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# ==================================================================== #
# 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/ #
# ==================================================================== #
#' Transform Input to a Microorganism ID
#'
#' Use this function to determine a valid microorganism ID ([`mo`]). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea 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*.
#' @inheritSection lifecycle Stable Lifecycle
#' @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 beta-haemolytic *Streptococci* 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 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 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_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 ID 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),
#' C (Chromista), F (Fungi), 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 Catalogue of Life (see below) and from one other source (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 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:)
#' @section Source:
#' 1. Becker K *et al.* **Coagulase-Negative Staphylococci**. 2014. Clin Microbiol Rev. 27(4): 870–926; \doi{10.1128/CMR.00109-13}
#' 2. Becker K *et al.* **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).** 2019. Clin Microbiol Infect; \doi{10.1016/j.cmi.2019.02.028}
#' 3. Becker K *et al.* **Emergence of coagulase-negative staphylococci** 2020. Expert Rev Anti Infect Ther. 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
#' 4. Lancefield RC **A serological differentiation of human and other groups of hemolytic streptococci**. 1933. J Exp Med. 57(4): 571–95; \doi{10.1084/jem.57.4.571}
#' 5. `r gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$version, fixed = TRUE)`, <http://www.catalogueoflife.org>
#' 6. List of Prokaryotic names with Standing in Nomenclature (`r CATALOGUE_OF_LIFE$yearmonth_LPSN`), \doi{10.1099/ijsem.0.004332}
#' 7. `r SNOMED_VERSION$current_source`, retrieved from the `r SNOMED_VERSION$title`, OID `r SNOMED_VERSION$current_oid`, version `r SNOMED_VERSION$current_version`; url: <`r SNOMED_VERSION$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
#' @inheritSection AMR Read more on Our Website!
#' @examples
#' \donttest{
#' # These examples all return "B_STPHY_AURS", the ID of S. aureus:
#' as.mo("sau") # WHONET code
#' as.mo("stau")
#' as.mo("STAU")
#' as.mo("staaur")
#' as.mo("S. aureus")
#' as.mo("S aureus")
#' as.mo("Staphylococcus aureus")
#' as.mo("Staphylococcus aureus (MRSA)")
#' as.mo("Zthafilokkoockus oureuz") # handles incorrect spelling
#' as.mo("MRSA") # Methicillin Resistant S. aureus
#' as.mo("VISA") # Vancomycin Intermediate S. aureus
#' as.mo("VRSA") # Vancomycin Resistant S. aureus
#' as.mo(115329001) # SNOMED CT code
#'
#' # Dyslexia is no problem - these all work:
#' as.mo("Ureaplasma urealyticum")
#' as.mo("Ureaplasma urealyticus")
#' as.mo("Ureaplasmium urealytica")
#' as.mo("Ureaplazma urealitycium")
#'
#' as.mo("Streptococcus group A")
#' as.mo("GAS") # Group A Streptococci
#' as.mo("GBS") # Group B Streptococci
#'
#' 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("E. coli") # returns "Escherichia"
#' mo_gramstain("E. coli") # returns "Gram negative"
#' mo_is_intrinsic_resistant("E. coli", "vanco") # returns TRUE
#' }
as.mo <- function(x,
Becker = FALSE,
Lancefield = FALSE,
allow_uncertain = TRUE,
reference_df = get_mo_source(),
ignore_pattern = getOption("AMR_ignore_pattern"),
language = get_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(allow_uncertain, allow_class = c("logical", "numeric", "integer"), has_length = 1)
meet_criteria(reference_df, allow_class = "data.frame", allow_NULL = TRUE)
meet_criteria(ignore_pattern, allow_class = "character", has_length = 1, allow_NULL = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
meet_criteria(info, allow_class = "logical", has_length = 1)
check_dataset_integrity()
if (tryCatch(all(x[!is.na(x)] %in% MO_lookup$mo)
& isFALSE(Becker)
& isFALSE(Lancefield), error = function(e) FALSE)) {
# don't look into valid MO codes, just return them
# is.mo() won't work - MO codes might change between package versions
return(set_clean_class(x, new_class = c("mo", "character")))
}
# start off with replaced language-specific non-ASCII characters with ASCII characters
x <- parse_and_convert(x)
# replace mo codes used in older package versions
x <- replace_old_mo_codes(x, property = "mo")
# ignore cases that match the ignore pattern
x <- replace_ignore_pattern(x, ignore_pattern)
# WHONET: xxx = no growth
x[tolower(as.character(paste0(x, ""))) %in% c("", "xxx", "na", "nan")] <- NA_character_
# Laboratory systems: remove (translated) entries like "no growth", etc.
x[trimws2(x) %like% translate_AMR("no .*growth", language = language)] <- NA_character_
x[trimws2(x) %like% paste0("^(", translate_AMR("no|not", language = language), ") [a-z]+")] <- "UNKNOWN"
uncertainty_level <- translate_allow_uncertain(allow_uncertain)
if (tryCatch(all(x == "" | gsub(".*(unknown ).*", "unknown name", tolower(x), perl = TRUE) %in% MO_lookup$fullname_lower, na.rm = TRUE)
& isFALSE(Becker)
& isFALSE(Lancefield), error = function(e) FALSE)) {
# to improve speed, special case for taxonomically correct full names (case-insensitive)
return(MO_lookup[match(gsub(".*(unknown ).*", "unknown name", tolower(x), perl = TRUE), MO_lookup$fullname_lower), "mo", drop = TRUE])
}
if (!is.null(reference_df)
&& check_validity_mo_source(reference_df)
&& isFALSE(Becker)
&& isFALSE(Lancefield)
&& all(x %in% unlist(reference_df), na.rm = TRUE)) {
reference_df <- repair_reference_df(reference_df)
suppressWarnings(
y <- data.frame(x = x, stringsAsFactors = FALSE) %pm>%
pm_left_join(reference_df, by = "x") %pm>%
pm_pull(mo)
)
} else if (all(x[!is.na(x)] %in% MO_lookup$mo)
& isFALSE(Becker)
& isFALSE(Lancefield)) {
y <- x
} else {
# will be checked for mo class in validation and uses exec_as.mo internally if necessary
y <- mo_validate(x = x, property = "mo",
Becker = Becker, Lancefield = Lancefield,
allow_uncertain = uncertainty_level,
reference_df = reference_df,
ignore_pattern = ignore_pattern,
language = language,
info = info,
...)
}
set_clean_class(y,
new_class = c("mo", "character"))
}
#' @rdname as.mo
#' @export
is.mo <- function(x) {
inherits(x, "mo")
}
# param property a column name of microorganisms
# param initial_search [logical] - is FALSE when coming from uncertain tries, which uses exec_as.mo internally too
# param dyslexia_mode [logical] - also check for characters that resemble others
# param debug [logical] - show different lookup texts while searching
# param reference_data_to_use [data.frame] - the data set to check for
# param actual_uncertainty - (only for initial_search = FALSE) the actual uncertainty level used in the function for score calculation (sometimes passed as 2 or 3 by uncertain_fn())
# param actual_input - (only for initial_search = FALSE) the actual, original input
# param language - used for translating "no growth", etc.
exec_as.mo <- function(x,
Becker = FALSE,
Lancefield = FALSE,
allow_uncertain = TRUE,
reference_df = get_mo_source(),
info = interactive(),
property = "mo",
initial_search = TRUE,
dyslexia_mode = FALSE,
debug = FALSE,
ignore_pattern = getOption("AMR_ignore_pattern"),
reference_data_to_use = MO_lookup,
actual_uncertainty = 1,
actual_input = NULL,
language = get_locale()) {
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(allow_uncertain, allow_class = c("logical", "numeric", "integer"), has_length = 1)
meet_criteria(reference_df, allow_class = "data.frame", allow_NULL = TRUE)
meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(microorganisms))
meet_criteria(initial_search, allow_class = "logical", has_length = 1)
meet_criteria(dyslexia_mode, allow_class = "logical", has_length = 1)
meet_criteria(debug, allow_class = "logical", has_length = 1)
meet_criteria(ignore_pattern, allow_class = "character", has_length = 1, allow_NULL = TRUE)
meet_criteria(reference_data_to_use, allow_class = "data.frame")
meet_criteria(actual_uncertainty, allow_class = "numeric", has_length = 1)
meet_criteria(actual_input, allow_class = "character", allow_NULL = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
check_dataset_integrity()
if (isTRUE(debug) && initial_search == TRUE) {
time_start_tracking()
}
lookup <- function(needle,
column = property,
haystack = reference_data_to_use,
n = 1,
debug_mode = debug,
initial = initial_search,
uncertainty = actual_uncertainty,
input_actual = actual_input) {
if (!is.null(input_actual)) {
input <- input_actual
} else {
input <- tryCatch(x_backup[i], error = function(e) "")
}
# `column` can be NULL for all columns, or a selection
# returns a [character] (vector) - if `column` > length 1 then with columns as names
if (isTRUE(debug_mode)) {
cat(font_silver("Looking up: ", substitute(needle), collapse = ""),
"\n ", time_track())
}
if (length(column) == 1) {
res_df <- haystack[which(eval(substitute(needle), envir = haystack, enclos = parent.frame())), , drop = FALSE]
if (NROW(res_df) > 1 & uncertainty != -1) {
# sort the findings on matching score
scores <- mo_matching_score(x = input,
n = res_df[, "fullname", drop = TRUE])
res_df <- res_df[order(scores, decreasing = TRUE), , drop = FALSE]
}
res <- as.character(res_df[, column, drop = TRUE])
if (length(res) == 0) {
if (isTRUE(debug_mode)) {
cat(font_red(" (no match)\n"))
}
NA_character_
} else {
if (isTRUE(debug_mode)) {
cat(font_green(paste0(" MATCH (", NROW(res_df), " results)\n")))
}
if ((length(res) > n | uncertainty > 1) & uncertainty != -1) {
# save the other possible results as well, but not for forced certain results (then uncertainty == -1)
uncertainties <<- rbind(uncertainties,
format_uncertainty_as_df(uncertainty_level = uncertainty,
input = input,
result_mo = res_df[1, "mo", drop = TRUE],
candidates = as.character(res_df[, "fullname", drop = TRUE])),
stringsAsFactors = FALSE)
}
res[seq_len(min(n, length(res)))]
}
} else {
if (is.null(column)) {
column <- names(haystack)
}
res <- haystack[which(eval(substitute(needle), envir = haystack, enclos = parent.frame())), , drop = FALSE]
res <- res[seq_len(min(n, nrow(res))), column, drop = TRUE]
if (NROW(res) == 0) {
if (isTRUE(debug_mode)) {
cat(font_red(" (no rows)\n"))
}
res <- rep(NA_character_, length(column))
} else {
if (isTRUE(debug_mode)) {
cat(font_green(paste0(" MATCH (", NROW(res), " rows)\n")))
}
}
res <- as.character(res)
names(res) <- column
res
}
}
# 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)
# ignore cases that match the ignore pattern
x <- replace_ignore_pattern(x, ignore_pattern)
# WHONET: xxx = no growth
x[tolower(as.character(paste0(x, ""))) %in% c("", "xxx", "na", "nan")] <- NA_character_
# Laboratory systems: remove (translated) entries like "no growth", etc.
x[trimws2(x) %like% translate_AMR("no .*growth", language = language)] <- NA_character_
x[trimws2(x) %like% paste0("^(", translate_AMR("no|not", language = language), ") [a-z]+")] <- "UNKNOWN"
if (initial_search == TRUE) {
# keep track of time - give some hints to improve speed if it takes a long time
start_time <- Sys.time()
pkg_env$mo_failures <- NULL
pkg_env$mo_uncertainties <- NULL
pkg_env$mo_renamed <- NULL
}
pkg_env$mo_renamed_last_run <- NULL
failures <- character(0)
uncertainty_level <- translate_allow_uncertain(allow_uncertain)
uncertainties <- data.frame(uncertainty = integer(0),
input = character(0),
fullname = character(0),
renamed_to = character(0),
mo = character(0),
candidates = character(0),
stringsAsFactors = FALSE)
x_input <- x
# already strip leading and trailing spaces
x <- trimws(x)
# only check the uniques, which is way faster
x <- unique(x)
# remove empty values (to later fill them in again with NAs)
# ("xxx" is WHONET code for 'no growth')
x <- x[!is.na(x)
& !is.null(x)
& !identical(x, "")
& !identical(x, "xxx")]
# defined df to check for
if (!is.null(reference_df)) {
check_validity_mo_source(reference_df)
reference_df <- repair_reference_df(reference_df)
}
# all empty
if (all(identical(trimws(x_input), "") | is.na(x_input) | length(x) == 0)) {
if (property == "mo") {
return(set_clean_class(rep(NA_character_, length(x_input)),
new_class = c("mo", "character")))
} else {
return(rep(NA_character_, length(x_input)))
}
} else if (all(x %in% reference_df[, 1][[1]])) {
# all in reference df
colnames(reference_df)[1] <- "x"
suppressWarnings(
x <- MO_lookup[match(reference_df[match(x, reference_df$x), "mo", drop = TRUE], MO_lookup$mo), property, drop = TRUE]
)
} else if (all(x %in% reference_data_to_use$mo)) {
x <- MO_lookup[match(x, MO_lookup$mo), property, drop = TRUE]
} else if (all(tolower(x) %in% reference_data_to_use$fullname_lower)) {
# we need special treatment for very prevalent full names, they are likely!
# e.g. as.mo("Staphylococcus aureus")
x <- MO_lookup[match(tolower(x), MO_lookup$fullname_lower), property, drop = TRUE]
} else if (all(x %in% reference_data_to_use$fullname)) {
# we need special treatment for very prevalent full names, they are likely!
# e.g. as.mo("Staphylococcus aureus")
x <- MO_lookup[match(x, MO_lookup$fullname), property, drop = TRUE]
} else if (all(toupper(x) %in% microorganisms.codes$code)) {
# commonly used MO codes
x <- MO_lookup[match(microorganisms.codes[match(toupper(x),
microorganisms.codes$code),
"mo",
drop = TRUE],
MO_lookup$mo),
property,
drop = TRUE]
} else if (!all(x %in% microorganisms[, property])) {
strip_whitespace <- function(x, dyslexia_mode) {
# all whitespaces (tab, new lines, etc.) should be one space
# and spaces before and after should be left blank
trimmed <- trimws2(x)
# also, make sure the trailing and leading characters are a-z or 0-9
# in case of non-regex
if (dyslexia_mode == FALSE) {
trimmed <- gsub("^[^a-zA-Z0-9)(]+", "", trimmed, perl = TRUE)
trimmed <- gsub("[^a-zA-Z0-9)(]+$", "", trimmed, perl = TRUE)
}
trimmed
}
x_backup_untouched <- x
x <- strip_whitespace(x, dyslexia_mode)
# translate 'unknown' names back to English
if (any(x %like% "unbekannt|onbekend|desconocid|sconosciut|iconnu|desconhecid", na.rm = TRUE)) {
trns <- subset(translations_file, pattern %like% "unknown" | affect_mo_name == TRUE)
langs <- LANGUAGES_SUPPORTED[LANGUAGES_SUPPORTED != "en"]
for (l in langs) {
for (i in seq_len(nrow(trns))) {
if (!is.na(trns[i, l, drop = TRUE])) {
x <- gsub(pattern = trns[i, l, drop = TRUE],
replacement = trns$pattern[i],
x = x,
ignore.case = TRUE,
perl = TRUE)
}
}
}
}
x_backup <- x
# from here on case-insensitive
x <- tolower(x)
x_backup[x %like_case% "^(fungus|fungi)$"] <- "(unknown fungus)" # will otherwise become the kingdom
x_backup[x_backup_untouched == "Fungi"] <- "Fungi" # is literally the kingdom
# Fill in fullnames and MO codes at once
known_names <- tolower(x_backup) %in% MO_lookup$fullname_lower
x[known_names] <- MO_lookup[match(tolower(x_backup)[known_names], MO_lookup$fullname_lower), property, drop = TRUE]
known_codes <- toupper(x_backup) %in% MO_lookup$mo
x[known_codes] <- MO_lookup[match(toupper(x_backup)[known_codes], MO_lookup$mo), property, drop = TRUE]
already_known <- known_names | known_codes
# now only continue where the right taxonomic output is not already known
if (any(!already_known)) {
x_known <- x[already_known]
# remove spp and species
x <- gsub(" +(spp.?|ssp.?|sp.? |ss ?.?|subsp.?|subspecies|biovar |serovar |species)", " ", x)
x <- gsub("(spp.?|subsp.?|subspecies|biovar|serovar|species)", "", x)
x <- gsub("^([a-z]{2,4})(spe.?)$", "\\1", x, perl = TRUE) # when ending in SPE instead of SPP and preceded by 2-4 characters
x <- strip_whitespace(x, dyslexia_mode)
x_backup_without_spp <- x
x_species <- paste(x, "species")
# translate to English for supported languages of mo_property
x <- gsub("(gruppe|groep|grupo|gruppo|groupe)", "group", x, perl = TRUE)
# no groups and complexes as ending
x <- gsub("(complex|group)$", "", x, perl = TRUE)
x <- gsub("(^|[^a-z])((an)?aero+b)[a-z]*", "", x, perl = TRUE)
x <- gsub("^atyp[a-z]*", "", x, perl = TRUE)
x <- gsub("(vergroen)[a-z]*", "viridans", x, perl = TRUE)
x <- gsub("[a-z]*diff?erent[a-z]*", "", x, perl = TRUE)
x <- gsub("(hefe|gist|gisten|levadura|lievito|fermento|levure)[a-z]*", "yeast", x, perl = TRUE)
x <- gsub("(schimmels?|mofo|molde|stampo|moisissure|fungi)[a-z]*", "fungus", x, perl = TRUE)
x <- gsub("fungus[ph|f]rya", "fungiphrya", x, perl = TRUE)
# no contamination
x <- gsub("(contamination|kontamination|mengflora|contaminaci.n|contamina..o)", "", x, perl = TRUE)
# remove non-text in case of "E. coli" except dots and spaces
x <- trimws(gsub("[^.a-zA-Z0-9/ \\-]+", " ", x, perl = TRUE))
# but make sure that dots are followed by a space
x <- gsub("[.] ?", ". ", x, perl = TRUE)
# replace minus by a space
x <- gsub("-+", " ", x, perl = TRUE)
# replace hemolytic by haemolytic
x <- gsub("ha?emoly", "haemoly", x, perl = TRUE)
# place minus back in streptococci
x <- gsub("(alpha|beta|gamma).?ha?emoly", "\\1-haemoly", x, perl = TRUE)
# remove genus as first word
x <- gsub("^genus ", "", x, perl = TRUE)
# remove 'uncertain'-like texts
x <- trimws(gsub("(uncertain|susp[ie]c[a-z]+|verdacht)", "", x, perl = TRUE))
# allow characters that resemble others = dyslexia_mode ----
if (dyslexia_mode == TRUE) {
x <- tolower(x)
x <- gsub("[iy]+", "[iy]+", x)
x <- gsub("(c|k|q|qu|s|z|x|ks)+", "(c|k|q|qu|s|z|x|ks)+", x)
x <- gsub("(ph|hp|f|v)+", "(ph|hp|f|v)+", x)
x <- gsub("(th|ht|t)+", "(th|ht|t)+", x)
x <- gsub("a+", "a+", x)
x <- gsub("u+", "u+", x)
# allow any ending of -um, -us, -ium, -icum, -ius, -icus, -ica, -ia and -a (needs perl for the negative backward lookup):
x <- gsub("(u\\+\\(c\\|k\\|q\\|qu\\+\\|s\\|z\\|x\\|ks\\)\\+)(?![a-z])",
"(u[s|m]|[iy][ck]?u[ms]|[iy]?[ck]?a)", x, perl = TRUE)
x <- gsub("(\\[iy\\]\\+\\(c\\|k\\|q\\|qu\\+\\|s\\|z\\|x\\|ks\\)\\+a\\+)(?![a-z])",
"(u[s|m]|[iy][ck]?u[ms]|[iy]?[ck]?a)", x, perl = TRUE)
x <- gsub("(\\[iy\\]\\+u\\+m)(?![a-z])",
"(u[s|m]|[iy][ck]?u[ms]|[iy]?[ck]?a)", x, perl = TRUE)
x <- gsub("(\\[iy\\]\\+a\\+)(?![a-z])",
"([iy]*a+|[iy]+a*)", x, perl = TRUE)
x <- gsub("e+", "e+", x)
x <- gsub("o+", "o+", x)
x <- gsub("(.)\\1+", "\\1+", x)
# allow multiplication of all other consonants
x <- gsub("([bdgjlnrw]+)", "\\1+", x, perl = TRUE)
# allow ending in -en or -us
x <- gsub("e\\+n(?![a-z[])", "(e+n|u+(c|k|q|qu|s|z|x|ks)+)", x, perl = TRUE)
# if the input is longer than 10 characters, allow any forgotten consonant between all characters, as some might just have forgotten one...
# this will allow "Pasteurella damatis" to be correctly read as "Pasteurella dagmatis".
consonants <- paste(letters[!letters %in% c("a", "e", "i", "o", "u")], collapse = "")
x[nchar(x_backup_without_spp) > 10] <- gsub("[+]", paste0("+[", consonants, "]?"), x[nchar(x_backup_without_spp) > 10])
# allow au and ou after all above regex implementations
x <- gsub("a+[bcdfghjklmnpqrstvwxyz]?u+[bcdfghjklmnpqrstvwxyz]?", "(a+u+|o+u+)[bcdfghjklmnpqrstvwxyz]?", x, fixed = TRUE)
x <- gsub("o+[bcdfghjklmnpqrstvwxyz]?u+[bcdfghjklmnpqrstvwxyz]?", "(a+u+|o+u+)[bcdfghjklmnpqrstvwxyz]?", x, fixed = TRUE)
}
x <- strip_whitespace(x, dyslexia_mode)
# make sure to remove regex overkill (will lead to errors)
x <- gsub("++", "+", x, fixed = TRUE)
x <- gsub("?+", "?", x, fixed = TRUE)
x_trimmed <- x
x_trimmed_species <- paste(x_trimmed, "species")
x_trimmed_without_group <- gsub(" gro.u.p$", "", x_trimmed, perl = TRUE)
# remove last part from "-" or "/"
x_trimmed_without_group <- gsub("(.*)[-/].*", "\\1", x_trimmed_without_group)
# replace space and dot by regex sign
x_withspaces <- gsub("[ .]+", ".* ", x, perl = TRUE)
x <- gsub("[ .]+", ".*", x, perl = TRUE)
# add start en stop regex
x <- paste0("^", x, "$")
x_withspaces_start_only <- paste0("^", x_withspaces)
x_withspaces_end_only <- paste0(x_withspaces, "$")
x_withspaces_start_end <- paste0("^", x_withspaces, "$")
if (isTRUE(debug)) {
cat(paste0(font_blue("x"), ' "', x, '"\n'))
cat(paste0(font_blue("x_species"), ' "', x_species, '"\n'))
cat(paste0(font_blue("x_withspaces_start_only"), ' "', x_withspaces_start_only, '"\n'))
cat(paste0(font_blue("x_withspaces_end_only"), ' "', x_withspaces_end_only, '"\n'))
cat(paste0(font_blue("x_withspaces_start_end"), ' "', x_withspaces_start_end, '"\n'))
cat(paste0(font_blue("x_backup"), ' "', x_backup, '"\n'))
cat(paste0(font_blue("x_backup_without_spp"), ' "', x_backup_without_spp, '"\n'))
cat(paste0(font_blue("x_trimmed"), ' "', x_trimmed, '"\n'))
cat(paste0(font_blue("x_trimmed_species"), ' "', x_trimmed_species, '"\n'))
cat(paste0(font_blue("x_trimmed_without_group"), ' "', x_trimmed_without_group, '"\n'))
}
if (initial_search == TRUE) {
progress <- progress_ticker(n = length(x[!already_known]), n_min = 25, print = info) # start if n >= 25
on.exit(close(progress))
}
for (i in which(!already_known)) {
if (initial_search == TRUE) {
progress$tick()
}
# valid MO code ----
found <- lookup(mo == toupper(x_backup[i]))
if (!is.na(found)) {
x[i] <- found[1L]
next
}
# valid fullname ----
found <- lookup(fullname_lower %in% gsub("[^a-zA-Z0-9_. -]", "", tolower(c(x_backup[i], x_backup_without_spp[i])), perl = TRUE))
# added the gsub() for "(unknown fungus)", since fullname_lower does not contain brackets
if (!is.na(found)) {
x[i] <- found[1L]
next
}
# old fullname ----
found <- lookup(fullname_lower %in% tolower(c(x_backup[i], x_backup_without_spp[i])),
column = NULL, # all columns
haystack = MO.old_lookup)
if (!all(is.na(found))) {
# when property is "ref" (which is the case in mo_ref, mo_authors and mo_year), return the old value, so:
# mo_ref() of "Chlamydia psittaci" will be "Page, 1968" (with warning)
# mo_ref() of "Chlamydophila psittaci" will be "Everett et al., 1999"
if (property == "ref") {
x[i] <- found["ref"]
} else {
x[i] <- lookup(fullname == found["fullname_new"], haystack = MO_lookup)
}
pkg_env$mo_renamed_last_run <- found["fullname"]
was_renamed(name_old = found["fullname"],
name_new = lookup(fullname == found["fullname_new"], "fullname", haystack = MO_lookup),
ref_old = found["ref"],
ref_new = lookup(fullname == found["fullname_new"], "ref", haystack = MO_lookup),
mo = lookup(fullname == found["fullname_new"], "mo", haystack = MO_lookup))
next
}
if (x_backup[i] %like_case% "\\(unknown [a-z]+\\)" | tolower(x_backup_without_spp[i]) %in% c("other", "none", "unknown")) {
# empty and nonsense values, ignore without warning
x[i] <- lookup(mo == "UNKNOWN")
next
}
# exact SNOMED code ----
if (x_backup[i] %like_case% "^[0-9]+$") {
snomed_found <- unlist(lapply(reference_data_to_use$snomed,
function(s) if (x_backup[i] %in% s) {
TRUE
} else {
FALSE
}))
if (sum(snomed_found, na.rm = TRUE) > 0) {
found <- reference_data_to_use[snomed_found == TRUE, property][[1]]
if (!is.na(found)) {
x[i] <- found[1L]
next
}
}
}
# very probable: is G. species ----
found <- lookup(g_species %in% gsub("[^a-z0-9/ \\-]+", "",
tolower(c(x_backup[i], x_backup_without_spp[i])), perl = TRUE))
if (!is.na(found)) {
x[i] <- found[1L]
next
}
# WHONET and other common LIS codes ----
found <- microorganisms.codes[which(microorganisms.codes$code %in% toupper(c(x_backup_untouched[i], x_backup[i], x_backup_without_spp[i]))), "mo", drop = TRUE][1L]
if (!is.na(found)) {
x[i] <- lookup(mo == found)
next
}
# user-defined reference ----
if (!is.null(reference_df)) {
if (x_backup[i] %in% reference_df[, 1]) {
# already checked integrity of reference_df, all MOs are valid
ref_mo <- reference_df[reference_df[, 1] == x_backup[i], "mo"][[1L]]
x[i] <- lookup(mo == ref_mo)
next
}
}
# WHONET: xxx = no growth
if (tolower(as.character(paste0(x_backup_without_spp[i], ""))) %in% c("", "xxx", "na", "nan")) {
x[i] <- NA_character_
next
}
# check for very small input, but ignore the O antigens of E. coli
if (nchar(gsub("[^a-zA-Z]", "", x_trimmed[i])) < 3
& toupper(x_backup_without_spp[i]) %unlike_case% "O?(26|103|104|104|111|121|145|157)") {
# fewer than 3 chars and not looked for species, add as failure
x[i] <- lookup(mo == "UNKNOWN")
if (initial_search == TRUE) {
failures <- c(failures, x_backup[i])
}
next
}
if (x_backup_without_spp[i] %like_case% "(virus|viridae)") {
# there is no fullname like virus or viridae, so don't try to coerce it
x[i] <- NA_character_
next
}
# translate known trivial abbreviations to genus + species ----
if (toupper(x_backup_without_spp[i]) %in% c("MRSA", "MSSA", "VISA", "VRSA", "BORSA", "GISA")
| x_backup_without_spp[i] %like_case% "(^| )(mrsa|mssa|visa|vrsa|borsa|gisa|la-?mrsa|ca-?mrsa)( |$)") {
x[i] <- lookup(fullname == "Staphylococcus aureus", uncertainty = -1)
next
}
if (toupper(x_backup_without_spp[i]) %in% c("MRSE", "MSSE")
| x_backup_without_spp[i] %like_case% "(^| )(mrse|msse)( |$)") {
x[i] <- lookup(fullname == "Staphylococcus epidermidis", uncertainty = -1)
next
}
if (toupper(x_backup_without_spp[i]) == "VRE"
| x_backup_without_spp[i] %like_case% "(^| )vre "
| x_backup_without_spp[i] %like_case% "(enterococci|enterokok|enterococo)[a-z]*?$") {
x[i] <- lookup(genus == "Enterococcus", uncertainty = -1)
next
}
# support for:
# - AIEC (Adherent-Invasive E. coli)
# - ATEC (Atypical Entero-pathogenic E. coli)
# - DAEC (Diffusely Adhering E. coli)
# - EAEC (Entero-Aggresive E. coli)
# - EHEC (Entero-Haemorrhagic E. coli)
# - EIEC (Entero-Invasive E. coli)
# - EPEC (Entero-Pathogenic E. coli)
# - ETEC (Entero-Toxigenic E. coli)
# - NMEC (Neonatal Meningitis‐causing E. coli)
# - STEC (Shiga-toxin producing E. coli)
# - UPEC (Uropathogenic E. coli)
if (toupper(x_backup_without_spp[i]) %in% c("AIEC", "ATEC", "DAEC", "EAEC", "EHEC", "EIEC", "EPEC", "ETEC", "NMEC", "STEC", "UPEC")
# also support O-antigens of E. coli: O26, O103, O104, O111, O121, O145, O157
| x_backup_without_spp[i] %like_case% "o?(26|103|104|111|121|145|157)") {
x[i] <- lookup(fullname == "Escherichia coli", uncertainty = -1)
next
}
if (toupper(x_backup_without_spp[i]) == "MRPA"
| x_backup_without_spp[i] %like_case% "(^| )mrpa( |$)") {
# multi resistant P. aeruginosa
x[i] <- lookup(fullname == "Pseudomonas aeruginosa", uncertainty = -1)
next
}
if (toupper(x_backup_without_spp[i]) == "CRSM") {
# co-trim resistant S. maltophilia
x[i] <- lookup(fullname == "Stenotrophomonas maltophilia", uncertainty = -1)
next
}
if (toupper(x_backup_without_spp[i]) %in% c("PISP", "PRSP", "VISP", "VRSP")
| x_backup_without_spp[i] %like_case% "(^| )(pisp|prsp|visp|vrsp)( |$)") {
# peni I, peni R, vanco I, vanco R: S. pneumoniae
x[i] <- lookup(fullname == "Streptococcus pneumoniae", uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "^g[abcdfghk]s$") {
# Streptococci, like GBS = Group B Streptococci (B_STRPT_GRPB)
x[i] <- lookup(mo == toupper(gsub("g([abcdfghk])s",
"B_STRPT_GRP\\1",
x_backup_without_spp[i],
perl = TRUE)), uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "(streptococ|streptokok).* [abcdfghk]$") {
# Streptococci in different languages, like "estreptococos grupo B"
x[i] <- lookup(mo == toupper(gsub(".*(streptococ|streptokok|estreptococ).* ([abcdfghk])$",
"B_STRPT_GRP\\2",
x_backup_without_spp[i],
perl = TRUE)), uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "group [abcdfghk] (streptococ|streptokok|estreptococ)") {
# Streptococci in different languages, like "Group A Streptococci"
x[i] <- lookup(mo == toupper(gsub(".*group ([abcdfghk]) (streptococ|streptokok|estreptococ).*",
"B_STRPT_GRP\\1",
x_backup_without_spp[i],
perl = TRUE)), uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "haemoly.*strep") {
# Haemolytic streptococci in different languages
x[i] <- lookup(mo == "B_STRPT_HAEM", uncertainty = -1)
next
}
# CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese)
if (x_backup_without_spp[i] %like_case% "[ck]oagulas[ea] negatie?[vf]"
| x_trimmed[i] %like_case% "[ck]oagulas[ea] negatie?[vf]"
| x_backup_without_spp[i] %like_case% "[ck]o?ns[^a-z]?$") {
# coerce S. coagulase negative
x[i] <- lookup(mo == "B_STPHY_CONS", uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "[ck]oagulas[ea] positie?[vf]"
| x_trimmed[i] %like_case% "[ck]oagulas[ea] positie?[vf]"
| x_backup_without_spp[i] %like_case% "[ck]o?ps[^a-z]?$") {
# coerce S. coagulase positive
x[i] <- lookup(mo == "B_STPHY_COPS", uncertainty = -1)
next
}
# streptococcal groups: milleri and viridans
if (x_trimmed[i] %like_case% "strepto.* mil+er+i"
| x_backup_without_spp[i] %like_case% "strepto.* mil+er+i"
| x_backup_without_spp[i] %like_case% "mgs[^a-z]?$") {
# Milleri Group Streptococcus (MGS)
x[i] <- lookup(mo == "B_STRPT_MILL", uncertainty = -1)
next
}
if (x_trimmed[i] %like_case% "strepto.* viridans"
| x_backup_without_spp[i] %like_case% "strepto.* viridans"
| x_backup_without_spp[i] %like_case% "vgs[^a-z]?$") {
# Viridans Group Streptococcus (VGS)
x[i] <- lookup(mo == "B_STRPT_VIRI", uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "gram[ -]?neg.*"
| x_backup_without_spp[i] %like_case% "negatie?[vf]"
| x_trimmed[i] %like_case% "gram[ -]?neg.*") {
# coerce Gram negatives
x[i] <- lookup(mo == "B_GRAMN", uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "gram[ -]?pos.*"
| x_backup_without_spp[i] %like_case% "positie?[vf]"
| x_trimmed[i] %like_case% "gram[ -]?pos.*") {
# coerce Gram positives
x[i] <- lookup(mo == "B_GRAMP", uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "mycoba[ck]teri.[nm]?$") {
# coerce mycobacteria in multiple languages
x[i] <- lookup(genus == "Mycobacterium", uncertainty = -1)
next
}
if (x_backup_without_spp[i] %like_case% "salmonella [a-z]+ ?.*") {
if (x_backup_without_spp[i] %like_case% "salmonella group") {
# Salmonella Group A to Z, just return S. species for now
x[i] <- lookup(genus == "Salmonella", uncertainty = -1)
next
} else if (x_backup[i] %like_case% "[sS]almonella [A-Z][a-z]+ ?.*" &
x_backup[i] %unlike% "t[iy](ph|f)[iy]") {
# Salmonella with capital letter species like "Salmonella Goettingen" - they're all S. enterica
# except for S. typhi, S. paratyphi, S. typhimurium
x[i] <- lookup(fullname == "Salmonella enterica", uncertainty = -1)
uncertainties <- rbind(uncertainties,
format_uncertainty_as_df(uncertainty_level = 1,
input = x_backup[i],
result_mo = lookup(fullname == "Salmonella enterica", "mo", uncertainty = -1)),
stringsAsFactors = FALSE)
next
}
}
# trivial names known to the field:
if ("meningococcus" %like_case% x_trimmed[i]) {
# coerce Neisseria meningitidis
x[i] <- lookup(fullname == "Neisseria meningitidis", uncertainty = -1)
next
}
if ("gonococcus" %like_case% x_trimmed[i]) {
# coerce Neisseria gonorrhoeae
x[i] <- lookup(fullname == "Neisseria gonorrhoeae", uncertainty = -1)
next
}
if ("pneumococcus" %like_case% x_trimmed[i]) {
# coerce Streptococcus penumoniae
x[i] <- lookup(fullname == "Streptococcus pneumoniae", uncertainty = -1)
next
}
if (x_backup[i] %in% pkg_env$mo_failed) {
# previously failed already in this session ----
# (at this point the latest reference_df has also been checked)
x[i] <- lookup(mo == "UNKNOWN")
if (initial_search == TRUE) {
failures <- c(failures, x_backup[i])
}
next
}
# NOW RUN THROUGH DIFFERENT PREVALENCE LEVELS
check_per_prevalence <- function(data_to_check,
data.old_to_check,
a.x_backup,
b.x_trimmed,
c.x_trimmed_without_group,
d.x_withspaces_start_end,
e.x_withspaces_start_only,
f.x_withspaces_end_only,
g.x_backup_without_spp,
h.x_species,
i.x_trimmed_species) {
# FIRST TRY FULLNAMES AND CODES ----
# if only genus is available, return only genus
if (all(c(x[i], b.x_trimmed) %unlike_case% " ")) {
found <- lookup(fullname_lower %in% c(h.x_species, i.x_trimmed_species),
haystack = data_to_check)
if (!is.na(found)) {
x[i] <- found[1L]
return(x[i])
}
if (nchar(g.x_backup_without_spp) >= 6) {
found <- lookup(fullname_lower %like_case% paste0("^", unregex(g.x_backup_without_spp), "[a-z]+"),
haystack = data_to_check)
if (!is.na(found)) {
x[i] <- found[1L]
return(x[i])
}
}
# rest of genus only is in allow_uncertain part.
}
# allow no codes less than 4 characters long, was already checked for WHONET earlier
if (nchar(g.x_backup_without_spp) < 4) {
x[i] <- lookup(mo == "UNKNOWN")
if (initial_search == TRUE) {
failures <- c(failures, a.x_backup)
}
return(x[i])
}
# try probable: trimmed version of fullname ----
found <- lookup(fullname_lower %in% tolower(g.x_backup_without_spp),
haystack = data_to_check)
if (!is.na(found)) {
return(found[1L])
}
# try any match keeping spaces ----
if (nchar(g.x_backup_without_spp) >= 6) {
found <- lookup(fullname_lower %like_case% d.x_withspaces_start_end,
haystack = data_to_check)
if (!is.na(found)) {
return(found[1L])
}
}
# try any match keeping spaces, not ending with $ ----
found <- lookup(fullname_lower %like_case% paste0(trimws(e.x_withspaces_start_only), " "),
haystack = data_to_check)
if (!is.na(found)) {
return(found[1L])
}
if (nchar(g.x_backup_without_spp) >= 6) {
found <- lookup(fullname_lower %like_case% e.x_withspaces_start_only,
haystack = data_to_check)
if (!is.na(found)) {
return(found[1L])
}
}
# try any match keeping spaces, not start with ^ ----
found <- lookup(fullname_lower %like_case% paste0(" ", trimws(f.x_withspaces_end_only)),
haystack = data_to_check)
if (!is.na(found)) {
return(found[1L])
}
# try a trimmed version
if (nchar(g.x_backup_without_spp) >= 6) {
found <- lookup(fullname_lower %like_case% b.x_trimmed |
fullname_lower %like_case% c.x_trimmed_without_group,
haystack = data_to_check)
if (!is.na(found)) {
return(found[1L])
}
}
# try splitting of characters in the middle and then find ID ----
# only when text length is 6 or lower
# like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus, staaur = S. aureus
if (nchar(g.x_backup_without_spp) <= 6) {
x_length <- nchar(g.x_backup_without_spp)
x_split <- paste0("^",
g.x_backup_without_spp %pm>% substr(1, x_length / 2),
".* ",
g.x_backup_without_spp %pm>% substr((x_length / 2) + 1, x_length))
found <- lookup(fullname_lower %like_case% x_split,
haystack = data_to_check)
if (!is.na(found)) {
return(found[1L])
}
}
# try fullname without start and without nchar limit of >= 6 ----
# like "K. pneu rhino" >> "Klebsiella pneumoniae (rhinoscleromatis)" = KLEPNERH
found <- lookup(fullname_lower %like_case% e.x_withspaces_start_only,
haystack = data_to_check)
if (!is.na(found)) {
return(found[1L])
}
# MISCELLANEOUS ----
# look for old taxonomic names ----
found <- lookup(fullname_lower %like_case% e.x_withspaces_start_only,
column = NULL, # all columns
haystack = data.old_to_check)
if (!all(is.na(found))) {
# when property is "ref" (which is the case in mo_ref, mo_authors and mo_year), return the old value, so:
# mo_ref() of "Chlamydia psittaci" will be "Page, 1968" (with warning)
# mo_ref() of "Chlamydophila psittaci" will be "Everett et al., 1999"
if (property == "ref") {
x[i] <- found["ref"]
} else {
x[i] <- lookup(fullname == found["fullname_new"], haystack = MO_lookup)
}
pkg_env$mo_renamed_last_run <- found["fullname"]
was_renamed(name_old = found["fullname"],
name_new = lookup(fullname == found["fullname_new"], "fullname", haystack = MO_lookup),
ref_old = found["ref"],
ref_new = lookup(fullname == found["fullname_new"], "ref", haystack = MO_lookup),
mo = lookup(fullname == found["fullname_new"], "mo", haystack = MO_lookup))
return(x[i])
}
# check for uncertain results ----
uncertain_fn <- function(a.x_backup,
b.x_trimmed,
d.x_withspaces_start_end,
e.x_withspaces_start_only,
f.x_withspaces_end_only,
g.x_backup_without_spp,
uncertain.reference_data_to_use) {
if (uncertainty_level == 0) {
# do not allow uncertainties
return(NA_character_)
}
# UNCERTAINTY LEVEL 1 ----
if (uncertainty_level >= 1) {
now_checks_for_uncertainty_level <- 1
# (1) look again for old taxonomic names, now for G. species ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (1) look again for old taxonomic names, now for G. species\n"))
}
if (isTRUE(debug)) {
message("Running '", d.x_withspaces_start_end, "' and '", e.x_withspaces_start_only, "'")
}
found <- lookup(fullname_lower %like_case% d.x_withspaces_start_end |
fullname_lower %like_case% e.x_withspaces_start_only,
column = NULL, # all columns
haystack = data.old_to_check)
if (!all(is.na(found)) & nchar(g.x_backup_without_spp) >= 6) {
if (property == "ref") {
# when property is "ref" (which is the case in mo_ref, mo_authors and mo_year), return the old value, so:
# mo_ref("Chlamydia psittaci") = "Page, 1968" (with warning)
# mo_ref("Chlamydophila psittaci") = "Everett et al., 1999"
x <- found["ref"]
} else {
x <- lookup(fullname == found["fullname_new"], haystack = MO_lookup)
}
was_renamed(name_old = found["fullname"],
name_new = lookup(fullname == found["fullname_new"], "fullname", haystack = MO_lookup),
ref_old = found["ref"],
ref_new = lookup(fullname == found["fullname_new"], "ref", haystack = MO_lookup),
mo = lookup(fullname == found["fullname_new"], "mo", haystack = MO_lookup))
pkg_env$mo_renamed_last_run <- found["fullname"]
uncertainties <<- rbind(uncertainties,
format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
input = a.x_backup,
result_mo = lookup(fullname == found["fullname_new"], "mo", haystack = MO_lookup)),
stringsAsFactors = FALSE)
return(x)
}
# (2) Try with misspelled input ----
# just rerun with dyslexia_mode = TRUE will used the extensive regex part above
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (2) Try with misspelled input\n"))
}
if (isTRUE(debug)) {
message("Running '", a.x_backup, "'")
}
# first try without dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(a.x_backup, initial_search = FALSE, dyslexia_mode = FALSE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 1, actual_input = a.x_backup)))
if (empty_result(found)) {
# then with dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(a.x_backup, initial_search = FALSE, dyslexia_mode = TRUE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 1, actual_input = a.x_backup)))
}
if (!empty_result(found)) {
found_result <- found
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
}
# UNCERTAINTY LEVEL 2 ----
if (uncertainty_level >= 2) {
now_checks_for_uncertainty_level <- 2
# (3) look for genus only, part of name ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (3) look for genus only, part of name\n"))
}
if (nchar(g.x_backup_without_spp) > 4 & b.x_trimmed %unlike_case% " ") {
if (b.x_trimmed %unlike_case% "^[A-Z][a-z]+") {
if (isTRUE(debug)) {
message("Running '", paste(b.x_trimmed, "species"), "'")
}
# not when input is like Genustext, because then Neospora would lead to Actinokineospora
found <- lookup(fullname_lower %like_case% paste(b.x_trimmed, "species"),
haystack = uncertain.reference_data_to_use)
if (!is.na(found)) {
found_result <- found
found <- lookup(mo == found)
uncertainties <<- rbind(uncertainties,
format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
input = a.x_backup,
result_mo = found_result),
stringsAsFactors = FALSE)
return(found)
}
}
}
# (4) strip values between brackets ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (4) strip values between brackets\n"))
}
a.x_backup_stripped <- gsub("( *[(].*[)] *)", " ", a.x_backup, perl = TRUE)
a.x_backup_stripped <- trimws(gsub(" +", " ", a.x_backup_stripped, perl = TRUE))
if (isTRUE(debug)) {
message("Running '", a.x_backup_stripped, "'")
}
# first try without dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(a.x_backup_stripped, initial_search = FALSE, dyslexia_mode = FALSE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
if (empty_result(found)) {
# then with dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(a.x_backup_stripped, initial_search = FALSE, dyslexia_mode = TRUE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
}
if (!empty_result(found) & nchar(g.x_backup_without_spp) >= 6) {
found_result <- found
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
# (5) inverse input ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (5) inverse input\n"))
}
a.x_backup_inversed <- paste(rev(unlist(strsplit(a.x_backup, split = " "))), collapse = " ")
if (isTRUE(debug)) {
message("Running '", a.x_backup_inversed, "'")
}
# first try without dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(a.x_backup_inversed, initial_search = FALSE, dyslexia_mode = FALSE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
if (empty_result(found)) {
# then with dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(a.x_backup_inversed, initial_search = FALSE, dyslexia_mode = TRUE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
}
if (!empty_result(found) & nchar(g.x_backup_without_spp) >= 6) {
found_result <- found
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
# (6) try to strip off half an element from end and check the remains ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (6) try to strip off half an element from end and check the remains\n"))
}
x_strip <- a.x_backup %pm>% strsplit("[ .]") %pm>% unlist()
if (length(x_strip) > 1) {
for (i in seq_len(length(x_strip) - 1)) {
lastword <- x_strip[length(x_strip) - i + 1]
lastword_half <- substr(lastword, 1, as.integer(nchar(lastword) / 2))
# remove last half of the second term
x_strip_collapsed <- paste(c(x_strip[seq_len(length(x_strip) - i)], lastword_half), collapse = " ")
if (nchar(x_strip_collapsed) >= 4 & nchar(lastword_half) > 2) {
if (isTRUE(debug)) {
message("Running '", x_strip_collapsed, "'")
}
# first try without dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = FALSE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
if (empty_result(found)) {
# then with dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = TRUE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
}
if (!empty_result(found)) {
found_result <- found
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
}
}
}
# (7) try to strip off one element from end and check the remains ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (7) try to strip off one element from end and check the remains\n"))
}
if (length(x_strip) > 1) {
for (i in seq_len(length(x_strip) - 1)) {
x_strip_collapsed <- paste(x_strip[seq_len(length(x_strip) - i)], collapse = " ")
if (nchar(x_strip_collapsed) >= 6) {
if (isTRUE(debug)) {
message("Running '", x_strip_collapsed, "'")
}
# first try without dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = FALSE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
if (empty_result(found)) {
# then with dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = TRUE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
}
if (!empty_result(found)) {
found_result <- found
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
}
}
}
# (8) check for unknown yeasts/fungi ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (8) check for unknown yeasts/fungi\n"))
}
if (b.x_trimmed %like_case% "yeast") {
found <- "F_YEAST"
found_result <- found
found <- lookup(mo == found)
uncertainties <<- rbind(uncertainties,
format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
input = a.x_backup,
result_mo = found_result),
stringsAsFactors = FALSE)
return(found)
}
if (b.x_trimmed %like_case% "(fungus|fungi)" & b.x_trimmed %unlike_case% "fungiphrya") {
found <- "F_FUNGUS"
found_result <- found
found <- lookup(mo == found)
uncertainties <<- rbind(uncertainties,
format_uncertainty_as_df(uncertainty_level = now_checks_for_uncertainty_level,
input = a.x_backup,
result_mo = found_result),
stringsAsFactors = FALSE)
return(found)
}
# (9) try to strip off one element from start and check the remains (only allow >= 2-part name outcome) ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (9) try to strip off one element from start and check the remains (only allow >= 2-part name outcome)\n"))
}
x_strip <- a.x_backup %pm>% strsplit("[ .]") %pm>% unlist()
if (length(x_strip) > 1 & nchar(g.x_backup_without_spp) >= 6) {
for (i in 2:(length(x_strip))) {
x_strip_collapsed <- paste(x_strip[i:length(x_strip)], collapse = " ")
if (isTRUE(debug)) {
message("Running '", x_strip_collapsed, "'")
}
# first try without dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = FALSE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
if (empty_result(found)) {
# then with dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = TRUE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 2, actual_input = a.x_backup)))
}
if (!empty_result(found)) {
found_result <- found
# uncertainty level 2 only if searched part contains a space (otherwise it will be found with lvl 3)
if (x_strip_collapsed %like_case% " ") {
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
}
}
}
}
# UNCERTAINTY LEVEL 3 ----
if (uncertainty_level >= 3) {
now_checks_for_uncertainty_level <- 3
# (10) try to strip off one element from start and check the remains (any text size) ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (10) try to strip off one element from start and check the remains (any text size)\n"))
}
x_strip <- a.x_backup %pm>% strsplit("[ .]") %pm>% unlist()
if (length(x_strip) > 1 & nchar(g.x_backup_without_spp) >= 6) {
for (i in 2:(length(x_strip))) {
x_strip_collapsed <- paste(x_strip[i:length(x_strip)], collapse = " ")
if (isTRUE(debug)) {
message("Running '", x_strip_collapsed, "'")
}
# first try without dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = FALSE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 3, actual_input = a.x_backup)))
if (empty_result(found)) {
# then with dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = TRUE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 3, actual_input = a.x_backup)))
}
if (!empty_result(found)) {
found_result <- found
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
}
}
# (11) try to strip off one element from end and check the remains (any text size) ----
# (this is in fact 7 but without nchar limit of >=6)
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (11) try to strip off one element from end and check the remains (any text size)\n"))
}
if (length(x_strip) > 1) {
for (i in seq_len(length(x_strip) - 1)) {
x_strip_collapsed <- paste(x_strip[seq_len(length(x_strip) - i)], collapse = " ")
if (isTRUE(debug)) {
message("Running '", x_strip_collapsed, "'")
}
# first try without dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = FALSE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 3, actual_input = a.x_backup)))
if (empty_result(found)) {
# then with dyslexia mode
found <- suppressMessages(suppressWarnings(exec_as.mo(x_strip_collapsed, initial_search = FALSE, dyslexia_mode = TRUE, allow_uncertain = FALSE, debug = debug, reference_data_to_use = uncertain.reference_data_to_use, actual_uncertainty = 3, actual_input = a.x_backup)))
}
if (!empty_result(found)) {
found_result <- found
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
}
}
# (12) part of a name (very unlikely match) ----
if (isTRUE(debug)) {
cat(font_bold("\n[ UNCERTAINTY LEVEL", now_checks_for_uncertainty_level, "] (12) part of a name (very unlikely match)\n"))
}
if (isTRUE(debug)) {
message("Running '", f.x_withspaces_end_only, "'")
}
if (nchar(g.x_backup_without_spp) >= 6) {
found <- lookup(fullname_lower %like_case% f.x_withspaces_end_only, column = "mo")
if (!is.na(found)) {
found_result <- lookup(mo == found)
uncertainties <<- rbind(uncertainties,
attr(found, which = "uncertainties", exact = TRUE),
stringsAsFactors = FALSE)
found <- lookup(mo == found)
return(found)
}
}
}
# didn't found in uncertain results too
return(NA_character_)
}
# uncertain results
x[i] <- uncertain_fn(a.x_backup = a.x_backup,
b.x_trimmed = b.x_trimmed,
d.x_withspaces_start_end = d.x_withspaces_start_end,
e.x_withspaces_start_only = e.x_withspaces_start_only,
f.x_withspaces_end_only = f.x_withspaces_end_only,
g.x_backup_without_spp = g.x_backup_without_spp,
uncertain.reference_data_to_use = MO_lookup)
if (!empty_result(x[i])) {
return(x[i])
}
# didn't found any
return(NA_character_)
}
# CHECK ALL IN ONE GO ----
x[i] <- check_per_prevalence(data_to_check = MO_lookup,
data.old_to_check = MO.old_lookup,
a.x_backup = x_backup[i],
b.x_trimmed = x_trimmed[i],
c.x_trimmed_without_group = x_trimmed_without_group[i],
d.x_withspaces_start_end = x_withspaces_start_end[i],
e.x_withspaces_start_only = x_withspaces_start_only[i],
f.x_withspaces_end_only = x_withspaces_end_only[i],
g.x_backup_without_spp = x_backup_without_spp[i],
h.x_species = x_species[i],
i.x_trimmed_species = x_trimmed_species[i])
if (!empty_result(x[i])) {
next
}
# no results found: make them UNKNOWN ----
x[i] <- lookup(mo == "UNKNOWN", uncertainty = -1)
if (initial_search == TRUE) {
failures <- c(failures, x_backup[i])
}
}
if (initial_search == TRUE) {
close(progress)
}
if (isTRUE(debug) && initial_search == TRUE) {
cat("Ended search", time_track(), "\n")
}
# handling failures ----
failures <- failures[!failures %in% c(NA, NULL, NaN)]
if (length(failures) > 0 & initial_search == TRUE) {
pkg_env$mo_failures <- sort(unique(failures))
pkg_env$mo_failed <- c(pkg_env$mo_failed, pkg_env$mo_failures)
plural <- c("value", "it", "was")
if (pm_n_distinct(failures) > 1) {
plural <- c("values", "them", "were")
}
x_input_clean <- trimws2(x_input)
total_failures <- length(x_input_clean[as.character(x_input_clean) %in% as.character(failures) & !x_input %in% c(NA, NULL, NaN)])
total_n <- length(x_input[!x_input %in% c(NA, NULL, NaN)])
msg <- paste0(nr2char(pm_n_distinct(failures)), " unique ", plural[1],
" (covering ", percentage(total_failures / total_n),
") could not be coerced and ", plural[3], " considered 'unknown'")
if (pm_n_distinct(failures) <= 10) {
msg <- paste0(msg, ": ", vector_and(failures, quotes = TRUE))
}
msg <- paste0(msg,
".\nUse `mo_failures()` to review ", plural[2], ". Edit the `allow_uncertain` argument if needed (see ?as.mo).\n",
"You can also use your own reference data with set_mo_source() or directly, e.g.:\n",
' as.mo("mycode", reference_df = data.frame(own = "mycode", mo = "', MO_lookup$mo[match("Escherichia coli", MO_lookup$fullname)], '"))\n',
' mo_name("mycode", reference_df = data.frame(own = "mycode", mo = "', MO_lookup$mo[match("Escherichia coli", MO_lookup$fullname)], '"))\n')
warning_(paste0("\n", msg),
add_fn = font_red,
call = FALSE,
immediate = TRUE) # thus will always be shown, even if >= warnings
}
# handling uncertainties ----
if (NROW(uncertainties) > 0 & initial_search == TRUE) {
uncertainties <- as.list(pm_distinct(uncertainties, input, .keep_all = TRUE))
pkg_env$mo_uncertainties <- uncertainties
plural <- c("", "it", "was")
if (length(uncertainties$input) > 1) {
plural <- c("s", "them", "were")
}
msg <- paste0("Translation is uncertain of ", nr2char(length(uncertainties$input)), " microorganism", plural[1],
". Use `mo_uncertainties()` to review ", plural[2], ".")
message_(msg)
}
x[already_known] <- x_known
}
}
# Becker ----
if (Becker == TRUE | Becker == "all") {
# warn when species found that are not in:
# - Becker et al. 2014, PMID 25278577
# - Becker et al. 2019, PMID 30872103
# - Becker et al. 2020, PMID 32056452
post_Becker <- character(0) # 2020-10-20 currently all are mentioned in above papers (otherwise uncomment the section below)
# nolint start
# if (any(x %in% MO_lookup[which(MO_lookup$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% MO_lookup[which(MO_lookup$species %in% post_Becker), property]]))),
# collapse = ", ")),
# ".",
# call = FALSE,
# immediate = TRUE)
# }
# nolint end
# 'MO_CONS' and 'MO_COPS' are <mo> vectors created in R/zzz.R
CoNS <- MO_lookup[which(MO_lookup$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)
}
}
# Lancefield ----
if (Lancefield == TRUE | Lancefield == "all") {
# group A - S. pyogenes
x[x %in% lookup(genus == "Streptococcus" & species == "pyogenes", n = Inf)] <- lookup(fullname == "Streptococcus group A", uncertainty = -1)
# group B - S. agalactiae
x[x %in% lookup(genus == "Streptococcus" & species == "agalactiae", n = Inf)] <- lookup(fullname == "Streptococcus group B", uncertainty = -1)
# group C
x[x %in% lookup(genus == "Streptococcus" &
species %in% c("equisimilis", "equi", "zooepidemicus", "dysgalactiae"),
n = Inf)] <- lookup(fullname == "Streptococcus group C", uncertainty = -1)
if (Lancefield == "all") {
# all Enterococci
x[x %in% lookup(genus == "Enterococcus", n = Inf)] <- lookup(fullname == "Streptococcus group D", uncertainty = -1)
}
# group F - S. anginosus
x[x %in% lookup(genus == "Streptococcus" & species == "anginosus", n = Inf)] <- lookup(fullname == "Streptococcus group F", uncertainty = -1)
# group H - S. sanguinis
x[x %in% lookup(genus == "Streptococcus" & species == "sanguinis", n = Inf)] <- lookup(fullname == "Streptococcus group H", uncertainty = -1)
# group K - S. salivarius
x[x %in% lookup(genus == "Streptococcus" & species == "salivarius", n = Inf)] <- lookup(fullname == "Streptococcus group K", uncertainty = -1)
}
# Wrap up ----------------------------------------------------------------
# comply to x, which is also unique and without empty values
x_input_unique_nonempty <- unique(x_input[!is.na(x_input)
& !is.null(x_input)
& !identical(x_input, "")
& !identical(x_input, "xxx")])
# left join the found results to the original input values (x_input)
df_found <- data.frame(input = as.character(x_input_unique_nonempty),
found = as.character(x),
stringsAsFactors = FALSE)
df_input <- data.frame(input = as.character(x_input),
stringsAsFactors = FALSE)
# super fast using match() which is a lot faster than merge()
x <- df_found$found[match(df_input$input, df_found$input)]
if (property == "mo") {
x <- set_clean_class(x, new_class = c("mo", "character"))
}
# keep track of time
end_time <- Sys.time()
if (length(mo_renamed()) > 0) {
print(mo_renamed())
}
if (initial_search == FALSE) {
# we got here from uncertain_fn().
if (NROW(uncertainties) == 0) {
# the stripped/transformed version of x_backup is apparently a full hit, like with: as.mo("Escherichia (hello there) coli")
uncertainties <- rbind(uncertainties,
format_uncertainty_as_df(uncertainty_level = actual_uncertainty,
input = actual_input,
result_mo = x,
candidates = ""),
stringsAsFactors = FALSE)
}
# this will save the uncertain items as attribute, so they can be bound to `uncertainties` in the uncertain_fn() function
x <- structure(x, uncertainties = uncertainties)
} else {
# keep track of time - give some hints to improve speed if it takes a long time
delta_time <- difftime(end_time, start_time, units = "secs")
if (delta_time >= 30) {
message_("Using `as.mo()` took ", round(delta_time), " seconds, which is a long time. Some suggestions to improve speed include:")
message_(word_wrap("- Try to use as many valid taxonomic names as possible for your input.",
extra_indent = 2),
as_note = FALSE)
message_(word_wrap("- Save the output and use it as input for future calculations, e.g. create a new variable to your data using `as.mo()`. All functions in this package that rely on microorganism codes will automatically use that new column where possible. All `mo_*()` functions also do not require you to set their `x` argument as long as you have a column of class <mo>.",
extra_indent = 2),
as_note = FALSE)
message_(word_wrap("- Use `set_mo_source()` to continually transform your organisation codes to microorganisms codes used by this package, see `?mo_source`.",
extra_indent = 2),
as_note = FALSE)
}
}
if (isTRUE(debug) && initial_search == TRUE) {
cat("Finished function", time_track(), "\n")
}
x
}
empty_result <- function(x) {
all(x %in% c(NA, "UNKNOWN"))
}
was_renamed <- function(name_old, name_new, ref_old = "", ref_new = "", mo = "") {
newly_set <- data.frame(old_name = name_old,
old_ref = ref_old,
new_name = name_new,
new_ref = ref_new,
mo = mo,
stringsAsFactors = FALSE)
already_set <- pkg_env$mo_renamed
if (!is.null(already_set)) {
pkg_env$mo_renamed = rbind(already_set,
newly_set,
stringsAsFactors = FALSE)
} else {
pkg_env$mo_renamed <- newly_set
}
}
format_uncertainty_as_df <- function(uncertainty_level,
input,
result_mo,
candidates = NULL) {
if (!is.null(pkg_env$mo_renamed_last_run)) {
fullname <- pkg_env$mo_renamed_last_run
pkg_env$mo_renamed_last_run <- NULL
renamed_to <- MO_lookup[match(result_mo, MO_lookup$mo), "fullname", drop = TRUE][1]
} else {
fullname <- MO_lookup[match(result_mo, MO_lookup$mo), "fullname", drop = TRUE][1]
renamed_to <- NA_character_
}
data.frame(uncertainty = uncertainty_level,
input = input,
fullname = fullname,
renamed_to = renamed_to,
mo = result_mo,
# save max 26 entries: the one to be chosen and 25 more
candidates = if (length(candidates) > 1) paste(candidates[c(2:min(26, length(candidates)))], collapse = ", ") else "",
stringsAsFactors = FALSE)
}
# 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")
if (!all(x[!is.na(x)] %in% MO_lookup$mo)) {
# markup old mo codes
out[!x %in% MO_lookup$mo] <- font_italic(font_na(x[!x %in% MO_lookup$mo],
collapse = NULL),
collapse = NULL)
# throw a warning with the affected column name
mo <- tryCatch(search_type_in_df(get_current_data(arg_name = "x", call = 0), type = "mo", info = FALSE),
error = function(e) NULL)
if (!is.null(mo)) {
col <- paste0("Column '", mo, "'")
} else {
col <- "The data"
}
warning_(col, " contains old MO codes (from a previous AMR package version). ",
"Please update your MO codes with `as.mo()`.",
call = FALSE)
}
# make it always fit exactly
max_char <- max(nchar(x))
if (is.na(max_char)) {
max_char <- 7
}
create_pillar_column(out,
align = "left",
width = max_char + ifelse(any(x %in% c(NA, "UNKNOWN")), 2, 0))
}
# will be exported using s3_register() in R/zzz.R
type_sum.mo <- function(x, ...) {
"mo"
}
# will be exported using s3_register() in R/zzz.R
freq.mo <- function(x, ...) {
x_noNA <- as.mo(x[!is.na(x)]) # as.mo() to get the newest mo codes
grams <- mo_gramstain(x_noNA, language = NULL)
digits <- list(...)$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$mo)) {
warning_("Some MO codes are from a previous AMR package version. ",
"Please update these MO codes with `as.mo()`.",
call = FALSE)
}
print.default(x, quote = FALSE)
}
#' @method summary mo
#' @export
#' @noRd
summary.mo <- function(object, ...) {
# unique and top 1-3
x <- as.mo(object) # force again, could be mo from older pkg version
top <- as.data.frame(table(x), responseName = "n", stringsAsFactors = FALSE)
top_3 <- top[order(-top$n), 1][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$mo)) {
warning_("The data contains old MO codes (from a previous AMR package version). ",
"Please update your MO codes with `as.mo()`.",
call = FALSE)
}
nm <- deparse1(substitute(x))
if (!"nm" %in% names(list(...))) {
as.data.frame.vector(x, ..., nm = nm)
} else {
as.data.frame.vector(x, ...)
}
}
#' @method [ mo
#' @export
#' @noRd
"[.mo" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method [[ mo
#' @export
#' @noRd
"[[.mo" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method [<- mo
#' @export
#' @noRd
"[<-.mo" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
# must only contain valid MOs
return_after_integrity_check(y, "microorganism code", c(as.character(microorganisms$mo),
as.character(microorganisms.translation$mo_old)))
}
#' @method [[<- mo
#' @export
#' @noRd
"[[<-.mo" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
# must only contain valid MOs
return_after_integrity_check(y, "microorganism code", c(as.character(microorganisms$mo),
as.character(microorganisms.translation$mo_old)))
}
#' @method c mo
#' @export
#' @noRd
c.mo <- function(...) {
x <- list(...)[[1L]]
y <- NextMethod()
attributes(y) <- attributes(x)
return_after_integrity_check(y, "microorganism code", c(as.character(microorganisms$mo),
as.character(microorganisms.translation$mo_old)))
}
#' @method unique mo
#' @export
#' @noRd
unique.mo <- function(x, incomparables = FALSE, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method rep mo
#' @export
#' @noRd
rep.mo <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @rdname as.mo
#' @export
mo_failures <- function() {
pkg_env$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)
}
message_("Matching scores are based on human pathogenic prevalence and the resemblance between the input and the full taxonomic name. See `?mo_matching_score`.", as_note = FALSE)
msg <- ""
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, ]$input, n = candidates)
# sort on descending scores
candidates <- candidates[order(1 - scores)]
scores_formatted <- trimws(formatC(round(scores, 3), format = "f", digits = 3))
n_candidates <- length(candidates)
candidates <- vector_and(paste0(candidates, " (", scores_formatted[order(1 - scores)], ")"),
quotes = FALSE,
sort = FALSE)
# align with input after arrow
candidates <- paste0("\n",
strwrap(paste0("Also matched",
ifelse(n_candidates >= 25, " (max 25)", ""), ": ",
candidates), # this is already max 25 due to format_uncertainty_as_df()
indent = nchar(x[i, ]$input) + 6,
exdent = nchar(x[i, ]$input) + 6,
width = 0.98 * getOption("width")),
collapse = "")
# after strwrap, make taxonomic names italic
candidates <- gsub("([A-Za-z]+)", font_italic("\\1"), candidates, perl = TRUE)
candidates <- gsub(font_italic("and"), "and", candidates, fixed = TRUE)
candidates <- gsub(paste(font_italic(c("Also", "matched"), collapse = NULL), collapse = " "),
"Also matched",
candidates, fixed = TRUE)
candidates <- gsub(font_italic("max"), "max", candidates, fixed = TRUE)
} else {
candidates <- ""
}
score <- trimws(formatC(round(mo_matching_score(x = x[i, ]$input,
n = x[i, ]$fullname),
3),
format = "f", digits = 3))
msg <- paste(msg,
paste0(
strwrap(
paste0('"', x[i, ]$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,
", matching score = ", score,
") ")),
width = 0.98 * getOption("width"),
exdent = nchar(x[i, ]$input) + 6),
collapse = "\n"),
candidates,
sep = "\n")
msg <- paste0(gsub("\n\n", "\n", msg), "\n\n")
}
cat(msg)
}
#' @rdname as.mo
#' @export
mo_renamed <- function() {
items <- pkg_env$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]]
}
}
x[is.null(x)] <- NA
parsed <- iconv(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)
}, error = function(e) stop(e$message, call. = FALSE)) # this will also be thrown when running `as.mo(no_existing_object)`
parsed
}
replace_old_mo_codes <- function(x, property) {
if (any(toupper(x) %in% microorganisms.translation$mo_old, na.rm = TRUE)) {
# get the ones that match
matched <- match(toupper(x), microorganisms.translation$mo_old)
# and their new codes
mo_new <- microorganisms.translation$mo_new[matched]
# assign on places where a match was found
x[which(!is.na(matched))] <- mo_new[which(!is.na(matched))]
n_matched <- length(matched[!is.na(matched)])
if (property != "mo") {
message_(font_blue(paste0("The input contained ", n_matched,
" old MO code", ifelse(n_matched == 1, "", "s"),
" (from a previous AMR package version). Please update your MO codes with `as.mo()`.")))
} else {
message_(font_blue(paste0(n_matched, " old MO code", ifelse(n_matched == 1, "", "s"),
" (from a previous AMR package version) ",
ifelse(n_matched == 1, "was", "were"),
" updated to ", ifelse(n_matched == 1, "a ", ""),
"currently used MO code", ifelse(n_matched == 1, "", "s"), ".")))
}
}
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) {
# 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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