Type:	Package
Title:	Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
Version:	0.4.2
Description:	Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like 'dplyr', 'broom', 'tidyr', and 'ggplot2'. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.
License:	MIT + file LICENSE
URL:	https://juliasilge.github.io/tidytext/, https://github.com/juliasilge/tidytext
BugReports:	https://github.com/juliasilge/tidytext/issues
Depends:	R (≥ 2.10)
Imports:	cli, dplyr (≥ 1.1.1), generics, janeaustenr, lifecycle, Matrix, methods, purrr (≥ 0.1.1), rlang (≥ 0.4.10), stringr, tibble, tokenizers, vctrs
Suggests:	broom, covr, data.table, ggplot2, hunspell, knitr, mallet, NLP, quanteda, readr, reshape2, rmarkdown, scales, stm, stopwords, testthat (≥ 2.1.0), textdata, tidyr, tm, topicmodels, vdiffr, wordcloud
VignetteBuilder:	knitr
Config/Needs/website:	ropensci/gutenbergr
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	TRUE
RoxygenNote:	7.3.1
NeedsCompilation:	no
Packaged:	2024-04-10 11:39:59 UTC; juliasilge
Author:	Gabriela De Queiroz [ctb], Colin Fay [ctb], Emil Hvitfeldt [ctb], Os Keyes [ctb], Kanishka Misra [ctb], Tim Mastny [ctb], Jeff Erickson [ctb], David Robinson [aut], Julia Silge [aut, cre]
Maintainer:	Julia Silge <julia.silge@gmail.com>
Repository:	CRAN
Date/Publication:	2024-04-10 12:50:06 UTC

tidytext: Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools

Description

Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like 'dplyr', 'broom', 'tidyr', and 'ggplot2'. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.

Author(s)

Maintainer: Julia Silge julia.silge@gmail.com (ORCID)

Authors:

• David Robinson admiral.david@gmail.com

Other contributors:

• Gabriela De Queiroz gabidequeiroz@gmail.com [contributor]

• Colin Fay contact@colinfay.me (ORCID) [contributor]

• Emil Hvitfeldt emilhhvitfeldt@gmail.com [contributor]

• Os Keyes ironholds@gmail.com (ORCID) [contributor]

• Kanishka Misra kmisra@purdue.edu [contributor]

• Tim Mastny tim.mastny@gmail.com [contributor]

• Jeff Erickson jeff@erick.so [contributor]

See Also

Useful links:

• https://juliasilge.github.io/tidytext/

• https://github.com/juliasilge/tidytext

• Report bugs at https://github.com/juliasilge/tidytext/issues

Bind the term frequency and inverse document frequency of a tidy text dataset to the dataset

Description

Calculate and bind the term frequency and inverse document frequency of a tidy text dataset, along with the product, tf-idf, to the dataset. Each of these values are added as columns. This function supports non-standard evaluation through the tidyeval framework.

Usage

bind_tf_idf(tbl, term, document, n)

Arguments

Details

The arguments term, document, and n are passed by expression and support quasiquotation; you can unquote strings and symbols.

If the dataset is grouped, the groups are ignored but are retained.

The dataset must have exactly one row per document-term combination for this to work.

Examples

library(dplyr)
library(janeaustenr)

book_words <- austen_books() %>%
  unnest_tokens(word, text) %>%
  count(book, word, sort = TRUE)

book_words

# find the words most distinctive to each document
book_words %>%
  bind_tf_idf(word, book, n) %>%
  arrange(desc(tf_idf))

Create a sparse matrix from row names, column names, and values in a table.

Description

This function supports non-standard evaluation through the tidyeval framework.

Usage

cast_sparse(data, row, column, value, ...)

Arguments

Details

Note that cast_sparse ignores groups in a grouped tbl_df. The arguments row, column, and value are passed by expression and support quasiquotation; you can unquote strings and symbols.

Value

A sparse Matrix object, with one row for each unique value in the row column, one column for each unique value in the column column, and with as many non-zero values as there are rows in data.

Examples

dat <- data.frame(a = c("row1", "row1", "row2", "row2", "row2"),
                  b = c("col1", "col2", "col1", "col3", "col4"),
                  val = 1:5)

cast_sparse(dat, a, b)

cast_sparse(dat, a, b, val)

Casting a data frame to a DocumentTermMatrix, TermDocumentMatrix, or dfm

Description

This turns a "tidy" one-term-per-document-per-row data frame into a DocumentTermMatrix or TermDocumentMatrix from the tm package, or a dfm from the quanteda package. These functions support non-standard evaluation through the tidyeval framework. Groups are ignored.

Usage

cast_tdm(data, term, document, value, weighting = tm::weightTf, ...)

cast_dtm(data, document, term, value, weighting = tm::weightTf, ...)

cast_dfm(data, document, term, value, ...)

Arguments

Details

The arguments term, document, and value are passed by expression and support quasiquotation; you can unquote strings and symbols.

Tidiers for a corpus object from the quanteda package

Description

Tidy a corpus object from the quanteda package. tidy returns a tbl_df with one-row-per-document, with a text column containing the document's text, and one column for each document-level metadata. glance returns a one-row tbl_df with corpus-level metadata, such as source and created. For Corpus objects from the tm package, see tidy.Corpus().

Usage

## S3 method for class 'corpus'
tidy(x, ...)

## S3 method for class 'corpus'
glance(x, ...)

Arguments

Details

For the most part, the tidy output is equivalent to the "documents" data frame in the corpus object, except that it is converted to a tbl_df, and texts column is renamed to text to be consistent with other uses in tidytext.

Similarly, the glance output is simply the "metadata" object, with NULL fields removed and turned into a one-row tbl_df.

Examples

if (requireNamespace("quanteda", quietly = TRUE)) {
 data("data_corpus_inaugural", package = "quanteda")

 data_corpus_inaugural

 tidy(data_corpus_inaugural)
}

Tidy dictionary objects from the quanteda package

Description

Tidy dictionary objects from the quanteda package

Usage

## S3 method for class 'dictionary2'
tidy(x, regex = FALSE, ...)

Arguments

Value

A data frame with two columns: category and word.

Get a tidy data frame of a single sentiment lexicon

Description

Get specific sentiment lexicons in a tidy format, with one row per word, in a form that can be joined with a one-word-per-row dataset. The "bing" option comes from the included sentiments() data frame, and others call the relevant function in the textdata package.

Usage

get_sentiments(lexicon = c("bing", "afinn", "loughran", "nrc"))

Arguments

Value

A tbl_df with a word column, and either a sentiment column (if lexicon is not "afinn") or a numeric value column (if lexicon is "afinn").

Examples

library(dplyr)

get_sentiments("bing")

## Not run: 
get_sentiments("afinn")
get_sentiments("nrc")

## End(Not run)

Get a tidy data frame of a single stopword lexicon

Description

Get a specific stop word lexicon via the stopwords package's stopwords function, in a tidy format with one word per row.

Usage

get_stopwords(language = "en", source = "snowball")

Arguments

Value

A tibble with two columns, word and lexicon. The parameter lexicon is "quanteda" in this case.

Examples

library(dplyr)
get_stopwords()
get_stopwords(source = "smart")
get_stopwords("es", "snowball")
get_stopwords("ru", "snowball")

Tidiers for LDA and CTM objects from the topicmodels package

Description

Tidy the results of a Latent Dirichlet Allocation or Correlated Topic Model.

Usage

## S3 method for class 'LDA'
tidy(x, matrix = c("beta", "gamma"), log = FALSE, ...)

## S3 method for class 'CTM'
tidy(x, matrix = c("beta", "gamma"), log = FALSE, ...)

## S3 method for class 'LDA'
augment(x, data, ...)

## S3 method for class 'CTM'
augment(x, data, ...)

## S3 method for class 'LDA'
glance(x, ...)

## S3 method for class 'CTM'
glance(x, ...)

Arguments

Value

tidy returns a tidied version of either the beta or gamma matrix.

If matrix == "beta" (default), returns a table with one row per topic and term, with columns

topic

Topic, as an integer

term

Term

beta

Probability of a term generated from a topic according to the multinomial model

If matrix == "gamma", returns a table with one row per topic and document, with columns

topic

Topic, as an integer

document

Document name or ID

gamma

Probability of topic given document

augment returns a table with one row per original document-term pair, such as is returned by tdm_tidiers:

document

Name of document (if present), or index

term

Term

.topic

Topic assignment

If the data argument is provided, any columns in the original data are included, combined based on the document and term columns.

glance always returns a one-row table, with columns

iter

Number of iterations used

terms

Number of terms in the model

alpha

If an LDA_VEM, the parameter of the Dirichlet distribution for topics over documents

Examples

if (requireNamespace("topicmodels", quietly = TRUE)) {
  set.seed(2016)
  library(dplyr)
  library(topicmodels)

  data("AssociatedPress", package = "topicmodels")
  ap <- AssociatedPress[1:100, ]
  lda <- LDA(ap, control = list(alpha = 0.1), k = 4)

  # get term distribution within each topic
  td_lda <- tidy(lda)
  td_lda

  library(ggplot2)

  # visualize the top terms within each topic
  td_lda_filtered <- td_lda %>%
    filter(beta > .004) %>%
    mutate(term = reorder(term, beta))

  ggplot(td_lda_filtered, aes(term, beta)) +
    geom_bar(stat = "identity") +
    facet_wrap(~ topic, scales = "free") +
    theme(axis.text.x = element_text(angle = 90, size = 15))

  # get classification of each document
  td_lda_docs <- tidy(lda, matrix = "gamma")
  td_lda_docs

  doc_classes <- td_lda_docs %>%
    group_by(document) %>%
    top_n(1) %>%
    ungroup()

  doc_classes

  # which were we most uncertain about?
  doc_classes %>%
    arrange(gamma)
}

Tidiers for Latent Dirichlet Allocation models from the mallet package

Description

Tidy LDA models fit by the mallet package, which wraps the Mallet topic modeling package in Java. The arguments and return values are similar to lda_tidiers().

Usage

## S3 method for class 'jobjRef'
tidy(
  x,
  matrix = c("beta", "gamma"),
  log = FALSE,
  normalized = TRUE,
  smoothed = TRUE,
  ...
)

## S3 method for class 'jobjRef'
augment(x, data, ...)

Arguments

Details

Note that the LDA models from mallet::MalletLDA() are technically a special case of S4 objects with class jobjRef. These are thus implemented as jobjRef tidiers, with a check for whether the toString output is as expected.

Value

augment must be provided a data argument containing one row per original document-term pair, such as is returned by tdm_tidiers, containing columns document and term. It returns that same data with an additional column .topic with the topic assignment for that document-term combination.

See Also

lda_tidiers(), mallet::mallet.doc.topics(), mallet::mallet.topic.words()

Examples

## Not run: 
library(mallet)
library(dplyr)

data("AssociatedPress", package = "topicmodels")
td <- tidy(AssociatedPress)

# mallet needs a file with stop words
tmp <- tempfile()
writeLines(stop_words$word, tmp)

# two vectors: one with document IDs, one with text
docs <- td %>%
  group_by(document = as.character(document)) %>%
  summarize(text = paste(rep(term, count), collapse = " "))

docs <- mallet.import(docs$document, docs$text, tmp)

# create and run a topic model
topic_model <- MalletLDA(num.topics = 4)
topic_model$loadDocuments(docs)
topic_model$train(20)

# tidy the word-topic combinations
td_beta <- tidy(topic_model)
td_beta

# Examine the four topics
td_beta %>%
  group_by(topic) %>%
  top_n(8, beta) %>%
  ungroup() %>%
  mutate(term = reorder(term, beta)) %>%
  ggplot(aes(term, beta)) +
  geom_col() +
  facet_wrap(~ topic, scales = "free") +
  coord_flip()

# find the assignments of each word in each document
assignments <- augment(topic_model, td)
assignments

## End(Not run)

English negators, modals, and adverbs

Description

English negators, modals, and adverbs, as a data frame. A few of these entries are two-word phrases instead of single words.

Usage

nma_words

Format

A data frame with 44 rows and 2 variables:

word

An English word or bigram

modifier

The modifier type for word, either "negator", "modal", or "adverb"

Source

http://saifmohammad.com/WebPages/SCL.html#NMA

Parts of speech for English words from the Moby Project

Description

Parts of speech for English words from the Moby Project by Grady Ward. Words with non-ASCII characters and items with a space have been removed.

Usage

parts_of_speech

Format

A data frame with 205,985 rows and 2 variables:

word

An English word

pos

The part of speech of the word. One of 13 options, such as "Noun", "Adverb", "Adjective"

Details

Another dataset of English parts of speech, available only for non-commercial use, is available as part of SUBTLEXus at https://www.ugent.be/pp/experimentele-psychologie/en/research/documents/subtlexus/.

Source

https://archive.org/details/mobypartofspeech03203gut

Examples

library(dplyr)

parts_of_speech

parts_of_speech %>%
  count(pos, sort = TRUE)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

augment, augment, glance, tidy

Reorder an x or y axis within facets

Description

Reorder a column before plotting with faceting, such that the values are ordered within each facet. This requires two functions: reorder_within applied to the column, then either scale_x_reordered or scale_y_reordered added to the plot. This is implemented as a bit of a hack: it appends ___ and then the facet at the end of each string.

Usage

reorder_within(x, by, within, fun = mean, sep = "___", ...)

scale_x_reordered(..., labels = reorder_func, sep = deprecated())

scale_y_reordered(..., labels = reorder_func, sep = deprecated())

reorder_func(x, sep = "___")

Arguments

Source

"Ordering categories within ggplot2 Facets" by Tyler Rinker: https://trinkerrstuff.wordpress.com/2016/12/23/ordering-categories-within-ggplot2-facets/

Examples

library(tidyr)
library(ggplot2)

iris_gathered <- gather(iris, metric, value, -Species)

# reordering doesn't work within each facet (see Sepal.Width):
ggplot(iris_gathered, aes(reorder(Species, value), value)) +
  geom_boxplot() +
  facet_wrap(~ metric)

# reorder_within and scale_x_reordered work.
# (Note that you need to set scales = "free_x" in the facet)
ggplot(iris_gathered, aes(reorder_within(Species, value, metric), value)) +
  geom_boxplot() +
  scale_x_reordered() +
  facet_wrap(~ metric, scales = "free_x")

# to reorder within multiple variables, set within to the list of
# facet variables.
ggplot(mtcars, aes(reorder_within(carb, mpg, list(vs, am)), mpg)) +
  geom_boxplot() +
  scale_x_reordered() +
  facet_wrap(vs ~ am, scales = "free_x")

Sentiment lexicon from Bing Liu and collaborators

Description

Lexicon for opinion and sentiment analysis in a tidy data frame. This dataset is included in this package with permission of the creators, and may be used in research, commercial, etc. contexts with attribution, using either the paper or URL below.

Usage

sentiments

Format

A data frame with 6,786 rows and 2 variables:

word

An English word

sentiment

A sentiment for that word, either positive or negative.

Details

This lexicon was first published in:

Minqing Hu and Bing Liu, “Mining and summarizing customer reviews.”, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2004), Seattle, Washington, USA, Aug 22-25, 2004.

Words with non-ASCII characters were removed.

Source

https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html

Tidiers for Structural Topic Models from the stm package

Description

Tidy topic models fit by the stm package. The arguments and return values are similar to lda_tidiers().

Usage

## S3 method for class 'STM'
tidy(
  x,
  matrix = c("beta", "gamma", "theta", "frex", "lift"),
  log = FALSE,
  document_names = NULL,
  ...
)

## S3 method for class 'estimateEffect'
tidy(x, ...)

## S3 method for class 'estimateEffect'
glance(x, ...)

## S3 method for class 'STM'
augment(x, data, ...)

## S3 method for class 'STM'
glance(x, ...)

Arguments

Value

tidy returns a tidied version of either the beta, gamma, FREX, or lift matrix if called on an object from stm::stm(), or a tidied version of the estimated regressions if called on an object from stm::estimateEffect().

glance returns a tibble with exactly one row of model summaries.

augment must be provided a data argument, either a dfm from quanteda or a table containing one row per original document-term pair, such as is returned by tdm_tidiers, containing columns document and term. It returns that same data with an additional column .topic with the topic assignment for that document-term combination.

See Also

lda_tidiers(), stm::calcfrex(), stm::calclift()

Examples

library(dplyr)
library(ggplot2)
library(stm)
library(janeaustenr)

austen_sparse <- austen_books() %>%
    unnest_tokens(word, text) %>%
    anti_join(stop_words) %>%
    count(book, word) %>%
    cast_sparse(book, word, n)
topic_model <- stm(austen_sparse, K = 12, verbose = FALSE)

# tidy the word-topic combinations
td_beta <- tidy(topic_model)
td_beta

# Examine the topics
td_beta %>%
    group_by(topic) %>%
    slice_max(beta, n = 10) %>%
    ungroup() %>%
    ggplot(aes(beta, term)) +
    geom_col() +
    facet_wrap(~ topic, scales = "free")

# high FREX words per topic
tidy(topic_model, matrix = "frex")

# high lift words per topic
tidy(topic_model, matrix = "lift")

# tidy the document-topic combinations, with optional document names
td_gamma <- tidy(topic_model, matrix = "gamma",
                 document_names = rownames(austen_sparse))
td_gamma

# using stm's gardarianFit, we can tidy the result of a model
# estimated with covariates
effects <- estimateEffect(1:3 ~ treatment, gadarianFit, gadarian)
glance(effects)
td_estimate <- tidy(effects)
td_estimate

Various lexicons for English stop words

Description

English stop words from three lexicons, as a data frame. The snowball and SMART sets are pulled from the tm package. Note that words with non-ASCII characters have been removed.

Usage

stop_words

Format

A data frame with 1149 rows and 2 variables:

word

An English word

lexicon

The source of the stop word. Either "onix", "SMART", or "snowball"

Source

• http://www.lextek.com/manuals/onix/stopwords1.html

• https://www.jmlr.org/papers/volume5/lewis04a/lewis04a.pdf

• http://snowball.tartarus.org/algorithms/english/stop.txt

Tidy DocumentTermMatrix, TermDocumentMatrix, and related objects from the tm package

Description

Tidy a DocumentTermMatrix or TermDocumentMatrix into a three-column data frame: term{}, and value (with zeros missing), with one-row-per-term-per-document.

Usage

## S3 method for class 'DocumentTermMatrix'
tidy(x, ...)

## S3 method for class 'TermDocumentMatrix'
tidy(x, ...)

## S3 method for class 'dfm'
tidy(x, ...)

## S3 method for class 'dfmSparse'
tidy(x, ...)

## S3 method for class 'simple_triplet_matrix'
tidy(x, row_names = NULL, col_names = NULL, ...)

Arguments

Examples

if (requireNamespace("topicmodels", quietly = TRUE)) {
  data("AssociatedPress", package = "topicmodels")
  AssociatedPress

  tidy(AssociatedPress)
}

Tidy a Corpus object from the tm package

Description

Tidy a Corpus object from the tm package. Returns a data frame with one-row-per-document, with a text column containing the document's text, and one column for each local (per-document) metadata tag. For corpus objects from the quanteda package, see tidy.corpus().

Usage

## S3 method for class 'Corpus'
tidy(x, collapse = "\n", ...)

Arguments

Examples

library(dplyr)   # displaying tbl_dfs

if (requireNamespace("tm", quietly = TRUE)) {
  library(tm)
  #' # tm package examples
  txt <- system.file("texts", "txt", package = "tm")
  ovid <- VCorpus(DirSource(txt, encoding = "UTF-8"),
                  readerControl = list(language = "lat"))

  ovid
  tidy(ovid)

  # choose different options for collapsing text within each
  # document
  tidy(ovid, collapse = "")$text
  tidy(ovid, collapse = NULL)$text

  # another example from Reuters articles
  reut21578 <- system.file("texts", "crude", package = "tm")
  reuters <- VCorpus(DirSource(reut21578),
                     readerControl = list(reader = readReut21578XMLasPlain))
  reuters

  tidy(reuters)
}

Utility function to tidy a simple triplet matrix

Description

Utility function to tidy a simple triplet matrix

Usage

tidy_triplet(x, triplets, row_names = NULL, col_names = NULL)

Arguments

Wrapper around unnest_tokens for characters and character shingles

Description

These functions are a wrapper around unnest_tokens( token = "characters" ) and unnest_tokens( token = "character_shingles" ).

Usage

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

unnest_character_shingles(
  tbl,
  output,
  input,
  n = 3L,
  n_min = n,
  strip_non_alphanum = TRUE,
  format = c("text", "man", "latex", "html", "xml"),
  to_lower = TRUE,
  drop = TRUE,
  collapse = NULL,
  ...
)

Arguments

See Also

• unnest_tokens()

Examples

library(dplyr)
library(janeaustenr)

d <- tibble(txt = prideprejudice)

d %>%
  unnest_characters(word, txt)

d %>%
  unnest_character_shingles(word, txt, n = 3)

Wrapper around unnest_tokens for n-grams

Description

These functions are wrappers around unnest_tokens( token = "ngrams" ) and unnest_tokens( token = "skip_ngrams" ) .

Usage

unnest_ngrams(
  tbl,
  output,
  input,
  n = 3L,
  n_min = n,
  ngram_delim = " ",
  format = c("text", "man", "latex", "html", "xml"),
  to_lower = TRUE,
  drop = TRUE,
  collapse = NULL,
  ...
)

unnest_skip_ngrams(
  tbl,
  output,
  input,
  n_min = 1,
  n = 3,
  k = 1,
  format = c("text", "man", "latex", "html", "xml"),
  to_lower = TRUE,
  drop = TRUE,
  collapse = NULL,
  ...
)

Arguments

See Also

• unnest_tokens()

Examples

library(dplyr)
library(janeaustenr)

d <- tibble(txt = prideprejudice)

d %>%
  unnest_ngrams(word, txt, n = 2)

d %>%
  unnest_skip_ngrams(word, txt, n = 3, k = 1)

Wrapper around unnest_tokens for Penn Treebank Tokenizer

Description

This function is a wrapper around unnest_tokens( token = "ptb" ).

Usage

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

Arguments

See Also

• unnest_tokens()

Examples

library(dplyr)
library(janeaustenr)

d <- tibble(txt = prideprejudice)

d %>%
  unnest_ptb(word, txt)

Wrapper around unnest_tokens for regular expressions

Description

This function is a wrapper around unnest_tokens( token = "regex" ).

Usage

unnest_regex(
  tbl,
  output,
  input,
  pattern = "\\s+",
  format = c("text", "man", "latex", "html", "xml"),
  to_lower = TRUE,
  drop = TRUE,
  collapse = NULL,
  ...
)

Arguments

See Also

• unnest_tokens()

Examples

library(dplyr)
library(janeaustenr)

d <- tibble(txt = prideprejudice)

d %>%
  unnest_regex(word, txt, pattern = "Chapter [\\\\d]")

Wrapper around unnest_tokens for sentences, lines, and paragraphs

Description

These functions are wrappers around unnest_tokens( token = "sentences" ) unnest_tokens( token = "lines" ) and unnest_tokens( token = "paragraphs" ).

Usage

unnest_sentences(
  tbl,
  output,
  input,
  strip_punct = FALSE,
  format = c("text", "man", "latex", "html", "xml"),
  to_lower = TRUE,
  drop = TRUE,
  collapse = NULL,
  ...
)

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

unnest_paragraphs(
  tbl,
  output,
  input,
  paragraph_break = "\n\n",
  format = c("text", "man", "latex", "html", "xml"),
  to_lower = TRUE,
  drop = TRUE,
  collapse = NULL,
  ...
)

Arguments

See Also

• unnest_tokens()

Examples

library(dplyr)
library(janeaustenr)

d <- tibble(txt = prideprejudice)

d %>%
  unnest_sentences(word, txt)

Split a column into tokens

Description

Split a column into tokens, flattening 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

Details

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

Support for token = "tweets" was removed in tidytext 0.4.0 because of changes in upstream dependencies.

Examples

library(dplyr)
library(janeaustenr)

d <- tibble(txt = prideprejudice)
d

d %>%
  unnest_tokens(output = word, input = txt)

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

d %>%
  unnest_tokens(output = ngram, input = 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")

Wrapper around unnest_tokens for tweets

Description

Usage

unnest_tweets(tbl, output, input, ...)

Arguments

See Also

• unnest_tokens()