new ggplot enhancement

man/abname.Rd

@@ -7,8 +7,8 @@
 \code{\link{antibiotics}}
 }
 \usage{
-abname(abcode, from = c("guess", "atc", "molis", "umcg"), to = "official",
-  textbetween = " + ", tolower = FALSE)
+abname(abcode, from = c("guess", "atc", "molis", "umcg"),
+  to = "official", textbetween = " + ", tolower = FALSE)
 }
 \arguments{
 \item{abcode}{a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}}

man/first_isolate.Rd

@@ -9,10 +9,11 @@ Methodology of this function is based on: \strong{M39 Analysis and Presentation
 \usage{
 first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,
   col_testcode = NA, col_specimen = NA, col_icu = NA,
-  col_keyantibiotics = NA, episode_days = 365, testcodes_exclude = "",
-  icu_exclude = FALSE, filter_specimen = NA, output_logical = TRUE,
-  type = "keyantibiotics", ignore_I = TRUE, points_threshold = 2,
-  info = TRUE, col_genus = NA, col_species = NA)
+  col_keyantibiotics = NA, episode_days = 365,
+  testcodes_exclude = "", icu_exclude = FALSE, filter_specimen = NA,
+  output_logical = TRUE, type = "keyantibiotics", ignore_I = TRUE,
+  points_threshold = 2, info = TRUE, col_genus = NA,
+  col_species = NA)
 }
 \arguments{
 \item{tbl}{a \code{data.frame} containing isolates.}

man/freq.Rd

@@ -7,9 +7,9 @@
 \alias{print.frequency_tbl}
 \title{Frequency table}
 \usage{
-frequency_tbl(x, ..., sort.count = TRUE, nmax = getOption("max.print.freq"),
-  na.rm = TRUE, row.names = TRUE, markdown = FALSE, digits = 2,
-  sep = " ")
+frequency_tbl(x, ..., sort.count = TRUE,
+  nmax = getOption("max.print.freq"), na.rm = TRUE, row.names = TRUE,
+  markdown = FALSE, digits = 2, sep = " ")
 
 freq(x, ..., sort.count = TRUE, nmax = getOption("max.print.freq"),
   na.rm = TRUE, row.names = TRUE, markdown = FALSE, digits = 2,
@@ -17,8 +17,8 @@ freq(x, ..., sort.count = TRUE, nmax = getOption("max.print.freq"),
 
 top_freq(f, n)
 
-\method{print}{frequency_tbl}(x, nmax = getOption("max.print.freq", default =
-  15), ...)
+\method{print}{frequency_tbl}(x, nmax = getOption("max.print.freq",
+  default = 15), ...)
 }
 \arguments{
 \item{x}{vector of any class or a \code{\link{data.frame}}, \code{\link{tibble}} or \code{\link{table}}}

man/g.test.Rd

@@ -12,7 +12,8 @@ This code is almost identical to \code{\link{chisq.test}}, except that:
 }
 }
 \usage{
-g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)
+g.test(x, y = NULL, p = rep(1/length(x), length(x)),
+  rescale.p = FALSE)
 }
 \arguments{
 \item{x}{a numeric vector or matrix. \code{x} and \code{y} can also

man/ggplot_rsi.Rd

@@ -0,0 +1,77 @@
+% 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}
+\title{AMR bar plots with \code{ggplot}}
+\usage{
+ggplot_rsi(data, x = "Antibiotic", facet = NULL)
+
+geom_rsi(position = "stack", x = c("Antibiotic", "Interpretation"))
+
+facet_rsi(facet = c("Interpretation", "Antibiotic"))
+
+scale_y_percent()
+
+scale_rsi_colours()
+
+theme_rsi()
+}
+\arguments{
+\item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})}
+
+\item{x}{parameter to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"}}
+
+\item{facet}{parameter to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"}}
+
+\item{position}{position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}}
+}
+\description{
+Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link{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{\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{facet_wrap}}.
+
+\code{scale_y_percent} transforms the y axis to a 0 to 100% range.
+
+\code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R.
+
+\code{theme_rsi} is a \code{\link{theme}} with minimal distraction.
+
+\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 it using some additional functions
+df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
+ggplot(df) +
+  geom_rsi(x = "Interpretation") +
+  facet_rsi(facet = "Antibiotic") +
+  scale_y_percent() +
+  scale_rsi_colours() +
+  theme_rsi()
+
+# or better yet, simplify this using the wrapper function - a single command:
+septic_patients \%>\%
+  select(amox, nitr, fosf, trim, cipr) \%>\%
+  ggplot_rsi()
+
+septic_patients \%>\%
+  select(amox, nitr, fosf, trim, cipr) \%>\%
+  ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
+}

man/portion.Rd

@@ -7,6 +7,7 @@
 \alias{portion_I}
 \alias{portion_SI}
 \alias{portion_S}
+\alias{portion_df}
 \title{Calculate resistance of isolates}
 \source{
 \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
@@ -21,6 +22,8 @@ portion_I(ab1, minimum = 30, as_percent = FALSE)
 portion_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
 
 portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+
+portion_df(data, translate = getOption("get_antibiotic_names", TRUE))
 }
 \arguments{
 \item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
@@ -30,6 +33,10 @@ portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
 \item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
 
 \item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
+
+\item{data}{a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
+
+\item{translate}{a logical value to indicate whether antibiotic abbreviations should be translated with \code{\link{abname}}}
 }
 \value{
 Double or, when \code{as_percent = TRUE}, a character.
@@ -42,6 +49,8 @@ These functions can be used to calculate the (co-)resistance of microbial isolat
 \details{
 \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
 
+\code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \code{data.frame} will have three rows (for R/I/S) and a column for each variable with class \code{"rsi"}.
+
 The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
 \if{html}{
   \cr\cr

man/resistance_predict.Rd

@@ -5,9 +5,10 @@
 \alias{rsi_predict}
 \title{Predict antimicrobial resistance}
 \usage{
-resistance_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
-  year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
-  preserve_measurements = TRUE, info = TRUE)
+resistance_predict(tbl, col_ab, col_date, year_min = NULL,
+  year_max = NULL, year_every = 1, minimum = 30,
+  model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
+  info = TRUE)
 
 rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
   year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,