check_text: Check Text For Potential Problems

In textclean: Text Cleaning Tools

Check Text For Potential Problems

Description

check_text - Uncleaned text may result in errors, warnings, and incorrect results in subsequent analysis. check_text checks text for potential problems and suggests possible fixes. Potential text anomalies that are detected include: factors, missing ending punctuation, empty cells, double punctuation, non-space after comma, no alphabetic characters, non-ASCII, missing value, and potentially misspelled words.

available_check - Provide a data.frame view of all the available checks in the check_text function.

Usage

check_text(x, file = NULL, checks = NULL, n = 10, ...)

available_checks()

Arguments

x	The text variable.
file	A connection, or a character string naming the file to print to. If NULL prints to the console. Note that this is assigned as an attribute and passed to print.
checks	A vector of checks to include from which_are. If checks = NULL, all checks from which_are which be used. Note that all meta checks will be conducted (see which_are for details on meta checks).
n	The number of affected elements to print out (the rest are truncated).
...	ignored.

Value

Returns a list with the following potential text faults report:

contraction	- Text elements that contain contractions
date	- Text elements that contain dates
digit	- Text elements that contain digits/numbers
email	- Text elements that contain email addresses
emoticon	- Text elements that contain emoticons
empty	- Text elements that contain empty text cells (all white space)
escaped	- Text elements that contain escaped back spaced characters
hash	- Text elements that contain Twitter style hash tags (e.g., #rstats)
html	- Text elements that contain HTML markup
incomplete	- Text elements that contain incomplete sentences (e.g., uses ending punctuation like ...)
kern	- Text elements that contain kerning (e.g., 'The B O M B!')
list_column	- Text variable that is a list column
missing_value	- Text elements that contain missing values
misspelled	- Text elements that contain potentially misspelled words
no_alpha	- Text elements that contain elements with no alphabetic (a-z) letters
no_endmark	- Text elements that contain elements with missing ending punctuation
no_space_after_comma	- Text elements that contain commas with no space afterwards
non_ascii	- Text elements that contain non-ASCII text
non_character	- Text variable that is not a character column (likely factor)
non_split_sentence	- Text elements that contain unsplit sentences (more than one sentence per element)
tag	- Text elements that contain Twitter style handle tags (e.g., @trinker)
time	- Text elements that contain timestamps
url	- Text elements that contain URLs

Note

The output is a list containing meta checks and elemental checks but prints as a pretty formatted output with potential problem elements, the accompanying text, and possible suggestions to fix the text.

Examples

## Not run: 
v <- list(c('foo', 'bar'), NA, c('hello', 'world'))
check_text(v)

w <- factor(unlist(v))
check_text(w)

x <- c("i like", "<p>i want. </p>thet them ther .", "I am ! that|", "", NA, 
    "&quot;they&quot;,were there", ".", "   ", "?", "3;", "I like goud eggs!", 
    "i 4like...", "\\tgreat",  'She said "yes"')
check_text(x)
print(check_text(x), include.text=FALSE)
check_text(x, checks = c('non_split_sentence', 'no_endmark'))
elementals <- available_checks()[is_meta != TRUE,][['fun']]
check_text(
    x, 
    checks = elementals[
        !elementals %in% c('non_split_sentence', 'no_endmark')
    ]
)

y <- c("A valid sentence.", "yet another!")
check_text(y)

z <- rep("dfsdsd'nt", 120)
check_text(z)

## End(Not run)

textclean documentation built on March 5, 2026, 9:06 a.m.