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When working with Tableau, sooner or later you stumble across the concept of Tableau sets. Explained next is what sets are and how they differ from groups. We'll also show you how to create sets in Tableau in order to increase interactivity.

What are sets?

Sets allow developers to divide a dimension, on the basis of its own content or that of another column, into subsets and create new perspectives as a result. This allows isolation of data segments and consequent generation of new insights.

There are three different types of sets:

A data record can be found inside or outside the defined condition or allocation, i.e. a binary view is involved here.

The binary view and different types of sets can be illustrated by means of examples:

  • Threshold - customer file at a store

A customer is to be considered regular as soon as they start buying there more than 5 times a month (threshold). A customer can therefore be either inside the segment of regular customers (> 5 purchases per month) or outside (< 5 purchases per month).

  • Top-ranking of sales outlets

A company’s headquarters wants to ascertain the top 3 outlets based on sales. This classification is performed without any fixed turnover threshold.

  • Manual selection - favourite customers

A craftsman decides to send a discount to his favourite customers. The group of favourite customers can only be created through manual assignment by a data-savvy developer.

Categorizations which cannot be represented by binary logic are not possible with sets.

How do sets differ from groups?

n addition to sets, Tableau also makes it possible to create groups.

Both functions seem very similar at first glance, but how do they differ?

As we already know, sets create segments of data fulfilling certain defined conditions. Several dimensions can be used for definition here. For example, the three most populous federal states are being sought: The dimension of federal states is divided into two segments (within the top 3 and the remainder) by means of the "population" key figure.

In the case of groups, on the other hand, the values of a dimension can be statically combined to form new categories at a higher level. How many categories are created does not matter. As an example, let us allocate each of the federal states to a region comprising north, east, south or west. Grouping allows each state to be manually assigned to a region.

Here is a comparison of the differences again:



Binary (in/out)

Any number of groups possible



Calculated before dimension filter

Calculated as dimension filter

Interaction with parameters

No parameters, instead manual determination

How to create sets

Data preparation is now concluded and we know that sets can be used to segment data on the basis of dimensions. But how to create a set, and what is the difference between fixed and dynamic sets?

Fixed sets contain several dimensions and are simply selected in a diagram. They are therefore particularly suitable for ad-hoc analyses. Once selected, the elements of the set are fixed and cannot be changed. However, the resulting field can be reused, e.g. for filters or calculations.

Dynamic sets are created by right-clicking the dimension to be segmented. An editing window opens with three options:

  • General - a defined set can also be created here; selected entries can be included or excluded. Any new data records are always added to the "miscellaneous group".
  • Condition - setting of a threshold for another dimension.
  • Topmost - determination of the top or bottom N entries after evaluation of a further dimension.

The condition and topmost functions both take new data records into account and categorize these according to the conditions specified. In addition, parameters can make thresholds and selection of N entries more interactive.




Sets are a good way to involve users and encourage interactivity. As a Tableau partner, we are happy to provide support in the implementation of your Tableau plans.

Contact us if you have specific questions about sets or participation in a training course.

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Julia Brandhofer
Julia has worked on BI projects involving DWHs and visualizations for several years. She does not rely on any one tool in particular and uses Tableau as well as Power BI. She considers these tools important in her own development and spends time helping others to use them.
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