Match Regular Expressions with a Nicer ‘API’
A small wrapper on regular expression matching functions regexpr and
gregexpr to return the results in tidy data frames.
Stable version:
install.packages("rematch2")Development version:
pak::pak("r-lib/rematch2")Note that rematch2 is not compatible with the original rematch
package. There are at least three major changes:
- The order of the arguments for the functions is different. In
rematch2thetextvector is first, andpatternis second. - In the result,
.matchis the last column instead of the first. rematch2returnstibbledata frames. See https://github.com/tidyverse/tibble.
library(rematch2)With capture groups:
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)
#> # A tibble: 7 × 5
#> `` `` `` .text .match
#> <chr> <chr> <chr> <chr> <chr>
#> 1 2016 04 20 2016-04-20 2016-04-20
#> 2 1977 08 08 1977-08-08 1977-08-08
#> 3 <NA> <NA> <NA> not a date <NA>
#> 4 <NA> <NA> <NA> 2016 <NA>
#> 5 <NA> <NA> <NA> 76-03-02 <NA>
#> 6 2012 06 30 2012-06-30 2012-06-30
#> 7 2015 01 21 2015-01-21 19:58 2015-01-21Named capture groups:
isodaten <- "(?<year>[0-9]{4})-(?<month>[0-1][0-9])-(?<day>[0-3][0-9])"
re_match(text = dates, pattern = isodaten)
#> # A tibble: 7 × 5
#> year month day .text .match
#> <chr> <chr> <chr> <chr> <chr>
#> 1 2016 04 20 2016-04-20 2016-04-20
#> 2 1977 08 08 1977-08-08 1977-08-08
#> 3 <NA> <NA> <NA> not a date <NA>
#> 4 <NA> <NA> <NA> 2016 <NA>
#> 5 <NA> <NA> <NA> 76-03-02 <NA>
#> 6 2012 06 30 2012-06-30 2012-06-30
#> 7 2015 01 21 2015-01-21 19:58 2015-01-21A slightly more complex example:
github_repos <- c(
"metacran/crandb",
"jeroenooms/curl@v0.9.3",
"jimhester/covr#47",
"hadley/dplyr@*release",
"r-lib/remotes@550a3c7d3f9e1493a2ba",
"/$&@R64&3"
)
owner_rx <- "(?:(?<owner>[^/]+)/)?"
repo_rx <- "(?<repo>[^/@#]+)"
subdir_rx <- "(?:/(?<subdir>[^@#]*[^@#/]))?"
ref_rx <- "(?:@(?<ref>[^*].*))"
pull_rx <- "(?:#(?<pull>[0-9]+))"
release_rx <- "(?:@(?<release>[*]release))"
subtype_rx <- sprintf("(?:%s|%s|%s)?", ref_rx, pull_rx, release_rx)
github_rx <- sprintf(
"^(?:%s%s%s%s|(?<catchall>.*))$",
owner_rx, repo_rx, subdir_rx, subtype_rx
)
re_match(text = github_repos, pattern = github_rx)
#> # A tibble: 6 × 9
#> owner repo subdir ref pull release catchall .text .match
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 "metacran" "crandb" "" "" "" "" "" meta… metac…
#> 2 "jeroenooms" "curl" "" "v0.9.3" "" "" "" jero… jeroe…
#> 3 "jimhester" "covr" "" "" "47" "" "" jimh… jimhe…
#> 4 "hadley" "dplyr" "" "" "" "*rele… "" hadl… hadle…
#> 5 "r-lib" "remotes" "" "550a3c7d3f… "" "" "" r-li… r-lib…
#> 6 "" "" "" "" "" "" "/$&@R6… /$&@… /$&@R…Extract all names, and also first names and last names:
name_rex <- paste0(
"(?<first>[[:upper:]][[:lower:]]+) ",
"(?<last>[[:upper:]][[:lower:]]+)"
)
notables <- c(
" Ben Franklin and Jefferson Davis",
"\tMillard Fillmore"
)
not <- re_match_all(notables, name_rex)
not
#> # A tibble: 2 × 4
#> first last .text .match
#> <list> <list> <chr> <list>
#> 1 <chr [2]> <chr [2]> " Ben Franklin and Jefferson Davis" <chr [2]>
#> 2 <chr [1]> <chr [1]> "\tMillard Fillmore" <chr [1]>not$first
#> [[1]]
#> [1] "Ben" "Jefferson"
#>
#> [[2]]
#> [1] "Millard"
not$last
#> [[1]]
#> [1] "Franklin" "Davis"
#>
#> [[2]]
#> [1] "Fillmore"
not$.match
#> [[1]]
#> [1] "Ben Franklin" "Jefferson Davis"
#>
#> [[2]]
#> [1] "Millard Fillmore"re_exec and re_exec_all are similar to re_match and
re_match_all, but they also return match positions. These functions
return match records. A match record has three components: match,
start, end, and each component can be a vector. It is similar to a
data frame in this respect.
pos <- re_exec(notables, name_rex)
pos
#> # A tibble: 2 × 4
#> first last .text .match
#> <rmtch_rc> <rmtch_rc> <chr> <rmtch_rc>
#> 1 <named list [3]> <named list [3]> " Ben Franklin and Jefferson … <named list>
#> 2 <named list [3]> <named list [3]> "\tMillard Fillmore" <named list>Unfortunately R does not allow hierarchical data frames (i.e. a column
of a data frame cannot be another data frame), but rematch2 defines
some special classes and an $ operator, to make it easier to extract
parts of re_exec and re_exec_all matches. You simply query the
match, start or end part of a column:
pos$first$match
#> [1] "Ben" "Millard"
pos$first$start
#> [1] 3 2
pos$first$end
#> [1] 5 8re_exec_all is very similar, but these queries return lists, with
arbitrary number of matches:
allpos <- re_exec_all(notables, name_rex)
allpos
#> # A tibble: 2 × 4
#> first last .text .match
#> <rmtch_ll> <rmtch_ll> <chr> <rmtch_ll>
#> 1 <named list [3]> <named list [3]> " Ben Franklin and Jefferson … <named list>
#> 2 <named list [3]> <named list [3]> "\tMillard Fillmore" <named list>allpos$first$match
#> [[1]]
#> [1] "Ben" "Jefferson"
#>
#> [[2]]
#> [1] "Millard"
allpos$first$start
#> [[1]]
#> [1] 3 20
#>
#> [[2]]
#> [1] 2
allpos$first$end
#> [[1]]
#> [1] 5 28
#>
#> [[2]]
#> [1] 8MIT © Mango Solutions, Gábor Csárdi