How to Create Chord Diagram in Tableau with Viz Extensions

Overview

A chord diagram is a graphical tool used to visualize interrelationships or flows between multiple entities (nodes) in a dataset. It arranges data radially around a circle, with arcs connecting the nodes to represent relationships. The thickness of each arc is proportional to the significance or magnitude of the flow or connection it represents.

Example of a Chord Diagram

Build similar viz in R: Explore how to create chord diagram in R with circlize package

Step-by-Step Guide

Using the same dataset used in creating a similar viz in R, I am going to shape the data by converting it from crosstab to columnar format (pivot the data to have only three columns namely, Source, Target, and Value) as shown below.

Connect the sample dataset to the Tableau Desktop app.

On the marks card select Add Extension.

On the search bar – search “Chord”.

And sort the results by Name.

Note that we’ve two extensions, created by LaDataViz and Infotopics respectively.

In this example, I am going to choose LaDataViz extension and open it to populate the view below.

Note: You can purchase the extension to enjoy its full features and capabilities.

To create a chord diagram with this extension.

• Drag the dimension Source to the Source shelf.

• Drag the dimension Target to the Target shelf.

• Drag the measure Value to the Size shelf.

• Drag the dimension Source to the Color shelf to add color to the view.

See the resulting view.

Formatting the View

To format the view, open Format Extension option.

Select color palette of choice, in this example I have selected “Miller Stone”

Next, change the color of the edges from the default “black” to the color of choice as shown below.

See the resulting view.

Note: Using the second extension by Infotopics, you can create a similar viz by following a similar procedure as the one shared above.

See the sample view created using the infotopics extension.

Conclusion

Chord diagrams are highly effective for illustrating interrelationships between entities in a matrix. Their radial layout and hierarchical edge bundling reduce visual complexity, making them ideal for analyzing flows in migration studies, economic exchanges, and genome mapping. Their aesthetic appeal further enhances their popularity in presenting weighted relationships between datasets.

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