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AMR/R/rsi_calc.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.gitlab.io/AMR. #
# ==================================================================== #
#' @importFrom rlang enquos as_label
dots2vars <- function(...) {
# this function is to give more informative output about
# variable names in count_* and proportion_* functions
paste(
unlist(
lapply(enquos(...),
function(x) {
l <- as_label(x)
if (l != ".") {
l
} else {
character(0)
}
})
),
collapse = ", ")
}
#' @importFrom dplyr %>% pull all_vars any_vars filter_all funs mutate_all
#' @importFrom cleaner percentage
rsi_calc <- function(...,
ab_result,
minimum = 0,
as_percent = FALSE,
only_all_tested = FALSE,
only_count = FALSE) {
data_vars <- dots2vars(...)
if (!is.numeric(minimum)) {
stop("`minimum` must be numeric", call. = FALSE)
}
if (!is.logical(as_percent)) {
stop("`as_percent` must be logical", call. = FALSE)
}
if (!is.logical(only_all_tested)) {
stop("`only_all_tested` must be logical", call. = FALSE)
}
dots_df <- ...elt(1) # it needs this evaluation
dots <- base::eval(base::substitute(base::alist(...)))
if ("also_single_tested" %in% names(dots)) {
stop("`also_single_tested` was replaced by `only_all_tested`. Please read Details in the help page (`?proportion`) as this may have a considerable impact on your analysis.", call. = FALSE)
}
ndots <- length(dots)
if ("data.frame" %in% class(dots_df)) {
# data.frame passed with other columns, like: example_isolates %>% proportion_S(amcl, gent)
dots <- as.character(dots)
dots <- dots[dots != "."]
if (length(dots) == 0 | all(dots == "df")) {
# for complete data.frames, like example_isolates %>% select(amcl, gent) %>% proportion_S()
# and the old rsi function, that has "df" as name of the first parameter
x <- dots_df
} else {
x <- dots_df[, dots]
}
} else if (ndots == 1) {
# only 1 variable passed (can also be data.frame), like: proportion_S(example_isolates$amcl) and example_isolates$amcl %>% proportion_S()
x <- dots_df
} else {
# multiple variables passed without pipe, like: proportion_S(example_isolates$amcl, example_isolates$gent)
x <- NULL
try(x <- as.data.frame(dots), silent = TRUE)
if (is.null(x)) {
# support for: with(example_isolates, proportion_S(amcl, gent))
x <- as.data.frame(rlang::list2(...))
}
}
if (is.null(x)) {
warning("argument is NULL (check if columns exist): returning NA", call. = FALSE)
return(NA)
}
print_warning <- FALSE
ab_result <- as.rsi(ab_result)
if (is.data.frame(x)) {
rsi_integrity_check <- character(0)
for (i in seq_len(ncol(x))) {
# check integrity of columns: force rsi class
if (!is.rsi(x %>% pull(i))) {
rsi_integrity_check <- c(rsi_integrity_check, x %>% pull(i) %>% as.character())
x[, i] <- suppressWarnings(x %>% pull(i) %>% as.rsi()) # warning will be given later
print_warning <- TRUE
}
}
if (length(rsi_integrity_check) > 0) {
# this will give a warning for invalid results, of all input columns (so only 1 warning)
rsi_integrity_check <- as.rsi(rsi_integrity_check)
}
if (only_all_tested == TRUE) {
# THE NUMBER OF ISOLATES WHERE *ALL* ABx ARE S/I/R
x <- apply(X = x %>% mutate_all(as.integer),
MARGIN = 1,
FUN = base::min)
numerator <- sum(as.integer(x) %in% as.integer(ab_result), na.rm = TRUE)
denominator <- length(x) - sum(is.na(x))
} else {
# THE NUMBER OF ISOLATES WHERE *ANY* ABx IS S/I/R
other_values <- base::setdiff(c(NA, levels(ab_result)), ab_result)
other_values_filter <- base::apply(x, 1, function(y) {
base::all(y %in% other_values) & base::any(is.na(y))
})
numerator <- x %>% filter_all(any_vars(. %in% ab_result)) %>% nrow()
denominator <- x %>% filter(!other_values_filter) %>% nrow()
}
} else {
# x is not a data.frame
if (!is.rsi(x)) {
x <- as.rsi(x)
print_warning <- TRUE
}
numerator <- sum(x %in% ab_result, na.rm = TRUE)
denominator <- sum(x %in% levels(ab_result), na.rm = TRUE)
}
if (print_warning == TRUE) {
warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_if(is.rsi.eligible, as.rsi)",
call. = FALSE)
}
if (only_count == TRUE) {
return(numerator)
}
if (denominator < minimum) {
if (data_vars != "") {
data_vars <- paste(" for", data_vars)
}
warning("Introducing NA: only ", denominator, " results available", data_vars, " (`minimum` was set to ", minimum, ").", call. = FALSE)
fraction <- NA
} else {
fraction <- numerator / denominator
}
if (as_percent == TRUE) {
percentage(fraction, digits = 1)
} else {
fraction
}
}
#' @importFrom dplyr %>% summarise_if mutate select everything bind_rows arrange
#' @importFrom tidyr pivot_longer
rsi_calc_df <- function(type, # "proportion" or "count"
data,
translate_ab = "name",
language = get_locale(),
minimum = 30,
as_percent = FALSE,
combine_SI = TRUE,
combine_IR = FALSE,
combine_SI_missing = FALSE) {
if (!"data.frame" %in% class(data)) {
stop(paste0("`", type, "_df` must be called on a data.frame"), call. = FALSE)
}
if (isTRUE(combine_IR) & isTRUE(combine_SI_missing)) {
combine_SI <- FALSE
}
if (isTRUE(combine_SI) & isTRUE(combine_IR)) {
stop("either `combine_SI` or `combine_IR` can be TRUE, not both", call. = FALSE)
}
if (!any(sapply(data, is.rsi), na.rm = TRUE)) {
stop("No columns with class 'rsi' found. See ?as.rsi.", call. = FALSE)
}
if (as.character(translate_ab) %in% c("TRUE", "official")) {
translate_ab <- "name"
}
get_summaryfunction <- function(int, type) {
# look for proportion_S, count_S, etc:
int_fn <- get(paste0(type, "_", int), envir = asNamespace("AMR"))
suppressWarnings(
if (type == "proportion") {
summ <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = int_fn,
minimum = minimum,
as_percent = as_percent)
} else if (type == "count") {
summ <- summarise_if(.tbl = data,
.predicate = is.rsi,
.funs = int_fn)
}
)
summ %>%
mutate(interpretation = int) %>%
select(interpretation, everything())
}
resS <- get_summaryfunction("S", type)
resI <- get_summaryfunction("I", type)
resR <- get_summaryfunction("R", type)
resSI <- get_summaryfunction("SI", type)
resIR <- get_summaryfunction("IR", type)
data.groups <- group_vars(data)
if (isFALSE(combine_SI) & isFALSE(combine_IR)) {
res <- bind_rows(resS, resI, resR) %>%
mutate(interpretation = factor(interpretation,
levels = c("S", "I", "R"),
ordered = TRUE))
} else if (isTRUE(combine_IR)) {
res <- bind_rows(resS, resIR) %>%
mutate(interpretation = factor(interpretation,
levels = c("S", "IR"),
ordered = TRUE))
} else if (isTRUE(combine_SI)) {
res <- bind_rows(resSI, resR) %>%
mutate(interpretation = factor(interpretation,
levels = c("SI", "R"),
ordered = TRUE))
}
res <- res %>%
pivot_longer(-c(interpretation, data.groups), names_to = "antibiotic") %>%
select(antibiotic, everything()) %>%
arrange(antibiotic, interpretation)
if (!translate_ab == FALSE) {
res <- res %>% mutate(antibiotic = AMR::ab_property(antibiotic, property = translate_ab, language = language))
}
as.data.frame(res, stringsAsFactors = FALSE)
}
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