Construct a corpus object

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 kwic object constructed by kwic().
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.

Usage

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, ...)

Arguments

x: a valid corpus source object
...: not used directly
docnames: 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.
docvars: a data.frame of document-level variables associated with each text
meta: 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().
unique_docnames: 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.
docid_field: 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").
text_field: 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.
split_context: 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.
extract_keyword: logical; if TRUE, save the keyword matching pattern as a new docvar keyword
concatenator: character between tokens, default is the whitespace.

Value

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.

Details

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().

Examples

# 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       136          BNP
#>     Coalition   142    260        12    Coalition
#>  Conservative   251    499        21 Conservative
#>        Greens   322    679        30       Greens
#>        Labour   298    683        33       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       136     NA 2026-07-23 01:01:38          NA
#>     Coalition   142    260        12     NA 2026-07-23 01:01:38          NA
#>  Conservative   251    499        21     NA 2026-07-23 01:01:38          NA
#>        Greens   322    679        30     NA 2026-07-23 01:01:38          NA
#>        Labour   298    683        33     NA 2026-07-23 01:01:38          NA
#>        LibDem   251    483        26     NA 2026-07-23 01:01:38          NA
#>            PC    77    114         5     NA 2026-07-23 01:01:38          NA
#>           SNP    88    134         4     NA 2026-07-23 01:01:38          NA
#>          UKIP   346    723        37     NA 2026-07-23 01:01:38          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
#>

Usage

Arguments

Value

Details

See also

Examples