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styled, unit test fix

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dr. M.S. (Matthijs) Berends
2022-08-28 10:31:50 +02:00
parent 4cb1db4554
commit 4d050aef7c
147 changed files with 10897 additions and 8169 deletions
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197
R/bug_drug_combinations.R
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@ -9,7 +9,7 @@
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
@ -24,39 +24,44 @@
# ==================================================================== #
#' 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_IR a [logical] to indicate whether values R and I should be summed
#' @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 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 rsi_df
#' @inheritParams base::formatC
#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_IR = FALSE` (default) to test R vs. S+I and `combine_IR = TRUE` to test R+I vs. S.
#' @details The function [format()] calculates the resistance per bug-drug combination. Use `combine_IR = FALSE` (default) to test R vs. S+I and `combine_IR = TRUE` 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".
#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @examples
#' @examples
#' \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"))
#' 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,
bug_drug_combinations <- function(x,
col_mo = NULL,
FUN = mo_shortname,
...) {
meet_criteria(x, allow_class = "data.frame", contains_column_class = "rsi")
@ -71,13 +76,13 @@ bug_drug_combinations <- function(x,
} 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
@ -87,21 +92,23 @@ bug_drug_combinations <- function(x,
data_has_groups <- FALSE
x <- x[, c(col_mo, names(which(vapply(FUN.VALUE = logical(1), x, is.rsi)))), 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)
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 R/SI columns
x_mo_filter <- x[which(x[, col_mo, drop = TRUE] == unique_mo[i]), names(which(vapply(FUN.VALUE = logical(1), x, is.rsi))), drop = FALSE]
@ -111,18 +118,21 @@ bug_drug_combinations <- function(x,
data.frame(S = m["S", ], I = m["I", ], R = m["R", ], stringsAsFactors = FALSE)
})
merged <- do.call(rbind, 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)
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))
each = nrow(out_group) / nrow(group_values)
)
group_values <- group_values[repeated, , drop = FALSE]
}
out_group <- cbind(group_values, out_group)
@ -141,7 +151,7 @@ bug_drug_combinations <- function(x,
}
res
}
if (data_has_groups) {
out <- apply_group(x, "run_it", groups)
} else {
@ -176,27 +186,31 @@ format.bug_drug_combinations <- function(x,
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 = sapply(idx, function(i) sum(x$S[i], na.rm = TRUE)),
I = sapply(idx, function(i) sum(x$I[i], na.rm = TRUE)),
R = sapply(idx, function(i) sum(x$R[i], na.rm = TRUE)),
total = sapply(idx, function(i) sum(x$S[i], na.rm = TRUE) +
sum(x$I[i], na.rm = TRUE) +
sum(x$R[i], na.rm = TRUE)),
stringsAsFactors = FALSE)
x <- data.frame(
mo = gsub("(.*)%%(.*)", "\\1", names(idx)),
ab = gsub("(.*)%%(.*)", "\\2", names(idx)),
S = sapply(idx, function(i) sum(x$S[i], na.rm = TRUE)),
I = sapply(idx, function(i) sum(x$I[i], na.rm = TRUE)),
R = sapply(idx, function(i) sum(x$R[i], na.rm = TRUE)),
total = sapply(idx, function(i) {
sum(x$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)
}
@ -205,7 +219,7 @@ format.bug_drug_combinations <- function(x,
} else {
x$isolates <- x$R + x$I
}
give_ab_name <- function(ab, format, language) {
format <- tolower(format)
ab_txt <- rep(format, length(ab))
@ -221,15 +235,16 @@ format.bug_drug_combinations <- function(x,
}
ab_txt
}
remove_NAs <- function(.data) {
cols <- colnames(.data)
.data <- as.data.frame(lapply(.data, function(x) ifelse(is.na(x), "", x)),
stringsAsFactors = FALSE)
stringsAsFactors = FALSE
)
colnames(.data) <- cols
.data
}
create_var <- function(.data, ...) {
dots <- list(...)
for (i in seq_len(length(dots))) {
@ -237,66 +252,74 @@ format.bug_drug_combinations <- function(x,
}
.data
}
y <- x %pm>%
create_var(ab = as.ab(x$ab),
ab_txt = give_ab_name(ab = x$ab, format = translate_ab, language = language)) %pm>%
pm_group_by(ab, ab_txt, mo) %pm>%
pm_summarise(isolates = sum(isolates, na.rm = TRUE),
total = sum(total, na.rm = TRUE)) %pm>%
create_var(
ab = as.ab(x$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$total, big.mark = big.mark)), ")")) %pm>%
y <- y %pm>%
create_var(txt = paste0(
percentage(y$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$mo == i), c("ab", "txt"), drop = FALSE]
colnames(mo_group) <- c("ab", i)
rownames(mo_group) <- NULL
y <- y %pm>%
y <- y %pm>%
pm_left_join(mo_group, by = "ab")
}
y <- y %pm>%
pm_distinct(ab, .keep_all = TRUE) %pm>%
pm_select(-mo, -txt) %pm>%
y <- y %pm>%
pm_distinct(ab, .keep_all = TRUE) %pm>%
pm_select(-mo, -txt) %pm>%
# replace tidyr::pivot_wider() until here
remove_NAs()
select_ab_vars <- function(.data) {
.data[, c("ab_group", "ab_txt", colnames(.data)[!colnames(.data) %in% c("ab_group", "ab_txt", "ab")]), drop = FALSE]
}
y <- y %pm>%
create_var(ab_group = ab_group(y$ab, language = language)) %pm>%
select_ab_vars() %pm>%
y <- y %pm>%
create_var(ab_group = ab_group(y$ab, language = language)) %pm>%
select_ab_vars() %pm>%
pm_arrange(ab_group, ab_txt)
y <- y %pm>%
y <- y %pm>%
create_var(ab_group = ifelse(y$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>%
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)
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) & !any(is.na(col))), drop = FALSE]
}
rownames(y) <- NULL
as_original_data_class(y, class(x.bak))
}
@ -305,10 +328,14 @@ format.bug_drug_combinations <- function(x,
#' @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")]),
...)
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)
ifelse(inherits(x, "grouped"), " Note: The grouping variable(s) will be ignored.", ""),
as_note = FALSE
)
}