Build Regular Expressions in a Human Readable Way.

rebus: Regular Expression Builder, Um, Something

Build regular expressions in a human readable way

Regular expressions are a very powerful tool, but the syntax is terse enough to be difficult to read. This makes bugs easy to introduce and hard to find. This package contains functions to make building regular expressions easier.

Package contents

The package contains constants for character classes (R-specific ones like ALNUM and GRAPH, generic ones like WORD, and compound ones like ISO_DATE), special characters (DOT, BACKSLASH), anchors (START, END).

There are functions for creating character classes, repetition, creating groups, capturing and all the basic regex functionality (char_class, repeated, group, capture).

Each of the class constants has a corresponding function that groups the class and allows repetition (alnum(3, 5)).

There are operators for concatenation (%R% or %c%) and alternation (%|%).

Examples

Match a hex colour, like "#99af01"

This reads Match a hash, followed by six hexadecimal values.

"#" %R% hex_digit(6)    

To match only a hex colour and nothing else, you can add anchors to the start and end of the expression.

START %R% "#" %R% hex_digit(6) %R% END

Simple email address matching.

This reads Match one or more letters, numbers, dots, underscores, percents, plusses or hyphens. Then match an 'at' symbol. Then match one or more letters, numbers, dots, or hyphens. Then match a dot. Then match two to four letters.

one_or_more(char_class(ASCII_ALNUM %R% "._%+-")) %R%
  "@" %R%
  one_or_more(char_class(ASCII_ALNUM %R% ".-")) %R%
  DOT %R%
  ascii_alpha(2, 4)

IP address matching.

First we need an expression to match numbers between 0 and 255. Both the following syntaxes read Match two then five then a number between zero and five. Or match two then a number between zero and four then a digit. Or match an optional zero or one followed by an optional digit folowed by a compulsory digit. Make this a single token, but don't capture it.

# Using the %|% operator
ip_element <- group(
  "25" %R% char_range(0, 5) %|%
  "2" %R% char_range(0, 4) %R% ascii_digit() %|%
  optional(char_class("01")) %R% optional(ascii_digit()) %R% ascii_digit()
)
    
# The same again, this time using the or function
ip_element <- or(
  "25" %R% char_range(0, 5),
  "2" %R% char_range(0, 4) %R% ascii_digit(),
  optional(char_class("01")) %R% optional(ascii_digit()) %R% ascii_digit()
)

# It's easier to write using number_range, though it isn't quite as optimal 
# as handcrafted regexes.
number_range(0, 255, allow_leading_zeroes = TRUE)

Now an IP address consists of 4 of these numbers separated by dots. This reads Match a word boundary. Then create a token from an ip_element followed by a dot, and repeat it three times. Then match another ip_element followed by a word boundary.

BOUNDARY %R% 
  repeated(group(ip_element %R% DOT), 3) %R% 
  ip_element %R%
  BOUNDARY

See also

The stringr and stringi packages provide tools for matching regular expressions and nicely complement this package.

The rex and Regularity packages are very similar to this package.

regular-expressions.info has good advice on using regular expression in R. In particular, see the R language page and the examples page.

debuggex.com is a visual regex debugging and testing site.

TODO

More high-level regexes for complex data types (phone numbers, post codes, car licenses, whatever).