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183 lines
7.5 KiB
R
183 lines
7.5 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 bar plots with \code{ggplot}}
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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 = "official", fun = count_df,
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nrow = NULL, datalabels = TRUE, datalabels.size = 3,
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datalabels.colour = "grey15", ...)
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geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
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fill = "Interpretation", translate_ab = "official", fun = count_df,
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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()
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theme_rsi()
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labels_rsi_count(position = NULL, x = "Antibiotic",
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datalabels.size = 3, datalabels.colour = "grey15")
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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 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.}
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\item{fun}{function to transform \code{data}, either \code{\link{count_df}} (default) or \code{\link{portion_df}}}
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\item{nrow}{(when using \code{facet}) number of rows}
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\item{datalabels}{show datalabels using \code{labels_rsi_count}, will at default only be shown when \code{fun = count_df}}
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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{...}{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}} 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{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)}.
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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{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.
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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: green for S, yellow for I and 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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\if{html}{\figure{logo.png}{options: height=40px style=margin-bottom:5px} \cr}
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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 omprehensive tutorial} about how to conduct AMR analysis and find \href{https://msberends.gitlab.io/AMR/reference}{the complete documentation of all functions}, which reads a lot easier than in R.
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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(amox, nitr, fosf, trim, cipr)) +
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geom_rsi()
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# prettify the plot using some additional functions:
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df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
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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(amox, nitr, fosf, trim, cipr) \%>\%
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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(amox, nitr, fosf, trim, cipr) \%>\%
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ggplot_rsi(fun = portion_df)
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# add other ggplot2 parameters as you like:
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septic_patients \%>\%
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select(amox, nitr, fosf, trim, cipr) \%>\%
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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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# 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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cipr) \%>\%
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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(amox, nitr, fosf, trim, cipr) \%>\%
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ggplot_rsi() + scale_fill_viridis_d()
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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, amox, nitr, fosf, trim, cipr) \%>\%
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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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labs(title = "AMR of Anti-UTI Drugs Per Hospital",
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x = "Hospital")
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# genuine analysis: check 2 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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# determine first isolates
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mutate(first_isolate = first_isolate(.,
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col_date = "date",
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col_patient_id = "patient_id",
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col_mo = "mo")) \%>\%
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# filter on first isolates
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filter(first_isolate == TRUE) \%>\%
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# get short MO names (like "E. coli")
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mutate(mo = mo_shortname(mo, Becker = TRUE)) \%>\%
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# select this short name and some antiseptic drugs
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select(mo, cfur, gent, cipr) \%>\%
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# group by MO
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group_by(mo) \%>\%
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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 = mo,
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translate_ab = FALSE,
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nrow = 1) +
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labs(title = "AMR of Top Three Microorganisms In Blood Culture Isolates",
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subtitle = "Only First Isolates, CoNS grouped according to Becker et al. (2014)",
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x = "Microorganisms")
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}
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}
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