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 bar plots with \code{ggplot}}
\usage{
ggplot_rsi(data, position = NULL, x = "Antibiotic",
  fill = "Interpretation", facet = NULL, breaks = seq(0, 1, 0.1),
  translate_ab = "official", fun = count_df, nrow = NULL,
  datalabels = TRUE, datalabels.size = 3,
  datalabels.colour = "grey15", ...)

geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
  fill = "Interpretation", translate_ab = "official", fun = count_df,
  ...)

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

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

scale_rsi_colours()

theme_rsi()

labels_rsi_count(position = NULL, x = "Antibiotic",
  datalabels.size = 3, datalabels.colour = "grey15")
}
\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{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable 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{datalabels}{show datalabels using \code{labels_rsi_count}, will at default only be shown when \code{fun = count_df}}

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

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

\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}} functions.
}
\details{
At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.

\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: green for S, yellow for I and 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.
}
\examples{
library(dplyr)
library(ggplot2)

# get antimicrobial results for drugs against a UTI:
ggplot(septic_patients \%>\% select(amox, nitr, fosf, trim, cipr)) +
  geom_rsi()

# prettify the plot using some additional functions:
df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
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(amox, nitr, fosf, trim, cipr) \%>\%
  ggplot_rsi()

# get only portions and no counts:
septic_patients \%>\%
  select(amox, nitr, fosf, trim, cipr) \%>\%
  ggplot_rsi(fun = portion_df)

# add other ggplot2 parameters as you like:
septic_patients \%>\%
  select(amox, nitr, fosf, trim, cipr) \%>\%
  ggplot_rsi(width = 0.5,
             colour = "black",
             size = 1,
             linetype = 2,
             alpha = 0.25)
\donttest{

# for colourblind mode, use divergent colours from the viridis package:
septic_patients \%>\%
  select(amox, nitr, fosf, trim, cipr) \%>\%
  ggplot_rsi() + scale_fill_viridis_d()


# it also supports groups (don't forget to use the group var on `x` or `facet`):
septic_patients \%>\%
  select(hospital_id, amox, nitr, fosf, trim, cipr) \%>\%
  group_by(hospital_id) \%>\%
  ggplot_rsi(x = hospital_id,
             facet = Antibiotic,
             nrow = 1) +
  labs(title = "AMR of Anti-UTI Drugs Per Hospital",
       x = "Hospital")

# genuine analysis: check 2 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)) \%>\%
  # determine first isolates
  mutate(first_isolate = first_isolate(.,
                                       col_date = "date",
                                       col_patient_id = "patient_id",
                                       col_mo = "mo")) \%>\%
  # filter on first isolates
  filter(first_isolate == TRUE) \%>\%
  # get short MO names (like "E. coli")
  mutate(mo = mo_shortname(mo, Becker = TRUE)) \%>\%
  # select this short name and some antiseptic drugs
  select(mo, cfur, gent, cipr) \%>\%
  # group by MO
  group_by(mo) \%>\%
  # plot the thing, putting MOs on the facet
  ggplot_rsi(x = Antibiotic,
             facet = mo,
             translate_ab = FALSE,
             nrow = 1) +
  labs(title = "AMR of Top Three Microorganisms In Blood Culture Isolates",
       subtitle = "Only First Isolates, CoNS grouped according to Becker et al. (2014)",
       x = "Microorganisms")
}
}