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AMR/R/bug_drug_combinations.R

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# ==================================================================== #
# TITLE #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# CITE 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. #
# doi: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/ #
# ==================================================================== #
#' Determine Bug-Drug Combinations
#'
#' Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use [format()] on the result to prettify it to a publishable/printable format, see *Examples*.
#' @inheritParams eucast_rules
#' @param combine_SI a [logical] to indicate whether values S and I should be summed, so resistance will be based on only R, defaults to `TRUE`
#' @param add_ab_group a [logical] to indicate where the group of the antimicrobials must be included as a first column
#' @param remove_intrinsic_resistant [logical] to indicate that rows and columns with 100% resistance for all tested antimicrobials must be removed from the table
#' @param FUN the function to call on the `mo` column to transform the microorganism codes, defaults to [mo_shortname()]
#' @param translate_ab a [character] of length 1 containing column names of the [antibiotics] data set
#' @param ... arguments passed on to `FUN`
#' @inheritParams sir_df
#' @inheritParams base::formatC
#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_SI = TRUE` (default) to test R vs. S+I and `combine_SI = FALSE` to test R+I vs. S.
#' @export
#' @rdname bug_drug_combinations
#' @return The function [bug_drug_combinations()] returns a [data.frame] with columns "mo", "ab", "S", "I", "R" and "total".
#' @examples
#' # example_isolates is a data set available in the AMR package.
#' # run ?example_isolates for more info.
#' example_isolates
#'
#' \donttest{
#' x <- bug_drug_combinations(example_isolates)
#' head(x)
#' format(x, translate_ab = "name (atc)")
#'
#' # Use FUN to change to transformation of microorganism codes
#' bug_drug_combinations(example_isolates,
#' FUN = mo_gramstain
#' )
#'
#' bug_drug_combinations(example_isolates,
#' FUN = function(x) {
#' ifelse(x == as.mo("Escherichia coli"),
#' "E. coli",
#' "Others"
#' )
#' }
#' )
#' }
bug_drug_combinations <- function(x,
col_mo = NULL,
FUN = mo_shortname,
...) {
meet_criteria(x, allow_class = "data.frame", contains_column_class = "sir")
meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), has_length = 1, allow_NULL = TRUE)
meet_criteria(FUN, allow_class = "function", has_length = 1)
# try to find columns based on type
# -- mo
if (is.null(col_mo)) {
col_mo <- search_type_in_df(x = x, type = "mo")
stop_if(is.null(col_mo), "`col_mo` must be set")
} else {
stop_ifnot(col_mo %in% colnames(x), "column '", col_mo, "' (`col_mo`) not found")
}
x.bak <- x
x <- as.data.frame(x, stringsAsFactors = FALSE)
x[, col_mo] <- FUN(x[, col_mo, drop = TRUE], ...)
unique_mo <- sort(unique(x[, col_mo, drop = TRUE]))
# select only groups and antibiotics
if (is_null_or_grouped_tbl(x.bak)) {
data_has_groups <- TRUE
groups <- get_group_names(x.bak)
x <- x[, c(groups, col_mo, colnames(x)[vapply(FUN.VALUE = logical(1), x, is.sir)]), drop = FALSE]
} else {
data_has_groups <- FALSE
x <- x[, c(col_mo, names(which(vapply(FUN.VALUE = logical(1), x, is.sir)))), drop = FALSE]
}
run_it <- function(x) {
out <- data.frame(
mo = character(0),
ab = character(0),
S = integer(0),
I = integer(0),
R = integer(0),
total = integer(0),
stringsAsFactors = FALSE
)
if (data_has_groups) {
group_values <- unique(x[, which(colnames(x) %in% groups), drop = FALSE])
rownames(group_values) <- NULL
x <- x[, which(!colnames(x) %in% groups), drop = FALSE]
}
for (i in seq_len(length(unique_mo))) {
# filter on MO group and only select SIR columns
x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(vapply(FUN.VALUE = logical(1), x, is.sir))), drop = FALSE]
# turn and merge everything
pivot <- lapply(x_mo_filter, function(x) {
m <- as.matrix(table(x))
data.frame(S = m["S", ], I = m["I", ], R = m["R", ], stringsAsFactors = FALSE)
})
merged <- do.call(rbind2, pivot)
out_group <- data.frame(
mo = rep(unique_mo[i], NROW(merged)),
ab = rownames(merged),
S = merged$S,
I = merged$I,
R = merged$R,
total = merged$S + merged$I + merged$R,
stringsAsFactors = FALSE
)
if (data_has_groups) {
if (nrow(group_values) < nrow(out_group)) {
# repeat group_values for the number of rows in out_group
repeated <- rep(seq_len(nrow(group_values)),
each = nrow(out_group) / nrow(group_values)
)
group_values <- group_values[repeated, , drop = FALSE]
}
out_group <- cbind(group_values, out_group)
}
out <- rbind2(out, out_group)
}
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(rbind2, 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(x, "run_it", groups)
} else {
out <- run_it(x)
}
rownames(out) <- NULL
out <- out %pm>% pm_arrange(mo, ab)
out <- as_original_data_class(out, class(x.bak)) # will remove tibble groups
structure(out, class = c("bug_drug_combinations", ifelse(data_has_groups, "grouped", character(0)), class(out)))
}
#' @method format bug_drug_combinations
#' @export
#' @rdname bug_drug_combinations
format.bug_drug_combinations <- function(x,
translate_ab = "name (ab, atc)",
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
add_ab_group = TRUE,
remove_intrinsic_resistant = FALSE,
decimal.mark = getOption("OutDec"),
big.mark = ifelse(decimal.mark == ",", ".", ","),
...) {
meet_criteria(x, allow_class = "data.frame")
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(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(add_ab_group, allow_class = "logical", has_length = 1)
meet_criteria(remove_intrinsic_resistant, allow_class = "logical", has_length = 1)
meet_criteria(decimal.mark, allow_class = "character", has_length = 1)
meet_criteria(big.mark, allow_class = "character", has_length = 1)
x.bak <- x
if (inherits(x, "grouped")) {
# bug_drug_combinations() has been run on groups, so de-group here
warning_("in `format()`: formatting the output of `bug_drug_combinations()` does not support grouped variables, they were ignored")
x <- as.data.frame(x, stringsAsFactors = FALSE)
idx <- split(seq_len(nrow(x)), paste0(x$mo, "%%", x$ab))
x <- data.frame(
mo = gsub("(.*)%%(.*)", "\\1", names(idx)),
ab = gsub("(.*)%%(.*)", "\\2", names(idx)),
S = vapply(FUN.VALUE = double(1), idx, function(i) sum(x$S[i], na.rm = TRUE)),
I = vapply(FUN.VALUE = double(1), idx, function(i) sum(x$I[i], na.rm = TRUE)),
R = vapply(FUN.VALUE = double(1), idx, function(i) sum(x$R[i], na.rm = TRUE)),
total = vapply(FUN.VALUE = double(1), idx, function(i) {
sum(x$S[i], na.rm = TRUE) +
sum(x$I[i], na.rm = TRUE) +
sum(x$R[i], na.rm = TRUE)
}),
stringsAsFactors = FALSE
)
}
x <- as.data.frame(x, stringsAsFactors = FALSE)
x <- subset(x, total >= minimum)
if (remove_intrinsic_resistant == TRUE) {
x <- subset(x, R != total)
}
if (combine_SI == TRUE) {
x$isolates <- x$R
} else {
x$isolates <- x$R + x$I
}
give_ab_name <- function(ab, format, language) {
format <- tolower(format)
ab_txt <- rep(format, length(ab))
for (i in seq_len(length(ab_txt))) {
ab_txt[i] <- gsub("ab", as.character(as.ab(ab[i])), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("cid", ab_cid(ab[i]), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("group", ab_group(ab[i], language = language), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("atc_group1", ab_atc_group1(ab[i], language = language), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("atc_group2", ab_atc_group2(ab[i], language = language), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("atc", ab_atc(ab[i], only_first = TRUE), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("name", ab_name(ab[i], language = language), ab_txt[i], fixed = TRUE)
ab_txt[i]
}
ab_txt
}
remove_NAs <- function(.data) {
cols <- colnames(.data)
.data <- as.data.frame(lapply(.data, function(x) ifelse(is.na(x), "", x)),
stringsAsFactors = FALSE
)
colnames(.data) <- cols
.data
}
create_var <- function(.data, ...) {
dots <- list(...)
for (i in seq_len(length(dots))) {
.data[, names(dots)[i]] <- dots[[i]]
}
.data
}
y <- x %pm>%
create_var(
ab = as.ab(x$ab),
ab_txt = give_ab_name(ab = x$ab, format = translate_ab, language = language)
) %pm>%
pm_group_by(ab, ab_txt, mo) %pm>%
pm_summarise(
isolates = sum(isolates, na.rm = TRUE),
total = sum(total, na.rm = TRUE)
) %pm>%
pm_ungroup()
y <- y %pm>%
create_var(txt = paste0(
percentage(y$isolates / y$total, decimal.mark = decimal.mark, big.mark = big.mark),
" (", trimws(format(y$isolates, big.mark = big.mark)), "/",
trimws(format(y$total, big.mark = big.mark)), ")"
)) %pm>%
pm_select(ab, ab_txt, mo, txt) %pm>%
pm_arrange(mo)
# replace tidyr::pivot_wider() from here
for (i in unique(y$mo)) {
mo_group <- y[which(y$mo == i), c("ab", "txt"), drop = FALSE]
colnames(mo_group) <- c("ab", i)
rownames(mo_group) <- NULL
y <- y %pm>%
pm_left_join(mo_group, by = "ab")
}
y <- y %pm>%
pm_distinct(ab, .keep_all = TRUE) %pm>%
pm_select(-mo, -txt) %pm>%
# replace tidyr::pivot_wider() until here
remove_NAs()
select_ab_vars <- function(.data) {
.data[, c("ab_group", "ab_txt", colnames(.data)[!colnames(.data) %in% c("ab_group", "ab_txt", "ab")]), drop = FALSE]
}
y <- y %pm>%
create_var(ab_group = ab_group(y$ab, language = language)) %pm>%
select_ab_vars() %pm>%
pm_arrange(ab_group, ab_txt)
y <- y %pm>%
create_var(ab_group = ifelse(y$ab_group != pm_lag(y$ab_group) | is.na(pm_lag(y$ab_group)), y$ab_group, ""))
if (add_ab_group == FALSE) {
y <- y %pm>%
pm_select(-ab_group) %pm>%
pm_rename("Drug" = ab_txt)
colnames(y)[1] <- translate_into_language(colnames(y)[1], language, only_unknown = FALSE)
} else {
y <- y %pm>%
pm_rename(
"Group" = ab_group,
"Drug" = ab_txt
)
}
if (!is.null(language)) {
colnames(y) <- translate_into_language(colnames(y), language, only_unknown = FALSE)
}
if (remove_intrinsic_resistant == TRUE) {
y <- y[, !vapply(FUN.VALUE = logical(1), y, function(col) all(col %like% "100", na.rm = TRUE) & !anyNA(col)), drop = FALSE]
}
rownames(y) <- NULL
as_original_data_class(y, class(x.bak)) # will remove tibble groups
}
#' @method print bug_drug_combinations
#' @export
print.bug_drug_combinations <- function(x, ...) {
x_class <- class(x)
print(
set_clean_class(x,
new_class = x_class[!x_class %in% c("bug_drug_combinations", "grouped")]
),
...
)
message_("Use 'format()' on this result to get a publishable/printable format.",
ifelse(inherits(x, "grouped"), " Note: The grouping variable(s) will be ignored.", ""),
as_note = FALSE
)
}
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