# ==================================================================== # # 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_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 #' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed. #' @inheritParams portion #' @inheritSection as.rsi Interpretation of S, I and R #' @details These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance. #' #' 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_S(...) + count_IR(...)}. #' #' 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 amounts of S, I and R. 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 function \code{rsi_df} works exactly like \code{count_df}, but adds the percentage of S, I and R. #' @inheritSection portion Combination therapy #' @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. #' @keywords resistance susceptibility rsi antibiotics isolate isolates #' @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 isolates #' count_R(example_isolates$AMX) #' count_IR(example_isolates$AMX) #' #' # Or susceptible isolates #' count_S(example_isolates$AMX) #' count_SI(example_isolates$AMX) #' #' # Count all available isolates #' count_all(example_isolates$AMX) #' n_rsi(example_isolates$AMX) #' #' # Since n_rsi counts available isolates, you can #' # calculate back to count e.g. non-susceptible isolates. #' # This results in the same: #' count_SI(example_isolates$AMX) #' portion_SI(example_isolates$AMX) * n_rsi(example_isolates$AMX) #' #' library(dplyr) #' example_isolates %>% #' group_by(hospital_id) %>% #' summarise(R = count_R(CIP), #' I = count_I(CIP), #' S = count_S(CIP), #' n1 = count_all(CIP), # the actual total; sum of all three #' n2 = n_rsi(CIP), # same - analogous to n_distinct #' total = n()) # NOT the number of tested isolates! #' #' # Count co-resistance between amoxicillin/clav acid and gentamicin, #' # so we can see that combination therapy does a lot more than mono therapy. #' # Please mind that `portion_SI` calculates percentages right away instead. #' count_SI(example_isolates$AMC) # 1433 #' count_all(example_isolates$AMC) # 1879 #' #' count_SI(example_isolates$GEN) # 1399 #' count_all(example_isolates$GEN) # 1855 #' #' with(example_isolates, #' count_SI(AMC, GEN)) # 1764 #' with(example_isolates, #' n_rsi(AMC, GEN)) # 1936 #' #' # Get portions S/I/R immediately of all rsi 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_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) { 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) { 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)) }