mirror of
https://github.com/msberends/AMR.git
synced 2024-12-27 07:26:11 +01:00
378 lines
15 KiB
R
Executable File
378 lines
15 KiB
R
Executable File
# ==================================================================== #
|
|
# TITLE: #
|
|
# AMR: An R Package for Working with Antimicrobial Resistance Data #
|
|
# #
|
|
# SOURCE CODE: #
|
|
# https://github.com/msberends/AMR #
|
|
# #
|
|
# PLEASE CITE THIS SOFTWARE AS: #
|
|
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
|
|
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
|
|
# Data. Journal of Statistical Software, 104(3), 1-31. #
|
|
# https://doi.org/10.18637/jss.v104.i03 #
|
|
# #
|
|
# Developed at the University of Groningen and the University Medical #
|
|
# Center Groningen in The Netherlands, in collaboration with many #
|
|
# colleagues from around the world, see our website. #
|
|
# #
|
|
# 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 the full manual and a complete tutorial about #
|
|
# how to conduct AMR data analysis: 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(...))
|
|
dots <- as.character(dots)[2:length(dots)]
|
|
paste0(dots[dots != "."], collapse = "+")
|
|
}
|
|
|
|
sir_calc <- function(...,
|
|
ab_result,
|
|
minimum = 0,
|
|
as_percent = FALSE,
|
|
only_all_tested = FALSE,
|
|
only_count = FALSE) {
|
|
meet_criteria(ab_result, allow_class = c("character", "numeric", "integer"), has_length = c(1, 2, 3))
|
|
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
|
|
meet_criteria(as_percent, allow_class = "logical", has_length = 1)
|
|
meet_criteria(only_all_tested, allow_class = "logical", has_length = 1)
|
|
meet_criteria(only_count, allow_class = "logical", has_length = 1)
|
|
|
|
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 %pm>% 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 %pm>% select(AMC, GEN) %pm>% proportion_S()
|
|
# and the old sir function, which has "df" as name of the first argument
|
|
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, any_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: ", vector_and(dots_not_exist, quotes = TRUE), 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 %pm>% 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 %pm>% group_by(ward) %pm>% 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")
|
|
if (as_percent == TRUE) {
|
|
return(NA_character_)
|
|
} else {
|
|
return(NA_real_)
|
|
}
|
|
}
|
|
|
|
print_warning <- FALSE
|
|
|
|
ab_result <- as.sir(ab_result)
|
|
|
|
if (is.data.frame(x)) {
|
|
sir_integrity_check <- character(0)
|
|
for (i in seq_len(ncol(x))) {
|
|
# check integrity of columns: force 'sir' class
|
|
if (!is.sir(x[, i, drop = TRUE])) {
|
|
sir_integrity_check <- c(sir_integrity_check, as.character(x[, i, drop = TRUE]))
|
|
x[, i] <- suppressWarnings(as.sir(x[, i, drop = TRUE])) # warning will be given later
|
|
print_warning <- TRUE
|
|
}
|
|
}
|
|
if (length(sir_integrity_check) > 0) {
|
|
# this will give a warning for invalid results, of all input columns (so only 1 warning)
|
|
sir_integrity_check <- as.sir(sir_integrity_check)
|
|
}
|
|
|
|
x_transposed <- as.list(as.data.frame(t(x), stringsAsFactors = FALSE))
|
|
if (isTRUE(only_all_tested)) {
|
|
# 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(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(anyNA(y))))
|
|
} else {
|
|
# may contain NAs in any column
|
|
other_values <- setdiff(c(NA, levels(ab_result)), ab_result)
|
|
numerator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) any(y %in% ab_result, na.rm = TRUE)))
|
|
denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(all(y %in% other_values) & anyNA(y))))
|
|
}
|
|
} else {
|
|
# x is not a data.frame
|
|
if (!is.sir(x)) {
|
|
x <- as.sir(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) {
|
|
if (message_not_thrown_before("sir_calc")) {
|
|
warning_("Increase speed by transforming to class 'sir' on beforehand:\n",
|
|
" your_data %>% mutate_if(is_sir_eligible, as.sir)",
|
|
call = FALSE
|
|
)
|
|
}
|
|
}
|
|
|
|
if (only_count == TRUE) {
|
|
return(numerator)
|
|
}
|
|
|
|
if (denominator < minimum) {
|
|
if (data_vars != "") {
|
|
data_vars <- paste(" for", data_vars)
|
|
# also add group name if used in dplyr::group_by()
|
|
cur_group <- import_fn("cur_group", "dplyr", error_on_fail = FALSE)
|
|
if (!is.null(cur_group)) {
|
|
group_df <- tryCatch(cur_group(), error = function(e) data.frame())
|
|
if (NCOL(group_df) > 0) {
|
|
# transform factors to characters
|
|
group <- vapply(FUN.VALUE = character(1), group_df, function(x) {
|
|
if (is.numeric(x)) {
|
|
format(x)
|
|
} else if (is.logical(x)) {
|
|
as.character(x)
|
|
} else {
|
|
paste0('"', x, '"')
|
|
}
|
|
})
|
|
data_vars <- paste0(data_vars, " in group: ", paste0(names(group), " = ", group, collapse = ", "))
|
|
}
|
|
}
|
|
}
|
|
warning_("Introducing NA: ",
|
|
ifelse(denominator == 0, "no", paste("only", denominator)),
|
|
" results available",
|
|
data_vars,
|
|
" (`minimum` = ", minimum, ").",
|
|
call = FALSE
|
|
)
|
|
fraction <- NA_real_
|
|
} else {
|
|
fraction <- numerator / denominator
|
|
fraction[is.nan(fraction)] <- NA_real_
|
|
}
|
|
|
|
if (as_percent == TRUE) {
|
|
percentage(fraction, digits = 1)
|
|
} else {
|
|
fraction
|
|
}
|
|
}
|
|
|
|
sir_calc_df <- function(type, # "proportion", "count" or "both"
|
|
data,
|
|
translate_ab = "name",
|
|
language = get_AMR_locale(),
|
|
minimum = 30,
|
|
as_percent = FALSE,
|
|
combine_SI = TRUE,
|
|
confidence_level = 0.95) {
|
|
meet_criteria(type, is_in = c("proportion", "count", "both"), has_length = 1)
|
|
meet_criteria(data, allow_class = "data.frame", contains_column_class = "sir")
|
|
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
|
|
language <- validate_language(language)
|
|
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
|
|
meet_criteria(as_percent, allow_class = "logical", has_length = 1)
|
|
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
|
|
meet_criteria(confidence_level, allow_class = "numeric", has_length = 1)
|
|
|
|
translate_ab <- get_translate_ab(translate_ab)
|
|
|
|
data.bak <- data
|
|
# select only groups and antibiotics
|
|
if (is_null_or_grouped_tbl(data)) {
|
|
data_has_groups <- TRUE
|
|
groups <- get_group_names(data)
|
|
data <- data[, c(groups, colnames(data)[vapply(FUN.VALUE = logical(1), data, is.sir)]), drop = FALSE]
|
|
} else {
|
|
data_has_groups <- FALSE
|
|
data <- data[, colnames(data)[vapply(FUN.VALUE = logical(1), data, is.sir)], drop = FALSE]
|
|
}
|
|
|
|
data <- as.data.frame(data, stringsAsFactors = FALSE)
|
|
if (isTRUE(combine_SI)) {
|
|
for (i in seq_len(ncol(data))) {
|
|
if (is.sir(data[, i, drop = TRUE])) {
|
|
data[, i] <- as.character(data[, i, drop = TRUE])
|
|
data[, i] <- gsub("(I|S)", "SI", data[, i, drop = TRUE])
|
|
}
|
|
}
|
|
}
|
|
|
|
sum_it <- function(.data) {
|
|
out <- data.frame(
|
|
antibiotic = character(0),
|
|
interpretation = character(0),
|
|
value = double(0),
|
|
ci_min = double(0),
|
|
ci_max = 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 {
|
|
values <- factor(values, levels = c("S", "I", "R"), ordered = TRUE)
|
|
}
|
|
col_results <- as.data.frame(as.matrix(table(values)), stringsAsFactors = FALSE)
|
|
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$isolates, na.rm = TRUE)
|
|
ci <- lapply(
|
|
col_results$isolates,
|
|
function(x) {
|
|
stats::binom.test(
|
|
x = x,
|
|
n = sum(col_results$isolates, na.rm = TRUE),
|
|
conf.level = confidence_level
|
|
)$conf.int
|
|
}
|
|
)
|
|
col_results$ci_min <- vapply(FUN.VALUE = double(1), ci, `[`, 1)
|
|
col_results$ci_max <- vapply(FUN.VALUE = double(1), ci, `[`, 2)
|
|
} else {
|
|
col_results$value <- rep(NA_real_, NROW(col_results))
|
|
# confidence intervals also to NA
|
|
col_results$ci_min <- col_results$value
|
|
col_results$ci_max <- col_results$value
|
|
}
|
|
out_new <- data.frame(
|
|
antibiotic = ifelse(isFALSE(translate_ab),
|
|
colnames(.data)[i],
|
|
ab_property(colnames(.data)[i], property = translate_ab, language = language)
|
|
),
|
|
interpretation = col_results$interpretation,
|
|
value = col_results$value,
|
|
ci_min = col_results$ci_min,
|
|
ci_max = col_results$ci_max,
|
|
isolates = col_results$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_AMR(out, out_new)
|
|
}
|
|
}
|
|
out
|
|
}
|
|
|
|
# based on pm_apply_grouped_function
|
|
apply_group <- function(.data, fn, groups, drop = FALSE, ...) {
|
|
grouped <- pm_split_into_groups(.data, groups, drop)
|
|
res <- do.call(rbind_AMR, unname(lapply(grouped, fn, ...)))
|
|
if (any(groups %in% colnames(res))) {
|
|
class(res) <- c("grouped_data", class(res))
|
|
res <- pm_set_groups(res, 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 {
|
|
# don't use as.sir() here, as it would add the class 'sir' 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 <- pm_ungroup(out[do.call("order", out[, seq_len(length(groups) + 2), drop = FALSE]), , drop = FALSE])
|
|
} else {
|
|
out <- out[order(out$antibiotic, out$interpretation), , drop = FALSE]
|
|
}
|
|
|
|
if (type == "proportion") {
|
|
# remove number of isolates
|
|
out <- subset(out, select = -c(isolates))
|
|
} else if (type == "count") {
|
|
# set value to be number of isolates
|
|
out$value <- out$isolates
|
|
# remove redundant columns
|
|
out <- subset(out, select = -c(ci_min, ci_max, isolates))
|
|
}
|
|
|
|
rownames(out) <- NULL
|
|
out <- as_original_data_class(out, class(data.bak)) # will remove tibble groups
|
|
structure(out, class = c("sir_df", class(out)))
|
|
}
|