unnest_tokens

From tidytext v0.1.3 by Julia Silge

Split a column into tokens using the tokenizers package

Split a column into tokens using the tokenizers package, splitting the table into one-token-per-row. unnest_tokens_ is the standard evaluation version.

Usage

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

Arguments

tbl: Data frame
output: Output column to be created as bare name.
input: Input column that gets split as bare name.
token: Unit for tokenizing, or a custom tokenizing function. Built-in options are "words" (default), "characters", "ngrams", "skip_ngrams", "sentences", "lines", "paragraphs", and "regex". 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 turn column lowercase.
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 the tokenizer, such as n and k for "ngrams" and "skip_ngrams" or 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.

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 <- data_frame(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(ngram, txt, token = "skip_ngrams", n = 4, k = 2)

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

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

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

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

# }

Documentation reproduced from package tidytext, version 0.1.3, License: MIT + file LICENSE

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