mirror of https://github.com/msberends/AMR.git
199 lines
8.5 KiB
R
Executable File
199 lines
8.5 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2022 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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# set up package environment, used by numerous AMR functions
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pkg_env <- new.env(hash = FALSE)
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pkg_env$mo_failed <- character(0)
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pkg_env$mo_uncertainties <- data.frame(
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uncertainty = integer(0),
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input = character(0),
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fullname = character(0),
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mo = character(0),
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candidates = character(0),
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stringsAsFactors = FALSE
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)
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pkg_env$mo_previously_coerced <- data.frame(
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x = character(0),
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mo = character(0),
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stringsAsFactors = FALSE
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)
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pkg_env$rsi_interpretation_history <- data.frame(
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datetime = Sys.time()[0],
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index = integer(0),
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ab_input = character(0),
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ab_considered = character(0),
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mo_input = character(0),
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mo_considered = character(0),
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guideline = character(0),
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ref_table = character(0),
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method = character(0),
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breakpoint_S = double(0),
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breakpoint_R = double(0),
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input = double(0),
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interpretation = character(0),
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stringsAsFactors = FALSE
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)
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# determine info icon for messages
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utf8_supported <- isTRUE(base::l10n_info()$`UTF-8`)
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is_latex <- tryCatch(import_fn("is_latex_output", "knitr", error_on_fail = FALSE)(),
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error = function(e) FALSE
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)
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if (utf8_supported && !is_latex) {
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# \u2139 is a symbol officially named 'information source'
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pkg_env$info_icon <- "\u2139"
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} else {
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pkg_env$info_icon <- "i"
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}
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.onLoad <- function(lib, pkg) {
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# Support for tibble headers (type_sum) and tibble columns content (pillar_shaft)
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# without the need to depend on other packages. This was suggested by the
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# developers of the vctrs package:
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# https://github.com/r-lib/vctrs/blob/05968ce8e669f73213e3e894b5f4424af4f46316/R/register-s3.R
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s3_register("pillar::pillar_shaft", "ab")
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s3_register("pillar::pillar_shaft", "mo")
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s3_register("pillar::pillar_shaft", "rsi")
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s3_register("pillar::pillar_shaft", "mic")
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s3_register("pillar::pillar_shaft", "disk")
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s3_register("tibble::type_sum", "ab")
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s3_register("tibble::type_sum", "mo")
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s3_register("tibble::type_sum", "rsi")
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s3_register("tibble::type_sum", "mic")
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s3_register("tibble::type_sum", "disk")
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# Support for frequency tables from the cleaner package
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s3_register("cleaner::freq", "mo")
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s3_register("cleaner::freq", "rsi")
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# Support for skim() from the skimr package
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if (pkg_is_available("skimr", also_load = FALSE, min_version = "2.0.0")) {
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s3_register("skimr::get_skimmers", "mo")
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s3_register("skimr::get_skimmers", "rsi")
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s3_register("skimr::get_skimmers", "mic")
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s3_register("skimr::get_skimmers", "disk")
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}
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# Support for autoplot() from the ggplot2 package
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s3_register("ggplot2::autoplot", "rsi")
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s3_register("ggplot2::autoplot", "mic")
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s3_register("ggplot2::autoplot", "disk")
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s3_register("ggplot2::autoplot", "resistance_predict")
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# Support for fortify from the ggplot2 package
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s3_register("ggplot2::fortify", "rsi")
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s3_register("ggplot2::fortify", "mic")
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s3_register("ggplot2::fortify", "disk")
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# Support vctrs package for use in e.g. dplyr verbs
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s3_register("vctrs::vec_ptype2", "ab.character")
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s3_register("vctrs::vec_ptype2", "character.ab")
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s3_register("vctrs::vec_cast", "character.ab")
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s3_register("vctrs::vec_ptype2", "mo.character")
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s3_register("vctrs::vec_ptype2", "character.mo")
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s3_register("vctrs::vec_cast", "character.mo")
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s3_register("vctrs::vec_ptype2", "ab_selector.character")
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s3_register("vctrs::vec_ptype2", "character.ab_selector")
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s3_register("vctrs::vec_cast", "character.ab_selector")
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s3_register("vctrs::vec_ptype2", "ab_selector_any_all.logical")
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s3_register("vctrs::vec_ptype2", "logical.ab_selector_any_all")
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s3_register("vctrs::vec_cast", "logical.ab_selector_any_all")
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s3_register("vctrs::vec_ptype2", "disk.integer")
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s3_register("vctrs::vec_ptype2", "integer.disk")
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s3_register("vctrs::vec_cast", "integer.disk")
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s3_register("vctrs::vec_cast", "character.mic")
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s3_register("vctrs::vec_cast", "double.mic")
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s3_register("vctrs::vec_math", "mic")
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# if mo source exists, fire it up (see mo_source())
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try(
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{
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if (file.exists(getOption("AMR_mo_source", "~/mo_source.rds"))) {
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invisible(get_mo_source())
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}
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},
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silent = TRUE
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)
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# be sure to print tibbles as tibbles
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if (pkg_is_available("tibble", also_load = FALSE)) {
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loadNamespace("tibble")
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}
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# reference data - they have additional columns compared to `antibiotics` and `microorganisms` to improve speed
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# they cannot be part of R/sysdata.rda since CRAN thinks it would make the package too large (+3 MB)
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assign(x = "AB_lookup", value = create_AB_lookup(), envir = asNamespace("AMR"))
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assign(x = "MO_lookup", value = create_MO_lookup(), envir = asNamespace("AMR"))
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# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
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assign(x = "INTRINSIC_R", value = create_intr_resistance(), envir = asNamespace("AMR"))
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}
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# Helper functions --------------------------------------------------------
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create_AB_lookup <- function() {
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cbind(AMR::antibiotics, AB_LOOKUP)
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}
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create_MO_lookup <- function() {
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MO_lookup <- AMR::microorganisms
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MO_lookup$kingdom_index <- NA_real_
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MO_lookup[which(MO_lookup$kingdom == "Bacteria" | MO_lookup$mo == "UNKNOWN"), "kingdom_index"] <- 1
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MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 2
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MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 3
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MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 4
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# all the rest
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MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
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# # use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
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# if (length(MO_FULLNAME_LOWER) == nrow(MO_lookup)) {
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# MO_lookup$fullname_lower <- MO_FULLNAME_LOWER
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# } else {
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# MO_lookup$fullname_lower <- ""
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# warning("MO table updated - Run: source(\"data-raw/_pre_commit_hook.R\")", call. = FALSE)
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# }
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MO_lookup$fullname_lower <- create_MO_fullname_lower()
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MO_lookup$full_first <- substr(MO_lookup$fullname_lower, 1, 1)
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MO_lookup$species_first <- substr(MO_lookup$species, 1, 1)
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# so arrange data on prevalence first, then kingdom, then full name
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MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower), , drop = FALSE]
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}
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create_MO_fullname_lower <- function() {
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MO_lookup <- AMR::microorganisms
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# use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
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MO_lookup$fullname_lower <- tolower(trimws(paste(
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MO_lookup$genus,
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MO_lookup$species,
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MO_lookup$subspecies
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)))
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ind <- MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname, perl = TRUE)
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MO_lookup[ind, "fullname_lower"] <- tolower(MO_lookup[ind, "fullname", drop = TRUE])
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MO_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower, perl = TRUE))
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MO_lookup$fullname_lower
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}
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create_intr_resistance <- function() {
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# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
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paste(AMR::intrinsic_resistant$mo, AMR::intrinsic_resistant$ab)
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}
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