regextable • regextable

Description

regextable extracts regular-expression-based pattern matches from a vector of text using a lookup table of regular expressions. It requires two inputs:

data: A data frame containing a text column, or a character vector.
regex_table: A lookup table (a data frame with a column of strings or regular expressions to search for, typically called pattern)

For each text entry, regextable::extract returns: - the row number of the input data - the matched pattern - the exact substring extracted from the text - Optionally, other metadata columns from data or regex_table

Users can choose to return all matches (resulting in multiple rows per text entry) or limit the output to just the first match found. ## Installation

devtools::install_github("judgelord/regextable")

library(regextable)

Data

The examples below use an example regex lookup table of members of Congress, members, and example text data from the Congressional Record, cr2007_03_01, from the legislators package, which are also included in this package, subset to congress == 107, for illustration. Users who need to search for legislators should use the legislators package.

data("members")
head(members)
#> # A tibble: 6 × 11
#>   chamber   congress bioname             pattern icpsr state state_abbrev district_code bioguide_id first_name last_name
#>   <chr>        <dbl> <chr>               <chr>   <dbl> <int> <chr>                <dbl> <chr>       <chr>      <chr>    
#> 1 President      107 BUSH, George Walker "georg… 99910    NA USA                      0 <NA>        George     BUSH     
#> 2 House          107 CALLAHAN, Herbert … "herbe… 15090     1 AL                       1 C000052     Herbert    CALLAHAN 
#> 3 House          107 CRAMER, Robert E. … "rober… 29100     1 AL                       5 C000868     Robert     CRAMER   
#> 4 House          107 EVERETT, Robert Te… "rober… 29300     1 AL                       2 E000268     Robert     EVERETT  
#> 5 House          107 BACHUS, Spencer T.… "spenc… 29301     1 AL                       6 B000013     Spencer    BACHUS   
#> 6 House          107 HILLIARD, Earl Fre… "earl … 29302     1 AL                       7 H000621     Earl       HILLIARD

data("cr2007_03_01")
head(cr2007_03_01)
#> # A tibble: 6 × 4
#>   date       text                                header                                                            url  
#>   <date>     <chr>                               <chr>                                                             <chr>
#> 1 2007-03-01 HON. SAM GRAVES;Mr. GRAVES          RECOGNIZING JARRETT MUCK FOR ACHIEVING THE RANK OF EAGLE SCOUT; … http…
#> 2 2007-03-01 HON. MARK UDALL;Mr. UDALL           INTRODUCING A CONCURRENT RESOLUTION HONORING THE 50TH ANNIVERSAR… http…
#> 3 2007-03-01 HON. JAMES R. LANGEVIN;Mr. LANGEVIN BIOSURVEILLANCE ENHANCEMENT ACT OF 2007; Congressional Record Vo… http…
#> 4 2007-03-01 HON. JIM COSTA;Mr. COSTA            A TRIBUTE TO THE LIFE OF MRS. VERNA DUTY; Congressional Record V… http…
#> 5 2007-03-01 HON. SAM GRAVES;Mr. GRAVES          RECOGNIZING JARRETT MUCK FOR ACHIEVING THE RANK OF EAGLE SCOUT    http…
#> 6 2007-03-01 HON. SANFORD D. BISHOP;Mr. BISHOP   IN HONOR OF SYNOVUS BEING NAMED ONE OF THE BEST COMPANIES IN AME… http…

Text cleaning

Before matching, by default, clean_text() is applied to standardize messy text. It converts text to lowercase, removes specific punctuation (+, —, !, ?, :, ;), replaces line breaks, tabs, periods, and dashes with spaces, and normalizes commas and excess whitespace. Text cleaning is applied only internally during matching and does not modify the original input data. Users can disable this behavior by setting do_clean_text = FALSE.

text <- "  HELLO---WORLD  "
cleaned_text <- clean_text(text)
print(cleaned_text)
#> [1] "hello world"

Extract regex-based matches from text

Description

extract() performs regex-based matching on a text column using a pattern lookup table. All patterns that match each row are returned, along with the corresponding pattern and optional metadata from the pattern table. If multiple patterns match the same text, multiple rows are returned, one per match.

Required Parameters

data: A data frame or character vector containing the text to search.
regex_table: A regex lookup table with at least one pattern column.

Optional Parameters

col_name: (default "text") Column name in the data frame containing text to search through. (Note: If data is a character vector, it is internally converted to a data frame and this argument is ignored).
pattern_col: (default "pattern") Name of the regex pattern column in regex_table.
data_return_cols: (default NULL) Vector of additional columns from data to include in the output.
regex_return_cols: (default NULL) Vector of additional columns from regex_table to include in the output.
date_col: (default NULL) Column in data containing dates for filtering.
date_start: (default NULL) Start date for filtering rows.
date_end: (default NULL) End date for filtering rows.
remove_acronyms: (default FALSE) If TRUE, removes all-uppercase patterns from regex_table.
do_clean_text: (default TRUE) If TRUE, cleans text before matching.
verbose: (default TRUE) If TRUE, displays progress messages.
unique_match (default FALSE) If TRUE, stops searching after first match to find at most one match per row.
cl: (default NULL) A cluster object or integer specifying child processes for parallel evaluation (ignored on Windows).
use_ner: (default FALSE) If TRUE, uses the ‘spacyr’ package to validate that matches are actual Named Entities (e.g., organizations). Requires ‘spacyr’ to be installed and initialized. Note: If ‘spacyr’ is missing or fails to initialize, the function will perform standard regex matching and issue a warning.
ner_entity_types: (default c(“ORG”)) Character vector; the types of spaCy Named Entities to keep if use_ner is TRUE (e.g., “ORG”, “PERSON”, “GPE”, “LAW”).

Returns

A data frame with one row per match, including:

row_id: the internal row number of the text in the input data
pattern: the regex pattern matched
match: the substring matched in the text
Additional columns from the input data (if data_return_cols specified)
Additional columns from the regex table (if regex_return_cols specified)

Basic Usage

The simplest use of extract() with only the required arguments and returned columns specified. This finds all matches in the text column using the provided regex table.

# Extract patterns using only required arguments
result <- extract(
  data = cr2007_03_01,
  regex_table = members,
  data_return_cols = c("text"),
  regex_return_cols = c("icpsr")
)

head(result)
#> # A tibble: 6 × 5
#>   row_id text                                icpsr pattern                                                         match
#>    <int> <chr>                               <dbl> <chr>                                                           <chr>
#> 1      1 HON. SAM GRAVES;Mr. GRAVES          20124 "samuel graves|\\bs graves|sam graves|(^|senator |representati… SAM …
#> 2      2 HON. MARK UDALL;Mr. UDALL           29906 "mark udall|\\bm udall|mark e udall|\\bna udall|(^|senator |re… MARK…
#> 3      3 HON. JAMES R. LANGEVIN;Mr. LANGEVIN 20136 "james langevin|\\bj langevin|james r langevin|jim langevin|ji… jame…
#> 4      5 HON. SAM GRAVES;Mr. GRAVES          20124 "samuel graves|\\bs graves|sam graves|(^|senator |representati… SAM …
#> 5      6 HON. SANFORD D. BISHOP;Mr. BISHOP   29339 "sanford bishop|sanford dixon bishop|\\bs bishop|sanford d bis… sanf…
#> 6      7 HON. EDOLPHUS TOWNS;Mr. TOWNS       15072 "edolphus towns|\\be towns|ed towns|(^|senator |representative… EDOL…

Advanced Usage

Shows how to use optional arguments for more control, such as filtering by date ranges and removing acronyms. This is useful when you want to narrow matches, disable text cleaning, control returned columns, or suppress messages.

# Advanced usage with optional filters
result_advanced <- extract(
  data = cr2007_03_01,
  regex_table = members,
  date_col = "date",
  date_start = "2007-01-01",
  date_end = "2007-12-31",
  remove_acronyms = TRUE,
  data_return_cols = c("text"),
  regex_return_cols = c("icpsr")
)

head(result_advanced)
#> # A tibble: 6 × 5
#>   row_id text                                icpsr pattern                                                         match
#>    <int> <chr>                               <dbl> <chr>                                                           <chr>
#> 1      1 HON. SAM GRAVES;Mr. GRAVES          20124 "samuel graves|\\bs graves|sam graves|(^|senator |representati… SAM …
#> 2      2 HON. MARK UDALL;Mr. UDALL           29906 "mark udall|\\bm udall|mark e udall|\\bna udall|(^|senator |re… MARK…
#> 3      3 HON. JAMES R. LANGEVIN;Mr. LANGEVIN 20136 "james langevin|\\bj langevin|james r langevin|jim langevin|ji… jame…
#> 4      5 HON. SAM GRAVES;Mr. GRAVES          20124 "samuel graves|\\bs graves|sam graves|(^|senator |representati… SAM …
#> 5      6 HON. SANFORD D. BISHOP;Mr. BISHOP   29339 "sanford bishop|sanford dixon bishop|\\bs bishop|sanford d bis… sanf…
#> 6      7 HON. EDOLPHUS TOWNS;Mr. TOWNS       15072 "edolphus towns|\\be towns|ed towns|(^|senator |representative… EDOL…

Future Development

Add support for typo_table to correct known text errors before matching.
Improve strict matching rules for patterns that may need more inclusive or more restrictive word boundaries.
Enable user-defined ID systems (e.g., corporations, campaigns) and control whether text is returned with matches.
Allow users to plug in their own regex_table without requiring a wrapper function.
For additional data, direct users to download tables from the package creator’s repository.