Extract a subset of a dfm — dfm

Returns document subsets of a dfm that meet certain conditions, including direct logical operations on docvars (document-level variables). dfm_subset functions identically to subset.data.frame(), using non-standard evaluation to evaluate conditions based on the docvars in the dfm.

Usage

dfm_subset(
  x,
  subset,
  min_ntoken = NULL,
  max_ntoken = NULL,
  drop_docid = TRUE,
  verbose = quanteda_options("verbose"),
  ...
)

Arguments

x: dfm object to be subsetted.
subset: logical expression indicating the documents to keep: missing values are taken as false.
min_ntoken, max_ntoken: minimum and maximum lengths of the documents to extract.
drop_docid: if TRUE, docid for documents are removed as the result of subsetting.
verbose: if TRUE print the number of tokens and documents before and after the function is applied. The number of tokens does not include paddings.
...: not used

Value

dfm object, with a subset of documents (and docvars) selected according to arguments

Details

To select or subset features, see dfm_select() instead.

Examples

corp <- corpus(c(d1 = "a b c d", d2 = "a a b e",
                 d3 = "b b c e", d4 = "e e f a b"),
               docvars = data.frame(grp = c(1, 1, 2, 3)))
dfmat <- dfm(tokens(corp))
# selecting on a docvars condition
dfm_subset(dfmat, grp > 1)
#> Document-feature matrix of: 2 documents, 6 features (41.67% sparse) and 1
#> docvar.
#>     features
#> docs a b c d e f
#>   d3 0 2 1 0 1 0
#>   d4 1 1 0 0 2 1
# selecting on a supplied vector
dfm_subset(dfmat, c(TRUE, FALSE, TRUE, FALSE))
#> Document-feature matrix of: 2 documents, 6 features (41.67% sparse) and 1
#> docvar.
#>     features
#> docs a b c d e f
#>   d1 1 1 1 1 0 0
#>   d3 0 2 1 0 1 0