# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Data Analysis for R # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2021 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. # # # # 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/ # # ==================================================================== # #' Key Antibiotics for First (Weighted) Isolates #' #' These function can be used to determine first isolates (see [first_isolate()]). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates can then be called first 'weighted' isolates. #' @inheritSection lifecycle Stable Lifecycle #' @param x a [data.frame] with antibiotics columns, like `AMX` or `amox`. Can be left blank to determine automatically #' @param y,z character vectors to compare #' @inheritParams first_isolate #' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of **broad-spectrum** antibiotics, case-insensitive. See details for which antibiotics will be used at default (which are guessed with [guess_ab_col()]). #' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for **Gram-positives**, case-insensitive. See details for which antibiotics will be used at default (which are guessed with [guess_ab_col()]). #' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for **Gram-negatives**, case-insensitive. See details for which antibiotics will be used at default (which are guessed with [guess_ab_col()]). #' @param warnings give a warning about missing antibiotic columns (they will be ignored) #' @param ... other arguments passed on to functions #' @details #' The [key_antibiotics()] function is context-aware. This means that then the `x` argument can be left blank, see *Examples*. #' #' The function [key_antibiotics()] returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using [key_antibiotics_equal()], to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (`"."`) by [key_antibiotics()] and ignored by [key_antibiotics_equal()]. #' #' The [first_isolate()] function only uses this function on the same microbial species from the same patient. Using this, e.g. an MRSA will be included after a susceptible *S. aureus* (MSSA) is found within the same patient episode. Without key antibiotic comparison it would not. See [first_isolate()] for more info. #' #' At default, the antibiotics that are used for **Gram-positive bacteria** are: #' - Amoxicillin #' - Amoxicillin/clavulanic acid #' - Cefuroxime #' - Piperacillin/tazobactam #' - Ciprofloxacin #' - Trimethoprim/sulfamethoxazole #' - Vancomycin #' - Teicoplanin #' - Tetracycline #' - Erythromycin #' - Oxacillin #' - Rifampin #' #' At default the antibiotics that are used for **Gram-negative bacteria** are: #' - Amoxicillin #' - Amoxicillin/clavulanic acid #' - Cefuroxime #' - Piperacillin/tazobactam #' - Ciprofloxacin #' - Trimethoprim/sulfamethoxazole #' - Gentamicin #' - Tobramycin #' - Colistin #' - Cefotaxime #' - Ceftazidime #' - Meropenem #' #' The function [key_antibiotics_equal()] checks the characters returned by [key_antibiotics()] for equality, and returns a [`logical`] vector. #' @inheritSection first_isolate Key Antibiotics #' @rdname key_antibiotics #' @export #' @seealso [first_isolate()] #' @inheritSection AMR Read more on Our Website! #' @examples #' # `example_isolates` is a data set available in the AMR package. #' # See ?example_isolates. #' #' # output of the `key_antibiotics()` function could be like this: #' strainA <- "SSSRR.S.R..S" #' strainB <- "SSSIRSSSRSSS" #' #' # those strings can be compared with: #' key_antibiotics_equal(strainA, strainB) #' # TRUE, because I is ignored (as well as missing values) #' #' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE) #' # FALSE, because I is not ignored and so the 4th character differs #' #' \donttest{ #' if (require("dplyr")) { #' # set key antibiotics to a new variable #' my_patients <- example_isolates %>% #' mutate(keyab = key_antibiotics()) %>% # no need to define `x` #' mutate( #' # now calculate first isolates #' first_regular = first_isolate(col_keyantibiotics = FALSE), #' # and first WEIGHTED isolates #' first_weighted = first_isolate(col_keyantibiotics = "keyab") #' ) #' #' # Check the difference, in this data set it results in a lot more isolates: #' sum(my_patients$first_regular, na.rm = TRUE) #' sum(my_patients$first_weighted, na.rm = TRUE) #' } #' } key_antibiotics <- function(x, col_mo = NULL, universal_1 = guess_ab_col(x, "amoxicillin"), universal_2 = guess_ab_col(x, "amoxicillin/clavulanic acid"), universal_3 = guess_ab_col(x, "cefuroxime"), universal_4 = guess_ab_col(x, "piperacillin/tazobactam"), universal_5 = guess_ab_col(x, "ciprofloxacin"), universal_6 = guess_ab_col(x, "trimethoprim/sulfamethoxazole"), GramPos_1 = guess_ab_col(x, "vancomycin"), GramPos_2 = guess_ab_col(x, "teicoplanin"), GramPos_3 = guess_ab_col(x, "tetracycline"), GramPos_4 = guess_ab_col(x, "erythromycin"), GramPos_5 = guess_ab_col(x, "oxacillin"), GramPos_6 = guess_ab_col(x, "rifampin"), GramNeg_1 = guess_ab_col(x, "gentamicin"), GramNeg_2 = guess_ab_col(x, "tobramycin"), GramNeg_3 = guess_ab_col(x, "colistin"), GramNeg_4 = guess_ab_col(x, "cefotaxime"), GramNeg_5 = guess_ab_col(x, "ceftazidime"), GramNeg_6 = guess_ab_col(x, "meropenem"), warnings = TRUE, ...) { if (missing(x)) { x <- get_current_data(arg_name = "x", call = -2) } meet_criteria(x, allow_class = "data.frame") meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(universal_1, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(universal_2, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(universal_3, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(universal_4, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(universal_5, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(universal_6, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramPos_1, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramPos_2, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramPos_3, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramPos_4, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramPos_5, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramPos_6, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramNeg_1, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramNeg_2, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramNeg_3, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramNeg_4, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramNeg_5, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(GramNeg_6, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE) meet_criteria(warnings, allow_class = "logical", has_length = 1) dots <- unlist(list(...)) if (length(dots) != 0) { # backwards compatibility with old arguments dots.names <- names(dots) if ("info" %in% dots.names) { warnings <- dots[which(dots.names == "info")] } } # 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") } # check columns col.list <- c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6, GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6, GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6) check_available_columns <- function(x, col.list, warnings = TRUE) { # check columns col.list <- col.list[!is.na(col.list) & !is.null(col.list)] names(col.list) <- col.list col.list.bak <- col.list # are they available as upper case or lower case then? for (i in seq_len(length(col.list))) { if (is.null(col.list[i]) | isTRUE(is.na(col.list[i]))) { col.list[i] <- NA } else if (toupper(col.list[i]) %in% colnames(x)) { col.list[i] <- toupper(col.list[i]) } else if (tolower(col.list[i]) %in% colnames(x)) { col.list[i] <- tolower(col.list[i]) } else if (!col.list[i] %in% colnames(x)) { col.list[i] <- NA } } if (!all(col.list %in% colnames(x))) { if (warnings == TRUE) { warning_("Some columns do not exist and will be ignored: ", col.list.bak[!(col.list %in% colnames(x))] %pm>% toString(), ".\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.", immediate = TRUE, call = FALSE) } } col.list } col.list <- check_available_columns(x = x, col.list = col.list, warnings = warnings) universal_1 <- col.list[universal_1] universal_2 <- col.list[universal_2] universal_3 <- col.list[universal_3] universal_4 <- col.list[universal_4] universal_5 <- col.list[universal_5] universal_6 <- col.list[universal_6] GramPos_1 <- col.list[GramPos_1] GramPos_2 <- col.list[GramPos_2] GramPos_3 <- col.list[GramPos_3] GramPos_4 <- col.list[GramPos_4] GramPos_5 <- col.list[GramPos_5] GramPos_6 <- col.list[GramPos_6] GramNeg_1 <- col.list[GramNeg_1] GramNeg_2 <- col.list[GramNeg_2] GramNeg_3 <- col.list[GramNeg_3] GramNeg_4 <- col.list[GramNeg_4] GramNeg_5 <- col.list[GramNeg_5] GramNeg_6 <- col.list[GramNeg_6] universal <- c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6) gram_positive <- c(universal, GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6) gram_positive <- gram_positive[!is.null(gram_positive)] gram_positive <- gram_positive[!is.na(gram_positive)] if (length(gram_positive) < 12 & message_not_thrown_before("key_antibiotics.grampos")) { warning_("Only using ", length(gram_positive), " different antibiotics as key antibiotics for Gram-positives. See ?key_antibiotics.", call = FALSE) remember_thrown_message("key_antibiotics.grampos") } gram_negative <- c(universal, GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6) gram_negative <- gram_negative[!is.null(gram_negative)] gram_negative <- gram_negative[!is.na(gram_negative)] if (length(gram_negative) < 12 & message_not_thrown_before("key_antibiotics.gramneg")) { warning_("Only using ", length(gram_negative), " different antibiotics as key antibiotics for Gram-negatives. See ?key_antibiotics.", call = FALSE) remember_thrown_message("key_antibiotics.gramneg") } x <- as.data.frame(x, stringsAsFactors = FALSE) x[, col_mo] <- as.mo(x[, col_mo, drop = TRUE]) x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL) x$key_ab <- NA_character_ # Gram + x$key_ab <- pm_if_else(x$gramstain == "Gram-positive", tryCatch(apply(X = x[, gram_positive], MARGIN = 1, FUN = function(x) paste(x, collapse = "")), error = function(e) paste0(rep(".", 12), collapse = "")), x$key_ab) # Gram - x$key_ab <- pm_if_else(x$gramstain == "Gram-negative", tryCatch(apply(X = x[, gram_negative], MARGIN = 1, FUN = function(x) paste(x, collapse = "")), error = function(e) paste0(rep(".", 12), collapse = "")), x$key_ab) # format key_abs <- toupper(gsub("[^SIR]", ".", gsub("(NA|NULL)", ".", x$key_ab))) if (pm_n_distinct(key_abs) == 1) { warning_("No distinct key antibiotics determined.", call = FALSE) } key_abs } #' @rdname key_antibiotics #' @export key_antibiotics_equal <- function(y, z, type = c("keyantibiotics", "points"), ignore_I = TRUE, points_threshold = 2, info = FALSE) { meet_criteria(y, allow_class = "character") meet_criteria(z, allow_class = "character") meet_criteria(type, allow_class = "character", has_length = c(1, 2)) meet_criteria(ignore_I, allow_class = "logical", has_length = 1) meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE) meet_criteria(info, allow_class = "logical", has_length = 1) stop_ifnot(length(y) == length(z), "length of `y` and `z` must be equal") # y is active row, z is lag x <- y y <- z type <- type[1] # only show progress bar on points or when at least 5000 isolates info_needed <- info == TRUE & (type == "points" | length(x) > 5000) result <- logical(length(x)) if (info_needed == TRUE) { p <- progress_ticker(length(x)) on.exit(close(p)) } for (i in seq_len(length(x))) { if (info_needed == TRUE) { p$tick() } if (is.na(x[i])) { x[i] <- "" } if (is.na(y[i])) { y[i] <- "" } if (x[i] == y[i]) { result[i] <- TRUE } else if (nchar(x[i]) != nchar(y[i])) { result[i] <- FALSE } else { x_split <- strsplit(x[i], "")[[1]] y_split <- strsplit(y[i], "")[[1]] if (type == "keyantibiotics") { if (ignore_I == TRUE) { x_split[x_split == "I"] <- "." y_split[y_split == "I"] <- "." } y_split[x_split == "."] <- "." x_split[y_split == "."] <- "." result[i] <- all(x_split == y_split) } else if (type == "points") { # count points for every single character: # - no change is 0 points # - I <-> S|R is 0.5 point # - S|R <-> R|S is 1 point # use the levels of as.rsi (S = 1, I = 2, R = 3) suppressWarnings(x_split <- x_split %pm>% as.rsi() %pm>% as.double()) suppressWarnings(y_split <- y_split %pm>% as.rsi() %pm>% as.double()) points <- (x_split - y_split) %pm>% abs() %pm>% sum(na.rm = TRUE) / 2 result[i] <- points >= points_threshold } else { stop("`", type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?key_antibiotics') } } } if (info_needed == TRUE) { close(p) } result }