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(v1.3.0.9035) mdro() for EUCAST 3.2, examples cleanup

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dr. M.S. (Matthijs) Berends
2020-09-29 23:35:46 +02:00
parent 68e6e1e329
commit 4e0374af29
94 changed files with 1143 additions and 1165 deletions
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61
R/mo.R
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@ -93,14 +93,7 @@
#'
#' ## Microbial prevalence of pathogens in humans
#'
#' The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the `prevalence` columns in the [microorganisms] and [microorganisms.old] data sets. The grouping into prevalence groups is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence.
#'
#' Group 1 (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Klebsiella*, *Pseudomonas* and *Legionella*.
#'
#' Group 2 consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Aspergillus*, *Bacteroides*, *Candida*, *Capnocytophaga*, *Chryseobacterium*, *Cryptococcus*, *Elisabethkingia*, *Flavobacterium*, *Fusobacterium*, *Giardia*, *Leptotrichia*, *Mycoplasma*, *Prevotella*, *Rhodotorula*, *Treponema*, *Trichophyton* or *Ureaplasma*. This group consequently contains all less common and rare human pathogens.
#'
#' Group 3 (least prevalent microorganisms) consists of all other microorganisms. This group contains microorganisms most probably not found in humans.
#'
#' The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the `prevalence` columns in the [microorganisms] and [microorganisms.old] data sets. The grouping into human pathogenic prevalence is explained in the section *Matching score for microorganisms* below.
#' @inheritSection mo_matching_score Matching score for microorganisms
#' @inheritSection catalogue_of_life Catalogue of Life
# (source as a section here, so it can be inherited by other man pages:)
@ -152,25 +145,6 @@
#' # All mo_* functions use as.mo() internally too (see ?mo_property):
#' mo_genus("E. coli") # returns "Escherichia"
#' mo_gramstain("E. coli") # returns "Gram negative"
#'
#' }
#' \dontrun{
#' df$mo <- as.mo(df$microorganism_name)
#'
#' # the select function of the Tidyverse is also supported:
#' library(dplyr)
#' df$mo <- df %>%
#' select(microorganism_name) %>%
#' as.mo()
#'
#' # and can even contain 2 columns, which is convenient
#' # for genus/species combinations:
#' df$mo <- df %>%
#' select(genus, species) %>%
#' as.mo()
#' # although this works easier and does the same:
#' df <- df %>%
#' mutate(mo = as.mo(paste(genus, species)))
#' }
as.mo <- function(x,
Becker = FALSE,
@ -1439,45 +1413,26 @@ exec_as.mo <- function(x,
# Becker ----
if (Becker == TRUE | Becker == "all") {
# See Source. It's this figure:
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187637/figure/F3/
MOs_staph <- MO_lookup[which(MO_lookup$genus == "Staphylococcus"), ]
CoNS <- MOs_staph[which(MOs_staph$species %in% c("arlettae", "auricularis", "capitis",
"caprae", "carnosus", "chromogenes", "cohnii", "condimenti",
"devriesei", "epidermidis", "equorum", "felis",
"fleurettii", "gallinarum", "haemolyticus",
"hominis", "jettensis", "kloosii", "lentus",
"lugdunensis", "massiliensis", "microti",
"muscae", "nepalensis", "pasteuri", "petrasii",
"pettenkoferi", "piscifermentans", "rostri",
"saccharolyticus", "saprophyticus", "sciuri",
"stepanovicii", "simulans", "succinus",
"vitulinus", "warneri", "xylosus")
| (MOs_staph$species == "schleiferi" & MOs_staph$subspecies %in% c("schleiferi", ""))),
property]
CoPS <- MOs_staph[which(MOs_staph$species %in% c("simiae", "agnetis",
"delphini", "lutrae",
"hyicus", "intermedius",
"pseudintermedius", "pseudointermedius",
"schweitzeri", "argenteus")
| (MOs_staph$species == "schleiferi" & MOs_staph$subspecies == "coagulans")),
property]
# warn when species found that are not in Becker (2014, PMID 25278577) and Becker (2019, PMID 30872103)
post_Becker <- c("argensis", "caeli", "cornubiensis", "edaphicus")
if (any(x %in% MOs_staph[which(MOs_staph$species %in% post_Becker), property])) {
if (any(x %in% MO_lookup[which(MO_lookup$species %in% post_Becker), property])) {
warning("Becker ", font_italic("et al."), " (2014, 2019) does not contain these species named after their publication: ",
font_italic(paste("S.",
sort(mo_species(unique(x[x %in% MOs_staph[which(MOs_staph$species %in% post_Becker), property]]))),
sort(mo_species(unique(x[x %in% MO_lookup[which(MO_lookup$species %in% post_Becker), property]]))),
collapse = ", ")),
".",
call. = FALSE,
immediate. = TRUE)
}
# 'MO_CONS' and 'MO_COPS' are <mo> vectors created in R/zzz.R
CoNS <- MO_lookup[which(MO_lookup$mo %in% MO_CONS), property, drop = TRUE]
x[x %in% CoNS] <- lookup(mo == "B_STPHY_CONS", uncertainty = -1)
CoPS <- MO_lookup[which(MO_lookup$mo %in% MO_COPS), property, drop = TRUE]
x[x %in% CoPS] <- lookup(mo == "B_STPHY_COPS", uncertainty = -1)
if (Becker == "all") {
x[x %in% lookup(fullname %like_case% "^Staphylococcus aureus", n = Inf)] <- lookup(mo == "B_STPHY_COPS", uncertainty = -1)
}