AMR/R/count.R

# ==================================================================== #
# TITLE                                                                #
# Antimicrobial Resistance (AMR) Analysis                              #
#                                                                      #
# SOURCE                                                               #
# https://gitlab.com/msberends/AMR                                     #
#                                                                      #
# LICENCE                                                              #
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)  #
#                                                                      #
# This R package is free software; you can freely use and distribute   #
# it for both personal and commercial purposes under the terms of the  #
# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
# the Free Software Foundation.                                        #
#                                                                      #
# This R package was created for academic research and was publicly    #
# released in the hope that it will be useful, but it comes WITHOUT    #
# ANY WARRANTY OR LIABILITY.                                           #
# Visit our website for more info: https://msberends.gitlab.io/AMR.    #
# ==================================================================== #

#' Count isolates
#'
#' @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_resistant()} should be used to count resistant isolates, \code{count_susceptible()} should be used to count susceptible isolates.\cr
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.
#' @inheritParams proportion
#' @inheritSection as.rsi Interpretation of S, I and R
#' @details These functions are meant to count isolates. Use the \code{\link{resistance}}/\code{\link{susceptibility}} functions to calculate microbial resistance/susceptibility.
#' 
#' The function \code{count_resistant()} is equal to the function \code{count_R()}. The function \code{count_susceptible()} is equal to the function \code{count_SI()}.
#'
#' The function \code{n_rsi()} is an alias of \code{count_all()}. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to \code{\link{n_distinct}()}. Their function is equal to \code{count_susceptible(...) + count_resistant(...)}.
#'
#' The function \code{count_df()} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}()}) and counts the number of S's, I's and R's. The function \code{rsi_df()} works exactly like \code{count_df()}, but adds the percentage of S, I and R.
#' @inheritSection proportion Combination therapy
#' @seealso \code{\link{proportion}_*} to calculate microbial resistance and susceptibility.
#' @return Integer
#' @rdname count
#' @name count
#' @export
#' @inheritSection AMR Read more on our website!
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' ?example_isolates
#' 
#' count_resistant(example_isolates$AMX)   # counts "R"
#' count_susceptible(example_isolates$AMX) # counts "S" and "I"
#' count_all(example_isolates$AMX)         # counts "S", "I" and "R"
#'
#' # be more specific
#' count_S(example_isolates$AMX)
#' count_SI(example_isolates$AMX)
#' count_I(example_isolates$AMX)
#' count_IR(example_isolates$AMX)
#' count_R(example_isolates$AMX)
#'
#' # Count all available isolates
#' count_all(example_isolates$AMX)
#' n_rsi(example_isolates$AMX)
#'
#' # n_rsi() is an alias of count_all().
#' # Since it counts all available isolates, you can
#' # calculate back to count e.g. susceptible isolates.
#' # These results are the same:
#' count_susceptible(example_isolates$AMX)
#' susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX)
#'
#' library(dplyr)
#' example_isolates %>%
#'   group_by(hospital_id) %>%
#'   summarise(R  = count_R(CIP),
#'             I  = count_I(CIP),
#'             S  = count_S(CIP),
#'             n1 = count_all(CIP),  # the actual total; sum of all three
#'             n2 = n_rsi(CIP),      # same - analogous to n_distinct
#'             total = n())          # NOT the number 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 `susceptibility()` calculates percentages right away instead.
#' example_isolates %>% count_susceptible(AMC) # 1433
#' example_isolates %>% count_all(AMC)         # 1879
#'
#' example_isolates %>% count_susceptible(GEN) # 1399
#' example_isolates %>% count_all(GEN)         # 1855
#'
#' example_isolates %>% count_susceptible(AMC, GEN) # 1764
#' example_isolates %>% count_all(AMC, GEN)         # 1936

#' # Get number of S+I vs. R immediately of selected columns
#' example_isolates %>%
#'   select(AMX, CIP) %>%
#'   count_df(translate = FALSE)
#'
#' # It also supports grouping variables
#' example_isolates %>%
#'   select(hospital_id, AMX, CIP) %>%
#'   group_by(hospital_id) %>%
#'   count_df(translate = FALSE)
#'
count_resistant <- function(..., only_all_tested = FALSE) {
  rsi_calc(...,
           ab_result = "R",
           only_all_tested = only_all_tested,
           only_count = TRUE)
}

#' @rdname count
#' @export
count_susceptible <- function(..., only_all_tested = FALSE) {
  rsi_calc(...,
           ab_result = c("S", "I"),
           only_all_tested = only_all_tested,
           only_count = TRUE)
}

#' @rdname count
#' @export
count_R <- function(..., only_all_tested = FALSE) {
  rsi_calc(...,
           ab_result = "R",
           only_all_tested = only_all_tested,
           only_count = TRUE)
}

#' @rdname count
#' @export
count_IR <- function(..., only_all_tested = FALSE) {
  warning("Using 'count_IR' is discouraged; use 'count_resistant()' instead to not consider \"I\" being resistant.", call. = FALSE)
  rsi_calc(...,
           ab_result = c("I", "R"),
           only_all_tested = only_all_tested,
           only_count = TRUE)
}

#' @rdname count
#' @export
count_I <- function(..., only_all_tested = FALSE) {
  rsi_calc(...,
           ab_result = "I",
           only_all_tested = only_all_tested,
           only_count = TRUE)
}

#' @rdname count
#' @export
count_SI <- function(..., only_all_tested = FALSE) {
  rsi_calc(...,
           ab_result = c("S", "I"),
           only_all_tested = only_all_tested,
           only_count = TRUE)
}

#' @rdname count
#' @export
count_S <- function(..., only_all_tested = FALSE) {
  warning("Using 'count_S' is discouraged; use 'count_susceptible()' instead to also consider \"I\" being susceptible.", call. = FALSE)
  rsi_calc(...,
           ab_result = "S",
           only_all_tested = only_all_tested,
           only_count = TRUE)
}

#' @rdname count
#' @export
count_all <- function(..., only_all_tested = FALSE) {
  rsi_calc(...,
           ab_result = c("S", "I", "R"),
           only_all_tested = only_all_tested,
           only_count = TRUE)
}

#' @rdname count
#' @export
n_rsi <- count_all

#' @rdname count
#' @export
count_df <- function(data,
                     translate_ab = "name",
                     language = get_locale(),
                     combine_SI = TRUE,
                     combine_IR = FALSE) {

  rsi_calc_df(type = "count",
              data = data,
              translate_ab = translate_ab,
              language = language,
              combine_SI = combine_SI,
              combine_IR = combine_IR,
              combine_SI_missing = missing(combine_SI))
}