I love RegEx, I use it every day and I will show you how to use it to easily get some smaller and larger tasks done.

But...

Don’t use it in production

Ok, first things first: Be very careful using RegEx for anything in production code if you're not absolutely certain it's actually necessary.

This is an example of what could happen. In 95% of the cases, it's much safer and easier to comprehend to use simple loops to go over data, using something like String.contains() or String.split(delimiter) to search and break strings up in a simple and readable way.

[EDIT] To be very clear: I mean what I said above. Don’t use anything I show you here in production. I personally only use that on log files, test data and manual data creation.

Tools

There is actually no special tool I use. Every more or less sophisticated text editor or IDE supports RegEx in search an replace. Most of the work I personally do in Sublime Text, sometimes in IntelliJ.

Useful RegEx

This is how I most often use RegEx in my day-to-day life.

Replace start end of line

Consider you have the following text

Flour
Eggs
Milk
Salt
Maple sirup

And you want to make a bulleted list. You could obviously enter a * in front of every line manually. But, you can use RegEx, of course.

This will result in:

* Flour
* Eggs
* Milk
* Salt
* Maple sirup

The ^ is a special character that matches the beginning of a line. Replacing this with one or more characters will prefix each line.

The same goes for end of a line. Let's say you need to add a comma at the end of each line.

"Foo"
"Bar"
"Baz"

"Foo",
"Bar",
"Baz",

The last comma might be unnecessary and thus must be removed manually. There is a more sophisticated search to fix this but most of the time it's not worth the effort. It's always good to let RegEx do the heavy lifting and fix the resulting 2% manually.

Swapping Columns

Assume we got the following data

"foo":8,
"bar":42,
"baz":13,

"8":"foo",
"42":"bar",
"13":"baz",

What's happening here? We are using groups. A group is delimited by parentheses. So we have (group1)(group2)(group3). The cool thing about groups is to use them later on. In Sublime, $n is used where n is the group index starting with 1. Notice that we did not include the , and " inside the groups. Inside each group, I am using \d which matches a single digit and \w matching a word character like a-z, A-Z, 0-9 and _, but no - e.g. + matches one ore more characters of the kind.

Convert CSV to JSON

Let's assume we have the following CSV:

1,35,"Bob"
2,42,"Eric"
3,27,"Jimi"

Result:

{"id":1,"age":35,"name":"Bob"},
{"id":2,"age":42,"name":"Eric"},
{"id":3,"age":27,"name":"Jimi"},

Again, we're using groups and digit or word matchers.

The transformed result could easily turned into valid JSON by adding a wrapper object and arrays as well as removing the last comma. But the heavy lifting is done by RegEx.

Create Test Data

Sometimes I need test data, a lot.

What I usually do, is to create a sequence of numbers using...Excel. Yep, Excel. Excel is pretty smart when it comes to sequences. E.g. you can enter something like:

Then select both an drag on the right bottom corner to fill the cells below. Excel is able to determine that the next number is 30. So based on that that, copy the rows in to Sublime:

10
20
30
40

Then I apply the same strategy as before:

{"id":10,"username":"user10"},
{"id":20,"username":"user20"},
{"id":30,"username":"user30"},
{"id":40,"username":"user40"},

Learning

RegEx101

There is RegEx101 where you can test if RegEx matches. Modern editors like Sublime and IntelliJ will dynamically highlight matches in your current window. However, this page is also great to find errors and to learn what actually matches and why by using hover and the explanation section.

RegEx Golf

Then, you can use RegEx Golf as a fun way to learn RegEx.

And of course, here on dev.to

A regex cheatsheet for all those regex haters (and lovers) 👀

catherine ・ Jan 10 '19

#javascript #beginners #programming #computerscience

Summary

As you can see there are plenty of use cases for RegEx to help you with small and larger tasks that would manually take hours, especially with large data sets.