Vec2Dtm: Convert a character vector to a document term matrix of class...
In ChengMengli/topic: Functions for Text Mining and Topic Modeling

Description Usage Arguments Value Examples

This function is deprecated. Use CreateDtm instead.

Vec2Dtm(vec, docnames = names(vec), min.n.gram = 1, max.n.gram = 1,
  remove.stopwords = TRUE, custom.stopwords = NULL, lower = TRUE,
  remove.punctuation = TRUE, remove.numbers = TRUE, stem.document = FALSE,
  ...)

`vec`	A character vector of documents.
`docnames`	A vector of names for your documents. Defaults to `names(doc_vec)`. If NULL, then docnames is set to be `1:length(doc_vec)`.
`min.n.gram`	The minimum size of n for creating n-grams. Defaults to 1.
`max.n.gram`	The maximum size of n for creating n-grams. Defaults to 1. Numbers greater than 3 are discouraged due to risk of overfitting.
`remove.stopwords`	Do you want to remove standard stopwords from your documents? Defaults to `TRUE`.
`custom.stopwords`	If not `NULL` (the default) a character vector of stopwords to remove from your corpus.
`lower`	Do you want all words coerced to lower case? Defaults to `TRUE`
`remove.punctuation`	Do you want to convert all non-alpha numeric characters to spaces? Defaults to `TRUE`
`remove.numbers`	Do you want to convert all numbers to spaces? Defaults to `TRUE`
`stem.document`	Do you want to stem the words in your document using Porter's word stemmer? Defaults to `FALSE`
`...`	Other arguments to be passed to `TmParallelApply`.

A document term matrix of class dgCMatrix. The rows index documents. The columns index terms. The i, j entries represent the count of term j appearing in document i.

## Not run: 
data(nih_sample)


dtm <- Vec2Dtm(vec = nih_sample$ABSTRACT_TEXT,
               docnames = nih_sample$APPLICATION_ID, 
               min.n.gram = 1, max.n.gram = 2)

dim(dtm)

head(colnames(dtm))

head(rownames(dtm))

## End(Not run)