terms of the GNU General Public License version 2.0, #
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# as published by the Free Software Foundation. #
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# #
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# This program is distributed in the hope that it will be useful, #
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# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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# GNU General Public License for more details. #
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# ==================================================================== #
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#' AMR bar plots with \code{ggplot}
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#'
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#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
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#' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})
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#' @param position position adjustment of bars, either \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) or \code{"dodge"} (default when \code{fun} is \code{\link{count_df}})
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#' @param x variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
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#' @param fill variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
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#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable
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#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.
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#' @param fun function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}
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#' @param nrow (when using \code{facet}) number of rows
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#' @param ... other parameters passed on to \code{geom_rsi}
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#' @details At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
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#'
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#' \strong{The functions}\cr
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#' \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{fun} (\code{\link{portion_df}} at default, could also be \code{\link{count_df}}) and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
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#'
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#' \code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
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#'
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#' \code{scale_y_percent} transforms the y axis to a 0 to 100\% range.
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#'
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#' \code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R.
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#'
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#' \code{theme_rsi} is a \code{\link[ggplot2]{theme}} with minimal distraction.
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#'
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#' \code{ggplot_rsi} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\code{\%>\%}). See Examples.
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#' @rdname ggplot_rsi
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#' @export
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#' @examples
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#' library(dplyr)
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#' library(ggplot2)
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#'
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#' # get antimicrobial results for drugs against a UTI:
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#' ggplot(septic_patients %>% select(amox, nitr, fosf, trim, cipr)) +
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#' geom_rsi()
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#'
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#' # prettify the plot using some additional functions:
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#' df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
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#' ggplot(df) +
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#' geom_rsi() +
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#' facet_rsi() +
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#' scale_y_percent() +
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#' scale_rsi_colours() +
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#' theme_rsi()
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#'
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#' # or better yet, simplify this using the wrapper function - a single command:
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#' septic_patients %>%
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#' select(amox, nitr, fosf, trim, cipr) %>%
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#' ggplot_rsi()
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#'
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#' # get counts instead of percentages:
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#' septic_patients %>%
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#' select(amox, nitr, fosf, trim, cipr) %>%
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#' ggplot_rsi(fun = count_df)
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#'
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#' # add other ggplot2 parameters as you like:
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#' septic_patients %>%
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#' select(amox, nitr, fosf, trim, cipr) %>%
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#' ggplot_rsi(width = 0.5,
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#' colour = "black",
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#' size = 1,
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#' linetype = 2,
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#' alpha = 0.25)
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#' \donttest{
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#'
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#' # for colourblind mode, use divergent colours from the viridis package:
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#' septic_patients %>%
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#' select(amox, nitr, fosf, trim, cipr) %>%
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#' ggplot_rsi() + scale_fill_viridis_d()
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#'
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#'
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#' # it also supports groups (don't forget to use the group on `x` or `facet`):
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#' septic_patients %>%
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#' select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
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#' group_by(hospital_id) %>%
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#' ggplot_rsi(x = "hospital_id",
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#' facet = "Antibiotic",
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#' nrow = 1) +
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#' labs(title = "AMR of Anti-UTI Drugs Per Hospital",
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#' x = "Hospital")
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#'
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#' # genuine analysis: check 2 most prevalent microorganisms
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#' septic_patients %>%
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#' # create new bacterial ID's, with all CoNS under the same group (Becker et al.)
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#' mutate(mo = as.mo(mo, Becker = TRUE)) %>%
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#' # filter on top 2 bacterial ID's
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#' filter(mo %in% top_freq(freq(.$mo), 2)) %>%
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#' # determine first isolates
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#' mutate(first_isolate = first_isolate(.,
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#' col_date = "date",
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#' col_patient_id = "patient_id",
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#' col_mo = "mo")) %>%
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#' # filter on first isolates
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#' filter(first_isolate == TRUE) %>%
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#' # join the `microorganisms` data set
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#' left_join_microorganisms() %>%
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#' # select full name and some antiseptic drugs
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#' select(mo = fullname,
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#' cfur, gent, cipr) %>%
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#' # group by MO
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#' group_by(mo) %>%
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#' # plot the thing, putting MOs on the facet
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#' ggplot_rsi(x = "Antibiotic",
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#' facet = "mo") +
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#' labs(title = "AMR of Top Two Microorganisms In Blood Culture Isolates",
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#' subtitle = "Only First Isolates, CoNS grouped according to Becker et al. (2014)",
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#' x = "Microorganisms")
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#' }
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ggplot_rsi <- function(data,
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position = NULL,
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x = "Antibiotic",
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fill = "Interpretation",
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# params = list(),
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facet = NULL,
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translate_ab = "official",
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fun = portion_df,
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nrow = NULL,
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...) {
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if (!"ggplot2" %in% rownames(installed.packages())) {
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stop('this function requires the ggplot2 package.', call. = FALSE)
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}
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fun_name <- deparse(substitute(fun))
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if (!fun_name %in% c("portion_df", "count_df")) {
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stop("`fun` must be portion_df or count_df")
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}
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p <- ggplot2::ggplot(data = data) +
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geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, fun = fun, ...) +
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theme_rsi()
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if (fill == "Interpretation") {
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# set RSI colours
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p <- p + scale_rsi_colours()
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}
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if (fun_name == "portion_df") {
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# portions, so use y scale with percentage
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p <- p + scale_y_percent()
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}
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if (!is.null(facet)) {
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p <- p + facet_rsi(facet = facet, nrow = nrow)
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}
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p
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}
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#' @rdname ggplot_rsi
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#' @export
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geom_rsi <- function(position = NULL,
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x = c("Antibiotic", "Interpretation"),
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fill = "Interpretation",
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translate_ab = "official",
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fun = portion_df,
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...) {
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fun_name <- deparse(substitute(fun))
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if (!fun_name %in% c("portion_df", "count_df", "fun")) {
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stop("`fun` must be portion_df or count_df")
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}
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if (identical(fun, count_df)) {
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y <- "Count"
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if (missing(position) | is.null(position)) {
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position <- "dodge"
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}
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} else {
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y <- "Percentage"
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if (missing(position) | is.null(position)) {
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position <- "stack"
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}
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}
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x <- x[1]
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if (tolower(x) %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
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x <- "Antibiotic"
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} else if (tolower(x) %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
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x <- "Interpretation"
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}
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options(get_antibiotic_names = translate_ab)
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ggplot2::layer(geom = "bar", stat = "identity", position = position,
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mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
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data = fun, params = list(...))
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}
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#' @rdname ggplot_rsi
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#' @export
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facet_rsi <- function(facet = c("Interpretation", "Antibiotic"), nrow = NULL) {
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facet <- facet[1]
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if (tolower(facet) %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
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facet <- "Interpretation"
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} else if (tolower(facet) %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
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facet <- "Antibiotic"
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}
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ggplot2::facet_wrap(facets = facet, scales = "free_x", nrow = nrow)
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}
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#' @rdname ggplot_rsi
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#' @export
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scale_y_percent <- function() {
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ggplot2::scale_y_continuous(breaks = seq(0, 1, 0.1),
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limits = c(0, 1),
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labels = percent(seq(0, 1, 0.1)))
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}
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#' @rdname ggplot_rsi
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#' @export
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scale_rsi_colours <- function() {
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ggplot2::scale_fill_brewer(palette = "RdYlGn")
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}
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#' @rdname ggplot_rsi
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#' @export
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theme_rsi <- function() {
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ggplot2::theme_minimal() +
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ggplot2::theme(panel.grid.major.x = ggplot2::element_blank(),
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panel.grid.minor = ggplot2::element_blank(),
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panel.grid.major.y = ggplot2::element_line(colour = "grey75"))
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}
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