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234 lines
9.0 KiB
R
234 lines
9.0 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/ggplot_sir.R
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\name{ggplot_sir}
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\alias{ggplot_sir}
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\alias{geom_sir}
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\alias{facet_sir}
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\alias{scale_y_percent}
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\alias{scale_sir_colours}
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\alias{theme_sir}
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\alias{labels_sir_count}
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\title{AMR Plots with \code{ggplot2}}
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\usage{
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ggplot_sir(
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data,
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position = NULL,
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x = "antibiotic",
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fill = "interpretation",
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facet = NULL,
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breaks = seq(0, 1, 0.1),
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limits = NULL,
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translate_ab = "name",
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combine_SI = TRUE,
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minimum = 30,
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language = get_AMR_locale(),
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nrow = NULL,
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colours = c(S = "#3CAEA3", SI = "#3CAEA3", I = "#F6D55C", IR = "#ED553B", R =
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"#ED553B"),
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datalabels = TRUE,
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datalabels.size = 2.5,
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datalabels.colour = "grey15",
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title = NULL,
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subtitle = NULL,
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caption = NULL,
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x.title = "Antimicrobial",
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y.title = "Proportion",
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...
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)
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geom_sir(
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position = NULL,
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x = c("antibiotic", "interpretation"),
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fill = "interpretation",
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translate_ab = "name",
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minimum = 30,
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language = get_AMR_locale(),
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combine_SI = TRUE,
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...
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)
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facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
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scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)
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scale_sir_colours(..., aesthetics = "fill")
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theme_sir()
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labels_sir_count(
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position = NULL,
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x = "antibiotic",
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translate_ab = "name",
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minimum = 30,
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language = get_AMR_locale(),
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combine_SI = TRUE,
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datalabels.size = 3,
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datalabels.colour = "grey15"
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)
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}
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\arguments{
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\item{data}{a \link{data.frame} with column(s) of class \code{\link{sir}} (see \code{\link[=as.sir]{as.sir()}})}
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\item{position}{position adjustment of bars, either \code{"fill"}, \code{"stack"} or \code{"dodge"}}
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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, 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"} or a grouping variable}
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\item{breaks}{a \link{numeric} vector of positions}
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\item{limits}{a \link{numeric} vector of length two providing limits of the scale, use \code{NA} to refer to the existing minimum or maximum}
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\item{translate_ab}{a column name of the \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{ab_property()}}}
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\item{combine_SI}{a \link{logical} to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant), defaults to \code{TRUE}}
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\item{minimum}{the minimum allowed number of available (tested) isolates. Any isolate count lower than \code{minimum} will return \code{NA} with a warning. The default number of \code{30} isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see \emph{Source}.}
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\item{language}{language of the returned text, defaults to system language (see \code{\link[=get_AMR_locale]{get_AMR_locale()}}) and can also be set with the option \code{\link[=AMR-options]{AMR_locale}}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
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\item{nrow}{(when using \code{facet}) number of rows}
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\item{colours}{a named vactor with colour to be used for filling. The default colours are colour-blind friendly.}
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\item{datalabels}{show datalabels using \code{\link[=labels_sir_count]{labels_sir_count()}}}
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\item{datalabels.size}{size of the datalabels}
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\item{datalabels.colour}{colour of the datalabels}
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\item{title}{text to show as title of the plot}
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\item{subtitle}{text to show as subtitle of the plot}
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\item{caption}{text to show as caption of the plot}
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\item{x.title}{text to show as x axis description}
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\item{y.title}{text to show as y axis description}
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\item{...}{other arguments passed on to \code{\link[=geom_sir]{geom_sir()}} or, in case of \code{\link[=scale_sir_colours]{scale_sir_colours()}}, named values to set colours. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red. See \emph{Examples}.}
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\item{aesthetics}{aesthetics to apply the colours to, defaults to "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"}
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}
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\description{
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Use these functions to create bar plots for AMR data analysis. All functions rely on \link[ggplot2:ggplot]{ggplot2} functions.
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}
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\details{
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At default, the names of antibiotics will be shown on the plots using \code{\link[=ab_name]{ab_name()}}. This can be set with the \code{translate_ab} argument. See \code{\link[=count_df]{count_df()}}.
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\subsection{The Functions}{
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\code{\link[=geom_sir]{geom_sir()}} will take any variable from the data that has an \code{\link{sir}} class (created with \code{\link[=as.sir]{as.sir()}}) using \code{\link[=sir_df]{sir_df()}} and will plot bars with the percentage S, I, and R. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
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\code{\link[=facet_sir]{facet_sir()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.
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\code{\link[=scale_y_percent]{scale_y_percent()}} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2:scale_continuous]{ggplot2::scale_y_continuous()}}.
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\code{\link[=scale_sir_colours]{scale_sir_colours()}} sets colours to the bars (green for S, yellow for I, and red for R). with multilingual support. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red.
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\code{\link[=theme_sir]{theme_sir()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
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\code{\link[=labels_sir_count]{labels_sir_count()}} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2:geom_text]{ggplot2::geom_text()}}.
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\code{\link[=ggplot_sir]{ggplot_sir()}} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\verb{\%>\%}). See \emph{Examples}.
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}
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}
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\examples{
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\donttest{
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if (require("ggplot2") && require("dplyr")) {
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# get antimicrobial results for drugs against a UTI:
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ggplot(example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
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geom_sir()
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}
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if (require("ggplot2") && require("dplyr")) {
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# prettify the plot using some additional functions:
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df <- example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)
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ggplot(df) +
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geom_sir() +
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scale_y_percent() +
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scale_sir_colours() +
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labels_sir_count() +
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theme_sir()
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}
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if (require("ggplot2") && require("dplyr")) {
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# or better yet, simplify this using the wrapper function - a single command:
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example_isolates \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_sir()
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}
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if (require("ggplot2") && require("dplyr")) {
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# get only proportions and no counts:
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example_isolates \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_sir(datalabels = FALSE)
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}
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if (require("ggplot2") && require("dplyr")) {
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# add other ggplot2 arguments as you like:
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example_isolates \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_sir(
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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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)
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}
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if (require("ggplot2") && require("dplyr")) {
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# you can alter the colours with colour names:
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example_isolates \%>\%
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select(AMX) \%>\%
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ggplot_sir(colours = c(SI = "yellow"))
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}
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if (require("ggplot2") && require("dplyr")) {
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# but you can also use the built-in colour-blind friendly colours for
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# your plots, where "S" is green, "I" is yellow and "R" is red:
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data.frame(
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x = c("Value1", "Value2", "Value3"),
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y = c(1, 2, 3),
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z = c("Value4", "Value5", "Value6")
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) \%>\%
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ggplot() +
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geom_col(aes(x = x, y = y, fill = z)) +
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scale_sir_colours(Value4 = "S", Value5 = "I", Value6 = "R")
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}
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if (require("ggplot2") && require("dplyr")) {
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# resistance of ciprofloxacine per age group
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example_isolates \%>\%
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mutate(first_isolate = first_isolate()) \%>\%
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filter(
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first_isolate == TRUE,
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mo == as.mo("Escherichia coli")
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) \%>\%
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# age_groups() is also a function in this AMR package:
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group_by(age_group = age_groups(age)) \%>\%
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select(age_group, CIP) \%>\%
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ggplot_sir(x = "age_group")
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}
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if (require("ggplot2") && require("dplyr")) {
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# a shorter version which also adjusts data label colours:
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example_isolates \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_sir(colours = FALSE)
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}
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if (require("ggplot2") && require("dplyr")) {
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# it also supports groups (don't forget to use the group var on `x` or `facet`):
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example_isolates \%>\%
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filter(mo_is_gram_negative(), ward != "Outpatient") \%>\%
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# select only UTI-specific drugs
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select(ward, AMX, NIT, FOS, TMP, CIP) \%>\%
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group_by(ward) \%>\%
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ggplot_sir(
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x = "ward",
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facet = "antibiotic",
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nrow = 1,
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title = "AMR of Anti-UTI Drugs Per Ward",
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x.title = "Ward",
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datalabels = FALSE
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)
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}
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}
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}
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