# ==================================================================== # # 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) Antimicrobials for First Weighted Isolates #' #' These functions can be used to determine first weighted isolates by considering the phenotype for isolate selection (see [first_isolate()]). Using a phenotype-based method to determine first isolates is more reliable than methods that disregard phenotypes. #' @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 names of **broad-spectrum** antimicrobial agents, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents. #' @param gram_negative names of antibiotic agents for **Gram-positives**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents. #' @param gram_positive names of antibiotic agents for **Gram-negatives**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents. #' @param antifungal names of antifungal agents for **fungi**, case-insensitive. Set to `NULL` to ignore. See *Details* for the default agents. #' @param only_rsi_columns a [logical] to indicate whether only columns must be included that were transformed to class `` (see [as.rsi()]) on beforehand (defaults to `FALSE`) #' @param ... ignored, only in place to allow future extensions #' @details #' The [key_antimicrobials()] and [all_antimicrobials()] functions are context-aware. This means that then the `x` argument can be left blank, see *Examples*. #' #' The function [key_antimicrobials()] returns a [character] vector with 12 antimicrobial results for every isolate. The function [all_antimicrobials()] returns a [character] vector with all antimicrobial results for every isolate. These vectors can then be compared using [antimicrobials_equal()], to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (`"."`) by [key_antimicrobials()] and ignored by [antimicrobials_equal()]. #' #' Please see the [first_isolate()] function how these important functions enable the 'phenotype-based' method for determination of first isolates. #' #' The default antimicrobial agents used for **all rows** (set in `universal`) are: #' #' - Ampicillin #' - Amoxicillin/clavulanic acid #' - Cefuroxime #' - Ciprofloxacin #' - Piperacillin/tazobactam #' - Trimethoprim/sulfamethoxazole #' #' The default antimicrobial agents used for **Gram-negative bacteria** (set in `gram_negative`) are: #' #' - Cefotaxime #' - Ceftazidime #' - Colistin #' - Gentamicin #' - Meropenem #' - Tobramycin #' #' The default antimicrobial agents used for **Gram-positive bacteria** (set in `gram_positive`) are: #' #' - Erythromycin #' - Oxacillin #' - Rifampin #' - Teicoplanin #' - Tetracycline #' - Vancomycin #' #' #' The default antimicrobial agents used for **fungi** (set in `antifungal`) are: #' #' - Anidulafungin #' - Caspofungin #' - Fluconazole #' - Miconazole #' - Nystatin #' - Voriconazole #' @rdname key_antimicrobials #' @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_antimicrobials()` function could be like this: #' strainA <- "SSSRR.S.R..S" #' strainB <- "SSSIRSSSRSSS" #' #' # those strings can be compared with: #' antimicrobials_equal(strainA, strainB, type = "keyantimicrobials") #' # TRUE, because I is ignored (as well as missing values) #' #' antimicrobials_equal(strainA, strainB, type = "keyantimicrobials", 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_antimicrobials(antifungal = NULL)) %>% # no need to define `x` #' mutate( #' # now calculate first isolates #' first_regular = first_isolate(col_keyantimicrobials = FALSE), #' # and first WEIGHTED isolates #' first_weighted = first_isolate(col_keyantimicrobials = "keyab") #' ) #' #' # Check the difference, in this data set it results in more isolates: #' sum(my_patients$first_regular, na.rm = TRUE) #' sum(my_patients$first_weighted, na.rm = TRUE) #' } #' } key_antimicrobials <- function(x = NULL, col_mo = NULL, universal = c("ampicillin", "amoxicillin/clavulanic acid", "cefuroxime", "piperacillin/tazobactam", "ciprofloxacin", "trimethoprim/sulfamethoxazole"), gram_negative = c("gentamicin", "tobramycin", "colistin", "cefotaxime", "ceftazidime", "meropenem"), gram_positive = c("vancomycin", "teicoplanin", "tetracycline", "erythromycin", "oxacillin", "rifampin"), antifungal = c("anidulafungin", "caspofungin", "fluconazole", "miconazole", "nystatin", "voriconazole"), only_rsi_columns = FALSE, ...) { if (is_null_or_grouped_tbl(x)) { # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all()) # is also fix for using a grouped df as input (a dot as first argument) x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x) } meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0 meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, allow_NA = TRUE, is_in = colnames(x)) meet_criteria(universal, allow_class = "character", allow_NULL = TRUE) meet_criteria(gram_negative, allow_class = "character", allow_NULL = TRUE) meet_criteria(gram_positive, allow_class = "character", allow_NULL = TRUE) meet_criteria(antifungal, allow_class = "character", allow_NULL = TRUE) meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1) # force regular [data.frame], not a tibble or data.table x <- as.data.frame(x, stringsAsFactors = FALSE) cols <- get_column_abx(x, info = FALSE, only_rsi_columns = only_rsi_columns) # try to find columns based on type # -- mo if (is.null(col_mo)) { col_mo <- search_type_in_df(x = x, type = "mo", info = FALSE) } if (is.null(col_mo)) { warning_("No column found for `col_mo`, ignoring antibiotics set in `gram_negative` and `gram_positive`, and antimycotics set in `antifungal`", call = FALSE) gramstain <- NA_character_ kingdom <- NA_character_ } else { x.mo <- as.mo(x[, col_mo, drop = TRUE]) gramstain <- mo_gramstain(x.mo, language = NULL) kingdom <- mo_kingdom(x.mo, language = NULL) } AMR_string <- function(x, values, name, filter, cols = cols) { if (is.null(values)) { return(rep(NA_character_, length(which(filter)))) } values_old_length <- length(values) values <- as.ab(values, flag_multiple_results = FALSE, info = FALSE) values <- cols[names(cols) %in% values] values_new_length <- length(values) if (values_new_length < values_old_length & any(filter, na.rm = TRUE) & message_not_thrown_before(paste0("key_antimicrobials.", name))) { warning_(ifelse(values_new_length == 0, "No columns available ", paste0("Only using ", values_new_length, " out of ", values_old_length, " defined columns ")), "as key antimicrobials for ", name, "s. See ?key_antimicrobials.", call = FALSE) remember_thrown_message(paste0("key_antimicrobials.", name)) } generate_antimcrobials_string(x[which(filter), c(universal, values), drop = FALSE]) } if (is.null(universal)) { universal <- character(0) } else { universal <- as.ab(universal, flag_multiple_results = FALSE, info = FALSE) universal <- cols[names(cols) %in% universal] } key_ab <- rep(NA_character_, nrow(x)) key_ab[which(gramstain == "Gram-negative")] <- AMR_string(x = x, values = gram_negative, name = "Gram-negative", filter = gramstain == "Gram-negative", cols = cols) key_ab[which(gramstain == "Gram-positive")] <- AMR_string(x = x, values = gram_positive, name = "Gram-positive", filter = gramstain == "Gram-positive", cols = cols) key_ab[which(kingdom == "Fungi")] <- AMR_string(x = x, values = antifungal, name = "antifungal", filter = kingdom == "Fungi", cols = cols) # back-up - only use `universal` key_ab[which(is.na(key_ab))] <- AMR_string(x = x, values = character(0), name = "", filter = is.na(key_ab), cols = cols) if (length(unique(key_ab)) == 1) { warning_("No distinct key antibiotics determined.", call = FALSE) } key_ab } #' @rdname key_antimicrobials #' @export all_antimicrobials <- function(x = NULL, only_rsi_columns = FALSE, ...) { if (is_null_or_grouped_tbl(x)) { # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all()) # is also fix for using a grouped df as input (a dot as first argument) x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x) } meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0 meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1) # force regular [data.frame], not a tibble or data.table x <- as.data.frame(x, stringsAsFactors = FALSE) cols <- get_column_abx(x, only_rsi_columns = only_rsi_columns, info = FALSE, sort = FALSE) generate_antimcrobials_string(x[ , cols, drop = FALSE]) } generate_antimcrobials_string <- function(df) { if (NCOL(df) == 0) { return(rep("", NROW(df))) } if (NROW(df) == 0) { return(character(0)) } out <- tryCatch( do.call(paste0, lapply(as.list(df), function(x) { x <- toupper(as.character(x)) x[!x %in% c("R", "S", "I")] <- "." paste(x) })), error = function(e) rep(strrep(".", NCOL(df)), NROW(df))) out } #' @rdname key_antimicrobials #' @export antimicrobials_equal <- function(y, z, type = c("points", "keyantimicrobials"), ignore_I = TRUE, points_threshold = 2, ...) { meet_criteria(y, allow_class = "character") meet_criteria(z, allow_class = "character") stop_if(missing(type), "argument \"type\" is missing, with no default") meet_criteria(type, allow_class = "character", has_length = 1, is_in = c("points", "keyantimicrobials")) 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) stop_ifnot(length(y) == length(z), "length of `y` and `z` must be equal") key2rsi <- function(val) { as.double(as.rsi(gsub(".", NA_character_, unlist(strsplit(val, "")), fixed = TRUE))) } y <- lapply(y, key2rsi) z <- lapply(z, key2rsi) determine_equality <- function(a, b, type, points_threshold, ignore_I) { if (length(a) != length(b)) { # incomparable, so not equal return(FALSE) } # ignore NAs on both sides NA_ind <- which(is.na(a) | is.na(b)) a[NA_ind] <- NA_real_ b[NA_ind] <- NA_real_ 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) # and divide by 2 (S = 0.5, I = 1, R = 1.5) (sum(abs(a - b), na.rm = TRUE) / 2) < points_threshold } else { if (ignore_I == TRUE) { ind <- which(a == 2 | b == 2) # since as.double(as.rsi("I")) == 2 a[ind] <- NA_real_ b[ind] <- NA_real_ } all(a == b, na.rm = TRUE) } } out <- unlist(mapply(FUN = determine_equality, y, z, MoreArgs = list(type = type, points_threshold = points_threshold, ignore_I = ignore_I), SIMPLIFY = FALSE, USE.NAMES = FALSE)) out[is.na(y) | is.na(z)] <- NA out }