quanteda (version 1.1.1)

corpus: Construct a corpus object

Description

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 textid_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 metacorpus information.

• a corpus object.

Usage

corpus(x, ...)
# S3 method for corpus
corpus(x, docnames = quanteda::docnames(x),
  docvars = quanteda::docvars(x), metacorpus = quanteda::metacorpus(x),
  compress = FALSE, ...)
# S3 method for character
corpus(x, docnames = NULL, docvars = NULL,
  metacorpus = NULL, compress = FALSE, ...)
# S3 method for data.frame
corpus(x, docid_field = NULL, text_field = "text",
  metacorpus = NULL, compress = FALSE, ...)
# S3 method for kwic
corpus(x, ...)
# S3 method for Corpus
corpus(x, metacorpus = NULL, compress = FALSE, ...)

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

metacorpus

a named list containing additional (character) information to be added to the corpus as corpus-level metadata. Special fields recognized in the summary.corpus are:

• source a description of the source of the texts, used for referencing;

• citation information on how to cite the corpus; and

• notes any additional information about who created the text, warnings, to do lists, etc.

compress

logical; if TRUE, compress the texts in memory using gzip compression. This significantly reduces the size of the corpus in memory, but will slow down operations that require the texts to be extracted.

docid_field

optional column index of a document identifier; if NULL, the constructor will use the row.names of the data.frame (if found)

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 (including those created by readtext).

Value

A corpus-class class object containing the original texts, document-level variables, document-level metadata, corpus-level metadata, and default settings for subsequent processing of the corpus.

A warning on accessing corpus elements

A corpus currently consists of an S3 specially classed list of elements, but you should not access these elements directly. 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 (as it inevitably will as we continue to develop the package, including moving corpus objects to the S4 class system).

Details

The texts and document variables of corpus objects can also be accessed using index notation. Indexing a corpus object as a vector will return its text, equivalent to texts(x). Note that this is not the same as subsetting the entire corpus -- this should be done using the subset method for a corpus.

Indexing a corpus using two indexes (integers or column names) will return the document variables, equivalent to docvars(x). It is also possible to access, create, or replace docvars using list notation, e.g.

myCorpus[["newSerialDocvar"]] <- paste0("tag", 1:ndoc(myCorpus)).

For details, see corpus-class.

See Also

corpus-class, docvars, metadoc, metacorpus, settings, texts, ndoc, docnames

Examples

# NOT RUN {
# create a corpus from texts
corpus(data_char_ukimmig2010)

# 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(texts(data_corpus_irishbudget2010))

# import a tm VCorpus
if (requireNamespace("tm", quietly = TRUE)) {
    data(crude, package = "tm")    # load in a tm example VCorpus
    mytmCorpus <- corpus(crude)
    summary(mytmCorpus, showmeta=TRUE)

    data(acq, package = "tm")
    summary(corpus(acq), 5, showmeta=TRUE)

    tmCorp <- tm::VCorpus(tm::VectorSource(data_char_ukimmig2010))
    quantCorp <- corpus(tmCorp)
    summary(quantCorp)
}

# construct a corpus from a data.frame
mydf <- 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))
mydf
summary(corpus(mydf, text_field = "some_text",
               metacorpus = list(source = "From a data.frame called mydf.")))

# construct a corpus from a kwic object
mykwic <- kwic(data_corpus_inaugural, "southern")
summary(corpus(mykwic))
# }