new ggplot enhancement

.Rbuildignore

@@ -2,3 +2,4 @@
 ^\.Rproj\.user$
 .travis.yml
 .zenodo.json
+^cran-comments\.md$

.gitignore

@@ -12,3 +12,4 @@ inst/doc
 *.dll
 vignettes/*.R
 .DS_Store
+^cran-comments\.md$

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 0.2.0.9022
+Version: 0.2.0.9023
-Date: 2018-08-10
+Date: 2018-08-11
 Title: Antimicrobial Resistance Analysis
 Authors@R: c(
     person(
@@ -46,7 +46,7 @@ Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR
     on antibiograms according to Leclercq (2013)
     <doi:10.1111/j.1469-0691.2011.03703.x>.
 Depends:
-    R (>= 3.0.0)
+    R (>= 3.1.0)
 Imports:
     backports,
     clipr,
@@ -57,19 +57,18 @@ Imports:
     Rcpp (>= 0.12.14),
     readr,
     rvest (>= 0.3.2),
-    tibble
+    tibble,
+    ggplot2
 Suggests:
     testthat (>= 1.0.2),
     covr (>= 3.0.1),
     rmarkdown,
     rstudioapi,
-    tidyr,
+    tidyr
-    ggplot2
-LinkingTo: Rcpp
 VignetteBuilder: knitr
 URL: https://github.com/msberends/AMR
 BugReports: https://github.com/msberends/AMR/issues
 License: GPL-2 | file LICENSE
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 6.0.1.9000
+RoxygenNote: 6.1.0

NAMESPACE

@@ -43,11 +43,14 @@ export(atc_groups)
 export(atc_property)
 export(clipboard_export)
 export(clipboard_import)
+export(facet_rsi)
 export(first_isolate)
 export(freq)
 export(frequency_tbl)
 export(full_join_microorganisms)
 export(g.test)
+export(geom_rsi)
+export(ggplot_rsi)
 export(guess_atc)
 export(guess_bactid)
 export(inner_join_microorganisms)
@@ -68,13 +71,17 @@ export(portion_IR)
 export(portion_R)
 export(portion_S)
 export(portion_SI)
+export(portion_df)
 export(ratio)
 export(resistance_predict)
 export(right_join_microorganisms)
 export(rsi)
 export(rsi_predict)
+export(scale_rsi_colours)
+export(scale_y_percent)
 export(semi_join_microorganisms)
 export(skewness)
+export(theme_rsi)
 export(top_freq)
 exportMethods(as.data.frame.bactid)
 exportMethods(as.data.frame.frequency_tbl)
@@ -105,7 +112,6 @@ exportMethods(skewness.default)
 exportMethods(skewness.matrix)
 exportMethods(summary.mic)
 exportMethods(summary.rsi)
-importFrom(Rcpp,evalCpp)
 importFrom(clipr,read_clip_tbl)
 importFrom(clipr,write_clip)
 importFrom(curl,nslookup)
@@ -114,6 +120,7 @@ importFrom(dplyr,arrange)
 importFrom(dplyr,arrange_at)
 importFrom(dplyr,as_tibble)
 importFrom(dplyr,between)
+importFrom(dplyr,bind_cols)
 importFrom(dplyr,case_when)
 importFrom(dplyr,desc)
 importFrom(dplyr,filter)
@@ -130,6 +137,7 @@ importFrom(dplyr,row_number)
 importFrom(dplyr,select)
 importFrom(dplyr,slice)
 importFrom(dplyr,summarise)
+importFrom(dplyr,summarise_if)
 importFrom(dplyr,tibble)
 importFrom(dplyr,top_n)
 importFrom(grDevices,boxplot.stats)
@@ -161,4 +169,3 @@ importFrom(utils,object.size)
 importFrom(utils,read.delim)
 importFrom(utils,write.table)
 importFrom(xml2,read_html)
-useDynLib(AMR, .registration = TRUE)

NEWS.md

@@ -1,12 +1,16 @@
 # 0.2.0.90xx (development version)
-**Published on CRAN: (unpublished)**
 
 #### New
-* **BREAKING**: `rsi_df` was removed in favour of new functions `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` to selectively calculate resistance or susceptibility. These functions use **hybrid evaluation**, which means that calculations are not done in R directly but rather in C++ using the `Rcpp` package, making them 20 to 30 times faster. The function `rsi` still works, but is deprecated.
+* **BREAKING**: `rsi_df` was removed in favour of new functions `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` to selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old `rsi` function. The old function still works, but is deprecated.
+  * New function `portion_df` to get all portions of S, I and R of a data set with antibiotic columns
 * **BREAKING**: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call *key antibiotics*) to include more first isolates (afterwards called first *weighted* isolates) are now as follows:
   * Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin,  trimethoprim/sulfamethoxazole
   * Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
   * Gram-negative: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
+* Support for `ggplot2`
+  * New functions `geom_rsi`, `facet_rsi`, `scale_y_percent`, `scale_rsi_colours` and `theme_rsi`
+  * New wrapper function `ggplot_rsi` to apply all above functions on a data set:
+    * `septic_patients %>% select(tobr, gent) %>% ggplot_rsi` will show portions of S, I and R immediately in a pretty plot
 * Determining bacterial ID:
   * New functions `as.bactid` and `is.bactid` to transform/ look up microbial ID's.
   * The existing function `guess_bactid` is now an alias of `as.bactid`

R/RcppExports.R

@@ -1,15 +0,0 @@
-# Generated by using Rcpp::compileAttributes() -> do not edit by hand
-# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-rsi_calc_S <- function(x, include_I) {
-    .Call(`_AMR_rsi_calc_S`, x, include_I)
-}
-
-rsi_calc_I <- function(x) {
-    .Call(`_AMR_rsi_calc_I`, x)
-}
-
-rsi_calc_R <- function(x, include_I) {
-    .Call(`_AMR_rsi_calc_R`, x, include_I)
-}
-

R/ggplot_rsi.R

@@ -0,0 +1,130 @@
+# ==================================================================== #
+# TITLE                                                                #
+# Antimicrobial Resistance (AMR) Analysis                              #
+#                                                                      #
+# AUTHORS                                                              #
+# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)           #
+#                                                                      #
+# LICENCE                                                              #
+# This program is free software; you can redistribute it and/or modify #
+# it under the terms of the GNU General Public License version 2.0,    #
+# as published by the Free Software Foundation.                        #
+#                                                                      #
+# This program is distributed in the hope that it will be useful,      #
+# but WITHOUT ANY WARRANTY; without even the implied warranty of       #
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the        #
+# GNU General Public License for more details.                         #
+# ==================================================================== #
+
+#' AMR bar plots with \code{ggplot}
+#'
+#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link{ggplot}} functions.
+#' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})
+#' @param position position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}
+#' @param x parameter to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"}
+#' @param facet parameter to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"}
+#' @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.
+#' @rdname ggplot_rsi
+#' @export
+#' @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")
+ggplot_rsi <- function(data,
+                       x = "Antibiotic",
+                       facet = NULL) {
+  p <- ggplot2::ggplot(data = data) +
+    geom_rsi(x = x) +
+    scale_y_percent() +
+    scale_rsi_colours() +
+    theme_rsi()
+
+  if (!is.null(facet)) {
+    p <- p + facet_rsi(facet = facet)
+  }
+
+  p
+}
+
+#' @rdname ggplot_rsi
+#' @export
+geom_rsi <- function(position = "stack", x = c("Antibiotic", "Interpretation"))  {
+
+  x <- x[1]
+  if (!x %in% c("Antibiotic", "Interpretation")) {
+    stop("`x` must be 'Antibiotic' or 'Interpretation'")
+  }
+
+  ggplot2::layer(geom = "bar", stat = "identity", position = position,
+        mapping = ggplot2::aes_string(x = x, y = "Percentage", fill = "Interpretation"),
+        data = AMR::portion_df, params = list())
+
+}
+
+#' @rdname ggplot_rsi
+#' @export
+facet_rsi <- function(facet = c("Interpretation", "Antibiotic")) {
+  facet <- facet[1]
+  if (!facet %in% c("Antibiotic", "Interpretation")) {
+    stop("`facet` must be 'Antibiotic' or 'Interpretation'")
+  }
+  ggplot2::facet_wrap(facets = facet, scales = "free")
+}
+
+#' @rdname ggplot_rsi
+#' @export
+scale_y_percent <- function() {
+  ggplot2::scale_y_continuous(name = "Percentage",
+                              breaks = seq(0, 1, 0.1),
+                              limits = c(0, 1),
+                              labels = percent(seq(0, 1, 0.1)))
+}
+
+#' @rdname ggplot_rsi
+#' @export
+scale_rsi_colours <- function() {
+  ggplot2::scale_fill_brewer(palette = "RdYlGn")
+}
+
+#' @rdname ggplot_rsi
+#' @export
+theme_rsi <- function() {
+  theme_minimal() +
+    theme(panel.grid.major.x = element_blank(),
+          panel.grid.minor = element_blank(),
+          panel.grid.major.y = element_line(colour = "grey75"))
+}

R/globals.R

@@ -17,6 +17,14 @@
 # ==================================================================== #
 
 globalVariables(c('abname',
+                  'Antibiotic',
+                  'Interpretation',
+                  'Percentage',
+                  'bind_rows',
+                  'element_blank',
+                  'element_line',
+                  'theme',
+                  'theme_minimal',
                   'antibiotic',
                   'antibiotics',
                   'atc',

R/portion.R

@@ -25,8 +25,12 @@
 #' @param ab2 like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
 #' @param 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.
 #' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
+#' @param data a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
+#' @param translate a logical value to indicate whether antibiotic abbreviations should be translated with \code{\link{abname}}
 #' @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
@@ -225,11 +229,11 @@ rsi_calc <- function(type,
   }
 
   if (type == "S") {
-    found <- .Call(`_AMR_rsi_calc_S`, x, include_I)
+    found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
   } else if (type == "I") {
-    found <- .Call(`_AMR_rsi_calc_I`, x)
+    found <- sum(as.integer(x) == 2, na.rm = TRUE)
   } else if (type == "R") {
-    found <- .Call(`_AMR_rsi_calc_R`, x, include_I)
+    found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
   } else {
     stop("invalid type")
   }
@@ -240,3 +244,29 @@ rsi_calc <- function(type,
     found / total
   }
 }
+
+#' @rdname portion
+#' @importFrom dplyr bind_cols summarise_if mutate
+#' @export
+portion_df <- function(data, translate = getOption("get_antibiotic_names", TRUE)) {
+  resS <- bind_cols(data.frame(Interpretation = "S", stringsAsFactors = FALSE),
+                    summarise_if(.tbl = data,
+                                 .predicate = is.rsi,
+                                 .funs = portion_S))
+  resI <- bind_cols(data.frame(Interpretation = "I", stringsAsFactors = FALSE),
+                    summarise_if(.tbl = data,
+                                 .predicate = is.rsi,
+                                 .funs = portion_I))
+  resR <- bind_cols(data.frame(Interpretation = "R", stringsAsFactors = FALSE),
+                    summarise_if(.tbl = data,
+                                 .predicate = is.rsi,
+                                 .funs = portion_R))
+
+  res <- bind_rows(resS, resI, resR) %>%
+    mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
+    tidyr::gather(Antibiotic, Percentage, -Interpretation)
+  if (translate == TRUE) {
+    res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = "official"))
+  }
+  res
+}

R/zzz.R

@@ -1,7 +1,3 @@
 .onLoad <- function(libname, pkgname) {
   backports::import(pkgname)
 }
-
-#' @importFrom Rcpp evalCpp
-#' @useDynLib AMR, .registration = TRUE
-NULL

README.md

@@ -50,8 +50,6 @@ And it contains:
 
 With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).
 
-The functions to calculate microbial resistance use expressions that are not evaluated by R itself, but by alternative C++ code that is 25 to 30 times faster than R and uses less memory. This is called *hybrid evaluation*.
-
 #### Read all changes and new functions in [NEWS.md](NEWS.md).
 
 ## How to get it?
@@ -145,27 +143,21 @@ rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
 ```
 These functions also try to coerce valid values.
 
-Quick overviews when just printing objects:
+Quick overviews with `summary`:
 ```r
-mic_data
+summary(rsi_data)
-# Class 'mic': 7 isolates
+#  Mode  :rsi  
-# 
+#  <NA>  :0    
-# <NA>  0
+#  Sum S :474  
-# 
+#  Sum IR:406  
-# <=0.128       1       8      16    >=32
+#  -Sum R:370  
-#       1       1       2       2       1
+#  -Sum I:36
 
-rsi_data
+summary(mic_data)
-# Class 'rsi': 880 isolates
+#  Mode:mic      
-# 
+#  <NA>:0        
-# <NA>:       0 
+#  Min.:<=0.128  
-# Sum of S:   474 
+#  Max.:>=32 
-# Sum of IR:  406 
-# - Sum of R: 370 
-# - Sum of I: 36 
-# 
-#   %S  %IR   %I   %R 
-# 53.9 46.1  4.1 42.0 
 ```
 
 A plot of `rsi_data`:

man/abname.Rd

@@ -7,8 +7,8 @@
 \code{\link{antibiotics}}
 }
 \usage{
-abname(abcode, from = c("guess", "atc", "molis", "umcg"), to = "official",
+abname(abcode, from = c("guess", "atc", "molis", "umcg"),
-  textbetween = " + ", tolower = FALSE)
+  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 = "",
+  col_keyantibiotics = NA, episode_days = 365,
-  icu_exclude = FALSE, filter_specimen = NA, output_logical = TRUE,
+  testcodes_exclude = "", icu_exclude = FALSE, filter_specimen = NA,
-  type = "keyantibiotics", ignore_I = TRUE, points_threshold = 2,
+  output_logical = TRUE, type = "keyantibiotics", ignore_I = TRUE,
-  info = TRUE, col_genus = NA, col_species = NA)
+  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"),
+frequency_tbl(x, ..., sort.count = TRUE,
-  na.rm = TRUE, row.names = TRUE, markdown = FALSE, digits = 2,
+  nmax = getOption("max.print.freq"), na.rm = TRUE, row.names = TRUE,
-  sep = " ")
+  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 =
+\method{print}{frequency_tbl}(x, nmax = getOption("max.print.freq",
-  15), ...)
+  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,
+resistance_predict(tbl, col_ab, col_date, year_min = NULL,
-  year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
+  year_max = NULL, year_every = 1, minimum = 30,
-  preserve_measurements = TRUE, info = TRUE)
+  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,

src/RcppExports.cpp

@@ -1,54 +0,0 @@
-// Generated by using Rcpp::compileAttributes() -> do not edit by hand
-// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
-
-#include <Rcpp.h>
-
-using namespace Rcpp;
-
-// rsi_calc_S
-int rsi_calc_S(DoubleVector x, bool include_I);
-RcppExport SEXP _AMR_rsi_calc_S(SEXP xSEXP, SEXP include_ISEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< DoubleVector >::type x(xSEXP);
-    Rcpp::traits::input_parameter< bool >::type include_I(include_ISEXP);
-    rcpp_result_gen = Rcpp::wrap(rsi_calc_S(x, include_I));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rsi_calc_I
-int rsi_calc_I(DoubleVector x);
-RcppExport SEXP _AMR_rsi_calc_I(SEXP xSEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< DoubleVector >::type x(xSEXP);
-    rcpp_result_gen = Rcpp::wrap(rsi_calc_I(x));
-    return rcpp_result_gen;
-END_RCPP
-}
-// rsi_calc_R
-int rsi_calc_R(DoubleVector x, bool include_I);
-RcppExport SEXP _AMR_rsi_calc_R(SEXP xSEXP, SEXP include_ISEXP) {
-BEGIN_RCPP
-    Rcpp::RObject rcpp_result_gen;
-    Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< DoubleVector >::type x(xSEXP);
-    Rcpp::traits::input_parameter< bool >::type include_I(include_ISEXP);
-    rcpp_result_gen = Rcpp::wrap(rsi_calc_R(x, include_I));
-    return rcpp_result_gen;
-END_RCPP
-}
-
-static const R_CallMethodDef CallEntries[] = {
-    {"_AMR_rsi_calc_S", (DL_FUNC) &_AMR_rsi_calc_S, 2},
-    {"_AMR_rsi_calc_I", (DL_FUNC) &_AMR_rsi_calc_I, 1},
-    {"_AMR_rsi_calc_R", (DL_FUNC) &_AMR_rsi_calc_R, 2},
-    {NULL, NULL, 0}
-};
-
-RcppExport void R_init_AMR(DllInfo *dll) {
-    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
-    R_useDynamicSymbols(dll, FALSE);
-}

src/rsi_calc.cpp

@@ -1,27 +0,0 @@
-#include <Rcpp.h>
-
-using namespace Rcpp;
-
-// [[Rcpp::export]]
-int rsi_calc_S(DoubleVector x, bool include_I) {
-  return count_if(x.begin(),
-                  x.end(),
-                  bind2nd(std::less_equal<double>(),
-                          1 + include_I));
-}
-
-// [[Rcpp::export]]
-int rsi_calc_I(DoubleVector x) {
-  return count_if(x.begin(),
-                  x.end(),
-                  bind2nd(std::equal_to<double>(),
-                          2));
-}
-
-// [[Rcpp::export]]
-int rsi_calc_R(DoubleVector x, bool include_I) {
-  return count_if(x.begin(),
-                  x.end(),
-                  bind2nd(std::greater_equal<double>(),
-                          3 - include_I));
-}