Packed bubble chart is a variation of scatter plot in which data point are replaced with bubbles. With the size of the bubble indicating the volume of data. Other measure can be illustrated using color to enable deeper comparisons.
Example of a bubble chart
Best practices for creating a bubble chart
Use color conservatively as this can lead to clutter – especially when adding more fields.
Provide additional details on the tooltip.
Where possible label the bubbles clearly
Step by step on how to create a bubble chart in Tableau
In this post I will show you how to build a bubble chart using Superstores data set pre-packaged with Tableau app. The goal here will be to visualize Sales and Profit for different product Sub-Category's.
If you check details on the Show Me tab of your Tableau screen. You will find that for you to create a bubble chart you'll need 1 or 2 dimensions against 1 or 2 measures.
In this article, I'll be using one dimension 'Sub-Category' against two measures 'Sales' and 'Profit' to build a bubble chart.
Building the Chart.
Drag dimension field 'Sub-category' to the Rows shelf.
Drag measure field 'Sales' and 'Profit' to the Columns shelf.
Under the Show Me tab, select Packed bubble , as shown below.
Executing above results to.
Note; The resulting chart has three negative values. This is because the field Profit was added to the Size tab and you can't represent negative values using Size, to correct this I will change SUM(Profit) from Size to color and SUM(Sales) from color to Size. This can be done by selecting the highlighted icons above and choose the replacement. Swapping the fields we have.
In our final chart. Sales have been presented using Size (Therefore, the bigger the bubble, the larger the amount of Sales generated) and Profits have been presented using Color (Therefore, dark blue color signifies more profit while brown color represents losses/less profit).
From this bubble chart, users can point out that Tables and Bookcases made a significant amount in Sales but were not profitable unlike Paper which despite having few Sales remained profitable.
This could prompt the analytics team to conduct further analysis onto what could be the root cause of the losses. Could it be the discounts are very high? or the shipping cost is extremely high? I don't know. But conducting further analysis could probably find the answer to these questions.
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