AMR/R/zzz.R

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R
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# ==================================================================== #
# TITLE #
# AMR: An R Package for Working with Antimicrobial Resistance Data #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# CITE AS #
# Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C #
# (2022). AMR: An R Package for Working with Antimicrobial Resistance #
# Data. Journal of Statistical Software, 104(3), 1-31. #
# doi:10.18637/jss.v104.i03 #
# #
# 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/ #
# ==================================================================== #
# set up package environment, used by numerous AMR functions
AMR_env <- new.env(hash = FALSE)
AMR_env$mo_uncertainties <- data.frame(
original_input = character(0),
input = character(0),
fullname = character(0),
mo = character(0),
candidates = character(0),
minimum_matching_score = integer(0),
keep_synonyms = logical(0),
stringsAsFactors = FALSE
)
AMR_env$mo_renamed <- list()
AMR_env$mo_previously_coerced <- data.frame(
x = character(0),
mo = character(0),
stringsAsFactors = FALSE
)
AMR_env$rsi_interpretation_history <- data.frame(
datetime = Sys.time()[0],
index = integer(0),
ab_input = character(0),
ab_considered = character(0),
mo_input = character(0),
mo_considered = character(0),
guideline = character(0),
ref_table = character(0),
method = character(0),
breakpoint_S = double(0),
breakpoint_R = double(0),
input = double(0),
interpretation = character(0),
stringsAsFactors = FALSE
)
AMR_env$has_data.table <- pkg_is_available("data.table", also_load = FALSE)
# determine info icon for messages
utf8_supported <- isTRUE(base::l10n_info()$`UTF-8`)
is_latex <- tryCatch(import_fn("is_latex_output", "knitr", error_on_fail = FALSE)(),
error = function(e) FALSE
)
if (utf8_supported && !is_latex) {
# \u2139 is a symbol officially named 'information source'
AMR_env$info_icon <- "\u2139"
} else {
AMR_env$info_icon <- "i"
}
.onLoad <- function(lib, pkg) {
# Support for tibble headers (type_sum) and tibble columns content (pillar_shaft)
# without the need to depend on other packages. This was suggested by the
# developers of the vctrs package:
# https://github.com/r-lib/vctrs/blob/05968ce8e669f73213e3e894b5f4424af4f46316/R/register-s3.R
s3_register("pillar::pillar_shaft", "ab")
s3_register("pillar::pillar_shaft", "mo")
s3_register("pillar::pillar_shaft", "rsi")
s3_register("pillar::pillar_shaft", "mic")
s3_register("pillar::pillar_shaft", "disk")
s3_register("tibble::type_sum", "ab")
s3_register("tibble::type_sum", "mo")
s3_register("tibble::type_sum", "rsi")
s3_register("tibble::type_sum", "mic")
s3_register("tibble::type_sum", "disk")
# Support for frequency tables from the cleaner package
s3_register("cleaner::freq", "mo")
s3_register("cleaner::freq", "rsi")
# Support for skim() from the skimr package
if (pkg_is_available("skimr", also_load = FALSE, min_version = "2.0.0")) {
s3_register("skimr::get_skimmers", "mo")
s3_register("skimr::get_skimmers", "rsi")
s3_register("skimr::get_skimmers", "mic")
s3_register("skimr::get_skimmers", "disk")
}
# Support for autoplot() from the ggplot2 package
s3_register("ggplot2::autoplot", "rsi")
s3_register("ggplot2::autoplot", "mic")
s3_register("ggplot2::autoplot", "disk")
s3_register("ggplot2::autoplot", "resistance_predict")
# Support for fortify from the ggplot2 package
s3_register("ggplot2::fortify", "rsi")
s3_register("ggplot2::fortify", "mic")
s3_register("ggplot2::fortify", "disk")
# Support vctrs package for use in e.g. dplyr verbs
s3_register("vctrs::vec_ptype2", "ab.character")
s3_register("vctrs::vec_ptype2", "character.ab")
s3_register("vctrs::vec_cast", "character.ab")
s3_register("vctrs::vec_ptype2", "mo.character")
s3_register("vctrs::vec_ptype2", "character.mo")
s3_register("vctrs::vec_cast", "character.mo")
s3_register("vctrs::vec_ptype2", "ab_selector.character")
s3_register("vctrs::vec_ptype2", "character.ab_selector")
s3_register("vctrs::vec_cast", "character.ab_selector")
s3_register("vctrs::vec_ptype2", "ab_selector_any_all.logical")
s3_register("vctrs::vec_ptype2", "logical.ab_selector_any_all")
s3_register("vctrs::vec_cast", "logical.ab_selector_any_all")
s3_register("vctrs::vec_ptype2", "disk.integer")
s3_register("vctrs::vec_ptype2", "integer.disk")
s3_register("vctrs::vec_cast", "integer.disk")
s3_register("vctrs::vec_cast", "character.mic")
s3_register("vctrs::vec_cast", "double.mic")
s3_register("vctrs::vec_math", "mic")
# if mo source exists, fire it up (see mo_source())
if (tryCatch(file.exists(getOption("AMR_mo_source", "~/mo_source.rds")), error = function(e) FALSE)) {
invisible(get_mo_source())
}
# be sure to print tibbles as tibbles
if (pkg_is_available("tibble", also_load = FALSE)) {
loadNamespace("tibble")
}
# reference data - they have additional columns compared to `antibiotics` and `microorganisms` to improve speed
# they cannot be part of R/sysdata.rda since CRAN thinks it would make the package too large (+3 MB)
assign(x = "AB_lookup", value = create_AB_lookup(), envir = asNamespace("AMR"))
assign(x = "MO_lookup", value = create_MO_lookup(), envir = asNamespace("AMR"))
# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
assign(x = "INTRINSIC_R", value = create_intr_resistance(), envir = asNamespace("AMR"))
}
# Helper functions --------------------------------------------------------
create_AB_lookup <- function() {
cbind(AMR::antibiotics, AB_LOOKUP)
}
create_MO_lookup <- function() {
MO_lookup <- AMR::microorganisms
MO_lookup$kingdom_index <- NA_real_
MO_lookup[which(MO_lookup$kingdom == "Bacteria" | MO_lookup$mo == "UNKNOWN"), "kingdom_index"] <- 1
MO_lookup[which(MO_lookup$kingdom == "Fungi"), "kingdom_index"] <- 2
MO_lookup[which(MO_lookup$kingdom == "Protozoa"), "kingdom_index"] <- 3
MO_lookup[which(MO_lookup$kingdom == "Archaea"), "kingdom_index"] <- 4
# all the rest
MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
# # use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
# if (length(MO_FULLNAME_LOWER) == nrow(MO_lookup)) {
# MO_lookup$fullname_lower <- MO_FULLNAME_LOWER
# } else {
# MO_lookup$fullname_lower <- ""
# warning("MO table updated - Run: source(\"data-raw/_pre_commit_hook.R\")", call. = FALSE)
# }
MO_lookup$fullname_lower <- create_MO_fullname_lower()
MO_lookup$full_first <- substr(MO_lookup$fullname_lower, 1, 1)
MO_lookup$species_first <- substr(MO_lookup$species, 1, 1)
# so arrange data on prevalence first, then kingdom, then full name
MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower), , drop = FALSE]
}
create_MO_fullname_lower <- function() {
MO_lookup <- AMR::microorganisms
# use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
MO_lookup$fullname_lower <- tolower(trimws(paste(
MO_lookup$genus,
MO_lookup$species,
MO_lookup$subspecies
)))
ind <- MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname, perl = TRUE)
MO_lookup[ind, "fullname_lower"] <- tolower(MO_lookup[ind, "fullname", drop = TRUE])
MO_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower, perl = TRUE))
MO_lookup$fullname_lower
}
create_intr_resistance <- function() {
# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
paste(AMR::intrinsic_resistant$mo, AMR::intrinsic_resistant$ab)
}