quanteda (version 4.1.0)

convert-wrappers: Convenience wrappers for dfm convert

Description

To make the usage as consistent as possible with other packages, quanteda also provides shortcut wrappers to convert(), designed to be similar in syntax to analogous commands in the packages to whose format they are converting.

Usage

dfm2austin(x)
dfm2tm(x, weighting = tm::weightTf)
dfm2lda(x, omit_empty = TRUE)
dtm2lda(x, omit_empty = TRUE)
dfm2dtm(x, omit_empty = TRUE)
dfm2stm(x, docvars = NULL, omit_empty = TRUE)

Value

A converted object determined by the value of to (see above). See conversion target package documentation for more detailed descriptions of the return formats.

Arguments

x

the dfm to be converted

weighting

a tm weight, see tm::weightTf()

omit_empty

logical; if TRUE, omit empty documents and features from the converted dfm. This is required for some formats (such as STM) that do not accept empty documents. Only used when to = "lda" or to = "topicmodels". For to = "stm" format, omit_empty is always TRUE.

docvars

optional data.frame of document variables used as the meta information in conversion to the stm package format. This aids in selecting the document variables only corresponding to the documents with non-zero counts. Only affects the "stm" format.

Details

dfm2lda provides converts a dfm into the list representation of terms in documents used by the lda package (a list with components "documents" and "vocab" as needed by lda::lda.collapsed.gibbs.sampler()).

dfm2ldaformat provides converts a dfm into the list representation of terms in documents used by the lda package (a list with components "documents" and "vocab" as needed by lda::lda.collapsed.gibbs.sampler()).

Examples

dfmat <- corpus_subset(data_corpus_inaugural, Year > 1970) |>
    tokens() |>
    dfm()

if (FALSE) {
# shortcut conversion to lda package list format
identical(quanteda:::dfm2lda(dfmat), convert(dfmat, to = "lda"))
}

if (FALSE) {
# shortcut conversion to lda package list format
identical(dfm2ldaformat(dfmat), convert(dfmat, to = "lda"))
}