Charts should be intuitive and easy to understand. Here are a few guidelines to follow to ensure people aren’t confused.
A Complete Guide to Bubble Charts
A bubble chart (aka bubble plot) is an extension of the scatter plot used to look at relationships between three numeric variables. Each dot in a bubble chart corresponds with a single data point, and the variables’ values for each point are indicated by horizontal position, vertical position, and dot size.
The example bubble chart above depicts the points scored per game by teams in the regular season of the National Football League in 2018. Each bubble represents a single team’s performance. A bubble’s horizontal position notes the average points scored against that team each game, and the vertical position notes the average points scored by that team each game. Each bubble’s size indicates the number of wins earned by each team, with larger bubbles corresponding to higher win rates. (Ties are worth half a win.)
From the plot, we can see that there is a lot more variance in points scored by teams than by their opponents, but there’s no particularly strong correlation between the two. Instead, the main takeaway from the plot comes from the third variable: as teams score more points and allow fewer points from their opponents (towards the upper left), they will earn more victories, as one might naturally expect.
The name “bubble chart” is sometimes used to refer to a different chart type, the packed circle chart. This is a completely different chart type that will be discussed briefly towards the end of the article.
When you should use a bubble chart
Like the scatter plot, a bubble chart is primarily used to depict and show relationships between numeric variables. However, the addition of marker size as a dimension allows for the comparison between three variables rather than just two. In a single bubble chart, we can make three different pairwise comparisons (X vs. Y, Y vs. Z, X vs. Z), as well as an overall three-way comparison. It would require multiple two-variable scatter plots in order to gain the same number of insights; even then, inferring a three-way relationship between data points will not be as direct as in a bubble chart.
The three scatter plots above show the same data as the original example bubble chart. While it is easier to get the specific win counts for each team from this series of plots, the relationship between all three variables is not as clearly stated as in the bubble chart.
Example of data structure
| avg_points_against | avg_points_for | wins |
|---|---|---|
| 26.56 | 14.06 | 3 |
| 26.44 | 25.88 | 7 |
| 17.94 | 24.31 | 10 |
| 23.38 | 16.81 | 6 |
| … | … | … |
A bubble chart is created from a data table with three columns. Two columns will correspond with the horizontal and vertical positions of each point, while the third will indicate each point’s size. One point will be plotted for each row in the table.
Best practices for using a bubble chart
Scale bubble area by value
One easy mistake that can be made is to scale the points’ diameters or radii to the third variable’s values. When this kind of scaling is performed, a point with twice the value of another point will end up with four times the area, making its value look much larger than is actually warranted.
Instead, make sure that the bubbles’ areas correspond with the third variable’s values. In the same scenario as above, a point with twice the value of another point should have sqrt(2) = 1.41 times the diameter or radius so that its area is twice the smaller point’s.
Depending on how you are creating your bubble chart, you may need to scale your data to account for how data values are mapped to point sizes. Many visualization tools will automatically match value to area, but be careful of those cases where value is matched to diameter or radius instead.
Limit number of points to plot
Bubble charts are commonly drawn with transparency on points since overlaps are a much easier occurrence than when all points are a small size. This overlapping also means that there are limitations to the number of data points that can be plotted while keeping a plot readable.
There aren’t any hard guidelines for whether a dataset is appropriate for a bubble chart or not, but it’s a point to be aware of when creating a bubble chart. If there appears to be too much overplotting, then it might be worth thinking about a way to summarize the data or choose a different chart type to represent your data. Reducing bubble size can help provide some physical separation between points, but doing so will also make it more difficult to read values from bubble sizes.
Include a legend
As another tip, it’s recommended to include a legend or other key on your plot to show how different bubble sizes correspond with values of your third variable. It is fairly easy to evaluate and compare values based on horizontal or vertical lengths and positions, thanks to the tick marks on the axes. A key for bubble sizes serves the same purpose as those tick marks for the third variable.
If you are using a visualization application with interactive capabilities, it can be a good idea to turn on the feature so that values are visible when individual points are selected or hovered over. For print, it is a good idea to label key points to improve a bubble chart’s communication abilities.
Present a clear trend
If you are thinking about using a bubble chart to present information to other people, make sure that it is able to present a clear trend with its use of point size as an indicator of value. When developing your chart, experiment with the order in which variables are plotted. The two most important variables or the most important relationship should end up on the vertical and horizontal axes. Avoid using a bubble chart if the third variable does not contribute significantly to the story told by the chart, and use additional, simpler plots instead.
Incorporating negative values
If a variable takes negative values, then it cannot be directly assigned to point size as an encoding: after all, how can a shape have a negative area? Additional information needs to be encoded into shape size in order to indicate negative values. For example, you might have filled circles indicate positive values and unfilled circles indicate negative values. As another alternative, you might have positive points in one color, and negative points in a distinct, different color.
Of course, it’s a good idea to check that such encodings make sense in the first place: the variable might be better off assigned to one of the positional axes instead! Try to avoid encoding negative values with bubbles unless it truly adds value to the plot.
What Is a Bubble Chart?
Also called a bubble plot, a bubble chart is a data visualization method that displays multiple circles in a two-dimensional plot. It is a cousin of the scatter chart, except data points are replaced with bubbles. Unlike other types of graphs, a bubble chart can illustrate the relationship between three variables instead of just two.
- Bubble charts illustrate three different variables, making them uniquely valuable in displaying certain types of information.
- There are different kinds of bubble charts, including some that use different colors, shapes and labels to convey information.
- Bubble charts can make it easy to digest a relatively large amount of information, but are not ideal for illustrating exact values.
Bubble Charts Explained
How do you read a bubble chart? Each dot corresponds to a data point — the bigger the bubble, the larger the value. The position of the bubble on the x- and y-axis illustrate two additional data points. Some examples where bubble charts are often useful include consumer satisfaction ratings, revenue projections and mapping, and production costs of particular products.
When Should You Use Bubble Charts?
A bubble chart is primarily used to depict and show relationships between numeric variables. However, the addition of marker size as a dimension allows for comparison of up to three variables within bubble charts. In a single bubble chart, we can examine the relationships between any three variables (A to B, B to C, and A to C), as well as make a three-way comparison between them.
Business Uses for Bubble Charts
For businesses, bubble charts are a great tool to show the relationship between variables and compare factors such as cost, value and risk. Visualizing project portfolios via bubble charts can help companies find “attractive” clusters in one area of a graph and compare those with “less attractive” projects or opportunities in a different area of the graph, such as low value, high cost and high risk. A bubble chart could also show total sales or sales growth by geography, using a map instead of a graph.
