dfm_select: Select features from a dfm or fcm

In quanteda: Quantitative Analysis of Textual Data

Select features from a dfm or fcm

Description

This function selects or removes features from a dfm or fcm, based on feature name matches with pattern. The most common usages are to eliminate features from a dfm already constructed, such as stopwords, or to select only terms of interest from a dictionary.

Usage

dfm_select(
  x,
  pattern = NULL,
  selection = c("keep", "remove"),
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  min_nchar = NULL,
  max_nchar = NULL,
  padding = FALSE,
  verbose = quanteda_options("verbose")
)

dfm_remove(x, ...)

dfm_keep(x, ...)

fcm_select(
  x,
  pattern = NULL,
  selection = c("keep", "remove"),
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  verbose = quanteda_options("verbose"),
  ...
)

fcm_remove(x, ...)

fcm_keep(x, ...)

Arguments

x	the dfm or fcm object whose features will be selected
pattern	a character vector, list of character vectors, dictionary, or collocations object. See pattern for details.
selection	whether to keep or remove the features
valuetype	the type of pattern matching: "glob" for "glob"-style wildcard expressions; "regex" for regular expressions; or "fixed" for exact matching. See valuetype for details.
case_insensitive	logical; if TRUE, ignore case when matching a pattern or dictionary values
min_nchar, max_nchar	optional numerics specifying the minimum and maximum length in characters for tokens to be removed or kept; defaults are NULL for no limits. These are applied after (and hence, in addition to) any selection based on pattern matches.
padding	if TRUE, record the number of removed tokens in the first column.
verbose	if TRUE, print message about how many pattern were removed
...	used only for passing arguments from dfm_remove or dfm_keep to dfm_select. Cannot include selection.

Details

dfm_remove and fcm_remove are simply a convenience wrappers to calling dfm_select and fcm_select with selection = "remove".

dfm_keep and fcm_keep are simply a convenience wrappers to calling dfm_select and fcm_select with selection = "keep".

Value

A dfm or fcm object, after the feature selection has been applied.

For compatibility with earlier versions, when pattern is a dfm object and selection = "keep", then this will be equivalent to calling dfm_match(). In this case, the following settings are always used: case_insensitive = FALSE, and valuetype = "fixed". This functionality is deprecated, however, and you should use dfm_match() instead.

Note

This function selects features based on their labels. To select features based on the values of the document-feature matrix, use dfm_trim().

See Also

dfm_match()

Examples

dfmat <- tokens(c("My Christmas was ruined by your opposition tax plan.",
               "Does the United_States or Sweden have more progressive taxation?")) |>
    dfm(tolower = FALSE)
dict <- dictionary(list(countries = c("United_States", "Sweden", "France"),
                        wordsEndingInY = c("by", "my"),
                        notintext = "blahblah"))
dfm_select(dfmat, pattern = dict)
dfm_select(dfmat, pattern = dict, case_insensitive = FALSE)
dfm_select(dfmat, pattern = c("s$", ".y"), selection = "keep", valuetype = "regex")
dfm_select(dfmat, pattern = c("s$", ".y"), selection = "remove", valuetype = "regex")
dfm_select(dfmat, pattern = stopwords("english"), selection = "keep", valuetype = "fixed")
dfm_select(dfmat, pattern = stopwords("english"), selection = "remove", valuetype = "fixed")

# select based on character length
dfm_select(dfmat, min_nchar = 5)

dfmat <- dfm(tokens(c("This is a document with lots of stopwords.",
                      "No if, and, or but about it: lots of stopwords.")))
dfmat
dfm_remove(dfmat, stopwords("english"))
toks <- tokens(c("this contains lots of stopwords",
                 "no if, and, or but about it: lots"),
               remove_punct = TRUE)
fcmat <- fcm(toks)
fcmat
fcm_remove(fcmat, stopwords("english"))

quanteda documentation built on Sept. 11, 2024, 6:08 p.m.