A complete guide to bubble chart presentation

What is a bubble chart?

A type of data visualisation, bubble charts are used to depict the relationship between three variables in a two-dimensional graph. Different data points are represented by coloured bubbles of varying sizes to highlight patterns within the data set. 

When you should use a bubble chart

Bubble charts are particularly useful to use when you need to:

• compare multiple data points
• highlight specific data points
• display proportional relationships
• make a comparison between multiple groups.

How to create a bubble chart in Excel

A quick step-by-step guide to crafting your own bubble chart using Excel.

1. Prepare your data

Organise your information into columns, being sure that each row represents a single data point. You will need at least three columns of data: x-axis, y-axis and one for the bubble sizes. 

2 . Select the data

Highlight the range of cells that contain the data that you want to use to create the bubble chart. 

3.  Insert the chart

Go to the ‘insert’ tab on the ribbon and click on the ‘scatter’ chart type. From the options choose ‘bubble’ chart type. 

4.  Format the chart

Change colours, labels and any other elements as needed.

5.  Customise the size of the bubbles

Right-click on one of the bubbles in the chart and select ‘format data series’. In the dialog box, select the ‘size and properties’ tab. Use the ‘bubble size’ control to adjust the size of the bubbles. 

6.  Add data labels

Right-click on one of the bubbles and select ‘add label’. Format the data label as needed to display the values of the sizes of the bubbles. 

7.  Save and share

Once you’ve finished you can save it and share by exporting to another file format, such as a PDF.

Best practices for creating a bubble chart

There are several best practices when it comes to crafting quality bubble charts. We’ve rounded up our top five.

Scale bubble area by value

This is useful if you want to highlight the significance of the third variable in a bubble chart, but it is important to choose an appropriate scale for the bubbles to ensure they’re accurately representing each variable being plotted. 

Present a clear trend

By choosing the right variables and scaling your bubbles consistently, a clear trend will be visually evident. 

Limit number of points to plot

By placing a limit on how many points you are plotting, you will avoid overloading the chart with too much information, which helps viewers understand the data with more ease. 

Consider colour selection

Clarify the different categories or data points with solid colour selection. Keep in mind how colours evoke certain moods and choose accordingly if you want to highlight specific data points, or show positive or negative values. 

Integrate interactivity

It’s worth considering using interactivity such as hover-over effects or tooltips so viewers can receive further insights to the data and uncover patterns that might not immediately be apparent. 

Alternatives to the bubble chart

If you are working with a larger data set, bubble charts might not be the best option, in which case you could explore the following approaches.

• The scatter plot

If you’re wanting to compare or understand the relationship between only two variables, the scatter plot is your answer. They’re particularly useful for identifying patterns and trends in the data, such as linear relationships, non linear and clusters. 

• Geographical bubble map

Best used when you need to visualise data with a geographical component, such as population distribution or environmental or economic data, geographical bubble maps help to show patterns between variables that are linked to specific locations. Population size and environmental impact or economic activity, for example. 

• Clustered bar chart

Also known as a grouped bar chart, this approach enables you to display  multiple datasets side by side. In a clustered bar chart, bars are grouped based on categories and within each group there are multiple bars representing different data sets. This approach is helpful when you need to compare multiple data sets within the one group.