AMR/man/ggplot_rsi.Rd

% 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 = "Antimicrobial",
  y.title = "Proportion",
  ...
)

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{\link{data.frame}} with column(s) of class \code{\link{rsi}} (see \code{\link[=as.rsi]{as.rsi()}})}

\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}{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 \link{antibiotics} data set to translate the antibiotic abbreviations to, using \code{\link[=ab_property]{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]{get_locale()}}) and can also be set with \code{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 [ggplot2][\code{\link[ggplot2:ggplot]{ggplot2::ggplot()}} colours.}

\item{datalabels}{show datalabels using \code{\link[=labels_rsi_count]{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{\link[=geom_rsi]{geom_rsi()}}}
}
\description{
Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \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} parameter. See \code{\link[=count_df]{count_df()}}.
\subsection{The functions}{

\code{\link[=geom_rsi]{geom_rsi()}} will take any variable from the data that has an \code{\link{rsi}} class (created with \code{\link[=as.rsi]{as.rsi()}}) using \code{\link[=rsi_df]{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{\link[=facet_rsi]{facet_rsi()}} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2:facet_wrap]{ggplot2::facet_wrap()}}.

\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_continuous()}}.

\code{\link[=scale_rsi_colours]{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]{ggplot2::scale_brewer()}}.

\code{\link[=theme_rsi]{theme_rsi()}} is a [ggplot2 theme][\code{\link[ggplot2:theme]{ggplot2::theme()}} with minimal distraction.

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

\code{\link[=ggplot_rsi]{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 (\verb{\%>\%}). See Examples.
}
}
\section{Maturing lifecycle}{

\if{html}{\figure{lifecycle_maturing.svg}{options: style=margin-bottom:5px} \cr}
The \link[AMR:lifecycle]{lifecycle} of this function is \strong{maturing}. The unlying code of a maturing function has been roughed out, but finer details might still change. We will strive to maintain backward compatibility, but the function needs wider usage and more extensive testing in order to optimise the unlying code.
}

\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 comprehensive 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(example_isolates \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
  geom_rsi()

# prettify the plot using some additional functions:
df <- example_isolates \%>\% 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:
example_isolates \%>\%
  select(AMX, NIT, FOS, TMP, CIP) \%>\%
  ggplot_rsi()

# get only proportions and no counts:
example_isolates \%>\%
  select(AMX, NIT, FOS, TMP, CIP) \%>\%
  ggplot_rsi(datalabels = FALSE)

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

example_isolates \%>\%
  select(AMX) \%>\%
  ggplot_rsi(colours = c(SI = "yellow"))
  
\dontrun{

# resistance of ciprofloxacine per age group
example_isolates \%>\%
  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")
  
# for colourblind mode, use divergent colours from the viridis package:
example_isolates \%>\%
  select(AMX, NIT, FOS, TMP, CIP) \%>\%
  ggplot_rsi() + scale_fill_viridis_d()
# a shorter version which also adjusts data label colours:
example_isolates \%>\%
  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`):
example_isolates \%>\%
  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
example_isolates \%>\%
  # 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)")
}
}