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(v1.8.1.9013) add Toxoplasma
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279
R/mo.R
279
R/mo.R
@ -2208,3 +2208,282 @@ strip_words <- function(text, n, side = "right") {
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})
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vapply(FUN.VALUE = character(1), out, paste, collapse = " ")
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}
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as.mo2 <- function(x,
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Becker = FALSE,
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Lancefield = FALSE,
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allow_uncertain = TRUE,
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reference_df = get_mo_source(),
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info = interactive(),
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property = "mo",
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initial_search = TRUE,
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dyslexia_mode = FALSE,
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debug = FALSE,
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ignore_pattern = getOption("AMR_ignore_pattern"),
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reference_data_to_use = MO_lookup,
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actual_uncertainty = 1,
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actual_input = NULL,
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language = get_AMR_locale()) {
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meet_criteria(x, allow_class = c("mo", "data.frame", "list", "character", "numeric", "integer", "factor"), allow_NA = TRUE)
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meet_criteria(Becker, allow_class = c("logical", "character"), has_length = 1)
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meet_criteria(Lancefield, allow_class = c("logical", "character"), has_length = 1)
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meet_criteria(allow_uncertain, allow_class = c("logical", "numeric", "integer"), has_length = 1)
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meet_criteria(reference_df, allow_class = "data.frame", allow_NULL = TRUE)
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meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(microorganisms))
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meet_criteria(initial_search, allow_class = "logical", has_length = 1)
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meet_criteria(dyslexia_mode, allow_class = "logical", has_length = 1)
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meet_criteria(debug, allow_class = "logical", has_length = 1)
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meet_criteria(ignore_pattern, allow_class = "character", has_length = 1, allow_NULL = TRUE)
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meet_criteria(reference_data_to_use, allow_class = "data.frame")
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meet_criteria(actual_uncertainty, allow_class = "numeric", has_length = 1)
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meet_criteria(actual_input, allow_class = "character", allow_NULL = TRUE)
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meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
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check_dataset_integrity()
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if (isTRUE(debug) && initial_search == TRUE) {
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time_start_tracking()
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}
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lookup <- function(needle,
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column = property,
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haystack = reference_data_to_use,
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n = 1,
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debug_mode = debug,
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initial = initial_search,
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uncertainty = actual_uncertainty,
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input_actual = actual_input) {
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if (!is.null(input_actual)) {
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input <- input_actual
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} else {
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input <- tryCatch(x_backup[i], error = function(e) "")
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}
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# `column` can be NULL for all columns, or a selection
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# returns a [character] (vector) - if `column` > length 1 then with columns as names
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if (isTRUE(debug_mode)) {
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cat(font_silver("Looking up: ", substitute(needle), collapse = ""),
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"\n ", time_track())
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}
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if (length(column) == 1) {
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res_df <- haystack[which(eval(substitute(needle), envir = haystack, enclos = parent.frame())), , drop = FALSE]
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if (NROW(res_df) > 1 & uncertainty != -1) {
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# sort the findings on matching score
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scores <- mo_matching_score(x = input,
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n = res_df[, "fullname", drop = TRUE])
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res_df <- res_df[order(scores, decreasing = TRUE), , drop = FALSE]
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}
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res <- as.character(res_df[, column, drop = TRUE])
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if (length(res) == 0) {
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if (isTRUE(debug_mode)) {
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cat(font_red(" (no match)\n"))
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}
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NA_character_
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} else {
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if (isTRUE(debug_mode)) {
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cat(font_green(paste0(" MATCH (", NROW(res_df), " results)\n")))
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}
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if ((length(res) > n | uncertainty > 1) & uncertainty != -1) {
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# save the other possible results as well, but not for forced certain results (then uncertainty == -1)
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uncertainties <<- rbind(uncertainties,
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format_uncertainty_as_df(uncertainty_level = uncertainty,
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input = input,
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result_mo = res_df[1, "mo", drop = TRUE],
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candidates = as.character(res_df[, "fullname", drop = TRUE])),
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stringsAsFactors = FALSE)
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}
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res[seq_len(min(n, length(res)))]
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}
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} else {
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if (is.null(column)) {
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column <- names(haystack)
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}
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res <- haystack[which(eval(substitute(needle), envir = haystack, enclos = parent.frame())), , drop = FALSE]
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res <- res[seq_len(min(n, nrow(res))), column, drop = TRUE]
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if (NROW(res) == 0) {
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if (isTRUE(debug_mode)) {
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cat(font_red(" (no rows)\n"))
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}
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res <- rep(NA_character_, length(column))
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} else {
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if (isTRUE(debug_mode)) {
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cat(font_green(paste0(" MATCH (", NROW(res), " rows)\n")))
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}
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}
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res <- as.character(res)
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names(res) <- column
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res
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}
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}
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# start off with replaced language-specific non-ASCII characters with ASCII characters
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x <- parse_and_convert(x)
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# replace mo codes used in older package versions
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x <- replace_old_mo_codes(x, property)
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# ignore cases that match the ignore pattern
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x <- replace_ignore_pattern(x, ignore_pattern)
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# WHONET: xxx = no growth
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x[tolower(as.character(paste0(x, ""))) %in% c("", "xxx", "na", "nan")] <- NA_character_
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# Laboratory systems: remove (translated) entries like "no growth", etc.
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x[trimws2(x) %like% translate_AMR("no .*growth", language = language)] <- NA_character_
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x[trimws2(x) %like% paste0("^(", translate_AMR("no|not", language = language), ") [a-z]+")] <- "UNKNOWN"
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if (initial_search == TRUE) {
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# keep track of time - give some hints to improve speed if it takes a long time
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start_time <- Sys.time()
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pkg_env$mo_failures <- NULL
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pkg_env$mo_uncertainties <- NULL
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pkg_env$mo_renamed <- NULL
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}
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pkg_env$mo_renamed_last_run <- NULL
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failures <- character(0)
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uncertainty_level <- translate_allow_uncertain(allow_uncertain)
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uncertainties <- data.frame(uncertainty = integer(0),
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input = character(0),
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fullname = character(0),
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renamed_to = character(0),
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mo = character(0),
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candidates = character(0),
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stringsAsFactors = FALSE)
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x_input <- x
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# already strip leading and trailing spaces
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x <- trimws(x)
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# only check the uniques, which is way faster
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x <- unique(x)
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# remove empty values (to later fill them in again with NAs)
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# ("xxx" is WHONET code for 'no growth')
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x <- x[!is.na(x)
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& !is.null(x)
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& !identical(x, "")
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& !identical(x, "xxx")]
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# defined df to check for
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if (!is.null(reference_df)) {
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check_validity_mo_source(reference_df)
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reference_df <- repair_reference_df(reference_df)
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}
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# all empty
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if (all(identical(trimws(x_input), "") | is.na(x_input) | length(x) == 0)) {
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if (property == "mo") {
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return(set_clean_class(rep(NA_character_, length(x_input)),
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new_class = c("mo", "character")))
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} else {
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return(rep(NA_character_, length(x_input)))
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}
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} else if (all(x %in% reference_df[, 1][[1]])) {
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# all in reference df
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colnames(reference_df)[1] <- "x"
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suppressWarnings(
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x <- MO_lookup[match(reference_df[match(x, reference_df$x), "mo", drop = TRUE], MO_lookup$mo), property, drop = TRUE]
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)
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} else if (all(x %in% reference_data_to_use$mo)) {
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x <- MO_lookup[match(x, MO_lookup$mo), property, drop = TRUE]
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} else if (all(tolower(x) %in% reference_data_to_use$fullname_lower)) {
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# we need special treatment for very prevalent full names, they are likely!
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# e.g. as.mo("Staphylococcus aureus")
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x <- MO_lookup[match(tolower(x), MO_lookup$fullname_lower), property, drop = TRUE]
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} else if (all(x %in% reference_data_to_use$fullname)) {
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# we need special treatment for very prevalent full names, they are likely!
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# e.g. as.mo("Staphylococcus aureus")
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x <- MO_lookup[match(x, MO_lookup$fullname), property, drop = TRUE]
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} else if (all(toupper(x) %in% microorganisms.codes$code)) {
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# commonly used MO codes
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x <- MO_lookup[match(microorganisms.codes[match(toupper(x),
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microorganisms.codes$code),
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"mo",
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drop = TRUE],
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MO_lookup$mo),
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property,
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drop = TRUE]
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} else if (!all(x %in% microorganisms[, property])) {
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strip_whitespace <- function(x, dyslexia_mode) {
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# all whitespaces (tab, new lines, etc.) should be one space
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# and spaces before and after should be left blank
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trimmed <- trimws2(x)
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# also, make sure the trailing and leading characters are a-z or 0-9
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# in case of non-regex
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if (dyslexia_mode == FALSE) {
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trimmed <- gsub("^[^a-zA-Z0-9)(]+", "", trimmed, perl = TRUE)
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trimmed <- gsub("[^a-zA-Z0-9)(]+$", "", trimmed, perl = TRUE)
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}
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trimmed
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}
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x_backup_untouched <- x
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x <- strip_whitespace(x, dyslexia_mode)
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# translate 'unknown' names back to English
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if (any(tolower(x) %like_case% "unbekannt|onbekend|desconocid|sconosciut|iconnu|desconhecid", na.rm = TRUE)) {
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trns <- subset(TRANSLATIONS, pattern %like% "unknown")
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langs <- LANGUAGES_SUPPORTED[LANGUAGES_SUPPORTED != "en"]
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for (l in langs) {
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for (i in seq_len(nrow(trns))) {
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if (!is.na(trns[i, l, drop = TRUE])) {
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x <- gsub(pattern = trns[i, l, drop = TRUE],
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replacement = trns$pattern[i],
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x = x,
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ignore.case = TRUE,
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perl = TRUE)
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}
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}
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}
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}
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# remove spp and species
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x <- gsub("(^| )[ .]*(spp|ssp|ss|sp|subsp|subspecies|biovar|biotype|serovar|species)[ .]*( |$)", "", x, ignore.case = TRUE, perl = TRUE)
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x <- strip_whitespace(x, dyslexia_mode)
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x_backup <- x
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# from here on case-insensitive
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x <- tolower(x)
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x_backup[x %like_case% "^(fungus|fungi)$"] <- "(unknown fungus)" # will otherwise become the kingdom
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x_backup[x_backup_untouched == "Fungi"] <- "Fungi" # is literally the kingdom
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# Fill in fullnames and MO codes directly
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known_names <- tolower(x_backup) %in% MO_lookup$fullname_lower
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x[known_names] <- MO_lookup[match(tolower(x_backup)[known_names], MO_lookup$fullname_lower), property, drop = TRUE]
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known_codes_mo <- toupper(x_backup) %in% MO_lookup$mo
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x[known_codes_mo] <- MO_lookup[match(toupper(x_backup)[known_codes_mo], MO_lookup$mo), property, drop = TRUE]
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known_codes_lis <- toupper(x_backup) %in% microorganisms.codes$code
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x[known_codes_lis] <- MO_lookup[match(microorganisms.codes[match(toupper(x_backup)[known_codes_lis],
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microorganisms.codes$code), "mo", drop = TRUE],
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MO_lookup$mo), property, drop = TRUE]
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already_known <- known_names | known_codes_mo | known_codes_lis
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# now only continue where the right taxonomic output is not already known
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if (any(!already_known)) {
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x_unknown <- x[!already_known]
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x_unknown <- gsub(" ?[(].*[)] ?", "", x_unknown, perl = TRUE)
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x_unknown <- gsub("[^a-z ]", " ", x_unknown, perl = TRUE)
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x_unknown <- gsub(" +", " ", x_unknown, perl = TRUE)
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print(x_unknown)
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x_search <- gsub("([a-z])[a-z]*( ([a-z])[a-z]*)?( ([a-z])[a-z]*)?", "^\\1.* \\3.* \\5.*", x_unknown, perl = TRUE)
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x_search <- gsub("( [.][*])+$", "", x_search, perl = TRUE)
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print(x_search)
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for (i in seq_len(length(x_unknown))) {
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# search first, second and third part
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mos_to_search <- MO_lookup[which(MO_lookup$fullname_lower %like_case% x_search[i]), "fullname", drop = TRUE]
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score <- mo_matching_score(x_unknown[i], mos_to_search)
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out <- mos_to_search[order(score, decreasing = TRUE)][1:25] # keep first 25
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print(score[order(score, decreasing = TRUE)][1])
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x[!already_known][i] <- MO_lookup$mo[match(out[1], MO_lookup$fullname)]
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}
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}
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}
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x
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}
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