quanteda (version 0.9.9-3)

dfm_select: select features from a dfm or fcm

Description

This function selects or discards features from a dfm or fcm, based on a pattern match with the feature names. 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, features, selection = c("keep", "remove"), valuetype = c("glob", "regex", "fixed"), case_insensitive = TRUE, verbose = TRUE, ...)
dfm_remove(x, features, ...)
fcm_select(x, features, selection = c("keep", "remove"), valuetype = c("glob", "regex", "fixed"), case_insensitive = TRUE, verbose = TRUE, ...)
fcm_remove(x, features, ...)

Arguments

x

the dfm or fcm object whose features will be selected

features

one of: a character vector of features to be selected, a dfm whose features will be used for selection, or a dictionary class object whose values (not keys) will provide the features to be selected. For dfm objects, see details in the Value section below.

selection

whether to keep or remove the features

valuetype

how to interpret keyword expressions: "glob" for "glob"-style wildcard expressions; "regex" for regular expressions; or "fixed" for exact matching. See valuetype for details.

case_insensitive

ignore the case of dictionary values if TRUE

verbose

if TRUE print message about how many features were removed

...

supplementary arguments passed to the underlying functions in stri_detect_regex. (This is how case_insensitive is passed, but you may wish to pass others.)

Value

A dfm or fcm after the feature selection has been applied.When features is a dfm object, then the returned object will be identical in its feature set to the dfm supplied as the features argument. This means that any features in x not in features will be discarded, and that any features in found in the dfm supplied as features but not found in x will be added with all zero counts. This is useful when you have trained a model on one dfm, and need to project this onto a test set whose features must be identical.

Details

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

Examples

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

# selecting on a dfm
textVec1 <- c("This is text one.", "This, the second text.", "Here: the third text.")
textVec2 <- c("Here are new words.", "New words in this text.")
(dfm1 <- dfm(textVec1, verbose = FALSE))
(dfm2a <- dfm(textVec2, verbose = FALSE))
(dfm2b <- dfm_select(dfm2a, dfm1))
setequal(featnames(dfm1), featnames(dfm2b))

# more selection on a dfm
dfm_select(dfm1, dfm2a)
dfm_select(dfm1, dfm2a, selection = "remove")

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