{"id":899,"date":"2017-03-25T12:58:01","date_gmt":"2017-03-25T08:58:01","guid":{"rendered":"https:\/\/optimizationup.com\/?p=899"},"modified":"2021-02-28T14:13:57","modified_gmt":"2021-02-28T10:13:57","slug":"regex-guide-google-data-studio","status":"publish","type":"post","link":"https:\/\/optimizationup.com\/regex-guide-google-data-studio\/","title":{"rendered":"Practical Guide to Regex in Google Data Studio"},"content":{"rendered":"

Regular expressions (regex or regexp) can help save time and effort when working with Google Analytics, Google Tag Manager<\/a> and now Google Data Studio. \u00a0They add extra flexibility to the way you create definitions or include &\u00a0exclude\u00a0data from your reports.<\/p>\n

You can think of regex as specific sequences of characters that broadly or narrowly match patterns in your data. And to build a pattern you need learn the regex metacharacters. In this post, I will be covering most of the metacharacters along with practical examples from Google Data Studio<\/a>.<\/p>\n

Before we start, I recommend you have a look and understand the formulas in the article below, because all of our regex examples are based on these formulas.<\/p>\n

Create Advanced Di<\/a>mensions in Google Data Studio via these 6 Formulas<\/a><\/p>\n

<\/h4>\n

Caret ^<\/h4>\n

It matches the start of a string. In other words, it means something begins\u00a0with.
\n^social\u00a0matches\u00a0social media and\u00a0social network, but not online social.<\/p>\n

Practical Use in Google Data Studio<\/p>\n

Let’s say you have 3 paid social sources in your Google Analytics<\/a> (facebook \/ paid-social,\u00a0instagram\/paid-social, twitter\/paid-social). And you want to group any medium that starts with the string paid under a channel grouping called “Paid” and anything else under “Organic”.<\/p>\n

CASE WHEN REGEXP_MATCH(meduim,\"^paid\") THEN \"Paid\"  ELSE \"Organic\" End<\/pre>\n
<\/h4>\n
Dot .<\/h4>\n
It matches any single character.<\/p>\n
go.gle\u00a0matches\u00a0google, goagle\u00a0and\u00a0gosgle but not gogle.<\/p>\n
The power of using the dot lies in combining it together with other characters.<\/p>\n
Practical Use in Google Data Studio<\/p>\n
Google Data Studio by default has a formula to convert all text to uppercase, but what if you want to capitalize only the first letter of your\u00a0traffic sources in Google\u00a0Analytics and create a report of the same.<\/p>\n
This can be done in three steps using the caret and the dot together\u00a0^. (which means select the first letter):
\n1- Extract the first letter using REGEXP_EXTRACT() with the regular expression ^.\u00a0which captures the first letter.<\/p>\n
2- Capitalize the extracted letter using UPPER().<\/p>\n
3- Replace the first letter in Source with nothing using\u00a0REGEXP_REPLACE() and concatenate it with the extracted CAP letter using CONCAT().<\/p>\n
So if we put all the formulas altogether, the final syntax will be as below:<\/p>\n
CONCAT(UPPER(REGEXP_EXTRACT(Source, \"(^.)\")), REGEXP_REPLACE(Source, \"^.\", \"\"))<\/pre>\n
Asterisk\u00a0*<\/h4>\n
It matches the preceding character 0 or more times.<\/p>\n
go*gle matches with google, gooogle, gooooogle\u00a0and gogle, but not goegle.<\/p>\n
One of the most powerful combinations of asterisk is with the dot (.*) It \u00a0actually matches everything,<\/p>\n
go.* matches with google, goooogle, goosssf and goelge.<\/p>\n
Practical Use in Google Data Studio<\/p>\n
You have search and display campaigns running within the same AdWords account, you want to review and compare the performance of each channel separately. Your campaigns naming includes the acronym SN for search and CN for display.<\/p>\n
^.* means text starts with anything, then contains (SN, CN) and finally ends with any string.<\/p>\n
So basically, the regex below means search for text contains my acronyms\u00a0(SN, CN) in the campaign name.<\/p>\n
CASE WHEN REGEXP_MATCH(Campaign,\"^.*(SN).*\") THEN \"Search\"\nWHEN REGEXP_MATCH(Campaign,\"^.*(CN).*\") THEN \"Display\" \nELSE \"Undefined\" End<\/pre>\n
Pipe |<\/h4>\n
It means OR.<\/p>\n
men|women match with men clothes\u00a0or\u00a0clothes for women.<\/p>\n
Practical Use in Google Data Studio<\/p>\n
You can run a performance report of your landing pages grouped into unified dimensions. For example, you’re running a fashion website that sells dresses, shirts, boots, sandals..etc. You\u00a0want to compare the performance of your website categories and review which category has the most sessions or transactions.<\/p>\n
CASE WHEN REGEXP_MATCH(Landing Page,\"^.*(shirt|dress|jeans\") THEN \"Clothes\" \nWHEN REGEXP_MATCH(Landing Page,\"^.*(boot|sandal|sneaker\") THEN \"Shoes\" \nELSE \"Undefined\"\nEnd<\/pre>\n
Dollar Sign $<\/h4>\n
It means text ends with.<\/p>\n
color$ matches red color and green color, but not color yellow.<\/p>\n
Practical Use in Google Data Studio<\/p>\n
You want to run a URL performance report of your website, but some of your links end with .php and you want to standardize\u00a0all the URLs by removing .php only from the end.<\/p>\n
REGEXP_REPLACE(Landing Page, \"(.php$)\", \"\")<\/pre>\n
Question Mark ?<\/h4>\n
It matches the preceding character zero or one time. In other words, it means the previous character is optional.<\/p>\n
colo?r matches colr\u00a0and color, but not colour.<\/p>\n
Practical Use in Google Data Studio<\/p>\n
You want to run a keywords performance report of your branded and non-branded terms.<\/p>\n
Optimization ?up matches both;\u00a0optimization up with space and optimizationup without space.<\/p>\n
CASE WHEN REGEXP_MATCH(Keyword,\".*(optimization ?up).*$\") THEN \"Paid\"  ELSE \"Organic\" End<\/pre>\n
Square brackets []<\/h4>\n
Matches the enclosed characters in any order anywhere in a string.<\/p>\n
col[oau]r matches color, colar\u00a0and\u00a0colur,\u00a0but not coloaur.<\/p>\n
Square brackets are very powerful when they are combined with dashes. Dashes create a range of characters between the brackets.<\/p>\n
[a-z] matches any lowercase letter between a to z.<\/p>\n
[0-9] matches any number from 0 to 9.<\/p>\n
Practical Use in Google Data Studio<\/p>\n
Let’s assume you sell cars and you want to create a performance report for all the sold car models between 2010 and 2017. You can create a filter for your query using the regular expression below.<\/p>\n
\"regex<\/p>\n
Parentheses ()<\/h4>\n
Matches the enclosed characters in exact order anywhere in a string. It also uses to group characters or string together.<\/p>\n
g(oo)gle matches only google.<\/p>\n
The power of parentheses lies in combing it with different metacharacters.<\/p>\n
Practical Use in Google Data Studio<\/p>\n
Let’s say you want to run a report of the categories included in your campaigns names. Your campaign naming looks like\u00a0below:<\/p>\n
SN-EN_Category:Cars_Exact<\/p>\n
SN-EN_Category:Bikes_BMM<\/p>\n
To extract the category name from the campaign name, we can write something as follows:<\/p>\n
REGEXP_EXTRACT(Campaign, \"Category:(.*?[^_]*)\")<\/pre>\n
.*? means capture everything after Category: The ? is added to make .*\u00a0non-greedy by matching little data.<\/p>\n
[^_]* to\u00a0stop at the _ underscore. Please note when the caret\u00a0comes between the square brackets [^], it means doesn’t contain.<\/p>\n","protected":false},"excerpt":{"rendered":"
Regular expressions (regex or regexp) can help save time and effort when working with Google Analytics, Google Tag Manager and now Google Data Studio. \u00a0They add extra flexibility to the way you create definitions or include &\u00a0exclude\u00a0data from your reports. You can think of regex as specific sequences of characters that broadly or narrowly match…<\/p>\n
Read More<\/a><\/p>\n","protected":false},"author":1,"featured_media":1262,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[42],"tags":[45],"class_list":["post-899","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analyze","tag-google-data-studio"],"yoast_head":"\nPractical Guide to Regex in Google Data Studio | Optimization Up<\/title>\n<meta name=\"description\" content=\"Learn how to use regular expressions, regex or regexp in Google Data Studio to create advanced formulas, calculated fields, filters and dimensions.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/optimizationup.com\/regex-guide-google-data-studio\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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