AMR/man/ggplot_rsi.Rd

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% Please edit documentation in R/ggplot_rsi.R
\name{ggplot_rsi}
\alias{ggplot_rsi}
\alias{geom_rsi}
\alias{facet_rsi}
\alias{scale_y_percent}
\alias{scale_rsi_colours}
\alias{theme_rsi}
\alias{labels_rsi_count}
\title{AMR plots with \code{ggplot2}}
\usage{
ggplot_rsi(data, position = NULL, x = "Antibiotic",
  fill = "Interpretation", facet = NULL, breaks = seq(0, 1, 0.1),
  limits = NULL, translate_ab = "name", combine_SI = TRUE,
  combine_IR = FALSE, language = get_locale(), fun = count_df,
  nrow = NULL, colours = c(S = "#61a8ff", SI = "#61a8ff", I =
  "#61f7ff", IR = "#ff6961", R = "#ff6961"), datalabels = TRUE,
  datalabels.size = 2.5, datalabels.colour = "gray15", title = NULL,
  subtitle = NULL, caption = NULL, x.title = NULL, y.title = NULL,
  ...)

geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
  fill = "Interpretation", translate_ab = "name",
  language = get_locale(), combine_SI = TRUE, combine_IR = FALSE,
  fun = count_df, ...)

facet_rsi(facet = c("Interpretation", "Antibiotic"), nrow = NULL)

scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)

scale_rsi_colours(colours = c(S = "#61a8ff", SI = "#61a8ff", I =
  "#61f7ff", IR = "#ff6961", R = "#ff6961"))

theme_rsi()

labels_rsi_count(position = NULL, x = "Antibiotic",
  translate_ab = "name", combine_SI = TRUE, combine_IR = FALSE,
  datalabels.size = 3, datalabels.colour = "gray15")
}
\arguments{
\item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})}

\item{position}{position adjustment of bars, either \code{"fill"} (default when \code{fun} is \code{\link{count_df}}), \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) 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}{numeric vector of positions}

\item{limits}{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 \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}}}

\item{combine_SI}{a 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). This used to be the parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is \code{TRUE}.}

\item{combine_IR}{a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter \code{combine_SI}.}

\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}

\item{fun}{function to transform \code{data}, either \code{\link{count_df}} (default) or \code{\link{portion_df}}}

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

\item{colours}{a named vector with colours for the bars. The names must be one or more of: S, SI, I, IR, R or be \code{FALSE} to use default \code{ggplot2} colours.}

\item{datalabels}{show datalabels using \code{labels_rsi_count}, will only be shown when \code{fun = count_df}}

\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 parameters passed on to \code{geom_rsi}}
}
\description{
Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}2} functions.
}
\details{
At default, the names of antibiotics will be shown on the plots using \code{\link{ab_name}}. This can be set with the \code{translate_ab} parameter. See \code{\link{count_df}}.

\strong{The functions}\cr
\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{count_df}} at default, can also be \code{\link{portion_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.

\code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.

\code{scale_y_percent} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2]{scale_continuous}}.

\code{scale_rsi_colours} sets colours to the bars: pastel blue for S, pastel turquoise for I and pastel red for R, using \code{\link[ggplot2]{scale_brewer}}.

\code{theme_rsi} is a \code{ggplot \link[ggplot2]{theme}} with minimal distraction.

\code{labels_rsi_count} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2]{geom_text}}

\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.
}
\section{Read more on our website!}{

On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
}

\examples{
library(dplyr)
library(ggplot2)

# get antimicrobial results for drugs against a UTI:
ggplot(septic_patients \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
  geom_rsi()

# prettify the plot using some additional functions:
df <- septic_patients \%>\% select(AMX, NIT, FOS, TMP, CIP)
ggplot(df) +
  geom_rsi() +
  scale_y_percent() +
  scale_rsi_colours() +
  labels_rsi_count() +
  theme_rsi()

# or better yet, simplify this using the wrapper function - a single command:
septic_patients \%>\%
  select(AMX, NIT, FOS, TMP, CIP) \%>\%
  ggplot_rsi()

# get only portions and no counts:
septic_patients \%>\%
  select(AMX, NIT, FOS, TMP, CIP) \%>\%
  ggplot_rsi(fun = portion_df)

# add other ggplot2 parameters as you like:
septic_patients \%>\%
  select(AMX, NIT, FOS, TMP, CIP) \%>\%
  ggplot_rsi(width = 0.5,
             colour = "black",
             size = 1,
             linetype = 2,
             alpha = 0.25)

septic_patients \%>\%
  select(AMX) \%>\%
  ggplot_rsi(colours = c(SI = "yellow"))

# resistance of ciprofloxacine per age group
septic_patients \%>\%
  mutate(first_isolate = first_isolate(.)) \%>\%
  filter(first_isolate == TRUE,
         mo == as.mo("E. coli")) \%>\%
  # `age_group` is also a function of this package:
  group_by(age_group = age_groups(age)) \%>\%
  select(age_group,
         CIP) \%>\%
  ggplot_rsi(x = "age_group")
\donttest{

# for colourblind mode, use divergent colours from the viridis package:
septic_patients \%>\%
  select(AMX, NIT, FOS, TMP, CIP) \%>\%
  ggplot_rsi() + scale_fill_viridis_d()
# a shorter version which also adjusts data label colours:
septic_patients \%>\%
  select(AMX, NIT, FOS, TMP, CIP) \%>\%
  ggplot_rsi(colours = FALSE)


# it also supports groups (don't forget to use the group var on `x` or `facet`):
septic_patients \%>\%
  select(hospital_id, AMX, NIT, FOS, TMP, CIP) \%>\%
  group_by(hospital_id) \%>\%
  ggplot_rsi(x = "hospital_id",
             facet = "Antibiotic",
             nrow = 1,
             title = "AMR of Anti-UTI Drugs Per Hospital",
             x.title = "Hospital",
             datalabels = FALSE)

# genuine analysis: check 3 most prevalent microorganisms
septic_patients \%>\%
  # create new bacterial ID's, with all CoNS under the same group (Becker et al.)
  mutate(mo = as.mo(mo, Becker = TRUE)) \%>\%
  # filter on top three bacterial ID's
  filter(mo \%in\% top_freq(freq(.$mo), 3)) \%>\%
  # filter on first isolates
  filter_first_isolate() \%>\%
  # get short MO names (like "E. coli")
  mutate(bug = mo_shortname(mo, Becker = TRUE)) \%>\%
  # select this short name and some antiseptic drugs
  select(bug, CXM, GEN, CIP) \%>\%
  # group by MO
  group_by(bug) \%>\%
  # plot the thing, putting MOs on the facet
  ggplot_rsi(x = "Antibiotic",
             facet = "bug",
             translate_ab = FALSE,
             nrow = 1,
             title = "AMR of Top Three Microorganisms In Blood Culture Isolates",
             subtitle = expression(paste("Only First Isolates, CoNS grouped according to Becker ",
                                         italic("et al."), " (2014)")),
             x.title = "Antibiotic (EARS-Net code)")
}
}