mirror of https://github.com/msberends/AMR.git
191 lines
7.4 KiB
R
Executable File
191 lines
7.4 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://gitlab.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# This R package was created for academic research and was publicly #
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# released in the hope that it will be useful, but it comes WITHOUT #
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# ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.gitlab.io/AMR. #
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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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#' @inheritSection as.rsi Interpretation of S, I and R
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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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#' 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(...)}.
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#'
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#' 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"}.
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#'
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#' The function \code{rsi_df} works exactly like \code{count_df}, but adds the percentage of S, I and R.
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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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#' @inheritSection AMR Read more on our website!
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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$AMX)
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#' count_IR(septic_patients$AMX)
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#'
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#' # Or susceptible isolates
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#' count_S(septic_patients$AMX)
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#' count_SI(septic_patients$AMX)
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#'
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#' # Count all available isolates
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#' count_all(septic_patients$AMX)
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#' n_rsi(septic_patients$AMX)
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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$AMX)
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#' portion_IR(septic_patients$AMX) * n_rsi(septic_patients$AMX)
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#'
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#' library(dplyr)
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(R = count_R(CIP),
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#' I = count_I(CIP),
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#' S = count_S(CIP),
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#' n1 = count_all(CIP), # the actual total; sum of all three
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#' n2 = n_rsi(CIP), # same - analogous to n_distinct
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#' total = n()) # NOT the number of tested isolates!
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#'
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#' # Count co-resistance between amoxicillin/clav acid and gentamicin,
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#' # so we can see that combination therapy does a lot more than mono therapy.
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#' # Please mind that `portion_S` calculates percentages right away instead.
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#' count_S(septic_patients$AMC) # S = 1342 (71.4%)
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#' count_all(septic_patients$AMC) # n = 1879
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#'
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#' count_S(septic_patients$GEN) # S = 1372 (74.0%)
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#' count_all(septic_patients$GEN) # n = 1855
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#'
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#' with(septic_patients,
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#' count_S(AMC, GEN)) # S = 1660 (92.3%)
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#' with(septic_patients, # n = 1798
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#' n_rsi(AMC, GEN))
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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(AMX, CIP) %>%
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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, AMX, CIP) %>%
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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 = "name",
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language = get_locale(),
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combine_SI = TRUE,
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combine_IR = FALSE) {
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rsi_calc_df(type = "count",
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data = data,
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translate_ab = translate_ab,
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language = language,
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combine_SI = combine_SI,
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combine_IR = combine_IR,
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combine_SI_missing = missing(combine_SI))
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}
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