term_matrix: Term Frequency Tabulation
In corpus: Text Corpus Analysis

Description Usage Arguments Details Value See Also Examples

Tokenize a set of texts and compute a term frequency matrix.

term_matrix(x, filter = NULL, ngrams = NULL, select = NULL,
            group = NULL, transpose = FALSE, ...)

term_counts(x, filter = NULL, ngrams = NULL, select = NULL,
            group = NULL, ...)

`x`	a text vector to tokenize.
`filter`	if non-`NULL`, a text filter to to use instead of the default text filter for `x`.
`ngrams`	an integer vector of n-gram lengths to include, or `NULL` to use the `select` argument to determine the n-gram lengths.
`select`	a character vector of terms to count, or `NULL` to count all terms that appear in `x`.
`group`	if non-`NULL`, a factor, character string, or integer vector the same length of `x` specifying the grouping behavior.
`transpose`	a logical value indicating whether to transpose the result, putting terms as rows instead of columns.
`...`	additional properties to set on the text filter.

term_matrix tokenizes a set of texts and computes the occurrence counts for each term, returning the result as a sparse matrix (texts-by-terms). term_counts returns the same information, but in a data frame.

If ngrams is non-NULL, then multi-type n-grams are included in the output for all lengths appearing in the ngrams argument. If ngrams is NULL but select is non-NULL, then all n-grams appearing in the select set are included. If both ngrams and select are NULL, then only unigrams (single type terms) are included.

If group is NULL, then the output has one set of term counts for each input text. Otherwise, we convert group to a factor and compute one set of term counts for each level. Texts with NA values for group get skipped.

term_matrix with transpose = FALSE returns a sparse matrix in "dgCMatrix" format with one column for each term and one row for each input text or (if group is non-NULL) for each grouping level. If filter$select is non-NULL, then the column names will be equal to filter$select. Otherwise, the columns are assigned in arbitrary order.

term_matrix with transpose = TRUE returns the transpose of the term matrix, in "dgCMatrix" format.

term_counts with group = NULL returns a data frame with one row for each entry of the term matrix, and columns "text", "term", and "count" giving the text ID, term, and count. The "term" column is a factor with levels equal to the selected terms. The "text" column is a factor with levels equal to names(as_corpus_text(x)); calling as.integer on the "text" column converts from the factor values to the integer row index in the term matrix.

term_counts with group non-NULL behaves similarly, but the result instead has columns named "group", "term", and "count", with "group" giving the grouping level, as a factor.

text_tokens, term_stats.

text <- c("A rose is a rose is a rose.",
          "A Rose is red, a violet is blue!",
          "A rose by any other name would smell as sweet.")
term_matrix(text)

# select certain terms
term_matrix(text, select = c("rose", "red", "violet", "sweet"))

# specify a grouping factor
term_matrix(text, group = c("Good", "Bad", "Good"))

# include higher-order n-grams
term_matrix(text, ngrams = 1:3)

# select certain multi-type terms
term_matrix(text, select = c("a rose", "a violet", "sweet", "smell"))

# transpose the result
term_matrix(text, ngrams = 1:2, transpose = TRUE)[1:10, ] # first 10 rows

# data frame
head(term_counts(text), n = 10) # first 10 rows

# with grouping
term_counts(text, group = c("Good", "Bad", "Good"))

# taking names from the input
term_counts(c(a = "One sentence.", b = "Another", c = "!!"))