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AMR/R/portion.R

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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# AUTHORS #
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
# #
# LICENCE #
# This program is free software; you can redistribute it and/or modify #
# it under the terms of the GNU General Public License version 2.0, #
# as published by the Free Software Foundation. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# ==================================================================== #
#' Calculate resistance of isolates
#'
#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
#'
#' \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
#' @param ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
#' @param ab2 like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.
#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
#' @param data a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
#' @param translate a logical value to indicate whether antibiotic abbreviations should be translated with \code{\link{abname}}
#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
#'
#' \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \code{data.frame} will have three rows (for R/I/S) and a column for each variable with class \code{"rsi"}.
#'
#' The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
#' \if{html}{
#' \cr\cr
#' To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
#' \out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
#' To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
#' \cr
#' For two antibiotics:
#' \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
#' \cr
#' Theoretically for three antibiotics:
#' \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
#' }
#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
#' @seealso \code{\link{n_rsi}} to count cases with antimicrobial results.
#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
#' @return Double or, when \code{as_percent = TRUE}, a character.
#' @rdname portion
#' @name portion
#' @export
#' @examples
#' # Calculate resistance
#' portion_R(septic_patients$amox)
#' portion_IR(septic_patients$amox)
#'
#' # Or susceptibility
#' portion_S(septic_patients$amox)
#' portion_SI(septic_patients$amox)
#'
#' # Since n_rsi counts available isolates (and is used as denominator),
#' # you can calculate back to count e.g. non-susceptible isolates:
#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
#'
#' library(dplyr)
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(p = portion_S(cipr),
#' n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(R = portion_R(cipr, as_percent = TRUE),
#' I = portion_I(cipr, as_percent = TRUE),
#' S = portion_S(cipr, as_percent = TRUE),
#' n = n_rsi(cipr), # works like n_distinct in dplyr
#' total = n()) # NOT the amount 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:
#' portion_S(septic_patients$amcl) # S = 67.3%
#' n_rsi(septic_patients$amcl) # n = 1570
#'
#' portion_S(septic_patients$gent) # S = 74.0%
#' n_rsi(septic_patients$gent) # n = 1842
#'
#' with(septic_patients,
#' portion_S(amcl, gent)) # S = 92.1%
#' with(septic_patients, # n = 1504
#' n_rsi(amcl, gent))
#'
#' septic_patients %>%
#' group_by(hospital_id) %>%
#' summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
#' cipro_n = n_rsi(cipr),
#' genta_p = portion_S(gent, as_percent = TRUE),
#' genta_n = n_rsi(gent),
#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
#' combination_n = n_rsi(cipr, gent))
#'
#' \dontrun{
#'
#' # calculate current empiric combination therapy of Helicobacter gastritis:
#' my_table %>%
#' filter(first_isolate == TRUE,
#' genus == "Helicobacter") %>%
#' summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
#' n = n_rsi(amox, metr))
#' }
portion_R <- function(ab1,
ab2 = NULL,
minimum = 30,
as_percent = FALSE) {
rsi_calc(type = "R",
ab1 = ab1,
ab2 = ab2,
include_I = FALSE,
minimum = minimum,
as_percent = as_percent)
}
#' @rdname portion
#' @export
portion_IR <- function(ab1,
ab2 = NULL,
minimum = 30,
as_percent = FALSE) {
rsi_calc(type = "R",
ab1 = ab1,
ab2 = ab2,
include_I = TRUE,
minimum = minimum,
as_percent = as_percent)
}
#' @rdname portion
#' @export
portion_I <- function(ab1,
minimum = 30,
as_percent = FALSE) {
rsi_calc(type = "I",
ab1 = ab1,
ab2 = NULL,
include_I = FALSE,
minimum = minimum,
as_percent = as_percent)
}
#' @rdname portion
#' @export
portion_SI <- function(ab1,
ab2 = NULL,
minimum = 30,
as_percent = FALSE) {
rsi_calc(type = "S",
ab1 = ab1,
ab2 = ab2,
include_I = TRUE,
minimum = minimum,
as_percent = as_percent)
}
#' @rdname portion
#' @export
portion_S <- function(ab1,
ab2 = NULL,
minimum = 30,
as_percent = FALSE) {
rsi_calc(type = "S",
ab1 = ab1,
ab2 = ab2,
include_I = FALSE,
minimum = minimum,
as_percent = as_percent)
}
rsi_calc <- function(type,
ab1,
ab2,
include_I,
minimum,
as_percent) {
if (NCOL(ab1) > 1) {
stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
}
if (!is.logical(include_I)) {
stop('`include_I` must be logical', call. = FALSE)
}
if (!is.numeric(minimum)) {
stop('`minimum` must be numeric', call. = FALSE)
}
if (!is.logical(as_percent)) {
stop('`as_percent` must be logical', call. = FALSE)
}
print_warning <- FALSE
if (!is.rsi(ab1)) {
ab1 <- as.rsi(ab1)
print_warning <- TRUE
}
if (!is.null(ab2)) {
# ab_name <- paste(deparse(substitute(ab1)), "and", deparse(substitute(ab2)))
if (NCOL(ab2) > 1) {
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
}
if (!is.rsi(ab2)) {
ab2 <- as.rsi(ab2)
print_warning <- TRUE
}
x <- apply(X = data.frame(ab1 = as.integer(ab1),
ab2 = as.integer(ab2)),
MARGIN = 1,
FUN = min)
} else {
x <- ab1
# ab_name <- deparse(substitute(ab1))
}
if (print_warning == TRUE) {
warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
}
total <- length(x) - sum(is.na(x))
if (total < minimum) {
return(NA)
}
if (type == "S") {
found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
} else if (type == "I") {
found <- sum(as.integer(x) == 2, na.rm = TRUE)
} else if (type == "R") {
found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
} else {
stop("invalid type")
}
if (as_percent == TRUE) {
percent(found / total, force_zero = TRUE)
} else {
found / total
}
}
#' @rdname portion
#' @importFrom dplyr bind_cols summarise_if mutate
#' @export
portion_df <- function(data, translate = getOption("get_antibiotic_names", TRUE)) {
resS <- bind_cols(data.frame(Interpretation = "S", stringsAsFactors = FALSE),
summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = portion_S))
resI <- bind_cols(data.frame(Interpretation = "I", stringsAsFactors = FALSE),
summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = portion_I))
resR <- bind_cols(data.frame(Interpretation = "R", stringsAsFactors = FALSE),
summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = portion_R))
res <- bind_rows(resS, resI, resR) %>%
mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
tidyr::gather(Antibiotic, Percentage, -Interpretation)
if (translate == TRUE) {
res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = "official"))
}
res
}
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