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AMR/R/count.R

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# ==================================================================== #
# 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.gitab.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
#' @details These functions are meant to count isolates. Use the \code{\link{portion}_*} functions to calculate microbial resistance.
#'
#' \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(...)}.
#'
#' \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"}.
#' @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
#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
#' ?septic_patients
#'
#' # Count resistant isolates
#' count_R(septic_patients$amox)
#' count_IR(septic_patients$amox)
#'
#' # Or susceptible isolates
#' count_S(septic_patients$amox)
#' count_SI(septic_patients$amox)
#'
#' # Count all available isolates
#' count_all(septic_patients$amox)
#' n_rsi(septic_patients$amox)
#'
#' # Since n_rsi counts available isolates, you can
#' # calculate back to count e.g. non-susceptible isolates.
#' # This results in the same:
#' count_IR(septic_patients$amox)
#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
#'
#' library(dplyr)
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(R = count_R(cipr),
#' I = count_I(cipr),
#' S = count_S(cipr),
#' n1 = count_all(cipr), # the actual total; sum of all three
#' n2 = n_rsi(cipr), # same - analogous to n_distinct
#' total = n()) # NOT the amount of tested isolates!
#'
#' # Count co-resistance between amoxicillin/clav acid and gentamicin,
#' # so we can see that combination therapy does a lot more than mono therapy.
#' # Please mind that `portion_S` calculates percentages right away instead.
#' count_S(septic_patients$amcl) # S = 1057 (67.1%)
#' count_all(septic_patients$amcl) # n = 1576
#'
#' count_S(septic_patients$gent) # S = 1372 (74.0%)
#' count_all(septic_patients$gent) # n = 1855
#'
#' with(septic_patients,
#' count_S(amcl, gent)) # S = 1396 (92.0%)
#' with(septic_patients, # n = 1517
#' n_rsi(amcl, gent))
#'
#' # Get portions S/I/R immediately of all rsi columns
#' septic_patients %>%
#' select(amox, cipr) %>%
#' count_df(translate = FALSE)
#'
#' # It also supports grouping variables
#' septic_patients %>%
#' select(hospital_id, amox, cipr) %>%
#' group_by(hospital_id) %>%
#' count_df(translate = FALSE)
#'
count_R <- function(..., also_single_tested = FALSE) {
rsi_calc(...,
type = "R",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
also_single_tested = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_IR <- function(..., also_single_tested = FALSE) {
rsi_calc(...,
type = "R",
include_I = TRUE,
minimum = 0,
as_percent = FALSE,
also_single_tested = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_I <- function(..., also_single_tested = FALSE) {
rsi_calc(...,
type = "I",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
also_single_tested = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_SI <- function(..., also_single_tested = FALSE) {
rsi_calc(...,
type = "S",
include_I = TRUE,
minimum = 0,
as_percent = FALSE,
also_single_tested = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_S <- function(..., also_single_tested = FALSE) {
rsi_calc(...,
type = "S",
include_I = FALSE,
minimum = 0,
as_percent = FALSE,
also_single_tested = FALSE,
only_count = TRUE)
}
#' @rdname count
#' @export
count_all <- function(...) {
# only print warnings once, if needed
count_S(...) + suppressWarnings(count_IR(...))
}
#' @rdname count
#' @export
n_rsi <- function(...) {
# only print warnings once, if needed
count_S(...) + suppressWarnings(count_IR(...))
}
#' @rdname count
#' @importFrom dplyr %>% select_if bind_rows summarise_if mutate group_vars select everything
#' @export
count_df <- function(data,
translate_ab = getOption("get_antibiotic_names", "official"),
combine_IR = FALSE) {
if (!"data.frame" %in% class(data)) {
stop("`count_df` must be called on a data.frame")
}
if (data %>% select_if(is.rsi) %>% ncol() == 0) {
stop("No columns with class 'rsi' found. See ?as.rsi.")
}
if (as.character(translate_ab) == "TRUE") {
translate_ab <- "official"
}
options(get_antibiotic_names = translate_ab)
resS <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = count_S) %>%
mutate(Interpretation = "S") %>%
select(Interpretation, everything())
if (combine_IR == FALSE) {
resI <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = count_I) %>%
mutate(Interpretation = "I") %>%
select(Interpretation, everything())
resR <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = count_R) %>%
mutate(Interpretation = "R") %>%
select(Interpretation, everything())
data.groups <- group_vars(data)
res <- bind_rows(resS, resI, resR) %>%
mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
} else {
resIR <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = count_IR) %>%
mutate(Interpretation = "IR") %>%
select(Interpretation, everything())
data.groups <- group_vars(data)
res <- bind_rows(resS, resIR) %>%
mutate(Interpretation = factor(Interpretation, levels = c("IR", "S"), ordered = TRUE)) %>%
tidyr::gather(Antibiotic, Value, -Interpretation, -data.groups)
}
if (!translate_ab == FALSE) {
if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
}
res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
}
res
}
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