tidytext (version 0.1.2)

unnest_tokens: Split a column into tokens using the tokenizers package

Description

Split a column into tokens using the tokenizers package

Usage

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

Arguments

tbl

Data frame

output_col

Output column to be created

input_col

Input column that gets split

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"

output

Output column to be created as bare name

input

Input column that gets split as bare name

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

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")