A Creative Twist: Radial Bar Charts Step-by-Step in Tableau.

What is Radial Bar Chart?

A radial chart is a type of chart that displays data in a circular or radial format, typically using polar coordinates.

It can be used to show relationships across multiple levels of categorical data or to visualize how a single value progresses toward a goal. It is a bar chart that is displayed on a polar coordinate system

Why to use Radial Bar Chart?

Visual Appeal:

Radial charts are inherently more engaging and visually appealing than traditional linear charts, making them suitable for situations where aesthetics play a role.

Compact Space:

They efficiently display a large amount of data in a compact space, which can be beneficial when limited space is available.

Cyclical Data:

Radial charts excel at illustrating cyclical or seasonal patterns, as seen in time series data where the values repeat in a cycle.

Comparing to a Whole:

They are well-suited for showing how individual categories contribute to a larger whole, making them effective for representing budget allocation, project management, or other scenarios where relative proportions are important.

Hierarchical Data:

Radial charts can effectively display hierarchical relationships, such as product categories and subcategories.

Now that we want to show our data in a colorful circular radial charts, Let's begin.

How to build a Radial Bar Chart with an example?

To create a Radial chart, We need to :

1. Understanding the Data

2. Perform Data Densification,

3. Create some Calculated Fields,

4. Build the visual using these fields,

5. Format the final outcome.

Here I have taken an example of SIRS (Systemic Inflammatory Response) Biomarkers analysis Radial bar chart which explains how various biomarkers with their abnormal values are responsible for causing SIRS.

1. Understanding the Data.

Introduction of SIRS:

SIRS (systemic inflammatory response syndrome) is an exaggerated defense response from your body to

a harmful stressor. It causes severe inflammation throughout your body. This can lead to reversible

or irreversible organ failure and even death.

The biomarkers that are responsible are :

Abnormal Body Temperature (> 38.5 C or < 35 C)

Abnormal Heart Rate or Abnormal HR (> 90 beats per min)

Abnormal Respiratory Rate or Abnormal RR (> 20 breaths per min)

Abnormal PaCo2 ( < 32 mmHg) i.e partial Carbon dioxide pressure

Abnormal WBC count (>12000 per microliter or <4 per microliter)

2. Perform Data Densification.

We can start with Dataset connection first in the Tableau Public. Select the dataset and add the dataset as text file.

Here the dataset I have chosen is Dataset1.csv file that contains Clinical Data for Early Prediction of Sepsis and their respective IDs of the patients.

Dataset1.csv

Secondly, Create a New ExcelFile as Path with 0, 270 values.

Select this file and add to the Data source.

Create a relationship between these two files by

• Clicking on the dropdown for each file (Dataset1.csv, path)

• Selecting Edit Calculation

• Adding 1

• Clicking OK for both the files.

  

Once we are done with the above step. We can go ahead and start with Creating the Calculated fields in Tableau Public worksheet.

2. Create Calculated fields

   • Index: By adding this formula- INDEX() -1

   • X: By adding this formula- cos(RADIANS([Index]))

   • Y: By adding this formula- SIN(RADIANS([Index]))

   • Rank:  By adding this formula- RANK_UNIQUE([SIRS_patients _count],"asc")

   • percentage: By adding This formula - [SIRS_patients _count]/[SIRS_patients_Biomarkers]

   • Size: By adding this formula- [percentage]/WINDOW_MAX([percentage])

   • SIRS_Biomarkers_Types:

     

   • SIRS_patients:

     

   • SIRS_patients_count:  By adding this formula-WINDOW_SUM(SUM([SIRS_patients]))

     It is the same formula for SIRS_Patients_Biomarkers too.

4. Build the visuals.

   Step 1-Start by creating Path bins by selecting 1 a s bin size.

   Drag Y into columns and X into rows.

   Add Path bins to detail.

   In both the Y and X dropdowns , go for compute using and select path bins.

Step 2- We can see a circle of 270 degree with radius 1 in our Tableau sheet. To see different biomarker circles, drag SIRS_Biomarkers_Types to color.

But we can see only individual circles at this point.

Step 3- To be able to get all the biomarkers in one chart. We need to make the following changes to the

X and Y calculated fields.

X- cos(RADIANS([Index]))*[Rank]

Y- SIN(RADIANS([Index]))*[Rank]

Once we are done updating the fields, we need to make few more changes to the Y and X in the

columns & rows also.

Step 4- Go to Y field and click on the drop down. Go to the Edit Table calculations,

Select Rank in the Nested calculations and select SIRS_Biomarkers_Types in the Specific Dimensions.

Then, Select SIRS_patients_count in the Nested calculations dropdown and Path (bin) in Specific Dimensions.

Repeat this step 4 for X field too.

It looks something like this with all the biomarkers but with same measurement or value which is

incorrect according to our dataset.

Step 5- For getting the correct alignment of various biomarkers, we need to further change the X and Y

fields formulae.

X- cos(RADIANS([Index]*[Size]))*[Rank]

Y- SIN(RADIANS([Index]*[Size]))*[Rank]

Step 6- Now select the Y dropdown again, select Edit Table Calculation. Select SIRS_patients_Biomarkers from the Nested Calculations dropdown. Compute Using Specific Dimensions and check both Path(bin) and SIRS_Biomarker_Types.

Repeat the Step 6 again for X field.

5. Format the Final outcome.

Remove all the gridlines and headers using format option. Finally, change the Marks pane to line and drag the path(bin) to Path, increase the width of the bars according to your convenience.

We can see the Final and completed Radial Bar chart with all the abnormal Biomarkers and the % of patients with these abnormal biomarkers.

Conclusion:

According to our Dataset, we can analyze from above Chart that Abnormal HR plays a crucial role in SIRS patients whereas Abnormal PaCo2 accounts for the least. Hence, Care should be planned accordingly.

The Radial Bar Chart proved to be a highly effective visualization tool for analyzing biomarker levels in SIRS patients. By mapping biomarker values into a circular format, we were able to highlight comparative differences across multiple markers while maintaining a clean, engaging visual structure.

This approach not only made it easier to spot outliers and dominant biomarkers but also enhanced the interpretability of cyclical or repeated measures in clinical data.

While radial charts are best used with caution — especially when precise comparisons are necessary — they are extremely powerful for showcasing overall patterns, trends, and highlighting key biomarkers at a glance.

Moving forward, such visual techniques can enable clinicians and researchers to more quickly identify significant biomarkers for early diagnosis and treatment planning in SIRS and related inflammatory conditions.

Happy Visualizing!