Google Data Studio

Google Data Studio filter control – Tip to reduce the number of item selections

Filter control is a simple but essential interactive feature of Google Data Studio. We can use it in our reports for very complicated use cases, especially if we take care about element scopes inside our data viz tool (report > page > group > chart). You can also build powerful advanced search tool if you combine several filter controls, including lists with item selections and search boxes with comparison operators.


Google Data Studio filter control - Tip to reduce the number of item selections

Here a small tip, usually applied inside spreadsheets softwares: Follow the steps below to avoid too much selections of a group of items, inside a filter control list. You just need to activate search box option where you configure you widget.

  • Unselect all items
  • Search the wished group of items with the search box based on keyword
  • Select all or a part of visible items
  • Clear your search and reproduce previous steps above until all your items are selected

Easier to understand and more obvious with a small video of filter control in action.


Filter control: Repeated list searches and incremental selections



Numeric dimension filter with additional search filter control


This tip can be applied only with string dimensions. If you need to do it with a numeric dimension (not an aggregated metrics but a dimension), so a numeric attribute, like here with product weight, you need to cascade an additional search filter control with your list filter control. This is a workaround to get numeric comparison operators like >, <, =, between… because search box list is only compatible with strings. By this way, the repeated searches and selections can not be applied but you can curate and filter your numeric items.



Checking, then filtering despite multi valued dimension


At last, The first time I used this trick inside Data Studio was not because of an important number of items to select but a multi valued dimension with char separator. The purpose would be just to filter quickly and the device doesn’t really require to build a data preparation layer behind the scenes of data viz design to split the values of this dimension (for a reliable model)…just a trick that does the job of filtering.

Let’s see it with an example of wordpress article tags, tracked in Google Analytics using a single content group.



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