Match Regular Expressions with a Nicer 'API'

Description

A small wrapper on 'regexpr' to extract the matches and captured groups from the match of a regular expression to a character vector. See re_match.

Author(s)

Maintainer: Gábor Csárdi csardi.gabor@gmail.com

Other contributors:

• Matthew Lincoln matthew.d.lincoln@gmail.com [contributor]

See Also

Useful links:

• https://github.com/r-lib/rematch2#readme

• Report bugs at https://github.com/r-lib/rematch2/issues

Match results from a data frame column and attach results

Description

Taking a data frame and a column name as input, this function will run re_match and bind the results as new columns to the original table., returning a tibble. This makes it friendly for pipe-oriented programming with magrittr.

Usage

bind_re_match(df, from, ..., keep_match = FALSE)

bind_re_match_(df, from, ..., keep_match = FALSE)

Arguments

Functions

• bind_re_match_: Standard-evaluation version that takes a quoted column name.

Note

If named capture groups will result in multiple columns with the same column name, repair_names will be called on the resulting table.

See Also

Standard-evaluation version bind_re_match_ that is suitable for programming.

Examples

match_cars <- tibble::rownames_to_column(mtcars)
bind_re_match(match_cars, rowname, "^(?<make>\\w+) ?(?<model>.+)?$")

Extract Data From First Regular Expression Match Into a Data Frame

Description

Match a regular expression to a string, and return matches, match positions, and capture groups. This function is like its match counterpart, except it returns match/capture group start and end positions in addition to the matched values.

Usage

re_exec(text, pattern, perl = TRUE, ...)

## S3 method for class 'rematch_records'
x$name

## S3 method for class 'rematch_allrecords'
x$name

Arguments

Value

A tidy data frame (see Section “Tidy Data”). Match record entries are one length vectors that are set to NA if there is no match.

Tidy Data

The return value is a tidy data frame where each row corresponds to an element of the input character vector text. The values from text appear for reference in the .text character column. All other columns are list columns containing the match data. The .match column contains the match information for full regular expression matches while other columns correspond to capture groups if there are any, and PCRE matches are enabled with perl = TRUE (this is on by default). If capture groups are named the corresponding columns will bear those names.

Each match data column list contains match records, one for each element in text. A match record is a named list, with entries match, start and end that are respectively the matching (sub) string, the start, and the end positions (using one based indexing).

Extracting Match Data

To make it easier to extract matching substrings or positions, a special $ operator is defined on match columns, both for the .match column and the columns corresponding to the capture groups. See examples below.

See Also

regexpr, which this function wraps

Other tidy regular expression matching: re_exec_all(), re_match_all(), re_match()

Examples

name_rex <- paste0(
  "(?<first>[[:upper:]][[:lower:]]+) ",
  "(?<last>[[:upper:]][[:lower:]]+)"
)
notables <- c(
  "  Ben Franklin and Jefferson Davis",
  "\tMillard Fillmore"
)
# Match first occurrence
pos <- re_exec(notables, name_rex)
pos

# Custom $ to extract matches and positions
pos$first$match
pos$first$start
pos$first$end

Extract Data From All Regular Expression Matches Into a Data Frame

Description

Match a regular expression to a string, and return matches, match positions, and capture groups. This function is like its match counterpart, except it returns match/capture group start and end positions in addition to the matched values.

Usage

re_exec_all(text, pattern, perl = TRUE, ...)

Arguments

Value

A tidy data frame (see Section “Tidy Data”). The entries within the match records within the list columns will be one vectors as long as there are matches for the corresponding text element.

Tidy Data

The return value is a tidy data frame where each row corresponds to an element of the input character vector text. The values from text appear for reference in the .text character column. All other columns are list columns containing the match data. The .match column contains the match information for full regular expression matches while other columns correspond to capture groups if there are any, and PCRE matches are enabled with perl = TRUE (this is on by default). If capture groups are named the corresponding columns will bear those names.

Each match data column list contains match records, one for each element in text. A match record is a named list, with entries match, start and end that are respectively the matching (sub) string, the start, and the end positions (using one based indexing).

Extracting Match Data

To make it easier to extract matching substrings or positions, a special $ operator is defined on match columns, both for the .match column and the columns corresponding to the capture groups. See examples below.

See Also

gregexpr, which this function wraps

Other tidy regular expression matching: re_exec(), re_match_all(), re_match()

Examples

name_rex <- paste0(
  "(?<first>[[:upper:]][[:lower:]]+) ",
  "(?<last>[[:upper:]][[:lower:]]+)"
)
notables <- c(
  "  Ben Franklin and Jefferson Davis",
  "\tMillard Fillmore"
)
# All occurrences
allpos <- re_exec_all(notables, name_rex)
allpos

# Custom $ to extract matches and positions
allpos$first$match
allpos$first$start
allpos$first$end

Extract Regular Expression Matches Into a Data Frame

Description

re_match wraps regexpr and returns the match results in a convenient data frame. The data frame has one column for each capture group if perl=TRUE, and one final columns called .match for the matching (sub)string. The columns of the capture groups are named if the groups themselves are named.

Usage

re_match(text, pattern, perl = TRUE, ...)

Arguments

Value

A data frame of character vectors: one column per capture group, named if the group was named, and additional columns for the input text and the first matching (sub)string. Each row corresponds to an element in the text vector.

Note

re_match uses PCRE compatible regular expressions by default (i.e. perl = TRUE in regexpr). You can switch this off but if you do so capture groups will no longer be reported as they are only supported by PCRE.

See Also

Other tidy regular expression matching: re_exec_all(), re_exec(), re_match_all()

Examples

dates <- c("2016-04-20", "1977-08-08", "not a date", "2016",
  "76-03-02", "2012-06-30", "2015-01-21 19:58")
isodate <- "([0-9]{4})-([0-1][0-9])-([0-3][0-9])"
re_match(text = dates, pattern = isodate)

# The same with named groups
isodaten <- "(?<year>[0-9]{4})-(?<month>[0-1][0-9])-(?<day>[0-3][0-9])"
re_match(text = dates, pattern = isodaten)

Extract All Regular Expression Matches Into a Data Frame

Description

This function is a thin wrapper on the gregexpr base R function, to extract the matching (sub)strings as a data frame. It extracts all matches, and potentially their capture groups as well.

Usage

re_match_all(text, pattern, perl = TRUE, ...)

Arguments

Value

A tidy data frame (see Section “Tidy Data”). The list columns contain character vectors with as many entries as there are matches for each input element.

Tidy Data

The return value is a tidy data frame where each row corresponds to an element of the input character vector text. The values from text appear for reference in the .text character column. All other columns are list columns containing the match data. The .match column contains the match information for full regular expression matches while other columns correspond to capture groups if there are any, and PCRE matches are enabled with perl = TRUE (this is on by default). If capture groups are named the corresponding columns will bear those names.

Each match data column list contains match records, one for each element in text. A match record is a named list, with entries match, start and end that are respectively the matching (sub) string, the start, and the end positions (using one based indexing).

Note

If the input text character vector has length zero, regexpr is called instead of gregexpr, because the latter cannot extract the number and names of the capture groups in this case.

See Also

Other tidy regular expression matching: re_exec_all(), re_exec(), re_match()

Examples

name_rex <- paste0(
  "(?<first>[[:upper:]][[:lower:]]+) ",
  "(?<last>[[:upper:]][[:lower:]]+)"
)
notables <- c(
  "  Ben Franklin and Jefferson Davis",
  "\tMillard Fillmore"
)
re_match_all(notables, name_rex)