quasiquotation, alpha for geom_rsi

2025-10-31 06:08:14 +01:00 · 2018-08-23 00:40:36 +02:00
parent 43ba16f8ed
commit da5379c881
18 changed files with 304 additions and 235 deletions
--- a/8
+++ b/8
@@ -1,6 +1,6 @@
 Package: AMR
-Version: 0.3.0.9001
-Date: 2018-08-21
+Version: 0.3.0.9002
+Date: 2018-08-23
 Title: Antimicrobial Resistance Analysis
 Authors@R: c(
    person(
@@ -52,8 +52,8 @@ Imports:
    xml2 (>= 1.0.0),
    knitr (>= 1.0.0),
    readr,
-    rvest (>= 0.3.2),
-    tibble
+    rlang,
+    rvest (>= 0.3.2)
 Suggests:
    testthat (>= 1.0.2),
    covr (>= 3.0.1),
--- a/2
+++ b/2
@@ -127,6 +127,7 @@ importFrom(dplyr,arrange)
 importFrom(dplyr,arrange_at)
 importFrom(dplyr,as_tibble)
 importFrom(dplyr,between)
+importFrom(dplyr,bind_cols)
 importFrom(dplyr,bind_rows)
 importFrom(dplyr,case_when)
 importFrom(dplyr,desc)
@@ -171,7 +172,6 @@ importFrom(stats,mad)
 importFrom(stats,pchisq)
 importFrom(stats,predict)
 importFrom(stats,sd)
-importFrom(tibble,tibble)
 importFrom(utils,View)
 importFrom(utils,browseVignettes)
 importFrom(utils,installed.packages)
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,13 +1,19 @@
 # 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
+* Functions `count_R`, `count_IR`, `count_I`, `count_SI` and `count_S` to selectively count resistant or susceptible 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_if(is.rsi.eligible, as.rsi)`

 #### Changed
 * Added parameters `minimum` and `as_percent` to `portion_df`
+* Support for quasiquotation in the functions series `count_*` and `portions_*`, and `n_rsi`. This allow to check for more than 2 vectors or columns.
+  * `septic_patients %>% select(amox, cipr) %>% count_R()`
+  * `septic_patients %>% portion_S(amcl)`
+  * `septic_patients %>% portion_S(amcl, gent)`
+  * `septic_patients %>% portion_S(amcl, gent, pita)`
 * 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
+* Fix for `ggplot_rsi` when the `ggplot2` package was not loaded
+* Added parameter `alpha` to `ggplot_rsi` and `geom_rsi`

 # 0.3.0 (latest stable version)
 **Published on CRAN: 2018-08-14**
--- a/R/count.R
+++ b/R/count.R
@@ -18,7 +18,7 @@

 #' 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}.
+#' @description These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, 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
@@ -87,11 +87,9 @@
 #'   group_by(hospital_id) %>%
 #'   count_df(translate = FALSE)
 #'
-count_R <- function(ab1,
-                    ab2 = NULL) {
-  rsi_calc(type = "R",
-           ab1 = ab1,
-           ab2 = ab2,
+count_R <- function(...) {
+  rsi_calc(...,
+           type = "R",
           include_I = FALSE,
           minimum = 0,
           as_percent = FALSE,
@@ -100,11 +98,9 @@ count_R <- function(ab1,

 #' @rdname count
 #' @export
-count_IR <- function(ab1,
-                     ab2 = NULL) {
-  rsi_calc(type = "R",
-           ab1 = ab1,
-           ab2 = ab2,
+count_IR <- function(...) {
+  rsi_calc(...,
+           type = "R",
           include_I = TRUE,
           minimum = 0,
           as_percent = FALSE,
@@ -113,10 +109,9 @@ count_IR <- function(ab1,

 #' @rdname count
 #' @export
-count_I <- function(ab1) {
-  rsi_calc(type = "I",
-           ab1 = ab1,
-           ab2 = NULL,
+count_I <- function(...) {
+  rsi_calc(...,
+           type = "I",
           include_I = FALSE,
           minimum = 0,
           as_percent = FALSE,
@@ -125,11 +120,9 @@ count_I <- function(ab1) {

 #' @rdname count
 #' @export
-count_SI <- function(ab1,
-                     ab2 = NULL) {
-  rsi_calc(type = "S",
-           ab1 = ab1,
-           ab2 = ab2,
+count_SI <- function(...) {
+  rsi_calc(...,
+           type = "S",
           include_I = TRUE,
           minimum = 0,
           as_percent = FALSE,
@@ -138,11 +131,9 @@ count_SI <- function(ab1,

 #' @rdname count
 #' @export
-count_S <- function(ab1,
-                    ab2 = NULL) {
-  rsi_calc(type = "S",
-           ab1 = ab1,
-           ab2 = ab2,
+count_S <- function(...) {
+  rsi_calc(...,
+           type = "S",
           include_I = FALSE,
           minimum = 0,
           as_percent = FALSE,
--- a/R/freq.R
+++ b/R/freq.R
@@ -55,9 +55,8 @@
 #' The function \code{top_freq} uses \code{\link[dplyr]{top_n}} internally and will include more than \code{n} rows if there are ties.
 #' @importFrom stats fivenum sd mad
 #' @importFrom grDevices boxplot.stats
-#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct
+#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct tibble
 #' @importFrom utils browseVignettes installed.packages
-#' @importFrom tibble tibble
 #' @keywords summary summarise frequency freq
 #' @rdname freq
 #' @name freq
@@ -378,12 +377,12 @@ frequency_tbl <- function(x,
  column_names_df <- c('item', 'count', 'percent', 'cum_count', 'cum_percent', 'factor_level')

  if (any(class(x) == 'factor')) {
-    df <- tibble::tibble(item = x,
+    df <- tibble(item = x,
                         fctlvl = x %>% as.integer()) %>%
      group_by(item, fctlvl)
    column_align <- c('l', 'r', 'r', 'r', 'r', 'r')
  } else {
-    df <- tibble::tibble(item = x) %>%
+    df <- tibble(item = x) %>%
      group_by(item)
    # strip factor lvl from col names
    column_names <- column_names[1:length(column_names) - 1]
--- a/R/ggplot_rsi.R
+++ b/R/ggplot_rsi.R
@@ -25,6 +25,7 @@
 #' @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 alpha opacity of the fill colours
 #' @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)}.
@@ -113,6 +114,7 @@ ggplot_rsi <- function(data,
                       fill = "Interpretation",
                       facet = NULL,
                       translate_ab = "official",
+                       alpha = 1,
                       fun = portion_df,
                       ...) {

@@ -126,7 +128,7 @@ ggplot_rsi <- function(data,
  }

  p <- ggplot2::ggplot(data = data) +
-    geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, fun = fun) +
+    geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab, alpha = alpha, fun = fun) +
    theme_rsi()

  if (fill == "Interpretation") {
@@ -151,6 +153,7 @@ geom_rsi <- function(position = NULL,
                     x = c("Antibiotic", "Interpretation"),
                     fill = "Interpretation",
                     translate_ab = "official",
+                     alpha = 1,
                     fun = portion_df)  {

  fun_name <- deparse(substitute(fun))
@@ -180,7 +183,7 @@ geom_rsi <- function(position = NULL,

  ggplot2::layer(geom = "bar", stat = "identity", position = position,
                 mapping = ggplot2::aes_string(x = x, y = y, fill = fill),
-                 data = fun, params = list())
+                 data = fun, params = list(alpha = alpha))

 }

--- a/R/n_rsi.R
+++ b/R/n_rsi.R
@@ -18,10 +18,11 @@

 #' Count cases with antimicrobial results
 #'
-#' This counts all cases where antimicrobial interpretations are available. Its use is equal to \code{\link{n_distinct}}.
-#' @param ab1,ab2 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
+#' This counts all cases where antimicrobial interpretations are available. The way it can be used is equal to \code{\link{n_distinct}}. Its function is equal to \code{count_S(...) + count_IR(...)}.
+#' @inheritParams portion
 #' @export
-#' @seealso The \code{\link{portion}} functions to calculate resistance and susceptibility.
+#' @seealso \code{\link[AMR]{count}_*} to count resistant and susceptibile isolates per interpretation type.\cr
+#' \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
 #' @examples
 #' library(dplyr)
 #'
@@ -33,22 +34,7 @@
 #'             genta_n = n_rsi(gent),
 #'             combination_p = portion_S(cipr, gent, as_percent = TRUE),
 #'             combination_n = n_rsi(cipr, gent))
-n_rsi <- function(ab1, ab2 = NULL) {
-  if (NCOL(ab1) > 1) {
-    stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
-  }
-  if (!is.rsi(ab1)) {
-    ab1 <- as.rsi(ab1)
-  }
-  if (!is.null(ab2)) {
-    if (NCOL(ab2) > 1) {
-      stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
-    }
-    if (!is.rsi(ab2)) {
-      ab2 <- as.rsi(ab2)
-    }
-    sum(!is.na(ab1) & !is.na(ab2))
-  } else {
-    sum(!is.na(ab1))
-  }
+n_rsi <- function(...) {
+  # only print warnings once, if needed
+  count_S(...) + suppressWarnings(count_IR(...))
 }
--- a/R/portion.R
+++ b/R/portion.R
@@ -18,11 +18,10 @@

 #' Calculate resistance of isolates
 #'
-#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
 #'
 #' \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
-#' @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 ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns 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 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}})
@@ -43,8 +42,10 @@
 #'   For two antibiotics:
 #'   \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
 #'   \cr
-#'   Theoretically for three antibiotics:
+#'   For three antibiotics:
 #'   \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
+#'   \cr
+#'   And so on.
 #' }
 #' @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/}.
 #'
@@ -68,11 +69,14 @@
 #' portion_S(septic_patients$amox)
 #' portion_SI(septic_patients$amox)
 #'
-#' # Since n_rsi counts available isolates (and is used as denominator),
-#' # you can calculate back to count e.g. non-susceptible isolates:
-#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
-#'
+
+#' # Do the above with pipes:
 #' library(dplyr)
+#' septic_patients %>% portion_R(amox)
+#' septic_patients %>% portion_IR(amox)
+#' septic_patients %>% portion_S(amox)
+#' septic_patients %>% portion_SI(amox)
+#'
 #' septic_patients %>%
 #'   group_by(hospital_id) %>%
 #'   summarise(p = portion_S(cipr),
@@ -88,16 +92,15 @@
 #'
 #' # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
 #' # so we can see that combination therapy does a lot more than mono therapy:
-#' portion_S(septic_patients$amcl) # S = 67.3%
-#' n_rsi(septic_patients$amcl)     # n = 1570
+#' septic_patients %>% portion_S(amcl)       # S = 67.3%
+#' septic_patients %>% n_rsi(amcl)           # n = 1570
 #'
-#' portion_S(septic_patients$gent) # S = 74.0%
-#' n_rsi(septic_patients$gent)     # n = 1842
+#' septic_patients %>% portion_S(gent)       # S = 74.0%
+#' septic_patients %>% n_rsi(gent)           # n = 1842
+#'
+#' septic_patients %>% portion_S(amcl, gent) # S = 92.1%
+#' septic_patients %>% n_rsi(amcl, gent)     # n = 1504
 #'
-#' with(septic_patients,
-#'      portion_S(amcl, gent))     # S = 92.1%
-#' with(septic_patients,           # n = 1504
-#'      n_rsi(amcl, gent))
 #'
 #' septic_patients %>%
 #'   group_by(hospital_id) %>%
@@ -129,13 +132,11 @@
 #'   summarise(p = portion_S(amox, metr),  # amoxicillin with metronidazole
 #'             n = n_rsi(amox, metr))
 #' }
-portion_R <- function(ab1,
-                      ab2 = NULL,
+portion_R <- function(...,
                      minimum = 30,
                      as_percent = FALSE) {
-  rsi_calc(type = "R",
-           ab1 = ab1,
-           ab2 = ab2,
+  rsi_calc(...,
+           type = "R",
           include_I = FALSE,
           minimum = minimum,
           as_percent = as_percent,
@@ -144,13 +145,11 @@ portion_R <- function(ab1,

 #' @rdname portion
 #' @export
-portion_IR <- function(ab1,
-                       ab2 = NULL,
+portion_IR <- function(...,
                       minimum = 30,
                       as_percent = FALSE) {
-  rsi_calc(type = "R",
-           ab1 = ab1,
-           ab2 = ab2,
+  rsi_calc(...,
+           type = "R",
           include_I = TRUE,
           minimum = minimum,
           as_percent = as_percent,
@@ -159,12 +158,11 @@ portion_IR <- function(ab1,

 #' @rdname portion
 #' @export
-portion_I <- function(ab1,
+portion_I <- function(...,
                      minimum = 30,
                      as_percent = FALSE) {
-  rsi_calc(type = "I",
-           ab1 = ab1,
-           ab2 = NULL,
+  rsi_calc(...,
+           type = "I",
           include_I = FALSE,
           minimum = minimum,
           as_percent = as_percent,
@@ -173,13 +171,11 @@ portion_I <- function(ab1,

 #' @rdname portion
 #' @export
-portion_SI <- function(ab1,
-                       ab2 = NULL,
+portion_SI <- function(...,
                       minimum = 30,
                       as_percent = FALSE) {
-  rsi_calc(type = "S",
-           ab1 = ab1,
-           ab2 = ab2,
+  rsi_calc(...,
+           type = "S",
           include_I = TRUE,
           minimum = minimum,
           as_percent = as_percent,
@@ -188,13 +184,11 @@ portion_SI <- function(ab1,

 #' @rdname portion
 #' @export
-portion_S <- function(ab1,
-                      ab2 = NULL,
+portion_S <- function(...,
                      minimum = 30,
                      as_percent = FALSE) {
-  rsi_calc(type = "S",
-           ab1 = ab1,
-           ab2 = ab2,
+  rsi_calc(...,
+           type = "S",
           include_I = FALSE,
           minimum = minimum,
           as_percent = as_percent,
@@ -257,77 +251,3 @@ portion_df <- function(data,

  res
 }
-
-rsi_calc <- function(type,
-                     ab1,
-                     ab2,
-                     include_I,
-                     minimum,
-                     as_percent,
-                     only_count) {
-
-  if (NCOL(ab1) > 1) {
-    stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
-  }
-  if (!is.logical(include_I)) {
-    stop('`include_I` must be logical', call. = FALSE)
-  }
-  if (!is.numeric(minimum)) {
-    stop('`minimum` must be numeric', call. = FALSE)
-  }
-  if (!is.logical(as_percent)) {
-    stop('`as_percent` must be logical', call. = FALSE)
-  }
-
-  print_warning <- FALSE
-  if (!is.rsi(ab1)) {
-    ab1 <- as.rsi(ab1)
-    print_warning <- TRUE
-  }
-  if (!is.null(ab2)) {
-    # ab_name <- paste(deparse(substitute(ab1)), "and", deparse(substitute(ab2)))
-    if (NCOL(ab2) > 1) {
-      stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
-    }
-    if (!is.rsi(ab2)) {
-      ab2 <- as.rsi(ab2)
-      print_warning <- TRUE
-    }
-    x <- apply(X = data.frame(ab1 = as.integer(ab1),
-                              ab2 = as.integer(ab2)),
-               MARGIN = 1,
-               FUN = min)
-  } else {
-    x <- ab1
-    # ab_name <- deparse(substitute(ab1))
-  }
-
-  if (print_warning == TRUE) {
-    warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
-  }
-
-  if (type == "S") {
-    found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
-  } else if (type == "I") {
-    found <- sum(as.integer(x) == 2, na.rm = TRUE)
-  } else if (type == "R") {
-    found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
-  } else {
-    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 {
-    found / total
-  }
-}
--- a/R/rsi.R
+++ b/R/rsi.R
@@ -20,9 +20,10 @@
 #'
 #' This function is deprecated. Use the \code{\link{portion}} functions instead.
 #' @inheritParams portion
+#' @param ab1,ab2  vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.
 #' @param interpretation antimicrobial interpretation to check for
 #' @param ... deprecated parameters to support usage on older versions
-#' @importFrom dplyr case_when
+#' @importFrom dplyr tibble case_when
 #' @export
 rsi <- function(ab1,
                ab2 = NULL,
@@ -31,12 +32,19 @@ rsi <- function(ab1,
                as_percent = FALSE,
                ...) {

+  if (all(is.null(ab2))) {
+    df <- tibble(ab1 = ab1)
+  } else {
+    df <- tibble(ab1 = ab1,
+                 ab2 = ab2)
+  }
+
  result <- case_when(
-    interpretation == "S"             ~ portion_S(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
-    interpretation %in% c("SI", "IS") ~ portion_SI(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
-    interpretation == "I"             ~ portion_I(ab1 = ab1, minimum = minimum, as_percent = FALSE),
-    interpretation %in% c("RI", "IR") ~ portion_IR(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
-    interpretation == "R"             ~ portion_R(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
+    interpretation == "S"             ~ portion_S(df, minimum = minimum, as_percent = FALSE),
+    interpretation %in% c("SI", "IS") ~ portion_SI(df, minimum = minimum, as_percent = FALSE),
+    interpretation == "I"             ~ portion_I(df, minimum = minimum, as_percent = FALSE),
+    interpretation %in% c("RI", "IR") ~ portion_IR(df, minimum = minimum, as_percent = FALSE),
+    interpretation == "R"             ~ portion_R(df, minimum = minimum, as_percent = FALSE),
    TRUE ~ -1
  )
  if (result == -1) {
--- a/R/rsi_calc.R
+++ b/R/rsi_calc.R
@@ -0,0 +1,115 @@
+# ==================================================================== #
+# 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.                         #
+# ==================================================================== #
+
+#' @importFrom dplyr %>% bind_cols pull
+rsi_calc <- function(...,
+                     type,
+                     include_I,
+                     minimum,
+                     as_percent,
+                     only_count) {
+
+  if (!is.logical(include_I)) {
+    stop('`include_I` must be logical', call. = FALSE)
+  }
+  if (!is.numeric(minimum)) {
+    stop('`minimum` must be numeric', call. = FALSE)
+  }
+  if (!is.logical(as_percent)) {
+    stop('`as_percent` must be logical', call. = FALSE)
+  }
+
+  dots_length <- ...length()
+  dots <- ...elt(1) # it needs this evaluation
+  dots <- rlang::exprs(...) # or this will be a list without actual values
+
+  if ("data.frame" %in% class(dots[[1]]) & dots_length > 1) {
+    # data.frame passed with other columns, like:
+    #   septic_patients %>% portion_S(amcl, gent)
+    df <- dots[[1]]
+    dots_df <- data.frame(col1 = df[,1])
+    for (i in 2:dots_length) {
+      dots_col <- as.character(dots[[i]])
+      if (!dots_col %in% colnames(df)) {
+        stop("variable not found: ", dots_col)
+      }
+      dots_df <- dots_df %>% bind_cols(data.frame(df %>% pull(dots_col)))
+    }
+    x <- dots_df[, -1]
+  } else if (dots_length == 1) {
+    # only 1 variable passed (count also be data.frame), like:
+    #   portion_S(septic_patients$amcl)
+    #   septic_patients$amcl %>% portion_S()
+    x <- dots[[1]]
+  } else {
+    # multiple variables passed without pipe, like:
+    #   portion_S(septic_patients$amcl, septic_patients$gent)
+    #   with(septic_patients, portion_S(amcl, gent))
+    x <- as.data.frame(rlang::list2(...))
+  }
+
+  print_warning <- FALSE
+  # check integrity of columns: force rsi class
+  if (is.data.frame(x)) {
+    for (i in 1:ncol(x)) {
+      if (!is.rsi(x %>% pull(i))) {
+        x[, i] <- as.rsi(x[, i])
+        print_warning <- TRUE
+      }
+      x[, i] <- x %>% pull(i) %>% as.integer()
+    }
+    x <- apply(X = x,
+               MARGIN = 1,
+               FUN = min)
+  } else {
+    if (!is.rsi(x)) {
+      x <- as.rsi(x)
+      print_warning <- TRUE
+    }
+  }
+
+  if (print_warning == TRUE) {
+    warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_if(is.rsi.eligible, as.rsi)",
+            call. = FALSE)
+  }
+
+  if (type == "S") {
+    found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
+  } else if (type == "I") {
+    found <- sum(as.integer(x) == 2, na.rm = TRUE)
+  } else if (type == "R") {
+    found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
+  } else {
+    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 {
+    found / total
+  }
+}
--- a/man/count.Rd
+++ b/man/count.Rd
@@ -13,23 +13,21 @@
 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_R(...)

-count_IR(ab1, ab2 = NULL)
+count_IR(...)

-count_I(ab1)
+count_I(...)

-count_SI(ab1, ab2 = NULL)
+count_SI(...)

-count_S(ab1, ab2 = NULL)
+count_S(...)

 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{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns 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}})}

@@ -39,7 +37,7 @@ count_df(data, translate_ab = getOption("get_antibiotic_names",
 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}.
+These functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, 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
 }
--- a/man/ggplot_rsi.Rd
+++ b/man/ggplot_rsi.Rd
@@ -11,10 +11,10 @@
 \usage{
 ggplot_rsi(data, position = NULL, x = "Antibiotic",
  fill = "Interpretation", facet = NULL, translate_ab = "official",
-  fun = portion_df, ...)
+  alpha = 1, fun = portion_df, ...)

 geom_rsi(position = NULL, x = c("Antibiotic", "Interpretation"),
-  fill = "Interpretation", translate_ab = "official",
+  fill = "Interpretation", translate_ab = "official", alpha = 1,
  fun = portion_df)

 facet_rsi(facet = c("Interpretation", "Antibiotic"), ...)
@@ -38,6 +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{alpha}{opacity of the fill colours}
+
 \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}}}
--- a/man/n_rsi.Rd
+++ b/man/n_rsi.Rd
@@ -4,13 +4,13 @@
 \alias{n_rsi}
 \title{Count cases with antimicrobial results}
 \usage{
-n_rsi(ab1, ab2 = NULL)
+n_rsi(...)
 }
 \arguments{
-\item{ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed}
+\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}
 }
 \description{
-This counts all cases where antimicrobial interpretations are available. Its use is equal to \code{\link{n_distinct}}.
+This counts all cases where antimicrobial interpretations are available. The way it can be used is equal to \code{\link{n_distinct}}. Its function is equal to \code{count_S(...) + count_IR(...)}.
 }
 \examples{
 library(dplyr)
@@ -25,5 +25,6 @@ septic_patients \%>\%
            combination_n = n_rsi(cipr, gent))
 }
 \seealso{
-The \code{\link{portion}} functions to calculate resistance and susceptibility.
+\code{\link[AMR]{count}_*} to count resistant and susceptibile isolates per interpretation type.\cr
+\code{\link{portion}_*} to calculate microbial resistance and susceptibility.
 }
--- a/man/portion.Rd
+++ b/man/portion.Rd
@@ -15,23 +15,21 @@
 Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
 }
 \usage{
-portion_R(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+portion_R(..., minimum = 30, as_percent = FALSE)

-portion_IR(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+portion_IR(..., minimum = 30, as_percent = FALSE)

-portion_I(ab1, minimum = 30, as_percent = FALSE)
+portion_I(..., minimum = 30, as_percent = FALSE)

-portion_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+portion_SI(..., minimum = 30, as_percent = FALSE)

-portion_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
+portion_S(..., minimum = 30, as_percent = FALSE)

 portion_df(data, translate_ab = getOption("get_antibiotic_names",
  "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}
-
-\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{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.}

 \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.}

@@ -45,7 +43,7 @@ portion_df(data, translate_ab = getOption("get_antibiotic_names",
 Double or, when \code{as_percent = TRUE}, a character.
 }
 \description{
-These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
+These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.

 \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
 }
@@ -66,8 +64,10 @@ The old \code{\link{rsi}} function is still available for backwards compatibilit
  For two antibiotics:
  \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
  \cr
-  Theoretically for three antibiotics:
+  For three antibiotics:
  \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
+  \cr
+  And so on.
 }
 }
 \examples{
@@ -82,11 +82,13 @@ portion_IR(septic_patients$amox)
 portion_S(septic_patients$amox)
 portion_SI(septic_patients$amox)

-# Since n_rsi counts available isolates (and is used as denominator),
-# you can calculate back to count e.g. non-susceptible isolates:
-portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
-
+# Do the above with pipes:
 library(dplyr)
+septic_patients \%>\% portion_R(amox)
+septic_patients \%>\% portion_IR(amox)
+septic_patients \%>\% portion_S(amox)
+septic_patients \%>\% portion_SI(amox)
+
 septic_patients \%>\%
  group_by(hospital_id) \%>\%
  summarise(p = portion_S(cipr),
@@ -102,16 +104,15 @@ septic_patients \%>\%

 # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
 # so we can see that combination therapy does a lot more than mono therapy:
-portion_S(septic_patients$amcl) # S = 67.3\%
-n_rsi(septic_patients$amcl)     # n = 1570
+septic_patients \%>\% portion_S(amcl)       # S = 67.3\%
+septic_patients \%>\% n_rsi(amcl)           # n = 1570

-portion_S(septic_patients$gent) # S = 74.0\%
-n_rsi(septic_patients$gent)     # n = 1842
+septic_patients \%>\% portion_S(gent)       # S = 74.0\%
+septic_patients \%>\% n_rsi(gent)           # n = 1842
+
+septic_patients \%>\% portion_S(amcl, gent) # S = 92.1\%
+septic_patients \%>\% n_rsi(amcl, gent)     # n = 1504

-with(septic_patients,
-     portion_S(amcl, gent))     # S = 92.1\%
-with(septic_patients,           # n = 1504
-     n_rsi(amcl, gent))

 septic_patients \%>\%
  group_by(hospital_id) \%>\%
--- a/man/rsi.Rd
+++ b/man/rsi.Rd
@@ -8,9 +8,7 @@ rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
  as_percent = FALSE, ...)
 }
 \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{ab1, ab2}{vector (or column) with antibiotic interpretations. It will be transformed internally with \code{\link{as.rsi}} if needed.}

 \item{interpretation}{antimicrobial interpretation to check for}

--- a/tests/testthat/test-atc.R
+++ b/tests/testthat/test-atc.R
@@ -1,8 +1,7 @@
 context("atc.R")

 test_that("atc_property works", {
-  skip_on_travis() # relies on internet connection of server, don't test
-
+  skip_on_cran() # relies on internet connection of server, don't test
  skip_on_appveyor() # security error on AppVeyor

  if (!is.null(curl::nslookup("www.whocc.no", error = FALSE))) {
--- a/tests/testthat/test-count.R
+++ b/tests/testthat/test-count.R
@@ -0,0 +1,41 @@
+context("count.R")
+
+test_that("counts work", {
+  # amox resistance in `septic_patients`
+  expect_equal(count_R(septic_patients$amox), 659)
+  expect_equal(count_I(septic_patients$amox), 3)
+  expect_equal(count_S(septic_patients$amox), 336)
+  expect_equal(count_R(septic_patients$amox) + count_I(septic_patients$amox),
+               count_IR(septic_patients$amox))
+  expect_equal(count_S(septic_patients$amox) + count_I(septic_patients$amox),
+               count_SI(septic_patients$amox))
+
+  expect_equal(septic_patients %>% count_S(amcl), 1056)
+  expect_equal(septic_patients %>% count_S(amcl, gent), 1385)
+
+  # count of cases
+  expect_equal(septic_patients %>%
+                 group_by(hospital_id) %>%
+                 summarise(cipro = count_S(cipr),
+                           genta = count_S(gent),
+                           combination = count_S(cipr, gent)) %>%
+                 pull(combination),
+               c(192, 440, 184, 474))
+
+  # warning for speed loss
+  expect_warning(count_R(as.character(septic_patients$amcl)))
+  expect_warning(count_I(as.character(septic_patients$amcl)))
+  expect_warning(count_S(as.character(septic_patients$amcl,
+                                      septic_patients$gent)))
+  expect_warning(count_S(septic_patients$amcl,
+                         as.character(septic_patients$gent)))
+
+  # check for errors
+  expect_error(count_IR("test", minimum = "test"))
+  expect_error(count_IR("test", as_percent = "test"))
+  expect_error(count_I("test", minimum = "test"))
+  expect_error(count_I("test", as_percent = "test"))
+  expect_error(count_S("test", minimum = "test"))
+  expect_error(count_S("test", as_percent = "test"))
+
+})
--- a/tests/testthat/test-portion.R
+++ b/tests/testthat/test-portion.R
@@ -11,12 +11,19 @@ test_that("portions works", {
  expect_equal(portion_S(septic_patients$amox) + portion_I(septic_patients$amox),
               portion_SI(septic_patients$amox))

-  # pita+genta susceptibility around 98.09%
-  expect_equal(suppressWarnings(rsi(septic_patients$pita,
+  expect_equal(septic_patients %>% portion_S(amcl),
+               0.673,
+               tolerance = 0.001)
+  expect_equal(septic_patients %>% portion_S(amcl, gent),
+               0.921,
+               tolerance = 0.001)
+
+  # amcl+genta susceptibility around 92.1%
+  expect_equal(suppressWarnings(rsi(septic_patients$amcl,
                                    septic_patients$gent,
                                    interpretation = "S")),
-               0.9535,
-               tolerance = 0.0001)
+               0.9208777,
+               tolerance = 0.000001)

  # percentages
  expect_equal(septic_patients %>%
@@ -46,25 +53,19 @@ test_that("portions works", {
  expect_warning(portion_S(as.character(septic_patients$amcl)))
  expect_warning(portion_S(as.character(septic_patients$amcl,
                                             septic_patients$gent)))
-  expect_equal(n_rsi(as.character(septic_patients$amcl,
-                                  septic_patients$gent)),
+  expect_warning(n_rsi(as.character(septic_patients$amcl,
+                                    septic_patients$gent)))
+  expect_equal(suppressWarnings(n_rsi(as.character(septic_patients$amcl,
+                                                   septic_patients$gent))),
               1570)

-
  # check for errors
-  expect_error(portion_IR(septic_patients %>% select(amox, amcl)))
  expect_error(portion_IR("test", minimum = "test"))
  expect_error(portion_IR("test", as_percent = "test"))
-  expect_error(portion_I(septic_patients %>% select(amox, amcl)))
  expect_error(portion_I("test", minimum = "test"))
  expect_error(portion_I("test", as_percent = "test"))
  expect_error(portion_S("test", minimum = "test"))
  expect_error(portion_S("test", as_percent = "test"))
-  expect_error(portion_S(septic_patients %>% select(amox, amcl)))
-  expect_error(portion_S("R", septic_patients %>% select(amox, amcl)))
-  expect_error(n_rsi(septic_patients %>% select(amox, amcl)))
-  expect_error(n_rsi(septic_patients$amox, septic_patients %>% select(amox, amcl)))
-

  # check too low amount of isolates
  expect_identical(portion_R(septic_patients$amox, minimum = nrow(septic_patients) + 1),