AMR/R/zzz.R

# ==================================================================== #
# 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/   #
# ==================================================================== #

# set up package environment, used by numerous AMR functions
pkg_env <- new.env(hash = FALSE)
pkg_env$mo_failed <- character(0)

# determine info icon for messages
utf8_supported <- isTRUE(base::l10n_info()$`UTF-8`)
is_latex <- tryCatch(import_fn("is_latex_output", "knitr", error_on_fail = FALSE)(),
                     error = function(e) FALSE)
if (utf8_supported && !is_latex) {
  # \u2139 is a symbol officially named 'information source'
  pkg_env$info_icon <- "\u2139"
} else {
  pkg_env$info_icon <- "i"
}

.onLoad <- function(...) {
  # 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
  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", "disk.integer")
  s3_register("vctrs::vec_ptype2", "integer.disk")
  s3_register("vctrs::vec_cast", "integer.disk")
  
  # if mo source exists, fire it up (see mo_source())
  try({
    if (file.exists(getOption("AMR_mo_source", "~/mo_source.rds"))) {
      invisible(get_mo_source())
    }
  }, silent = TRUE)
  
  
  # reference data - they have additional columns compared to `antibiotics` and `microorganisms` to improve speed
  # they cannott 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"))
  assign(x = "MO.old_lookup", value = create_MO.old_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"))
  
  # for building the website, only print first 5 rows of a data set
  # if (Sys.getenv("IN_PKGDOWN") != "" && !interactive()) {
  #   ...
  # }
}

# Helper functions --------------------------------------------------------

create_AB_lookup <- function() {
  AB_lookup <- AMR::antibiotics
  AB_lookup$generalised_name <- generalise_antibiotic_name(AB_lookup$name)
  AB_lookup$generalised_synonyms <- lapply(AB_lookup$synonyms, generalise_antibiotic_name)
  AB_lookup$generalised_abbreviations <- lapply(AB_lookup$abbreviations, generalise_antibiotic_name)
  AB_lookup$generalised_loinc <- lapply(AB_lookup$loinc, generalise_antibiotic_name)
  AB_lookup$generalised_all <- unname(lapply(as.list(as.data.frame(t(AB_lookup[, 
                                                                               c("ab", "atc", "cid", "name",
                                                                                 colnames(AB_lookup)[colnames(AB_lookup) %like% "generalised"]),
                                                                               drop = FALSE]),
                                                                   stringsAsFactors = FALSE)),
                                             function(x) {
                                               x <- generalise_antibiotic_name(unname(unlist(x)))
                                               x[x != ""]
                                             }))
  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.
  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"])
  MO_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower, perl = TRUE))
  
  # add a column with only "e coli" like combinations
  MO_lookup$g_species <- gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO_lookup$fullname_lower, perl = TRUE)
  
  # 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), ]
}

create_MO.old_lookup <- function() {
  MO.old_lookup <- AMR::microorganisms.old
  MO.old_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", tolower(trimws(MO.old_lookup$fullname))))
  
  # add a column with only "e coli"-like combinations
  MO.old_lookup$g_species <- trimws(gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO.old_lookup$fullname_lower))
  
  # so arrange data on prevalence first, then full name
  MO.old_lookup[order(MO.old_lookup$prevalence, MO.old_lookup$fullname_lower), ]
}

create_intr_resistance <- function() {
  # for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
  paste(AMR::microorganisms[match(AMR::intrinsic_resistant$microorganism, AMR::microorganisms$fullname), "mo", drop = TRUE],
        AMR::antibiotics[match(AMR::intrinsic_resistant$antibiotic, AMR::antibiotics$name), "ab", drop = TRUE])
}