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# ==================================================================== #
# TITLE: #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE CODE: #
# https://github.com/msberends/AMR #
# #
# PLEASE CITE THIS SOFTWARE AS: #
# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
# AMR: An R Package for Working with Antimicrobial Resistance Data. #
# Journal of Statistical Software, 104(3), 1-31. #
# https://doi.org/10.18637/jss.v104.i03 #
# #
# Developed at the University of Groningen and the University Medical #
# Center Groningen in the Netherlands, in collaboration with many #
# colleagues from around the world, see our website. #
# #
# 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://amr-for-r.org #
# ==================================================================== #
# ====================================================== #
# || Change the EUCAST version numbers in R/globals.R || #
# ====================================================== #
format_eucast_version_nr <- function(version, markdown = TRUE) {
# for documentation - adds title, version number, year and url in markdown language
lst <- c(EUCAST_VERSION_BREAKPOINTS, EUCAST_VERSION_EXPECTED_PHENOTYPES, EUCAST_VERSION_EXPERT_RULES)
version <- format(unique(version), nsmall = 1)
txt <- character(0)
for (i in seq_len(length(version))) {
v <- version[i]
if (markdown == TRUE) {
txt <- c(txt, paste0(
"[", lst[[v]]$title, " ", lst[[v]]$version_txt, "](", lst[[v]]$url, ")",
" (", lst[[v]]$year, ")"
))
} else {
txt <- c(txt, paste0(
lst[[version]]$title, " ", lst[[v]]$version_txt,
" (", lst[[v]]$year, ")"
))
}
}
vector_and(txt, quotes = FALSE)
}
#' Apply Interpretive Rules
#'
#' @description
#' Apply rules from clinical breakpoints notes and expected resistant phenotypes as defined by e.g. the European Committee on Antimicrobial Susceptibility Testing (EUCAST, <https://www.eucast.org>), see *Source*. Use [eucast_dosage()] to get a [data.frame] with advised dosages of a certain bug-drug combination, which is based on the [dosage] data set.
#'
#' To improve the interpretation of the antibiogram before CLSI/EUCAST interpretive rules are applied, some AMR-specific rules can be applied at default, see *Details*.
#' @param x A data set with antimicrobials columns, such as `amox`, `AMX` and `AMC`.
#' @param info A [logical] to indicate whether progress should be printed to the console - the default is only print while in interactive sessions.
#' @param guideline A guideline name, either "EUCAST" (default) or "CLSI". This can be set with the package option [`AMR_guideline`][AMR-options].
#' @param rules A [character] vector that specifies which rules should be applied. Must be one or more of `"breakpoints"`, `"expected_phenotypes"`, `"expert"`, `"other"`, `"custom"`, `"all"`, and defaults to `c("breakpoints", "expected_phenotypes")`. The default value can be set to another value using the package option [`AMR_interpretive_rules`][AMR-options]: `options(AMR_interpretive_rules = "all")`. If using `"custom"`, be sure to fill in argument `custom_rules` too. Custom rules can be created with [custom_interpretive_rules()].
#' @param verbose A [logical] to turn Verbose mode on and off (default is off). In Verbose mode, the function does not apply rules to the data, but instead returns a data set in logbook form with extensive info about which rows and columns would be effected and in which way. Using Verbose mode takes a lot more time.
#' @param version_breakpoints The version number to use for the EUCAST Clinical Breakpoints guideline. Can be `r vector_or(names(EUCAST_VERSION_BREAKPOINTS), documentation = TRUE, reverse = TRUE)`.
#' @param version_expected_phenotypes The version number to use for the EUCAST Expected Phenotypes. Can be `r vector_or(names(EUCAST_VERSION_EXPECTED_PHENOTYPES), documentation = TRUE, reverse = TRUE)`.
#' @param version_expertrules The version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be `r vector_or(names(EUCAST_VERSION_EXPERT_RULES), documentation = TRUE, reverse = TRUE)`.
#' @param ampc_cephalosporin_resistance (only applies when `rules` contains `"expert"` or `"all"`) a [character] value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants - the default is `NA`. Currently only works when `version_expertrules` is `3.2` and higher; these versions of '*EUCAST Expert Rules on Enterobacterales*' state that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three drugs. A value of `NA` (the default) for this argument will remove results for these three drugs, while e.g. a value of `"R"` will make the results for these drugs resistant. Use `NULL` or `FALSE` to not alter results for these three drugs of AmpC de-repressed cephalosporin-resistant mutants. Using `TRUE` is equal to using `"R"`. \cr For *EUCAST Expert Rules* v3.2, this rule applies to: `r vector_and(gsub("[^a-zA-Z ]+", "", unlist(strsplit(INTERPRETIVE_RULES_DF[which(INTERPRETIVE_RULES_DF$reference.version %in% c(3.2, 3.3) & INTERPRETIVE_RULES_DF$reference.rule %like% "ampc"), "this_value"][1], "|", fixed = TRUE))), quotes = "*")`.
#' @param ... Column names of antimicrobials. To automatically detect antimicrobial column names, do not provide any named arguments; [guess_ab_col()] will then be used for detection. To manually specify a column, provide its name (case-insensitive) as an argument, e.g. `AMX = "amoxicillin"`. To skip a specific antimicrobial, set it to `NULL`, e.g. `TIC = NULL` to exclude ticarcillin. If a manually defined column does not exist in the data, it will be skipped with a warning.
#' @param ab Any (vector of) text that can be coerced to a valid antimicrobial drug code with [as.ab()].
#' @param administration Route of administration, either `r vector_or(dosage$administration, documentation = TRUE)`.
#' @param only_sir_columns A [logical] to indicate whether only antimicrobial columns must be included that were transformed to class [sir][as.sir()] on beforehand. Defaults to `FALSE` if no columns of `x` have a class [sir][as.sir()].
#' @param custom_rules Custom rules to apply, created with [custom_interpretive_rules()].
#' @param overwrite A [logical] indicating whether to overwrite existing SIR values (default: `FALSE`). When `FALSE`, only non-SIR values are modified (i.e., any value that is not already S, I or R). To ensure compliance with EUCAST guidelines, **this should remain** `FALSE`, as EUCAST notes often state that an organism "should be tested for susceptibility to individual agents or be reported resistant".
#' @param add_if_missing A [logical] indicating whether rules should also be applied to missing (`NA`) values (default: `TRUE`). When `FALSE`, rules are only applied to cells that already contain an SIR value; cells with `NA` are left untouched. This is particularly useful when using `overwrite = TRUE` with custom rules and you want to update reported results without imputing values for untested drugs.
#' @inheritParams first_isolate
#' @details
#' **Note:** This function does not translate MIC or disk values to SIR values. Use [as.sir()] for that. \cr
#' **Note:** When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance. \cr
#'
#' The file containing all interpretive rules is located here: <https://github.com/msberends/AMR/blob/main/data-raw/interpretive_rules.tsv>. **Note:** Old taxonomic names are replaced with the current taxonomy where applicable. For example, *Ochrobactrum anthropi* was renamed to *Brucella anthropi* in 2020; older interpretive rules may not yet contain this new taxonomic name. The `AMR` package contains the full microbial taxonomy updated until `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date))`, see [microorganisms].
#'
#' ### Custom Rules
#'
#' Custom rules can be created using [custom_interpretive_rules()], e.g.:
#'
#' ```r
#' x <- custom_interpretive_rules(AMC == "R" & genus == "Klebsiella" ~ aminopenicillins == "R",
#' AMC == "I" & genus == "Klebsiella" ~ aminopenicillins == "I")
#'
#' interpretive_rules(example_isolates, rules = "custom", custom_rules = x)
#' ```
#'
#' ### 'Other' Rules
#'
#' Before further processing, two non-EUCAST rules about drug combinations can be applied to improve the efficacy of the EUCAST rules, and the reliability of your data (analysis). These rules are:
#'
#' 1. A drug **with** enzyme inhibitor will be set to S if the same drug **without** enzyme inhibitor is S
#' 2. A drug **without** enzyme inhibitor will be set to R if the same drug **with** enzyme inhibitor is R
#'
#' Important examples include amoxicillin and amoxicillin/clavulanic acid, and trimethoprim and trimethoprim/sulfamethoxazole. Needless to say, for these rules to work, both drugs must be available in the data set.
#'
#' Since these rules are not officially approved by EUCAST, they are not applied at default. To use these rules, include `"other"` to the `rules` argument, or use `interpretive_rules(..., rules = "all")`. You can also set the package option [`AMR_interpretive_rules`][AMR-options], i.e. run `options(AMR_interpretive_rules = "all")`.
#' @aliases EUCAST
#' @rdname interpretive_rules
#' @export
#' @return The input of `x`, possibly with edited values of antimicrobials. Or, if `verbose = TRUE`, a [data.frame] with all original and new values of the affected bug-drug combinations.
#' @references
#' - EUCAST Expert Rules. Version 2.0, 2012.\cr
#' Leclercq et al. **EUCAST expert rules in antimicrobial susceptibility testing.** *Clin Microbiol Infect.* 2013;19(2):141-60; \doi{https://doi.org/10.1111/j.1469-0691.2011.03703.x}
#' - EUCAST Expected Phenotypes. [(link)](https://www.eucast.org/bacteria/important-additional-information/expected-phenotypes/)
#' - EUCAST Breakpoint tables for interpretation of MICs and zone diameters. [(link)](https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/clinical-breakpoint-tables/)
#' @inheritSection AMR Download Our Reference Data
#' @examples
#' \donttest{
#' a <- data.frame(
#' mo = c(
#' "Staphylococcus aureus",
#' "Enterococcus faecalis",
#' "Escherichia coli",
#' "Klebsiella pneumoniae",
#' "Pseudomonas aeruginosa"
#' ),
#' VAN = "-", # Vancomycin
#' AMX = "-", # Amoxicillin
#' COL = "-", # Colistin
#' CAZ = "-", # Ceftazidime
#' CXM = "-", # Cefuroxime
#' PEN = "S", # Benzylpenicillin
#' FOX = "S", # Cefoxitin
#' stringsAsFactors = FALSE
#' )
#'
#' head(a)
#'
#'
#' # apply EUCAST rules: some results wil be changed
#' b <- eucast_rules(a, overwrite = TRUE)
#'
#' head(b)
#'
#'
#' # do not apply EUCAST rules, but rather get a data.frame
#' # containing all details about the transformations:
#' c <- eucast_rules(a, overwrite = TRUE, verbose = TRUE)
#' head(c)
#' }
#'
#' # Dosage guidelines:
#'
#' eucast_dosage(c("tobra", "genta", "cipro"), "iv")
#'
#' eucast_dosage(c("tobra", "genta", "cipro"), "iv", version_breakpoints = 10)
interpretive_rules <- function(x,
col_mo = NULL,
info = interactive(),
rules = getOption("AMR_interpretive_rules", default = c("breakpoints", "expected_phenotypes")),
guideline = getOption("AMR_guideline", "EUCAST"),
verbose = FALSE,
version_breakpoints = 16.0,
version_expected_phenotypes = 1.2,
version_expertrules = 3.3,
ampc_cephalosporin_resistance = NA,
only_sir_columns = any(is.sir(x)),
custom_rules = NULL,
overwrite = FALSE,
add_if_missing = TRUE,
...) {
meet_criteria(x, allow_class = "data.frame")
meet_criteria(col_mo, allow_class = "character", has_length = 1, is_in = colnames(x), allow_NULL = TRUE)
if (guideline %like% "EUCAST") {
guideline <- "EUCAST"
} else if (guideline %like% "CLSI") {
guideline <- "CLSI"
}
meet_criteria(guideline, allow_class = "character", has_length = 1, is_in = c("EUCAST", "CLSI"))
meet_criteria(info, allow_class = "logical", has_length = 1)
meet_criteria(rules, allow_class = "character", has_length = c(1, 2, 3, 4, 5, 6), is_in = c("breakpoints", "expected_phenotypes", "expert", "other", "all", "custom"))
meet_criteria(verbose, allow_class = "logical", has_length = 1)
meet_criteria(version_breakpoints, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_BREAKPOINTS)))
meet_criteria(version_expected_phenotypes, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_EXPECTED_PHENOTYPES)))
meet_criteria(version_expertrules, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_EXPERT_RULES)))
meet_criteria(ampc_cephalosporin_resistance, allow_class = c("logical", "character", "sir"), has_length = 1, allow_NA = TRUE, allow_NULL = TRUE)
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(custom_rules, allow_class = c("custom_interpretive_rules", "custom_eucast_rules"), allow_NULL = TRUE)
meet_criteria(overwrite, allow_class = "logical", has_length = 1)
meet_criteria(add_if_missing, allow_class = "logical", has_length = 1)
stop_if(
!overwrite && !add_if_missing,
"Either set {.arg overwrite} or {.arg add_if_missing} to {.code TRUE}, or both."
)
stop_if(
!is.na(ampc_cephalosporin_resistance) && !any(c("expert", "all") %in% rules),
"For the {.arg ampc_cephalosporin_resistance} argument to work, the {.arg rules} argument must contain {.code \"expert\"} or {.code \"all\"}."
)
add_MO_lookup_to_AMR_env()
if ("custom" %in% rules && is.null(custom_rules)) {
warning_("in {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}: no custom rules were set with the {.arg custom_rules} argument",
immediate = TRUE
)
rules <- rules[rules != "custom"]
if (length(rules) == 0) {
if (isTRUE(info)) {
message_("No other rules were set, returning original data", as_note = FALSE)
}
return(x)
}
}
x_deparsed <- deparse(substitute(x))
if (length(x_deparsed) > 1 || any(x_deparsed %unlike% "[a-z]+")) {
x_deparsed <- "your_data"
}
breakpoints_info <- EUCAST_VERSION_BREAKPOINTS[[which(as.double(names(EUCAST_VERSION_BREAKPOINTS)) == version_breakpoints)]]
expected_phenotypes_info <- EUCAST_VERSION_EXPECTED_PHENOTYPES[[which(as.double(names(EUCAST_VERSION_EXPECTED_PHENOTYPES)) == version_expected_phenotypes)]]
expertrules_info <- EUCAST_VERSION_EXPERT_RULES[[which(as.double(names(EUCAST_VERSION_EXPERT_RULES)) == version_expertrules)]]
if (interactive() && isTRUE(verbose) && isTRUE(info)) {
txt <- paste0(
"WARNING: In Verbose mode, the interpretive_rules() function does not apply rules to the data, but instead returns a data set in logbook form with comprehensive info about which rows and columns would be effected and in which way.",
"\n\nThis may overwrite your existing data if you use e.g.:",
"\ndata <- interpretive_rules(data, verbose = TRUE)\n\nDo you want to continue?"
)
showQuestion <- import_fn("showQuestion", "rstudioapi", error_on_fail = FALSE)
if (!is.null(showQuestion)) {
q_continue <- showQuestion("Using verbose = TRUE with interpretive_rules()", txt)
} else {
q_continue <- utils::menu(choices = c("OK", "Cancel"), graphics = FALSE, title = txt)
}
if (q_continue %in% c(FALSE, 2)) {
message_("Cancelled, returning original data", as_note = FALSE)
return(x)
}
}
# try to find columns based on type
# -- mo
if (is.null(col_mo)) {
col_mo <- search_type_in_df(x = x, type = "mo", info = info)
stop_if(is.null(col_mo), "{.arg col_mo} must be set")
}
decimal.mark <- getOption("OutDec")
big.mark <- ifelse(decimal.mark != ",", ",", " ")
formatnr <- function(x, big = big.mark, dec = decimal.mark) {
trimws(format(x, big.mark = big, decimal.mark = dec))
}
warned <- FALSE
warn_lacking_sir_class <- character(0)
overwrite_skipped <- FALSE
txt_ok <- function(n_added, n_changed, warned = FALSE) {
if (warned == FALSE) {
if (n_added + n_changed == 0) {
cat(" ", font_subtle("(no changes)"), "\n", sep = "")
} else {
status <- character(0)
if (n_added > 0) {
status <- c(status, font_bold(font_green(formatnr(n_added), ifelse(n_added == 1, "value added", "values added"))))
}
if (n_changed > 0) {
status <- c(status, font_bold(font_blue(formatnr(n_changed), ifelse(n_changed == 1, "value changed", "values changed"))))
}
cat(" ", font_subtle("("), paste0(status, collapse = font_subtle(", ")), font_subtle(")"), "\n", sep = "")
}
warned <<- FALSE
}
}
cols_ab <- get_column_abx(
x = x,
soft_dependencies = c(
"AMC",
"AMP",
"AMX",
"CIP",
"ERY",
"FOX-S",
"GEN",
"MFX",
"NAL-S",
"NOR-S",
"OXA-S",
"PEN-S",
"PIP",
"TCY",
"TIC",
"TOB"
),
hard_dependencies = NULL,
verbose = verbose,
info = info,
only_sir_columns = only_sir_columns,
fn = "interpretive_rules",
...
)
if (!"AMP" %in% names(cols_ab) && "AMX" %in% names(cols_ab)) {
# ampicillin column is missing, but amoxicillin is available
if (isTRUE(info)) {
message_("Using column {.field ", font_bold(cols_ab[names(cols_ab) == "AMX"]), "} as input for ampicillin since many EUCAST rules depend on it.")
}
cols_ab <- c(cols_ab, c(AMP = unname(cols_ab[names(cols_ab) == "AMX"])))
}
# data preparation ----
if (isTRUE(info) && NROW(x) > 10000) {
message_("Preparing data...", appendLF = FALSE, as_note = FALSE)
}
# Some helper functions ---------------------------------------------------
get_antibiotic_names <- function(x) {
x <- x %pm>%
strsplit(",") %pm>%
unlist() %pm>%
trimws2() %pm>%
vapply(FUN.VALUE = character(1), function(x) if (x %in% AMR::antimicrobials$ab) ab_name(x, language = NULL, tolower = TRUE, fast_mode = TRUE) else x) %pm>%
sort() %pm>%
paste(collapse = ", ")
x <- gsub("_", " ", x, fixed = TRUE)
x <- gsub("except CAZ", paste("except", ab_name("CAZ", language = NULL, tolower = TRUE)), x, fixed = TRUE)
x <- gsub("except TGC", paste("except", ab_name("TGC", language = NULL, tolower = TRUE)), x, fixed = TRUE)
x <- gsub("cephalosporins (1st|2nd|3rd|4th|5th)", "cephalosporins (\\1 gen.)", x)
x
}
format_antibiotic_names <- function(ab_names, ab_results) {
ab_names <- trimws2(unlist(strsplit(ab_names, ",")))
ab_results <- trimws2(unlist(strsplit(ab_results, ",")))
if (length(ab_results) == 1) {
if (length(ab_names) == 1) {
# like FOX S
x <- paste(ab_names, "is")
} else if (length(ab_names) == 2) {
# like PEN,FOX S
x <- paste(paste0(ab_names, collapse = " and "), "are both")
} else {
# like PEN,FOX,GEN S
x <- paste(paste0(ab_names, collapse = " and "), "are all")
}
return(paste0(x, " '", ab_results, "'"))
} else {
if (length(ab_names) == 2) {
# like PEN,FOX S,R
paste0(
ab_names[1], " is '", ab_results[1], "' and ",
ab_names[2], " is '", ab_results[2], "'"
)
} else {
# like PEN,FOX,GEN S,R,R
paste0(
ab_names[1], " is '", ab_results[1], "' and ",
ab_names[2], " is '", ab_results[2], "' and ",
ab_names[3], " is '", ab_results[3], "'"
)
}
}
}
like_unique <- function(vals, pattern) {
# %like% on the unique values only, as organism columns contain many duplicates
vals_unique <- unique(vals)
(vals_unique %like% pattern)[match(vals, vals_unique)]
}
sir_values <- function(col) {
# SIR values of a column of the current data as character, to compare without the overhead of Ops.sir()
vals <- x[[col]]
if (!is.sir(vals)) {
vals <- suppressWarnings(as.sir(vals))
}
as.character(vals)
}
expand_groups <- function(entry) {
parts <- trimws(strsplit(entry, ",")[[1]])
group_names <- tolower(AMR::microorganisms.groups$mo_group_name)
mo_names <- AMR::microorganisms.groups$mo_name
group_names_lc <- tolower(group_names)
result <- unlist(lapply(parts, function(part) {
match_idx <- which(group_names_lc == tolower(part))
if (length(match_idx) > 0) {
mo_names[match_idx]
} else {
part
}
}))
# only the ones with genus or genus/species, not subspecies (as genus_species will be matched)
spaces <- vapply(FUN.VALUE = integer(1), strsplit(result, " "), length)
result <- result[spaces < 3]
return(paste0(unique(result), collapse = ", "))
}
# the accepted taxa of literal organism names in a rule, e.g. Thomasclavelia ramosa for 'Clostridium ramosum'
add_MO_lookup_to_AMR_env()
fullnames_lower <- tolower(AMR_env$MO_lookup$fullname)
names_to_accepted_mo <- function(mo_names) {
mo <- AMR_env$MO_lookup$mo[which(fullnames_lower %in% tolower(mo_names))]
synonym_mo_to_accepted_mo(mo, fill_in_accepted = TRUE)
}
# Preparing the data ------------------------------------------------------
verbose_info <- data.frame(
rowid = character(0),
col = character(0),
mo_fullname = character(0),
old = as.sir(character(0)),
new = as.sir(character(0)),
rule = character(0),
rule_group = character(0),
rule_name = character(0),
rule_source = character(0),
stringsAsFactors = FALSE
)
old_cols <- colnames(x)
old_attributes <- attributes(x)
x <- as.data.frame(x, stringsAsFactors = FALSE) # no tibbles, data.tables, etc.
rownames(x) <- NULL # will later be restored with old_attributes
# create unique row IDs - combination of the MO and all ABx columns (so they will only run once per unique combination)
# (with a separator, so that e.g. "S" + "IR" and "SI" + "R" cannot get the same ID)
x$`.rowid` <- do.call(paste, c(
lapply(c(col_mo, cols_ab), function(col) {
vals <- as.character(x[[col]])
vals[is.na(vals)] <- "."
vals
}),
sep = "\r"
))
# save original table, with the new .rowid column
x.bak <- x
# keep only unique rows for MO and ABx
x <- x %pm>%
pm_arrange(`.rowid`) %pm>%
# big speed gain! only analyse unique rows:
pm_distinct(`.rowid`, .keep_all = TRUE) %pm>%
as.data.frame(stringsAsFactors = FALSE)
x[, col_mo] <- as.mo(as.character(x[, col_mo, drop = TRUE]), info = FALSE)
# rename col_mo to prevent interference with joined columns
colnames(x)[colnames(x) == col_mo] <- ".col_mo"
col_mo <- ".col_mo"
# join to microorganisms data set
x <- left_join_microorganisms(x, by = col_mo, suffix = c("_oldcols", ""))
x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL, info = FALSE)
x$genus_species <- trimws(paste(x$genus, x$species))
# the accepted name of each organism, so that a rule can name it by any of its synonyms (see 'Get rule from file')
x$`.mo_accepted` <- synonym_mo_to_accepted_mo(as.character(x[, col_mo, drop = TRUE]), fill_in_accepted = TRUE)
if (isTRUE(info) && NROW(x.bak) > 10000) {
message_("OK.", as_note = FALSE)
}
n_added <- 0
n_changed <- 0
rule_current <- ""
rule_group_current <- ""
rule_group_previous <- ""
rule_next <- ""
rule_previous <- ""
rule_text <- ""
# >>> Apply Other rules: enzyme inhibitors <<< ------------------------------------------
if (any(c("all", "other") %in% rules)) {
if (isTRUE(info)) {
cat_rules_header(
title = paste0(
"Rules by the ", font_bold(paste0("AMR package v", utils::packageDescription("AMR")$Version)),
" (", format(as.Date(utils::packageDescription("AMR")$Date), format = "%Y"), ")"
),
subtitle = format_inline_("See {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}")
)
}
ab_enzyme <- subset(AMR::antimicrobials, name %like% "/")[, c("ab", "name"), drop = FALSE]
colnames(ab_enzyme) <- c("enzyme_ab", "enzyme_name")
ab_enzyme$base_name <- gsub("^([a-zA-Z0-9]+).*", "\\1", ab_enzyme$enzyme_name)
ab_enzyme$base_ab <- AMR::antimicrobials[match(ab_enzyme$base_name, AMR::antimicrobials$name), "ab", drop = TRUE]
ab_enzyme <- subset(ab_enzyme, !is.na(base_ab))
# make ampicillin and amoxicillin interchangable
ampi <- subset(ab_enzyme, base_ab == "AMX")
ampi$base_ab <- "AMP"
ampi$base_name <- ab_name("AMP", language = NULL)
amox <- subset(ab_enzyme, base_ab == "AMP")
amox$base_ab <- "AMX"
amox$base_name <- ab_name("AMX", language = NULL)
# merge and sort
ab_enzyme <- rbind_AMR(ab_enzyme, ampi, amox)
ab_enzyme <- ab_enzyme[order(ab_enzyme$enzyme_name), , drop = FALSE]
for (i in seq_len(nrow(ab_enzyme))) {
# check if both base and base + enzyme inhibitor are part of the data set
if (all(c(ab_enzyme$base_ab[i], ab_enzyme$enzyme_ab[i]) %in% names(cols_ab), na.rm = TRUE)) {
col_base <- unname(cols_ab[ab_enzyme$base_ab[i]])
col_enzyme <- unname(cols_ab[ab_enzyme$enzyme_ab[i]])
## Set base to R where base + enzyme inhibitor is R ----
rule_current <- paste0(
ab_enzyme$base_name[i], " (", col_base, ") = R if ",
tolower(ab_enzyme$enzyme_name[i]), " (", col_enzyme, ") = R"
)
if (isTRUE(info)) {
cat_rule_name(paste0(
ab_enzyme$base_name[i], " (", format_field(col_base), ") = ", format_sir_badge("R", guideline = guideline), " if ",
tolower(ab_enzyme$enzyme_name[i]), " (", format_field(col_enzyme), ") = ", format_sir_badge("R", guideline = guideline)
))
}
run_changes <- edit_sir(
x = x,
to = "R",
rule = c(
rule_current, "Other rules", "",
paste0("Non-EUCAST: AMR package v", utils::packageDescription("AMR")$Version)
),
rows = which(sir_values(col_enzyme) == "R"),
cols = col_base,
last_verbose_info = verbose_info,
original_data = x.bak,
warned = warned,
info = info,
verbose = verbose,
overwrite = overwrite,
add_if_missing = add_if_missing
)
n_added <- n_added + run_changes$added
n_changed <- n_changed + run_changes$changed
verbose_info <- run_changes$verbose_info
x <- run_changes$output
warn_lacking_sir_class <- c(warn_lacking_sir_class, run_changes$sir_warn)
overwrite_skipped <- overwrite_skipped || isTRUE(run_changes$overwrite_skipped)
# Print number of new changes
if (isTRUE(info)) {
# print only on last one of rules in this group
txt_ok(n_added = n_added, n_changed = n_changed, warned = warned)
# and reset counters
n_added <- 0
n_changed <- 0
}
## Set base + enzyme inhibitor to S where base is S ----
rule_current <- paste0(
ab_enzyme$enzyme_name[i], " (", col_enzyme, ") = S if ",
tolower(ab_enzyme$base_name[i]), " (", col_base, ") = S"
)
if (isTRUE(info)) {
cat_rule_name(paste0(
ab_enzyme$enzyme_name[i], " (", format_field(col_enzyme), ") = ", format_sir_badge("S", guideline = guideline), " if ",
tolower(ab_enzyme$base_name[i]), " (", format_field(col_base), ") = ", format_sir_badge("S", guideline = guideline)
))
}
run_changes <- edit_sir(
x = x,
to = "S",
rule = c(
rule_current, "Other rules", "",
paste0("Non-EUCAST: AMR package v", utils::packageDescription("AMR")$Version)
),
rows = which(sir_values(col_base) == "S"),
cols = col_enzyme,
last_verbose_info = verbose_info,
original_data = x.bak,
warned = warned,
info = info,
verbose = verbose,
overwrite = overwrite,
add_if_missing = add_if_missing
)
n_added <- n_added + run_changes$added
n_changed <- n_changed + run_changes$changed
verbose_info <- run_changes$verbose_info
x <- run_changes$output
warn_lacking_sir_class <- c(warn_lacking_sir_class, run_changes$sir_warn)
overwrite_skipped <- overwrite_skipped || isTRUE(run_changes$overwrite_skipped)
# Print number of new changes
if (isTRUE(info)) {
# print only on last one of rules in this group
txt_ok(n_added = n_added, n_changed = n_changed, warned = warned)
# and reset counters
n_added <- 0
n_changed <- 0
}
}
}
} else {
if (isTRUE(info)) {
cat("\n")
message_("Skipping inhibitor-inheritance rules defined by this AMR package: setting S to drug+inhibitor where drug is S, and setting R to drug where drug+inhibitor is R. Add {.val other} or {.val all} to the {.arg rules} argument to apply those rules.")
}
}
if (!any(c("all", "custom") %in% rules) && !is.null(custom_rules)) {
if (isTRUE(info)) {
message_("Skipping custom interpretive rules, since the {.arg rules} argument does not contain {.code \"custom\"}.")
}
custom_rules <- NULL
}
# >>> Apply Official interpretive rules <<< ---------------------------------------------------
eucast_notification_shown <- FALSE
if (!is.null(list(...)$interpretive_rules_df)) {
# this allows: interpretive_rules(x, interpretive_rules_df = AMR:::INTERPRETIVE_RULES_DF |> filter(is.na(have_these_values)))
interpretive_rules_df_total <- list(...)$interpretive_rules_df
} else if (!is.null(list(...)$eucast_rules_df)) {
# deprecated parameter name kept for backward compatibility
interpretive_rules_df_total <- list(...)$eucast_rules_df
warning("Used interpretive_rules(x, eucast_rules_df = ...) - Do use newer argument interpretive_rules_df now.")
} else {
# internal data file, created in data-raw/_pre_commit_checks.R
interpretive_rules_df_total <- INTERPRETIVE_RULES_DF
}
## filter on guideline provider and user-set guideline versions ----
interpretive_rules_df <- data.frame()
if (any(c("all", "breakpoints") %in% rules)) {
interpretive_rules_df <- interpretive_rules_df %pm>%
rbind_AMR(interpretive_rules_df_total %pm>%
subset(rule.provider == guideline & reference.rule_group %like% "breakpoint" & reference.version == version_breakpoints))
}
if (any(c("all", "expected_phenotypes") %in% rules)) {
interpretive_rules_df <- interpretive_rules_df %pm>%
rbind_AMR(interpretive_rules_df_total %pm>%
subset(rule.provider == guideline & reference.rule_group %like% "expected" & reference.version == version_expected_phenotypes))
}
if (any(c("all", "expert") %in% rules)) {
interpretive_rules_df <- interpretive_rules_df %pm>%
rbind_AMR(interpretive_rules_df_total %pm>%
subset(rule.provider == guideline & reference.rule_group %like% "expert" & reference.version == version_expertrules))
}
## filter out AmpC de-repressed cephalosporin-resistant mutants ----
# no need to filter on version number here - the rules contain these version number, so are inherently filtered
# cefotaxime, ceftriaxone, ceftazidime
if (is.null(ampc_cephalosporin_resistance) || isFALSE(ampc_cephalosporin_resistance)) {
interpretive_rules_df <- subset(
interpretive_rules_df,
reference.rule %unlike% "ampc"
)
} else {
if (isTRUE(ampc_cephalosporin_resistance)) {
ampc_cephalosporin_resistance <- "R"
}
# old R versions (at least 3.0) give an error when assigning to no rows of a data.frame without rows
ampc_rows <- if (is.null(interpretive_rules_df$reference.rule)) integer(0) else which(interpretive_rules_df$reference.rule %like% "ampc")
if (length(ampc_rows) > 0) {
interpretive_rules_df[ampc_rows, "to_value"] <- as.character(ampc_cephalosporin_resistance)
}
}
# sometimes, the screenings are missing but the names are actually available
# we only hints on remaining rows in `interpretive_rules_df`
screening_abx <- as.character(AMR::antimicrobials$ab[which(AMR::antimicrobials$ab %like% "-S$")])
screening_abx <- screening_abx[screening_abx %in% unique(unlist(strsplit(interpretive_rules_df_total$and_these_antibiotics[!is.na(interpretive_rules_df_total$and_these_antibiotics)], ", *")))]
for (ab_s in screening_abx) {
ab <- gsub("-S$", "", ab_s)
if (ab %in% names(cols_ab) && !ab_s %in% names(cols_ab)) {
if (isTRUE(info)) {
message_(
"Using column {.field ", font_bold(cols_ab[names(cols_ab) == ab]),
"} as ", ab_name(ab_s, language = NULL, tolower = TRUE),
" since a column {.code ", ab_s, "} is missing but required for the chosen rules"
)
}
cols_ab <- c(cols_ab, stats::setNames(unname(cols_ab[names(cols_ab) == ab]), ab_s))
}
}
## Go over all rules and apply them ----
mo_match_cache <- new.env(parent = emptyenv())
# the type of each rule group, determined once instead of for every rule
rule_groups <- as.character(interpretive_rules_df$reference.rule_group)
rule_group_types <- rep("", length(rule_groups))
rule_group_types[rule_groups %like% "expert"] <- "expert"
rule_group_types[rule_groups %like% "expected"] <- "expected"
rule_group_types[rule_groups %like% "breakpoint"] <- "breakpoint"
rule_group_is_other <- rule_groups %like% "other"
for (i in seq_len(nrow(interpretive_rules_df))) {
rule_previous <- interpretive_rules_df[max(1, i - 1), "reference.rule", drop = TRUE]
rule_current <- interpretive_rules_df[i, "reference.rule", drop = TRUE]
rule_next <- interpretive_rules_df[min(nrow(interpretive_rules_df), i + 1), "reference.rule", drop = TRUE]
rule_group_previous <- interpretive_rules_df[max(1, i - 1), "reference.rule_group", drop = TRUE]
rule_group_current <- interpretive_rules_df[i, "reference.rule_group", drop = TRUE]
# don't apply rules if user doesn't want to apply them
if (rule_group_types[i] == "breakpoint" && !any(c("all", "breakpoints") %in% rules)) {
next
}
if (rule_group_types[i] == "expected" && !any(c("all", "expected_phenotypes") %in% rules)) {
next
}
if (rule_group_types[i] == "expert" && !any(c("all", "expert") %in% rules)) {
next
}
if (isFALSE(info) || isFALSE(verbose)) {
rule_text <- ""
} else {
if (is.na(interpretive_rules_df[i, "and_these_antibiotics", drop = TRUE])) {
rule_text <- paste0("always report as '", interpretive_rules_df[i, "to_value", drop = TRUE], "': ", get_antibiotic_names(interpretive_rules_df[i, "then_change_these_antibiotics", drop = TRUE]))
} else {
rule_text <- paste0(
"report as '", interpretive_rules_df[i, "to_value", drop = TRUE], "' when ",
format_antibiotic_names(
ab_names = get_antibiotic_names(interpretive_rules_df[i, "and_these_antibiotics", drop = TRUE]),
ab_results = interpretive_rules_df[i, "have_these_values", drop = TRUE]
), ": ",
get_antibiotic_names(interpretive_rules_df[i, "then_change_these_antibiotics", drop = TRUE])
)
}
}
if (i == 1) {
rule_previous <- ""
rule_group_previous <- ""
}
if (i == nrow(interpretive_rules_df)) {
rule_next <- ""
}
if (isTRUE(info)) {
## Print EUCAST intro ------------------------------------------------------
if (!rule_group_is_other[i] && eucast_notification_shown == FALSE) {
cat_rules_header(
title = paste0("Rules by ", font_bold("EUCAST")),
subtitle = format_inline_("European Committee on Antimicrobial Susceptibility Testing, {.url https://eucast.org/}")
)
eucast_notification_shown <- TRUE
}
## Print rule (group) ------------------------------------------------------
if (rule_group_current != rule_group_previous) {
# is new rule group, one of Breakpoints, Expected Phenotypes and Expert Rules
if (rule_group_types[i] == "breakpoint") {
group_info <- breakpoints_info
} else if (rule_group_types[i] == "expected") {
group_info <- expected_phenotypes_info
} else if (rule_group_types[i] == "expert") {
group_info <- expertrules_info
} else {
group_info <- NULL
}
if (!is.null(group_info)) {
# the first group directly follows the EUCAST header, which already ends with an empty line
cat(if (i > 1) "\n", wrap_rules_text(paste0(font_bold(group_info$title), " ", font_red(paste0("(", group_info$version_txt, ", ", group_info$year, ")")))), "\n", sep = "")
}
}
## Print rule -------------------------------------------------------------
if (rule_current != rule_previous) {
# is new rule within group, print its name
cat_rule_name(italicise_taxonomy(rule_current, type = "ansi"))
warned <- FALSE
}
}
## Get rule from file ------------------------------------------------------
if_mo_property <- trimws(interpretive_rules_df[i, "if_mo_property", drop = TRUE])
like_is_one_of <- trimws(interpretive_rules_df[i, "like.is.one_of", drop = TRUE])
mo_value <- trimws(interpretive_rules_df[i, "this_value", drop = TRUE])
source_antibiotics <- interpretive_rules_df[i, "and_these_antibiotics", drop = TRUE]
source_value <- trimws(unlist(strsplit(interpretive_rules_df[i, "have_these_values", drop = TRUE], ",", fixed = TRUE)))
target_antibiotics <- interpretive_rules_df[i, "then_change_these_antibiotics", drop = TRUE]
target_value <- interpretive_rules_df[i, "to_value", drop = TRUE]
# the organism columns do not change while applying rules, so rules for the same organisms can reuse the matches
mo_key <- paste(if_mo_property, like_is_one_of, mo_value, sep = "\r")
mo_match <- mo_match_cache[[mo_key]]
if (is.null(mo_match)) {
# if amo_value contains a group name, expand that name with all species in it
if (any(trimws(strsplit(mo_value, ",")[[1]]) %in% AMR::microorganisms.groups$mo_group_name, na.rm = TRUE)) {
like_is_one_of <- "one_of"
mo_value <- expand_groups(mo_value)
}
# Organisms match by the names in the data. Species-level rules with literal names also match by the accepted
# taxon, so that the rule and the data may use different names of it (e.g. 'Clostridium ramosum' in a rule, while
# its accepted name is Thomasclavelia ramosa). Not for genera, as a former genus also contains species that now
# belong to other genera (e.g. Pseudomonas maltophilia), and not for regular expressions, as these would then also
# match synonyms they were written to exclude (e.g. Klebsiella mobilis for '^Klebsiella(?! aerogenes)').
accepted_mo_of_rule <- NULL
if (like_is_one_of %in% c("is", "one_of") && if_mo_property %in% c("genus_species", "fullname")) {
accepted_mo_of_rule <- names_to_accepted_mo(trimws(strsplit(mo_value, ",", fixed = TRUE)[[1]]))
}
if (like_is_one_of == "is") {
# so e.g. 'Enterococcus' will turn into '^Enterococcus$'
mo_value <- paste0("^", mo_value, "$")
} else if (like_is_one_of == "one_of") {
# so 'Clostridium, Actinomyces, ...' will turn into '^(Clostridium|Actinomyces|...)$'
mo_value <- paste0(
"^(",
paste(trimws(unlist(strsplit(mo_value, ",", fixed = TRUE))),
collapse = "|"
),
")$"
)
} else if (like_is_one_of != "like") {
stop("invalid value for column {.field like.is.one_of}", call. = FALSE)
}
mo_match <- tryCatch(
like_unique(x[, if_mo_property, drop = TRUE], mo_value) | like_unique(x$fullname, mo_value),
error = function(e) rep(FALSE, nrow(x))
)
if (!is.null(accepted_mo_of_rule)) {
mo_match <- mo_match | x$`.mo_accepted` %in% accepted_mo_of_rule
}
mo_match_cache[[mo_key]] <- mo_match
}
if (is.na(source_antibiotics)) {
rows <- which(mo_match)
} else {
source_antibiotics <- get_ab_from_namespace(source_antibiotics, cols_ab)
if (length(source_value) == 1 && length(source_antibiotics) > 1) {
source_value <- rep(source_value, length(source_antibiotics))
}
if (length(source_antibiotics) == 0) {
rows <- integer(0)
} else if (length(source_antibiotics) == 1) {
rows <- tryCatch(
which(mo_match &
sir_values(source_antibiotics[1L]) == source_value[1L]),
error = function(e) integer(0)
)
} else if (length(source_antibiotics) == 2) {
rows <- tryCatch(
which(mo_match &
sir_values(source_antibiotics[1L]) == source_value[1L] &
sir_values(source_antibiotics[2L]) == source_value[2L]),
error = function(e) integer(0)
)
} else {
stop_("only 2 antimicrobials supported for source_antibiotics")
}
}
cols <- get_ab_from_namespace(target_antibiotics, cols_ab)
## Apply rule on data ------------------------------------------------------
# this will return the unique number of changes
run_changes <- edit_sir(
x = x,
to = target_value,
rule = c(
rule_text, rule_group_current, rule_current,
ifelse(rule_group_types[i] == "breakpoint",
paste0(breakpoints_info$title, " ", breakpoints_info$version_txt, ", ", breakpoints_info$year),
ifelse(rule_group_types[i] == "expected",
paste0(expected_phenotypes_info$title, " ", expected_phenotypes_info$version_txt, ", ", expected_phenotypes_info$year),
paste0(expertrules_info$title, " ", expertrules_info$version_txt, ", ", expertrules_info$year)
)
)
),
rows = rows,
cols = cols,
last_verbose_info = verbose_info,
original_data = x.bak,
warned = warned,
info = info,
verbose = verbose,
overwrite = overwrite,
add_if_missing = add_if_missing
)
n_added <- n_added + run_changes$added
n_changed <- n_changed + run_changes$changed
verbose_info <- run_changes$verbose_info
x <- run_changes$output
warn_lacking_sir_class <- c(warn_lacking_sir_class, run_changes$sir_warn)
overwrite_skipped <- overwrite_skipped || isTRUE(run_changes$overwrite_skipped)
## Print number of new changes ---------------------------------------------
if (isTRUE(info) && rule_next != rule_current) {
# print only on last one of rules in this group
txt_ok(n_added = n_added, n_changed = n_changed, warned = warned)
# and reset counters
n_added <- 0
n_changed <- 0
}
} # end of going over all rules
# >>> Apply custom rules <<< ----
if (!is.null(custom_rules)) {
if (isTRUE(info)) {
cat_rules_header(title = paste0(font_bold("Custom interpretive rules"), ", set by user"))
}
for (i in seq_len(length(custom_rules))) {
rule <- custom_rules[[i]]
rows <- tryCatch(which(eval(parse(text = rule$query), envir = x)),
error = function(e) stop_(conditionMessage(e), " (check available data and compare with the custom rules set)", call = FALSE)
)
cols <- as.character(rule$result_group)
cols <- c(
cols[cols %in% colnames(x)], # direct column names
unname(cols_ab[names(cols_ab) %in% cols])
) # based on previous cols_ab finding
cols <- unique(cols)
target_value <- as.character(rule$result_value)
rule_text <- paste0(
"report as '", target_value, "' when ",
format_custom_query_rule(rule$query, colours = FALSE), ": ",
get_antibiotic_names(cols)
)
if (isTRUE(info)) {
# formatted like print.custom_interpretive_rules()
cat_rule_name(paste0(
font_bold("If "), font_blue(format_custom_query_rule(rule$query, guideline = guideline)),
font_bold(" then "), "set to ", format_sir_badge(target_value, guideline = guideline), ": ",
italicise_taxonomy(get_antibiotic_names(cols), type = "ansi")
))
warned <- FALSE
}
run_changes <- edit_sir(
x = x,
to = target_value,
rule = c(
rule_text,
"Custom interpretive rules",
paste0("Custom interpretive rule ", i),
paste0(
"Object '", deparse(substitute(custom_rules)),
"' consisting of ", length(custom_rules), " custom rules"
)
),
rows = rows,
cols = cols,
last_verbose_info = verbose_info,
original_data = x.bak,
warned = warned,
info = info,
verbose = verbose,
overwrite = overwrite,
add_if_missing = add_if_missing
)
n_added <- n_added + run_changes$added
n_changed <- n_changed + run_changes$changed
verbose_info <- run_changes$verbose_info
x <- run_changes$output
warn_lacking_sir_class <- c(warn_lacking_sir_class, run_changes$sir_warn)
overwrite_skipped <- overwrite_skipped || isTRUE(run_changes$overwrite_skipped)
## Print number of new changes ---------------------------------------------
if (isTRUE(info)) {
# every custom rule is its own group
txt_ok(n_added = n_added, n_changed = n_changed, warned = warned)
# and reset counters
n_added <- 0
n_changed <- 0
}
}
}
# Print overview ----------------------------------------------------------
if (isTRUE(info) || isTRUE(verbose)) {
verbose_info <- x.bak %pm>%
pm_mutate(row = pm_row_number()) %pm>%
pm_select(`.rowid`, row) %pm>%
pm_right_join(verbose_info,
by = c(".rowid" = "rowid")
) %pm>%
pm_select(-`.rowid`) %pm>%
pm_select(row, pm_everything()) %pm>%
pm_filter(!is.na(new) | is.na(new) & !is.na(old)) %pm>%
pm_arrange(row, rule_group, rule_name, col)
if (isFALSE(overwrite)) {
verbose_info <- verbose_info %pm>%
pm_filter(!old %in% levels(NA_sir_))
}
rownames(verbose_info) <- NULL
}
if (isTRUE(info)) {
if (isTRUE(verbose)) {
wouldve <- "would have "
} else {
wouldve <- ""
}
cat_rules_header(title = font_bold("Summary"))
cat(wrap_rules_text(paste0(
"The rules ", wouldve, "affected ",
font_bold(formatnr(pm_n_distinct(verbose_info$row)), "out of", formatnr(nrow(x.bak)), "rows"),
", making a total of ",
font_bold(formatnr(nrow(verbose_info)), ifelse(nrow(verbose_info) == 1, "edit", "edits"))
)), "\n", sep = "")
added_rows <- verbose_info[which(is.na(verbose_info$old)), , drop = FALSE]
changed_rows <- verbose_info[which(!is.na(verbose_info$old)), , drop = FALSE]
total_n_added <- nrow(added_rows)
total_n_changed <- nrow(changed_rows)
arrow <- if (has_cli_rlang()) cli::symbol$arrow_right else "=>"
test_results <- function(n) paste(formatnr(n), ifelse(n == 1, "test result", "test results"))
count_old_new <- function(df) {
# count combinations of old and new values, keeping NA as a value (pm_count() does not support NA)
old <- as.character(df$old)
new <- as.character(df$new)
key <- paste(ifelse(is.na(old), "\001", old), ifelse(is.na(new), "\001", new), sep = "\002")
tbl <- table(key)
parts <- strsplit(names(tbl), "\002", fixed = TRUE)
out <- data.frame(
old = vapply(FUN.VALUE = character(1), parts, function(p) p[1]),
new = vapply(FUN.VALUE = character(1), parts, function(p) p[2]),
n = as.integer(tbl),
stringsAsFactors = FALSE
)
out$old[out$old == "\001"] <- NA_character_
out$new[out$new == "\001"] <- NA_character_
# sort on the order of SIR levels, with NA and other values last
out[order(match(out$old, VALID_SIR_LEVELS), match(out$new, VALID_SIR_LEVELS), out$old, out$new), , drop = FALSE]
}
# print added values
cat(
if (total_n_added == 0) arrow else font_green(arrow),
" ", wouldve, "added ",
if (total_n_added == 0) font_bold(test_results(0)) else font_bold(font_green(test_results(total_n_added))),
"\n",
sep = ""
)
if (total_n_added > 0) {
added_summary <- count_old_new(added_rows)
cat(
paste0(
" ", AMR_env$bullet_icon, " ",
formatnr(added_summary$n), " test result", ifelse(added_summary$n > 1, "s", ""),
" ", wouldve, "added as ", format_sir_badge(added_summary$new, guideline = guideline),
"\n"
),
sep = ""
)
}
# print changed values
cat(
if (total_n_changed == 0) arrow else font_blue(arrow),
" ", wouldve, "changed ",
if (total_n_changed == 0) font_bold(test_results(0)) else font_bold(font_blue(test_results(total_n_changed))),
"\n",
sep = ""
)
if (total_n_changed > 0) {
changed_summary <- count_old_new(changed_rows)
cat(
paste0(
" ", AMR_env$bullet_icon, " ",
formatnr(changed_summary$n), " test result", ifelse(changed_summary$n > 1, "s", ""),
" ", wouldve, "changed from ", format_sir_badge(changed_summary$old, guideline = guideline),
" to ", format_sir_badge(changed_summary$new, guideline = guideline),
"\n"
),
sep = ""
)
}
if (isFALSE(verbose) && total_n_added + total_n_changed > 0) {
cat("\n", wrap_rules_text(format_inline_(
"Use {.code interpretive_rules(..., verbose = TRUE)} (on your original data) to get a data.frame with all specified edits instead."
)), "\n\n", sep = "")
} else if (isTRUE(verbose)) {
cat("\n", wrap_rules_text(format_inline_(
"Used 'Verbose mode' ({.code verbose = TRUE}), which returns a data.frame with all specified edits. Use {.code verbose = FALSE} to apply the rules on your data."
)), "\n\n", sep = "")
}
}
if (isTRUE(overwrite_skipped) && message_not_thrown_before("edit_sir.warning_overwrite")) {
warning_("in {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}: some columns had SIR values which were not overwritten, since {.code overwrite = FALSE}.")
}
if (length(warn_lacking_sir_class) > 0) {
warn_lacking_sir_class <- unique(warn_lacking_sir_class)
# take order from original data set
warn_lacking_sir_class <- warn_lacking_sir_class[order(colnames(x.bak))]
warn_lacking_sir_class <- warn_lacking_sir_class[!is.na(warn_lacking_sir_class)]
warning_(
"in {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}: not all columns with antimicrobial results are of class {.cls sir}. Transform them on beforehand, e.g.:\n\n",
"\u00a0\u00a0", AMR_env$bullet_icon, " ", highlight_code(paste0(x_deparsed, " |> as.sir(", ifelse(length(warn_lacking_sir_class) == 1,
warn_lacking_sir_class,
paste0(warn_lacking_sir_class[1], ":", warn_lacking_sir_class[length(warn_lacking_sir_class)])
), ")")), "\n\n",
"\u00a0\u00a0", AMR_env$bullet_icon, " ", highlight_code(paste0(x_deparsed, " |> mutate_if(is_sir_eligible, as.sir)")), "\n\n",
"\u00a0\u00a0", AMR_env$bullet_icon, " ", highlight_code(paste0(x_deparsed, " |> mutate(across(where(is_sir_eligible), as.sir))"))
)
}
# Return data set ---------------------------------------------------------
if (isTRUE(verbose)) {
as_original_data_class(verbose_info, old_attributes$class) # will remove tibble groups
} else {
# x was analysed with only unique rows, so join everything together again
x <- x[, c(cols_ab, ".rowid"), drop = FALSE]
x.bak <- x.bak[, setdiff(colnames(x.bak), cols_ab), drop = FALSE]
x.bak <- x.bak %pm>%
pm_left_join(x, by = ".rowid")
x.bak <- x.bak[, old_cols, drop = FALSE]
# reset original attributes
attributes(x.bak) <- old_attributes
x.bak <- as_original_data_class(x.bak, old_class = class(x.bak)) # will remove tibble groups
x.bak
}
}
#' @rdname interpretive_rules
#' @export
eucast_rules <- function(x,
col_mo = NULL,
info = interactive(),
rules = getOption("AMR_interpretive_rules", default = c("breakpoints", "expected_phenotypes")),
...) {
if (!is.null(getOption("AMR_eucastrules", default = NULL))) {
warning_("The global option {.code AMR_eucastrules} that you have set is now invalid was ignored - set {.code AMR_interpretive_rules} instead. See {.topic [AMR-options](AMR::AMR-options)}.")
}
interpretive_rules(x = x, col_mo = col_mo, info = info, rules = rules, guideline = "EUCAST", ...)
}
#' @rdname interpretive_rules
#' @export
clsi_rules <- function(x,
col_mo = NULL,
info = interactive(),
rules = getOption("AMR_interpretive_rules", default = c("breakpoints", "expected_phenotypes")),
...) {
interpretive_rules(x = x, col_mo = col_mo, info = info, rules = rules, guideline = "CLSI", ...)
}
# helper function for editing the table ----
edit_sir <- function(x,
to,
rule,
rows,
cols,
last_verbose_info,
original_data,
warned,
info,
verbose,
overwrite,
add_if_missing) {
cols <- unique(cols[!is.na(cols) & !is.null(cols)])
rows <- unique(rows)
# for Verbose Mode, keep track of all changes and return them
track_changes <- list(
added = 0,
changed = 0,
output = x,
verbose_info = last_verbose_info,
sir_warn = character(0),
overwrite_skipped = FALSE
)
txt_error <- function() {
if (isTRUE(info)) cat("", font_red_bg(" ERROR "), "\n\n")
}
txt_warning <- function() {
if (warned == FALSE) {
if (isTRUE(info)) cat(" ", font_orange_bg(" WARNING "), sep = "")
}
warned <<- TRUE
}
if (length(rows) > 0 && length(cols) > 0) {
new_edits <- x
if (any(!vapply(FUN.VALUE = logical(1), x[, cols, drop = FALSE], is.sir), na.rm = TRUE)) {
track_changes$sir_warn <- cols[!vapply(FUN.VALUE = logical(1), x[, cols, drop = FALSE], is.sir)]
}
# current values as a character matrix (rows x cols), to compare without the overhead of Ops.sir()
current <- matrix(
unlist(lapply(cols, function(col) as.character(new_edits[[col]][rows])), use.names = FALSE),
nrow = length(rows)
)
isSIR <- !is.na(current) & current %in% VALID_SIR_LEVELS
# the warning about this is thrown once by interpretive_rules(), as message_not_thrown_before() is too slow to call for every rule
track_changes$overwrite_skipped <- isFALSE(overwrite) && any(isSIR)
# determine which cells to modify based on overwrite and add_if_missing
if (isTRUE(overwrite)) {
if (isTRUE(add_if_missing)) {
apply_mask <- rep(TRUE, length(isSIR))
} else {
apply_mask <- isSIR
}
} else {
# overwrite = FALSE, add_if_missing = TRUE: fill missing and placeholder cells only
apply_mask <- !isSIR
}
# most rules leave the data as they are, so skip writing (and tracking) if no value would change
if (is.na(to)) {
will_change <- apply_mask & !is.na(current)
} else {
will_change <- apply_mask & (is.na(current) | current != to)
}
if (!any(will_change)) {
return(track_changes)
}
do_assign <- function() {
subset <- new_edits[rows, cols, drop = FALSE]
mask <- matrix(apply_mask, nrow = nrow(subset), ncol = ncol(subset))
subset[mask] <- to
new_edits[rows, cols] <<- subset
}
tryCatch(
do_assign(),
warning = function(w) {
if (w$message %like% "invalid factor level") {
vapply(FUN.VALUE = logical(1), cols, function(col) {
new_edits[, col] <<- factor(
x = as.character(pm_pull(new_edits, col)),
levels = unique(c(to, levels(pm_pull(new_edits, col))))
)
TRUE
})
suppressWarnings(do_assign())
warning_(
"in {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}: value \"", to, "\" added to the factor levels of column",
ifelse(length(cols) == 1, "", "s"),
" ", vector_and(cols, quotes = "`", sort = FALSE),
" because this value was not an existing factor level."
)
txt_warning()
warned <<- FALSE
} else {
warning_("in {.help [{.fun interpretive_rules}](AMR::interpretive_rules)}: ", w$message)
txt_warning()
}
},
error = function(e) {
txt_error()
stop(
paste0(
"In row(s) ", paste(rows[seq_len(min(length(rows), 10))], collapse = ","),
ifelse(length(rows) > 10, "...", ""),
" while writing value '", to,
"' to column(s) `", paste(cols, collapse = "`, `"),
"`:\n", conditionMessage(e)
),
call. = FALSE
)
}
)
track_changes$output <- new_edits
if (isTRUE(info) || isTRUE(verbose)) {
# the number of rows in the original data that the (unique) row IDs stand for
n_original_rows <- function(rowids) {
sum(original_data$.rowid %in% rowids)
}
for (i in seq_len(length(cols))) {
verbose_new <- data.frame(
rowid = new_edits[rows, ".rowid", drop = TRUE],
col = cols[i],
mo_fullname = new_edits[rows, "fullname", drop = TRUE],
old = x[rows, cols[i], drop = TRUE],
# the value after the edit, so that cells left as they were (due to `overwrite` or `add_if_missing`) are not counted
new = as.character(new_edits[rows, cols[i], drop = TRUE]),
rule = font_stripstyle(rule[1]),
rule_group = font_stripstyle(rule[2]),
rule_name = font_stripstyle(rule[3]),
rule_source = font_stripstyle(rule[4]),
stringsAsFactors = FALSE
)
colnames(verbose_new) <- c(
"rowid", "col", "mo_fullname", "old", "new",
"rule", "rule_group", "rule_name", "rule_source"
)
is_edited <- ifelse(
is.na(verbose_new$old) | is.na(verbose_new$new),
xor(is.na(verbose_new$old), is.na(verbose_new$new)),
as.character(verbose_new$old) != verbose_new$new
)
verbose_new <- verbose_new[which(is_edited), , drop = FALSE]
# save changes to data set 'verbose_info'
track_changes$verbose_info <- rbind_AMR(
track_changes$verbose_info,
verbose_new
)
# count adds and changes
track_changes$added <- track_changes$added + n_original_rows(verbose_new$rowid[is.na(verbose_new$old)])
track_changes$changed <- track_changes$changed + n_original_rows(verbose_new$rowid[!is.na(verbose_new$old)])
}
}
}
return(track_changes)
}
# helper functions for printing ----
# The output of interpretive_rules() is printed to stdout with cat(). It is styled with cli where available (see has_cli_rlang()), with a plain-text fallback for old R versions or when cli is not installed.
rules_console_width <- function() {
if (has_cli_rlang()) {
cli::console_width()
} else {
getOption("width", 80)
}
}
wrap_rules_text <- function(x, width = rules_console_width(), indent = 0, exdent = 0) {
if (has_cli_rlang()) {
out <- cli::ansi_strwrap(x, width = width, indent = indent, exdent = exdent)
} else {
# strwrap() would count ANSI styling as characters, so wrap on visible width
words <- strsplit(x, " ", fixed = TRUE)[[1]]
words <- words[words != ""]
out <- character(0)
line <- strrep(" ", indent)
line_has_words <- FALSE
for (word in words) {
if (line_has_words && nchar(font_stripstyle(line)) + 1 + nchar(font_stripstyle(word)) > width) {
out <- c(out, line)
line <- paste0(strrep(" ", exdent), word)
} else {
line <- paste0(line, if (line_has_words) " ", word)
}
line_has_words <- TRUE
}
out <- c(out, line)
}
paste0(out, collapse = "\n")
}
cat_rules_header <- function(title, subtitle = NULL) {
if (has_cli_rlang()) {
header <- as.character(cli::rule(left = title))
} else {
header <- paste0(font_grey("--"), " ", title, " ")
header <- paste0(header, font_grey(strrep("-", max(3, rules_console_width() - nchar(font_stripstyle(header))))))
}
cat("\n", header, "\n", sep = "")
if (!is.null(subtitle)) {
cat(wrap_rules_text(subtitle), "\n", sep = "")
}
cat("\n")
}
cat_rule_name <- function(x) {
# printed without line ending, since edit_sir() and txt_ok() will print on the same line
cat(wrap_rules_text(
paste0(AMR_env$bullet_icon, " ", x),
indent = 2,
exdent = 4
))
}
format_field <- function(x) {
if (has_cli_rlang()) {
cli::format_inline("{.field {x}}")
} else {
x
}
}
#' @rdname interpretive_rules
#' @export
eucast_dosage <- function(ab, administration = "iv", version_breakpoints = 15) {
meet_criteria(ab, allow_class = c("character", "numeric", "integer", "factor"))
meet_criteria(administration, allow_class = "character", is_in = dosage$administration[!is.na(dosage$administration)], has_length = 1)
meet_criteria(version_breakpoints, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_BREAKPOINTS)))
# show used version_breakpoints number once per session (AMR_env will reload every session)
if (missing(version_breakpoints) && message_not_thrown_before("eucast_dosage", "v", gsub("[^0-9]", "", version_breakpoints), entire_session = TRUE)) {
message_(
"Dosages for antimicrobial drugs, as meant for ",
format_eucast_version_nr(version_breakpoints, markdown = FALSE), ". ",
font_red("This note will be shown once per session.")
)
}
ab <- as.ab(ab)
lst <- vector("list", length = length(ab))
for (i in seq_len(length(ab))) {
df <- AMR::dosage[which(AMR::dosage$eucast_version == version_breakpoints & AMR::dosage$ab == ab[i] & AMR::dosage$administration == administration), , drop = FALSE]
lst[[i]] <- list(
ab = "",
name = "",
standard_dosage = ifelse("standard_dosage" %in% df$type,
trimws2(df[which(df$type == "standard_dosage"), "original_txt", drop = TRUE]),
NA_character_
),
high_dosage = ifelse("high_dosage" %in% df$type,
trimws2(df[which(df$type == "high_dosage"), "original_txt", drop = TRUE]),
NA_character_
),
eucast_version = df$eucast_version[1]
)
}
out <- do.call(rbind_AMR, lapply(lst, as.data.frame, stringsAsFactors = FALSE))
rownames(out) <- NULL
out$ab <- ab
out$name <- ab_name(ab, language = NULL)
if (pkg_is_available("tibble")) {
import_fn("as_tibble", "tibble")(out)
} else {
out
}
}
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