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.
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, ...)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
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.
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.
character; the name of the column containing document
names used when to = "data.frame". Unused for other conversions.
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.
A converted object determined by the value of to (see above).
See conversion target package documentation for more detailed descriptions
of the return formats.
## 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")))
#> [1] TRUE
# stm package format
stmmat <- convert(dfmat1, to = "stm")
str(stmmat)
#> List of 3
#> $ documents:List of 14
#> ..$ 1973-Nixon : int [1:2, 1:515] 2 2 6 96 7 17 8 69 20 1 ...
#> ..$ 1977-Carter : int [1:2, 1:501] 2 4 4 1 5 1 6 65 7 12 ...
#> ..$ 1981-Reagan : int [1:2, 1:850] 2 20 6 174 7 10 8 130 19 1 ...
#> ..$ 1985-Reagan : int [1:2, 1:876] 2 6 3 1 4 1 5 1 6 177 ...
#> ..$ 1989-Bush : int [1:2, 1:756] 2 6 6 166 7 8 8 142 25 2 ...
#> ..$ 1993-Clinton : int [1:2, 1:605] 2 4 6 139 8 81 29 1 42 5 ...
#> ..$ 1997-Clinton : int [1:2, 1:726] 2 4 6 131 7 13 8 108 18 1 ...
#> ..$ 2001-Bush : int [1:2, 1:592] 2 2 6 110 7 2 8 96 30 1 ...
#> ..$ 2005-Bush : int [1:2, 1:734] 2 6 3 1 6 120 7 2 8 98 ...
#> ..$ 2009-Obama : int [1:2, 1:900] 1 1 2 2 6 130 7 22 8 118 ...
#> ..$ 2013-Obama : int [1:2, 1:786] 6 99 7 13 8 89 13 1 25 1 ...
#> ..$ 2017-Trump : int [1:2, 1:547] 2 2 3 1 6 96 7 11 8 88 ...
#> ..$ 2025-Trump : int [1:2, 1:744] 2 10 6 147 8 210 9 1 10 1 ...
#> ..$ 2021-Biden.txt: int [1:2, 1:744] 2 10 6 147 8 210 9 1 10 1 ...
#> $ vocab : chr [1:3616] "!" "\"" "'" "(" ...
#> $ meta :'data.frame': 14 obs. of 4 variables:
#> ..$ Year : int [1:14] 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 ...
#> ..$ President: chr [1:14] "Nixon" "Carter" "Reagan" "Reagan" ...
#> ..$ FirstName: chr [1:14] "Richard Milhous" "Jimmy" "Ronald" "Ronald" ...
#> ..$ Party : Factor w/ 6 levels "Democratic","Democratic-Republican",..: 5 1 5 5 5 1 1 5 5 1 ...
# triplet
tripletmat <- convert(dfmat1, to = "tripletlist")
str(tripletmat)
#> List of 3
#> $ document : chr [1:9876] "1973-Nixon" "1981-Reagan" "1989-Bush" "2005-Bush" ...
#> $ feature : chr [1:9876] "mr" "mr" "mr" "mr" ...
#> $ frequency: num [1:9876] 3 3 6 1 1 69 52 130 124 142 ...
if (FALSE) { # \dontrun{
# 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"))
} # }
## 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")
#> doc_id text dvar1 dvar2
#> 1 d1 Text one. 1 one
#> 2 d2 Text two. 2 two
convert(corp, to = "json")
#> [{"doc_id":"d1","text":"Text one.","dvar1":1,"dvar2":"one"},{"doc_id":"d2","text":"Text two.","dvar1":2,"dvar2":"two"}]