convert: Convert quanteda objects to non-quanteda formats

In quanteda: Quantitative Analysis of Textual Data

Convert quanteda objects to non-quanteda formats

Description

Convert a quanteda dfm or corpus object to a format useable by other packages. The general function convert provides easy conversion from a dfm to the document-term representations used in all other text analysis packages for which conversions are defined. For corpus objects, convert provides an easy way to make a corpus and its document variables into a data.frame.

Usage

convert(x, to, ...)

## S3 method for class 'dfm'
convert(
  x,
  to = c("lda", "tm", "stm", "austin", "topicmodels", "lsa", "matrix", "data.frame",
    "tripletlist"),
  docvars = NULL,
  omit_empty = TRUE,
  docid_field = "doc_id",
  ...
)

## S3 method for class 'corpus'
convert(x, to = c("data.frame", "json"), pretty = FALSE, ...)

Arguments

x	a dfm or corpus to be converted
to	target conversion format, one of: "lda" a list with components "documents" and "vocab" as needed by the function lda.collapsed.gibbs.sampler from the lda package "tm" a DocumentTermMatrix from the tm package. Note: The tm package version of as.TermDocumentMatrix() allows a weighting argument, which supplies a weighting function for TermDocumentMatrix(). Here the default is for term frequency weighting. If you want a different weighting, apply the weights after converting using one of the tm functions. For other available weighting functions from the tm package, see TermDocumentMatrix. "stm" the format for the stm package "austin" the wfm format from the austin package "topicmodels" the "dtm" format as used by the topicmodels package "lsa" the "textmatrix" format as used by the lsa package "data.frame" a data.frame of without row.names, in which documents are rows, and each feature is a variable (for a dfm), or each text and its document variables form a row (for a corpus) "json" (corpus only) convert a corpus and its document variables into JSON format, using the format described in jsonlite::toJSON() "tripletlist" a named "triplet" format list consisting of document, feature, and frequency
...	unused directly
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.
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.
docid_field	character; the name of the column containing document names used when to = "data.frame". Unused for other conversions.
pretty	adds indentation whitespace to JSON output. Can be TRUE/FALSE or a number specifying the number of spaces to indent (default is 2). Use a negative number for tabs instead of spaces.

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.

Examples

## convert a dfm

toks <- corpus_subset(data_corpus_inaugural, Year > 1970) |>
    tokens()
dfmat1 <- dfm(toks)

# austin's wfm format
identical(dim(dfmat1), dim(convert(dfmat1, to = "austin")))

# stm package format
stmmat <- convert(dfmat1, to = "stm")
str(stmmat)

# triplet
tripletmat <- convert(dfmat1, to = "tripletlist")
str(tripletmat)

## Not run: 
# tm's DocumentTermMatrix format
tmdfm <- convert(dfmat1, to = "tm")
str(tmdfm)

# topicmodels package format
str(convert(dfmat1, to = "topicmodels"))

# lda package format
str(convert(dfmat1, to = "lda"))

## End(Not run)

## convert a corpus into a data.frame

corp <- corpus(c(d1 = "Text one.", d2 = "Text two."),
               docvars = data.frame(dvar1 = 1:2, dvar2 = c("one", "two"),
                                    stringsAsFactors = FALSE))
convert(corp, to = "data.frame")
convert(corp, to = "json")

quanteda documentation built on June 8, 2025, 9:41 p.m.