dfm: Create a document-feature matrix

View source: R/dfm.R

dfmR Documentation

Create a document-feature matrix

Description

Construct a sparse document-feature matrix, from a character, corpus, tokens, or even other dfm object.

Usage

dfm(
  x,
  tolower = TRUE,
  remove_padding = FALSE,
  verbose = quanteda_options("verbose"),
  ...
)

Arguments

x

a tokens or dfm object

tolower

convert all features to lowercase

remove_padding

logical; if TRUE, remove the "pads" left as empty tokens after calling tokens() or tokens_remove() with padding = TRUE

verbose

display messages if TRUE

...

not used directly

Value

a dfm object

Changes in version 3

In quanteda v3, many convenience functions formerly available in dfm() were deprecated. Formerly, dfm() could be called directly on a character or corpus object, but we now steer users to tokenise their inputs first using tokens(). Other convenience arguments to dfm() were also removed, such as select, dictionary, thesaurus, and groups. All of these functions are available elsewhere, e.g. through dfm_group(). See news(Version >= "2.9", package = "quanteda") for details.

See Also

dfm_select(), dfm

Examples

## for a corpus
toks <- data_corpus_inaugural %>%
  corpus_subset(Year > 1980) %>%
  tokens()
dfm(toks)

# removal options
toks <- tokens(c("a b c", "A B C D")) %>%
    tokens_remove("b", padding = TRUE)
toks
dfm(toks)
dfm(toks) %>%
 dfm_remove(pattern = "") # remove "pads"

# preserving case
dfm(toks, tolower = FALSE)

quanteda documentation built on May 31, 2023, 8:28 p.m.