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AMR/man/ggplot_rsi.Rd

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8.9 KiB
R

% Generated by roxygen2: do not edit by hand
% 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(), 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,
...)
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{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}}
\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{\link{rsi_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(datalabels = FALSE)
# 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)")
}
}