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(v1.5.0.9008) Internal data sets to pkg, speed for auto col determination

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dr. M.S. (Matthijs) Berends
2021-01-22 10:20:41 +01:00
parent 27f084d819
commit 1ba44776a1
87 changed files with 408 additions and 292 deletions
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113
R/zzz.R
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@ -28,35 +28,6 @@ pkg_env <- new.env(hash = FALSE)
pkg_env$mo_failed <- character(0)
.onLoad <- function(libname, pkgname) {
assign(x = "AB_lookup",
value = create_AB_lookup(),
envir = asNamespace("AMR"))
assign(x = "MO_lookup",
value = create_MO_lookup(),
envir = asNamespace("AMR"))
assign(x = "MO.old_lookup",
value = create_MO.old_lookup(),
envir = asNamespace("AMR"))
assign(x = "INTRINSIC_R",
value = create_intr_resistance(),
envir = asNamespace("AMR"))
assign(x = "LANGUAGES_SUPPORTED",
value = sort(c("en", unique(translations_file$lang))),
envir = asNamespace("AMR"))
assign(x = "MO_CONS",
value = create_species_cons_cops("CoNS"),
envir = asNamespace("AMR"))
assign(x = "MO_COPS",
value = create_species_cons_cops("CoPS"),
envir = asNamespace("AMR"))
# 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:
@ -102,89 +73,5 @@ pkg_env$mo_failed <- character(0)
font_bold("options(AMR_silentstart = TRUE)"), "]"))
}
create_intr_resistance <- function() {
# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
paste(AMR::microorganisms[match(AMR::intrinsic_resistant$microorganism, AMR::microorganisms$fullname), "mo", drop = TRUE],
AMR::antibiotics[match(AMR::intrinsic_resistant$antibiotic, AMR::antibiotics$name), "ab", drop = TRUE])
}
create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
# Determination of which staphylococcal species are CoNS/CoPS according to:
# - Becker et al. 2014, PMID 25278577
# - Becker et al. 2019, PMID 30872103
# - Becker et al. 2020, PMID 32056452
# this function returns class <mo>
MO_staph <- AMR::microorganisms
MO_staph <- MO_staph[which(MO_staph$genus == "Staphylococcus"), , drop = FALSE]
if (type == "CoNS") {
MO_staph[which(MO_staph$species %in% c("coagulase-negative", "argensis", "arlettae",
"auricularis", "caeli", "capitis", "caprae",
"carnosus", "chromogenes", "cohnii", "condimenti",
"debuckii", "devriesei", "edaphicus", "epidermidis",
"equorum", "felis", "fleurettii", "gallinarum",
"haemolyticus", "hominis", "jettensis", "kloosii",
"lentus", "lugdunensis", "massiliensis", "microti",
"muscae", "nepalensis", "pasteuri", "petrasii",
"pettenkoferi", "piscifermentans", "pseudoxylosus",
"rostri", "saccharolyticus", "saprophyticus",
"sciuri", "simulans", "stepanovicii", "succinus",
"vitulinus", "warneri", "xylosus")
| (MO_staph$species == "schleiferi" & MO_staph$subspecies %in% c("schleiferi", ""))),
"mo", drop = TRUE]
} else if (type == "CoPS") {
MO_staph[which(MO_staph$species %in% c("coagulase-positive",
"simiae", "agnetis",
"delphini", "lutrae",
"hyicus", "intermedius",
"pseudintermedius", "pseudointermedius",
"schweitzeri", "argenteus")
| (MO_staph$species == "schleiferi" & MO_staph$subspecies == "coagulans")),
"mo", drop = TRUE]
}
}
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
}
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$fullname)
MO_lookup[ind, "fullname_lower"] <- tolower(MO_lookup[ind, "fullname"])
MO_lookup$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), ]
}