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(v1.4.0.9017) stringsAsFactors definitions
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 1.4.0.9016
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Date: 2020-11-10
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Version: 1.4.0.9017
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Date: 2020-11-11
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Title: Antimicrobial Resistance Analysis
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Authors@R: c(
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person(role = c("aut", "cre"),
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5
NEWS.md
5
NEWS.md
@ -1,5 +1,5 @@
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# AMR 1.4.0.9016
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## <small>Last updated: 10 November 2020</small>
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# AMR 1.4.0.9017
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## <small>Last updated: 11 November 2020</small>
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### New
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* Functions `is_gram_negative()` and `is_gram_positive()` as wrappers around `mo_gramstain()`. They always return `TRUE` or `FALSE` (except when the input is `NA` or the MO code is `UNKNOWN`), thus always return `FALSE` for species outside the taxonomic kingdom of Bacteria. If you have the `dplyr` package installed, they can even determine the column with microorganisms themselves inside `dplyr` functions:
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@ -11,6 +11,7 @@
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* Functions `%not_like%` and `%not_like_case%` as wrappers around `%like%` and `%like_case%`. The RStudio addin to insert the text " %like% " as provided in this package now iterates over all like variants. So if you have defined the keyboard shortcut Ctrl/Cmd + L to this addin, it will first insert ` %like% ` and by pressing it again it will be replaced with ` %not_like% `, etc.
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### Changed
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* Reference data used for `as.rsi()` can now be set by the user, using the `reference_data` parameter.
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* For all function parameters in the code, it is now defined what the exact type of user input should be (inspired by the [`typed`](https://github.com/moodymudskipper/typed) package). If the user input for a certain function does not meet the requirements for a specific parameter (such as the class or length), an informative error will be thrown. This makes the package more robust and the use of it more reproducible and reliable. In total, more than 400 arguments were defined.
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* Deprecated function `p_symbol()` that not really fits the scope of this package. It will be removed in a future version. See [here](https://github.com/msberends/AMR/blob/v1.4.0/R/p_symbol.R) for the source code to preserve it.
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* Better determination of disk zones and MIC values when running `as.rsi()` on a data.frame
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@ -153,7 +153,7 @@ ab_selector <- function(ab_class, function_name) {
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peek_vars_tidyselect <- import_fn("peek_vars", "tidyselect")
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vars_vct <- peek_vars_tidyselect(fn = function_name)
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vars_df <- data.frame(as.list(vars_vct))[1, , drop = FALSE]
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vars_df <- data.frame(as.list(vars_vct), stringsAsFactors = FALSE)[1, , drop = FALSE]
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colnames(vars_df) <- vars_vct
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ab_in_data <- get_column_abx(vars_df, info = FALSE)
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@ -85,7 +85,8 @@ availability <- function(tbl, width = NULL) {
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available = percentage(x),
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visual_availabilty = paste0("|", x_chars, x_chars_empty, "|"),
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resistant = R_print,
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visual_resistance = vis_resistance)
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visual_resistance = vis_resistance,
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stringsAsFactors = FALSE)
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if (length(R[is.na(R)]) == ncol(tbl)) {
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df[, 1:3]
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} else {
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@ -79,13 +79,13 @@ bug_drug_combinations <- function(x,
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unique_mo <- sort(unique(x[, col_mo, drop = TRUE]))
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out <- data.frame(
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mo = character(0),
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out <- data.frame(mo = character(0),
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ab = character(0),
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S = integer(0),
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I = integer(0),
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R = integer(0),
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total = integer(0))
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total = integer(0),
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stringsAsFactors = FALSE)
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for (i in seq_len(length(unique_mo))) {
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# filter on MO group and only select R/SI columns
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@ -101,8 +101,9 @@ bug_drug_combinations <- function(x,
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S = merged$S,
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I = merged$I,
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R = merged$R,
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total = merged$S + merged$I + merged$R)
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out <- rbind(out, out_group)
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total = merged$S + merged$I + merged$R,
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stringsAsFactors = FALSE)
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out <- rbind(out, out_group, stringsAsFactors = FALSE)
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}
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structure(.Data = out, class = c("bug_drug_combinations", x_class))
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@ -163,7 +164,8 @@ format.bug_drug_combinations <- function(x,
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remove_NAs <- function(.data) {
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cols <- colnames(.data)
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.data <- as.data.frame(sapply(.data, function(x) ifelse(is.na(x), "", x), simplify = FALSE))
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.data <- as.data.frame(sapply(.data, function(x) ifelse(is.na(x), "", x), simplify = FALSE),
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stringsAsFactors = FALSE)
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colnames(.data) <- cols
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.data
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}
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@ -1088,7 +1088,9 @@ edit_rsi <- function(x,
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"rule", "rule_group", "rule_name", "rule_source")
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verbose_new <- verbose_new %pm>% pm_filter(old != new | is.na(old))
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# save changes to data set 'verbose_info'
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track_changes$verbose_info <- rbind(track_changes$verbose_info, verbose_new)
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track_changes$verbose_info <- rbind(track_changes$verbose_info,
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verbose_new,
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stringsAsFactors = FALSE)
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# count adds and changes
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track_changes$added <- track_changes$added + verbose_new %pm>%
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pm_filter(is.na(old)) %pm>%
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@ -163,7 +163,7 @@ filter_ab_class <- function(x,
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"` (", ab_name(names(agents), tolower = TRUE, language = NULL), ")"),
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collapse = scope_txt),
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operator, toString(result), as_note = FALSE)
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x_transposed <- as.list(as.data.frame(t(x[, agents, drop = FALSE])))
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x_transposed <- as.list(as.data.frame(t(x[, agents, drop = FALSE]), stringsAsFactors = FALSE))
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filtered <- sapply(x_transposed, function(y) scope_fn(y %in% result, na.rm = TRUE))
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x <- x[which(filtered), , drop = FALSE]
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class(x) <- x_class
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@ -314,11 +314,13 @@ pca_calculations <- function(pca_model,
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# Scores
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choices <- pmin(choices, ncol(u))
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obs.scale <- 1 - as.integer(scale)
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df.u <- as.data.frame(sweep(u[, choices], 2, d[choices] ^ obs.scale, FUN = "*"))
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df.u <- as.data.frame(sweep(u[, choices], 2, d[choices] ^ obs.scale, FUN = "*"),
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stringsAsFactors = FALSE)
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# Directions
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v <- sweep(v, 2, d ^ as.integer(scale), FUN = "*")
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df.v <- as.data.frame(v[, choices])
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df.v <- as.data.frame(v[, choices],
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stringsAsFactors = FALSE)
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names(df.u) <- c("xvar", "yvar")
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names(df.v) <- names(df.u)
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@ -356,7 +358,8 @@ pca_calculations <- function(pca_model,
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if (nrow(x) <= 2) {
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return(data.frame(X1 = numeric(0),
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X2 = numeric(0),
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groups = character(0)))
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groups = character(0),
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stringsAsFactors = FALSE))
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}
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sigma <- var(cbind(x$xvar, x$yvar))
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mu <- c(mean(x$xvar), mean(x$yvar))
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@ -145,8 +145,9 @@ get_column_abx <- function(x,
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vectr_antibiotics <- vectr_antibiotics[!is.na(vectr_antibiotics) & nchar(vectr_antibiotics) >= 3]
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x_columns <- sapply(colnames(x), function(col, df = x_bak) {
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if (toupper(col) %in% vectr_antibiotics |
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is.rsi(as.data.frame(df)[, col, drop = TRUE]) |
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is.rsi.eligible(as.data.frame(df)[, col, drop = TRUE], threshold = 0.5)) {
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is.rsi(as.data.frame(df, stringsAsFactors = FALSE)[, col, drop = TRUE]) |
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is.rsi.eligible(as.data.frame(df, stringsAsFactors = FALSE)[, col, drop = TRUE],
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threshold = 0.5)) {
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return(col)
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} else {
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return(NA_character_)
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@ -156,7 +157,8 @@ get_column_abx <- function(x,
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x <- x[, x_columns, drop = FALSE] # without drop = TRUE, x will become a vector when x_columns is length 1
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df_trans <- data.frame(colnames = colnames(x),
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abcode = suppressWarnings(as.ab(colnames(x), info = FALSE)))
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abcode = suppressWarnings(as.ab(colnames(x), info = FALSE)),
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stringsAsFactors = FALSE)
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df_trans <- df_trans[!is.na(df_trans$abcode), , drop = FALSE]
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x <- as.character(df_trans$colnames)
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names(x) <- df_trans$abcode
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16
R/mdro.R
16
R/mdro.R
@ -574,7 +574,9 @@ mdro <- function(x,
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cols <- cols[!ab_missing(cols)]
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cols <- cols[!is.na(cols)]
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if (length(rows) > 0 & length(cols) > 0) {
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x[, cols] <- as.data.frame(lapply(x[, cols, drop = FALSE], function(col) as.rsi(col)))
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x[, cols] <- as.data.frame(lapply(x[, cols, drop = FALSE],
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function(col) as.rsi(col)),
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stringsAsFactors = FALSE)
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x[rows, "columns_nonsusceptible"] <<- sapply(rows,
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function(row, group_vct = cols) {
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cols_nonsus <- sapply(x[row, group_vct, drop = FALSE],
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@ -589,7 +591,8 @@ mdro <- function(x,
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} else if (any_all == "all") {
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search_function <- all
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}
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x_transposed <- as.list(as.data.frame(t(x[, cols, drop = FALSE])))
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x_transposed <- as.list(as.data.frame(t(x[, cols, drop = FALSE]),
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stringsAsFactors = FALSE))
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row_filter <- sapply(x_transposed, function(y) search_function(y %in% search_result, na.rm = TRUE))
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row_filter <- x[which(row_filter), "row_number", drop = TRUE]
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rows <- rows[rows %in% row_filter]
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@ -604,7 +607,9 @@ mdro <- function(x,
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if (length(rows) > 0) {
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# function specific for the CMI paper of 2012 (Magiorakos et al.)
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lst_vector <- unlist(lst)[!is.na(unlist(lst))]
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x[, lst_vector] <- as.data.frame(lapply(x[, lst_vector, drop = FALSE], function(col) as.rsi(col)))
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x[, lst_vector] <- as.data.frame(lapply(x[, lst_vector, drop = FALSE],
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function(col) as.rsi(col)),
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stringsAsFactors = FALSE)
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x[rows, "classes_in_guideline"] <<- length(lst)
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x[rows, "classes_available"] <<- sapply(rows,
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function(row, group_tbl = lst) {
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@ -627,13 +632,14 @@ mdro <- function(x,
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na.rm = TRUE)
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})
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# for PDR; all agents are R (or I if combine_SI = FALSE)
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x_transposed <- as.list(as.data.frame(t(x[rows, lst_vector, drop = FALSE])))
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x_transposed <- as.list(as.data.frame(t(x[rows, lst_vector, drop = FALSE]),
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stringsAsFactors = FALSE))
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row_filter <- sapply(x_transposed, function(y) all(y %in% search_result, na.rm = TRUE))
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x[which(row_filter), "classes_affected"] <<- 999
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}
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if (info == TRUE) {
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message_(" OK", as_note = FALSE)
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message_(" OK.", add_fn = list(font_green, font_bold), as_note = FALSE)
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}
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}
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54
R/mo.R
54
R/mo.R
@ -324,7 +324,8 @@ exec_as.mo <- function(x,
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format_uncertainty_as_df(uncertainty_level = uncertainty,
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input = input,
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result_mo = res_df[1, "mo", drop = TRUE],
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candidates = as.character(res_df[, "fullname", drop = TRUE])))
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candidates = as.character(res_df[, "fullname", drop = TRUE])),
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stringsAsFactors = FALSE)
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}
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res[seq_len(min(n, length(res)))]
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}
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@ -819,7 +820,8 @@ exec_as.mo <- function(x,
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uncertainties <- rbind(uncertainties,
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format_uncertainty_as_df(uncertainty_level = 1,
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input = x_backup[i],
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result_mo = lookup(fullname == "Salmonella enterica", "mo", uncertainty = -1)))
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result_mo = lookup(fullname == "Salmonella enterica", "mo", uncertainty = -1)),
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stringsAsFactors = FALSE)
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next
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}
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}
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@ -1022,7 +1024,8 @@ exec_as.mo <- function(x,
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uncertainties <<- rbind(uncertainties,
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format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
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input = a.x_backup,
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result_mo = lookup(fullname == found["fullname_new"], "mo", haystack = MO_lookup)))
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result_mo = lookup(fullname == found["fullname_new"], "mo", haystack = MO_lookup)),
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stringsAsFactors = FALSE)
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return(x)
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}
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@ -1043,7 +1046,8 @@ exec_as.mo <- function(x,
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if (!empty_result(found)) {
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found_result <- found
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1071,7 +1075,8 @@ exec_as.mo <- function(x,
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uncertainties <<- rbind(uncertainties,
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format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
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input = a.x_backup,
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result_mo = found_result))
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result_mo = found_result),
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stringsAsFactors = FALSE)
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return(found)
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}
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}
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@ -1095,7 +1100,8 @@ exec_as.mo <- function(x,
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if (!empty_result(found) & nchar(g.x_backup_without_spp) >= 6) {
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found_result <- found
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1118,7 +1124,8 @@ exec_as.mo <- function(x,
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if (!empty_result(found) & nchar(g.x_backup_without_spp) >= 6) {
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found_result <- found
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1147,7 +1154,8 @@ exec_as.mo <- function(x,
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if (!empty_result(found)) {
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found_result <- found
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1175,7 +1183,8 @@ exec_as.mo <- function(x,
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if (!empty_result(found)) {
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found_result <- found
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1193,7 +1202,8 @@ exec_as.mo <- function(x,
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uncertainties <<- rbind(uncertainties,
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format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
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input = a.x_backup,
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result_mo = found_result))
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result_mo = found_result),
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stringsAsFactors = FALSE)
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return(found)
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}
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if (b.x_trimmed %like_case% "(fungus|fungi)" & !b.x_trimmed %like_case% "fungiphrya") {
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@ -1203,7 +1213,8 @@ exec_as.mo <- function(x,
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uncertainties <<- rbind(uncertainties,
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format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
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input = a.x_backup,
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result_mo = found_result))
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result_mo = found_result),
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stringsAsFactors = FALSE)
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return(found)
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}
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# (9) try to strip off one element from start and check the remains (only allow >= 2-part name outcome) ----
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@ -1228,7 +1239,8 @@ exec_as.mo <- function(x,
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# uncertainty level 2 only if searched part contains a space (otherwise it will be found with lvl 3)
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if (x_strip_collapsed %like_case% " ") {
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1261,7 +1273,8 @@ exec_as.mo <- function(x,
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if (!empty_result(found)) {
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found_result <- found
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1287,7 +1300,8 @@ exec_as.mo <- function(x,
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if (!empty_result(found)) {
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found_result <- found
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1305,7 +1319,8 @@ exec_as.mo <- function(x,
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if (!is.na(found) & nchar(g.x_backup_without_spp) >= 6) {
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found_result <- lookup(mo == found)
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uncertainties <<- rbind(uncertainties,
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attr(found, which = "uncertainties", exact = TRUE))
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attr(found, which = "uncertainties", exact = TRUE),
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stringsAsFactors = FALSE)
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found <- lookup(mo == found)
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return(found)
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}
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@ -1498,7 +1513,8 @@ exec_as.mo <- function(x,
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format_uncertainty_as_df(uncertainty_level = actual_uncertainty,
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input = actual_input,
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result_mo = x,
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candidates = ""))
|
||||
candidates = ""),
|
||||
stringsAsFactors = FALSE)
|
||||
}
|
||||
# this will save the uncertain items as attribute, so they can be bound to `uncertainties` in the uncertain_fn() function
|
||||
x <- structure(x, uncertainties = uncertainties)
|
||||
@ -1520,7 +1536,9 @@ was_renamed <- function(name_old, name_new, ref_old = "", ref_new = "", mo = "")
|
||||
stringsAsFactors = FALSE)
|
||||
already_set <- getOption("mo_renamed")
|
||||
if (!is.null(already_set)) {
|
||||
options(mo_renamed = rbind(already_set, newly_set))
|
||||
options(mo_renamed = rbind(already_set,
|
||||
newly_set,
|
||||
stringsAsFactors = FALSE))
|
||||
} else {
|
||||
options(mo_renamed = newly_set)
|
||||
}
|
||||
@ -1791,7 +1809,7 @@ print.mo_uncertainties <- function(x, ...) {
|
||||
mo_renamed <- function() {
|
||||
items <- getOption("mo_renamed", default = NULL)
|
||||
if (is.null(items)) {
|
||||
items <- data.frame()
|
||||
items <- data.frame(stringsAsFactors = FALSE)
|
||||
} else {
|
||||
items <- pm_distinct(items, old_name, .keep_all = TRUE)
|
||||
}
|
||||
|
||||
@ -186,7 +186,8 @@ resistance_predict <- function(x,
|
||||
# remove rows with NAs
|
||||
df <- subset(df, !is.na(df[, col_ab, drop = TRUE]))
|
||||
df$year <- year(df[, col_date, drop = TRUE])
|
||||
df <- as.data.frame(rbind(table(df[, c("year", col_ab)])), stringsAsFactors = FALSE)
|
||||
df <- as.data.frame(rbind(table(df[, c("year", col_ab)])),
|
||||
stringsAsFactors = FALSE)
|
||||
df$year <- as.integer(rownames(df))
|
||||
rownames(df) <- NULL
|
||||
|
||||
|
||||
90
R/rsi.R
90
R/rsi.R
@ -36,6 +36,7 @@
|
||||
#' @param guideline defaults to the latest included EUCAST guideline, see Details for all options
|
||||
#' @param conserve_capped_values a logical to indicate that MIC values starting with `">"` (but not `">="`) must always return "R" , and that MIC values starting with `"<"` (but not `"<="`) must always return "S"
|
||||
#' @param add_intrinsic_resistance *(only useful when using a EUCAST guideline)* a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in *Klebsiella* species. Determination is based on the [intrinsic_resistant] data set, that itself is based on 'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes', version `r EUCAST_VERSION_EXPERT_RULES`.
|
||||
#' @param reference_data a [data.frame] to be used for interpretation, which defaults to the [rsi_translation] data set. Changing this parameter allows for using own interpretation guidelines. This parameter must contain a data set that is equal in structure to the [rsi_translation] data set (same column names and column types). Please note that the 'guideline' column in this data set must contain values set in the 'guideline' parameter of [as.rsi()].
|
||||
#' @param threshold maximum fraction of invalid antimicrobial interpretations of `x`, please see *Examples*
|
||||
#' @param ... for using on a [data.frame]: names of columns to apply [as.rsi()] on (supports tidy selection like `AMX:VAN`). Otherwise: parameters passed on to methods.
|
||||
#' @details
|
||||
@ -66,7 +67,7 @@
|
||||
#'
|
||||
#' For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the `guideline` parameter are: `r paste0('"', sort(unique(AMR::rsi_translation$guideline)), '"', collapse = ", ")`.
|
||||
#'
|
||||
#' Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline.
|
||||
#' Simply using `"CLSI"` or `"EUCAST"` as input will automatically select the latest version of that guideline. You can set your own data set using the `reference_data` parameter.
|
||||
#'
|
||||
#' ## After interpretation
|
||||
#'
|
||||
@ -294,6 +295,7 @@ as.rsi.mic <- function(x,
|
||||
uti = FALSE,
|
||||
conserve_capped_values = FALSE,
|
||||
add_intrinsic_resistance = FALSE,
|
||||
reference_data = AMR::rsi_translation,
|
||||
...) {
|
||||
meet_criteria(x)
|
||||
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
|
||||
@ -302,6 +304,8 @@ as.rsi.mic <- function(x,
|
||||
meet_criteria(uti, allow_class = "logical", has_length = c(1, length(x)))
|
||||
meet_criteria(conserve_capped_values, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(add_intrinsic_resistance, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(reference_data, allow_class = "data.frame")
|
||||
check_reference_data(reference_data)
|
||||
|
||||
# for dplyr's across()
|
||||
cur_column_dplyr <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
|
||||
@ -328,8 +332,7 @@ as.rsi.mic <- function(x,
|
||||
}
|
||||
if (is.null(mo)) {
|
||||
stop_('No information was supplied about the microorganisms (missing parameter "mo"). See ?as.rsi.\n\n',
|
||||
"To transform certain columns with e.g. mutate_at(), use\n",
|
||||
"`data %>% mutate_at(vars(...), as.rsi, mo = .$x)`, where x is your column with microorganisms.\n\n",
|
||||
"To transform certain columns with e.g. mutate_at(), use `data %>% mutate_at(vars(...), as.rsi, mo = .$x)`, where x is your column with microorganisms.\n",
|
||||
"To tranform all MIC values in a data set, use `data %>% as.rsi()` or data %>% mutate_if(is.mic, as.rsi).", call = FALSE)
|
||||
}
|
||||
if (length(ab) == 1 && ab %like% "as.mic") {
|
||||
@ -338,7 +341,7 @@ as.rsi.mic <- function(x,
|
||||
|
||||
ab_coerced <- suppressWarnings(as.ab(ab))
|
||||
mo_coerced <- suppressWarnings(as.mo(mo))
|
||||
guideline_coerced <- get_guideline(guideline)
|
||||
guideline_coerced <- get_guideline(guideline, reference_data)
|
||||
if (is.na(ab_coerced)) {
|
||||
message_("Returning NAs for unknown drug: `", font_bold(ab),
|
||||
"`. Rename this column to a drug name or code, and check the output with as.ab().",
|
||||
@ -367,7 +370,8 @@ as.rsi.mic <- function(x,
|
||||
guideline = guideline_coerced,
|
||||
uti = uti,
|
||||
conserve_capped_values = conserve_capped_values,
|
||||
add_intrinsic_resistance = add_intrinsic_resistance) # exec_as.rsi will return message_(" OK.")
|
||||
add_intrinsic_resistance = add_intrinsic_resistance,
|
||||
reference_data = reference_data) # exec_as.rsi will return message 'OK'
|
||||
result
|
||||
}
|
||||
|
||||
@ -379,6 +383,7 @@ as.rsi.disk <- function(x,
|
||||
guideline = "EUCAST",
|
||||
uti = FALSE,
|
||||
add_intrinsic_resistance = FALSE,
|
||||
reference_data = AMR::rsi_translation,
|
||||
...) {
|
||||
meet_criteria(x)
|
||||
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
|
||||
@ -386,6 +391,8 @@ as.rsi.disk <- function(x,
|
||||
meet_criteria(guideline, allow_class = "character", has_length = 1)
|
||||
meet_criteria(uti, allow_class = "logical", has_length = c(1, length(x)))
|
||||
meet_criteria(add_intrinsic_resistance, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(reference_data, allow_class = "data.frame")
|
||||
check_reference_data(reference_data)
|
||||
|
||||
# for dplyr's across()
|
||||
cur_column_dplyr <- import_fn("cur_column", "dplyr", error_on_fail = FALSE)
|
||||
@ -412,8 +419,7 @@ as.rsi.disk <- function(x,
|
||||
}
|
||||
if (is.null(mo)) {
|
||||
stop_('No information was supplied about the microorganisms (missing parameter "mo"). See ?as.rsi.\n\n',
|
||||
"To transform certain columns with e.g. mutate_at(), use\n",
|
||||
"`data %>% mutate_at(vars(...), as.rsi, mo = .$x)`, where x is your column with microorganisms.\n\n",
|
||||
"To transform certain columns with e.g. mutate_at(), use `data %>% mutate_at(vars(...), as.rsi, mo = .$x)`, where x is your column with microorganisms.\n",
|
||||
"To tranform all disk diffusion zones in a data set, use `data %>% as.rsi()` or data %>% mutate_if(is.disk, as.rsi).", call = FALSE)
|
||||
}
|
||||
if (length(ab) == 1 && ab %like% "as.disk") {
|
||||
@ -422,7 +428,7 @@ as.rsi.disk <- function(x,
|
||||
|
||||
ab_coerced <- suppressWarnings(as.ab(ab))
|
||||
mo_coerced <- suppressWarnings(as.mo(mo))
|
||||
guideline_coerced <- get_guideline(guideline)
|
||||
guideline_coerced <- get_guideline(guideline, reference_data)
|
||||
if (is.na(ab_coerced)) {
|
||||
message_("Returning NAs for unknown drug: `", font_bold(ab),
|
||||
"`. Rename this column to a drug name or code, and check the output with as.ab().",
|
||||
@ -449,7 +455,8 @@ as.rsi.disk <- function(x,
|
||||
guideline = guideline_coerced,
|
||||
uti = uti,
|
||||
conserve_capped_values = FALSE,
|
||||
add_intrinsic_resistance = add_intrinsic_resistance) # exec_as.rsi will return message_(" OK.")
|
||||
add_intrinsic_resistance = add_intrinsic_resistance,
|
||||
reference_data = reference_data) # exec_as.rsi will return message 'OK'
|
||||
result
|
||||
}
|
||||
|
||||
@ -461,13 +468,15 @@ as.rsi.data.frame <- function(x,
|
||||
guideline = "EUCAST",
|
||||
uti = NULL,
|
||||
conserve_capped_values = FALSE,
|
||||
add_intrinsic_resistance = FALSE) {
|
||||
add_intrinsic_resistance = FALSE,
|
||||
reference_data = rsi_translation) {
|
||||
meet_criteria(x, allow_class = "data.frame") # will also check for dimensions > 0
|
||||
meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), allow_NULL = TRUE)
|
||||
meet_criteria(guideline, allow_class = "character", has_length = 1)
|
||||
meet_criteria(uti, allow_class = c("logical", "character"), allow_NULL = TRUE)
|
||||
meet_criteria(conserve_capped_values, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(add_intrinsic_resistance, allow_class = "logical", has_length = 1)
|
||||
meet_criteria(reference_data, allow_class = "data.frame")
|
||||
|
||||
for (i in seq_len(ncol(x))) {
|
||||
# don't keep factors
|
||||
@ -574,18 +583,28 @@ as.rsi.data.frame <- function(x,
|
||||
|
||||
for (i in seq_len(length(ab_cols))) {
|
||||
if (types[i] == "mic") {
|
||||
x[, ab_cols[i]] <- as.rsi.mic(x = x %pm>% pm_pull(ab_cols[i]),
|
||||
x[, ab_cols[i]] <- as.rsi(x = x %pm>%
|
||||
pm_pull(ab_cols[i]) %pm>%
|
||||
as.character() %pm>%
|
||||
as.mic(),
|
||||
mo = x_mo,
|
||||
ab = ab_cols[i],
|
||||
guideline = guideline,
|
||||
uti = uti,
|
||||
conserve_capped_values = conserve_capped_values)
|
||||
conserve_capped_values = conserve_capped_values,
|
||||
add_intrinsic_resistance = add_intrinsic_resistance,
|
||||
reference_data = reference_data)
|
||||
} else if (types[i] == "disk") {
|
||||
x[, ab_cols[i]] <- as.rsi.disk(x = x %pm>% pm_pull(ab_cols[i]),
|
||||
x[, ab_cols[i]] <- as.rsi(x = x %pm>%
|
||||
pm_pull(ab_cols[i]) %pm>%
|
||||
as.character() %pm>%
|
||||
as.disk(),
|
||||
mo = x_mo,
|
||||
ab = ab_cols[i],
|
||||
guideline = guideline,
|
||||
uti = uti)
|
||||
uti = uti,
|
||||
add_intrinsic_resistance = add_intrinsic_resistance,
|
||||
reference_data = reference_data)
|
||||
} else if (types[i] == "rsi") {
|
||||
ab <- ab_cols[i]
|
||||
ab_coerced <- suppressWarnings(as.ab(ab))
|
||||
@ -602,19 +621,19 @@ as.rsi.data.frame <- function(x,
|
||||
x
|
||||
}
|
||||
|
||||
get_guideline <- function(guideline) {
|
||||
get_guideline <- function(guideline, reference_data) {
|
||||
guideline_param <- toupper(guideline)
|
||||
if (guideline_param %in% c("CLSI", "EUCAST")) {
|
||||
guideline_param <- rev(sort(subset(rsi_translation, guideline %like% guideline_param)$guideline))[1L]
|
||||
guideline_param <- rev(sort(subset(reference_data, guideline %like% guideline_param)$guideline))[1L]
|
||||
}
|
||||
if (!guideline_param %like% " ") {
|
||||
# like 'EUCAST2020', should be 'EUCAST 2020'
|
||||
guideline_param <- gsub("([a-z]+)([0-9]+)", "\\1 \\2", guideline_param, ignore.case = TRUE)
|
||||
}
|
||||
|
||||
stop_ifnot(guideline_param %in% rsi_translation$guideline,
|
||||
stop_ifnot(guideline_param %in% reference_data$guideline,
|
||||
"invalid guideline: '", guideline,
|
||||
"'.\nValid guidelines are: ", paste0("'", unique(rsi_translation$guideline), "'", collapse = ", "), call = FALSE)
|
||||
"'.\nValid guidelines are: ", paste0("'", unique(reference_data$guideline), "'", collapse = ", "), call = FALSE)
|
||||
|
||||
guideline_param
|
||||
|
||||
@ -631,7 +650,7 @@ exec_as.rsi <- function(method,
|
||||
|
||||
metadata_mo <- get_mo_failures_uncertainties_renamed()
|
||||
|
||||
x_bak <- data.frame(x_mo = paste0(x, mo))
|
||||
x_bak <- data.frame(x_mo = paste0(x, mo), stringsAsFactors = FALSE)
|
||||
df <- unique(data.frame(x, mo), stringsAsFactors = FALSE)
|
||||
x <- df$x
|
||||
mo <- df$mo
|
||||
@ -661,14 +680,14 @@ exec_as.rsi <- function(method,
|
||||
}
|
||||
mo_other <- as.mo(rep("UNKNOWN", length(mo)))
|
||||
|
||||
guideline_coerced <- get_guideline(guideline)
|
||||
guideline_coerced <- get_guideline(guideline, reference_data)
|
||||
if (guideline_coerced != guideline) {
|
||||
message_("Using guideline ", font_bold(guideline_coerced), " as input for `guideline`.")
|
||||
}
|
||||
|
||||
new_rsi <- rep(NA_character_, length(x))
|
||||
ab_param <- ab
|
||||
trans <- rsi_translation %pm>%
|
||||
trans <- reference_data %pm>%
|
||||
subset(guideline == guideline_coerced & method == method_param & ab == ab_param)
|
||||
trans$lookup <- paste(trans$mo, trans$ab)
|
||||
|
||||
@ -682,7 +701,7 @@ exec_as.rsi <- function(method,
|
||||
|
||||
if (all(trans$uti == TRUE, na.rm = TRUE) & all(uti == FALSE)) {
|
||||
message_("WARNING.", add_fn = list(font_red, font_bold), as_note = FALSE)
|
||||
warning_("Interpretation of ", font_bold(ab_name(ab, tolower = TRUE)), " for some microorganisms is only available for (uncomplicated) urinary tract infections (UTI).\n Use parameter 'uti' to set which isolates are from urine. See ?as.rsi.", call = FALSE)
|
||||
warning_("Interpretation of ", font_bold(ab_name(ab, tolower = TRUE)), " for some microorganisms is only available for (uncomplicated) urinary tract infections (UTI). Use parameter 'uti' to set which isolates are from urine. See ?as.rsi.", call = FALSE)
|
||||
warned <- TRUE
|
||||
}
|
||||
|
||||
@ -757,7 +776,9 @@ exec_as.rsi <- function(method,
|
||||
}
|
||||
|
||||
new_rsi <- x_bak %pm>%
|
||||
pm_left_join(data.frame(x_mo = paste0(df$x, df$mo), new_rsi), by = "x_mo") %pm>%
|
||||
pm_left_join(data.frame(x_mo = paste0(df$x, df$mo), new_rsi,
|
||||
stringsAsFactors = FALSE),
|
||||
by = "x_mo") %pm>%
|
||||
pm_pull(new_rsi)
|
||||
|
||||
if (warned == FALSE) {
|
||||
@ -928,13 +949,16 @@ plot.rsi <- function(x,
|
||||
data$s <- round((data$n / sum(data$n)) * 100, 1)
|
||||
|
||||
if (!"S" %in% data$x) {
|
||||
data <- rbind(data, data.frame(x = "S", n = 0, s = 0))
|
||||
data <- rbind(data, data.frame(x = "S", n = 0, s = 0, stringsAsFactors = FALSE),
|
||||
stringsAsFactors = FALSE)
|
||||
}
|
||||
if (!"I" %in% data$x) {
|
||||
data <- rbind(data, data.frame(x = "I", n = 0, s = 0))
|
||||
data <- rbind(data, data.frame(x = "I", n = 0, s = 0, stringsAsFactors = FALSE),
|
||||
stringsAsFactors = FALSE)
|
||||
}
|
||||
if (!"R" %in% data$x) {
|
||||
data <- rbind(data, data.frame(x = "R", n = 0, s = 0))
|
||||
data <- rbind(data, data.frame(x = "R", n = 0, s = 0, stringsAsFactors = FALSE),
|
||||
stringsAsFactors = FALSE)
|
||||
}
|
||||
|
||||
# don't use as.rsi() here, it will confuse plot()
|
||||
@ -1037,3 +1061,17 @@ unique.rsi <- function(x, incomparables = FALSE, ...) {
|
||||
attributes(y) <- attributes(x)
|
||||
y
|
||||
}
|
||||
|
||||
check_reference_data <- function(reference_data) {
|
||||
if (!identical(reference_data, AMR::rsi_translation)) {
|
||||
class_rsi <- sapply(rsi_translation, function(x) paste0("<", class(x), ">", collapse = " and "))
|
||||
class_ref <- sapply(reference_data, function(x) paste0("<", class(x), ">", collapse = " and "))
|
||||
if (!all(names(class_rsi) == names(class_ref))) {
|
||||
stop_("'reference_data' must have the same column names as the 'rsi_translation' data set.", call = -2)
|
||||
}
|
||||
if (!all(class_rsi == class_ref)) {
|
||||
class_rsi[class_rsi != class_ref][1]
|
||||
stop_("'reference_data' must be the same structure as the 'rsi_translation' data set. Column '", names(class_ref[class_rsi != class_ref][1]), "' is of class ", class_ref[class_rsi != class_ref][1], ", but should be of class ", class_rsi[class_rsi != class_ref][1], ".", call = -2)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -118,7 +118,7 @@ rsi_calc <- function(...,
|
||||
rsi_integrity_check <- as.rsi(rsi_integrity_check)
|
||||
}
|
||||
|
||||
x_transposed <- as.list(as.data.frame(t(x)))
|
||||
x_transposed <- as.list(as.data.frame(t(x), stringsAsFactors = FALSE))
|
||||
if (only_all_tested == TRUE) {
|
||||
# no NAs in any column
|
||||
y <- apply(X = as.data.frame(lapply(x, as.integer), stringsAsFactors = FALSE),
|
||||
@ -240,7 +240,7 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
|
||||
} else {
|
||||
values <- factor(values, levels = c("S", "I", "R"), ordered = TRUE)
|
||||
}
|
||||
col_results <- as.data.frame(as.matrix(table(values)))
|
||||
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) {
|
||||
@ -265,7 +265,7 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
|
||||
}
|
||||
out_new <- cbind(group_values, out_new)
|
||||
}
|
||||
out <- rbind(out, out_new)
|
||||
out <- rbind(out, out_new, stringsAsFactors = FALSE)
|
||||
}
|
||||
}
|
||||
out
|
||||
|
||||
@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="https://msberends.github.io/AMR//index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9016</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9016</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9016</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9016</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@ -43,7 +43,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9016</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9016</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@ -236,13 +236,13 @@
|
||||
<small>Source: <a href='https://github.com/msberends/AMR/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
|
||||
</div>
|
||||
|
||||
<div id="amr-1409016" class="section level1">
|
||||
<h1 class="page-header" data-toc-text="1.4.0.9016">
|
||||
<a href="#amr-1409016" class="anchor"></a>AMR 1.4.0.9016<small> Unreleased </small>
|
||||
<div id="amr-1409017" class="section level1">
|
||||
<h1 class="page-header" data-toc-text="1.4.0.9017">
|
||||
<a href="#amr-1409017" class="anchor"></a>AMR 1.4.0.9017<small> Unreleased </small>
|
||||
</h1>
|
||||
<div id="last-updated-10-november-2020" class="section level2">
|
||||
<div id="last-updated-11-november-2020" class="section level2">
|
||||
<h2 class="hasAnchor">
|
||||
<a href="#last-updated-10-november-2020" class="anchor"></a><small>Last updated: 10 November 2020</small>
|
||||
<a href="#last-updated-11-november-2020" class="anchor"></a><small>Last updated: 11 November 2020</small>
|
||||
</h2>
|
||||
<div id="new" class="section level3">
|
||||
<h3 class="hasAnchor">
|
||||
@ -252,7 +252,7 @@
|
||||
<p>Functions <code><a href="../reference/mo_property.html">is_gram_negative()</a></code> and <code><a href="../reference/mo_property.html">is_gram_positive()</a></code> as wrappers around <code><a href="../reference/mo_property.html">mo_gramstain()</a></code>. They always return <code>TRUE</code> or <code>FALSE</code> (except when the input is <code>NA</code> or the MO code is <code>UNKNOWN</code>), thus always return <code>FALSE</code> for species outside the taxonomic kingdom of Bacteria. If you have the <code>dplyr</code> package installed, they can even determine the column with microorganisms themselves inside <code>dplyr</code> functions:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit">
|
||||
<span class="va">example_isolates</span> <span class="op">%>%</span>
|
||||
<span class="fu"><a href="https://rdrr.io/r/stats/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">is_gram_positive</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
|
||||
<span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">is_gram_positive</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>
|
||||
<span class="co">#> NOTE: Using column `mo` as input for 'x'</span></pre></div>
|
||||
</li>
|
||||
<li><p>Functions <code><a href="../reference/like.html">%not_like%</a></code> and <code><a href="../reference/like.html">%not_like_case%</a></code> as wrappers around <code><a href="../reference/like.html">%like%</a></code> and <code><a href="../reference/like.html">%like_case%</a></code>. The RStudio addin to insert the text " %like% " as provided in this package now iterates over all like variants. So if you have defined the keyboard shortcut Ctrl/Cmd + L to this addin, it will first insert <code><a href="../reference/like.html">%like%</a></code> and by pressing it again it will be replaced with <code><a href="../reference/like.html">%not_like%</a></code>, etc.</p></li>
|
||||
@ -262,6 +262,7 @@
|
||||
<h3 class="hasAnchor">
|
||||
<a href="#changed" class="anchor"></a>Changed</h3>
|
||||
<ul>
|
||||
<li>Reference data used for <code><a href="../reference/as.rsi.html">as.rsi()</a></code> can now be set by the user, using the <code>reference_data</code> parameter.</li>
|
||||
<li>For all function parameters in the code, it is now defined what the exact type of user input should be (inspired by the <a href="https://github.com/moodymudskipper/typed"><code>typed</code></a> package). If the user input for a certain function does not meet the requirements for a specific parameter (such as the class or length), an informative error will be thrown. This makes the package more robust and the use of it more reproducible and reliable. In total, more than 400 arguments were defined.</li>
|
||||
<li>Deprecated function <code><a href="../reference/AMR-deprecated.html">p_symbol()</a></code> that not really fits the scope of this package. It will be removed in a future version. See <a href="https://github.com/msberends/AMR/blob/v1.4.0/R/p_symbol.R">here</a> for the source code to preserve it.</li>
|
||||
<li>Better determination of disk zones and MIC values when running <code><a href="../reference/as.rsi.html">as.rsi()</a></code> on a data.frame</li>
|
||||
|
||||
@ -12,7 +12,7 @@ articles:
|
||||
datasets: datasets.html
|
||||
resistance_predict: resistance_predict.html
|
||||
welcome_to_AMR: welcome_to_AMR.html
|
||||
last_built: 2020-11-10T18:58Z
|
||||
last_built: 2020-11-11T15:44Z
|
||||
urls:
|
||||
reference: https://msberends.github.io/AMR//reference
|
||||
article: https://msberends.github.io/AMR//articles
|
||||
|
||||
@ -82,7 +82,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9008</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
@ -257,6 +257,7 @@
|
||||
uti <span class='op'>=</span> <span class='cn'>FALSE</span>,
|
||||
conserve_capped_values <span class='op'>=</span> <span class='cn'>FALSE</span>,
|
||||
add_intrinsic_resistance <span class='op'>=</span> <span class='cn'>FALSE</span>,
|
||||
reference_data <span class='op'>=</span> <span class='fu'>AMR</span><span class='fu'>::</span><span class='va'><a href='rsi_translation.html'>rsi_translation</a></span>,
|
||||
<span class='va'>...</span>
|
||||
<span class='op'>)</span>
|
||||
|
||||
@ -268,6 +269,7 @@
|
||||
guideline <span class='op'>=</span> <span class='st'>"EUCAST"</span>,
|
||||
uti <span class='op'>=</span> <span class='cn'>FALSE</span>,
|
||||
add_intrinsic_resistance <span class='op'>=</span> <span class='cn'>FALSE</span>,
|
||||
reference_data <span class='op'>=</span> <span class='fu'>AMR</span><span class='fu'>::</span><span class='va'><a href='rsi_translation.html'>rsi_translation</a></span>,
|
||||
<span class='va'>...</span>
|
||||
<span class='op'>)</span>
|
||||
|
||||
@ -279,7 +281,8 @@
|
||||
guideline <span class='op'>=</span> <span class='st'>"EUCAST"</span>,
|
||||
uti <span class='op'>=</span> <span class='cn'>NULL</span>,
|
||||
conserve_capped_values <span class='op'>=</span> <span class='cn'>FALSE</span>,
|
||||
add_intrinsic_resistance <span class='op'>=</span> <span class='cn'>FALSE</span>
|
||||
add_intrinsic_resistance <span class='op'>=</span> <span class='cn'>FALSE</span>,
|
||||
reference_data <span class='op'>=</span> <span class='va'>rsi_translation</span>
|
||||
<span class='op'>)</span></pre>
|
||||
|
||||
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
|
||||
@ -322,6 +325,10 @@
|
||||
<td><p><em>(only useful when using a EUCAST guideline)</em> a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in <em>Klebsiella</em> species. Determination is based on the <a href='intrinsic_resistant.html'>intrinsic_resistant</a> data set, that itself is based on 'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes', version list(version_txt = "v3.1", year = 2016, title = "EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes")
|
||||
list(version_txt = "v3.2", year = 2020, title = "EUCAST Expert Rules / EUCAST Intrinsic Resistance and Unusual Phenotypes").</p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>reference_data</th>
|
||||
<td><p>a <a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> to be used for interpretation, which defaults to the <a href='rsi_translation.html'>rsi_translation</a> data set. Changing this parameter allows for using own interpretation guidelines. This parameter must contain a data set that is equal in structure to the <a href='rsi_translation.html'>rsi_translation</a> data set (same column names and column types). Please note that the 'guideline' column in this data set must contain values set in the 'guideline' parameter of <code>as.rsi()</code>.</p></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>col_mo</th>
|
||||
<td><p>column name of the IDs of the microorganisms (see <code><a href='as.mo.html'>as.mo()</a></code>), defaults to the first column of class <code><a href='as.mo.html'>mo</a></code>. Values will be coerced using <code><a href='as.mo.html'>as.mo()</a></code>.</p></td>
|
||||
@ -358,7 +365,7 @@ list(version_txt = "v3.2", year = 2020, title = "EUCAST Expert Rules / EUCAST In
|
||||
|
||||
|
||||
<p>For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the <code>guideline</code> parameter are: "CLSI 2010", "CLSI 2011", "CLSI 2012", "CLSI 2013", "CLSI 2014", "CLSI 2015", "CLSI 2016", "CLSI 2017", "CLSI 2018", "CLSI 2019", "EUCAST 2011", "EUCAST 2012", "EUCAST 2013", "EUCAST 2014", "EUCAST 2015", "EUCAST 2016", "EUCAST 2017", "EUCAST 2018", "EUCAST 2019", "EUCAST 2020".</p>
|
||||
<p>Simply using <code>"CLSI"</code> or <code>"EUCAST"</code> as input will automatically select the latest version of that guideline.</p>
|
||||
<p>Simply using <code>"CLSI"</code> or <code>"EUCAST"</code> as input will automatically select the latest version of that guideline. You can set your own data set using the <code>reference_data</code> parameter.</p>
|
||||
|
||||
<h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>After interpretation</h3>
|
||||
|
||||
|
||||
@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="../index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9016</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@ -81,7 +81,7 @@
|
||||
</button>
|
||||
<span class="navbar-brand">
|
||||
<a class="navbar-link" href="index.html">AMR (for R)</a>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9016</span>
|
||||
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9017</span>
|
||||
</span>
|
||||
</div>
|
||||
|
||||
|
||||
@ -24,6 +24,7 @@ is.rsi.eligible(x, threshold = 0.05)
|
||||
uti = FALSE,
|
||||
conserve_capped_values = FALSE,
|
||||
add_intrinsic_resistance = FALSE,
|
||||
reference_data = AMR::rsi_translation,
|
||||
...
|
||||
)
|
||||
|
||||
@ -34,6 +35,7 @@ is.rsi.eligible(x, threshold = 0.05)
|
||||
guideline = "EUCAST",
|
||||
uti = FALSE,
|
||||
add_intrinsic_resistance = FALSE,
|
||||
reference_data = AMR::rsi_translation,
|
||||
...
|
||||
)
|
||||
|
||||
@ -44,7 +46,8 @@ is.rsi.eligible(x, threshold = 0.05)
|
||||
guideline = "EUCAST",
|
||||
uti = NULL,
|
||||
conserve_capped_values = FALSE,
|
||||
add_intrinsic_resistance = FALSE
|
||||
add_intrinsic_resistance = FALSE,
|
||||
reference_data = rsi_translation
|
||||
)
|
||||
}
|
||||
\arguments{
|
||||
@ -67,6 +70,8 @@ is.rsi.eligible(x, threshold = 0.05)
|
||||
\item{add_intrinsic_resistance}{\emph{(only useful when using a EUCAST guideline)} a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in \emph{Klebsiella} species. Determination is based on the \link{intrinsic_resistant} data set, that itself is based on 'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes', version list(version_txt = "v3.1", year = 2016, title = "EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes")
|
||||
list(version_txt = "v3.2", year = 2020, title = "EUCAST Expert Rules / EUCAST Intrinsic Resistance and Unusual Phenotypes").}
|
||||
|
||||
\item{reference_data}{a \link{data.frame} to be used for interpretation, which defaults to the \link{rsi_translation} data set. Changing this parameter allows for using own interpretation guidelines. This parameter must contain a data set that is equal in structure to the \link{rsi_translation} data set (same column names and column types). Please note that the 'guideline' column in this data set must contain values set in the 'guideline' parameter of \code{\link[=as.rsi]{as.rsi()}}.}
|
||||
|
||||
\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
|
||||
}
|
||||
\value{
|
||||
@ -102,7 +107,7 @@ your_data \%>\% mutate(across(where(is.disk), as.rsi)) # since dplyr 1.0.0
|
||||
|
||||
For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} parameter are: "CLSI 2010", "CLSI 2011", "CLSI 2012", "CLSI 2013", "CLSI 2014", "CLSI 2015", "CLSI 2016", "CLSI 2017", "CLSI 2018", "CLSI 2019", "EUCAST 2011", "EUCAST 2012", "EUCAST 2013", "EUCAST 2014", "EUCAST 2015", "EUCAST 2016", "EUCAST 2017", "EUCAST 2018", "EUCAST 2019", "EUCAST 2020".
|
||||
|
||||
Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline.
|
||||
Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline. You can set your own data set using the \code{reference_data} parameter.
|
||||
}
|
||||
|
||||
\subsection{After interpretation}{
|
||||
|
||||
@ -90,12 +90,12 @@ test_that("EUCAST rules work", {
|
||||
"R")
|
||||
|
||||
# Azithromycin and Clarythromycin must be equal to Erythromycin
|
||||
a <- as.rsi(eucast_rules(data.frame(mo = example_isolates$mo,
|
||||
a <- suppressWarnings(as.rsi(eucast_rules(data.frame(mo = example_isolates$mo,
|
||||
ERY = example_isolates$ERY,
|
||||
AZM = as.rsi("R"),
|
||||
CLR = factor("R"),
|
||||
stringsAsFactors = FALSE),
|
||||
version_expertrules = 3.1)$CLR)
|
||||
version_expertrules = 3.1)$CLR))
|
||||
b <- example_isolates$ERY
|
||||
expect_identical(a[!is.na(b)],
|
||||
b[!is.na(b)])
|
||||
|
||||
Loading…
Reference in New Issue
Block a user