hunspell (version 3.0.2)

hunspell: Hunspell Spell Checking and Morphological Analysis


The hunspell function is a high-level wrapper for finding spelling errors within a text document. It takes a character vector with text (plain, latex, man, html or xml format), parses out the words and returns a list with incorrect words for each line. It effectively combines hunspell_parse with hunspell_check in a single step. Other functions in the package operate on individual words, see details.


  format = c("text", "man", "latex", "html", "xml"),
  dict = dictionary("en_US"),
  ignore = en_stats

hunspell_parse( text, format = c("text", "man", "latex", "html", "xml"), dict = dictionary("en_US") )

hunspell_check(words, dict = dictionary("en_US"))

hunspell_suggest(words, dict = dictionary("en_US"))

hunspell_analyze(words, dict = dictionary("en_US"))

hunspell_stem(words, dict = dictionary("en_US"))

hunspell_info(dict = dictionary("en_US"))

dictionary(lang = "en_US", affix = NULL, add_words = NULL, cache = TRUE)




character vector with arbitrary input text


input format; supported parsers are text, latex, man, xml and html.


a dictionary object or string which can be passed to dictionary.


character vector with additional approved words added to the dictionary


character vector with individual words to spell check


dictionary file or language, see details


file path to corresponding affix file. If NULL it is is assumed to be the same path as dict with extension .aff.


a character vector of additional words to add to the dictionary


speed up loading of dictionaries by caching


Hunspell uses a special dictionary format that defines which stems and affixes are valid in a given language. The hunspell_analyze function shows how a word breaks down into a valid stem plus affix. The hunspell_stem function is similar but only returns valid stems for a given word. Stemming can be used to summarize text (e.g in a wordcloud). The hunspell_check function takes a vector of individual words and tests each one for correctness. Finally hunspell_suggest is used to suggest correct alternatives for each (incorrect) input word.

Because spell checking is usually done on a document, the package includes some parsers to extract words from various common formats. With hunspell_parse we can parse plain-text, latex and man format. R also has a few built-in parsers such as RdTextFilter and SweaveTeXFilter, see also ?aspell.

The package searches for dictionaries in the working directory as well as in the standard system locations. list_dictionaries provides a list of all dictionaries it can find. Additional search paths can be specified by setting the DICPATH environment variable. A US English dictionary (en_US) is included with the package; other dictionaries need to be installed by the system. Most operating systems already include compatible dictionaries with names such as hunspell-en-gb or myspell-en-gb.

To manually install dictionaries, copy the corresponding .aff and .dic file to ~/Library/Spelling or a custom directory specified in DICPATH. Alternatively you can pass the entire path to the .dic file as the dict parameter. Some popular sources of dictionaries are SCOWL, OpenOffice, debian, github/titoBouzout or github/wooorm.

Note that hunspell uses iconv to convert input text to the encoding used by the dictionary. This will fail if text contains characters which are unsupported by that particular encoding. For this reason UTF-8 dictionaries are preferable over legacy 8-bit dictionaries.


Run this code
# Check individual words
words <- c("beer", "wiskey", "wine")
correct <- hunspell_check(words)

# Find suggestions for incorrect words

# Extract incorrect from a piece of text
bad <- hunspell("spell checkers are not neccessairy for langauge ninja's")

# Stemming
words <- c("love", "loving", "lovingly", "loved", "lover", "lovely", "love")

# Check an entire latex document
tmpfile <- file.path(tempdir(), "1406.4806v1.tar.gz")
download.file("", tmpfile,  mode = "wb")
untar(tmpfile, exdir = tempdir())
text <- readLines(file.path(tempdir(), "content.tex"), warn = FALSE)
bad_words <- hunspell(text, format = "latex")

# Summarize text by stems (e.g. for wordcloud)
allwords <- hunspell_parse(text, format = "latex")
stems <- unlist(hunspell_stem(unlist(allwords)))
words <- head(sort(table(stems), decreasing = TRUE), 200)

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