tidytext (version 0.2.6)

unnest_tokens: Split a column into tokens using the tokenizers package

Description

Split a column into tokens using the tokenizers package, splitting the table into one-token-per-row. This function supports non-standard evaluation through the tidyeval framework.

Usage

unnest_tokens(
  tbl,
  output,
  input,
  token = "words",
  format = c("text", "man", "latex", "html", "xml"),
  to_lower = TRUE,
  drop = TRUE,
  collapse = NULL,
  ...
)

Arguments

tbl

A data frame

output

Output column to be created as string or symbol.

input

Input column that gets split as string or symbol.

The output/input arguments are passed by expression and support quasiquotation; you can unquote strings and symbols.

token

Unit for tokenizing, or a custom tokenizing function. Built-in options are "words" (default), "characters", "character_shingles", "ngrams", "skip_ngrams", "sentences", "lines", "paragraphs", "regex", "tweets" (tokenization by word that preserves usernames, hashtags, and URLS ), and "ptb" (Penn Treebank). If a function, should take a character vector and return a list of character vectors of the same length.

format

Either "text", "man", "latex", "html", or "xml". If not text, this uses the hunspell tokenizer, and can tokenize only by "word"

to_lower

Whether to convert tokens to lowercase. If tokens include URLS (such as with token = "tweets"), such converted URLs may no longer be correct.

drop

Whether original input column should get dropped. Ignored if the original input and new output column have the same name.

collapse

Whether to combine text with newlines first in case tokens (such as sentences or paragraphs) span multiple lines. If NULL, collapses when token method is "ngrams", "skip_ngrams", "sentences", "lines", "paragraphs", or "regex".

...

Extra arguments passed on to tokenizers, such as strip_punct for "words" and "tweets", n and k for "ngrams" and "skip_ngrams", strip_url for "tweets", and pattern for "regex".

Details

If the unit for tokenizing is ngrams, skip_ngrams, sentences, lines, paragraphs, or regex, the entire input will be collapsed together before tokenizing unless collapse = FALSE.

If format is anything other than "text", this uses the hunspell_parse tokenizer instead of the tokenizers package. This does not yet have support for tokenizing by any unit other than words.

Examples

# NOT RUN {
library(dplyr)
library(janeaustenr)

d <- tibble(txt = prideprejudice)
d

d %>%
  unnest_tokens(word, txt)

d %>%
  unnest_tokens(sentence, txt, token = "sentences")

d %>%
  unnest_tokens(ngram, txt, token = "ngrams", n = 2)

d %>%
  unnest_tokens(chapter, txt, token = "regex", pattern = "Chapter [\\\\d]")

d %>%
  unnest_tokens(shingle, txt, token = "character_shingles", n = 4)

# custom function
d %>%
  unnest_tokens(word, txt, token = stringr::str_split, pattern = " ")

# tokenize HTML
h <- tibble(row = 1:2,
                text = c("<h1>Text <b>is</b>", "<a href='example.com'>here</a>"))

h %>%
  unnest_tokens(word, text, format = "html")

# }