textclean is a collection of tools to clean and process text. Many of these tools have been taken from the qdap package and revamped to be more intuitive, better named, and faster.
The main functions, task category, & descriptions are summarized in the table below:
| Function | Task | Description |
|---|---|---|
mgsub |
subbing | Multiple gsub |
sub_holder |
subbing | Hold a value prior to a strip |
strip |
deletion | Remove all non word characters |
filter_empty_row |
filter rows | Remove empty rows |
filter_row |
filter rows | Remove rows matching a regex |
filter_NA |
filter rows | Remove NA text rows |
replace_contractions |
replacement | Replace contractions with both words |
replace_incomplete |
replacement | Replace incomplete sentence end-marks |
replace_non_ascii |
replacement | Replace non-ascii with equivalent or remove |
replace_number |
replacement | Replace common numbers |
replace_ordinal |
replacement | Replace common ordinal number form |
replace_symbol |
replacement | Replace common symbols |
replace_white |
replacement | Replace regex white space characters |
add_comma_space |
repalcement | Replace non-space after comma |
check_text |
check | Text report of potential issues |
has_endmark |
check | Check if an element has an end-mark |
To download the development version of textclean:
Download the zip
ball or tar
ball, decompress
and run R CMD INSTALL on it, or use the pacman package to install
the development version:
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh(
"trinker/lexicon",
"trinker/textclean"
)
You are welcome to:
- submit suggestions and bug-reports at: https://github.com/trinker/textclean/issues
- send a pull request on: https://github.com/trinker/textclean/
- compose a friendly e-mail to: tyler.rinker@gmail.com
if (!require("pacman")) install.packages("pacman")
pacman::p_load(dplyr)
pacman::p_load_gh("trinker/textclean", "trinker/textshape", "trinker/lexicon")
One of the most useful tools in textclean is check_text which
scans text variables and reports potential problems. Not all potential
problems are definite problems for analysis but the report provides an
overview of what may need further preparation. The report also provides
suggested functions for the reported problems. The report provides
information on the following:
- non_character - Text that is
factor. - missing_ending_punctuation - Text with no endmark at the end of the string.
- empty - Text that contains an empty element (i.e.,
""). - double_punctuation - Text that contains two punctuation marks in the same string.
- non_space_after_comma - Text that contains commas with no space after them.
- no_alpha - Text that contains string elements with no alphabetic characters.
- non_ascii - Text that contains non-ASCII characters.
- missing_value - Text that contains missing values (i.e.,
NA). - containing_escaped - Text that contains escaped (see
?Quotes). - containing_digits - Text that contains digits.
- indicating_incomplete - Text that contains endmarks that are
indicative of incomplete/trailing sentences (e.g.,
...). - potentially_misspelled - Text that contains potentially misspelled words.
Here is an example:
x <- as.factor(c("i like", "i want. thet them ther .", "I am ! that|", "", NA,
"they,were there", ".", " ", "?", "3;", "I like goud eggs!",
"i 4like...", "\\tgreat", "She said \"yes\""))
check_text(x)
##
## =============
## NON CHARACTER
## =============
##
## Text is a factor.
##
## *Suggestion: Consider using `as.character` or `stringsAsFactors = FALSE` when reading in
##
## ==========================
## MISSING ENDING PUNCTUATION
## ==========================
##
## The following observations were missing ending punctuation:
##
## 1, 3, 4, 5, 6, 8, 10, 13, 14
##
## The following text is missing ending punctuation:
##
## 1: i like
## 3: I am ! that|
## 4:
## 5: NA
## 6: they,were there
## 8:
## 10: 3;
## 13: \tgreat
## 14: She said "yes"
##
## *Suggestion: Consider cleaning the raw text or running `replace_incomplete`
##
##
## =====
## EMPTY
## =====
##
## The following observations were empty:
##
## 4, 8
##
## The following text is empty:
##
## 4:
## 8:
##
## *Suggestion: Consider running `filter_empty`
##
##
## =====================
## NON SPACE AFTER COMMA
## =====================
##
## The following observations were non space after comma:
##
## 6
##
## The following text is non space after comma:
##
## 6: they,were there
##
## *Suggestion: Consider running `add_comma_space`
##
##
## ========
## NO ALPHA
## ========
##
## The following observations were no alpha:
##
## 4, 7, 8, 9, 10
##
## The following text is no alpha:
##
## 4:
## 7: .
## 8:
## 9: ?
## 10: 3;
##
## *Suggestion: Consider cleaning the raw text or running `filter_row`
##
##
## =========
## NON ASCII
## =========
##
## The following observations were non ascii:
##
## 4, 5
##
## The following text is non ascii:
##
## 4:
## 5: NA
##
## *Suggestion: Consider running `replace_non_ascii`
##
##
## =============
## MISSING VALUE
## =============
##
## The following observations were missing value:
##
## 5
##
## *Suggestion: Consider running `filter_NA`
##
##
## ==================
## CONTAINING ESCAPED
## ==================
##
## The following observations were containing escaped:
##
## 13
##
## The following text is containing escaped:
##
## 13: \tgreat
##
## *Suggestion: Consider using `replace_white`
##
##
## =================
## CONTAINING DIGITS
## =================
##
## The following observations were containing digits:
##
## 10, 12
##
## The following text is containing digits:
##
## 10: 3;
## 12: i 4like...
##
## *Suggestion: Consider using `replace_number`
##
##
## =====================
## INDICATING INCOMPLETE
## =====================
##
## The following observations were indicating incomplete:
##
## 12
##
## The following text is indicating incomplete:
##
## 12: i 4like...
##
## *Suggestion: Consider using `replace_incomplete`
##
##
## ======================
## POTENTIALLY MISSPELLED
## ======================
##
## The following observations were potentially misspelled:
##
## 2, 11, 12, 13
##
## The following text is potentially misspelled:
##
## 2: i want. <<thet>> them <<ther>> .
## 11: I like <<goud>> eggs!
## 12: i <<4like>>...
## 13: \<<tgreat>>
##
## *Suggestion: Consider running `hunspell::hunspell_find` & `hunspell::hunspell_suggest`
And if all is well the user should be greeted by a cow:
y <- c("A valid sentence.", "yet another!")
check_text(y)
##
## -------
## No problems found!
## You are prodigious!
## --------
## \ ^__^
## \ (oo)\ ________
## (__)\ )\ /\
## ||------w|
## || ||
It is useful to filter/remove empty rows or unwanted rows (for example
the researcher dialogue from a transcript). The filter_empty_row &
filter_row do empty row do just this. First I'll demo the removal of
empty rows.
## create a data set wit empty rows
(dat <- rbind.data.frame(DATA[, c(1, 4)], matrix(rep(" ", 4),
ncol =2, dimnames=list(12:13, colnames(DATA)[c(1, 4)]))))
## person state
## 1 sam Computer is fun. Not too fun.
## 2 greg No it's not, it's dumb.
## 3 teacher What should we do?
## 4 sam You liar, it stinks!
## 5 greg I am telling the truth!
## 6 sally How can we be certain?
## 7 greg There is no way.
## 8 sam I distrust you.
## 9 sally What are you talking about?
## 10 researcher Shall we move on? Good then.
## 11 greg I'm hungry. Let's eat. You already?
## 12
## 13
filter_empty_row(dat)
## person state
## 1 sam Computer is fun. Not too fun.
## 2 greg No it's not, it's dumb.
## 3 teacher What should we do?
## 4 sam You liar, it stinks!
## 5 greg I am telling the truth!
## 6 sally How can we be certain?
## 7 greg There is no way.
## 8 sam I distrust you.
## 9 sally What are you talking about?
## 10 researcher Shall we move on? Good then.
## 11 greg I'm hungry. Let's eat. You already?
Next we filter out rows. The filter_row function takes a data set, a
column (named or numeric position) and regex terms to search for. The
terms argument takes regex(es) allowing for partial matching. terms
is case sensitive but can be changed via the ignore.case argument.
filter_row(dataframe = DATA, column = "person", terms = c("sam", "greg"))
## person sex adult state code
## 1 teacher m 1 What should we do? K3
## 2 sally f 0 How can we be certain? K6
## 3 sally f 0 What are you talking about? K9
## 4 researcher f 1 Shall we move on? Good then. K10
filter_row(DATA, 1, c("sam", "greg"))
## person sex adult state code
## 1 teacher m 1 What should we do? K3
## 2 sally f 0 How can we be certain? K6
## 3 sally f 0 What are you talking about? K9
## 4 researcher f 1 Shall we move on? Good then. K10
filter_row(DATA, "state", c("Comp"))
## person sex adult state code
## 1 greg m 0 No it's not, it's dumb. K2
## 2 teacher m 1 What should we do? K3
## 3 sam m 0 You liar, it stinks! K4
## 4 greg m 0 I am telling the truth! K5
## 5 sally f 0 How can we be certain? K6
## 6 greg m 0 There is no way. K7
## 7 sam m 0 I distrust you. K8
## 8 sally f 0 What are you talking about? K9
## 9 researcher f 1 Shall we move on? Good then. K10
## 10 greg m 0 I'm hungry. Let's eat. You already? K11
filter_row(DATA, "state", c("I "))
## person sex adult state code
## 1 sam m 0 Computer is fun. Not too fun. K1
## 2 greg m 0 No it's not, it's dumb. K2
## 3 teacher m 1 What should we do? K3
## 4 sam m 0 You liar, it stinks! K4
## 5 sally f 0 How can we be certain? K6
## 6 greg m 0 There is no way. K7
## 7 sally f 0 What are you talking about? K9
## 8 researcher f 1 Shall we move on? Good then. K10
## 9 greg m 0 I'm hungry. Let's eat. You already? K11
filter_row(DATA, "state", c("you"), ignore.case = TRUE)
## person sex adult state code
## 1 sam m 0 Computer is fun. Not too fun. K1
## 2 greg m 0 No it's not, it's dumb. K2
## 3 teacher m 1 What should we do? K3
## 4 greg m 0 I am telling the truth! K5
## 5 sally f 0 How can we be certain? K6
## 6 greg m 0 There is no way. K7
## 7 researcher f 1 Shall we move on? Good then. K10
Often it is useful to remove all non relevant symbols and case from a
text (letters, spaces, and apostrophes are retained). The strip
function accomplishes this. The char.keep argument allows the user to
retain characters.
strip(DATA$state)
## [1] "computer is fun not too fun" "no it's not it's dumb"
## [3] "what should we do" "you liar it stinks"
## [5] "i am telling the truth" "how can we be certain"
## [7] "there is no way" "i distrust you"
## [9] "what are you talking about" "shall we move on good then"
## [11] "i'm hungry let's eat you already"
strip(DATA$state, apostrophe.remove = TRUE)
## [1] "computer is fun not too fun" "no its not its dumb"
## [3] "what should we do" "you liar it stinks"
## [5] "i am telling the truth" "how can we be certain"
## [7] "there is no way" "i distrust you"
## [9] "what are you talking about" "shall we move on good then"
## [11] "im hungry lets eat you already"
strip(DATA$state, char.keep = c("?", "."))
## [1] "computer is fun. not too fun."
## [2] "no it's not it's dumb."
## [3] "what should we do?"
## [4] "you liar it stinks"
## [5] "i am telling the truth"
## [6] "how can we be certain?"
## [7] "there is no way."
## [8] "i distrust you."
## [9] "what are you talking about?"
## [10] "shall we move on? good then."
## [11] "i'm hungry. let's eat. you already?"
gsub is a great tool but often the user wants to replace a vector of
elements with another vector. mgsub allows for a vector of patterns
and replacements. Note that the first argument of mgsub is the data,
not the pattern as is standard with base R's gsub. This allows
mgsub to be used in a magrittr pipeline more easily. Also note
that by default fixed = TRUE. This means the search pattern is not a
regex per-se. This makes the replacement much faster when a regex search
is not needed. mgsub also reorders the patterns to ensure patterns
contained within patterns don't over write the longer pattern. For
example if the pattern c('i', 'it') is given the longer 'it' is
replaced first (though order.pattern = FALSE can be used to negate
this feature).
mgsub(DATA$state, c("it's", "I'm"), c("<<it is>>", "<<I am>>"))
## [1] "Computer is fun. Not too fun."
## [2] "No <<it is>> not, <<it is>> dumb."
## [3] "What should we do?"
## [4] "You liar, it stinks!"
## [5] "I am telling the truth!"
## [6] "How can we be certain?"
## [7] "There is no way."
## [8] "I distrust you."
## [9] "What are you talking about?"
## [10] "Shall we move on? Good then."
## [11] "<<I am>> hungry. Let's eat. You already?"
mgsub(DATA$state, "[[:punct:]]", "<<PUNCT>>", fixed = FALSE)
## [1] "Computer is fun<<PUNCT>> Not too fun<<PUNCT>>"
## [2] "No it<<PUNCT>>s not<<PUNCT>> it<<PUNCT>>s dumb<<PUNCT>>"
## [3] "What should we do<<PUNCT>>"
## [4] "You liar<<PUNCT>> it stinks<<PUNCT>>"
## [5] "I am telling the truth<<PUNCT>>"
## [6] "How can we be certain<<PUNCT>>"
## [7] "There is no way<<PUNCT>>"
## [8] "I distrust you<<PUNCT>>"
## [9] "What are you talking about<<PUNCT>>"
## [10] "Shall we move on<<PUNCT>> Good then<<PUNCT>>"
## [11] "I<<PUNCT>>m hungry<<PUNCT>> Let<<PUNCT>>s eat<<PUNCT>> You already<<PUNCT>>"
mgsub(DATA$state, c("i", "it"), c("<<I>>", "[[IT]]"))
## [1] "Computer <<I>>s fun. Not too fun."
## [2] "No [[IT]]'s not, [[IT]]'s dumb."
## [3] "What should we do?"
## [4] "You l<<I>>ar, [[IT]] st<<I>>nks!"
## [5] "I am tell<<I>>ng the truth!"
## [6] "How can we be certa<<I>>n?"
## [7] "There <<I>>s no way."
## [8] "I d<<I>>strust you."
## [9] "What are you talk<<I>>ng about?"
## [10] "Shall we move on? Good then."
## [11] "I'm hungry. Let's eat. You already?"
mgsub(DATA$state, c("i", "it"), c("<<I>>", "[[IT]]"), order.pattern = FALSE)
## [1] "Computer <<I>>s fun. Not too fun."
## [2] "No <<I>>t's not, <<I>>t's dumb."
## [3] "What should we do?"
## [4] "You l<<I>>ar, <<I>>t st<<I>>nks!"
## [5] "I am tell<<I>>ng the truth!"
## [6] "How can we be certa<<I>>n?"
## [7] "There <<I>>s no way."
## [8] "I d<<I>>strust you."
## [9] "What are you talk<<I>>ng about?"
## [10] "Shall we move on? Good then."
## [11] "I'm hungry. Let's eat. You already?"
There are times the user may want to stash a set of characters before
subbing out and then return the stashed characters. An example of this
is when a researcher wants to remove punctuation but not emoticons. The
subholder function provides tooling to stash the emoticons, allow a
punctuation stripping, and then return the emoticons. First I'll create
some fake text data with emoticons, then stash the emoticons (using a
unique text key to hold their place), then I'll strip out the
punctuation, and last put the stashed emoticons back.
(fake_dat <- paste(key_emoticons[1:11, 1, with=FALSE][[1]], DATA$state))
## [1] "#-o Computer is fun. Not too fun."
## [2] "$_$ No it's not, it's dumb."
## [3] "(*v*) What should we do?"
## [4] "(-: You liar, it stinks!"
## [5] "(-}{-) I am telling the truth!"
## [6] "(.V.) How can we be certain?"
## [7] ")-: There is no way."
## [8] "*-* I distrust you."
## [9] "*<:o) What are you talking about?"
## [10] "//_^ Shall we move on? Good then."
## [11] "0;) I'm hungry. Let's eat. You already?"
(m <- sub_holder(fake_dat, key_emoticons[[1]]))
## [1] "zzzplaceholderazzz Computer is fun. Not too fun."
## [2] "zzzplaceholderbzzz No it's not, it's dumb."
## [3] "zzzplaceholderczzz What should we do?"
## [4] "zzzplaceholderdzzz You liar, it stinks!"
## [5] "zzzplaceholderezzz I am telling the truth!"
## [6] "zzzplaceholderfzzz How can we be certain?"
## [7] "zzzplaceholdergzzz There is no way."
## [8] "zzzplaceholderhzzz I distrust you."
## [9] "zzzplaceholderizzz What are you talking about?"
## [10] "zzzplaceholderjzzz Shall we move on? Good then."
## [11] "zzzplaceholderkzzz I'm hungry. Let's eat. You already?"
(m_stripped <-strip(m$output))
## [1] "zzzplaceholderazzz computer is fun not too fun"
## [2] "zzzplaceholderbzzz no it's not it's dumb"
## [3] "zzzplaceholderczzz what should we do"
## [4] "zzzplaceholderdzzz you liar it stinks"
## [5] "zzzplaceholderezzz i am telling the truth"
## [6] "zzzplaceholderfzzz how can we be certain"
## [7] "zzzplaceholdergzzz there is no way"
## [8] "zzzplaceholderhzzz i distrust you"
## [9] "zzzplaceholderizzz what are you talking about"
## [10] "zzzplaceholderjzzz shall we move on good then"
## [11] "zzzplaceholderkzzz i'm hungry let's eat you already"
m$unhold(m_stripped)
## [1] "#-o computer is fun not too fun"
## [2] "$_$ no it's not it's dumb"
## [3] "(*v*) what should we do"
## [4] "(-: you liar it stinks"
## [5] "(-}{-) i am telling the truth"
## [6] "(.V.) how can we be certain"
## [7] ")-: there is no way"
## [8] "*-* i distrust you"
## [9] "*<:o) what are you talking about"
## [10] "//_^ shall we move on good then"
## [11] "0;) i'm hungry let's eat you already"
textclean contains tools to replace substrings within text with other substrings that may be easier to analyze. This section outlines the uses of these tools.
Some analysis techniques require contractions to be replaced with their
multi-word forms (e.g., "I'll" -> "I will"). replace_contrction
provides this functionality.
x <- c("Mr. Jones isn't going.",
"Check it out what's going on.",
"He's here but didn't go.",
"the robot at t.s. wasn't nice",
"he'd like it if i'd go away")
replace_contraction(x)
## [1] "Mr. Jones is not going."
## [2] "Check it out what is going on."
## [3] "he is here but did not go."
## [4] "the robot at t.s. was not nice"
## [5] "he would like it if I would go away"
Sometimes an incomplete sentence is denoted with multiple end marks or
no punctuation at all. replace_incomplete standardizes these sentences
with a pipe (|) endmark (or one of the users choice).
x <- c("the...", "I.?", "you.", "threw..", "we?")
replace_incomplete(x)
## [1] "the|" "I|" "you." "threw|" "we?"
replace_incomplete(x, '...')
## [1] "the..." "I..." "you." "threw..." "we?"
R can choke on non-ASCII characters. They can be re-encoded but the new
encoding may lack iterpretablity (e.g., ¢ may be converted to \xA2
which is not easily understood or likely to be matched in a hash look
up). replace_non_ascii attempts to replace common non-ASCII characters
with a text representation (e.g., ¢ becomes "cent") Non recognized
non-ASCII characters are simply removed (unless
remove.nonconverted = FALSE).
x <- c(
"Hello World", "6 Ekstr\xf8m", "J\xf6reskog", "bi\xdfchen Z\xfcrcher",
'This is a \xA9 but not a \xAE', '6 \xF7 2 = 3', 'fractions \xBC, \xBD, \xBE',
'cows go \xB5', '30\xA2'
)
Encoding(x) <- "latin1"
x
## [1] "Hello World" "6 Ekstrøm"
## [3] "Jöreskog" "bißchen Zürcher"
## [5] "This is a © but not a ®" "6 ÷ 2 = 3"
## [7] "fractions ¼, ½, ¾" "cows go µ"
## [9] "30¢"
replace_non_ascii(x)
## [1] "Hello World"
## [2] "6 Ekstrm"
## [3] "Jreskog"
## [4] "bichen Zrcher"
## [5] "This is a copyright but not a registered trademark"
## [6] "6 / 2 = 3"
## [7] "fractions 1/2, 1/4, 3/4"
## [8] "cows go mu"
## [9] "30 cent"
replace_non_ascii(x, remove.nonconverted = FALSE)
## [1] "Hello World"
## [2] "6 Ekstr<f8>m"
## [3] "J<f6>reskog"
## [4] "bi<df>chen Z<fc>rcher"
## [5] "This is a copyright but not a registered trademark"
## [6] "6 / 2 = 3"
## [7] "fractions 1/2, 1/4, 3/4"
## [8] "cows go mu"
## [9] "30 cent"
Some analysis requires numbers to be converted to text form.
replace_number attempts to perform this task. replace_number handles
comma separated numbers as well.
x <- c("I like 346,457 ice cream cones.", "They are 99 percent good")
y <- c("I like 346457 ice cream cones.", "They are 99 percent good")
replace_number(x)
## [1] "I like three hundred forty six thousand four hundred fifty seven ice cream cones."
## [2] "They are ninety nine percent good"
replace_number(y)
## [1] "I like three hundred forty six thousand four hundred fifty seven ice cream cones."
## [2] "They are ninety nine percent good"
replace_number(x, num.paste = TRUE)
## [1] "I like threehundredfortysixthousandfourhundredfiftyseven ice cream cones."
## [2] "They are ninetynine percent good"
replace_number(x, remove=TRUE)
## [1] "I like , ice cream cones." "They are percent good"
Afain, some analysis requires numbers, including ordinal numbers, to be
converted to text form. replace_ordinal attempts to perform this task
for ordinal number 1-100 (i.e., 1st - 100th).
x <- c(
"I like the 1st one not the 22nd one.",
"For the 100th time stop those 3 things!",
"I like the 3rd 1 not the 12th 1."
)
replace_ordinal(x)
## [1] "I like the first one not the twenty second one."
## [2] "For the hundredth time stop those 3 things!"
## [3] "I like the third 1 not the twelfth 1."
replace_ordinal(x, TRUE)
## [1] "I like the first one not the twentysecond one."
## [2] "For the hundredth time stop those 3 things!"
## [3] "I like the third 1 not the twelfth 1."
replace_ordinal(x, remove = TRUE)
## [1] "I like the one not the one." "For the time stop those 3 things!"
## [3] "I like the 1 not the 1."
replace_number(replace_ordinal(x))
## [1] "I like the first one not the twenty second one."
## [2] "For the hundredth time stop those three things!"
## [3] "I like the third one not the twelfth one."
Text often contains short-hand representations of words/phrases. These
symbols may contain analyzable information but in the symbolic form they
cannot be parsed. The replace_symbol function attempts to replace the
symbols c("$", "%", "#", "@", "& "w/") with their word equivalents.
x <- c("I am @ Jon's & Jim's w/ Marry",
"I owe $41 for food",
"two is 10% of a #"
)
replace_symbol(x)
## [1] "I am at Jon's and Jim's with Marry"
## [2] "I owe dollar 41 for food"
## [3] "two is 10 percent of a number"
Regex white space characters (e.g., \n, \t, \r) matched by \s
may impede analysis. These can be replaced with a single space " " via
the replace_white function.
x <- "I go \r
to the \tnext line"
x
## [1] "I go \r\n to the \tnext line"
cat(x)
## I go
## to the next line
replace_white(x)
## [1] "I go to the next line"