mirror of
https://github.com/msberends/AMR.git
synced 2024-12-25 21:26:11 +01:00
325 lines
13 KiB
R
Executable File
325 lines
13 KiB
R
Executable File
# ==================================================================== #
|
|
# TITLE #
|
|
# Antimicrobial Resistance (AMR) Analysis #
|
|
# #
|
|
# SOURCE #
|
|
# https://github.com/msberends/AMR #
|
|
# #
|
|
# LICENCE #
|
|
# (c) 2018-2020 Berends MS, Luz CF et al. #
|
|
# #
|
|
# 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. #
|
|
# #
|
|
# We created this package for both routine data analysis and academic #
|
|
# research and it 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.github.io/AMR. #
|
|
# ==================================================================== #
|
|
|
|
dots2vars <- function(...) {
|
|
# this function is to give more informative output about
|
|
# variable names in count_* and proportion_* functions
|
|
dots <- substitute(list(...))
|
|
paste(as.character(dots)[2:length(dots)], collapse = ", ")
|
|
}
|
|
|
|
rsi_calc <- function(...,
|
|
ab_result,
|
|
minimum = 0,
|
|
as_percent = FALSE,
|
|
only_all_tested = FALSE,
|
|
only_count = FALSE) {
|
|
|
|
stop_ifnot(is.numeric(minimum), "`minimum` must be numeric", call = -2)
|
|
stop_ifnot(is.logical(as_percent), "`as_percent` must be logical", call = -2)
|
|
stop_ifnot(is.logical(only_all_tested), "`only_all_tested` must be logical", call = -2)
|
|
|
|
data_vars <- dots2vars(...)
|
|
|
|
dots_df <- switch(1, ...)
|
|
if (is.data.frame(dots_df)) {
|
|
# make sure to remove all other classes like tibbles, data.tables, etc
|
|
dots_df <- as.data.frame(dots_df, stringsAsFactors = FALSE)
|
|
}
|
|
|
|
dots <- eval(substitute(alist(...)))
|
|
stop_if(length(dots) == 0, "no variables selected", call = -2)
|
|
|
|
stop_if("also_single_tested" %in% names(dots),
|
|
"`also_single_tested` was replaced by `only_all_tested`.\n",
|
|
"Please read Details in the help page (`?proportion`) as this may have a considerable impact on your analysis.", call = -2)
|
|
ndots <- length(dots)
|
|
|
|
if (is.data.frame(dots_df)) {
|
|
# data.frame passed with other columns, like: example_isolates %>% proportion_S(AMC, GEN)
|
|
|
|
dots <- as.character(dots)
|
|
# remove first element, it's the data.frame
|
|
if (length(dots) == 1) {
|
|
dots <- character(0)
|
|
} else {
|
|
dots <- dots[2:length(dots)]
|
|
}
|
|
if (length(dots) == 0 | all(dots == "df")) {
|
|
# for complete data.frames, like example_isolates %>% select(AMC, GEN) %>% proportion_S()
|
|
# and the old rsi function, which has "df" as name of the first parameter
|
|
x <- dots_df
|
|
} else {
|
|
# get dots that are in column names already, and the ones that will be once evaluated using dots_df or global env
|
|
# this is to support susceptibility(example_isolates, AMC, dplyr::all_of(some_vector_with_AB_names))
|
|
dots <- c(dots[dots %in% colnames(dots_df)],
|
|
eval(parse(text = dots[!dots %in% colnames(dots_df)]), envir = dots_df, enclos = globalenv()))
|
|
dots_not_exist <- dots[!dots %in% colnames(dots_df)]
|
|
stop_if(length(dots_not_exist) > 0, "column(s) not found: ", paste0("'", dots_not_exist, "'", collapse = ", "), call = -2)
|
|
x <- dots_df[, dots, drop = FALSE]
|
|
}
|
|
} else if (ndots == 1) {
|
|
# only 1 variable passed (can also be data.frame), like: proportion_S(example_isolates$AMC) and example_isolates$AMC %>% proportion_S()
|
|
x <- dots_df
|
|
} else {
|
|
# multiple variables passed without pipe, like: proportion_S(example_isolates$AMC, example_isolates$GEN)
|
|
x <- NULL
|
|
try(x <- as.data.frame(dots, stringsAsFactors = FALSE), silent = TRUE)
|
|
if (is.null(x)) {
|
|
# support for example_isolates %>% group_by(hospital_id) %>% summarise(amox = susceptibility(GEN, AMX))
|
|
x <- as.data.frame(list(...), stringsAsFactors = FALSE)
|
|
}
|
|
}
|
|
|
|
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[, i, drop = TRUE])) {
|
|
rsi_integrity_check <- c(rsi_integrity_check, as.character(x[, i, drop = TRUE]))
|
|
x[, i] <- suppressWarnings(as.rsi(x[, i, drop = TRUE])) # 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)
|
|
}
|
|
|
|
x_transposed <- as.list(as.data.frame(t(x)))
|
|
if (only_all_tested == TRUE) {
|
|
# no NAs in any column
|
|
y <- apply(X = as.data.frame(lapply(x, as.integer), stringsAsFactors = FALSE),
|
|
MARGIN = 1,
|
|
FUN = min)
|
|
numerator <- sum(as.integer(y) %in% as.integer(ab_result), na.rm = TRUE)
|
|
denominator <- sum(sapply(x_transposed, function(y) !(any(is.na(y)))))
|
|
} else {
|
|
# may contain NAs in any column
|
|
other_values <- setdiff(c(NA, levels(ab_result)), ab_result)
|
|
numerator <- sum(sapply(x_transposed, function(y) any(y %in% ab_result, na.rm = TRUE)))
|
|
denominator <- sum(sapply(x_transposed, function(y) !(all(y %in% other_values) & any(is.na(y)))))
|
|
}
|
|
} 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: your_data %>% 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` = ", minimum, ").", call. = FALSE)
|
|
fraction <- NA
|
|
} else {
|
|
fraction <- numerator / denominator
|
|
}
|
|
|
|
if (as_percent == TRUE) {
|
|
percentage(fraction, digits = 1)
|
|
} else {
|
|
fraction
|
|
}
|
|
}
|
|
|
|
rsi_calc_df <- function(type, # "proportion", "count" or "both"
|
|
data,
|
|
translate_ab = "name",
|
|
language = get_locale(),
|
|
minimum = 30,
|
|
as_percent = FALSE,
|
|
combine_SI = TRUE,
|
|
combine_IR = FALSE,
|
|
combine_SI_missing = FALSE) {
|
|
|
|
check_dataset_integrity()
|
|
stop_ifnot(is.data.frame(data), "`data` must be a data.frame", call = -2)
|
|
stop_if(any(dim(data) == 0), "`data` must contain rows and columns", call = -2)
|
|
stop_ifnot(any(sapply(data, is.rsi), na.rm = TRUE), "no columns with class <rsi> found. See ?as.rsi.", call = -2)
|
|
if (isTRUE(combine_IR) & isTRUE(combine_SI_missing)) {
|
|
combine_SI <- FALSE
|
|
}
|
|
stop_if(isTRUE(combine_SI) & isTRUE(combine_IR), "either `combine_SI` or `combine_IR` can be TRUE, not both", call = -2)
|
|
stop_ifnot(is.numeric(minimum), "`minimum` must be numeric", call = -2)
|
|
stop_ifnot(is.logical(as_percent), "`as_percent` must be logical", call = -2)
|
|
|
|
translate_ab <- get_translate_ab(translate_ab)
|
|
|
|
# select only groups and antibiotics
|
|
if (has_groups(data)) {
|
|
data_has_groups <- TRUE
|
|
groups <- setdiff(names(get_groups(data)), ".rows") # get_groups is from poorman.R
|
|
data <- data[, c(groups, colnames(data)[sapply(data, is.rsi)]), drop = FALSE]
|
|
} else {
|
|
data_has_groups <- FALSE
|
|
data <- data[, colnames(data)[sapply(data, is.rsi)], drop = FALSE]
|
|
}
|
|
|
|
data <- as.data.frame(data, stringsAsFactors = FALSE)
|
|
if (isTRUE(combine_SI) | isTRUE(combine_IR)) {
|
|
for (i in seq_len(ncol(data))) {
|
|
if (is.rsi(data[, i, drop = TRUE])) {
|
|
data[, i] <- as.character(data[, i, drop = TRUE])
|
|
if (isTRUE(combine_SI)) {
|
|
data[, i] <- gsub("(I|S)", "SI", data[, i, drop = TRUE])
|
|
} else if (isTRUE(combine_IR)) {
|
|
data[, i] <- gsub("(I|R)", "IR", data[, i, drop = TRUE])
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
sum_it <- function(.data) {
|
|
out <- data.frame(antibiotic = character(0),
|
|
interpretation = character(0),
|
|
value = double(0),
|
|
isolates = integer(0),
|
|
stringsAsFactors = FALSE)
|
|
if (data_has_groups) {
|
|
group_values <- unique(.data[, which(colnames(.data) %in% groups), drop = FALSE])
|
|
rownames(group_values) <- NULL
|
|
.data <- .data[, which(!colnames(.data) %in% groups), drop = FALSE]
|
|
}
|
|
for (i in seq_len(ncol(.data))) {
|
|
values <- .data[, i, drop = TRUE]
|
|
if (isTRUE(combine_SI)) {
|
|
values <- factor(values, levels = c("SI", "R"), ordered = TRUE)
|
|
} else if (isTRUE(combine_IR)) {
|
|
values <- factor(values, levels = c("S", "IR"), ordered = TRUE)
|
|
} else {
|
|
values <- factor(values, levels = c("S", "I", "R"), ordered = TRUE)
|
|
}
|
|
col_results <- as.data.frame(as.matrix(table(values)))
|
|
col_results$interpretation <- rownames(col_results)
|
|
col_results$isolates <- col_results[, 1, drop = TRUE]
|
|
if (NROW(col_results) > 0 && sum(col_results$isolates, na.rm = TRUE) > 0) {
|
|
if (sum(col_results$isolates, na.rm = TRUE) >= minimum) {
|
|
col_results$value <- col_results$isolates / sum(col_results$isolates, na.rm = TRUE)
|
|
} else {
|
|
col_results$value <- rep(NA_real_, NROW(col_results))
|
|
}
|
|
out_new <- data.frame(antibiotic = ifelse(isFALSE(translate_ab),
|
|
colnames(.data)[i],
|
|
ab_property(colnames(.data)[i], property = translate_ab, language = language)),
|
|
interpretation = col_results$interpretation,
|
|
value = col_results$value,
|
|
isolates = col_results$isolates,
|
|
stringsAsFactors = FALSE)
|
|
if (data_has_groups) {
|
|
if (nrow(group_values) < nrow(out_new)) {
|
|
# repeat group_values for the number of rows in out_new
|
|
repeated <- rep(seq_len(nrow(group_values)),
|
|
each = nrow(out_new) / nrow(group_values))
|
|
group_values <- group_values[repeated, , drop = FALSE]
|
|
}
|
|
out_new <- cbind(group_values, out_new)
|
|
}
|
|
out <- rbind(out, out_new)
|
|
}
|
|
}
|
|
out
|
|
}
|
|
|
|
# support dplyr groups
|
|
apply_group <- function(.data, fn, groups, ...) {
|
|
grouped <- split(x = .data, f = lapply(groups, function(x, .data) as.factor(.data[, x]), .data))
|
|
res <- do.call(rbind, unname(lapply(grouped, fn, ...)))
|
|
if (any(groups %in% colnames(res))) {
|
|
class(res) <- c("grouped_data", class(res))
|
|
attr(res, "groups") <- groups[groups %in% colnames(res)]
|
|
}
|
|
res
|
|
}
|
|
|
|
if (data_has_groups) {
|
|
out <- apply_group(data, "sum_it", groups)
|
|
} else {
|
|
out <- sum_it(data)
|
|
}
|
|
|
|
# apply factors for right sorting in interpretation
|
|
if (isTRUE(combine_SI)) {
|
|
out$interpretation <- factor(out$interpretation, levels = c("SI", "R"), ordered = TRUE)
|
|
} else if (isTRUE(combine_IR)) {
|
|
out$interpretation <- factor(out$interpretation, levels = c("S", "IR"), ordered = TRUE)
|
|
} else {
|
|
# don't use as.rsi() here, as it would add the class <rsi> and we would like
|
|
# the same data structure as output, regardless of input
|
|
out$interpretation <- factor(out$interpretation, levels = c("S", "I", "R"), ordered = TRUE)
|
|
}
|
|
|
|
if (data_has_groups) {
|
|
# ordering by the groups and two more: "antibiotic" and "interpretation"
|
|
out <- ungroup(out[do.call("order", out[, seq_len(length(groups) + 2)]), ])
|
|
} else {
|
|
out <- out[order(out$antibiotic, out$interpretation), ]
|
|
}
|
|
|
|
if (type == "proportion") {
|
|
out <- subset(out, select = -c(isolates))
|
|
} else if (type == "count") {
|
|
out$value <- out$isolates
|
|
out <- subset(out, select = -c(isolates))
|
|
}
|
|
|
|
rownames(out) <- NULL
|
|
out
|
|
}
|
|
|
|
get_translate_ab <- function(translate_ab) {
|
|
translate_ab <- as.character(translate_ab)[1L]
|
|
if (translate_ab %in% c("TRUE", "official")) {
|
|
return("name")
|
|
} else if (translate_ab %in% c(NA_character_, "FALSE")) {
|
|
return(FALSE)
|
|
} else {
|
|
translate_ab <- tolower(translate_ab)
|
|
stop_ifnot(translate_ab %in% colnames(AMR::antibiotics),
|
|
"invalid value for 'translate_ab', this must be a column name of the antibiotics data set\n",
|
|
"or TRUE (equals 'name') or FALSE to not translate at all.",
|
|
call = FALSE)
|
|
translate_ab
|
|
}
|
|
}
|