# ==================================================================== # # TITLE: # # AMR: An R Package for Working with Antimicrobial Resistance Data # # # # SOURCE CODE: # # https://github.com/msberends/AMR # # # # PLEASE CITE THIS SOFTWARE AS: # # Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C # # (2022). AMR: An R Package for Working with Antimicrobial Resistance # # Data. Journal of Statistical Software, 104(3), 1-31. # # https://doi.org/10.18637/jss.v104.i03 # # # # Developed at the University of Groningen and the University Medical # # Center Groningen in The Netherlands, in collaboration with many # # colleagues from around the world, see our website. # # # # 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 Arbitrary Input to Valid Microbial Taxonomy #' #' Use this function to get a valid microorganism code ([`mo`]) based on arbitrary user input. Determination is done using intelligent rules and the complete taxonomic tree of the 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.* (see *Source*). Please see *Details* for a full list of staphylococcal species that will be converted. #' #' 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 (see *Source*). 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. . Please see *Details* for a full list of streptococcal species that will be converted. #' #' 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 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`, which will return a note if old taxonomic names were processed. The default can be set with the [package option][AMR-options] [`AMR_keep_synonyms`][AMR-options], i.e. `options(AMR_keep_synonyms = TRUE)` or `options(AMR_keep_synonyms = FALSE)`. #' @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 Perl-compatible [regular expression][base::regex] (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 [package option][AMR-options] [`AMR_ignore_pattern`][AMR-options], e.g. `options(AMR_ignore_pattern = "(not reported|contaminated flora)")`. #' @param cleaning_regex a Perl-compatible [regular expression][base::regex] (case-insensitive) to clean the input of `x`. Every matched part in `x` will be removed. At default, this is the outcome of [mo_cleaning_regex()], which removes texts between brackets and texts such as "species" and "serovar". The default can be set with the [package option][AMR-options] [`AMR_cleaning_regex`][AMR-options]. #' @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 - the default is `TRUE` only in interactive mode #' @param ... other arguments passed on to functions #' @rdname as.mo #' @aliases mo #' @details #' 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 3-5 letter acronym #' | | \----> species, a 3-6 letter acronym #' | \----> genus, a 4-8 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 be returned as the MO code `UNKNOWN` with a warning. #' #' Use the [`mo_*`][mo_property()] functions to get properties based on the returned code, see *Examples*. #' #' The [as.mo()] function uses a novel [matching score algorithm][mo_matching_score()] (see *Matching Score for Microorganisms* below) to match input against the [available microbial taxonomy][microorganisms] in this package. 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. #' #' With `Becker = TRUE`, the following `r length(MO_CONS[MO_CONS != "B_STPHY_CONS"])` staphylococci will be converted to the **coagulase-negative group**: `r vector_and(gsub("Staphylococcus", "S.", mo_name(MO_CONS[MO_CONS != "B_STPHY_CONS"], keep_synonyms = TRUE)), quotes = "*")`.\cr The following `r length(MO_COPS[MO_COPS != "B_STPHY_COPS"])` staphylococci will be converted to the **coagulase-positive group**: `r vector_and(gsub("Staphylococcus", "S.", mo_name(MO_COPS[MO_COPS != "B_STPHY_COPS"], keep_synonyms = TRUE)), quotes = "*")`. #' #' With `Lancefield = TRUE`, the following streptococci will be converted to their corresponding Lancefield group: `r vector_and(gsub("Streptococcus", "S.", paste0("*", mo_name(MO_LANCEFIELD, keep_synonyms = TRUE), "* (", mo_species(MO_LANCEFIELD, keep_synonyms = TRUE, Lancefield = TRUE), ")")), quotes = FALSE)`. #' #' ### Coping with Uncertain Results #' #' Results of non-exact taxonomic input are based on their [matching score][mo_matching_score()]. The lowest allowed score can be set with the `minimum_matching_score` argument. At default this will be determined based on the character length of the input, and the [taxonomic kingdom][microorganisms] and [human pathogenicity][mo_matching_score()] of the taxonomic outcome. If values are matched with uncertainty, a message will be shown to suggest the user to evaluate the results with [mo_uncertainties()], which returns a [data.frame] with all specifications. #' #' To increase the quality of matching, the `cleaning_regex` argument can be used to clean the input (i.e., `x`). This must be a [regular expression][base::regex] that matches parts of the input that should be removed before the input is matched against the [available microbial taxonomy][microorganisms]. It will be matched Perl-compatible and case-insensitive. The default value of `cleaning_regex` is the outcome of the helper function [mo_cleaning_regex()]. #' #' 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, which is available as the `prevalence` column 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. Berends MS *et al.* (2022). **AMR: An R Package for Working with Antimicrobial Resistance Data**. *Journal of Statistical Software*, 104(3), 1-31; \doi{10.18637/jss.v104.i03} #' 2. Becker K *et al.* (2014). **Coagulase-Negative Staphylococci.** *Clin Microbiol Rev.* 27(4): 870-926; \doi{10.1128/CMR.00109-13} #' 3. 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} #' 4. 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} #' 5. Lancefield RC (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} #' 6. Berends MS *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/** *Micro.rganisms* 10(9), 1801; \doi{10.3390/microorganisms10091801} #' 7. `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`. #' 8. `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`. #' 9. `r TAXONOMY_VERSION$BacDive$citation` Accessed from <`r TAXONOMY_VERSION$BacDive$url`> on `r documentation_date(TAXONOMY_VERSION$BacDive$accessed_date)`. #' 10. `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`> #' 11. Bartlett A *et al.* (2022). **A comprehensive list of bacterial pathogens infecting humans** *Microbiology* 168:001269; \doi{10.1099/mic.0.001269} #' @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", #' "Sthafilokkockus aureus", # handles incorrect spelling #' "Staphylococcus aureus (MRSA)", #' "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" #' )) #' #' # input will get cleaned up with the input given in the `cleaning_regex` argument, #' # which defaults to `mo_cleaning_regex()`: #' cat(mo_cleaning_regex(), "\n") #' #' 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("E. coli") #' mo_gramstain("ESCO") #' mo_is_intrinsic_resistant("ESCCOL", ab = "vanco") #' } as.mo <- function(x, Becker = FALSE, Lancefield = FALSE, minimum_matching_score = NULL, keep_synonyms = getOption("AMR_keep_synonyms", FALSE), reference_df = get_mo_source(), ignore_pattern = getOption("AMR_ignore_pattern", NULL), cleaning_regex = getOption("AMR_cleaning_regex", mo_cleaning_regex()), 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(minimum_matching_score, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive_or_zero = TRUE, is_finite = TRUE) meet_criteria(keep_synonyms, allow_class = "logical", 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(cleaning_regex, allow_class = "character", has_length = 1, allow_NULL = TRUE) language <- validate_language(language) meet_criteria(info, allow_class = "logical", has_length = 1) add_MO_lookup_to_AMR_env() if (tryCatch(all(x %in% c(AMR_env$MO_lookup$mo, NA)), error = function(e) FALSE) && isFALSE(Becker) && isFALSE(Lancefield) && isTRUE(keep_synonyms)) { # 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) x_lower <- tolower(x) # WHONET: xxx = no growth x[x_lower %in% c("", "xxx", "na", "nan")] <- NA_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) & toupper(x) %in% AMR_env$MO_lookup$mo] <- toupper(x[is.na(out) & toupper(x) %in% AMR_env$MO_lookup$mo]) # From full name ---- out[is.na(out) & x_lower %in% AMR_env$MO_lookup$fullname_lower] <- AMR_env$MO_lookup$mo[match(x_lower[is.na(out) & x_lower %in% AMR_env$MO_lookup$fullname_lower], AMR_env$MO_lookup$fullname_lower)] # one exception: "Fungi" matches the kingdom, but instead it should return the 'unknown' code for fungi out[out == "F_[KNG]_FUNGI"] <- "F_FUNGUS" # From known codes ---- out[is.na(out) & toupper(x) %in% AMR::microorganisms.codes$code] <- AMR::microorganisms.codes$mo[match(toupper(x)[is.na(out) & toupper(x) %in% AMR::microorganisms.codes$code], AMR::microorganisms.codes$code)] # From SNOMED ---- # based on this extremely fast gem: https://stackoverflow.com/a/11002456/4575331 snomeds <- unlist(AMR_env$MO_lookup$snomed) snomeds <- snomeds[!is.na(snomeds)] out[is.na(out) & x %in% snomeds] <- AMR_env$MO_lookup$mo[rep(seq_along(AMR_env$MO_lookup$snomed), vapply(FUN.VALUE = double(1), AMR_env$MO_lookup$snomed, length))[match(x[is.na(out) & x %in% snomeds], snomeds)]] # From other familiar output ---- # such as Salmonella groups, colloquial names, etc. out[is.na(out)] <- convert_colloquial_input(x[is.na(out)]) # From previous hits in this session ---- old <- out out[is.na(out) & paste(x, minimum_matching_score) %in% AMR_env$mo_previously_coerced$x] <- AMR_env$mo_previously_coerced$mo[match(paste(x, minimum_matching_score)[is.na(out) & paste(x, minimum_matching_score) %in% AMR_env$mo_previously_coerced$x], AMR_env$mo_previously_coerced$x)] new <- out if (isTRUE(info) && message_not_thrown_before("as.mo", old, 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. This note will be shown once per session for this input." ) } # For all other input ---- if (any(is.na(out) & !is.na(x))) { # reset uncertainties AMR_env$mo_uncertainties <- AMR_env$mo_uncertainties[0, ] AMR_env$mo_failures <- NULL # Laboratory systems: remove (translated) entries like "no growth", "not E. coli", etc. x[trimws2(x) %like% translate_into_language("no .*growth", language = language)] <- NA_character_ x[trimws2(x) %like% paste0("^(", translate_into_language("no|not", language = language), ") ")] <- NA_character_ # groups are in our taxonomic table with a capital G x <- gsub(" group( |$)", " Group\\1", x, perl = TRUE) # 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, title = "Converting microorganism input") on.exit(close(progress)) msg <- character(0) # run it x_coerced <- vapply(FUN.VALUE = character(1), x_unique, function(x_search) { progress$tick() # some required cleaning steps x_out <- trimws2(x_search) # this applies the `cleaning_regex` argument, which defaults to mo_cleaning_regex() x_out <- gsub(cleaning_regex, " ", x_out, ignore.case = TRUE, perl = TRUE) x_out <- trimws2(gsub(" +", " ", x_out, perl = TRUE)) x_search_cleaned <- x_out x_out <- tolower(x_out) # when x_search_cleaned are only capitals (such as in codes), make them lowercase to increase matching score x_search_cleaned[x_search_cleaned == toupper(x_search_cleaned)] <- x_out[x_search_cleaned == toupper(x_search_cleaned)] # first check if cleaning led to an exact result, case-insensitive if (x_out %in% AMR_env$MO_lookup$fullname_lower) { return(as.character(AMR_env$MO_lookup$mo[match(x_out, AMR_env$MO_lookup$fullname_lower)])) } # input must not be too short if (nchar(x_out) < 3) { return("UNKNOWN") } # take out the parts, split by space x_parts <- strsplit(gsub("-", " ", x_out, fixed = TRUE), " ", fixed = TRUE)[[1]] # do a pre-match on first character (and if it contains a space, first chars of first two terms) if (length(x_parts) %in% c(2, 3)) { # for genus + species + subspecies if (nchar(gsub("[^a-z]", "", x_parts[1], perl = TRUE)) <= 3) { filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) & (AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) | AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1) | AMR_env$MO_lookup$subspecies_first == substr(x_parts[3], 1, 1))) } else { filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts[1], 1, 1) | AMR_env$MO_lookup$species_first == substr(x_parts[2], 1, 1) | AMR_env$MO_lookup$subspecies_first == substr(x_parts[2], 1, 1) | AMR_env$MO_lookup$subspecies_first == substr(x_parts[3], 1, 1)) } } else if (length(x_parts) > 3) { first_chars <- paste0("(^| )[", paste(substr(x_parts, 1, 1), collapse = ""), "]") filtr <- which(AMR_env$MO_lookup$full_first %like_case% first_chars) } else if (nchar(x_out) == 3) { # no space and 3 characters - probably a code such as SAU or ECO msg <<- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on \"", totitle(substr(x_out, 1, 1)), AMR_env$dots, " ", substr(x_out, 2, 3), AMR_env$dots, "\"")) filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", substr(x_out, 1, 1), ".* ", substr(x_out, 2, 3))) } else if (nchar(x_out) == 4) { # no space and 4 characters - probably a code such as STAU or ESCO msg <<- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on \"", totitle(substr(x_out, 1, 2)), AMR_env$dots, " ", substr(x_out, 3, 4), AMR_env$dots, "\"")) filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", substr(x_out, 1, 2), ".* ", substr(x_out, 3, 4))) } else if (nchar(x_out) <= 6) { # no space and 5-6 characters - probably a code such as STAAUR or ESCCOL first_part <- paste0(substr(x_out, 1, 2), "[a-z]*", substr(x_out, 3, 3)) second_part <- substr(x_out, 4, nchar(x_out)) msg <<- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on \"", gsub("[a-z]*", AMR_env$dots, totitle(first_part), fixed = TRUE), " ", second_part, AMR_env$dots, "\"")) filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", first_part, ".* ", second_part)) } else { # for genus or species or subspecies filtr <- which(AMR_env$MO_lookup$full_first == substr(x_parts, 1, 1) | AMR_env$MO_lookup$species_first == substr(x_parts, 1, 1) | AMR_env$MO_lookup$subspecies_first == substr(x_parts, 1, 1)) } if (length(filtr) == 0) { mo_to_search <- AMR_env$MO_lookup$fullname } else { mo_to_search <- AMR_env$MO_lookup$fullname[filtr] } AMR_env$mo_to_search <- mo_to_search # determine the matching score on the original search value m <- mo_matching_score(x = x_search_cleaned, n = mo_to_search) if (is.null(minimum_matching_score)) { minimum_matching_score_current <- min(0.6, min(10, nchar(x_search_cleaned)) * 0.08) # correct back for prevalence minimum_matching_score_current <- minimum_matching_score_current / AMR_env$MO_lookup$prevalence[match(mo_to_search, AMR_env$MO_lookup$fullname)] # correct back for kingdom minimum_matching_score_current <- minimum_matching_score_current / AMR_env$MO_lookup$kingdom_index[match(mo_to_search, AMR_env$MO_lookup$fullname)] minimum_matching_score_current <- pmax(minimum_matching_score_current, m) if (length(x_parts) > 1 && all(m <= 0.55, na.rm = TRUE)) { # if the highest score is 0.5, we have nothing serious - 0.5 is the lowest for pathogenic group 1 # make everything NA so the results will get removed below # (we added length(x_parts) > 1 to exclude microbial codes from this rule, such as "STAU") m[seq_len(length(m))] <- NA_real_ } } else { # minimum_matching_score was set, so remove everything below it m[m < minimum_matching_score] <- NA_real_ minimum_matching_score_current <- minimum_matching_score } 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), paste0("NULL (=", round(min(minimum_matching_score_current, na.rm = TRUE), 3), ")"), minimum_matching_score), ". Try setting this value lower or even to 0.", call = FALSE) result_mo <- NA_character_ } else { result_mo <- AMR_env$MO_lookup$mo[match(top_hits[1], AMR_env$MO_lookup$fullname)] AMR_env$mo_uncertainties <- rbind_AMR( AMR_env$mo_uncertainties, data.frame( original_input = x_search, input = x_search_cleaned, fullname = top_hits[1], mo = result_mo, candidates = ifelse(length(top_hits) > 1, paste(top_hits[2:min(99, length(top_hits))], collapse = ", "), ""), minimum_matching_score = ifelse(is.null(minimum_matching_score), "NULL", minimum_matching_score), keep_synonyms = keep_synonyms, stringsAsFactors = FALSE ) ) # save to package env to save time for next time AMR_env$mo_previously_coerced <- unique(rbind_AMR( AMR_env$mo_previously_coerced, data.frame( x = paste(x_search, minimum_matching_score), mo = result_mo, stringsAsFactors = FALSE ) )) } # the actual result: as.character(result_mo) }) # remove progress bar from console close(progress) # expand from unique again out[is.na(out)] <- x_coerced[match(x[is.na(out)], x_unique)] # Throw note about uncertainties ---- if (isTRUE(info) && NROW(AMR_env$mo_uncertainties) > 0) { if (message_not_thrown_before("as.mo", "uncertainties", AMR_env$mo_uncertainties$original_input)) { plural <- c("", "this") if (length(AMR_env$mo_uncertainties$original_input) > 1) { plural <- c("s", "these uncertainties") } if (length(AMR_env$mo_uncertainties$original_input) <= 3) { examples <- vector_and( paste0( '"', AMR_env$mo_uncertainties$original_input, '" (assumed ', italicise(AMR_env$mo_uncertainties$fullname), ")" ), quotes = FALSE ) } else { examples <- paste0(nr2char(length(AMR_env$mo_uncertainties$original_input)), " microorganism", plural[1]) } msg <- c(msg, paste0( "Microorganism translation was uncertain for ", examples, ". Run `mo_uncertainties()` to review ", plural[2], ", or use `add_custom_microorganisms()` to add custom entries." )) for (m in msg) { message_(m) } } } } # end of loop over all yet unknowns # Keep or replace synonyms ---- lpsn_matches <- AMR_env$MO_lookup$lpsn_renamed_to[match(out, AMR_env$MO_lookup$mo)] lpsn_matches[!lpsn_matches %in% AMR_env$MO_lookup$lpsn] <- NA # GBIF only for non-bacteria, since we use LPSN as primary source for bacteria # (an example is Strep anginosus, renamed according to GBIF, not according to LPSN) gbif_matches <- AMR_env$MO_lookup$gbif_renamed_to[AMR_env$MO_lookup$kingdom != "Bacteria"][match(out, AMR_env$MO_lookup$mo[AMR_env$MO_lookup$kingdom != "Bacteria"])] gbif_matches[!gbif_matches %in% AMR_env$MO_lookup$gbif] <- NA AMR_env$mo_renamed <- list( old = out[!is.na(gbif_matches) | !is.na(lpsn_matches)], gbif_matches = gbif_matches[!is.na(gbif_matches) | !is.na(lpsn_matches)], lpsn_matches = lpsn_matches[!is.na(gbif_matches) | !is.na(lpsn_matches)] ) if (isFALSE(keep_synonyms)) { out[which(!is.na(gbif_matches))] <- AMR_env$MO_lookup$mo[match(gbif_matches[which(!is.na(gbif_matches))], AMR_env$MO_lookup$gbif)] out[which(!is.na(lpsn_matches))] <- AMR_env$MO_lookup$mo[match(lpsn_matches[which(!is.na(lpsn_matches))], AMR_env$MO_lookup$lpsn)] if (isTRUE(info) && length(AMR_env$mo_renamed$old) > 0) { print(mo_renamed(), extra_txt = " (use `keep_synonyms = TRUE` to leave uncorrected)") } } else if (is.null(getOption("AMR_keep_synonyms")) && length(AMR_env$mo_renamed$old) > 0 && message_not_thrown_before("as.mo", "keep_synonyms_warning", entire_session = TRUE)) { # keep synonyms is TRUE, so check if any do have synonyms warning_("Function `as.mo()` returned ", nr2char(length(unique(AMR_env$mo_renamed$old))), " old taxonomic name", ifelse(length(unique(AMR_env$mo_renamed$old)) > 1, "s", ""), ". Use `as.mo(..., keep_synonyms = FALSE)` to clean the input to currently accepted taxonomic names, or set the R option `AMR_keep_synonyms` to `FALSE`. This warning will be shown once per session.", call = FALSE) } # 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_env$MO_lookup$mo[match(post_Becker, AMR_env$MO_lookup$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, call = FALSE ) } } # '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") { # (using `%like_case%` to also match subspecies) # group A - S. pyogenes out[out %like_case% "^B_STRPT_PYGN(_|$)"] <- "B_STRPT_GRPA" # group B - S. agalactiae out[out %like_case% "^B_STRPT_AGLC(_|$)"] <- "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 - Milleri group == S. anginosus group, which incl. S. anginosus, S. constellatus, S. intermedius out[out %like_case% "^B_STRPT_(ANGN|CNST|INTR)(_|$)"] <- "B_STRPT_GRPF" # group G - S. dysgalactiae and S. canis (though dysgalactiae is also group C and will be matched there) out[out %like_case% "^B_STRPT_(DYSG|CANS)(_|$)"] <- "B_STRPT_GRPG" # group H - S. sanguinis out[out %like_case% "^B_STRPT_SNGN(_|$)"] <- "B_STRPT_GRPH" # group K - S. salivarius, incl. S. salivarius salivarius and S. salivarius thermophilus out[out %like_case% "^B_STRPT_SLVR(_|$)"] <- "B_STRPT_GRPK" # group L - only S. dysgalactiae which is also group C & G, so ignore it here } # All unknowns ---- out[is.na(out) & !is.na(x)] <- "UNKNOWN" AMR_env$mo_failures <- unique(x[out == "UNKNOWN" & !toupper(x) %in% c("UNKNOWN", "CON", "UNK") & !x %like_case% "^[(]unknown [a-z]+[)]$" & !is.na(x)]) if (length(AMR_env$mo_failures) > 0) { warning_("The following input could not be coerced and was returned as \"UNKNOWN\": ", vector_and(AMR_env$mo_failures, quotes = TRUE), ".\nYou can retrieve this list with `mo_failures()`.", call = FALSE) } # Return class ---- set_clean_class(out, new_class = c("mo", "character") ) } # OTHER DOCUMENTED FUNCTIONS ---------------------------------------------- #' @rdname as.mo #' @export is.mo <- function(x) { inherits(x, "mo") } #' @rdname as.mo #' @export mo_uncertainties <- function() { set_clean_class(AMR_env$mo_uncertainties, new_class = c("mo_uncertainties", "data.frame")) } #' @rdname as.mo #' @export mo_renamed <- function() { add_MO_lookup_to_AMR_env() x <- AMR_env$mo_renamed x$new <- synonym_mo_to_accepted_mo(x$old) mo_old <- AMR_env$MO_lookup$fullname[match(x$old, AMR_env$MO_lookup$mo)] mo_new <- AMR_env$MO_lookup$fullname[match(x$new, AMR_env$MO_lookup$mo)] ref_old <- AMR_env$MO_lookup$ref[match(x$old, AMR_env$MO_lookup$mo)] ref_new <- AMR_env$MO_lookup$ref[match(x$new, AMR_env$MO_lookup$mo)] df_renamed <- data.frame( old = mo_old, new = mo_new, ref_old = ref_old, ref_new = ref_new, stringsAsFactors = FALSE ) df_renamed <- unique(df_renamed) df_renamed <- df_renamed[order(df_renamed$old), , drop = FALSE] set_clean_class(df_renamed, new_class = c("mo_renamed", "data.frame")) } #' @rdname as.mo #' @export mo_failures <- function() { AMR_env$mo_failures } #' @rdname as.mo #' @export mo_reset_session <- function() { if (NROW(AMR_env$mo_previously_coerced) > 0) { message_("Reset ", nr2char(NROW(AMR_env$mo_previously_coerced)), " previously matched input value", ifelse(NROW(AMR_env$mo_previously_coerced) > 1, "s", ""), ".") AMR_env$mo_previously_coerced <- AMR_env$mo_previously_coerced[0, , drop = FALSE] AMR_env$mo_uncertainties <- AMR_env$mo_uncertainties[0, , drop = FALSE] } else { message_("No previously matched input values to reset.") } } #' @rdname as.mo #' @export mo_cleaning_regex <- function() { parts_to_remove <- c("e?spp([^a-z]+|$)", "e?ssp([^a-z]+|$)", "e?ss([^a-z]+|$)", "e?sp([^a-z]+|$)", "e?subsp", "sube?species", "e?species", "biovar[a-z]*", "biotype", "serovar[a-z]*", "var([^a-z]+|$)", "serogr.?up[a-z]*", "titer", "dummy", "Ig[ADEGM]") paste0( "(", "[^A-Za-z- \\(\\)\\[\\]{}]+", "|", "([({]|\\[).+([})]|\\])", "|(^| )(", paste0(parts_to_remove[order(1 - nchar(parts_to_remove))], collapse = "|"), "))") } # UNDOCUMENTED METHODS ---------------------------------------------------- # will be exported using s3_register() in R/zzz.R pillar_shaft.mo <- function(x, ...) { add_MO_lookup_to_AMR_env() out <- trimws(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") # markup manual codes out[x %in% AMR_env$MO_lookup$mo & !x %in% AMR::microorganisms$mo] <- font_blue(out[x %in% AMR_env$MO_lookup$mo & !x %in% AMR::microorganisms$mo], collapse = NULL) 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 } all_mos <- c(AMR_env$MO_lookup$mo, NA) if (!all(x %in% all_mos) || (!is.null(df) && !all(unlist(df[, which(mo_cols), drop = FALSE]) %in% all_mos))) { # markup old mo codes out[!x %in% all_mos] <- font_italic( font_na(x[!x %in% all_mos], 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()`.", call = FALSE ) } # add the names to the bugs as mouse-over! if (tryCatch(isTRUE(getExportedValue("ansi_has_hyperlink_support", ns = asNamespace("cli"))()), error = function(e) FALSE)) { out[!x %in% c("UNKNOWN", NA)] <- font_url(url = paste0(x[!x %in% c("UNKNOWN", NA)], ": ", mo_name(x[!x %in% c("UNKNOWN", NA)], keep_synonyms = TRUE)), txt = out[!x %in% c("UNKNOWN", NA)]) } # 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(...)$digits if (is.null(digits)) { digits <- 2 } cleaner::freq.default( x = x, ..., .add_header = list( `Gram-negative` = paste0( format(sum(grams == "Gram-negative", na.rm = TRUE), big.mark = " ", decimal.mark = "." ), " (", percentage(sum(grams == "Gram-negative", na.rm = TRUE) / length(grams), digits = digits ), ")" ), `Gram-positive` = paste0( format(sum(grams == "Gram-positive", na.rm = TRUE), big.mark = " ", decimal.mark = "." ), " (", percentage(sum(grams == "Gram-positive", na.rm = TRUE) / length(grams), digits = digits ), ")" ), `Nr. of genera` = pm_n_distinct(mo_genus(x_noNA, language = NULL)), `Nr. of species` = pm_n_distinct(paste( mo_genus(x_noNA, language = NULL), mo_species(x_noNA, language = NULL) )) ) ) } # will be exported using s3_register() in R/zzz.R get_skimmers.mo <- function(column) { skimr::sfl( skim_type = "mo", unique_total = ~ length(unique(stats::na.omit(.))), gram_negative = ~ sum(mo_is_gram_negative(.), na.rm = TRUE), gram_positive = ~ sum(mo_is_gram_positive(.), na.rm = TRUE), top_genus = ~ names(sort(-table(mo_genus(stats::na.omit(.), language = NULL))))[1L], top_species = ~ names(sort(-table(mo_name(stats::na.omit(.), language = NULL))))[1L] ) } #' @method print mo #' @export #' @noRd print.mo <- function(x, print.shortnames = FALSE, ...) { add_MO_lookup_to_AMR_env() cat("Class 'mo'\n") x_names <- names(x) if (is.null(x_names) & print.shortnames == TRUE) { x_names <- tryCatch(mo_shortname(x, ...), error = function(e) NULL) } x <- as.character(x) names(x) <- x_names if (!all(x %in% c(AMR_env$MO_lookup$mo, NA))) { warning_( "Some MO codes are from a previous AMR package version. ", "Please update the MO codes with `as.mo()`.", call = FALSE ) } print.default(x, quote = FALSE) } #' @method summary mo #' @export #' @noRd summary.mo <- function(object, ...) { # unique and top 1-3 x <- object top_3 <- names(sort(-table(x[!is.na(x)])))[1:3] out <- c( "Class" = "mo", "" = length(x[is.na(x)]), "Unique" = length(unique(x[!is.na(x)])), "#1" = top_3[1], "#2" = top_3[2], "#3" = top_3[3] ) class(out) <- c("summaryDefault", "table") out } #' @method as.data.frame mo #' @export #' @noRd as.data.frame.mo <- function(x, ...) { add_MO_lookup_to_AMR_env() if (!all(x %in% c(AMR_env$MO_lookup$mo, NA))) { warning_( "The data contains old MO codes (from a previous AMR package version). ", "Please update your MO codes with `as.mo()`." ) } nm <- deparse1(substitute(x)) if (!"nm" %in% names(list(...))) { as.data.frame.vector(x, ..., nm = nm) } else { as.data.frame.vector(x, ...) } } #' @method [ mo #' @export #' @noRd "[.mo" <- function(x, ...) { y <- NextMethod() attributes(y) <- attributes(x) y } #' @method [[ mo #' @export #' @noRd "[[.mo" <- function(x, ...) { y <- NextMethod() attributes(y) <- attributes(x) y } #' @method [<- mo #' @export #' @noRd "[<-.mo" <- function(i, j, ..., value) { y <- NextMethod() attributes(y) <- attributes(i) # must only contain valid MOs add_MO_lookup_to_AMR_env() return_after_integrity_check(y, "microorganism code", as.character(AMR_env$MO_lookup$mo)) } #' @method [[<- mo #' @export #' @noRd "[[<-.mo" <- function(i, j, ..., value) { y <- NextMethod() attributes(y) <- attributes(i) # must only contain valid MOs add_MO_lookup_to_AMR_env() return_after_integrity_check(y, "microorganism code", as.character(AMR_env$MO_lookup$mo)) } #' @method c mo #' @export #' @noRd c.mo <- function(...) { x <- list(...)[[1L]] y <- NextMethod() attributes(y) <- attributes(x) add_MO_lookup_to_AMR_env() return_after_integrity_check(y, "microorganism code", as.character(AMR_env$MO_lookup$mo)) } #' @method unique mo #' @export #' @noRd unique.mo <- function(x, incomparables = FALSE, ...) { y <- NextMethod() attributes(y) <- attributes(x) y } #' @method rep mo #' @export #' @noRd rep.mo <- function(x, ...) { y <- NextMethod() attributes(y) <- attributes(x) y } #' @method print mo_uncertainties #' @export #' @noRd print.mo_uncertainties <- function(x, n = 10, ...) { more_than_50 <- FALSE if (NROW(x) == 0) { cat(word_wrap("No uncertainties to show. Only uncertainties of the last call to `as.mo()` or any `mo_*()` function are stored.\n\n", add_fn = font_blue)) return(invisible(NULL)) } else if (NROW(x) > 50) { more_than_50 <- TRUE x <- x[1:50, , drop = FALSE] } cat(word_wrap("Matching scores are based on the resemblance between the input and the full taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`.\n\n", add_fn = font_blue)) add_MO_lookup_to_AMR_env() col_red <- function(x) font_rose_bg(x, collapse = NULL) col_orange <- function(x) font_orange_bg(x, collapse = NULL) col_yellow <- function(x) font_yellow_bg(x, collapse = NULL) col_green <- function(x) font_green_bg(x, collapse = NULL) if (has_colour()) { cat(word_wrap("Colour keys: ", col_red(" 0.000-0.549 "), col_orange(" 0.550-0.649 "), col_yellow(" 0.650-0.749 "), col_green(" 0.750-1.000"), add_fn = font_blue ), font_green_bg(" "), "\n", sep = "") } score_set_colour <- function(text, scores) { # set colours to scores text[scores >= 0.75] <- col_green(text[scores >= 0.75]) text[scores >= 0.65 & scores < 0.75] <- col_yellow(text[scores >= 0.65 & scores < 0.75]) text[scores >= 0.55 & scores < 0.65] <- col_orange(text[scores >= 0.55 & scores < 0.65]) text[scores < 0.55] <- col_red(text[scores < 0.55]) text } txt <- "" any_maxed_out <- FALSE for (i in seq_len(nrow(x))) { if (x[i, ]$candidates != "") { candidates <- unlist(strsplit(x[i, ]$candidates, ", ", fixed = TRUE)) if (length(candidates) > n) { any_maxed_out <- TRUE candidates <- candidates[seq_len(n)] } scores <- mo_matching_score(x = x[i, ]$input, n = candidates) n_candidates <- length(candidates) candidates_formatted <- italicise(candidates) 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 ) ), extra_indent = nchar("Also matched: "), width = 0.9 * getOption("width", 100) ) } 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( paste0( "", strrep(font_grey("-"), times = getOption("width", 100)), "\n", '"', x[i, ]$original_input, '"', " -> ", paste0( font_bold(italicise(x[i, ]$fullname)), " (", x[i, ]$mo, ", ", score_set_colour(score_formatted, score), ")" ) ), collapse = "\n" ), # Add note if result was coerced to accepted taxonomic name ifelse(x[i, ]$keep_synonyms == FALSE & x[i, ]$mo %in% AMR_env$MO_lookup$mo[which(AMR_env$MO_lookup$status == "synonym")], paste0( strrep(" ", nchar(x[i, ]$original_input) + 6), font_red(paste0("This old taxonomic name was converted to ", font_italic(AMR_env$MO_lookup$fullname[match(synonym_mo_to_accepted_mo(x[i, ]$mo), AMR_env$MO_lookup$mo)], collapse = NULL), " (", synonym_mo_to_accepted_mo(x[i, ]$mo), ")."), collapse = NULL) ), "" ), candidates, sep = "\n" ) txt <- gsub("[\n]+", "\n", txt) # remove first and last break txt <- gsub("(^[\n]|[\n]$)", "", txt) txt <- paste0("\n", txt, "\n") } cat(txt) if (isTRUE(any_maxed_out)) { cat(font_blue(word_wrap("\nOnly the first ", n, " other matches of each record are shown. Run `print(mo_uncertainties(), n = ...)` to view more entries, or save `mo_uncertainties()` to an object."))) } if (isTRUE(more_than_50)) { cat(font_blue(word_wrap("\nOnly the first 50 uncertainties are shown. Run `View(mo_uncertainties())` to view all entries, or save `mo_uncertainties()` to an object."))) } } #' @method print mo_renamed #' @export #' @noRd print.mo_renamed <- function(x, extra_txt = "", n = 25, ...) { if (NROW(x) == 0) { cat(word_wrap("No renamed taxonomy to show. Only renamed taxonomy of the last call of `as.mo()` or any `mo_*()` function are stored.\n", add_fn = font_blue)) return(invisible(NULL)) } x$ref_old[!is.na(x$ref_old)] <- paste0(" (", gsub("et al.", font_italic("et al."), x$ref_old[!is.na(x$ref_old)], fixed = TRUE), ")") x$ref_new[!is.na(x$ref_new)] <- paste0(" (", gsub("et al.", font_italic("et al."), x$ref_new[!is.na(x$ref_new)], fixed = TRUE), ")") x$ref_old[is.na(x$ref_old)] <- " (author unknown)" x$ref_new[is.na(x$ref_new)] <- " (author unknown)" rows <- seq_len(min(NROW(x), n)) message_( "The following microorganism", ifelse(NROW(x) > 1, "s were", " was"), " taxonomically renamed", extra_txt, ":\n", paste0(" ", AMR_env$bullet_icon, " ", font_italic(x$old[rows], collapse = NULL), x$ref_old[rows], " -> ", font_italic(x$new[rows], collapse = NULL), x$ref_new[rows], collapse = "\n" ), ifelse(NROW(x) > n, paste0("\n\nOnly the first ", n, " (out of ", NROW(x), ") are shown. Run `print(mo_renamed(), n = ...)` to view more entries (might be slow), or save `mo_renamed()` to an object."), "") ) } # UNDOCUMENTED HELPER FUNCTIONS ------------------------------------------- convert_colloquial_input <- function(x) { x.bak <- trimws2(x) x <- trimws2(tolower(x)) out <- rep(NA_character_, length(x)) # Streptococci, like GBS = Group B Streptococci (B_STRPT_GRPB) out[x %like_case% "^g[abcdefghijkl]s$"] <- gsub("g([abcdefghijkl])s", "B_STRPT_GRP\\U\\1", x[x %like_case% "^g[abcdefghijkl]s$"], perl = TRUE ) # Streptococci in different languages, like "estreptococos grupo B" out[x %like_case% "strepto[ck]o[ck][a-zA-Z ]* [abcdefghijkl]$"] <- gsub(".*e?strepto[ck]o[ck].* ([abcdefghijkl])$", "B_STRPT_GRP\\U\\1", x[x %like_case% "strepto[ck]o[ck][a-zA-Z ]* [abcdefghijkl]$"], perl = TRUE ) out[x %like_case% "strep[a-z]* group [abcdefghijkl]$"] <- gsub(".* ([abcdefghijkl])$", "B_STRPT_GRP\\U\\1", x[x %like_case% "strep[a-z]* group [abcdefghijkl]$"], perl = TRUE ) out[x %like_case% "group [abcdefghijkl] strepto[ck]o[ck]"] <- gsub(".*group ([abcdefghijkl]) strepto[ck]o[ck].*", "B_STRPT_GRP\\U\\1", x[x %like_case% "group [abcdefghijkl] strepto[ck]o[ck]"], perl = TRUE ) out[x %like_case% "ha?emoly.*strep"] <- "B_STRPT_HAEM" out[x %like_case% "(strepto.* [abcg, ]{2,4}$)"] <- "B_STRPT_ABCG" out[x %like_case% "(strepto.* mil+er+i|^mgs[^a-z]*$)"] <- "B_STRPT_MILL" out[x %like_case% "mil+er+i gr"] <- "B_STRPT_MILL" out[x %like_case% "((strepto|^s).* viridans|^vgs[^a-z]*$)"] <- "B_STRPT_VIRI" out[x %like_case% "(viridans.* (strepto|^s).*|^vgs[^a-z]*$)"] <- "B_STRPT_VIRI" # Salmonella in different languages, like "Salmonella grupo B" out[x %like_case% "salmonella.* [abcdefgh]$"] <- gsub(".*salmonella.* ([abcdefgh])$", "B_SLMNL_GRP\\U\\1", x[x %like_case% "salmonella.* [abcdefgh]$"], perl = TRUE ) out[x %like_case% "group [abcdefgh] salmonella"] <- gsub(".*group ([abcdefgh]) salmonella*", "B_SLMNL_GRP\\U\\1", x[x %like_case% "group [abcdefgh] salmonella"], perl = TRUE ) # CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese) out[x %like_case% "([ck]oagulas[ea].negatie?[vf]|^[ck]o?ns[^a-z]*$)"] <- "B_STPHY_CONS" out[x %like_case% "([ck]oagulas[ea].positie?[vf]|^[ck]o?ps[^a-z]*$)"] <- "B_STPHY_COPS" # Gram stains out[x %like_case% "gram[ -]?neg.*"] <- "B_GRAMN" out[x %like_case% "( |^)gram[-]( |$)"] <- "B_GRAMN" out[x %like_case% "gram[ -]?pos.*"] <- "B_GRAMP" out[x %like_case% "( |^)gram[+]( |$)"] <- "B_GRAMP" out[x %like_case% "anaerob[a-z]+ .*gram[ -]?neg.*"] <- "B_ANAER-NEG" out[x %like_case% "anaerob[a-z]+ .*gram[ -]?pos.*"] <- "B_ANAER-POS" out[is.na(out) & x %like_case% "anaerob[a-z]+ (micro)?.*organism"] <- "B_ANAER" # coryneform bacteria out[x %like_case% "^coryneform"] <- "B_CORYNF" # yeasts and fungi out[x %like_case% "^yeast?"] <- "F_YEAST" out[x %like_case% "^fung(us|i)"] <- "F_FUNGUS" # trivial names known to the field out[x %like_case% "meningo[ck]o[ck]"] <- "B_NESSR_MNNG" out[x %like_case% "gono[ck]o[ck]"] <- "B_NESSR_GNRR" out[x %like_case% "pneumo[ck]o[ck]"] <- "B_STRPT_PNMN" out[x %like_case% "hacek"] <- "B_HACEK" out[x %like_case% "haemophilus" & x %like_case% "aggregatibacter" & x %like_case% "cardiobacterium" & x %like_case% "eikenella" & x %like_case% "kingella"] <- "B_HACEK" out[x %like_case% "slow.* grow.* mycobact"] <- "B_MYCBC_SGM" out[x %like_case% "rapid.* grow.* mycobact"] <- "B_MYCBC_RGM" # unexisting names (con is the WHONET code for contamination) out[x %in% c("con", "other", "none", "unknown") | x %like_case% "virus"] <- "UNKNOWN" # WHONET has a lot of E. coli and Vibrio cholerae names out[x %like_case% "escherichia coli"] <- "B_ESCHR_COLI" out[x %like_case% "vibrio cholerae"] <- "B_VIBRI_CHLR" out } italicise <- function(x) { if (!has_colour()) { return(x) } out <- font_italic(x, collapse = NULL) # city-like serovars of Salmonella (start with a capital) out[x %like_case% "Salmonella [A-Z]"] <- paste( font_italic("Salmonella"), gsub("Salmonella ", "", x[x %like_case% "Salmonella [A-Z]"]) ) # streptococcal groups out[x %like_case% "Streptococcus [A-Z]"] <- paste( font_italic("Streptococcus"), gsub("Streptococcus ", "", x[x %like_case% "Streptococcus [A-Z]"]) ) # be sure not to make these italic out <- gsub("([ -]*)(Group|group|Complex|complex)(\033\\[23m)?", "\033[23m\\1\\2", out, perl = TRUE) out <- gsub("(\033\\[3m)?(Beta[-]haemolytic|Coagulase[-](postive|negative)) ", "\\2 \033[3m", out, perl = TRUE) out } 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 } } parse_and_convert <- function(x) { if (tryCatch(is.character(x) && all(Encoding(x) == "unknown", na.rm = TRUE), error = function(e) FALSE)) { out <- x } else { out <- 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$B) } else { # support Tidyverse selection like: df %>% select(colA) x <- as.data.frame(x, stringsAsFactors = FALSE)[[1]] } } parsed <- iconv(as.character(x), to = "UTF-8") parsed[is.na(parsed) & !is.na(x)] <- iconv(x[is.na(parsed) & !is.na(x)], from = "Latin1", to = "ASCII//TRANSLIT") parsed <- gsub('"', "", parsed, fixed = TRUE) parsed }, error = function(e) stop(e$message, call. = FALSE) ) # this will also be thrown when running `as.mo(no_existing_object)` } out <- trimws2(out) out <- gsub(" +", " ", out, perl = TRUE) out <- gsub(" ?/ ? ", "/", out, perl = TRUE) out } 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_]+$" & !x %in% AMR_env$MO_lookup$mo if (any(ind, na.rm = TRUE)) { add_MO_lookup_to_AMR_env() # get the ones that match affected <- x[ind] affected_unique <- unique(affected) all_direct_matches <- TRUE # find their new codes, once per code solved_unique <- unlist(lapply( strsplit(affected_unique, ""), function(m) { kingdom <- paste0("^", m[1]) name <- m[3:length(m)] name[name == "_"] <- " " name <- tolower(paste0(name, ".*", collapse = "")) name <- gsub(" .*", " ", name, fixed = TRUE) name <- paste0("^", name) results <- AMR_env$MO_lookup$mo[AMR_env$MO_lookup$kingdom %like_case% kingdom & AMR_env$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 = NULL) reference_df } get_mo_uncertainties <- function() { remember <- list(uncertainties = AMR_env$mo_uncertainties) # empty them, otherwise e.g. mo_shortname("Chlamydophila psittaci") will give 3 notes AMR_env$mo_uncertainties <- NULL remember } load_mo_uncertainties <- function(metadata) { AMR_env$mo_uncertainties <- metadata$uncertainties } synonym_mo_to_accepted_mo <- function(x, fill_in_accepted = FALSE) { x_gbif <- AMR_env$MO_lookup$gbif_renamed_to[match(x, AMR_env$MO_lookup$mo)] x_lpsn <- AMR_env$MO_lookup$lpsn_renamed_to[match(x, AMR_env$MO_lookup$mo)] x_gbif[!x_gbif %in% AMR_env$MO_lookup$gbif] <- NA x_lpsn[!x_lpsn %in% AMR_env$MO_lookup$lpsn] <- NA out <- ifelse(is.na(x_lpsn), AMR_env$MO_lookup$mo[match(x_gbif, AMR_env$MO_lookup$gbif)], AMR_env$MO_lookup$mo[match(x_lpsn, AMR_env$MO_lookup$lpsn)] ) if (isTRUE(fill_in_accepted)) { x_accepted <- which(AMR_env$MO_lookup$status[match(x, AMR_env$MO_lookup$mo)] == "accepted") out[x_accepted] <- x[x_accepted] } out }