Radial chart in Tableau

When I started doing my Tableau assignments, my visualizations were only with basic visuals consisting of bar charts, lines, and a few pie and tree maps. But when I attended the Sepsis presentation of the earlier batches, they were using advanced visualizations such as Radial charts, Sankey diagrams, etc. It was really inspiring to see those presentations. So this is an effort from my side to learn some of them.

Here are so advanced visualizations

1. Radial chart

2. Nested Bar Chart

3. Waffle chart

4. Word Clouds

In this blog, you can learn how to make a radial chart in a step-by-step format

1. Radial Chart

1. Radial chart

What is a Radial Chart?

• Definition: A chart that plots data points in a circular format, with positions determined by angles (theta) and distances from the center (radius).

• Uses: Ideal for displaying cyclical data (e.g., seasons, time of day) or hierarchical data in a multi-layered circular view (like a sunburst or multi-level pie chart).

• Types:

  • Multi-level Pie/Sunburst: Uses nested rings to show hierarchy (dimension on Level, dimension on Color, measure on Angle).

  • Radial Bar Chart: Uses arcs for bars, with radius representing magnitude, often requiring data densification and trigonometry. 

• Let’s learn how to visualize a Radial bubble chart and where we can use them.

• A radial bubble chart is a data visualization that arranges data points as circles (bubbles) on a circular layout, using bubble size, color, and position (distance from center/angle) to represent multiple variables, effectively extending the standard X-Y scatter plot into a circular, multi-dimensional format, great for showing relationships and comparisons in a compact, visually appealing way. 

• When to Use It:

  • Three-Variable Relationships: When you have X-axis, Y-axis, and bubble size representing three distinct numerical values (e.g., cost, effort, return).

  • Cyclical Data: Excellent for time-based data (months, hours) where a circular layout connects the start and end points (like December and January).

  • Identifying Patterns: To spot trends, correlations, clusters, and outliers in complex datasets.

  • Comparing Data Sets: To see how different groups (like states' rent vs. property count) interact.

  High-Level Overview: For a compact, holistic view of value and performance across many items (e.g., customer segments). 

  
  

• Here I have used Sample - Superstore from https://public.tableau.com/, and I have created an Excel file with all the dates from 2018 to 2021. 

Sample - Superstore.xlsx

Download XLSX • 1.13MB

Daily Dates.xlsx

Download XLSX • 37KB

Connecting with the Daily Dates Excel file and Sample -Superstore Excel Data

Making Joins - Here, making a left join between dates and order date

Adding Filters

1. Creating an Angle

No of days in each year from the order date: 360/ count the number of days in the order date. Here, 360⁰  of a circle is divided by the 365 days of a year.

2. Angle Running Total

3. Angle in Radians

4. Calculating the Radius

X and Y axis

Now lets build our visual using the calculated field

We can adjust the value of the radius dynamically, too. We are going to adjust radius and the density dynamically using parameters

1. Date level parameter

1. 2. Distance from the center

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   3. Spacing between the radicals

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Advanced Visual

Key Limitations:

• Difficulty with Exact Values: It's hard to judge precise numerical differences from bubble areas, making them better for trends than exact comparisons.

• Overlapping & Clutter: Too many bubbles, or bubbles with similar positions, overlap, obscuring data and making interpretation difficult.

• Misleading Size Perception: Viewers often focus on radius or diameter, not area, leading to incorrect judgments about magnitude.

• Limited Data Density: Can't handle large datasets well; complexity reduces clarity.

• Inability to Show Zero/Negative Values: Bubbles can't effectively represent zero or negative quantities.

• Not for Categorical Axes: X and Y axes are meant for numerical data, not categories, which breaks the scatter plot foundation.

• Geographical Inaccuracy (Bubble Maps): Placing bubbles in the center of large regions (like states) misrepresents the actual location of data, notes this Evergreen Data article, writes Stephanie Evergreen. 

When to Avoid Them:

• When exact values or precise comparisons are crucial.

• With complex datasets or many data points.

• If showing zero or negative values.

• When one axis needs to be categorical.