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121 lines
4.9 KiB
R
121 lines
4.9 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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\title{AMR bar plots with \code{ggplot}}
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\usage{
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ggplot_rsi(data, position = "stack", x = "Antibiotic",
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fill = "Interpretation", facet = NULL, translate_ab = "official",
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...)
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geom_rsi(position = "stack", x = c("Antibiotic", "Interpretation"),
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fill = "Interpretation", translate_ab = "official")
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facet_rsi(facet = c("Interpretation", "Antibiotic"), ...)
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scale_y_percent()
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scale_rsi_colours()
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theme_rsi()
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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{"stack"} (default) 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{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{...}{other parameters passed on to \code{\link[ggplot2]{facet_wrap}}}
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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{\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.
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\code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R.
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\code{theme_rsi} is a \code{\link[ggplot2]{theme}} with minimal distraction.
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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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\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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facet_rsi() +
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scale_y_percent() +
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scale_rsi_colours() +
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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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\donttest{
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# it also supports groups (don't forget to use the group 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(bactid = as.bactid(bactid, Becker = TRUE)) \%>\%
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# filter on top 2 bacterial ID's
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filter(bactid \%in\% top_freq(freq(.$bactid), 2)) \%>\%
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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_bactid = "bactid")) \%>\%
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# filter on first isolates
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filter(first_isolate == TRUE) \%>\%
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# join the `microorganisms` data set
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left_join_microorganisms() \%>\%
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# select full name and some antiseptic drugs
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select(mo = fullname,
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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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labs(title = "AMR of Top Two Microorganisms In Blood Culture Isolates",
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subtitle = "Only First Isolates, CoNS grouped according to Becker et al.",
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x = "Microorganisms")
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}
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}
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