Looker Studio - Functions

Master dynamic data transformations with Looker Studio’s powerful functions for smarter visualizations and deeper insights.

Ceyhun Enki Aksan Entrepreneur, Maker

Sep 15, 2025

Looker Studio (GDS) visualization tasks involve acquiring and visualizing data directly from data sources or through connectors (agents). At this stage, the data we obtain is listed in the form of metrics and dimensions. These fields, listed in relation to the data source, can be controlled through various functions based on user requirements.

In simple terms, Looker Studio functions can be described as calculated fields (calculated fields), which allow us to manipulate dates, numerical operations, values (value), and formats, and can be recorded either at the data chart level (chart level) or at the data source level. For more details on this topic, please refer to the article titled Looker Studio Calculated Fields (Calculated Fields). In this article, I will discuss the functions we can use within calculated fields. First, let’s briefly recall the differences between calculated fields at the data chart level and at the report/data source level.

Operation	At Data Source Level	At Data Chart Level
Is a data source editor required?	+
Will blended data (blended) be used?		+
Will other calculated fields be used?	+
Will it be used in other reports and charts?	+

It is beneficial to take into account these differences when creating calculated fields. Otherwise, you might end up creating separate calculated fields that perform the same operation but are calculated individually for each data visualization, and you may be forced to manually adjust each calculated field whenever any updates or edits are required. On the other hand, for calculated fields that will be used only in a few visualizations, it may not be necessary to perform operations at the data source level; in such cases, operations at the data visualization level (depending on the conditions in the table) will suffice.

To create a calculated field at the data visualization (chart) level, you can click the Create new field link in the Metrics and/or Dimensions section, either during or after the visualization stage. Then, the relevant operations can be performed within the opened field.

Blended data (blended data) also works similarly at the data visualization level. A field (field) is created again for the metric and/or dimension, and the newly created calculated field is added to the relevant section¹. Of course, a calculated field intended to be used as a key must return the same data for both data tables, including data types, etc. Otherwise, an error will occur.

Unlike operations at the data visualization level, at the data source level, as shown in the table above, different calculated fields can also be used within formulas and across different charts and reports.

Thus, managing the corresponding calculated fields becomes significantly easier, and a much broader range of operations can be visualized through data charts.

We can also define parameters at the data source level. However, for now, I am keeping the topic of parameters for a separate article, outside the scope of this post.

Functions

Data Studio functions are predefined formulas that enable a wide range of operations—including controls, value manipulation, merging, data type conversions, etc.—beyond basic mathematical operations, and can be used within calculated fields (for metrics and dimensions).

Similar to functions available in electronic spreadsheet applications such as Microsoft Excel and Google Sheets, or in query languages such as SQL, Data Studio functions are designed for specific purposes and must follow specific syntax rules to be used.

CASE
    WHEN condition1 THEN result1
    WHEN condition2 THEN result2
    WHEN conditionN THEN resultN
    ELSE result
END;

Currently, over 50 functions are available for use in calculated fields, with this number expected to grow over time². These functions are grouped under various headings—data aggregation (aggregation), arithmetic (arithmetic), conditional (conditional), date (date), geographic/geo (geo), text (text), and miscellaneous (miscellaneous) operations—based on their intended purposes³ ⁴.

Function Usage

Before moving on to the most commonly used formulas and example operations, it’s worth remembering a few key points regarding formula usage.

We can use formulas (at the data visualization and/or data source level) by either creating a new field (field) or by modifying an existing (non-default) custom metric or dimension. We cannot perform operations on a standard field (field).
If a formula runs successfully, a green checkmark appears under the formula editor. Once this step is completed, the formula can be saved and applied to the data pulled from the data source.
When a formula is entered into the formula editor, a help window appears, providing examples of usage and relevant information.
Calculated fields created at the data source level are marked with fx.

Finally, text operations within metric fields are processed as binary (boolean) values, and depending on the selected operation, if Count is selected, the total number of processed data items is returned, while if Count Distinct is selected, the total number of unique values is returned.

Data Types

Data Studio supports various data types (numerical, text, boolean, URL, etc.) to describe operations and results within calculated fields. These data types apply to all data obtained through predefined data sources and connectors. ⁵

Data type	Expected data	Example
Numeric (numeric)	Numeric value	1,234.56
Percent (percentage)	Numeric ratio	61.73%
Duration (duration)	A whole number representing a duration in seconds. This will be formatted as a standard time display in reports.	01:43:19
Currency (currency)	Monetary value. This will be formatted with the appropriate currency symbol in reports. Currency codes are also selected.	$1,234.56, 173.9 €, ¥30,093, etc.
Text (text)	Zero or more letters, digits, characters, or symbols	A1B2C3
Date & Time	Calendar date, date and time, or an abstract date or time index. This will be formatted according to local settings.	2020-09-21, 2020-09-21 12:35 PM, Q4 2020, Week 1, 13, etc.

Field definitions for data types can be adjusted through selection among appropriate options. Additionally, data type adjustments can be made within calculated fields using the CAST function⁶. However, conversions between types are subject to specific constraints.

Source data type	Target data type
Text	DATE, DATETIME, NUMBER
Number	TEXT
Date	DATETIME, TEXT
Date & Time	DATETIME, TEXT

After covering the essential topics, we can briefly discuss a few prominent functions.

Example Functions

Below, I’ve attempted to provide descriptions and examples for some commonly used functions. New examples and functions may be added over time⁷ ⁸. You can also share your frequently used calculated field formulas and functions in the comments.

CASE

Enables conditional operations. It is treated similarly to SQL⁹. Different formulas can be used for condition checks within a formula. In the example below, the utm_medium parameter within the Page content is being checked and the value is assigned to a channel group accordingly. As shown, values are being checked using REGEXP_MATCH within the CASE statement.

CASE 
WHEN REGEXP_MATCH(Page,'.*utm_medium=e-mail.*') THEN "Email"
WHEN REGEXP_MATCH(Page,'.*utm_medium=paid-search.*') THEN "Paid Search" 
ELSE Default Channel Grouping 
END

CONCAT

Combines multiple text strings into a single text string¹⁰. The example below combines the domain name with the page URL to create a complete URL, and the resulting link is associated with the page title using the HYPERLINK function.

HYPERLINK(CONCAT(Hostname, Page), Page Title)

HYPERLINK

As seen in the previous example, the HYPERLINK function allows defining a label (label) for a link and makes that label clickable¹¹.

REGEXP_EXTRACT

Allows splitting a value into components¹². For example, you can use the REGEXP_EXTRACT function to extract query parameters from within the Page and access the parameter values as well.

REGEXP_EXTRACT(Page, 'q=([^&]+)')

When we want to check whether the values satisfy the specified conditions, we use the REGEXP_MATCH function¹³. Of course, this is not the only option available; other functions such as REGEXP_CONTAINS, STARTS_WITH, and ENDS_WITH also offer different capabilities. In the following example, it will return True if either apple or iphone is present in the Query content.

REGEXP_MATCH(Query, '(apple|iphone)')

REGEXP_REPLACE

Allows replacing values with a specified value when the given values match a specified pattern. For instance, you can reevaluate issues such as typos in search queries or campaigns using the REGEXP_REPLACE function¹⁴.

REGEXP_REPLACE(Page, 'stanley', 'thermos')

UPPER / LOWER

Converts all characters in the given value to¹⁵ or lowercase¹⁶.

UPPER(Campaign)
LOWER(Campaign)

In addition to the examples above, sample reports accessible through search engines can also be very helpful in understanding the usage patterns. At this stage, it is important to verify the relevant formulas and ensure that the values associated with the data sources are accurate and consistent. In such cases, I recommend using a table (table) visualization to validate the operations.

Looker Studio Functions

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