count_* functions

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 0.3.0
+Version: 0.3.0.9001
-Date: 2018-08-14
+Date: 2018-08-21
 Title: Antimicrobial Resistance Analysis
 Authors@R: c(
     person(

NAMESPACE

@@ -43,6 +43,12 @@ export(atc_groups)
 export(atc_property)
 export(clipboard_export)
 export(clipboard_import)
+export(count_I)
+export(count_IR)
+export(count_R)
+export(count_S)
+export(count_SI)
+export(count_df)
 export(facet_rsi)
 export(first_isolate)
 export(freq)
@@ -58,6 +64,7 @@ export(interpretive_reading)
 export(is.bactid)
 export(is.mic)
 export(is.rsi)
+export(is.rsi.eligible)
 export(key_antibiotics)
 export(key_antibiotics_equal)
 export(kurtosis)
@@ -137,6 +144,7 @@ importFrom(dplyr,progress_estimated)
 importFrom(dplyr,pull)
 importFrom(dplyr,row_number)
 importFrom(dplyr,select)
+importFrom(dplyr,select_if)
 importFrom(dplyr,slice)
 importFrom(dplyr,summarise)
 importFrom(dplyr,summarise_if)

NEWS.md

@@ -1,4 +1,15 @@
-# 0.3.0
+# 0.3.0.90xx (latest development version)
+
+#### New
+* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptibile isolates
+* Function `is.rsi.eligible` to check for columns that have valid antimicrobial results, but do not have the `rsi` class yet. Transform the columns of your raw data with: `data %>% mutate_at(is.rsi.eligible, as.rsi)`
+
+#### Changed
+* Added parameters `minimum` and `as_percent` to `portion_df`
+* Edited `ggplot_rsi` and `geom_rsi` so they can cope with `count_df`. The new `fun` parameter has value `portion_df` at default, but can be set to `count_df`.
+* Fix for `ggplot_rsi` when the `ggplot2` was not loaded
+
+# 0.3.0 (latest stable version)
 **Published on CRAN: 2018-08-14**
 
 #### New

R/classes.R

@@ -21,6 +21,7 @@
 #' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
 #' @rdname as.rsi
 #' @param x vector
+#' @details The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
 #' @return Ordered factor with new class \code{rsi} and new attribute \code{package}
 #' @keywords rsi
 #' @export
@@ -37,6 +38,12 @@
 #' plot(rsi_data)    # for percentages
 #' barplot(rsi_data) # for frequencies
 #' freq(rsi_data)    # frequency table with informative header
+#'
+#' # fastest way to transform all columns with already valid AB results to class `rsi`:
+#' library(dplyr)
+#' septic_patients %>%
+#'   mutate_if(is.rsi.eligible,
+#'             as.rsi)
 as.rsi <- function(x) {
   if (is.rsi(x)) {
     x
@@ -88,6 +95,18 @@ is.rsi <- function(x) {
   class(x) %>% identical(c('rsi', 'ordered', 'factor'))
 }
 
+#' @rdname as.rsi
+#' @export
+#' @importFrom dplyr %>%
+is.rsi.eligible <- function(x) {
+  distinct_val <- x %>% unique() %>% sort() %>% as.character()
+  distinct_val <- distinct_val[!is.na(distinct_val) & trimws(distinct_val) != ""]
+  distinct_val_rsi <- as.character(suppressWarnings(as.rsi(distinct_val)))
+
+  length(distinct_val) > 0 &
+    identical(distinct_val, distinct_val_rsi)
+}
+
 #' @exportMethod print.rsi
 #' @export
 #' @importFrom dplyr %>%

R/count.R

@@ -0,0 +1,199 @@
+# ==================================================================== #
+# 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.                         #
+# ==================================================================== #
+
+#' Count isolates
+#'
+#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+#'
+#' \code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
+#' @inheritParams portion
+#' @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.
+#'
+#' These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
+#'
+#' \code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
+#' @source Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
+#' @seealso \code{\link{portion}_*} to calculate microbial resistance and susceptibility.\cr
+#' \code{\link{n_rsi}} to count all cases where antimicrobial results are available.
+#' @keywords resistance susceptibility rsi antibiotics isolate isolates
+#' @return Integer
+#' @rdname count
+#' @name count
+#' @export
+#' @examples
+#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
+#' ?septic_patients
+#'
+#' # Count resistant isolates
+#' count_R(septic_patients$amox)
+#' count_IR(septic_patients$amox)
+#'
+#' # Or susceptibile isolates
+#' count_S(septic_patients$amox)
+#' count_SI(septic_patients$amox)
+#'
+#' # Since n_rsi counts available isolates, you can
+#' # calculate back to count e.g. non-susceptible isolates.
+#' # This results in the same:
+#' count_IR(septic_patients$amox)
+#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
+#'
+#' library(dplyr)
+#' septic_patients %>%
+#'   group_by(hospital_id) %>%
+#'   summarise(R = count_R(cipr),
+#'             I = count_I(cipr),
+#'             S = count_S(cipr),
+#'             n = n_rsi(cipr), # the actual total; sum of all three
+#'             total = n())     # NOT the amount of tested isolates!
+#'
+#' # Count co-resistance between amoxicillin/clav acid and gentamicin,
+#' # so we can see that combination therapy does a lot more than mono therapy.
+#' # Please mind that `portion_S` calculates percentages right away instead.
+#' count_S(septic_patients$amcl)   # S = 1056 (67.3%)
+#' n_rsi(septic_patients$amcl)     # n = 1570
+#'
+#' count_S(septic_patients$gent)   # S = 1363 (74.0%)
+#' n_rsi(septic_patients$gent)     # n = 1842
+#'
+#' with(septic_patients,
+#'      count_S(amcl, gent))       # S = 1385 (92.1%)
+#' with(septic_patients,           # n = 1504
+#'      n_rsi(amcl, gent))
+#'
+#' # Get portions S/I/R immediately of all rsi columns
+#' septic_patients %>%
+#'   select(amox, cipr) %>%
+#'   count_df(translate = FALSE)
+#'
+#' # It also supports grouping variables
+#' septic_patients %>%
+#'   select(hospital_id, amox, cipr) %>%
+#'   group_by(hospital_id) %>%
+#'   count_df(translate = FALSE)
+#'
+count_R <- function(ab1,
+                    ab2 = NULL) {
+  rsi_calc(type = "R",
+           ab1 = ab1,
+           ab2 = ab2,
+           include_I = FALSE,
+           minimum = 0,
+           as_percent = FALSE,
+           only_count = TRUE)
+}
+
+#' @rdname count
+#' @export
+count_IR <- function(ab1,
+                     ab2 = NULL) {
+  rsi_calc(type = "R",
+           ab1 = ab1,
+           ab2 = ab2,
+           include_I = TRUE,
+           minimum = 0,
+           as_percent = FALSE,
+           only_count = TRUE)
+}
+
+#' @rdname count
+#' @export
+count_I <- function(ab1) {
+  rsi_calc(type = "I",
+           ab1 = ab1,
+           ab2 = NULL,
+           include_I = FALSE,
+           minimum = 0,
+           as_percent = FALSE,
+           only_count = TRUE)
+}
+
+#' @rdname count
+#' @export
+count_SI <- function(ab1,
+                     ab2 = NULL) {
+  rsi_calc(type = "S",
+           ab1 = ab1,
+           ab2 = ab2,
+           include_I = TRUE,
+           minimum = 0,
+           as_percent = FALSE,
+           only_count = TRUE)
+}
+
+#' @rdname count
+#' @export
+count_S <- function(ab1,
+                    ab2 = NULL) {
+  rsi_calc(type = "S",
+           ab1 = ab1,
+           ab2 = ab2,
+           include_I = FALSE,
+           minimum = 0,
+           as_percent = FALSE,
+           only_count = TRUE)
+}
+
+#' @rdname count
+#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
+#' @export
+count_df <- function(data,
+                     translate_ab = getOption("get_antibiotic_names", "official")) {
+
+  if (data %>% select_if(is.rsi) %>% ncol() == 0) {
+    stop("No columns with class 'rsi' found. See ?as.rsi.")
+  }
+
+  if (as.character(translate_ab) == "TRUE") {
+    translate_ab <- "official"
+  }
+  options(get_antibiotic_names = translate_ab)
+
+  resS <- summarise_if(.tbl = data,
+                       .predicate = is.rsi,
+                       .funs = count_S) %>%
+    mutate(Interpretation = "S") %>%
+    select(Interpretation, everything())
+
+  resI <- summarise_if(.tbl = data,
+                       .predicate = is.rsi,
+                       .funs = count_I) %>%
+    mutate(Interpretation = "I") %>%
+    select(Interpretation, everything())
+
+  resR <- summarise_if(.tbl = data,
+                       .predicate = is.rsi,
+                       .funs = count_R) %>%
+    mutate(Interpretation = "R") %>%
+    select(Interpretation, everything())
+
+  data.groups <- group_vars(data)
+
+  res <- bind_rows(resS, resI, resR) %>%
+    mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
+    tidyr::gather(Antibiotic, Count, -Interpretation, -data.groups)
+
+  if (!translate_ab == FALSE) {
+    if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
+      stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
+    }
+    res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
+  }
+
+  res
+}

R/ggplot_rsi.R

@@ -20,16 +20,17 @@
 #'
 #' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{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 position position adjustment of bars, either \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) or \code{"dodge"} (default when \code{fun} is \code{\link{count_df}})
 #' @param x variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
 #' @param fill variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
 #' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable
 #' @param 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.
+#' @param fun function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}
 #' @param ... other parameters passed on to \code{\link[ggplot2]{facet_wrap}}
 #' @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{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{portion_df}} at default, could also be \code{\link{count_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[ggplot2]{facet_wrap}}.
 #'
@@ -63,6 +64,11 @@
 #' septic_patients %>%
 #'   select(amox, nitr, fosf, trim, cipr) %>%
 #'   ggplot_rsi()
+#'
+#' # get counts instead of percentages:
+#' septic_patients %>%
+#'   select(amox, nitr, fosf, trim, cipr) %>%
+#'   ggplot_rsi(fun = count_df)
 #' \donttest{
 #' # it also supports groups (don't forget to use the group on `x` or `facet`):
 #' septic_patients %>%
@@ -102,26 +108,35 @@
 #'        x = "Microorganisms")
 #' }
 ggplot_rsi <- function(data,
-                       position = "stack",
+                       position = NULL,
                        x = "Antibiotic",
                        fill = "Interpretation",
                        facet = NULL,
                        translate_ab = "official",
+                       fun = portion_df,
                        ...) {
 
   if (!"ggplot2" %in% rownames(installed.packages())) {
     stop('this function requires the ggplot2 package.', call. = FALSE)
   }
 
+  fun_name <- deparse(substitute(fun))
+  if (!fun_name %in% c("portion_df", "count_df")) {
+    stop("`fun` must be portion_df or count_df")
+  }
+
   p <- ggplot2::ggplot(data = data) +
-    geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab) +
+    geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, fun = fun) +
-    scale_y_percent() +
     theme_rsi()
 
   if (fill == "Interpretation") {
     # set RSI colours
     p <- p + scale_rsi_colours()
   }
+  if (fun_name == "portion_df") {
+    # portions, so use y scale with percentage
+    p <- p + scale_y_percent()
+  }
 
   if (!is.null(facet)) {
     p <- p + facet_rsi(facet = facet, ...)
@@ -132,10 +147,27 @@ ggplot_rsi <- function(data,
 
 #' @rdname ggplot_rsi
 #' @export
-geom_rsi <- function(position = "stack",
+geom_rsi <- function(position = NULL,
                      x = c("Antibiotic", "Interpretation"),
                      fill = "Interpretation",
-                     translate_ab = "official")  {
+                     translate_ab = "official",
+                     fun = portion_df)  {
+
+  fun_name <- deparse(substitute(fun))
+  if (!fun_name %in% c("portion_df", "count_df", "fun")) {
+    stop("`fun` must be portion_df or count_df")
+  }
+  if (identical(fun, count_df)) {
+    y <- "Count"
+    if (missing(position) | is.null(position)) {
+      position <- "dodge"
+    }
+  } else {
+    y <- "Percentage"
+    if (missing(position) | is.null(position)) {
+      position <- "stack"
+    }
+  }
 
   x <- x[1]
   if (x %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
@@ -147,8 +179,8 @@ geom_rsi <- function(position = "stack",
   options(get_antibiotic_names = translate_ab)
 
   ggplot2::layer(geom = "bar", stat = "identity", position = position,
-                 mapping = ggplot2::aes_string(x = x, y = "Percentage", fill = fill),
+                 mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
-                 data = AMR::portion_df, params = list())
+                 data = fun, params = list())
 
 }
 
@@ -163,14 +195,13 @@ facet_rsi <- function(facet = c("Interpretation", "Antibiotic"), ...) {
     facet <- "Antibiotic"
   }
 
-  ggplot2::facet_wrap(facets = facet, scales = "free", ...)
+  ggplot2::facet_wrap(facets = facet, scales = "free_x", ...)
 }
 
 #' @rdname ggplot_rsi
 #' @export
 scale_y_percent <- function() {
-  ggplot2::scale_y_continuous(name = "Percentage",
+  ggplot2::scale_y_continuous(breaks = seq(0, 1, 0.1),
-                              breaks = seq(0, 1, 0.1),
                               limits = c(0, 1),
                               labels = percent(seq(0, 1, 0.1)))
 }
@@ -184,8 +215,8 @@ scale_rsi_colours <- function() {
 #' @rdname ggplot_rsi
 #' @export
 theme_rsi <- function() {
-  theme_minimal() +
+  ggplot2::theme_minimal() +
-    theme(panel.grid.major.x = element_blank(),
+    ggplot2::theme(panel.grid.major.x = ggplot2::element_blank(),
-          panel.grid.minor = element_blank(),
+                   panel.grid.minor = ggplot2::element_blank(),
-          panel.grid.major.y = element_line(colour = "grey75"))
+                   panel.grid.major.y = ggplot2::element_line(colour = "grey75"))
 }

R/globals.R

@@ -32,6 +32,7 @@ globalVariables(c('abname',
                   'C_chisq_sim',
                   'cnt',
                   'count',
+                  'Count',
                   'counts',
                   'cum_count',
                   'cum_percent',

R/misc.R

@@ -138,21 +138,18 @@ tbl_parse_guess <- function(tbl,
                                                             asciify = FALSE))
     }
     if (any(tbl %>% pull(i) %>% class() %in% c('factor', 'character'))) {
-      # get values
-      distinct_val <- tbl %>% pull(i) %>% unique() %>% sort()
       if (remove_ASCII_escape_char == TRUE) {
         # remove ASCII escape character: https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
         tbl[, i] <- tbl %>% pull(i) %>% gsub('\033', ' ', ., fixed = TRUE)
       }
-      # look for RSI, shouldn't all be "" and must be valid antibiotic interpretations
+      if (tbl %>% pull(i) %>% is.rsi.eligible()) {
-      if (!all(distinct_val[!is.na(distinct_val)] == '')
+        # look for RSI
-          & all(distinct_val[!is.na(distinct_val)] %in% c('', 'I', 'I;I', 'R', 'R;R', 'S', 'S;S'))) {
+        tbl[, i] <- as.rsi(tbl[, i])
-        tbl[, i] <- tbl %>% pull(i) %>% as.rsi()
       }
     }
-    # convert to MIC class
+    # convert to MIC class when ends on `_mic`
     if (colnames(tbl)[i] %like% '_mic$') {
-      tbl[, i] <- tbl %>% pull(i) %>% as.mic()
+      tbl[, i] <- as.mic(tbl[, i])
     }
   }
   tbl

R/portion.R

@@ -24,11 +24,13 @@
 #' @param ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
 #' @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 as_percent logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.
-#' @param data a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
+#' @param data a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
 #' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
 #' @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.
 #'
+#' These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. If a column has been transformed with \code{\link{as.rsi}}, just use e.g. \code{isolates[isolates == "R"]} to get the resistant ones. You could then calculate the \code{\link{length}} of it.
+#'
 #' \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 \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) 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.
@@ -47,7 +49,8 @@
 #' @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/}.
 #'
 #' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
-#' @seealso \code{\link{n_rsi}} to count cases with antimicrobial results.
+#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.\cr
+#' \code{\link{n_rsi}} to count all cases where antimicrobial results are available.
 #' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
 #' @return Double or, when \code{as_percent = TRUE}, a character.
 #' @rdname portion
@@ -135,7 +138,8 @@ portion_R <- function(ab1,
            ab2 = ab2,
            include_I = FALSE,
            minimum = minimum,
-           as_percent = as_percent)
+           as_percent = as_percent,
+           only_count = FALSE)
 }
 
 #' @rdname portion
@@ -149,7 +153,8 @@ portion_IR <- function(ab1,
            ab2 = ab2,
            include_I = TRUE,
            minimum = minimum,
-           as_percent = as_percent)
+           as_percent = as_percent,
+           only_count = FALSE)
 }
 
 #' @rdname portion
@@ -162,7 +167,8 @@ portion_I <- function(ab1,
            ab2 = NULL,
            include_I = FALSE,
            minimum = minimum,
-           as_percent = as_percent)
+           as_percent = as_percent,
+           only_count = FALSE)
 }
 
 #' @rdname portion
@@ -176,7 +182,8 @@ portion_SI <- function(ab1,
            ab2 = ab2,
            include_I = TRUE,
            minimum = minimum,
-           as_percent = as_percent)
+           as_percent = as_percent,
+           only_count = FALSE)
 }
 
 #' @rdname portion
@@ -190,13 +197,21 @@ portion_S <- function(ab1,
            ab2 = ab2,
            include_I = FALSE,
            minimum = minimum,
-           as_percent = as_percent)
+           as_percent = as_percent,
+           only_count = FALSE)
 }
 
 #' @rdname portion
-#' @importFrom dplyr bind_rows summarise_if mutate group_vars select everything
+#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
 #' @export
-portion_df <- function(data, translate_ab = getOption("get_antibiotic_names", "official")) {
+portion_df <- function(data,
+                       translate_ab = getOption("get_antibiotic_names", "official"),
+                       minimum = 30,
+                       as_percent = FALSE) {
+
+  if (data %>% select_if(is.rsi) %>% ncol() == 0) {
+    stop("No columns with class 'rsi' found. See ?as.rsi.")
+  }
 
   if (as.character(translate_ab) == "TRUE") {
     translate_ab <- "official"
@@ -205,19 +220,25 @@ portion_df <- function(data, translate_ab = getOption("get_antibiotic_names", "o
 
   resS <- summarise_if(.tbl = data,
                        .predicate = is.rsi,
-                       .funs = portion_S) %>%
+                       .funs = portion_S,
+                       minimum = minimum,
+                       as_percent = as_percent) %>%
     mutate(Interpretation = "S") %>%
     select(Interpretation, everything())
 
   resI <- summarise_if(.tbl = data,
                        .predicate = is.rsi,
-                       .funs = portion_I) %>%
+                       .funs = portion_I,
+                       minimum = minimum,
+                       as_percent = as_percent) %>%
     mutate(Interpretation = "I") %>%
     select(Interpretation, everything())
 
   resR <- summarise_if(.tbl = data,
                        .predicate = is.rsi,
-                       .funs = portion_R) %>%
+                       .funs = portion_R,
+                       minimum = minimum,
+                       as_percent = as_percent) %>%
     mutate(Interpretation = "R") %>%
     select(Interpretation, everything())
 
@@ -242,7 +263,8 @@ rsi_calc <- function(type,
                      ab2,
                      include_I,
                      minimum,
-                     as_percent) {
+                     as_percent,
+                     only_count) {
 
   if (NCOL(ab1) > 1) {
     stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
@@ -284,11 +306,6 @@ rsi_calc <- function(type,
     warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
   }
 
-  total <- length(x) - sum(is.na(x))
-  if (total < minimum) {
-    return(NA)
-  }
-
   if (type == "S") {
     found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
   } else if (type == "I") {
@@ -299,6 +316,15 @@ rsi_calc <- function(type,
     stop("invalid type")
   }
 
+  if (only_count == TRUE) {
+    return(found)
+  }
+
+  total <- length(x) - sum(is.na(x))
+  if (total < minimum) {
+    return(NA)
+  }
+
   if (as_percent == TRUE) {
     percent(found / total, force_zero = TRUE)
   } else {

README.md

@@ -67,7 +67,7 @@ With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you c
 **Read all changes and new functions in [NEWS.md](NEWS.md).**
 
 ## How to get it?
-This package [is published on CRAN](http://cran.r-project.org/package=AMR), the official R network.
+All versions of this package [are published on CRAN](http://cran.r-project.org/package=AMR), the official R network with a peer-reviewed submission process.
 
 ### Install from CRAN
 [![CRAN_Badge](https://www.r-pkg.org/badges/version/AMR)](http://cran.r-project.org/package=AMR) [![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)
@@ -87,10 +87,16 @@ This package [is published on CRAN](http://cran.r-project.org/package=AMR), the
 [![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg)](https://github.com/msberends/AMR/commits/master)
 [![Code_Coverage](https://codecov.io/gh/msberends/AMR/branch/master/graph/badge.svg)](https://codecov.io/gh/msberends/AMR)
 
-```r install.packages("devtools") 
+```r
+install.packages("devtools") 
 devtools::install_github("msberends/AMR")
 ```
 
+### Install from Zenodo
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1305355.svg)](https://doi.org/10.5281/zenodo.1305355)
+
+This package was also published on Zenodo: https://doi.org/10.5281/zenodo.1305355
+
 ## How to use it?
 ```r
 # Call it with:

man/as.rsi.Rd

@@ -3,11 +3,14 @@
 \name{as.rsi}
 \alias{as.rsi}
 \alias{is.rsi}
+\alias{is.rsi.eligible}
 \title{Class 'rsi'}
 \usage{
 as.rsi(x)
 
 is.rsi(x)
+
+is.rsi.eligible(x)
 }
 \arguments{
 \item{x}{vector}
@@ -18,6 +21,9 @@ Ordered factor with new class \code{rsi} and new attribute \code{package}
 \description{
 This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
 }
+\details{
+The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
+}
 \examples{
 rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
 rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
@@ -29,6 +35,12 @@ as.rsi("<= 0.002; S") # will return S
 plot(rsi_data)    # for percentages
 barplot(rsi_data) # for frequencies
 freq(rsi_data)    # frequency table with informative header
+
+# fastest way to transform all columns with already valid AB results to class `rsi`:
+library(dplyr)
+septic_patients \%>\%
+  mutate_if(is.rsi.eligible,
+            as.rsi)
 }
 \seealso{
 \code{\link{as.mic}}

man/count.Rd

@@ -0,0 +1,115 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/count.R
+\name{count}
+\alias{count}
+\alias{count_R}
+\alias{count_IR}
+\alias{count_I}
+\alias{count_SI}
+\alias{count_S}
+\alias{count_df}
+\title{Count isolates}
+\source{
+Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
+}
+\usage{
+count_R(ab1, ab2 = NULL)
+
+count_IR(ab1, ab2 = NULL)
+
+count_I(ab1)
+
+count_SI(ab1, ab2 = NULL)
+
+count_S(ab1, ab2 = NULL)
+
+count_df(data, translate_ab = getOption("get_antibiotic_names",
+  "official"))
+}
+\arguments{
+\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
+
+\item{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.}
+
+\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
+
+\item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.}
+}
+\value{
+Integer
+}
+\description{
+These functions can be used to count resistant/susceptible microbial isolates. All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+
+\code{count_R} and \code{count_IR} can be used to count resistant isolates, \code{count_S} and \code{count_SI} can be used to count susceptible isolates.\cr
+}
+\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.
+
+These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
+
+\code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
+}
+\examples{
+# septic_patients is a data set available in the AMR package. It is true, genuine data.
+?septic_patients
+
+# Count resistant isolates
+count_R(septic_patients$amox)
+count_IR(septic_patients$amox)
+
+# Or susceptibile isolates
+count_S(septic_patients$amox)
+count_SI(septic_patients$amox)
+
+# Since n_rsi counts available isolates, you can
+# calculate back to count e.g. non-susceptible isolates.
+# This results in the same:
+count_IR(septic_patients$amox)
+portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
+
+library(dplyr)
+septic_patients \%>\%
+  group_by(hospital_id) \%>\%
+  summarise(R = count_R(cipr),
+            I = count_I(cipr),
+            S = count_S(cipr),
+            n = n_rsi(cipr), # the actual total; sum of all three
+            total = n())     # NOT the amount of tested isolates!
+
+# Count co-resistance between amoxicillin/clav acid and gentamicin,
+# so we can see that combination therapy does a lot more than mono therapy.
+# Please mind that `portion_S` calculates percentages right away instead.
+count_S(septic_patients$amcl)   # S = 1056 (67.3\%)
+n_rsi(septic_patients$amcl)     # n = 1570
+
+count_S(septic_patients$gent)   # S = 1363 (74.0\%)
+n_rsi(septic_patients$gent)     # n = 1842
+
+with(septic_patients,
+     count_S(amcl, gent))       # S = 1385 (92.1\%)
+with(septic_patients,           # n = 1504
+     n_rsi(amcl, gent))
+
+# Get portions S/I/R immediately of all rsi columns
+septic_patients \%>\%
+  select(amox, cipr) \%>\%
+  count_df(translate = FALSE)
+
+# It also supports grouping variables
+septic_patients \%>\%
+  select(hospital_id, amox, cipr) \%>\%
+  group_by(hospital_id) \%>\%
+  count_df(translate = FALSE)
+
+}
+\seealso{
+\code{\link{portion}_*} to calculate microbial resistance and susceptibility.\cr
+\code{\link{n_rsi}} to count all cases where antimicrobial results are available.
+}
+\keyword{antibiotics}
+\keyword{isolate}
+\keyword{isolates}
+\keyword{resistance}
+\keyword{rsi}
+\keyword{susceptibility}

man/ggplot_rsi.Rd

@@ -9,12 +9,13 @@
 \alias{theme_rsi}
 \title{AMR bar plots with \code{ggplot}}
 \usage{
-ggplot_rsi(data, position = "stack", x = "Antibiotic",
+ggplot_rsi(data, position = NULL, x = "Antibiotic",
   fill = "Interpretation", facet = NULL, translate_ab = "official",
-  ...)
+  fun = portion_df, ...)
 
-geom_rsi(position = "stack", x = c("Antibiotic", "Interpretation"),
+geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
-  fill = "Interpretation", translate_ab = "official")
+  fill = "Interpretation", translate_ab = "official",
+  fun = portion_df)
 
 facet_rsi(facet = c("Interpretation", "Antibiotic"), ...)
 
@@ -27,7 +28,7 @@ theme_rsi()
 \arguments{
 \item{data}{a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})}
 
-\item{position}{position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}}
+\item{position}{position adjustment of bars, either \code{"stack"} (default when \code{fun} is \code{\link{portion_df}}) or \code{"dodge"} (default when \code{fun} is \code{\link{count_df}})}
 
 \item{x}{variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable}
 
@@ -37,6 +38,8 @@ theme_rsi()
 
 \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.}
 
+\item{fun}{function to transform \code{data}, either \code{\link{portion_df}} (default) or \code{\link{count_df}}}
+
 \item{...}{other parameters passed on to \code{\link[ggplot2]{facet_wrap}}}
 }
 \description{
@@ -46,7 +49,7 @@ Use these functions to create bar plots for antimicrobial resistance analysis. A
 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{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{portion_df}} at default, could also be \code{\link{count_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[ggplot2]{facet_wrap}}.
 
@@ -79,6 +82,11 @@ ggplot(df) +
 septic_patients \%>\%
   select(amox, nitr, fosf, trim, cipr) \%>\%
   ggplot_rsi()
+
+# get counts instead of percentages:
+septic_patients \%>\%
+  select(amox, nitr, fosf, trim, cipr) \%>\%
+  ggplot_rsi(fun = count_df)
 \donttest{
 # it also supports groups (don't forget to use the group on `x` or `facet`):
 septic_patients \%>\%

man/portion.Rd

@@ -26,7 +26,7 @@ portion_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
 portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
 
 portion_df(data, translate_ab = getOption("get_antibiotic_names",
-  "official"))
+  "official"), minimum = 30, as_percent = FALSE)
 }
 \arguments{
 \item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
@@ -35,9 +35,9 @@ portion_df(data, translate_ab = getOption("get_antibiotic_names",
 
 \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{as_percent}{logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.}
 
-\item{data}{a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
+\item{data}{a \code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})}
 
 \item{translate_ab}{a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.}
 }
@@ -52,6 +52,8 @@ 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.
 
+These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. If a column has been transformed with \code{\link{as.rsi}}, just use e.g. \code{isolates[isolates == "R"]} to get the resistant ones. You could then calculate the \code{\link{length}} of it.
+
 \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 \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) 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.
@@ -143,7 +145,8 @@ my_table \%>\%
 }
 }
 \seealso{
-\code{\link{n_rsi}} to count cases with antimicrobial results.
+\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates.\cr
+\code{\link{n_rsi}} to count all cases where antimicrobial results are available.
 }
 \keyword{antibiotics}
 \keyword{isolate}

man/rsi.Rd

@@ -16,7 +16,7 @@ rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
 
 \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{as_percent}{logical to indicate whether the output must be returned as a hundred fold with \% sign (a character). A value of \code{0.123456} will then be returned as \code{"12.3\%"}.}
 
 \item{...}{deprecated parameters to support usage on older versions}
 }

tests/testthat/test-ggplot_rsi.R

@@ -29,4 +29,13 @@ test_that("ggplot_rsi works", {
       summarise_all(portion_IR) %>% as.double()
   )
 
+  expect_equal(
+    (septic_patients %>% select(amcl, cipr) %>% ggplot_rsi(x = "Antibiotic",
+                                                           facet = "Interpretation",
+                                                           fun = count_df))$data %>%
+      summarise_all(count_IR) %>% as.double(),
+    septic_patients %>% select(amcl, cipr) %>%
+      summarise_all(count_IR) %>% as.double()
+  )
+
 })