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improve top_n_microorganisms(): add property_for_each, fix property=NULL, enforce rank order (#297)
This commit is contained in:
Matthijs Berends
GitHub
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R/sysdata.rda
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R/sysdata.rda
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@@ -120,13 +120,14 @@ all_disk_predictors <- function() {
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#' @rdname amr-tidymodels
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#' @export
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step_mic_log2 <- function(
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recipe,
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...,
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role = NA,
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trained = FALSE,
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columns = NULL,
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skip = FALSE,
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id = recipes::rand_id("mic_log2")) {
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recipe,
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...,
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role = NA,
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trained = FALSE,
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columns = NULL,
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skip = FALSE,
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id = recipes::rand_id("mic_log2")
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) {
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recipes::add_step(
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recipe,
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step_mic_log2_new(
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@@ -195,13 +196,14 @@ tidy.step_mic_log2 <- function(x, ...) {
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#' @rdname amr-tidymodels
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#' @export
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step_sir_numeric <- function(
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recipe,
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...,
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role = NA,
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trained = FALSE,
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columns = NULL,
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skip = FALSE,
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id = recipes::rand_id("sir_numeric")) {
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recipe,
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...,
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role = NA,
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trained = FALSE,
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columns = NULL,
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skip = FALSE,
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id = recipes::rand_id("sir_numeric")
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) {
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recipes::add_step(
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recipe,
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step_sir_numeric_new(
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@@ -29,73 +29,88 @@
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#' Filter Top *n* Microorganisms
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#'
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#' This function filters a data set to include only the top *n* microorganisms based on a specified property, such as taxonomic family or genus. For example, it can filter a data set to the top 3 species, or to any species in the top 5 genera, or to the top 3 species in each of the top 5 genera.
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#' Filters a data set to include only the top *n* microorganisms based on a specified property, such as taxonomic family or genus. For example, it can filter a data set to the top 3 species, to any species in the top 5 genera, or to the top 3 species in each of the top 5 genera.
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#' @param x A data frame containing microbial data.
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#' @param n An integer specifying the maximum number of unique values of the `property` to include in the output.
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#' @param property A character string indicating the microorganism property to use for filtering. Must be one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, documentation = TRUE)`. If `NULL`, the raw values from `col_mo` will be used without transformation. When using `"species"` (default) or `"subpecies"`, the genus will be added to make sure each (sub)species still belongs to the right genus.
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#' @param n_for_each An optional integer specifying the maximum number of rows to retain for each value of the selected property. If `NULL`, all rows within the top *n* groups will be included.
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#' @param n A positive whole number specifying the maximum number of unique values of `property` to include in the output.
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#' @param property A character string indicating the microorganism property to use for filtering. Must be one of the column names of the [microorganisms] data set: `r vector_or(colnames(microorganisms), sort = FALSE, documentation = TRUE)`. If `NULL`, the raw values from `col_mo` will be used without transformation. When using `"species"` (default) or `"subspecies"`, the genus is prepended to ensure each name is unambiguous.
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#' @param n_for_each An optional positive whole number specifying the maximum number of distinct microorganism groups at the level of `property_for_each` to retain within each of the top *n* groups. Only used when `property_for_each` is also set.
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#' @param property_for_each The microorganism property to use for sub-grouping within each top *n* group. Must be one of the column names of the [microorganisms] data set and at a strictly lower taxonomic rank than `property` (allowed order: domain > kingdom > phylum > class > order > family > genus > species > subspecies). Defaults to `"species"`. Only relevant when `n_for_each` is set.
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#' @param col_mo A character string indicating the column in `x` that contains microorganism names or codes. Defaults to the first column of class [`mo`]. Values will be coerced using [as.mo()].
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#' @param ... Additional arguments passed on to [mo_property()] when `property` is not `NULL`.
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#' @details This function is useful for preprocessing data before creating [antibiograms][antibiogram()] or other analyses that require focused subsets of microbial data. For example, it can filter a data set to only include isolates from the top 10 species.
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#' @details This function is useful for preprocessing data before creating [antibiograms][antibiogram()] or other analyses that require focused subsets of microbial data.
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#' @export
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#' @seealso [mo_property()], [as.mo()], [antibiogram()]
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#' @examples
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#' # filter to the top 3 species:
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#' top_n_microorganisms(example_isolates,
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#' n = 3
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#' )
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#' top_n_microorganisms(example_isolates, n = 3)
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#'
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#' # filter to any species in the top 5 genera:
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#' top_n_microorganisms(example_isolates,
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#' n = 5, property = "genus"
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#' )
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#' top_n_microorganisms(example_isolates, n = 5, property = "genus")
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#'
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#' # filter to the top 3 species in each of the top 5 genera:
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#' top_n_microorganisms(example_isolates,
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#' n = 5, property = "genus", n_for_each = 3
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#' )
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top_n_microorganisms <- function(x, n, property = "species", n_for_each = NULL, col_mo = NULL, ...) {
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#'
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#' # filter to the top 2 genera in each of the top 3 families:
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#' top_n_microorganisms(example_isolates,
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#' n = 3, property = "family", n_for_each = 2, property_for_each = "genus"
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#' )
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top_n_microorganisms <- function(x, n, property = "species", n_for_each = NULL, property_for_each = "species", col_mo = NULL, ...) {
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meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
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meet_criteria(n, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE)
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meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(AMR::microorganisms))
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meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(AMR::microorganisms), allow_NULL = TRUE)
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meet_criteria(n_for_each, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE, allow_NULL = TRUE)
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meet_criteria(property_for_each, allow_class = "character", has_length = 1, is_in = colnames(AMR::microorganisms), allow_NULL = TRUE)
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meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
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if (is.null(col_mo)) {
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col_mo <- search_type_in_df(x = x, type = "mo", info = TRUE)
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stop_if(is.null(col_mo), "{.arg col_mo} must be set")
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}
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x.bak <- x
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.taxonomic_ranks <- c("domain", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies")
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if (!is.null(n_for_each) && !is.null(property) && !is.null(property_for_each)) {
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prop_rank <- match(property, .taxonomic_ranks)
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each_rank <- match(property_for_each, .taxonomic_ranks)
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if (!is.na(prop_rank) && !is.na(each_rank) && each_rank <= prop_rank) {
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stop_(
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"`property_for_each` (\"", property_for_each, "\") must be at a lower ",
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"taxonomic rank than `property` (\"", property, "\")"
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)
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}
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}
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x.bak <- x
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x[, col_mo] <- as.mo(x[, col_mo, drop = TRUE], keep_synonyms = TRUE)
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if (is.null(property)) {
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x$prop_val <- x[[col_mo]]
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} else if (property == "species") {
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x$prop_val <- paste(mo_genus(x[[col_mo]], ...), mo_species(x[[col_mo]], ...))
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} else if (property == "subspecies") {
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x$prop_val <- paste(mo_genus(x[[col_mo]], ...), mo_species(x[[col_mo]], ...), mo_subspecies(x[[col_mo]], ...))
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} else {
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x$prop_val <- mo_property(x[[col_mo]], property = property, ...)
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get_prop_val <- function(prop) {
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if (is.null(prop)) {
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x[[col_mo]]
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} else if (prop == "species") {
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paste(mo_genus(x[[col_mo]], ...), mo_species(x[[col_mo]], ...))
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} else if (prop == "subspecies") {
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paste(mo_genus(x[[col_mo]], ...), mo_species(x[[col_mo]], ...), mo_subspecies(x[[col_mo]], ...))
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} else {
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mo_property(x[[col_mo]], property = prop, ...)
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}
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}
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counts <- sort(table(x$prop_val), decreasing = TRUE)
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n <- as.integer(n)
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if (length(counts) < n) {
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n <- length(counts)
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}
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count_values <- names(counts)[seq_len(n)]
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filtered_rows <- which(x$prop_val %in% count_values)
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x$prop_val <- get_prop_val(property)
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counts <- sort(table(x$prop_val), decreasing = TRUE)
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n <- min(as.integer(n), length(counts))
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filtered_rows <- which(x$prop_val %in% names(counts)[seq_len(n)])
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if (!is.null(n_for_each)) {
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n_for_each <- as.integer(n_for_each)
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x$prop_val_each <- get_prop_val(property_for_each)
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filtered_x <- x[filtered_rows, , drop = FALSE]
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filtered_x$.orig_row <- filtered_rows
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filtered_rows <- do.call(
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c,
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lapply(split(filtered_x, filtered_x$prop_val), function(group) {
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top_values <- names(sort(table(group[[col_mo]]), decreasing = TRUE)[seq_len(n_for_each)])
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top_values <- top_values[!is.na(top_values)]
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which(x[[col_mo]] %in% top_values)
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top_each <- names(sort(table(group$prop_val_each), decreasing = TRUE)[seq_len(n_for_each)])
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group$.orig_row[group$prop_val_each %in% top_each[!is.na(top_each)]]
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})
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)
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}
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