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(v1.3.0.9022) mo_matching_score(), poorman update, as.rsi() fix
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@ -35,7 +35,7 @@
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#' @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.
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#' @export
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#' @rdname bug_drug_combinations
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#' @return The function [bug_drug_combinations()] returns a [`data.frame`] with columns "mo", "ab", "S", "I", "R" and "total".
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#' @return The function [bug_drug_combinations()] returns a [data.frame] with columns "mo", "ab", "S", "I", "R" and "total".
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#' @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/>.
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#' @inheritSection AMR Read more on our website!
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#' @examples
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@ -160,32 +160,33 @@ format.bug_drug_combinations <- function(x,
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.data
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}
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y <- x %>%
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y <- x %pm>%
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create_var(ab = as.ab(x$ab),
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ab_txt = give_ab_name(ab = x$ab, format = translate_ab, language = language)) %>%
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group_by(ab, ab_txt, mo) %>%
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summarise(isolates = sum(isolates, na.rm = TRUE),
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total = sum(total, na.rm = TRUE)) %>%
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ungroup()
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ab_txt = give_ab_name(ab = x$ab, format = translate_ab, language = language)) %pm>%
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pm_group_by(ab, ab_txt, mo) %pm>%
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pm_summarise(isolates = sum(isolates, na.rm = TRUE),
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total = sum(total, na.rm = TRUE)) %pm>%
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pm_ungroup()
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y <- y %>%
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y <- y %pm>%
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create_var(txt = paste0(percentage(y$isolates / y$total, decimal.mark = decimal.mark, big.mark = big.mark),
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" (", trimws(format(y$isolates, big.mark = big.mark)), "/",
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trimws(format(y$total, big.mark = big.mark)), ")")) %>%
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select(ab, ab_txt, mo, txt) %>%
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arrange(mo)
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trimws(format(y$total, big.mark = big.mark)), ")")) %pm>%
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pm_select(ab, ab_txt, mo, txt) %pm>%
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pm_arrange(mo)
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# replace tidyr::pivot_wider() from here
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for (i in unique(y$mo)) {
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mo_group <- y[which(y$mo == i), c("ab", "txt")]
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colnames(mo_group) <- c("ab", i)
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rownames(mo_group) <- NULL
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y <- y %>%
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left_join(mo_group, by = "ab")
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y <- y %pm>%
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pm_left_join(mo_group, by = "ab")
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}
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y <- y %>%
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distinct(ab, .keep_all = TRUE) %>%
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select(-mo, -txt) %>%
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y <<- y
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y <- y %pm>%
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pm_distinct(ab, .keep_all = TRUE) %pm>%
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pm_select(-mo, -txt) %pm>%
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# replace tidyr::pivot_wider() until here
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remove_NAs()
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@ -193,21 +194,22 @@ format.bug_drug_combinations <- function(x,
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.data[, c("ab_group", "ab_txt", colnames(.data)[!colnames(.data) %in% c("ab_group", "ab_txt", "ab")])]
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}
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y <- y %>%
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create_var(ab_group = ab_group(y$ab, language = language)) %>%
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select_ab_vars() %>%
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arrange(ab_group, ab_txt)
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y <- y %>%
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create_var(ab_group = ifelse(y$ab_group != lag(y$ab_group) | is.na(lag(y$ab_group)), y$ab_group, ""))
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y <- y %pm>%
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create_var(ab_group = ab_group(y$ab, language = language)) %pm>%
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select_ab_vars() %pm>%
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pm_arrange(ab_group, ab_txt)
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y <- y %pm>%
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create_var(ab_group = ifelse(y$ab_group != lag(y$ab_group) | is.na(pm_lag(y$ab_group)), y$ab_group, ""))
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if (add_ab_group == FALSE) {
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y <- y %>%
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select(-ab_group) %>%
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rename("Drug" = ab_txt)
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y <- y %pm>%
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pm_select(-ab_group) %pm>%
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pm_rename("Drug" = ab_txt)
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colnames(y)[1] <- translate_AMR(colnames(y)[1], language = get_locale(), only_unknown = FALSE)
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} else {
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y <- y %>% rename("Group" = ab_group,
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"Drug" = ab_txt)
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y <- y %pm>%
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pm_rename("Group" = ab_group,
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"Drug" = ab_txt)
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colnames(y)[1:2] <- translate_AMR(colnames(y)[1:2], language = get_locale(), only_unknown = FALSE)
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}
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