AMR/R/proportion.R

# ==================================================================== #
# TITLE                                                                #
# AMR: An R Package for Working with Antimicrobial Resistance Data     #
#                                                                      #
# SOURCE                                                               #
# https://github.com/msberends/AMR                                     #
#                                                                      #
# CITE AS                                                              #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C    #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance  #
# Data. Journal of Statistical Software, 104(3), 1-31.                 #
# doi:10.18637/jss.v104.i03                                            #
#                                                                      #
# Developed at the University of Groningen, the Netherlands, in        #
# collaboration with non-profit organisations Certe Medical            #
# Diagnostics & Advice, and University Medical Center Groningen.       #
#                                                                      #
# 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.                                        #
# We created this package for both routine data analysis and academic  #
# research and it was publicly released in the hope that it will be    #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
#                                                                      #
# Visit our website for the full manual and a complete tutorial about  #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
# ==================================================================== #

#' Calculate Microbial Resistance
#'
#' @description These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in `summarise()` from the `dplyr` package and also support grouped variables, see *Examples*.
#'
#' [resistance()] should be used to calculate resistance, [susceptibility()] should be used to calculate susceptibility.\cr
#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with [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 the minimum allowed number of available (tested) isolates. Any isolate count lower than `minimum` will return `NA` with a warning. The default number of `30` isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see *Source*.
#' @param as_percent a [logical] to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of `0.123456` will then be returned as `"12.3%"`.
#' @param only_all_tested (for combination therapies, i.e. using more than one variable for `...`): a [logical] to indicate that isolates must be tested for all antibiotics, see section *Combination Therapy* below
#' @param data a [data.frame] containing columns with class [`rsi`] (see [as.rsi()])
#' @param translate_ab a column name of the [antibiotics] data set to translate the antibiotic abbreviations to, using [ab_property()]
#' @inheritParams ab_property
#' @param combine_SI a [logical] to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the argument `combine_IR`, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is `TRUE`.
#' @param combine_IR a [logical] to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see argument `combine_SI`.
#' @inheritSection as.rsi Interpretation of R and S/I
#' @details
#' The function [resistance()] is equal to the function [proportion_R()]. The function [susceptibility()] is equal to the function [proportion_SI()].
#'
#' **Remember that you should filter your data to let it contain only first isolates!** This is needed to exclude duplicates and to reduce selection bias. Use [first_isolate()] to determine them in your data set.
#'
#' These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the [`count()`][AMR::count()] functions to count isolates. The function [susceptibility()] is essentially equal to `count_susceptible() / count_all()`. *Low counts can influence the outcome - the `proportion` functions may camouflage this, since they only return the proportion (albeit being dependent on the `minimum` argument).*
#'
#' The function [proportion_df()] takes any variable from `data` that has an [`rsi`] class (created with [as.rsi()]) and calculates the proportions R, I and S. It also supports grouped variables. The function [rsi_df()] works exactly like [proportion_df()], but adds the number of isolates.
#' @section Combination Therapy:
#' When using more than one variable for `...` (= combination therapy), use `only_all_tested` to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Drug A and Drug B, about how [susceptibility()] works to calculate the %SI:
#'
#' ```
#' --------------------------------------------------------------------
#'                     only_all_tested = FALSE  only_all_tested = TRUE
#'                     -----------------------  -----------------------
#'  Drug A    Drug B   include as  include as   include as  include as
#'                     numerator   denominator  numerator   denominator
#' --------  --------  ----------  -----------  ----------  -----------
#'  S or I    S or I       X            X            X            X
#'    R       S or I       X            X            X            X
#'   <NA>     S or I       X            X            -            -
#'  S or I      R          X            X            X            X
#'    R         R          -            X            -            X
#'   <NA>       R          -            -            -            -
#'  S or I     <NA>        X            X            -            -
#'    R        <NA>        -            -            -            -
#'   <NA>      <NA>        -            -            -            -
#' --------------------------------------------------------------------
#' ```
#'
#' Please note that, in combination therapies, for `only_all_tested = TRUE` applies that:
#' ```
#'     count_S()    +   count_I()    +   count_R()    = count_all()
#'   proportion_S() + proportion_I() + proportion_R() = 1
#' ```
#' and that, in combination therapies, for `only_all_tested = FALSE` applies that:
#' ```
#'     count_S()    +   count_I()    +   count_R()    >= count_all()
#'   proportion_S() + proportion_I() + proportion_R() >= 1
#' ```
#'
#' Using `only_all_tested` has no impact when only using one antibiotic as input.
#' @source **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @seealso [AMR::count()] to count resistant and susceptible isolates.
#' @return A [double] or, when `as_percent = TRUE`, a [character].
#' @rdname proportion
#' @aliases portion
#' @name proportion
#' @export
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' # run ?example_isolates for more info.
#'
#' # base R ------------------------------------------------------------
#' resistance(example_isolates$AMX) # determines %R
#' susceptibility(example_isolates$AMX) # determines %S+I
#'
#' # be more specific
#' proportion_S(example_isolates$AMX)
#' proportion_SI(example_isolates$AMX)
#' proportion_I(example_isolates$AMX)
#' proportion_IR(example_isolates$AMX)
#' proportion_R(example_isolates$AMX)
#'
#' # dplyr -------------------------------------------------------------
#' \donttest{
#' if (require("dplyr")) {
#'   example_isolates %>%
#'     group_by(ward) %>%
#'     summarise(
#'       r = resistance(CIP),
#'       n = n_rsi(CIP)
#'     ) # n_rsi works like n_distinct in dplyr, see ?n_rsi
#'
#'   example_isolates %>%
#'     group_by(ward) %>%
#'     summarise(
#'       R = resistance(CIP, as_percent = TRUE),
#'       SI = susceptibility(CIP, as_percent = TRUE),
#'       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!
#'
#'   # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
#'   # so we can see that combination therapy does a lot more than mono therapy:
#'   example_isolates %>% susceptibility(AMC) # %SI = 76.3%
#'   example_isolates %>% count_all(AMC) #   n = 1879
#'
#'   example_isolates %>% susceptibility(GEN) # %SI = 75.4%
#'   example_isolates %>% count_all(GEN) #   n = 1855
#'
#'   example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%
#'   example_isolates %>% count_all(AMC, GEN) #   n = 1939
#'
#'
#'   # See Details on how `only_all_tested` works. Example:
#'   example_isolates %>%
#'     summarise(
#'       numerator = count_susceptible(AMC, GEN),
#'       denominator = count_all(AMC, GEN),
#'       proportion = susceptibility(AMC, GEN)
#'     )
#'
#'   example_isolates %>%
#'     summarise(
#'       numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
#'       denominator = count_all(AMC, GEN, only_all_tested = TRUE),
#'       proportion = susceptibility(AMC, GEN, only_all_tested = TRUE)
#'     )
#'
#'
#'   example_isolates %>%
#'     group_by(ward) %>%
#'     summarise(
#'       cipro_p = susceptibility(CIP, as_percent = TRUE),
#'       cipro_n = count_all(CIP),
#'       genta_p = susceptibility(GEN, as_percent = TRUE),
#'       genta_n = count_all(GEN),
#'       combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
#'       combination_n = count_all(CIP, GEN)
#'     )
#'
#'   # Get proportions S/I/R immediately of all rsi columns
#'   example_isolates %>%
#'     select(AMX, CIP) %>%
#'     proportion_df(translate = FALSE)
#'
#'   # It also supports grouping variables
#'   # (use rsi_df to also include the count)
#'   example_isolates %>%
#'     select(ward, AMX, CIP) %>%
#'     group_by(ward) %>%
#'     rsi_df(translate = FALSE)
#' }
#' }
resistance <- function(...,
                       minimum = 30,
                       as_percent = FALSE,
                       only_all_tested = FALSE) {
  tryCatch(
    rsi_calc(...,
      ab_result = "R",
      minimum = minimum,
      as_percent = as_percent,
      only_all_tested = only_all_tested,
      only_count = FALSE
    ),
    error = function(e) stop_(e$message, call = -5)
  )
}

#' @rdname proportion
#' @export
susceptibility <- function(...,
                           minimum = 30,
                           as_percent = FALSE,
                           only_all_tested = FALSE) {
  tryCatch(
    rsi_calc(...,
      ab_result = c("S", "I"),
      minimum = minimum,
      as_percent = as_percent,
      only_all_tested = only_all_tested,
      only_count = FALSE
    ),
    error = function(e) stop_(e$message, call = -5)
  )
}

#' @rdname proportion
#' @export
proportion_R <- function(...,
                         minimum = 30,
                         as_percent = FALSE,
                         only_all_tested = FALSE) {
  tryCatch(
    rsi_calc(...,
      ab_result = "R",
      minimum = minimum,
      as_percent = as_percent,
      only_all_tested = only_all_tested,
      only_count = FALSE
    ),
    error = function(e) stop_(e$message, call = -5)
  )
}

#' @rdname proportion
#' @export
proportion_IR <- function(...,
                          minimum = 30,
                          as_percent = FALSE,
                          only_all_tested = FALSE) {
  tryCatch(
    rsi_calc(...,
      ab_result = c("I", "R"),
      minimum = minimum,
      as_percent = as_percent,
      only_all_tested = only_all_tested,
      only_count = FALSE
    ),
    error = function(e) stop_(e$message, call = -5)
  )
}

#' @rdname proportion
#' @export
proportion_I <- function(...,
                         minimum = 30,
                         as_percent = FALSE,
                         only_all_tested = FALSE) {
  tryCatch(
    rsi_calc(...,
      ab_result = "I",
      minimum = minimum,
      as_percent = as_percent,
      only_all_tested = only_all_tested,
      only_count = FALSE
    ),
    error = function(e) stop_(e$message, call = -5)
  )
}

#' @rdname proportion
#' @export
proportion_SI <- function(...,
                          minimum = 30,
                          as_percent = FALSE,
                          only_all_tested = FALSE) {
  tryCatch(
    rsi_calc(...,
      ab_result = c("S", "I"),
      minimum = minimum,
      as_percent = as_percent,
      only_all_tested = only_all_tested,
      only_count = FALSE
    ),
    error = function(e) stop_(e$message, call = -5)
  )
}

#' @rdname proportion
#' @export
proportion_S <- function(...,
                         minimum = 30,
                         as_percent = FALSE,
                         only_all_tested = FALSE) {
  tryCatch(
    rsi_calc(...,
      ab_result = "S",
      minimum = minimum,
      as_percent = as_percent,
      only_all_tested = only_all_tested,
      only_count = FALSE
    ),
    error = function(e) stop_(e$message, call = -5)
  )
}

#' @rdname proportion
#' @export
proportion_df <- function(data,
                          translate_ab = "name",
                          language = get_AMR_locale(),
                          minimum = 30,
                          as_percent = FALSE,
                          combine_SI = TRUE,
                          combine_IR = FALSE) {
  tryCatch(
    rsi_calc_df(
      type = "proportion",
      data = data,
      translate_ab = translate_ab,
      language = language,
      minimum = minimum,
      as_percent = as_percent,
      combine_SI = combine_SI,
      combine_IR = combine_IR,
      combine_SI_missing = missing(combine_SI)
    ),
    error = function(e) stop_(e$message, call = -5)
  )
}