mirror of https://github.com/msberends/AMR.git
209 lines
8.9 KiB
R
209 lines
8.9 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/ggplot_rsi.R
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\name{ggplot_rsi}
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\alias{ggplot_rsi}
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\alias{geom_rsi}
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\alias{facet_rsi}
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\alias{scale_y_percent}
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\alias{scale_rsi_colours}
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\alias{theme_rsi}
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\alias{labels_rsi_count}
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\title{AMR plots with \code{ggplot2}}
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\usage{
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ggplot_rsi(data, position = NULL, x = "antibiotic",
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fill = "interpretation", facet = NULL, breaks = seq(0, 1, 0.1),
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limits = NULL, translate_ab = "name", combine_SI = TRUE,
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combine_IR = FALSE, language = get_locale(), nrow = NULL,
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colours = c(S = "#61a8ff", SI = "#61a8ff", I = "#61f7ff", IR =
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"#ff6961", R = "#ff6961"), datalabels = TRUE, datalabels.size = 2.5,
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datalabels.colour = "gray15", title = NULL, subtitle = NULL,
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caption = NULL, x.title = NULL, y.title = NULL, ...)
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geom_rsi(position = NULL, x = c("antibiotic", "interpretation"),
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fill = "interpretation", translate_ab = "name",
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language = get_locale(), combine_SI = TRUE, combine_IR = FALSE,
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...)
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facet_rsi(facet = c("interpretation", "antibiotic"), nrow = NULL)
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scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)
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scale_rsi_colours(colours = c(S = "#61a8ff", SI = "#61a8ff", I =
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"#61f7ff", IR = "#ff6961", R = "#ff6961"))
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theme_rsi()
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labels_rsi_count(position = NULL, x = "antibiotic",
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translate_ab = "name", combine_SI = TRUE, combine_IR = FALSE,
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datalabels.size = 3, datalabels.colour = "gray15")
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}
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\arguments{
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\item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})}
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\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"}}
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\item{x}{variable to show on x axis, either \code{"antibiotic"} (default) or \code{"interpretation"} or a grouping variable}
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\item{fill}{variable to categorise using the plots legend, either \code{"antibiotic"} (default) or \code{"interpretation"} or a grouping variable}
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\item{facet}{variable to split plots by, either \code{"interpretation"} (default) or \code{"antibiotic"} or a grouping variable}
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\item{breaks}{numeric vector of positions}
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\item{limits}{numeric vector of length two providing limits of the scale, use \code{NA} to refer to the existing minimum or maximum}
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\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{ab_property}}}
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\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}.}
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\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}.}
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\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.}
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\item{nrow}{(when using \code{facet}) number of rows}
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\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.}
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\item{datalabels}{show datalabels using \code{labels_rsi_count}}
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\item{datalabels.size}{size of the datalabels}
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\item{datalabels.colour}{colour of the datalabels}
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\item{title}{text to show as title of the plot}
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\item{subtitle}{text to show as subtitle of the plot}
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\item{caption}{text to show as caption of the plot}
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\item{x.title}{text to show as x axis description}
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\item{y.title}{text to show as y axis description}
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\item{...}{other parameters passed on to \code{geom_rsi}}
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}
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\description{
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Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}2} functions.
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}
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\details{
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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}}.
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\strong{The functions}\cr
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\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.
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\code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
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\code{scale_y_percent} transforms the y axis to a 0 to 100\% range using \code{\link[ggplot2]{scale_continuous}}.
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\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}}.
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\code{theme_rsi} is a \code{ggplot \link[ggplot2]{theme}} with minimal distraction.
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\code{labels_rsi_count} print datalabels on the bars with percentage and amount of isolates using \code{\link[ggplot2]{geom_text}}
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\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.
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}
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\section{Read more on our website!}{
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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}.
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}
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\examples{
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library(dplyr)
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library(ggplot2)
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# get antimicrobial results for drugs against a UTI:
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ggplot(septic_patients \%>\% select(AMX, NIT, FOS, TMP, CIP)) +
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geom_rsi()
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# prettify the plot using some additional functions:
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df <- septic_patients \%>\% select(AMX, NIT, FOS, TMP, CIP)
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ggplot(df) +
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geom_rsi() +
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scale_y_percent() +
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scale_rsi_colours() +
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labels_rsi_count() +
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theme_rsi()
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# or better yet, simplify this using the wrapper function - a single command:
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septic_patients \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_rsi()
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# get only portions and no counts:
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septic_patients \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_rsi(datalabels = FALSE)
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# add other ggplot2 parameters as you like:
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septic_patients \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_rsi(width = 0.5,
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colour = "black",
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size = 1,
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linetype = 2,
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alpha = 0.25)
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septic_patients \%>\%
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select(AMX) \%>\%
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ggplot_rsi(colours = c(SI = "yellow"))
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# resistance of ciprofloxacine per age group
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septic_patients \%>\%
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mutate(first_isolate = first_isolate(.)) \%>\%
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filter(first_isolate == TRUE,
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mo == as.mo("E. coli")) \%>\%
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# `age_group` is also a function of this package:
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group_by(age_group = age_groups(age)) \%>\%
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select(age_group,
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CIP) \%>\%
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ggplot_rsi(x = "age_group")
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\donttest{
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# for colourblind mode, use divergent colours from the viridis package:
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septic_patients \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_rsi() + scale_fill_viridis_d()
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# a shorter version which also adjusts data label colours:
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septic_patients \%>\%
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select(AMX, NIT, FOS, TMP, CIP) \%>\%
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ggplot_rsi(colours = FALSE)
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# it also supports groups (don't forget to use the group var on `x` or `facet`):
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septic_patients \%>\%
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select(hospital_id, AMX, NIT, FOS, TMP, CIP) \%>\%
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group_by(hospital_id) \%>\%
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ggplot_rsi(x = "hospital_id",
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facet = "antibiotic",
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nrow = 1,
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title = "AMR of Anti-UTI Drugs Per Hospital",
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x.title = "Hospital",
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datalabels = FALSE)
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# genuine analysis: check 3 most prevalent microorganisms
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septic_patients \%>\%
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# create new bacterial ID's, with all CoNS under the same group (Becker et al.)
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mutate(mo = as.mo(mo, Becker = TRUE)) \%>\%
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# filter on top three bacterial ID's
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filter(mo \%in\% top_freq(freq(.$mo), 3)) \%>\%
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# filter on first isolates
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filter_first_isolate() \%>\%
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# get short MO names (like "E. coli")
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mutate(bug = mo_shortname(mo, Becker = TRUE)) \%>\%
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# select this short name and some antiseptic drugs
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select(bug, CXM, GEN, CIP) \%>\%
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# group by MO
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group_by(bug) \%>\%
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# plot the thing, putting MOs on the facet
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ggplot_rsi(x = "antibiotic",
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facet = "bug",
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translate_ab = FALSE,
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nrow = 1,
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title = "AMR of Top Three Microorganisms In Blood Culture Isolates",
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subtitle = expression(paste("Only First Isolates, CoNS grouped according to Becker ",
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italic("et al."), " (2014)")),
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x.title = "Antibiotic (EARS-Net code)")
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}
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}
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