# 9 Regular Expressions

## 9.1 Introduction

So far you have learned some basic and intermediate functions for handling and working with text in R. These are very useful functions and they allow you to do many interesting things. However, if you truly want to unleash the power of strings manipulation, you need to take things to the next level and learn about regular expressions.

## 9.2 What are Regular Expressions?

The name “Regular Expression” does not say much. However, regular expressions are all about text. Think about how much text is all around you in our modern digital world: email, text messages, news articles, blogs, computer code, contacts in your address book—all these things are text. Regular expressions are a tool that allows us to work with these text by describing text patterns.

A regular expression is a special text string for describing a certain amount of text. This “certain amount of text” receives the formal name of pattern. In other words, a regular expression is a set of symbols that describes a text pattern. More formally we say that a regular expression is a pattern that describes a set of strings. In addition to this first meaning, the term regular expression can also be used in a slightly different but related way: as the formal language of these symbols that needs to be interpreted by a regular expression processor. Because the term “regular expression” is rather long, most people use the word regex as a shortcut term. And you will even find the plural regexes.

It is also worth noting what regular expressions are not. They’re not a programming language. They may look like some sort of programming language because they are a formal language with a defined set of rules that gets a computer to do what we want it to do. However, there are no variables in regex and you can’t do computations like adding 1 + 1.

### 9.2.1 What are Regular Expressions used for?

We use regular expressions to work with text. Some of its common uses involve testing if a phone number has the correct number of digits, if a date follows a specifc format (e.g. mm/dd/yy), if an email address is in a valid format, or if a password has numbers and special characters. You could also use regular expressions to search a document for gray spelt either as “gray” or “grey”. You could search a document and replace all occurrences of “Will”, “Bill”, or “W.” with William. Or you could count the number of times in a document that the word “analysis” is immediately preceded by the words “data”, “computer” or “statistical” only in those cases. You could use it to convert a comma-delimited file into a tab-delimited file or to find duplicate words in a text.

In each of these cases, you are going to use a regular expression to write up a description of what you are looking for using symbols. In the case of a phone number, that pattern might be three digits followed by a dash, followed by three digits and another dash, followed by four digits. Once you have defined a pattern then the regex processor will use our description to return matching results, or in the case of the test, to return true or false for whether or not it matched.

### 9.2.2 A word of caution about regex

If you have never used regular expressions before, their syntax may seem a bit scary and cryptic. You will see strings formed by a bunch of letters, digits, and other punctuation symbols combined in seemingly nonsensical ways. As with any other topic that has to do with programming and data analysis, learning the principles of regex and becoming fluent in defining regex patterns takes time and requires a lot of practice. The more you use them, the better you will become at defining more complex patterns and getting the most out of them.

Regular Expressions is a wide topic and there are books entirely dedicated to this subject. The material offered in this book is not extensive and there are many subtopics that I don’t cover here. Despite the initial barriers that you may encounter when entering the regex world, the pain and frustration of learning this tool will payoff in your data science career.

### 9.2.3 Regular Expressions in R

Tools for working with regular expressions can be found in virtually all scripting languages (e.g. Perl, Python, Java, Ruby, etc). R has some functions for working with regular expressions but it does not provide the wide range of capabilities that other scripting languages do. Nevertheless, they can take you very far with some workarounds (and a bit of patience).

One of the best tools you must have in your toolkit is the R package "stringr" (by Hadley Wickham). It provides functions that have similar behavior to those of the base distribution in R. But it also provides many more facilities for working with regular expressions.

## 9.3 Regex Basics

The main purpose of working with regular expressions is to describe patterns that are used to match against text strings. Simply put, working with regular expressions is nothing more than pattern matching. The result of a match is either successful or not.

The simplest version of pattern matching is to search for one occurrence (or all occurrences) of some specific characters in a string. For example, we might want to search for the word "programming" in a large text document, or we might want to search for all occurrences of the string "apply" in a series of files containing R scripts.

Typically, regular expression patterns consist of a combination of alphanumeric characters as well as special characters. A regex pattern can be as simple as a single character, or it can be formed by several characters with a more complex structure. In all cases we construct regular expressions much in the same form in which we construct arithmetic expressions, by using various operators to combine smaller expressions.