textclean is a collection of tools to clean and normalize text. Many
of these tools have been taken from the qdap package and revamped to
be more intuitive, better named, and faster. Tools are geared at
checking for substrings that are not optimal for analysis and replacing
or removing them (normalizing) with more analysis friendly substrings
(see Sproat, Black, Chen, Kumar, Ostendorf, & Richards, 2001,
doi:10.1006/csla.2001.0169)
or extracting them into new variables. For example, emoticons are often
used in text but not always easily handled by analysis algorithms. The
replace_emoticon() function replaces emoticons with word equivalents.
Other R packages provide some of the same functionality (e.g.,
english, gsubfn, mgsub, stringi, stringr,
qdapRegex). textclean differs from these packages in that it is
designed to handle all of the common cleaning and normalization tasks
with a single, consistent, pre-configured toolset (note that
textclean uses many of these terrific packages as a backend). This
means that the researcher spends less time on munging, leading to
quicker analysis. This package is meant to be used jointly with the
textshape package, which
provides text extraction and reshaping functionality. textclean
works well with the
qdapRegex package which
provides tooling for substring substitution and extraction of pre-canned
regular expressions. In addition, the functions of textclean are
designed to work within the piping of the tidyverse framework by
consistently using the first argument of functions as the data source.
The textclean subbing and replacement tools are particularly
effective within a dplyr::mutate statement.
- Functions
- Installation
- Contact
- Contributing
- Demonstration
The main functions, task category, & descriptions are summarized in the table below:
| Function | Task | Description |
|---|---|---|
mgsub |
subbing | Multiple gsub |
fgsub |
subbing | Functional matching replacement gsub |
sub_holder |
subbing | Hold a value prior to a strip |
swap |
subbing | Simultaneously swap patterns 1 & 2 |
strip |
deletion | Remove all non word characters |
drop_empty_row |
filter rows | Remove empty rows |
drop_row/keep_row |
filter rows | Filter rows matching a regex |
drop_NA |
filter rows | Remove NA text rows |
drop_element/keep_element |
filter elements | Filter matching elements from a vector |
match_tokens |
filter elements | Filter out tokens from strings that match a regex criteria |
replace_contractions |
replacement | Replace contractions with both words |
replace_date |
replacement | Replace dates |
replace_email |
replacement | Replace emails |
replace_emoji |
replacement | Replace emojis with word equivalent or unique identifier |
replace_emoticon |
replacement | Replace emoticons with word equivalent |
replace_grade |
replacement | Replace grades (e.g., “A+”) with word equivalent |
replace_hash |
replacement | Replace Twitter style hash tags (e.g., #rstats) |
replace_html |
replacement | Replace HTML tags and symbols |
replace_incomplete |
replacement | Replace incomplete sentence end-marks |
replace_internet_slang |
replacement | Replace Internet slang with word equivalents |
replace_kern |
replacement | Replace spaces for >2 letter, all cap, words containing spaces in between letters |
replace_misspelling |
replacement | Replace misspelled words with their most likely replacement |
replace_money |
replacement | Replace money in the form of $\d+.?\d{0,2} |
replace_names |
replacement | Replace common first/last names |
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_rating |
replacement | Replace ratings (e.g., “10 out of 10”, “3 stars”) with word equivalent |
replace_symbol |
replacement | Replace common symbols |
replace_tag |
replacement | Replace Twitter style handle tag (e.g., @trinker) |
replace_time |
replacement | Replace time stamps |
replace_to/replace_from |
replacement | Remove from/to begin/end of string to/from a character(s) |
replace_tokens |
replacement | Remove or replace a vector of tokens with a single value |
replace_url |
replacement | Replace URLs |
replace_white |
replacement | Replace regex white space characters |
replace_word_elongation |
replacement | Replace word elongations with shortened form |
add_comma_space |
replacement | Replace non-space after comma |
add_missing_endmark |
replacement | Replace missing endmarks with desired symbol |
make_plural |
replacement | Add plural endings to singular noun forms |
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
Contributions are welcome from anyone subject to the following rules:
- Abide by the code of conduct.
- Follow the style conventions of the package (indentation, function & argument naming, commenting, etc.)
- All contributions must be consistent with the package license (GPL-2)
- Submit contributions as a pull request. Clearly state what the changes are and try to keep the number of changes per pull request as low as possible.
- If you make big changes, add your name to the ‘Author’ field.
library(dplyr)
library(textshape)
library(lexicon)
library(textclean)
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:
- 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 that check_text is running multiple checks and may be slower on
larger texts. The user may provide a sample of text for review or use
the checks argument to specify the exact checks to conduct and thus
limit the compute time.
Here is an example:
x <- c("i like", "<p>i want. </p>. thet them ther .", "I am ! that|", "", NA,
""they" they,were there", ".", " ", "?", "3;", "I like goud eggs!",
"bi\xdfchen Z\xfcrcher", "i 4like...", "\\tgreat", "She said \"yes\"")
Encoding(x) <- "latin1"
x <- as.factor(x)
check_text(x)
##
## =============
## NON CHARACTER
## =============
##
## The text variable is not a character column (likely `factor`):
##
##
## *Suggestion: Consider using `as.character` or `stringsAsFactors = FALSE` when reading in
## Also, consider rerunning `check_text` after fixing
##
##
## =====
## DIGIT
## =====
##
## The following observations contain digits/numbers:
##
## 10, 13
##
## This issue affected the following text:
##
## 10: 3;
## 13: i 4like...
##
## *Suggestion: Consider using `replace_number`
##
##
## ========
## EMOTICON
## ========
##
## The following observations contain emoticons:
##
## 6
##
## This issue affected the following text:
##
## 6: "they" they,were there
##
## *Suggestion: Consider using `replace_emoticons`
##
##
## =====
## EMPTY
## =====
##
## The following observations contain empty text cells (all white space):
##
## 4, 8
##
## This issue affected the following text:
##
## 4:
## 8:
##
## *Suggestion: Consider running `drop_empty_row`
##
##
## =======
## ESCAPED
## =======
##
## The following observations contain escaped back spaced characters:
##
## 14
##
## This issue affected the following text:
##
## 14: \tgreat
##
## *Suggestion: Consider using `replace_white`
##
##
## ====
## HTML
## ====
##
## The following observations contain HTML markup:
##
## 2, 6
##
## This issue affected the following text:
##
## 2: <p>i want. </p>. thet them ther .
## 6: "they" they,were there
##
## *Suggestion: Consider running `replace_html`
##
##
## ==========
## INCOMPLETE
## ==========
##
## The following observations contain incomplete sentences (e.g., uses ending punctuation like '...'):
##
## 13
##
## This issue affected the following text:
##
## 13: i 4like...
##
## *Suggestion: Consider using `replace_incomplete`
##
##
## =============
## MISSING VALUE
## =============
##
## The following observations contain missing values:
##
## 5
##
## *Suggestion: Consider running `drop_NA`
##
##
## ========
## NO ALPHA
## ========
##
## The following observations contain elements with no alphabetic (a-z) letters:
##
## 4, 7, 8, 9, 10
##
## This issue affected the following text:
##
## 4:
## 7: .
## 8:
## 9: ?
## 10: 3;
##
## *Suggestion: Consider cleaning the raw text or running `filter_row`
##
##
## ==========
## NO ENDMARK
## ==========
##
## The following observations contain elements with missing ending punctuation:
##
## 1, 3, 4, 6, 8, 10, 12, 14, 15
##
## This issue affected the following text:
##
## 1: i like
## 3: I am ! that|
## 4:
## 6: "they" they,were there
## 8:
## 10: 3;
## 12: bißchen Zürcher
## 14: \tgreat
## 15: She said "yes"
##
## *Suggestion: Consider cleaning the raw text or running `add_missing_endmark`
##
##
## ====================
## NO SPACE AFTER COMMA
## ====================
##
## The following observations contain commas with no space afterwards:
##
## 6
##
## This issue affected the following text:
##
## 6: "they" they,were there
##
## *Suggestion: Consider running `add_comma_space`
##
##
## =========
## NON ASCII
## =========
##
## The following observations contain non-ASCII text:
##
## 12
##
## This issue affected the following text:
##
## 12: bißchen Zürcher
##
## *Suggestion: Consider running `replace_non_ascii`
##
##
## ==================
## NON SPLIT SENTENCE
## ==================
##
## The following observations contain unsplit sentences (more than one sentence per element):
##
## 2, 3
##
## This issue affected the following text:
##
## 2: <p>i want. </p>. thet them ther .
## 3: I am ! that|
##
## *Suggestion: Consider running `textshape::split_sentence`
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!
## This text is stunning!
## ----------------
## \ ^__^
## \ (oo)\ ________
## (__)\ )\ /\
## ||------w|
## || ||
It is useful to drop/remove empty rows or unwanted rows (for example the
researcher dialogue from a transcript). The drop_empty_row &
drop_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
drop_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 drop out rows. The drop_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.
drop_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
drop_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
keep_row(DATA, 1, c("sam", "greg"))
## 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 sam m 0 You liar, it stinks! K4
## 4 greg m 0 I am telling the truth! K5
## 5 greg m 0 There is no way. K7
## 6 sam m 0 I distrust you. K8
## 7 greg m 0 I'm hungry. Let's eat. You already? K11
drop_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
drop_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
drop_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" "what should we do" "you liar it stinks"
## [5] "i am telling the truth" "how can we be certain" "there is no way" "i distrust you"
## [9] "what are you talking about" "shall we move on good then" "i'm hungry let's eat you already"
strip(DATA$state, apostrophe.remove = TRUE)
## [1] "computer is fun not too fun" "no its not its dumb" "what should we do" "you liar it stinks" "i am telling the truth"
## [6] "how can we be certain" "there is no way" "i distrust you" "what are you talking about" "shall we move on good then"
## [11] "im hungry lets eat you already"
strip(DATA$state, char.keep = c("?", "."))
## [1] "computer is fun. not too fun." "no it's not it's dumb." "what should we do?" "you liar it stinks"
## [5] "i am telling the truth" "how can we be certain?" "there is no way." "i distrust you."
## [9] "what are you talking about?" "shall we move on? good then." "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$state, c("it's", "I'm"), c("<<it is>>", "<<I am>>"))
## [1] "Computer is fun. Not too fun." "No <<it is>> not, <<it is>> dumb." "What should we do?"
## [4] "You liar, it stinks!" "I am telling the truth!" "How can we be certain?"
## [7] "There is no way." "I distrust you." "What are you talking about?"
## [10] "Shall we move on? Good then." "<<I am>> hungry. Let's eat. You already?"
mgsub(DATA$state, "[[:punct:]]", "<<PUNCT>>", fixed = FALSE)
## [1] "Computer is fun<<PUNCT>> Not too fun<<PUNCT>>" "No it<<PUNCT>>s not<<PUNCT>> it<<PUNCT>>s dumb<<PUNCT>>"
## [3] "What should we do<<PUNCT>>" "You liar<<PUNCT>> it stinks<<PUNCT>>"
## [5] "I am telling the truth<<PUNCT>>" "How can we be certain<<PUNCT>>"
## [7] "There is no way<<PUNCT>>" "I distrust you<<PUNCT>>"
## [9] "What are you talking about<<PUNCT>>" "Shall we move on<<PUNCT>> Good then<<PUNCT>>"
## [11] "I<<PUNCT>>m hungry<<PUNCT>> Let<<PUNCT>>s eat<<PUNCT>> You already<<PUNCT>>"
mgsub(DATA$state, c("i", "it"), c("<<I>>", "[[IT]]"))
## [1] "Computer <<I>>s fun. Not too fun." "No [[IT]]'s not, [[IT]]'s dumb." "What should we do?" "You l<<I>>ar, [[IT]] st<<I>>nks!"
## [5] "I am tell<<I>>ng the truth!" "How can we be certa<<I>>n?" "There <<I>>s no way." "I d<<I>>strust you."
## [9] "What are you talk<<I>>ng about?" "Shall we move on? Good then." "I'm hungry. Let's eat. You already?"
mgsub(DATA$state, c("i", "it"), c("<<I>>", "[[IT]]"), order.pattern = FALSE)
## [1] "Computer <<I>>s fun. Not too fun." "No <<I>>t's not, <<I>>t's dumb." "What should we do?" "You l<<I>>ar, <<I>>t st<<I>>nks!"
## [5] "I am tell<<I>>ng the truth!" "How can we be certa<<I>>n?" "There <<I>>s no way." "I d<<I>>strust you."
## [9] "What are you talk<<I>>ng about?" "Shall we move on? Good then." "I'm hungry. Let's eat. You already?"
The default behavior of mgsub is optimized for speed. This means that
it is very fast at multiple substitutions and in most cases works
efficiently. However, it is not what Mark Ewing describes as “safe”
substitution. In his vignette for the
mgsub package, Mark defines
“safe” as:
- Longer matches are preferred over shorter matches for substitution first
- No placeholders are used so accidental string collisions don’t occur
Because safety is sometimes required, textclean::mgsub provides a
safe argument using the mgsub package as the backend. In addition
to the safe argument the mgsub_regex_safe function is available to
make the usage more explicit. The safe mode comes at the cost of speed.
x <- "Dopazamine is a fake chemical"
pattern <- c("dopazamin", "do.*ne")
replacement <- c("freakout", "metazamine")
## Unsafe
mgsub(x, pattern, replacement, ignore.case=TRUE, fixed = FALSE)
## [1] "freakoute is a fake chemical"
## Safe
mgsub(x, pattern, replacement, ignore.case=TRUE, fixed = FALSE, safe = TRUE)
## [1] "metazamine is a fake chemical"
## Or alternatively
mgsub_regex_safe(x, pattern, replacement, ignore.case=TRUE)
## [1] "metazamine is a fake chemical"
x <- "hey, how are you?"
pattern <- c("hey", "how", "are", "you")
replacement <- c("how", "are", "you", "hey")
## Unsafe
mgsub(x, pattern,replacement)
## [1] "how, are you how?"
## Safe
mgsub_regex_safe(x, pattern,replacement)
## [1] "how, are you hey?"
Again, gsub is a great tool but sometimes the user wants to match a
pattern, extract that pattern, operate a function over that pattern, and
then replace the original match. The fgsub function allows the user to
perform this operation. It is a stripped down version of gsubfn from
the gsubfn package. For more versatile needs please see the
gsubfn package.
In this example the regex looks for words that contain a lower case letter followed by the same letter at least 2 more times. It then extracts these words, splits them appart into letters, reverses the string, pastes them back together, wraps them with double angle braces, and then puts them back at the original locations.
fgsub(
x = c(NA, 'df dft sdf', 'sd fdggg sd dfhhh d', 'ddd'),
pattern = "\\b\\w*([a-z])(\\1{2,})\\w*\\b",
fun = function(x) {paste0('<<', paste(rev(strsplit(x, '')[[1]]), collapse =''), '>>')}
)
## [1] NA "df dft sdf" "sd <<gggdf>> sd <<hhhfd>> d" "<<ddd>>"
In this example we extract numbers, strip out non-digits, coerce them to numeric, cut them in half, round up to the closest integer, add the commas back, and replace back into the original locations.
fgsub(
x = c(NA, 'I want 32 grapes', 'he wants 4 ice creams', 'they want 1,234,567 dollars'),
pattern = "[\\d,]+",
fun = function(x) {prettyNum(ceiling(as.numeric(gsub('[^0-9]', '', x))/2), big.mark = ',')}
)
## [1] NA "I want 16 grapes" "he wants 2 ice creams" "they want 617,284 dollars"
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(hash_emoticons[1:11, 1, with=FALSE][[1]], DATA$state))
## [1] "#-) Computer is fun. Not too fun." "%) No it's not, it's dumb." "%-) What should we do?"
## [4] "',:-l You liar, it stinks!" "',:-| I am telling the truth!" "*) How can we be certain?"
## [7] "*-) There is no way." "*<|:-) I distrust you." "*\\0/* What are you talking about?"
## [10] "0:) Shall we move on? Good then." "0:-) I'm hungry. Let's eat. You already?"
(m <- sub_holder(fake_dat, hash_emoticons[[1]]))
## [1] "zzzplaceholderaazzz Computer is fun. Not too fun." "zzzplaceholderbazzz No it's not, it's dumb."
## [3] "zzzplaceholdercazzz What should we do?" "zzzplaceholderdazzz You liar, it stinks!"
## [5] "zzzplaceholdereazzz I am telling the truth!" "zzzplaceholderfazzz How can we be certain?"
## [7] "zzzplaceholdergazzz There is no way." "zzzplaceholderhazzz I distrust you."
## [9] "zzzplaceholderiazzz What are you talking about?" "zzzplaceholderjazzz Shall we move on? Good then."
## [11] "zzzplaceholderkazzz I'm hungry. Let's eat. You already?"
(m_stripped <-strip(m$output))
## [1] "zzzplaceholderaazzz computer is fun not too fun" "zzzplaceholderbazzz no it's not it's dumb" "zzzplaceholdercazzz what should we do"
## [4] "zzzplaceholderdazzz you liar it stinks" "zzzplaceholdereazzz i am telling the truth" "zzzplaceholderfazzz how can we be certain"
## [7] "zzzplaceholdergazzz there is no way" "zzzplaceholderhazzz i distrust you" "zzzplaceholderiazzz what are you talking about"
## [10] "zzzplaceholderjazzz shall we move on good then" "zzzplaceholderkazzz i'm hungry let's eat you already"
m$unhold(m_stripped)
## [1] "#-) computer is fun not too fun" "%) no it's not it's dumb" "%-) what should we do" "',:-l you liar it stinks"
## [5] "',:-| i am telling the truth" "*) how can we be certain" "*-) there is no way" "*<|:-) i distrust you"
## [9] "*\\0/* what are you talking about" "0:) shall we move on good then" "0:-) i'm hungry let's eat you already"
Of course with clever regexes you can achieve the same thing:
ord_emos <- hash_emoticons[[1]][order(nchar(hash_emoticons[[1]]))]
## This step ensures that longer strings are matched first but can
## fail in cases that use quantifiers. These can appear short but in
## reality can match long strings and would be ordered last in the
## replacement, meaning that the shorter regex took precedent.
emos <- paste(
gsub('([().\\|[{}^$*+?])', '\\\\\\1', ord_emos),
collapse = '|'
)
gsub(
sprintf('(%s)(*SKIP)(*FAIL)|[^\'[:^punct:]]', emos),
'',
fake_dat,
perl = TRUE
)
## [1] "#-) Computer is fun Not too fun" "%) No it's not it's dumb" "%-) What should we do" "',:-l You liar it stinks"
## [5] "',:-| I am telling the truth" "*) How can we be certain" "*-) There is no way" "*<|:-) I distrust you"
## [9] "*\\0/* What are you talking about" "0:) Shall we move on Good then" "0:-) I'm hungry Let's eat You already"
The pure regex approach can be a bit trickier (less safe) and more
difficult to reason about. It also relies on the less general
(*SKIP)(*FAIL) backtracking control verbs that are only implemented in
a few applications like Perl & PCRE. Still, it’s nice to see an
alternative regex approach for comparison.
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." "Check it out what is going on." "he is here but did not go." "the robot at t.s. was not nice"
## [5] "he would like it if I would go away"
x <- c(NA, '11-16-1980 and 11/16/1980', "and 2017-02-08 but then there's 2/8/2017 too")
replace_date(x)
## [1] NA
## [2] "November sixteenth, one thousand nine hundred eighty and November sixteenth, one thousand nine hundred eighty"
## [3] "and February eighth, two thousand seventeen but then there's February eighth, two thousand seventeen too"
replace_date(x, replacement = '<<DATE>>')
## [1] NA "<<DATE>> and <<DATE>>" "and <<DATE>> but then there's <<DATE>> too"
Similar to emoticons, emoji tokens may be ignored if they are not in a
computer readable form. replace_emoji replaces emojis with their word
forms equivalents.
x <- read.delim(system.file("docs/r_tweets.txt", package = "textclean"),
stringsAsFactors = FALSE)[[3]][1:3]
x
## [1] "Hello, helpful! 📦â\235ŒðŸ‘¾ debugme: Easy & efficient debugging for R packages 👨ðŸ\217»â\200\215💻 @GaborCsardi https://buff.ly/2nNKcps #rstats"
## [2] "Did you ever get bored and accidentally create a 📦 to make #Rstats speak on a Mac? I have -> "
## [3] "A gift to my fellow nfl loving #rstats folks this package is 💥💥"
replace_emoji(x)
## [1] "Hello, helpful! package cross mark alien monster debugme: Easy & efficient debugging for R packages man <f0><9f><8f><bb><e2><80><8d> laptop computer @GaborCsardi https://buff.ly/2nNKcps #rstats"
## [2] "Did you ever get bored and accidentally create a package to make #Rstats speak on a Mac? I have -> "
## [3] "A gift to my fellow nfl loving #rstats folks this package is collision collision "
Some analysis techniques examine words, meaning emoticons may be
ignored. replace_emoticon replaces emoticons with their word forms
equivalents.
x <- c(
"text from: http://www.webopedia.com/quick_ref/textmessageabbreviations_02.asp",
"... understanding what different characters used in smiley faces mean:",
"The close bracket represents a sideways smile )",
"Add in the colon and you have sideways eyes :",
"Put them together to make a smiley face :)",
"Use the dash - to add a nose :-)",
"Change the colon to a semi-colon ; and you have a winking face ;) with a nose ;-)",
"Put a zero 0 (halo) on top and now you have a winking, smiling angel 0;) with a nose 0;-)",
"Use the letter 8 in place of the colon for sunglasses 8-)",
"Use the open bracket ( to turn the smile into a frown :-("
)
replace_emoticon(x)
## [1] "text from: http://www.webopedia.com/quick_ref/textmessageabbreviations_02.asp"
## [2] "... understanding what different characters used in smiley faces mean:"
## [3] "The close bracket represents a sideways smile )"
## [4] "Add in the colon and you have sideways eyes :"
## [5] "Put them together to make a smiley face :)"
## [6] "Use the dash - to add a nose :-)"
## [7] "Change the colon to a semi-colon ; and you have a winking face ;) with a nose ;-)"
## [8] "Put a zero 0 (halo) on top and now you have a winking, smiling angel 0;) with a nose 0 wink"
## [9] "Use the letter 8 in place of the colon for sunglasses smiley"
## [10] "Use the open bracket ( to turn the smile into a frown :-("
In analysis where grades may be discussed it may be useful to convert
the letter forms into word meanings. The replace_grade can be used for
this task.
text <- c(
"I give an A+",
"He deserves an F",
"It's C+ work",
"A poor example deserves a C!"
)
replace_grade(text)
## [1] "I give an very excellent excellent" "He deserves an very very bad" "It's slightly above average work" "A poor example deserves a average!"
Sometimes HTML tags and symbols stick around like pesky gnats. The
replace_html function makes light work of them.
x <- c(
"<bold>Random</bold> text with symbols: < > & " '",
"<p>More text</p> ¢ £ ¥ € © ®"
)
replace_html(x)
## [1] " Random text with symbols: < > & \" '" " More text cents pounds yen euro (c) (r)"
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 user’s 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?"
Often in informal written and spoken communication (e.g., Twitter,
texting, Facebook, etc.) people use Internet slang, shorter
abbreviations and acronyms, to replace longer word sequences. These
replacements may obfuscate the meaning when the machine attempts to
analyze the text. The replace_internet_slang function replaces the
slang with longer word equivalents that are more easily analyzed by
machines.
x <- c(
"TGIF and a big w00t! The weekend is GR8!",
"NP it was my pleasure: EOM",
'w8...this n00b needs me to say LMGTFY...lol.',
NA
)
replace_internet_slang(x)
## [1] "thank god, it's friday and a big hooray! The weekend is great!" "no problem it was my pleasure: end of message"
## [3] "wait...this newbie needs me to say let me google that for you...laughing out loud." NA
In typography kerning is the adjustment of spacing. Often, in informal
writing, adding manual spaces (a form of kerning) coupled with all
capital letters is used for emphasis (e.g., "She's the B O M B!").
These word forms would look like noise in most analysis and would likely
be removed as a stopword when in fact they likely carry a great deal of
meaning. The replace_kern function looks for 3 or more consecutive
capital letters with spaces in between and removes the spaces.
x <- c(
"Welcome to A I: the best W O R L D!",
"Hi I R is the B O M B for sure: we A G R E E indeed.",
"A sort C A T indeed!",
NA
)
replace_kern(x)
## [1] "Welcome to A I: the best WORLD!" "Hi I R is the BOMB for sure: we AGREE indeed." "A sort CAT indeed!"
## [4] NA
There are times one may want to replace money mentions with text or
normalized versions. The replace_money function is designed to
complete this task.
x <- c(NA, '$3.16 into "three dollars, sixteen cents"', "-$20,333.18 too", 'fff')
replace_money(x)
## [1] NA "three dollars and sixteen cents into \"three dollars, sixteen cents\""
## [3] "negative twenty thousand three hundred thirty-three dollars and eighteen cents too" "fff"
replace_money(x, replacement = '<<MONEY>>')
## [1] NA "<<MONEY>> into \"three dollars, sixteen cents\"" "<<MONEY>> too"
## [4] "fff"
Often one will want to standardize text by removing first and last
names. The replace_names function quickly removes/replaces common
first and last names. This can be made more targeted by feeding a vector
of names extracted via a named entity extractor.
x <- c(
"Mary Smith is not here",
"Karen is not a nice person",
"Will will do it",
NA
)
replace_names(x)
## [1] " is not here" " is not a nice person" " will do it" NA
replace_names(x, replacement = '<<NAME>>')
## [1] "<<NAME>> <<NAME>> is not here" "<<NAME>> is not a nice person" "<<NAME>> will do it" NA
R can choke on non-ASCII characters. They can be re-encoded but the new
encoding may lack interpretability (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" "Jöreskog" "bißchen Zürcher" "This is a © but not a ®" "6 ÷ 2 = 3"
## [7] "fractions ¼, ½, ¾" "cows go µ" "30¢"
replace_non_ascii(x)
## [1] "Hello World" "6 Ekstrom" "Joreskog" "bisschen Zurcher" "This is a (C) but not a (R)"
## [6] "6 / 2 = 3" "fractions 1/4, 1/2, 3/4" "cows go mu" "30 cent"
replace_non_ascii(x, remove.nonconverted = FALSE)
## [1] "Hello World" "6 Ekstrom" "Joreskog" "bisschen Zurcher" "This is a (C) but not a (R)"
## [6] "6 / 2 = 3" "fractions 1/4, 1/2, 3/4" "cows go <c2> mu " "30<c2> 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." "They are ninety-nine percent good"
replace_number(y)
## [1] "I like three hundred forty-six thousand four hundred fifty-seven ice cream cones." "They are ninety-nine percent good"
replace_number(x, num.paste = TRUE)
## [1] "I like threehundredforty-sixthousandfourhundredfifty-seven ice cream cones." "They are ninety-nine percent good"
replace_number(x, remove=TRUE)
## [1] "I like ice cream cones." "They are percent good"
Some texts use ratings to convey satisfaction with a particular object.
The replace_rating function replaces the more abstract rating with
word equivalents.
x <- c("This place receives 5 stars for their APPETIZERS!!!",
"Four stars for the food & the guy in the blue shirt for his great vibe!",
"10 out of 10 for both the movie and trilogy.",
"* Both the Hot & Sour & the Egg Flower Soups were absolutely 5 Stars!",
"For service, I give them no stars.", "This place deserves no stars.",
"10 out of 10 stars.",
"My rating: just 3 out of 10.",
"If there were zero stars I would give it zero stars.",
"Rating: 1 out of 10.",
"I gave it 5 stars because of the sound quality.",
"If it were possible to give them 0/10, they'd have it."
)
replace_rating(x)
## [1] "This place receives best for their APPETIZERS!!!" " better for the food & the guy in the blue shirt for his great vibe!"
## [3] " best for both the movie and trilogy." "* Both the Hot & Sour & the Egg Flower Soups were absolutely best !"
## [5] "For service, I give them terrible ." "This place deserves terrible ."
## [7] " best stars." "My rating: just below average ."
## [9] "If there were terrible I would give it terrible ." "Rating: extremely below average ."
## [11] "I gave it best because of the sound quality." "If it were possible to give them terrible , they'd have it."
Again, 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." "For the hundredth time stop those 3 things!" "I like the third 1 not the twelfth 1."
replace_ordinal(x, TRUE)
## [1] "I like the first one not the twentysecond one." "For the hundredth time stop those 3 things!" "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!" "I like the 1 not the 1."
replace_number(replace_ordinal(x))
## [1] "I like the first one not the twenty second one." "For the hundredth time stop those three things!" "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" "I owe dollar 41 for food" "two is 10 percent of a number "
Often times the researcher will want to replace times with a text or
normalized version. The replace_time function works well for this
task. Notice that replacement takes a function that can operate on the
extracted pattern.
x <- c(
NA, '12:47 to "twelve forty-seven" and also 8:35:02',
'what about 14:24.5', 'And then 99:99:99?'
)
## Textual: Word version
replace_time(x)
## [1] NA
## [2] "twelve forty-seven to \"twelve forty-seven\" and also eight thirty-five and two seconds"
## [3] "what about fourteen twenty-four and five seconds"
## [4] "And then 99:99:99?"
## Normalization: <<TIME>>
replace_time(x, replacement = '<<TIME>>')
## [1] NA "<<TIME>> to \"twelve forty-seven\" and also <<TIME>>" "what about <<TIME>>"
## [4] "And then 99:99:99?"
## Normalization: hh:mm:ss or hh:mm
replace_time(x, replacement = function(y){
z <- unlist(strsplit(y, '[:.]'))
z[1] <- 'hh'
z[2] <- 'mm'
if(!is.na(z[3])) z[3] <- 'ss'
textclean::glue_collapse(z, ':')
}
)
## [1] NA "hh:mm to \"twelve forty-seven\" and also hh:mm:ss" "what about hh:mm:ss"
## [4] "And then 99:99:99?"
## Textual: Word version (forced seconds)
replace_time(x, replacement = function(y){
z <- replace_number(unlist(strsplit(y, '[:.]')))
z[3] <- paste0('and ', ifelse(is.na(z[3]), '0', z[3]), ' seconds')
paste(z, collapse = ' ')
}
)
## [1] NA
## [2] "twelve forty-seven and 0 seconds to \"twelve forty-seven\" and also eight thirty-five and two seconds"
## [3] "what about fourteen twenty-four and five seconds"
## [4] "And then 99:99:99?"
Often an analysis requires converting tokens of a certain type into a
common form or removing them entirely. The mgsub function can do this
task, however it is regex based and time consuming when the number of
tokens to replace is large. For example, one may want to replace all
proper nouns that are first names with the word name. The
replace_token provides a fast way to replace a group of tokens with a
single replacement.
This example shows a use case for replace_token:
## Set Up the Tokens to Replace
nms <- gsub("(^.)(.*)", "\\U\\1\\L\\2", lexicon::common_names, perl = TRUE)
head(nms)
## [1] "Mary" "Patricia" "Linda" "Barbara" "Elizabeth" "Jennifer"
## Set Up the Data
x <- textshape::split_portion(sample(c(sample(lexicon::grady_augmented, 20000),
sample(nms, 10000, TRUE))), n.words = 12)
x$text.var <- paste0(x$text.var, sample(c('.', '!', '?'), length(x$text.var), TRUE))
head(x$text.var)
## [1] "penitent turbary Reita fruitages Larissa Wendi Francis Jimmy unmelted Stacey Dolly marinaras!"
## [2] "Darlena Bernie slathered Madalene Everette subletting amalgam vang Alysa detractor Waldo Alberto."
## [3] "souring extensor pugilist specificity Marchelle spirit Monet immovably sidelined Idella bots Lee."
## [4] "wager estimated Xiao malorie dynamic optimist Rosena Jocelyn interviewing bromism overexhausting ladyloves."
## [5] "wedded spiraeas partake coxswained Chia warnings istvan Mona jemidars Gena idolatries brailed?"
## [6] "outhowls ballet epitaxy mentality compunction escheating unkennelled Candyce espresso baleen Katelyn disconnect."
head(replace_tokens(x$text.var, nms, 'NAME'))
## [1] "penitent turbary NAME fruitages NAME NAME NAME NAME unmelted NAME NAME marinaras!"
## [2] "NAME NAME slathered NAME NAME subletting amalgam vang NAME detractor NAME NAME."
## [3] "souring extensor pugilist specificity NAME spirit NAME immovably sidelined NAME bots NAME."
## [4] "wager estimated NAME malorie dynamic optimist NAME NAME interviewing bromism overexhausting ladyloves."
## [5] "wedded spiraeas partake coxswained NAME warnings istvan NAME jemidars NAME idolatries brailed?"
## [6] "outhowls ballet epitaxy mentality compunction escheating unkennelled NAME espresso baleen NAME disconnect."
This demonstration shows how fast token replacement can be with
replace_token:
## mgsub
tic <- Sys.time()
head(mgsub(x$text.var, nms, "NAME"))
## [1] "penitent turbary NAME fruitages NAME NAME NAME NAME unmelted NAME NAME marinaras!"
## [2] "NAME NAME slathered NAME NAME subletting amalgam vang NAME detractor NAME NAME."
## [3] "souring extensor pugilist specificity NAME spirit NAME immovably sidelined NAME bots NAME."
## [4] "wager estimated NAME malorie dynamic optimist NAME NAME interviewing bromism overexhausting ladyloves."
## [5] "wedded spiraeas partake coxswained NAME warnings istvan NAME jemidars NAME idolatries brailed?"
## [6] "outhowls ballet epitaxy mentality compunction escheating unkennelled NAME espresso baleen NAME disconnect."
(toc <- Sys.time() - tic)
## Time difference of 4.642003 secs
## replace_tokens
tic <- Sys.time()
head(replace_tokens(x$text.var, nms, "NAME"))
## [1] "penitent turbary NAME fruitages NAME NAME NAME NAME unmelted NAME NAME marinaras!"
## [2] "NAME NAME slathered NAME NAME subletting amalgam vang NAME detractor NAME NAME."
## [3] "souring extensor pugilist specificity NAME spirit NAME immovably sidelined NAME bots NAME."
## [4] "wager estimated NAME malorie dynamic optimist NAME NAME interviewing bromism overexhausting ladyloves."
## [5] "wedded spiraeas partake coxswained NAME warnings istvan NAME jemidars NAME idolatries brailed?"
## [6] "outhowls ballet epitaxy mentality compunction escheating unkennelled NAME espresso baleen NAME disconnect."
(toc <- Sys.time() - tic)
## Time difference of 0.04703093 secs
Now let’s amp it up with 20x more text data. That’s 50,000 rows of text (600,140 words) and 5,493 replacement tokens in 1.2 seconds.
tic <- Sys.time()
out <- replace_tokens(rep(x$text.var, 20), nms, "NAME")
(toc <- Sys.time() - tic)
## Time difference of 1.237006 secs
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"
In informal writing people may use a form of text embellishment to emphasize or alter word meanings called elongation (a.k.a. “word lengthening”). For example, the use of “Whyyyyy” conveys frustration. Other times the usage may be to be more sexy (e.g., “Heyyyy there”). Other times it may be used for emphasis (e.g., “This is so gooood”).
The replace_word_elongation function replaces these un-normalized
forms with the most likely normalized form. The impart.meaning
argument can replace a short list of known elongations with semantic
replacements.
x <- c('look', 'noooooo!', 'real coooool!', "it's sooo goooood", 'fsdfds',
'fdddf', 'as', "aaaahahahahaha", "aabbccxccbbaa", 'I said heyyy!',
"I'm liiiike whyyyyy me?", "Wwwhhatttt!", NA)
replace_word_elongation(x)
## [1] "look" "no!" "real cool!" "it's so good" "fsdfds" "fdf" "as" "ahahahahaha" "aabbccxccbbaa"
## [10] "I said hey!" "I'm like why me?" "what!" NA
replace_word_elongation(x, impart.meaning = TRUE)
## [1] "look" "sarcastic!" "real cool!" "it's so good" "fsdfds" "fdf"
## [7] "as" "ahahahahaha" "aabbccxccbbaa" "I said hey sexy!" "I'm like frustration me?" "what!"
## [13] NA