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.
Usage
hunspell(
text,
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)
list_dictionaries()Arguments
- text
character vector with arbitrary input text
- format
input format; supported parsers are
text,latex,man,xmlandhtml.- dict
a dictionary object or string which can be passed to
dictionary.- ignore
character vector with additional approved words added to the dictionary
- words
character vector with individual words to spell check
- lang
dictionary file or language, see details
- affix
file path to corresponding affix file. If
NULLit is is assumed to be the same path asdictwith extension.aff.- add_words
a character vector of additional words to add to the dictionary
- cache
speed up loading of dictionaries by caching
Details
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.
Examples
# Check individual words
words <- c("beer", "wiskey", "wine")
correct <- hunspell_check(words)
print(correct)
#> [1] TRUE FALSE TRUE
# Find suggestions for incorrect words
hunspell_suggest(words[!correct])
#> [[1]]
#> [1] "whiskey" "whiskery"
#>
# Extract incorrect from a piece of text
bad <- hunspell("spell checkers are not neccessairy for langauge ninja's")
print(bad[[1]])
#> [1] "neccessairy" "langauge"
hunspell_suggest(bad[[1]])
#> [[1]]
#> [1] "necessary" "necessarily"
#>
#> [[2]]
#> [1] "language" "melange"
#>
# Stemming
words <- c("love", "loving", "lovingly", "loved", "lover", "lovely", "love")
hunspell_stem(words)
#> [[1]]
#> [1] "love"
#>
#> [[2]]
#> [1] "loving" "love"
#>
#> [[3]]
#> [1] "loving"
#>
#> [[4]]
#> [1] "loved" "love"
#>
#> [[5]]
#> [1] "lover" "love"
#>
#> [[6]]
#> [1] "lovely" "love"
#>
#> [[7]]
#> [1] "love"
#>
hunspell_analyze(words)
#> [[1]]
#> [1] " st:love"
#>
#> [[2]]
#> [1] " st:loving" " st:love fl:G"
#>
#> [[3]]
#> [1] " st:loving fl:Y"
#>
#> [[4]]
#> [1] " st:loved" " st:love fl:D"
#>
#> [[5]]
#> [1] " st:lover" " st:love fl:R"
#>
#> [[6]]
#> [1] " st:lovely" " st:love fl:Y"
#>
#> [[7]]
#> [1] " st:love"
#>
# \donttest{
# Check an entire latex document
tmpfile <- file.path(tempdir(), "1406.4806v1.tar.gz")
download.file("https://arxiv.org/e-print/1406.4806v1", tmpfile, mode = "wb")
untar(tmpfile, exdir = tempdir())
text <- readLines(file.path(tempdir(), "content.tex"), warn = FALSE)
bad_words <- hunspell(text, format = "latex")
sort(unique(unlist(bad_words)))
#> [1] "CORBA" "CTRL" "DCOM"
#> [4] "DOM" "DSL" "ESC"
#> [7] "JRI" "OAuth" "OpenCPU"
#> [10] "RInside" "RPC" "RProtoBuf"
#> [13] "RStudio" "Reproducibility" "RinRuby"
#> [16] "Rserve" "SIGINT" "STATA"
#> [19] "STDOUT" "Stateful" "auth"
#> [22] "cpu" "cran" "cron"
#> [25] "css" "csv" "de"
#> [28] "dec" "decompositions" "dir"
#> [31] "eol" "facto" "grDevices"
#> [34] "httpuv" "ignorable" "interoperable"
#> [37] "js" "json" "jsonlite"
#> [40] "knitr" "md" "memcached"
#> [43] "mydata" "myfile" "nondegenerateness"
#> [46] "ocpu" "opencpu" "pandoc"
#> [49] "pb" "php" "png"
#> [52] "prescripted" "priori" "protobuf"
#> [55] "rApache" "rda" "rds"
#> [58] "reproducibility" "rlm" "rmd"
#> [61] "rnorm" "rnw" "rpy"
#> [64] "saveRDS" "scalability" "scalable"
#> [67] "schemas" "se" "sep"
#> [70] "stateful" "statefulness" "stdout"
#> [73] "suboptimal" "svg" "sweave"
#> [76] "tex" "texi" "tmp"
#> [79] "toJSON" "urlencoded" "www"
#> [82] "xyz"
# 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)
# }