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# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 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/ #
# ==================================================================== #
#' Transform Input to a Microorganism Code
#'
#' Use this function to determine a valid microorganism code ([`mo`]). Determination is done using intelligent rules and the complete taxonomic kingdoms `r vector_and(unique(microorganisms$kingdom[which(!grepl("(unknown|Fungi)", microorganisms$kingdom))]), quotes = FALSE)`, and most microbial species from the kingdom Fungi (see *Source*). The input can be almost anything: a full name (like `"Staphylococcus aureus"`), an abbreviated name (such as `"S. aureus"`), an abbreviation known in the field (such as `"MRSA"`), or just a genus. See *Examples*.
#' @param x a [character] vector or a [data.frame] with one or two columns
#' @param Becker a [logical] to indicate whether staphylococci should be categorised into coagulase-negative staphylococci ("CoNS") and coagulase-positive staphylococci ("CoPS") instead of their own species, according to Karsten Becker *et al.* (1,2,3).
#'
#' This excludes *Staphylococcus aureus* at default, use `Becker = "all"` to also categorise *S. aureus* as "CoPS".
#' @param Lancefield a [logical] to indicate whether a beta-haemolytic *Streptococcus* should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield (4). These streptococci will be categorised in their first group, e.g. *Streptococcus dysgalactiae* will be group C, although officially it was also categorised into groups G and L.
#'
#' This excludes enterococci at default (who are in group D), use `Lancefield = "all"` to also categorise all enterococci as group D.
#' @param minimum_matching_score a numeric value to set as the lower limit for the [MO matching score][mo_matching_score()]. When left blank, this will be determined automatically based on the character length of `x`, its [taxonomic kingdom][microorganisms] and [human pathogenicity][mo_matching_score()].
#' @param allow_uncertain a number between `0` (or `"none"`) and `3` (or `"all"`), or `TRUE` (= `2`) or `FALSE` (= `0`) to indicate whether the input should be checked for less probable results, see *Details*
#' @param keep_synonyms a [logical] to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is `FALSE` to always return the currently accepted names.
#' @param reference_df a [data.frame] to be used for extra reference when translating `x` to a valid [`mo`]. See [set_mo_source()] and [get_mo_source()] to automate the usage of your own codes (e.g. used in your analysis or organisation).
#' @param ignore_pattern a regular expression (case-insensitive) of which all matches in `x` must return `NA`. This can be convenient to exclude known non-relevant input and can also be set with the option `AMR_ignore_pattern`, e.g. `options(AMR_ignore_pattern = "(not reported|contaminated flora)")`.
#' @param language language to translate text like "no growth", which defaults to the system language (see [get_AMR_locale()])
#' @param info a [logical] to indicate if a progress bar should be printed if more than 25 items are to be coerced, defaults to `TRUE` only in interactive mode
#' @param ... other arguments passed on to functions
#' @rdname as.mo
#' @aliases mo
#' @keywords mo Becker becker Lancefield lancefield guess
#' @details
#' ## General Info
#'
#' A microorganism (MO) code from this package (class: [`mo`]) is human readable and typically looks like these examples:
#' ```
#' Code Full name
#' --------------- --------------------------------------
#' B_KLBSL Klebsiella
#' B_KLBSL_PNMN Klebsiella pneumoniae
#' B_KLBSL_PNMN_RHNS Klebsiella pneumoniae rhinoscleromatis
#' | | | |
#' | | | |
#' | | | \---> subspecies, a 4-5 letter acronym
#' | | \----> species, a 4-5 letter acronym
#' | \----> genus, a 5-7 letter acronym
#' \----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),
#' F (Fungi), PL (Plantae), P (Protozoa)
#' ```
#'
#' Values that cannot be coerced will be considered 'unknown' and will get the MO code `UNKNOWN`.
#'
#' Use the [`mo_*`][mo_property()] functions to get properties based on the returned code, see *Examples*.
#'
#' The algorithm uses data from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF) (see [microorganisms]).
#'
#' The [as.mo()] function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:
#'
#' 1. Human pathogenic prevalence: the function starts with more prevalent microorganisms, followed by less prevalent ones;
#' 2. Taxonomic kingdom: the function starts with determining Bacteria, then Fungi, then Protozoa, then others;
#' 3. Breakdown of input values to identify possible matches.
#'
#' This will lead to the effect that e.g. `"E. coli"` (a microorganism highly prevalent in humans) will return the microbial ID of *Escherichia coli* and not *Entamoeba coli* (a microorganism less prevalent in humans), although the latter would alphabetically come first.
#'
#' ## Coping with Uncertain Results
#'
#' In addition, the [as.mo()] function can differentiate four levels of uncertainty to guess valid results:
#' - Uncertainty level 0: no additional rules are applied;
#' - Uncertainty level 1: allow previously accepted (but now invalid) taxonomic names and minor spelling errors;
#' - Uncertainty level 2: allow all of level 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;
#' - Uncertainty level 3: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name.
#'
#' The level of uncertainty can be set using the argument `allow_uncertain`. The default is `allow_uncertain = TRUE`, which is equal to uncertainty level 2. Using `allow_uncertain = FALSE` is equal to uncertainty level 0 and will skip all rules. You can also use e.g. `as.mo(..., allow_uncertain = 1)` to only allow up to level 1 uncertainty.
#'
#' With the default setting (`allow_uncertain = TRUE`, level 2), below examples will lead to valid results:
#' - `"Streptococcus group B (known as S. agalactiae)"`. The text between brackets will be removed and a warning will be thrown that the result *Streptococcus group B* (``r as.mo("Streptococcus group B")``) needs review.
#' - `"S. aureus - please mind: MRSA"`. The last word will be stripped, after which the function will try to find a match. If it does not, the second last word will be stripped, etc. Again, a warning will be thrown that the result *Staphylococcus aureus* (``r as.mo("Staphylococcus aureus")``) needs review.
#' - `"Fluoroquinolone-resistant Neisseria gonorrhoeae"`. The first word will be stripped, after which the function will try to find a match. A warning will be thrown that the result *Neisseria gonorrhoeae* (``r as.mo("Neisseria gonorrhoeae")``) needs review.
#'
#' There are three helper functions that can be run after using the [as.mo()] function:
#' - Use [mo_uncertainties()] to get a [data.frame] that prints in a pretty format with all taxonomic names that were guessed. The output contains the matching score for all matches (see *Matching Score for Microorganisms* below).
#' - Use [mo_failures()] to get a [character] [vector] with all values that could not be coerced to a valid value.
#' - Use [mo_renamed()] to get a [data.frame] with all values that could be coerced based on old, previously accepted taxonomic names.
#'
#' ## Microbial Prevalence of Pathogens in Humans
#'
#' The coercion rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the `prevalence` columns in the [microorganisms] data set. The grouping into human pathogenic prevalence is explained in the section *Matching Score for Microorganisms* below.
#' @inheritSection mo_matching_score Matching Score for Microorganisms
# (source as a section here, so it can be inherited by other man pages:)
#' @section Source:
#' 1. Becker K. *et al.* (2014). **Coagulase-Negative Staphylococci.** *Clin Microbiol Rev.* 27(4): 870-926; \doi{10.1128/CMR.00109-13}
#' 2. Becker K. *et al.* (2019). **Implications of identifying the recently defined members of the *S. aureus* complex, *S. argenteus* and *S. schweitzeri*: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).** *Clin Microbiol Infect*; \doi{10.1016/j.cmi.2019.02.028}
#' 3. Becker K. *et al.* (2020). **Emergence of coagulase-negative staphylococci** *Expert Rev Anti Infect Ther.* 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
#' 4. Lancefield R.C. (1933). **A serological differentiation of human and other groups of hemolytic streptococci**. *J Exp Med.* 57(4): 571-95; \doi{10.1084/jem.57.4.571}
#' 5. Berends M.S. *et al.* (2022). **Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Human Blood in the Northern Netherlands between 2013 and 2019** *Microorganisms* 10(9), 1801; \doi{10.3390/microorganisms10091801}
#' 6. `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
#' 7. `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
#' 8. `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
#' @export
#' @return A [character] [vector] with additional class [`mo`]
#' @seealso [microorganisms] for the [data.frame] that is being used to determine ID's.
#'
#' The [`mo_*`][mo_property()] functions (such as [mo_genus()], [mo_gramstain()]) to get properties based on the returned code.
#' @inheritSection AMR Reference Data Publicly Available
#' @examples
#' \donttest{
#' # These examples all return "B_STPHY_AURS", the ID of S. aureus:
#' as.mo(c(
#' "sau", # WHONET code
#' "stau",
#' "STAU",
#' "staaur",
#' "S. aureus",
#' "S aureus",
#' "Staphylococcus aureus",
#' "Staphylococcus aureus (MRSA,",
#' "Zthafilokkoockus oureuz", # handles incorrect spelling
#' "MRSA", # Methicillin Resistant S. aureus
#' "VISA", # Vancomycin Intermediate S. aureus
#' "VRSA", # Vancomycin Resistant S. aureus
#' 115329001
#' )) # SNOMED CT code
#'
#' # Dyslexia is no problem - these all work:
#' as.mo(c(
#' "Ureaplasma urealyticum",
#' "Ureaplasma urealyticus",
#' "Ureaplasmium urealytica",
#' "Ureaplazma urealitycium"
#' ))
#'
#' as.mo("Streptococcus group A")
#'
#' as.mo("S. epidermidis") # will remain species: B_STPHY_EPDR
#' as.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CONS
#'
#' as.mo("S. pyogenes") # will remain species: B_STRPT_PYGN
#' as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA
#'
#' # All mo_* functions use as.mo() internally too (see ?mo_property):
#' mo_genus("Esch coli")
#' mo_gramstain("E. coli")
#' mo_is_intrinsic_resistant("E. coli", "vanco")
#' }
as.mo <- function(x,
Becker = FALSE,
Lancefield = FALSE,
minimum_matching_score = NULL,
allow_uncertain = TRUE,
keep_synonyms = FALSE,
reference_df = get_mo_source(),
ignore_pattern = getOption("AMR_ignore_pattern"),
language = get_AMR_locale(),
info = interactive(),
...) {
meet_criteria(x, allow_class = c("mo", "data.frame", "list", "character", "numeric", "integer", "factor"), allow_NA = TRUE)
meet_criteria(Becker, allow_class = c("logical", "character"), has_length = 1)
meet_criteria(Lancefield, allow_class = c("logical", "character"), has_length = 1)
meet_criteria(keep_synonyms, allow_class = c("logical", "character"), has_length = 1)
meet_criteria(minimum_matching_score, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE)
meet_criteria(reference_df, allow_class = "data.frame", allow_NULL = TRUE)
meet_criteria(ignore_pattern, allow_class = "character", has_length = 1, allow_NULL = TRUE)
language <- validate_language(language)
meet_criteria(info, allow_class = "logical", has_length = 1)
if (tryCatch(all(x[!is.na(x)] %in% AMR::microorganisms$mo) &
isFALSE(Becker) &
isTRUE(keep_synonyms) &&
isFALSE(Lancefield), error = function(e) FALSE)) {
# don't look into valid MO codes, just return them
# is.mo() won't work - MO codes might change between package versions
return(set_clean_class(x, new_class = c("mo", "character")))
}
# start off with replaced language-specific non-ASCII characters with ASCII characters
x <- parse_and_convert(x)
# replace mo codes used in older package versions
x <- replace_old_mo_codes(x, property = "mo")
# ignore cases that match the ignore pattern
x <- replace_ignore_pattern(x, ignore_pattern)
# WHONET: xxx = no growth
x[tolower(x) %in% c("", "xxx", "na", "nan")] <- NA_character_
if (tryCatch(all(x == "" | gsub(".*(unknown ).*", "unknown name", tolower(x), perl = TRUE) %in% MO_lookup$fullname_lower, na.rm = TRUE) &&
isFALSE(Becker) &&
isTRUE(keep_synonyms) &&
isFALSE(Lancefield), error = function(e) FALSE)) {
# to improve speed, special case for taxonomically correct full names (case-insensitive)
return(set_clean_class(MO_lookup[match(
gsub(".*(unknown ).*", "unknown name",
tolower(x),
perl = TRUE
),
MO_lookup$fullname_lower
), "mo", drop = TRUE],
new_class = c("mo", "character")
))
}
out <- rep(NA_character_, length(x))
# below we use base R's match(), known for powering '%in%', and incredibly fast!
# From reference_df ----
reference_df <- repair_reference_df(reference_df)
if (!is.null(reference_df)) {
out[x %in% reference_df[[1]]] <- reference_df[[2]][match(x[x %in% reference_df[[1]]], reference_df[[1]])]
}
# From MO code ----
out[is.na(out) & x %in% AMR::microorganisms$mo] <- x[is.na(out) & x %in% AMR::microorganisms$mo]
# From full name ----
out[is.na(out) & x %in% AMR::microorganisms$fullname] <- AMR::microorganisms$mo[match(x[is.na(out) & x %in% AMR::microorganisms$fullname], AMR::microorganisms$fullname)]
# From known codes ----
out[is.na(out) & x %in% AMR::microorganisms.codes$code] <- AMR::microorganisms.codes$mo[match(x[is.na(out) & x %in% AMR::microorganisms.codes$code], AMR::microorganisms.codes$code)]
# From SNOMED ----
if (any(is.na(out) & x %in% unlist(microorganisms$snomed), na.rm = TRUE)) {
# found this extremely fast gem here: https://stackoverflow.com/a/11002456/4575331
out[is.na(out) & x %in% unlist(microorganisms$snomed)] <- microorganisms$mo[rep(seq_along(microorganisms$snomed), vapply(FUN.VALUE = double(1), microorganisms$snomed, length))[match(x[is.na(out) & x %in% unlist(microorganisms$snomed)], unlist(microorganisms$snomed))]]
}
# From previous hits in this session ----
old <- out
out[is.na(out) & x %in% pkg_env$mo_previously_coerced$x] <- pkg_env$mo_previously_coerced$mo[match(x[is.na(out) & x %in% pkg_env$mo_previously_coerced$x], pkg_env$mo_previously_coerced$x)]
new <- out
if (isTRUE(info) && message_not_thrown_before("as.mo", old[seq_len(min(100, length(old)))], new[seq_len(min(100, length(new)))], entire_session = TRUE) && any(is.na(old) & !is.na(new), na.rm = TRUE)) {
message_(
"Returning previously coerced value", ifelse(sum(is.na(old) & !is.na(new)) > 1, "s", ""),
" for ", vector_and(x[is.na(old) & !is.na(new)]), ". Run `mo_reset_session()` to reset this."
)
}
# For all other input ----
if (any(is.na(out) & !is.na(x))) {
# reset uncertainties
pkg_env$mo_uncertainties <- pkg_env$mo_uncertainties[0, ]
# Laboratory systems: remove (translated) entries like "no growth", "not E. coli", etc.
x[trimws2(x) %like% translate_into_language("no .*growth", language = language)] <- NA_character_
x[trimws2(x) %like% paste0("^(", translate_into_language("no|not", language = language), ") ")] <- NA_character_
# run over all unique leftovers
x_unique <- unique(x[is.na(out) & !is.na(x)])
# set up progress bar
progress <- progress_ticker(n = length(x_unique), n_min = 10, print = info)
on.exit(close(progress))
# run it
x_coerced <- lapply(x_unique, function(x_search) {
progress$tick()
x_out <- trimws(tolower(x_search))
x_parts <- strsplit(gsub("-", " ", x_out, fixed = TRUE), " ", fixed = TRUE)[[1]]
# do a pre-match on first character (and if it contains a space, first chars of first two terms)
if (length(x_parts) == 2) {
filtr <- which(MO_lookup$full_first == substr(x_parts[1], 1, 1) & MO_lookup$species_first == substr(x_parts[2], 1, 1))
} else if (length(x_parts) > 2) {
first_chars <- paste0("(^| )", "[", paste(substr(x_parts, 1, 1), collapse = ""), "]")
filtr <- which(MO_lookup$full_first %like_case% first_chars)
} else if (nchar(x_out) == 4) {
# no space and 4 characters - probably a code such as STAU or ESCO!
if (isTRUE(info)) {
message_("Input \"", x_search, "\" is assumed to be a microorganism code - trying to match on ", vector_and(c(substr(x_out, 1, 2), substr(x_out, 3, 4)), sort = FALSE))
}
filtr <- which(MO_lookup$fullname_lower %like_case% paste0("(^| )", substr(x_out, 1, 2), ".* ", substr(x_out, 3, 4)))
} else if (nchar(x_out) <= 6) {
# no space and 5-6 characters - probably a code such as STAAUR or ESCCOL!
first_part <- paste0(substr(x_out, 1, 2), "[a-z]*", substr(x_out, 3, 3))
second_part <- substr(x_out, 4, nchar(x_out))
if (isTRUE(info)) {
message_("Input \"", x_search, "\" is assumed to be a microorganism code - trying to match on ", vector_and(c(gsub("[a-z]*", "(...)", first_part, fixed = TRUE), second_part), sort = FALSE))
}
filtr <- which(MO_lookup$fullname_lower %like_case% paste0("(^| )", first_part, ".* ", second_part))
} else {
filtr <- which(MO_lookup$full_first == substr(x_out, 1, 1))
}
if (length(filtr) == 0) {
mo_to_search <- MO_lookup$fullname
} else {
mo_to_search <- MO_lookup$fullname[filtr]
}
pkg_env$mo_to_search <- mo_to_search
# determine the matching score on the original search value
m <- mo_matching_score(x = x_search, n = mo_to_search)
if (is.null(minimum_matching_score)) {
minimum_matching_score_current <- min(0.7, min(10, nchar(x_search)) * 0.08)
# correct back for prevalence
minimum_matching_score_current <- minimum_matching_score_current / MO_lookup$prevalence[match(mo_to_search, MO_lookup$fullname)]
# correct back for kingdom
minimum_matching_score_current <- minimum_matching_score_current / MO_lookup$kingdom_index[match(mo_to_search, MO_lookup$fullname)]
} else {
minimum_matching_score_current <- minimum_matching_score
}
m[m < minimum_matching_score_current] <- NA_real_
top_hits <- mo_to_search[order(m, decreasing = TRUE, na.last = NA)] # na.last = NA will remove the NAs
if (length(top_hits) == 0) {
warning_("No hits found for \"", x_search, "\" with minimum_matching_score = ", ifelse(is.null(minimum_matching_score), "NULL", minimum_matching_score), ". Try setting this value higher.")
result_mo <- NA_character_
} else {
result_mo <- MO_lookup$mo[match(top_hits[1], MO_lookup$fullname)]
pkg_env$mo_uncertainties <- rbind(pkg_env$mo_uncertainties,
data.frame(
minimum_matching_score = ifelse(is.null(minimum_matching_score), "NULL", minimum_matching_score),
input = x_search,
fullname = top_hits[1],
mo = result_mo,
candidates = ifelse(length(top_hits) > 1, paste(top_hits[2:min(26, length(top_hits))], collapse = ", "), ""),
stringsAsFactors = FALSE
),
stringsAsFactors = FALSE
)
# save to package env to save time for next time
pkg_env$mo_previously_coerced <- unique(rbind(pkg_env$mo_previously_coerced,
data.frame(
x = x_search,
mo = result_mo,
stringsAsFactors = FALSE
),
stringsAsFactors = FALSE
))
}
# the actual result:
result_mo
})
# remove progress bar from console
close(progress)
# expand from unique again
out[is.na(out)] <- unlist(x_coerced)[match(x[is.na(out)], x_unique)]
# Throw note about uncertainties ----
if (isTRUE(info) && NROW(pkg_env$mo_uncertainties) > 0) {
if (message_not_thrown_before("as.mo", "uncertainties", pkg_env$mo_uncertainties$input)) {
plural <- c("", "this")
if (length(pkg_env$mo_uncertainties$input) > 1) {
plural <- c("s", "these uncertainties")
}
if (length(pkg_env$mo_uncertainties$input) <= 3) {
examples <- vector_and(paste0(
'"', pkg_env$mo_uncertainties$input,
'" (assumed ', font_italic(pkg_env$mo_uncertainties$fullname, collapse = NULL), ")"
),
quotes = FALSE
)
} else {
examples <- paste0(nr2char(length(pkg_env$mo_uncertainties$input)), " microorganism", plural[1])
}
msg <- paste0(
"Microorganism translation was uncertain for ", examples,
". Run `mo_uncertainties()` to review ", plural[2], "."
)
message_(msg)
}
}
} # end of loop over all yet unknowns
# Keep or replace synonyms ----
if (isFALSE(keep_synonyms)) {
out_old <- out
gbif_matches <- AMR::microorganisms$gbif_renamed_to[match(out, AMR::microorganisms$mo)]
gbif_matches[!gbif_matches %in% AMR::microorganisms$gbif] <- NA
out[which(!is.na(gbif_matches))] <- AMR::microorganisms$mo[match(gbif_matches[which(!is.na(gbif_matches))], AMR::microorganisms$gbif)]
lpsn_matches <- AMR::microorganisms$lpsn_renamed_to[match(out, AMR::microorganisms$mo)]
lpsn_matches[!lpsn_matches %in% AMR::microorganisms$lpsn] <- NA
out[which(!is.na(lpsn_matches))] <- AMR::microorganisms$mo[match(lpsn_matches[which(!is.na(lpsn_matches))], AMR::microorganisms$lpsn)]
if (isTRUE(info) && (any(!is.na(gbif_matches)) || any(!is.na(lpsn_matches))) && message_not_thrown_before("as.mo", gbif_matches[which(!is.na(gbif_matches))][1:5], lpsn_matches[which(!is.na(lpsn_matches))][1:5]) && length(c(lpsn_matches, gbif_matches)) > 0) {
total_old <- out_old[which(!is.na(gbif_matches) | !is.na(lpsn_matches))]
total_new <- out[which(!is.na(gbif_matches) | !is.na(lpsn_matches))]
total_new <- total_new[!duplicated(total_old)]
total_old <- total_old[!duplicated(total_old)]
total_new <- total_new[order(total_old)]
total_old <- total_old[order(total_old)]
refs_old <- microorganisms$ref[match(total_old, microorganisms$mo)]
refs_old[!is.na(refs_old)] <- paste0(" (", refs_old[!is.na(refs_old)], ")")
refs_old[is.na(refs_old)] <- ""
refs_new <- microorganisms$ref[match(total_new, microorganisms$mo)]
refs_new[!is.na(refs_new)] <- paste0(" (", refs_new[!is.na(refs_new)], ")")
refs_new[is.na(refs_new)] <- ""
message_(
"The following microorganism", ifelse(length(total_old) > 1, "s were", " was"), " taxonomically renamed (use `keep_synonyms = TRUE` to leave uncorrected):\n",
paste0(" ", microorganisms$fullname[match(total_old, microorganisms$mo)],
refs_old,
" -> ", microorganisms$fullname[match(total_new, microorganisms$mo)],
refs_new,
collapse = "\n"
)
)
}
}
# Apply Becker ----
if (isTRUE(Becker) || Becker == "all") {
# warn when species found that are not in:
# - Becker et al. 2014, PMID 25278577
# - Becker et al. 2019, PMID 30872103
# - Becker et al. 2020, PMID 32056452
# comment below code if all staphylococcal species are categorised as CoNS/CoPS
post_Becker <- paste(
"Staphylococcus",
c("caledonicus", "canis", "durrellii", "lloydii", "ratti", "roterodami", "singaporensis", "taiwanensis")
)
if (any(out %in% AMR::microorganisms$mo[match(post_Becker, AMR::microorganisms$fullname)])) {
if (message_not_thrown_before("as.mo", "becker")) {
warning_("in `as.mo()`: Becker ", font_italic("et al."), " (2014, 2019, 2020) does not contain these species named after their publication: ",
vector_and(font_italic(gsub("Staphylococcus", "S.", post_Becker, fixed = TRUE), collapse = NULL), quotes = FALSE),
". Categorisation to CoNS/CoPS was taken from the original scientific publication(s).",
immediate = TRUE
)
}
}
# 'MO_CONS' and 'MO_COPS' are <mo> vectors created in R/_pre_commit_hook.R
out[out %in% MO_CONS] <- "B_STPHY_CONS"
out[out %in% MO_COPS] <- "B_STPHY_COPS"
if (Becker == "all") {
out[out == "B_STPHY_AURS"] <- "B_STPHY_COPS"
}
}
# Apply Lancefield ----
if (isTRUE(Lancefield) || Lancefield == "all") {
# group A - S. pyogenes
out[out == "B_STRPT_PYGN"] <- "B_STRPT_GRPA"
# group B - S. agalactiae
out[out == "B_STRPT_AGLC"] <- "B_STRPT_GRPB"
# group C - all subspecies within S. dysgalactiae and S. equi (such as S. equi zooepidemicus)
out[out %like_case% "^B_STRPT_(DYSG|EQUI)(_|$)"] <- "B_STRPT_GRPC"
if (Lancefield == "all") {
# group D - all enterococci
out[out %like_case% "^B_ENTRC(_|$)"] <- "B_STRPT_GRPD"
}
# group F - S. anginosus, incl. S. anginosus anginosus and S. anginosus whileyi
out[out %like_case% "^B_STRPT_ANGN(_|$)"] <- "B_STRPT_GRPF"
# group G - only S. dysgalactiae which is also group C, so ignore it here
# group H - S. sanguinis
out[out == "B_STRPT_SNGN"] <- "B_STRPT_GRPH"
# group K - S. salivarius, incl. S. salivarius salivariuss and S. salivarius thermophilus
out[out %like_case% "^B_STRPT_SLVR(_|$)"] <- "B_STRPT_GRPK"
# group L - only S. dysgalactiae which is also group C, so ignore it here
}
# Return class ----
set_clean_class(out,
new_class = c("mo", "character")
)
}
#' @rdname as.mo
#' @export
is.mo <- function(x) {
inherits(x, "mo")
}
# will be exported using s3_register() in R/zzz.R
pillar_shaft.mo <- function(x, ...) {
out <- format(x)
# grey out the kingdom (part until first "_")
out[!is.na(x)] <- gsub("^([A-Z]+_)(.*)", paste0(font_subtle("\\1"), "\\2"), out[!is.na(x)], perl = TRUE)
# and grey out every _
out[!is.na(x)] <- gsub("_", font_subtle("_"), out[!is.na(x)])
# markup NA and UNKNOWN
out[is.na(x)] <- font_na(" NA")
out[x == "UNKNOWN"] <- font_na(" UNKNOWN")
df <- tryCatch(get_current_data(arg_name = "x", call = 0),
error = function(e) NULL
)
if (!is.null(df)) {
mo_cols <- vapply(FUN.VALUE = logical(1), df, is.mo)
} else {
mo_cols <- NULL
}
if (!all(x[!is.na(x)] %in% MO_lookup$mo) |
(!is.null(df) && !all(unlist(df[, which(mo_cols), drop = FALSE]) %in% MO_lookup$mo))) {
# markup old mo codes
out[!x %in% MO_lookup$mo] <- font_italic(font_na(x[!x %in% MO_lookup$mo],
collapse = NULL
),
collapse = NULL
)
# throw a warning with the affected column name(s)
if (!is.null(mo_cols)) {
col <- paste0("Column ", vector_or(colnames(df)[mo_cols], quotes = TRUE, sort = FALSE))
} else {
col <- "The data"
}
warning_(
col, " contains old MO codes (from a previous AMR package version). ",
"Please update your MO codes with `as.mo()`."
)
}
# make it always fit exactly
max_char <- max(nchar(x))
if (is.na(max_char)) {
max_char <- 12
}
create_pillar_column(out,
align = "left",
width = max_char + ifelse(any(x %in% c(NA, "UNKNOWN")), 2, 0)
)
}
# will be exported using s3_register() in R/zzz.R
type_sum.mo <- function(x, ...) {
"mo"
}
# will be exported using s3_register() in R/zzz.R
freq.mo <- function(x, ...) {
x_noNA <- as.mo(x[!is.na(x)]) # as.mo() to get the newest mo codes
grams <- mo_gramstain(x_noNA, language = NULL)
digits <- list(...)$digits
if (is.null(digits)) {
digits <- 2
}
cleaner::freq.default(
x = x,
...,
.add_header = list(
`Gram-negative` = paste0(
format(sum(grams == "Gram-negative", na.rm = TRUE),
big.mark = ",",
decimal.mark = "."
),
" (", percentage(sum(grams == "Gram-negative", na.rm = TRUE) / length(grams),
digits = digits
),
")"
),
`Gram-positive` = paste0(
format(sum(grams == "Gram-positive", na.rm = TRUE),
big.mark = ",",
decimal.mark = "."
),
" (", percentage(sum(grams == "Gram-positive", na.rm = TRUE) / length(grams),
digits = digits
),
")"
),
`Nr. of genera` = pm_n_distinct(mo_genus(x_noNA, language = NULL)),
`Nr. of species` = pm_n_distinct(paste(
mo_genus(x_noNA, language = NULL),
mo_species(x_noNA, language = NULL)
))
)
)
}
# will be exported using s3_register() in R/zzz.R
get_skimmers.mo <- function(column) {
skimr::sfl(
skim_type = "mo",
unique_total = ~ length(unique(stats::na.omit(.))),
gram_negative = ~ sum(mo_is_gram_negative(.), na.rm = TRUE),
gram_positive = ~ sum(mo_is_gram_positive(.), na.rm = TRUE),
top_genus = ~ names(sort(-table(mo_genus(stats::na.omit(.), language = NULL))))[1L],
top_species = ~ names(sort(-table(mo_name(stats::na.omit(.), language = NULL))))[1L]
)
}
#' @method print mo
#' @export
#' @noRd
print.mo <- function(x, print.shortnames = FALSE, ...) {
cat("Class <mo>\n")
x_names <- names(x)
if (is.null(x_names) & print.shortnames == TRUE) {
x_names <- tryCatch(mo_shortname(x, ...), error = function(e) NULL)
}
x <- as.character(x)
names(x) <- x_names
if (!all(x[!is.na(x)] %in% MO_lookup$mo)) {
warning_(
"Some MO codes are from a previous AMR package version. ",
"Please update the MO codes with `as.mo()`."
)
}
print.default(x, quote = FALSE)
}
#' @method summary mo
#' @export
#' @noRd
summary.mo <- function(object, ...) {
# unique and top 1-3
x <- as.mo(object) # force again, could be mo from older pkg version
top <- as.data.frame(table(x), responseName = "n", stringsAsFactors = FALSE)
top_3 <- top[order(-top$n), 1, drop = TRUE][1:3]
value <- c(
"Class" = "mo",
"<NA>" = length(x[is.na(x)]),
"Unique" = pm_n_distinct(x[!is.na(x)]),
"#1" = top_3[1],
"#2" = top_3[2],
"#3" = top_3[3]
)
class(value) <- c("summaryDefault", "table")
value
}
#' @method as.data.frame mo
#' @export
#' @noRd
as.data.frame.mo <- function(x, ...) {
if (!all(x[!is.na(x)] %in% MO_lookup$mo)) {
warning_(
"The data contains old MO codes (from a previous AMR package version). ",
"Please update your MO codes with `as.mo()`."
)
}
nm <- deparse1(substitute(x))
if (!"nm" %in% names(list(...))) {
as.data.frame.vector(x, ..., nm = nm)
} else {
as.data.frame.vector(x, ...)
}
}
#' @method [ mo
#' @export
#' @noRd
"[.mo" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method [[ mo
#' @export
#' @noRd
"[[.mo" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method [<- mo
#' @export
#' @noRd
"[<-.mo" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
# must only contain valid MOs
return_after_integrity_check(y, "microorganism code", as.character(AMR::microorganisms$mo))
}
#' @method [[<- mo
#' @export
#' @noRd
"[[<-.mo" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
# must only contain valid MOs
return_after_integrity_check(y, "microorganism code", as.character(AMR::microorganisms$mo))
}
#' @method c mo
#' @export
#' @noRd
c.mo <- function(...) {
x <- list(...)[[1L]]
y <- NextMethod()
attributes(y) <- attributes(x)
return_after_integrity_check(y, "microorganism code", as.character(AMR::microorganisms$mo))
}
#' @method unique mo
#' @export
#' @noRd
unique.mo <- function(x, incomparables = FALSE, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method rep mo
#' @export
#' @noRd
rep.mo <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @rdname as.mo
#' @export
mo_failures <- function() {
pkg_env$mo_failures
}
#' @rdname as.mo
#' @export
mo_uncertainties <- function() {
if (is.null(pkg_env$mo_uncertainties)) {
return(NULL)
}
set_clean_class(as.data.frame(pkg_env$mo_uncertainties,
stringsAsFactors = FALSE
),
new_class = c("mo_uncertainties", "data.frame")
)
}
#' @method print mo_uncertainties
#' @export
#' @noRd
print.mo_uncertainties <- function(x, ...) {
if (NROW(x) == 0) {
return(NULL)
}
cat(word_wrap("Matching scores are based on pathogenicity in humans and the resemblance between the input and the full taxonomic name. See `?mo_matching_score`.\n\n", add_fn = font_blue))
if (has_colour()) {
cat(word_wrap("Colour keys: ",
font_red_bg(" 0.000-0.499 "),
font_orange_bg(" 0.500-0.599 "),
font_yellow_bg(" 0.600-0.699 "),
font_green_bg(" 0.700-1.000"),
add_fn = font_blue
), font_green_bg(" "), "\n", sep = "")
}
score_set_colour <- function(text, scores) {
# set colours to scores
text[scores >= 0.7] <- font_green_bg(text[scores >= 0.7], collapse = NULL)
text[scores >= 0.6 & scores < 0.7] <- font_yellow_bg(text[scores >= 0.6 & scores < 0.7], collapse = NULL)
text[scores >= 0.5 & scores < 0.6] <- font_orange_bg(text[scores >= 0.5 & scores < 0.6], collapse = NULL)
text[scores < 0.5] <- font_red_bg(text[scores < 0.5], collapse = NULL)
text
}
txt <- ""
for (i in seq_len(nrow(x))) {
if (x[i, ]$candidates != "") {
candidates <- unlist(strsplit(x[i, ]$candidates, ", ", fixed = TRUE))
scores <- mo_matching_score(x = x[i, ]$input, n = candidates)
n_candidates <- length(candidates)
candidates_formatted <- font_italic(candidates, collapse = NULL)
scores_formatted <- trimws(formatC(round(scores, 3), format = "f", digits = 3))
scores_formatted <- score_set_colour(scores_formatted, scores)
# sort on descending scores
candidates_formatted <- candidates_formatted[order(1 - scores)]
scores_formatted <- scores_formatted[order(1 - scores)]
candidates <- word_wrap(paste0(
"Also matched: ",
vector_and(paste0(
candidates_formatted,
font_blue(paste0(" (", scores_formatted, ")"), collapse = NULL)
),
quotes = FALSE, sort = FALSE
),
ifelse(n_candidates == 25,
font_grey(" [showing first 25]"),
""
)
),
extra_indent = nchar("Also matched: ")
)
} else {
candidates <- ""
}
score <- mo_matching_score(
x = x[i, ]$input,
n = x[i, ]$fullname
)
score_formatted <- trimws(formatC(round(score, 3), format = "f", digits = 3))
txt <- paste(txt,
paste0(
strwrap(
paste0(
'"', x[i, ]$input, '"',
" -> ",
paste0(
font_bold(font_italic(x[i, ]$fullname)),
ifelse(!is.na(x[i, ]$renamed_to), paste(", renamed to", font_italic(x[i, ]$renamed_to)), ""),
" (", x[i, ]$mo,
", ", score_set_colour(score_formatted, score),
") "
)
),
width = 0.98 * getOption("width"),
exdent = nchar(x[i, ]$input) + 6
),
collapse = "\n"
),
candidates,
sep = "\n"
)
txt <- paste0(gsub("\n\n", "\n", txt), "\n\n")
}
cat(txt)
}
#' @rdname as.mo
#' @export
mo_reset_session <- function() {
if (NROW(pkg_env$mo_previously_coerced) > 0) {
message_("Reset ", NROW(pkg_env$mo_previously_coerced), " previously matched input values.")
pkg_env$mo_previously_coerced <- pkg_env$mo_previously_coerced[0, , drop = FALSE]
} else {
message_("No previously matched input values to reset.")
}
}
#' @rdname as.mo
#' @export
mo_renamed <- function() {
items <- pkg_env$mo_renamed
if (is.null(items)) {
items <- data.frame(stringsAsFactors = FALSE)
} else {
items <- pm_distinct(items, old_name, .keep_all = TRUE)
}
set_clean_class(as.data.frame(items,
stringsAsFactors = FALSE
),
new_class = c("mo_renamed", "data.frame")
)
}
#' @method print mo_renamed
#' @export
#' @noRd
print.mo_renamed <- function(x, ...) {
if (NROW(x) == 0) {
return(invisible())
}
for (i in seq_len(nrow(x))) {
message_(
font_italic(x$old_name[i]),
ifelse(x$old_ref[i] %in% c("", NA),
"",
paste0(" (", gsub("et al.", font_italic("et al."), x$old_ref[i]), ")")
),
" was renamed ",
ifelse(!x$new_ref[i] %in% c("", NA) && as.integer(gsub("[^0-9]", "", x$new_ref[i])) < as.integer(gsub("[^0-9]", "", x$old_ref[i])),
font_bold("back to "),
""
),
font_italic(x$new_name[i]),
ifelse(x$new_ref[i] %in% c("", NA),
"",
paste0(" (", gsub("et al.", font_italic("et al."), x$new_ref[i]), ")")
),
" [", x$mo[i], "]"
)
}
}
nr2char <- function(x) {
if (x %in% c(1:10)) {
v <- c(
"one" = 1, "two" = 2, "three" = 3, "four" = 4, "five" = 5,
"six" = 6, "seven" = 7, "eight" = 8, "nine" = 9, "ten" = 10
)
names(v[x])
} else {
x
}
}
unregex <- function(x) {
gsub("[^a-zA-Z0-9 -]", "", x)
}
translate_allow_uncertain <- function(allow_uncertain) {
if (isTRUE(allow_uncertain)) {
# default to uncertainty level 2
allow_uncertain <- 2
} else {
allow_uncertain[tolower(allow_uncertain) == "none"] <- 0
allow_uncertain[tolower(allow_uncertain) == "all"] <- 3
allow_uncertain <- as.integer(allow_uncertain)
stop_ifnot(allow_uncertain %in% c(0:3),
'`allow_uncertain` must be a number between 0 (or "none") and 3 (or "all"), or TRUE (= 2) or FALSE (= 0)',
call = FALSE
)
}
allow_uncertain
}
get_mo_failures_uncertainties_renamed <- function() {
remember <- list(
failures = pkg_env$mo_failures,
uncertainties = pkg_env$mo_uncertainties,
renamed = pkg_env$mo_renamed
)
# empty them, otherwise mo_shortname("Chlamydophila psittaci") will give 3 notes
pkg_env$mo_failures <- NULL
pkg_env$mo_uncertainties <- NULL
pkg_env$mo_renamed <- NULL
remember
}
load_mo_failures_uncertainties_renamed <- function(metadata) {
pkg_env$mo_failures <- metadata$failures
pkg_env$mo_uncertainties <- metadata$uncertainties
pkg_env$mo_renamed <- metadata$renamed
}
trimws2 <- function(x) {
trimws(gsub("[\\s]+", " ", x, perl = TRUE))
}
parse_and_convert <- function(x) {
tryCatch(
{
if (!is.null(dim(x))) {
if (NCOL(x) > 2) {
stop("a maximum of two columns is allowed", call. = FALSE)
} else if (NCOL(x) == 2) {
# support Tidyverse selection like: df %>% select(colA, colB)
# paste these columns together
x <- as.data.frame(x, stringsAsFactors = FALSE)
colnames(x) <- c("A", "B")
x <- paste(x$A, x$B)
} else {
# support Tidyverse selection like: df %>% select(colA)
x <- as.data.frame(x, stringsAsFactors = FALSE)[[1]]
}
}
parsed <- iconv(as.character(x), to = "UTF-8")
parsed[is.na(parsed) & !is.na(x)] <- iconv(x[is.na(parsed) & !is.na(x)], from = "Latin1", to = "ASCII//TRANSLIT")
parsed <- gsub('"', "", parsed, fixed = TRUE)
parsed <- gsub(" +", " ", parsed, perl = TRUE)
parsed <- trimws(parsed)
parsed
},
error = function(e) stop(e$message, call. = FALSE)
) # this will also be thrown when running `as.mo(no_existing_object)`
parsed
}
replace_old_mo_codes <- function(x, property) {
# this function transform old MO codes to current codes, such as:
# B_ESCH_COL (AMR v0.5.0) -> B_ESCHR_COLI
ind <- x %like_case% "^[A-Z]_[A-Z_]+$" & !x %in% MO_lookup$mo
if (any(ind)) {
# get the ones that match
affected <- x[ind]
affected_unique <- unique(affected)
all_direct_matches <- TRUE
# find their new codes, once per code
solved_unique <- unlist(lapply(
strsplit(affected_unique, ""),
function(m) {
kingdom <- paste0("^", m[1])
name <- m[3:length(m)]
name[name == "_"] <- " "
name <- tolower(paste0(name, ".*", collapse = ""))
name <- gsub(" .*", " ", name, fixed = TRUE)
name <- paste0("^", name)
results <- MO_lookup$mo[MO_lookup$kingdom %like_case% kingdom &
MO_lookup$fullname_lower %like_case% name]
if (length(results) > 1) {
all_direct_matches <<- FALSE
}
results[1L]
}
), use.names = FALSE)
solved <- solved_unique[match(affected, affected_unique)]
# assign on places where a match was found
x[ind] <- solved
n_matched <- length(affected[!is.na(affected)])
n_solved <- length(affected[!is.na(solved)])
n_unsolved <- length(affected[is.na(solved)])
n_unique <- length(affected_unique[!is.na(affected_unique)])
if (n_unique < n_matched) {
n_unique <- paste0(n_unique, " unique, ")
} else {
n_unique <- ""
}
if (property != "mo") {
warning_(
"in `mo_", property, "()`: the input contained ", n_matched,
" old MO code", ifelse(n_matched == 1, "", "s"),
" (", n_unique, "from a previous AMR package version). ",
"Please update your MO codes with `as.mo()` to increase speed."
)
} else {
warning_(
"in `as.mo()`: the input contained ", n_matched,
" old MO code", ifelse(n_matched == 1, "", "s"),
" (", n_unique, "from a previous AMR package version). ",
n_solved, " old MO code", ifelse(n_solved == 1, "", "s"),
ifelse(n_solved == 1, " was", " were"),
ifelse(all_direct_matches, " updated ", font_bold(" guessed ")),
"to ", ifelse(n_solved == 1, "a ", ""),
"currently used MO code", ifelse(n_solved == 1, "", "s"),
ifelse(n_unsolved > 0,
paste0(" and ", n_unsolved, " old MO code", ifelse(n_unsolved == 1, "", "s"), " could not be updated."),
"."
)
)
}
}
x
}
replace_ignore_pattern <- function(x, ignore_pattern) {
if (!is.null(ignore_pattern) && !identical(trimws2(ignore_pattern), "")) {
ignore_cases <- x %like% ignore_pattern
if (sum(ignore_cases) > 0) {
message_(
"The following input was ignored by `ignore_pattern = \"", ignore_pattern, "\"`: ",
vector_and(x[ignore_cases], quotes = TRUE)
)
x[ignore_cases] <- NA_character_
}
}
x
}
repair_reference_df <- function(reference_df) {
if (is.null(reference_df)) {
return(NULL)
}
# has valid own reference_df
reference_df <- reference_df %pm>%
pm_filter(!is.na(mo))
# keep only first two columns, second must be mo
if (colnames(reference_df)[1] == "mo") {
reference_df <- reference_df %pm>% pm_select(2, "mo")
} else {
reference_df <- reference_df %pm>% pm_select(1, "mo")
}
# remove factors, just keep characters
colnames(reference_df)[1] <- "x"
reference_df[, "x"] <- as.character(reference_df[, "x", drop = TRUE])
reference_df[, "mo"] <- as.character(reference_df[, "mo", drop = TRUE])
# some MO codes might be old
reference_df[, "mo"] <- as.mo(reference_df[, "mo", drop = TRUE])
reference_df
}
strip_words <- function(text, n, side = "right") {
out <- lapply(strsplit(text, " "), function(x) {
if (side %like% "^r" & length(x) > n) {
x[seq_len(length(x) - n)]
} else if (side %like% "^l" & length(x) > n) {
x[2:length(x)]
}
})
vapply(FUN.VALUE = character(1), out, paste, collapse = " ")
}
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