An overview of the different Data Studio Filter Types

When you start with Data Studio and you want to filter in one of the reports, you get a broad offer of many different filters. In this Blogpost, I want to show the different kinds of filters and give some recommendations for their best use cases. Additionally I want to speak about which filter types we use most of the time and the reasons for that. The audience of this article are people taking the first steps with Data Studio.

With filters, you can select the data you want to see in the report. The filters vary in the used data types, visualization and interaction possibilities. This article deals with the filters users can interact with, not with the chart filters. Those particular filters are called controls in Data Studio too.

The first filter is maybe also the most important one, so we use it in many of our reports. You have to select a dimension and an optional metric. Then the user can click on the filter and he/she sees every variation of the dimensional values. Then it is possible to select at least one dimensional value. The chosen metric is shown on the right side, aggregated by every dimensional value.

A special type of this filter is the “single select” (in the style tab of the filter). When this type is active, the user can select just one value at once.

The advantage of this filter type is that you can choose any dimension (independent of the data type) and the interaction is easy.

It is nearly the same as the drop-down list. The big difference is, that the user can already see the selectable values by default. All other addressed aspects remain the same.

One very important aspect of these two filter types is the default selection. In this field you can enter a dimensional value that is preselected for the filter until the user changes it. The Date Range Control filter has the same feature and gets explained later.

For this filter, you just select the dimension you want to filter for. The user can enter the search term. Furthermore you can select, which search type it is. You can choose between f.e. equals, contains, starts with (depends on the data type).

This filter fits for some cases too. So especially the handling of integer data types is intuitive and powerful. We did not work much with this filter, because mostly you do not want to filter for numbers. For text dimensions especially the types contains or regex are useful, for example if the user wants to filter for complex product names.

This filter is nearly the same as the input box, just that here the user can choose the search type. The offered types are just limited by the data type of the underlying dimension.
When we use one of them we mostly used the advanced filter, because the user has more control over the report.

This type just accepts integer fields. The user can interact with a slide, where he/she can select a range of numbers of this dimensional field. This filter type comes to action in rare cases, because as I mentioned before, to filter for integers is not a really ordinary case.

In this filter you can just put a boolean dimension to filter for that value. The handling of that filter is a bit tricky. It has three states

To see the minus again, you have to right-click on the filter and select “Reset action”.

We rarely use this control type, because to filter for just a boolean value is a rare case. Sometimes it is necessary to filter out some test data in the report, which is indicated by a boolean value. But for that possibility, a default selection option is needed and the Tickbox does not offer it.

This Control Type works a little bit differently in connection with the charts. In the charts you can select a date dimension as the date range dimension. Another option is to partition the data source, then the date range dimension of the chart is always the partitioned date column.

The Date Range Control always affects the Date Range Control field of the charts. The filter has predefined values you can filter for (Last Year, Today, Last Month, …) and you can also define an own date range.

This control type is in most cases a standard filter in our reports, because it is a powerful and intuitive control unit. Furthermore analyzing different date periods is a common case and the use of a Date Range Control is the preferred way for this.

Data Studio offers a lot of different filter types, but in my opinion only a few of them are intuitive, efficient and fitting for most situations. We frequently use the drop-down list and sometimes the advanced filter.

For sure we also use the date range control in many reports, but it has a special role in terms of functionality and mostly there is no real replacement. The problem with some filters is that you can just filter on data types (f.e. numbers or booleans), which you want to filter for in rare cases.

Now you have the first impressions of the different filter types in Data Studio and you can try them on your own.

This post is part of our Google Data Studio series in the Data School of datadice. We will be sharing the best insights that we’ve learned through years of creating data solutions.

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