fugly

This package provides a single function (str_capture) for using named capture groups to extract values from strings. This function is just a wrapper around stringr.

fugly::str_capture() is very similar to both unglue and utils::strcapture().

This package was written because stringr doesn’t yet do named capture groups (See issues for stringr and stringi), and I needed something faster than unglue

	fugly::str_capture	unglue:::unglue	utils::strcapture
Speed	fastest	slow	faster
Naming Groups	inline capture group naming	inline capture group naming	use of prototype to define names
Safe + Robust?	dodgy	quite safe	quite safe

What do I mean when I say `fugly::str_capture()` is unsafe/dodgy/non-robust?

It doesn’t adhere to standard regular expression syntax for named capture groups as used in perl, python etc.
It doesn’t really adhere to glue syntax (although it looks similar at a surface level).
If you specify delimiters which appear in your string input, then you’re going to have a bad time.
It’s generally only been tested on data which is:
- highly structured
- only ASCII
- non-pathological

What’s in the box?

fugly::str_capture(string, pattern, delim)
- capture named groups with regular expressions
- returns a data.frame with all columns containing character strings
- can mix-and-match with non-capturing regular expressions
- if no regular expression specified for a named group then .*? is used.
- does not do any type guessing/conversion.

Installation

You can install from GitHub with:

# install.package('remotes')
remotes::install_github('coolbutuseless/fugly')

Example 1

In the following example:

Input consists of multiple strings
capture groups are delimited by {} by default.
the regex for the capture group for name is unspecified, so .*? will be used
the regex for the capture group for age is \d+ i.e. match must consist of 1-or-more digits

library(fugly)

string <- c(
  "information: Name:greg Age:27 ",
  "information: Name:mary Age:34 "
)

str_capture(string, pattern = "Name:{name} Age:{age=\\d+}")

##   name age
## 1 greg  27
## 2 mary  34

Example 2

A more complicated example:

Note the mixture of capturing groups and a bare .*? in the pattern which is not returned as a result

string <- c(
'{"type":"Feature","properties":{"hash":"1348778913c0224a","number":"27","street":"BANAMBILA STREET","unit":"","city":"ARANDA","district":"","region":"ACT","postcode":"2614","id":"GAACT714851647"},"geometry":{"type":"Point","coordinates":[149.0826143,-35.2545558]}}',
'{"type":"Feature","properties":{"hash":"dc776871c868bc7e","number":"139","street":"BOUVERIE STREET","unit":"UNIT 711","city":"CARLTON","district":"","region":"VIC","postcode":"3053","id":"GAVIC423944917"},"geometry":{"type":"Point","coordinates":[144.9617149,-37.8032551]}}',
'{"type":"Feature","properties":{"hash":"8197f34a40ccad47","number":"6","street":"MOGRIDGE STREET","unit":"","city":"WARWICK","district":"","region":"QLD","postcode":"4370","id":"GAQLD155949502"},"geometry":{"type":"Point","coordinates":[152.0230999,-28.2230133]}}',
'{"type":"Feature","properties":{"hash":"18edc96308fc1a8e","number":"22","street":"ORR STREET","unit":"UNIT 507","city":"CARLTON","district":"","region":"VIC","postcode":"3053","id":"GAVIC424282716"},"geometry":{"type":"Point","coordinates":[144.9653484,-37.8063371]}}'
)


str_capture(string, pattern = '"number":"{number}","street":"{street}".*?"coordinates":\\[{coords}\\]')

##   number           street                  coords
## 1     27 BANAMBILA STREET 149.0826143,-35.2545558
## 2    139  BOUVERIE STREET 144.9617149,-37.8032551
## 3      6  MOGRIDGE STREET 152.0230999,-28.2230133
## 4     22       ORR STREET 144.9653484,-37.8063371

Simple Benchmark

I acknowledge that this isn’t the greatest benchmark, but it is relevant to my current use-case.

For large inputs (1000+ input strings), fugly is the fastest
For small inputs, fugly and utils::strcapture are about equivalent
unglue is the slowest of the methods.
ore lies somewhere between unglue and utils::strcapture

# remotes::install_github("jonclayden/ore")
library(ore)
library(unglue)
library(ggplot2)

# meaningless strings for benchmarking
string <- paste0("Information name:greg age:", 1:1000)

res <- bench::mark(
  `fugly::str_capture()` = fugly::str_capture(string, "name:{name} age:{age=\\d+}"),
  `unglue::unglue()` = unglue::unglue_data(string, "Information name:{name} age:{age=\\d+}"),
  `utils::strcapture()` = utils::strcapture("Information name:(.*?) age:(\\d+)", string, 
                    proto = data.frame(name=character(), age=character())),
  `ore::ore_search()` = do.call(rbind.data.frame, lapply(ore_search(ore('name:(?<name>.*?) age:(?<age>\\d+)', encoding='utf8'), string, all=TRUE), function(x) {x$groups$matches}))
)

Acknowledgements

R Core for developing and maintaining the language.
CRAN maintainers, for patiently shepherding packages onto CRAN and maintaining the repository

Introducing 'fugly' - fast regular expression substring extraction with named capture groups