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#' Randomly sample documents from a dfm
#'
#' Take a random sample of documents of the specified size from a dfm, with
#' or without replacement, optionally by grouping variables or with probability
#' weights.
#' @param x the [dfm] object whose documents will be sampled
#' @inheritParams corpus_sample
#' @export
#' @return a [dfm] object (re)sampled on the documents, containing the document
#' variables for the documents sampled.
#' @seealso [sample]
#' @keywords dfm
#' @examples
#' set.seed(10)
#' dfmat <- dfm(tokens(c("a b c c d", "a a c c d d d", "a b b c")))
#' dfmat
#' dfm_sample(dfmat)
#' dfm_sample(dfmat, replace = TRUE)
#'
#' # by groups
#' dfmat <- dfm(tokens(data_corpus_inaugural[50:58]))
#' dfm_sample(dfmat, by = Party, size = 2)
dfm_sample <- function(x, size = NULL, replace = FALSE, prob = NULL, by = NULL) {
UseMethod("dfm_sample")
}
#' @export
dfm_sample.default <- function(x, size = NULL, replace = FALSE, prob = NULL, by = NULL) {
check_class(class(x), "dfm_sample")
}
#' @export
dfm_sample.dfm <- function(x, size = NULL, replace = FALSE, prob = NULL, by = NULL) {
x <- as.dfm(x)
if (!missing(by)) {
by <- eval(substitute(by), get_docvars(x, user = TRUE, system = TRUE), parent.frame())
if (is.factor(by)) by <- droplevels(by)
}
i <- resample(seq_len(ndoc(x)), size = size, replace = replace, prob = prob, by = by)
return(x[i, ])
}
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