mirror of https://github.com/msberends/AMR.git
234 lines
8.7 KiB
R
234 lines
8.7 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# AUTHORS #
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# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# LICENCE #
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# This package is free software; you can redistribute it and/or modify #
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# it under the terms of the GNU General Public License version 2.0, #
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# as published by the Free Software Foundation. #
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# #
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# This R package is distributed in the hope that it will be useful, #
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# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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# GNU General Public License version 2.0 for more details. #
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# ==================================================================== #
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#' Count isolates
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#'
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#' @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}.
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#'
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#' \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
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#' @param ... one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.
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#' @inheritParams portion
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#' @details These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
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#'
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#' \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(...)}.
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#'
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#' \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"}.
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#' @source Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
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#' @seealso \code{\link{portion}_*} to calculate microbial resistance and susceptibility.
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#' @keywords resistance susceptibility rsi antibiotics isolate isolates
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#' @return Integer
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#' @rdname count
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#' @name count
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#' @export
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#' @examples
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#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
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#' ?septic_patients
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#'
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#' # Count resistant isolates
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#' count_R(septic_patients$amox)
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#' count_IR(septic_patients$amox)
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#'
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#' # Or susceptible isolates
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#' count_S(septic_patients$amox)
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#' count_SI(septic_patients$amox)
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#'
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#' # Count all available isolates
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#' count_all(septic_patients$amox)
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#' n_rsi(septic_patients$amox)
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#'
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#' # Since n_rsi counts available isolates, you can
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#' # calculate back to count e.g. non-susceptible isolates.
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#' # This results in the same:
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#' count_IR(septic_patients$amox)
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#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
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#'
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#' library(dplyr)
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(R = count_R(cipr),
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#' I = count_I(cipr),
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#' S = count_S(cipr),
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#' n1 = count_all(cipr), # the actual total; sum of all three
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#' n2 = n_rsi(cipr), # same - analogous to n_distinct
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#' total = n()) # NOT the amount of tested isolates!
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#'
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#' # Count co-resistance between amoxicillin/clav acid and gentamicin,
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#' # so we can see that combination therapy does a lot more than mono therapy.
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#' # Please mind that `portion_S` calculates percentages right away instead.
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#' count_S(septic_patients$amcl) # S = 1057 (67.1%)
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#' count_all(septic_patients$amcl) # n = 1576
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#'
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#' count_S(septic_patients$gent) # S = 1372 (74.0%)
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#' count_all(septic_patients$gent) # n = 1855
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#'
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#' with(septic_patients,
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#' count_S(amcl, gent)) # S = 1396 (92.0%)
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#' with(septic_patients, # n = 1517
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#' n_rsi(amcl, gent))
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#'
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#' # Get portions S/I/R immediately of all rsi columns
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#' septic_patients %>%
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#' select(amox, cipr) %>%
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#' count_df(translate = FALSE)
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#'
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#' # It also supports grouping variables
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#' septic_patients %>%
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#' select(hospital_id, amox, cipr) %>%
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#' group_by(hospital_id) %>%
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#' count_df(translate = FALSE)
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#'
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count_R <- function(..., also_single_tested = FALSE) {
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rsi_calc(...,
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type = "R",
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include_I = FALSE,
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minimum = 0,
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as_percent = FALSE,
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also_single_tested = FALSE,
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only_count = TRUE)
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}
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#' @rdname count
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#' @export
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count_IR <- function(..., also_single_tested = FALSE) {
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rsi_calc(...,
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type = "R",
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include_I = TRUE,
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minimum = 0,
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as_percent = FALSE,
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also_single_tested = FALSE,
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only_count = TRUE)
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}
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#' @rdname count
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#' @export
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count_I <- function(..., also_single_tested = FALSE) {
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rsi_calc(...,
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type = "I",
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include_I = FALSE,
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minimum = 0,
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as_percent = FALSE,
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also_single_tested = FALSE,
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only_count = TRUE)
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}
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#' @rdname count
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#' @export
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count_SI <- function(..., also_single_tested = FALSE) {
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rsi_calc(...,
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type = "S",
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include_I = TRUE,
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minimum = 0,
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as_percent = FALSE,
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also_single_tested = FALSE,
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only_count = TRUE)
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}
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#' @rdname count
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#' @export
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count_S <- function(..., also_single_tested = FALSE) {
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rsi_calc(...,
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type = "S",
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include_I = FALSE,
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minimum = 0,
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as_percent = FALSE,
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also_single_tested = FALSE,
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only_count = TRUE)
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}
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#' @rdname count
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#' @export
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count_all <- function(...) {
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# only print warnings once, if needed
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count_S(...) + suppressWarnings(count_IR(...))
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}
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#' @rdname count
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#' @export
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n_rsi <- function(...) {
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# only print warnings once, if needed
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count_S(...) + suppressWarnings(count_IR(...))
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}
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#' @rdname count
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#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
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#' @export
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count_df <- function(data,
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translate_ab = getOption("get_antibiotic_names", "official"),
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combine_IR = FALSE) {
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if (!"data.frame" %in% class(data)) {
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stop("`count_df` must be called on a data.frame")
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}
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if (data %>% select_if(is.rsi) %>% ncol() == 0) {
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stop("No columns with class 'rsi' found. See ?as.rsi.")
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}
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if (as.character(translate_ab) == "TRUE") {
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translate_ab <- "official"
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}
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options(get_antibiotic_names = translate_ab)
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resS <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = count_S) %>%
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mutate(Interpretation = "S") %>%
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select(Interpretation, everything())
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if (combine_IR == FALSE) {
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resI <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = count_I) %>%
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mutate(Interpretation = "I") %>%
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select(Interpretation, everything())
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resR <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = count_R) %>%
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mutate(Interpretation = "R") %>%
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select(Interpretation, everything())
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data.groups <- group_vars(data)
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res <- bind_rows(resS, resI, resR) %>%
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mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
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tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
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} else {
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resIR <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = count_IR) %>%
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mutate(Interpretation = "IR") %>%
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select(Interpretation, everything())
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data.groups <- group_vars(data)
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res <- bind_rows(resS, resIR) %>%
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mutate(Interpretation = factor(Interpretation, levels = c("IR", "S"), ordered = TRUE)) %>%
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tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
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}
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if (!translate_ab == FALSE) {
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if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
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stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
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}
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res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
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}
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res
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}
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