mirror of https://github.com/msberends/AMR.git
ggplot_rsi improvements
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NEWS.md
3
NEWS.md
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@ -2,7 +2,7 @@
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#### New
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* **BREAKING**: `rsi_df` was removed in favour of new functions `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` to selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old `rsi` function. The old function still works, but is deprecated.
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* New function `portion_df` to get all portions of S, I and R of a data set with antibiotic columns
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* New function `portion_df` to get all portions of S, I and R of a data set with antibiotic columns, with support for grouped variables
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* **BREAKING**: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call *key antibiotics*) to include more first isolates (afterwards called first *weighted* isolates) are now as follows:
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* Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole
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* Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
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@ -11,6 +11,7 @@
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* New functions `geom_rsi`, `facet_rsi`, `scale_y_percent`, `scale_rsi_colours` and `theme_rsi`
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* New wrapper function `ggplot_rsi` to apply all above functions on a data set:
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* `septic_patients %>% select(tobr, gent) %>% ggplot_rsi` will show portions of S, I and R immediately in a pretty plot
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* Support for grouped variables, see `?ggplot_rsi`
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* Determining bacterial ID:
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* New functions `as.bactid` and `is.bactid` to transform/ look up microbial ID's.
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* The existing function `guess_bactid` is now an alias of `as.bactid`
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@ -21,9 +21,11 @@
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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) or \code{"dodge"}
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#' @param x variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"}
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#' @param fill variable to categorise using the plots legend
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#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"}
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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 ... other parameters passed on to \code{\link[ggplot2]{facet_wrap}}
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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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@ -31,7 +33,7 @@
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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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#' \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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@ -48,11 +50,11 @@
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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 it using some additional functions
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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(x = "Interpretation") +
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#' facet_rsi(facet = "Antibiotic") +
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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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@ -61,30 +63,58 @@
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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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#' \donttest{
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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(amox, nitr, fosf, trim, cipr) %>%
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#' ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
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#'
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#' # it also supports groups (don't forget to use facet on the group):
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#' septic_patients %>%
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#' select(hospital_id, amox, cipr) %>%
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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() +
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#' facet_wrap("hospital_id", nrow = 1) +
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#' labs(title = "AMR of Amoxicillin And Ciprofloxacine Per Hospital")
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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(bactid = as.bactid(bactid, Becker = TRUE)) %>%
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#' # filter on top 2 bacterial ID's
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#' filter(bactid %in% top_freq(freq(.$bactid), 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_bactid = "bactid")) %>%
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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.",
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#' x = "Microorganisms")
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#' }
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ggplot_rsi <- function(data,
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position = "stack",
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x = "Antibiotic",
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fill = "Interpretation",
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facet = NULL) {
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facet = NULL,
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translate_ab = "official",
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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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p <- ggplot2::ggplot(data = data) +
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geom_rsi(position = position, x = x, fill = fill) +
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geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab) +
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scale_y_percent() +
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theme_rsi()
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@ -94,7 +124,7 @@ ggplot_rsi <- function(data,
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}
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if (!is.null(facet)) {
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p <- p + facet_rsi(facet = facet)
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p <- p + facet_rsi(facet = facet, ...)
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}
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p
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@ -102,13 +132,20 @@ ggplot_rsi <- function(data,
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#' @rdname ggplot_rsi
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#' @export
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geom_rsi <- function(position = "stack", x = c("Antibiotic", "Interpretation"), fill = "Interpretation") {
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geom_rsi <- function(position = "stack",
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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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x <- x[1]
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if (!x %in% c("Antibiotic", "Interpretation")) {
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stop("`x` must be 'Antibiotic' or 'Interpretation'")
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if (x %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
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x <- "Antibiotic"
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} else if (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 = "Percentage", fill = fill),
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data = AMR::portion_df, params = list())
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@ -117,12 +154,16 @@ geom_rsi <- function(position = "stack", x = c("Antibiotic", "Interpretation"),
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#' @rdname ggplot_rsi
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#' @export
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facet_rsi <- function(facet = c("Interpretation", "Antibiotic")) {
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facet_rsi <- function(facet = c("Interpretation", "Antibiotic"), ...) {
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facet <- facet[1]
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if (!facet %in% c("Antibiotic", "Interpretation")) {
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stop("`facet` must be 'Antibiotic' or 'Interpretation'")
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if (facet %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
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facet <- "Interpretation"
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} else if (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")
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ggplot2::facet_wrap(facets = facet, scales = "free", ...)
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}
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#' @rdname ggplot_rsi
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18
R/portion.R
18
R/portion.R
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#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.
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#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
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#' @param data a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
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#' @param translate a logical value to indicate whether antibiotic abbreviations should be translated with \code{\link{abname}}
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#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
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#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
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#'
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#' \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
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@ -196,7 +196,12 @@ portion_S <- function(ab1,
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#' @rdname portion
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#' @importFrom dplyr bind_rows summarise_if mutate group_vars select everything
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#' @export
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portion_df <- function(data, translate = getOption("get_antibiotic_names", TRUE)) {
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portion_df <- function(data, translate_ab = getOption("get_antibiotic_names", "official")) {
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if (as.character(translate_ab) == "TRUE") {
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translate_ab <- "official"
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}
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options(get_antibiotic_names = translate_ab)
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resS <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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@ -221,9 +226,14 @@ portion_df <- function(data, translate = getOption("get_antibiotic_names", TRUE)
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res <- bind_rows(resS, resI, resR) %>%
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mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
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tidyr::gather(Antibiotic, Percentage, -Interpretation, -data.groups)
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if (translate == TRUE) {
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res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = "official"))
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if (!translate_ab == FALSE) {
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if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
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stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
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}
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res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
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}
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res
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}
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@ -42,6 +42,7 @@ This R package was intended to make microbial epidemiology easier. Most function
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With `AMR` you can:
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* Calculate the resistance (and even co-resistance) of microbial isolates with the `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` functions, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`)
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* Plot AMR results with `geom_rsi`, a function made for the `ggplot` package
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* Predict antimicrobial resistance for the nextcoming years with the `resistance_predict` function
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* Apply [EUCAST rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) with the `EUCAST_rules` function
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* Identify first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute) with the `first_isolate` function
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\title{AMR bar plots with \code{ggplot}}
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\usage{
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ggplot_rsi(data, position = "stack", x = "Antibiotic",
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fill = "Interpretation", facet = NULL)
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fill = "Interpretation", facet = NULL, translate_ab = "official",
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...)
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geom_rsi(position = "stack", x = c("Antibiotic", "Interpretation"),
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fill = "Interpretation")
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fill = "Interpretation", translate_ab = "official")
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facet_rsi(facet = c("Interpretation", "Antibiotic"))
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facet_rsi(facet = c("Interpretation", "Antibiotic"), ...)
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scale_y_percent()
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@ -28,11 +29,15 @@ theme_rsi()
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\item{position}{position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}}
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\item{x}{variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"}}
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\item{x}{variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable}
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\item{fill}{variable to categorise using the plots legend}
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\item{fill}{variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable}
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\item{facet}{variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"}}
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\item{facet}{variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable}
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\item{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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\item{...}{other parameters passed on to \code{\link[ggplot2]{facet_wrap}}}
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}
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\description{
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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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\code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
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\code{scale_y_percent} transforms the y axis to a 0 to 100% range.
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\code{scale_y_percent} transforms the y axis to a 0 to 100\% range.
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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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@ -61,11 +66,11 @@ library(ggplot2)
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ggplot(septic_patients \%>\% select(amox, nitr, fosf, trim, cipr)) +
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geom_rsi()
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# prettify it using some additional functions
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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(x = "Interpretation") +
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facet_rsi(facet = "Antibiotic") +
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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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@ -74,16 +79,42 @@ ggplot(df) +
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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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\donttest{
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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(amox, nitr, fosf, trim, cipr) \%>\%
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ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
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# it also supports groups (don't forget to use facet on the group):
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septic_patients \%>\%
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select(hospital_id, amox, cipr) \%>\%
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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() +
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facet_wrap("hospital_id", nrow = 1) +
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labs(title = "AMR of Amoxicillin And Ciprofloxacine Per Hospital")
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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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# 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(bactid = as.bactid(bactid, Becker = TRUE)) \%>\%
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# filter on top 2 bacterial ID's
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filter(bactid \%in\% top_freq(freq(.$bactid), 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_bactid = "bactid")) \%>\%
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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.",
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||||
x = "Microorganisms")
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -25,7 +25,8 @@ portion_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
|
|||
|
||||
portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
|
||||
|
||||
portion_df(data, translate = getOption("get_antibiotic_names", TRUE))
|
||||
portion_df(data, translate_ab = getOption("get_antibiotic_names",
|
||||
"official"))
|
||||
}
|
||||
\arguments{
|
||||
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
|
||||
|
|
@ -38,7 +39,7 @@ portion_df(data, translate = getOption("get_antibiotic_names", TRUE))
|
|||
|
||||
\item{data}{a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
|
||||
|
||||
\item{translate}{a logical value to indicate whether antibiotic abbreviations should be translated with \code{\link{abname}}}
|
||||
\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.}
|
||||
}
|
||||
\value{
|
||||
Double or, when \code{as_percent = TRUE}, a character.
|
||||
|
|
|
|||
|
|
@ -29,16 +29,4 @@ test_that("ggplot_rsi works", {
|
|||
summarise_all(portion_IR) %>% as.double()
|
||||
)
|
||||
|
||||
expect_error(geom_rsi(x = "test"))
|
||||
expect_error(facet_rsi(facet = "test"))
|
||||
|
||||
# support for groups
|
||||
print(
|
||||
septic_patients %>%
|
||||
select(hospital_id, amox, cipr) %>%
|
||||
group_by(hospital_id) %>%
|
||||
ggplot_rsi() +
|
||||
facet_grid("hospital_id")
|
||||
)
|
||||
|
||||
})
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ test_that("old rsi works", {
|
|||
|
||||
# portion_df
|
||||
expect_equal(
|
||||
septic_patients %>% select(amox) %>% portion_df(TRUE) %>% pull(Percentage),
|
||||
septic_patients %>% select(amox) %>% portion_df() %>% pull(Percentage),
|
||||
c(septic_patients$amox %>% portion_S(),
|
||||
septic_patients$amox %>% portion_I(),
|
||||
septic_patients$amox %>% portion_R())
|
||||
|
|
|
|||
Loading…
Reference in New Issue