Creates a corpus object from available sources. The currently available sources are:
a character vector, consisting of one document per element; if the elements are named, these names will be used as document names.
a data.frame (or a tibble tbl_df), whose default
document id is a variable identified by docid_field; the text of the
document is a variable identified by text_field; and other variables
are imported as document-level meta-data. This matches the format of
data.frames constructed by the the readtext package.
a tm VCorpus or SimpleCorpus class object, with the fixed metadata fields imported as docvars and corpus-level metadata imported as meta information.
a corpus object.
corpus(x, ...)
# S3 method for class 'corpus'
corpus(
x,
docnames = quanteda::docnames(x),
docvars = quanteda::docvars(x),
meta = quanteda::meta(x),
...
)
# S3 method for class 'character'
corpus(
x,
docnames = NULL,
docvars = NULL,
meta = list(),
unique_docnames = TRUE,
...
)
# S3 method for class 'data.frame'
corpus(
x,
docid_field = "doc_id",
text_field = "text",
meta = list(),
unique_docnames = TRUE,
...
)
# S3 method for class 'kwic'
corpus(
x,
split_context = TRUE,
extract_keyword = TRUE,
meta = list(),
concatenator = " ",
...
)
# S3 method for class 'Corpus'
corpus(x, ...)a valid corpus source object
not used directly
Names to be assigned to the texts. Defaults to the names of
the character vector (if any); doc_id for a data.frame; the document
names in a tm corpus; or a vector of user-supplied labels equal in
length to the number of documents. If none of these are round, then
"text1", "text2", etc. are assigned automatically.
a data.frame of document-level variables associated with each text
a named list that will be added to the corpus as corpus-level,
user meta-data. This can later be accessed or updated using
meta().
logical; if TRUE, enforce strict uniqueness in
docnames; otherwise, rename duplicated docnames using an added serial
number, and treat them as segments of the same document.
optional column index of a document identifier; defaults
to "doc_id", but if this is not found, then will use the rownames of the
data.frame; if the rownames are not set, it will use the default sequence
based on ([quanteda_options]("base_docname").
the character name or numeric index of the source
data.frame indicating the variable to be read in as text, which must
be a character vector. All other variables in the data.frame will be
imported as docvars. This argument is only used for data.frame
objects.
logical; if TRUE, split each kwic row into two
"documents", one for "pre" and one for "post", with this designation saved
in a new docvar context and with the new number of documents
therefore being twice the number of rows in the kwic.
logical; if TRUE, save the keyword matching
pattern as a new docvar keyword
character between tokens, default is the whitespace.
A corpus class object containing the original texts, document-level variables, document-level metadata, corpus-level metadata, and default settings for subsequent processing of the corpus.
For quanteda >= 2.0, this is a specially classed character vector. It has many additional attributes but you should not access these attributes directly, especially if you are another package author. Use the extractor and replacement functions instead, or else your code is not only going to be uglier, but also likely to break should the internal structure of a corpus object change. Using the accessor and replacement functions ensures that future code to manipulate corpus objects will continue to work.
The texts and document variables of corpus objects can also be
accessed using index notation and the $ operator for accessing or assigning
docvars. For details, see [.corpus().
# create a corpus from texts
corpus(data_char_ukimmig2010)
#> Corpus consisting of 9 documents.
#> BNP :
#> "IMMIGRATION: AN UNPARALLELED CRISIS WHICH ONLY THE BNP CAN S..."
#>
#> Coalition :
#> "IMMIGRATION. The Government believes that immigration has e..."
#>
#> Conservative :
#> "Attract the brightest and best to our country. Immigration h..."
#>
#> Greens :
#> "Immigration. Migration is a fact of life. People have alway..."
#>
#> Labour :
#> "Crime and immigration The challenge for Britain We will cont..."
#>
#> LibDem :
#> "firm but fair immigration system Britain has always been an ..."
#>
#> [ reached max_ndoc ... 3 more documents ]
# create a corpus from texts and assign meta-data and document variables
summary(corpus(data_char_ukimmig2010,
docvars = data.frame(party = names(data_char_ukimmig2010))), 5)
#> Corpus consisting of 9 documents, showing 5 documents:
#>
#> Text Types Tokens Sentences party
#> BNP 1125 3280 88 BNP
#> Coalition 142 260 4 Coalition
#> Conservative 251 499 15 Conservative
#> Greens 322 679 21 Greens
#> Labour 298 683 29 Labour
#>
# import a tm VCorpus
if (requireNamespace("tm", quietly = TRUE)) {
data(crude, package = "tm") # load in a tm example VCorpus
vcorp <- corpus(crude)
summary(vcorp)
data(acq, package = "tm")
summary(corpus(acq), 5)
vcorp2 <- tm::VCorpus(tm::VectorSource(data_char_ukimmig2010))
corp <- corpus(vcorp2)
summary(corp)
}
#> Corpus consisting of 9 documents, showing 9 documents:
#>
#> Text Types Tokens Sentences author datetimestamp description
#> BNP 1125 3280 88 NA 2025-05-21 12:50:38 NA
#> Coalition 142 260 4 NA 2025-05-21 12:50:38 NA
#> Conservative 251 499 15 NA 2025-05-21 12:50:38 NA
#> Greens 322 679 21 NA 2025-05-21 12:50:38 NA
#> Labour 298 683 29 NA 2025-05-21 12:50:38 NA
#> LibDem 251 483 14 NA 2025-05-21 12:50:38 NA
#> PC 77 114 5 NA 2025-05-21 12:50:38 NA
#> SNP 88 134 4 NA 2025-05-21 12:50:38 NA
#> UKIP 346 723 26 NA 2025-05-21 12:50:38 NA
#> heading id language origin
#> NA 1 en NA
#> NA 2 en NA
#> NA 3 en NA
#> NA 4 en NA
#> NA 5 en NA
#> NA 6 en NA
#> NA 7 en NA
#> NA 8 en NA
#> NA 9 en NA
#>
# construct a corpus from a data.frame
dat <- data.frame(letter_factor = factor(rep(letters[1:3], each = 2)),
some_ints = 1L:6L,
some_text = paste0("This is text number ", 1:6, "."),
stringsAsFactors = FALSE,
row.names = paste0("fromDf_", 1:6))
dat
#> letter_factor some_ints some_text
#> fromDf_1 a 1 This is text number 1.
#> fromDf_2 a 2 This is text number 2.
#> fromDf_3 b 3 This is text number 3.
#> fromDf_4 b 4 This is text number 4.
#> fromDf_5 c 5 This is text number 5.
#> fromDf_6 c 6 This is text number 6.
summary(corpus(dat, text_field = "some_text",
meta = list(source = "From a data.frame called mydf.")))
#> Corpus consisting of 6 documents, showing 6 documents:
#>
#> Text Types Tokens Sentences letter_factor some_ints
#> fromDf_1 6 6 1 a 1
#> fromDf_2 6 6 1 a 2
#> fromDf_3 6 6 1 b 3
#> fromDf_4 6 6 1 b 4
#> fromDf_5 6 6 1 c 5
#> fromDf_6 6 6 1 c 6
#>