| Title: | An Automated Cleaning Tool for Semantic and Linguistic Data |
| Version: | 2.0.0 |
| Date: | 2026-10-14 |
| Maintainer: | Alexander P. Christensen <alexpaulchristensen@gmail.com> |
| Description: | Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible. |
| License: | GPL (≥ 3.0) |
| URL: | https://github.com/AlexChristensen/SemNetCleaner |
| BugReports: | https://github.com/AlexChristensen/SemNetCleaner/issues |
| NeedsCompilation: | no |
| Encoding: | UTF-8 |
| Language: | en-US |
| LazyData: | true |
| Depends: | R (≥ 4.0.1), SemNetDictionaries (≥ 0.3.0) |
| Imports: | future, future.apply, hunspell, phonics, progressr, searcher, stringi, stringdist |
| Suggests: | DT, foreign, promises, R.matlab, readxl, rstudioapi, shiny (≥ 1.8.0), tcltk, testthat (≥ 3.0.0), withr |
| Config/testthat/edition: | 3 |
| Config/roxygen2/version: | 8.1.1 |
| Packaged: | 2026-10-15 19:46:15 UTC; alextops |
| Author: | Alexander P. Christensen
 [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2026-10-16 13:30:22 UTC |
SemNetCleaner–package
Description
Implements several functions that automates the cleaning and
spell-checking of text data. Also converges, finalizes, removes plurals and
continuous strings, and puts text data in binary format for semantic network analysis.
Uses the SemNetDictionaries package to make
the cleaning process more accurate, efficient, and reproducible.
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
See Also
Useful links:
Report bugs at https://github.com/AlexChristensen/SemNetCleaner/issues
Advance the textcleaner_shiny() Wizard by One Decision
Description
Wraps the mechanics common to every
textcleaner_shiny wizard button click: builds a one-shot
queued reader out of answers (typically from
build_answer_burst), resumes textcleaner(continue =
...) with it, and returns whatever it stops at next – either a further
$stop = TRUE/$pending decision, or the finished result.
Exported (not just internal) because inst/Shiny/'s own module
files are sourced into the global environment by Shiny's own app-loading
mechanism, not into the package namespace – an unexported function is
not reachable from there by its bare name.
Usage
advance_wizard(
continue,
answers = character(0),
search_fn = searcher::search_site,
edit_fn = function(x) x,
cores = 1
)
Arguments
continue |
List.
The current |
answers |
Character vector.
The answers to queue for this one resume call, typically built by
|
search_fn |
Function.
Passed through to |
edit_fn |
Function.
Passed through to |
cores |
Numeric.
Passed through to |
Value
Whatever textcleaner() returns for this resume call – a
further $stop = TRUE/$pending decision, or the finished
textcleaner-classed result
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Automated Spell-Check Review App Server
Description
The server half of the review app used to hand-correct
automated spell-check changes mid-textcleaner pipeline
(see automated_edit()). Exposed as a reusable, namespaceable
building block so a larger app (textcleaner_shiny())
can embed it as one module of a bigger wizard instead of only launching
it standalone.
Usage
automated_edit_server(automated, on_finish = shiny::stopApp, id = NULL)
Arguments
automated |
Matrix or data frame.
The automated spell-check changes to review – a |
on_finish |
Function.
Called with the edited matrix once either "Done" button is pressed.
Defaults to |
id |
Character or |
Value
A function(input, output, session) suitable for
shiny::shinyApp()'s server argument (when id is
NULL), or a module server usable inside a larger app (when
id is supplied)
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Automated Spell-Check Review App UI
Description
The UI half of the review app used to hand-correct
automated spell-check changes mid-textcleaner pipeline
(see automated_edit()). Exposed as a reusable, namespaceable
building block so a larger app (textcleaner_shiny())
can embed it as one step of a bigger wizard instead of only launching it
standalone.
Usage
automated_edit_ui(id = NULL)
Arguments
id |
Character or |
Value
A shiny UI definition
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Changes Bad Responses to NA
Description
Determines whether responses are good or bad.
Bad responses are replaced with missing (NA). Good responses are returned
Usage
bad_response(word, ...)
bad.response(word, ...)
Arguments
word |
Character. A word to be tested for whether it is bad |
... |
Vector. Additional responses to be considered bad |
Value
If response is bad, then returns NA.
If response is valid, then returns the response
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Examples
# Bad response
bad_response(word = " ")
# Good response
bad_response(word = "hello")
# Make a good response bad
bad_response(word = "hello", "hello")
# Add additional bad responses
bad_response(word = "hello", c("hello", "world"))
Makes Best Guess for Spelling Correction
Description
A wrapper function for the best guess of a spelling mistake
based on the letters, the ordering of those letters, and the potential
for letters to be interchanged. The
Damerau-Levenshtein distance
is used to guide inferences into what word the participant was trying to spell from a dictionary
(see SemNetDictionaries)
Usage
best_guess(word, full_dictionary, dictionary = NULL, tolerance = 1)
best.guess(word, full.dictionary, dictionary = NULL, tolerance = 1)
Arguments
word |
Character. A word to get best guess spelling options from dictionary |
full_dictionary |
Character vector.
The dictionary to search for best guesses in.
See |
dictionary |
Character.
A dictionary from |
tolerance |
Numeric.
The distance tolerance set for automatic spell-correction purposes.
This function uses the function Unique words (i.e., n = 1) that are within the (distance) tolerance are automatically output as best guess responses. This default is based on Damerau's (1964) proclamation that more than 80% of all human misspellings can be expressed by a single error (e.g., insertion, deletion, substitution, and transposition). If there is more than one word that is within or below the distance tolerance, then these will be provided as potential options. The recommended and default distance tolerance is |
full.dictionary |
Legacy name for 'full_dictionary'. |
Value
The best guess(es) of the word
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
References
Damerau, F. J. (1964). A technique for computer detection and correction of spelling errors. Communications of the ACM, 7, 171-176.
Philips, L. (1990). Hanging on the metaphone. Computer Language, 7(12), 39-43.
Examples
# Misspelled "bombay"
best_guess("bomba", full_dictionary = SemNetDictionaries::animals.dictionary)
Binary Responses to Character Responses
Description
Converts a binary response matrix into characters for each participant
Usage
binary_to_responses(binary_matrix, to_data_frame = FALSE)
bin2resp(rmat, to.data.frame = FALSE)
Arguments
binary_matrix |
Matrix. A binarized response matrix of verbal fluency or linguistic data |
to_data_frame |
Boolean.
Should output be a data frame where participants are columns?
Defaults to |
rmat |
Legacy name for 'binary_matrix'. |
to.data.frame |
Legacy name for 'to_data_frame'. |
Value
A list containing objects for each participant and their responses
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Examples
# Binary matrix of the first 10 participants of the cleaned data
binary_matrix <- responses_to_binary(open.clean[1:10, ])
# Each participant's responses, as a list
binary_to_responses(binary_matrix)
# As a participants by responses matrix
binary_to_responses(binary_matrix, to_data_frame = TRUE)
Build a Wizard Answer Burst
Description
Builds the exact queued-answer burst one
textcleaner_shiny wizard button click needs to feed a
single resume call (textcleaner(continue = ..., reader = <queue
reader>, ...)) all the way through the one decision it represents,
without starving or overrunning the console wizard's own prompts. See
advance_wizard.
Exported (not just internal) because inst/Shiny/'s own module
files are sourced into the global environment by Shiny's own app-loading
mechanism, not into the package namespace – an unexported function is
not reachable from there by its bare name.
Usage
build_answer_burst(
menu_number,
typed_text = NULL,
add_to_dictionary = FALSE,
full_dictionary = NULL,
original_check = NULL
)
Arguments
menu_number |
Character or numeric, length 1.
The real, currently-displayed menu position (or letter, for GO BACK/HELP/
a response-option suggestion) to send – see |
typed_text |
Character or |
add_to_dictionary |
Logical.
Whether to answer "yes" to any dictionary-add prompt this decision
triggers. Defaults to |
full_dictionary |
Character vector.
The current full dictionary, used to determine how many (if any)
dictionary-add prompts |
original_check |
Character or |
Value
A character vector: the answers to queue, in order
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Converts textcleaner object
to a SNAFU GUI format
Description
Converts textcleaner object
to a SNAFU GUI format (only works for fluency data)
Usage
convert2snafu(..., category, path, reader = readline)
Arguments
... |
Matrix or data frame. A clean response matrices |
category |
Character. Category of verbal fluency data |
path |
Character.
Directory to save the SNAFU-formatted |
reader |
Function.
The input reader used to prompt for the output file name. Defaults to
|
Details
The format of the file has 7 columns:
id — Defaults to the row names of the inputted
datalistnum — The list number for the fluency category. Defaults to 0. Future implementations will allow more lists
category — The verbal fluency category that is input into the
categoryargumentitem — The verbal fluency responses for every participant
RT — Response time. Currently not implemented. Defaults to 0
RTstart — Start of response time. Currently not implemented. Defaults to 0
group — Names of groups. Defaults to the names of the objects input into the function (
...)
Value
A .csv file formatted for SNAFU
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
References
# For SNAFU, see: Zemla, J. C., Cao, K., Mueller, K. D., & Austerweil, J. L. (2020). SNAFU: The Semantic Network and Fluency Utility. Behavior Research Methods, 1-19. https://doi.org/10.3758/s13428-019-01343-w
Examples
# Convert data to SNAFU (\code{reader} supplies the file name)
convert2snafu(
open.clean[1:5, ], category = "animals", path = tempdir(),
reader = function(prompt = "") "snafu_example"
)
if(interactive())
{
# Asks for the file name
convert2snafu(open.clean, category = "animals", path = tempdir())
}
Finalize a textcleaner() Result
Description
Reviews automated spell-check corrections and builds the
final cleaned/binary/behavioral output – the exact logic
textcleaner itself runs once, internally, via a pure code
motion. Exposed so textcleaner_shiny's
corrections-review module can re-run this same, already-tested logic a
second time once the user's real edits (from the embedded
automated_edit_server) are known, instead of duplicating
it. Byte-identical behavior to textcleaner()'s own original inline
block, covered by the full test-textcleaner.R suite unchanged.
Exported (not just internal) because inst/Shiny/'s own module
files are sourced into the global environment by Shiny's own app-loading
mechanism, not into the package namespace – an unexported function is
not reachable from there by its bare name.
Usage
finalize_textcleaner(
res,
data,
type,
ids,
reader = readline,
edit_fn = automated_edit,
spell_check = res,
keep_repeats = FALSE
)
Arguments
res |
List.
The in-progress |
data |
Matrix or data frame.
The already-spell-checked response data ( |
type |
Character.
|
ids |
Character vector.
Participant IDs ( |
reader |
Function.
Passed through to |
edit_fn |
Function.
The automated-corrections review function. Defaults to
|
spell_check |
List.
The error-path fallback return value, matching |
keep_repeats |
Boolean.
Should repeated responses (perseverations) be kept in the cleaned
responses? Only applies to |
Value
A finished, textcleaner-classed result
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Letter Frequencies Based on 40,000 Words
Description
A vector corresponding the frequency of letters across 40,000 words. Retrieved from: http://pi.math.cornell.edu/~mec/2003-2004/cryptography/subs/frequencies.html
Usage
data(letter.freq)
Format
letter.freq (26-element numeric vector)
Examples
data("letter.freq")
Embedded-Search search_fn Factory for textcleaner_shiny()
Description
Returns a search_fn-shaped closure (matching
textcleaner()'s own search_fn contract) for
textcleaner_shiny's wizard: instead of opening a real
browser window, it writes a DuckDuckGo results URL into store for
the app's own search modal to render in an embedded iframe. See this
file's own header comment for why a real Google results page cannot be
embedded this way at all.
Exported (not just internal) because inst/Shiny/'s own module
files are sourced into the global environment by Shiny's own app-loading
mechanism, not into the package namespace – an unexported function is
not reachable from there by its bare name.
Usage
make_shiny_search_fn(store)
Arguments
store |
Function.
A |
Value
A function(query, site, rlang) suitable for
textcleaner()'s own search_fn argument
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Openness and Verbal Fluency
Description
Raw Animals verbal fluency data (n = 516) from Christensen et al. (2018).
Usage
data(open.animals)
Format
open.animals (matrix 516 x 38)
Details
First column is a grouping variable ("Group") with 1 corresponding
to low openness to experience and 2 to high openness to experience
Second column is the latent variable of openness to experience with Intellect items removed (see Christensen et al., 2018 for more details).
Third column is the ID variable for each participant.
Columns 4-38 are raw fluency data.
References
Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32, 480-492.
Examples
data("open.animals")
Cleaned Response Matrices (Openness and Verbal Fluency)
Description
Cleaned response matrices for the Animals verbal fluency data (n = 516) from Christensen et al. (2018).
Usage
data(open.clean)
Format
open.clean (matrix, 516 x 35)
References
Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32, 480-492.
Examples
data("open.clean")
Preprocessed textcleaner Object (Openness and Verbal Fluency)
Description
Preprocessed textcleaner object for the Animals verbal fluency data (n = 516)
from Christensen and Kenett (2020).
Usage
data(open.preprocess)
Format
open.preprocess (list, length = 4)
References
Christensen, A. P., & Kenett, Y. N. (2020). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. PsyArxiv.
Examples
data("open.preprocess")
Converts Words to their Plural Form
Description
A function to change words to their plural form. The rules for converting words to their plural forms are based on the grammar rules.
This function handles most special cases and some irregular cases (see examples) but caution is necessary. If no plural form is identified, then the original word is returned.
Usage
pluralize(word)
Arguments
word |
A word |
Value
Returns the word in plural form, unless a plural form could not be found (then the original word is returned)
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Examples
# Handles any prototypical cases
"dogs"
pluralize("dog")
"foxes"
pluralize("fox")
"wolves"
pluralize("wolf")
"octopi"
pluralize("octopus")
"taxa"
pluralize("taxon")
# And most special cases:
"wives"
pluralize("wife")
"roofs"
pluralize("roof")
"photos"
pluralize("photo")
# And some irregular cases:
"children"
pluralize("child")
"teeth"
pluralize("tooth")
"mice"
pluralize("mouse")
Prepare the Automated Corrections Matrix for Review
Description
Reconstructs the automated spell-check corrections matrix
correct_changes() itself hands to its edit_fn – restoring
matrix-ness and the correspondence-matrix row name for a single-row
selection (see this file's own header comment for the real bug this
fixes), and padding with enough "to_#" columns for the widest
multi-word "from" entry. Exposed as its own function
so textcleaner_shiny's corrections-review module can render
the exact same shape correct_changes() itself will later expect
back from edit_fn – computing this independently (e.g. from the
raw, un-padded res$spellcheck$automated) produces a
narrower matrix than correct_changes()'s own internal copy,
which errors with "non-conformable arrays" the moment they're compared.
Exported (not just internal) because inst/Shiny/'s own module
files are sourced into the global environment by Shiny's own app-loading
mechanism, not into the package namespace – an unexported function is
not reachable from there by its bare name.
Usage
prepare_automated_for_review(res)
Arguments
res |
List.
The in-progress |
Value
The padded automated-corrections matrix, ready to hand to
automated_edit()/automated_edit_server (or any
other edit_fn)
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
QWERTY Distance for Same Length Words
Description
Computes QWERTY distance for words that have the same number of characters. Distance is computed based on the number of keys a character is away from another character on a QWERTY keyboard
Usage
qwerty.dist(wordA, wordB)
qwerty_distance(word_a, word_b)
Arguments
wordA |
Legacy name for 'word_a'. |
wordB |
Legacy name for 'word_b'. |
word_a |
Character. Word to be compared |
word_b |
Character. Word to be compared |
Value
Numeric value for distance between word_a and word_b
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Examples
# Identical values for Damerau-Levenshtein
stringdist::stringdist("big", "pig", method = "dl")
stringdist::stringdist("big", "bug", method = "dl")
# Different distances for QWERTY
qwerty_distance("big", "pig")
qwerty_distance("big", "bug") # Probably meant to type "bug"
Read in Common Data File Extensions
Description
A single function to read in common data file extensions. Note that this function is specialized for reading in text data in the format necessary for functions in SemNetCleaner
File extensions supported:
.Rdata
.rds
.csv
.xlsx
.xls
.sav
.txt
.mat
.dat
Usage
read.data(file = file.choose(), header = TRUE, sep = ",", ...)
read_data(file = file.choose(), header = TRUE, sep = ",", ...)
Arguments
file |
Character.
A path to the file to load.
Defaults to interactive file selection using |
header |
Boolean.
A logical value indicating whether the file contains the
names of the variables as its first line.
If missing, the value is determined from the file format:
header is set to |
sep |
Character.
The field separator character.
Values on each line of the file are separated by this character.
If sep = "" (the default for |
... |
Additional arguments. Allows for additional arguments to be passed onto the respective read functions. See documentation in the list below:
|
Value
A data frame containing a representation of the data in the file. If file extension is ".Rdata", then data will be read to the global environment
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
References
# R Core Team
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
# readxl
Hadley Wickham and Jennifer Bryan (2019). readxl: Read Excel Files. R package version 1.3.1. https://CRAN.R-project.org/package=readxl
# R.matlab
Henrik Bengtsson (2018). R.matlab: Read and Write MAT Files and Call MATLAB from Within R. R package version 3.6.2. https://CRAN.R-project.org/package=R.matlab
Examples
# Use this example for your data
if(interactive())
{read_data()}
# Example for CRAN tests
## Create test data
test1 <- c(1:5, "6,7", "8,9,10")
## Path to temporary file
tf <- tempfile()
## Create test file
writeLines(test1, tf)
## Read in data
read_data(tf)
# See documentation of respective R functions for specific examples
Read a File Uploaded Through Shiny
Description
Reads a file uploaded via shiny::fileInput() (Phase
11, textcleaner_shiny()'s own upload step). Shiny stores an
uploaded file at a temporary path (datapath) that does not
necessarily carry the original file's extension, but read_data
dispatches purely by sniffing the path's own extension – this function
bridges the two by renaming/copying datapath to one ending in the
real, original extension (from filename, the name the browser
reported) before handing off.
Usage
read_uploaded_file(datapath, filename, header = TRUE, sep = ",")
Arguments
datapath |
Character.
The temporary file path |
filename |
Character.
The original file name the browser reported
( |
header |
Boolean.
Passed through to |
sep |
Character.
Passed through to |
Value
The data read from the uploaded file (a data frame for every
supported extension except .Rdata, an unmodified round-trip)
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Converts a Response Matrix to a Binary Response Matrix
Description
Converts a response matrix (verbal fluency or other linguistic data, one participant per row) into a binary (participant x vocabulary) response matrix
Usage
resp2bin(resp)
responses_to_binary(responses)
Arguments
resp |
Legacy name for 'responses'. |
responses |
Matrix or data frame. A response matrix of verbal fluency or linguistic data |
Value
A binary matrix with one row per participant and one column per unique response observed across the whole sample
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Examples
# First 10 participants of the cleaned data
binary_matrix <- responses_to_binary(open.clean[1:10, ])
# Participants by responses (0 = not given, 1 = given)
binary_matrix[, 1:10]
Converts Words to their Singular Form
Description
A function to change words to their singular form. The rules for converting words to their singular forms are based on the inverse of the grammar rules. This function handles most special cases and some irregular cases (see examples) but caution is necessary. If no singular form is identified, then the original word is returned.
Usage
singularize(word, dictionary = TRUE)
Arguments
word |
Character. A word |
dictionary |
Boolean.
Should dictionary be used to verify word exists?
Default to |
Value
Returns the word in singular form, unless a singular form could not be found (then the original word is returned)
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Examples
# Handles any prototypical cases
# "dog"
singularize("dogs")
# "fox"
singularize("foxes")
# "wolf"
singularize("wolves")
# "octopus"
singularize("octopi")
# "taxon"
singularize("taxa")
# And most special cases:
# "wife"
singularize("wives")
# "fez"
singularize("fezzes")
# "roof"
singularize("roofs")
# "photo"
singularize("photos")
# And some irregular cases:
# "child"
singularize("children")
# "tooth"
singularize("teeth")
# "mouse"
singularize("mice")
Text Cleaner
Description
An automated cleaning function for spell-checking, de-pluralizing, removing duplicates, and binarizing text data
Usage
textcleaner(
data = NULL,
type = c("fluency", "free"),
miss = 99,
partBY = c("row", "col"),
dictionary = NULL,
spelling = c("UK", "US"),
add.path = NULL,
keepStrings = FALSE,
allowPunctuations,
allowNumbers = FALSE,
keepLength = NULL,
keepCue = FALSE,
keepRepeats = FALSE,
continue = NULL,
reader = readline,
search_fn = searcher::search_site,
cores = 1,
edit_fn = automated_edit
)
Arguments
data |
Matrix or data frame. For
For
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
type |
Character vector. Type of task to be preprocessed.
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
miss |
Numeric or character.
Value for missing data.
Defaults to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
partBY |
Character.
Are participants by row or column?
Set to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
dictionary |
Character vector.
The name(s) of the dictionary (or dictionaries) to spell-check against,
e.g., Use | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
spelling |
Character vector. English spelling to be used.
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
add.path |
Character.
Path to additional dictionaries to be found.
DOES NOT search recursively (through all folders in path)
to avoid time intensive search.
Set to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
keepStrings |
Boolean.
Should strings be retained or separated?
Defaults to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
allowPunctuations |
Character vector.
Allows punctuation characters to be included in responses.
Defaults to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
allowNumbers |
Boolean.
Defaults to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
keepLength |
Numeric.
Maximum number of words allowed in a response.
Defaults to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
keepCue |
Boolean.
Should cue words be retained in the responses?
Defaults to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
keepRepeats |
Boolean.
Should repeated responses (perseverations) within a participant be
retained in | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
continue |
List.
A result previously unfinished that still needs to be completed.
Allows you to continue to manually spell-check your data
after you've exited early ( | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
reader |
Function.
The input reader used for every interactive prompt in the manual
spell-check wizard. Defaults to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
search_fn |
Function.
The function used to open a browser search for the "GOOGLE WORD"/
"GOOGLE STRING" manual spell-check options. Defaults to
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
cores |
Numeric.
Number of computer cores to use in parallelized computations (the
"ad hoc" moniker re-check after manual spell-check). Defaults to | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
edit_fn |
Function.
The spreadsheet-editor function used to review automated spell-check
changes. Defaults to |
Details
Responses are cleaned before they are spell-checked: they are
lowercased, accented and typographic characters are transliterated to
ASCII ("caf\u00e9" becomes "cafe"), and characters with no
ASCII equivalent (e.g., Chinese or Cyrillic text) are removed, so a response
made only of such characters is treated as missing. The dictionaries
supplied by SemNetDictionaries are English.
Value
A list of class "textcleaner". For type = "fluency":
responses |
A list of three matrices:
|
behavioral |
A data frame, one row per participant, with the number of
|
spellcheck |
A list of three matrices: |
moniker |
The |
dictionary, added_words |
Only present if words were added to the
dictionary and "In my results" was chosen when asked where to save them
(or a file could not be written). |
For type = "free", data holds the original and
clean long-format data, responses$clean is a response-by-cue
frequency matrix, and behavioral, spellcheck and the
dictionary objects are as above. If the manual spell-check is exited
early, the object instead holds the saved state and can be passed back
with continue =
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
References
Christensen, A. P., & Kenett, Y. N. (2021). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. Psychological Methods. doi:10.1037/met0000463
Hornik, K., & Murdoch, D. (2010). Watch Your Spelling!. The R Journal, 3, 22-28.
Examples
if(interactive())
{
# Full example with the manual spell-check
clean <- textcleaner(open.animals[, -c(1, 2)], partBY = "row", dictionary = "animals")
}
Point-and-Click textcleaner() Wizard
Description
Internal and experimental: not exported while the
app's known bugs are triaged. Call it as
SemNetCleaner:::textcleaner_shiny().
Launches a Shiny app wrapping the entire
textcleaner pipeline end to end – upload, options,
automated spell-check, manual spell-check wizard, automated-corrections
review, and download – as a point-and-click alternative to the
interactive console wizard. Every menu option becomes a real clickable
button (plus a free-text box for typing a custom word/string directly);
the "GOOGLE WORD"/"GOOGLE STRING"/"GOOGLE IT" options open an embedded
in-app search instead of a new browser window.
Usage
textcleaner_shiny(...)
Arguments
... |
Additional arguments passed to |
Value
Called for its side effect (launching the Shiny app). Nothing is returned to the calling R session – results are downloaded from within the app itself (see the app's own Export tab).
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Wizard Menu Labels
Description
The exact, position-ordered menu labels
spellcheck_menu() itself displays for a given context/
keep_strings combination – used by
textcleaner_shiny's wizard module to render one button per
meta-option, each labeled with its real text and sending its real
1-indexed display position as the raw answer (see
build_answer_burst).
Exported (not just internal) because inst/Shiny/'s own module
files are sourced into the global environment by Shiny's own app-loading
mechanism, not into the package namespace – an unexported function is
not reachable from there by its bare name.
Usage
wizard_menu_labels(context, keep_strings)
Arguments
context |
Character vector or |
keep_strings |
Logical.
The pending decision's own |
Value
A character vector of menu labels, in real display order
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
Wizard Response Letters
Description
The QWERTY letters appropriate_answer() assigns to
non-default choices (possible, i.e. best_guess's own
candidate list) in display order – used by textcleaner_shiny's
wizard module to label its suggestion buttons and to build the right raw
answer for one (see build_answer_burst).
Exported (not just internal) for the same reason as
wizard_menu_labels – reachability from inst/Shiny/.
Usage
wizard_response_letters(n)
Arguments
n |
Numeric.
The number of candidates to label ( |
Value
A character vector of QWERTY letters, in display order
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>