How to Use Regular Expressions in Google Data Studio

Regular expressions, also known as regex, are powerful tools for working with text and data. They can be used to search for, match, and extract specific patterns of characters from a larger body of text. In Google Data Studio, regular expressions can be used to filter and transform data from your data sources, allowing you to create more customized and accurate reports and dashboards.

In this blog, we'll provide an overview of regular expressions and show you how to use them in Google Data Studio. We'll also provide some examples of regular expressions that you can use in your own projects.

What are Regular Expressions?

Regular expressions are a type of text pattern that can be used to match and manipulate text. They are often used in programming languages and text editors to search for and manipulate strings of text, and can be very powerful and efficient tools for working with large amounts of data.

A regular expression consists of a pattern of characters that defines the text that it matches. This pattern can include special characters, called metacharacters, which have specific meanings and functions in regular expressions. For example, the metacharacter "." (dot) can be used to match any single character, while the metacharacter "*" (asterisk) can be used to match zero or more occurrences of the preceding character.

How to Use Regular Expressions in Google Data Studio

Google Data Studio allows you to use regular expressions in the filters and data transformations for your data sources. This means that you can use regular expressions to extract specific patterns of characters from your data and use them in your reports and dashboards.

To use regular expressions in Google Data Studio, you first need to create a data source and connect it to your data. Then, you can use the Filter and Calculated Field options in the Data Source settings to apply regular expressions to your data.

In the Filter section, you can use regular expressions to specify which data to include or exclude from your data source. For example, you could use a regular expression to only include rows of data that contain specific keywords or to exclude rows of data that contain certain characters.

Here are some examples of regular expressions that you can use in Google Data Studio:

1. Extracting email addresses: You can use the following regular expression to extract email addresses from a larger body of the text: [A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}. This regular expression will match any text that follows the format of a typical email address (e.g. "name@domain.com"), and will exclude any text that does not match this format.
2. Removing special characters: You can use the following regular expression to remove special characters from a string of text: [^A-Za-z0-9]. This regular expression will match any character that is not a letter or a number and will remove these characters from the text. This can be useful for cleaning up data and making it easier to work with.
3. Extracting numbers: You can use the following regular expression to extract numbers from a larger body of the text: [0-9]. This regular expression will match any single digit (0-9), and will exclude any other characters. You can use this regular expression to extract numbers from text and use them in your calculations and data transformations.
4. Matching dates: You can use the following regular expression to match dates in a specific format: \d{2}/\d{2}/\d{4}. This regular expression will match dates that have the format "MM/DD/YYYY" (e.g. "01/01/2021"), and will exclude any dates that do not match this format. This can be useful for working with dates in your data.

These are just a few examples of the many ways that you can use regular expressions in Google Data Studio. With a little practice and experimentation, you can create your own regular expressions to match and manipulate the data in your reports and dashboards.