AMR/man/ggplot_sir.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggplot_sir.R
\name{ggplot_sir}
\alias{ggplot_sir}
\alias{geom_sir}
\title{AMR Plots with \code{ggplot2}}
\usage{
ggplot_sir(
  data,
  position = NULL,
  x = "antibiotic",
  fill = "interpretation",
  facet = NULL,
  breaks = seq(0, 1, 0.1),
  limits = NULL,
  translate_ab = "name",
  combine_SI = TRUE,
  minimum = 30,
  language = get_AMR_locale(),
  nrow = NULL,
  colours = c(S = "#3CAEA3", SI = "#3CAEA3", I = "#F6D55C", IR = "#ED553B", R =
    "#ED553B"),
  datalabels = TRUE,
  datalabels.size = 2.5,
  datalabels.colour = "grey15",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  x.title = "Antimicrobial",
  y.title = "Proportion",
  ...
)

geom_sir(
  position = NULL,
  x = c("antibiotic", "interpretation"),
  fill = "interpretation",
  translate_ab = "name",
  minimum = 30,
  language = get_AMR_locale(),
  combine_SI = TRUE,
  ...
)
}
\arguments{
\item{data}{a \link{data.frame} with column(s) of class \code{\link{sir}} (see \code{\link[=as.sir]{as.sir()}})}

\item{position}{position adjustment of bars, either \code{"fill"}, \code{"stack"} or \code{"dodge"}}

\item{x}{variable to show on x axis, either \code{"antibiotic"} (default) or \code{"interpretation"} or a grouping variable}

\item{fill}{variable to categorise using the plots legend, either \code{"antibiotic"} (default) or \code{"interpretation"} or a grouping variable}

\item{facet}{variable to split plots by, either \code{"interpretation"} (default) or \code{"antibiotic"} or a grouping variable}

\item{breaks}{a \link{numeric} vector of positions}

\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}

\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()}}}

\item{combine_SI}{a \link{logical} to indicate whether all values of S, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is \code{TRUE}}

\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}.}

\item{language}{language of the returned text - the default is the current system language (see \code{\link[=get_AMR_locale]{get_AMR_locale()}}) and can also be set with the package option \code{\link[=AMR-options]{AMR_locale}}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}

\item{nrow}{(when using \code{facet}) number of rows}

\item{colours}{a named vactor with colour to be used for filling. The default colours are colour-blind friendly.}

\item{datalabels}{show datalabels using \code{\link[=labels_sir_count]{labels_sir_count()}}}

\item{datalabels.size}{size of the datalabels}

\item{datalabels.colour}{colour of the datalabels}

\item{title}{text to show as title of the plot}

\item{subtitle}{text to show as subtitle of the plot}

\item{caption}{text to show as caption of the plot}

\item{x.title}{text to show as x axis description}

\item{y.title}{text to show as y axis description}

\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}.}

\item{aesthetics}{aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"}
}
\description{
Use these functions to create bar plots for AMR data analysis. All functions rely on \link[ggplot2:ggplot]{ggplot2} functions.
}
\details{
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()}}.

\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.

Additional functions include:
\itemize{
\item \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()}}.
\item \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()}}.
\item \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.
\item \code{\link[=theme_sir]{theme_sir()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.
\item \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()}}.
}

\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}.
}
\examples{
\donttest{
if (require("ggplot2") && require("dplyr")) {
  # get antimicrobial results for drugs against a UTI:
  ggplot(example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
    geom_sir()
}
if (require("ggplot2") && require("dplyr")) {
  # prettify the plot using some additional functions:
  df <- example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)
  ggplot(df) +
    geom_sir() +
    scale_y_percent() +
    scale_sir_colours() +
    labels_sir_count() +
    theme_sir()
}
if (require("ggplot2") && require("dplyr")) {
  # or better yet, simplify this using the wrapper function - a single command:
  example_isolates \%>\%
    select(AMX, NIT, FOS, TMP, CIP) \%>\%
    ggplot_sir()
}
if (require("ggplot2") && require("dplyr")) {
  # get only proportions and no counts:
  example_isolates \%>\%
    select(AMX, NIT, FOS, TMP, CIP) \%>\%
    ggplot_sir(datalabels = FALSE)
}
if (require("ggplot2") && require("dplyr")) {
  # add other ggplot2 arguments as you like:
  example_isolates \%>\%
    select(AMX, NIT, FOS, TMP, CIP) \%>\%
    ggplot_sir(
      width = 0.5,
      colour = "black",
      size = 1,
      linetype = 2,
      alpha = 0.25
    )
}
if (require("ggplot2") && require("dplyr")) {
  # you can alter the colours with colour names:
  example_isolates \%>\%
    select(AMX) \%>\%
    ggplot_sir(colours = c(SI = "yellow"))
}
if (require("ggplot2") && require("dplyr")) {
  # but you can also use the built-in colour-blind friendly colours for
  # your plots, where "S" is green, "I" is yellow and "R" is red:
  data.frame(
    x = c("Value1", "Value2", "Value3"),
    y = c(1, 2, 3),
    z = c("Value4", "Value5", "Value6")
  ) \%>\%
    ggplot() +
    geom_col(aes(x = x, y = y, fill = z)) +
    scale_sir_colours(Value4 = "S", Value5 = "I", Value6 = "R")
}
if (require("ggplot2") && require("dplyr")) {
  # resistance of ciprofloxacine per age group
  example_isolates \%>\%
    mutate(first_isolate = first_isolate()) \%>\%
    filter(
      first_isolate == TRUE,
      mo == as.mo("Escherichia coli")
    ) \%>\%
    # age_groups() is also a function in this AMR package:
    group_by(age_group = age_groups(age)) \%>\%
    select(age_group, CIP) \%>\%
    ggplot_sir(x = "age_group")
}
if (require("ggplot2") && require("dplyr")) {
  # a shorter version which also adjusts data label colours:
  example_isolates \%>\%
    select(AMX, NIT, FOS, TMP, CIP) \%>\%
    ggplot_sir(colours = FALSE)
}
if (require("ggplot2") && require("dplyr")) {
  # it also supports groups (don't forget to use the group var on `x` or `facet`):
  example_isolates \%>\%
    filter(mo_is_gram_negative(), ward != "Outpatient") \%>\%
    # select only UTI-specific drugs
    select(ward, AMX, NIT, FOS, TMP, CIP) \%>\%
    group_by(ward) \%>\%
    ggplot_sir(
      x = "ward",
      facet = "antibiotic",
      nrow = 1,
      title = "AMR of Anti-UTI Drugs Per Ward",
      x.title = "Ward",
      datalabels = FALSE
    )
}
}
}