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A complete guide to bubble charts

Posted by: mike yi, what is a bubble chart.

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

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.

Without transparency, the smaller data point would not have been visible against the larger ones.

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.

Other charts related to bubble charts

Scatter plot.

The bubble chart is, of course, built upon the  scatter plot  as a base, just with the addition of a third variable through point size. It’s worth mentioning, however, that third variables can be added to scatter plots through other point encodings. Most common among these is color. When we have a categorical third variable (taking discrete values that may or may not be ordered), we can assign a distinct hue to each category of points. It is actually possible to use hue as a fourth variable in conjunction with point size, but this should be used carefully since it can result in information overload – the earlier cautions regarding presenting a clear trend are magnified greatly with a fourth variable.

Color can also be used as an encoding for numeric variables. If we have a  color palette  where colors have a continuous relationship (e.g. light to dark), we can use color to indicate value for a third variable, rather than size. Note that perception of value based on color has similar limitations as using size, so a legend is just as necessary when using color as it is for point size.

If the two positional variables represent geographical coordinates (i.e. latitude and longitude), we can overlay bubbles over a map in the background and get a bubble map. A bubble map is an interesting extension of the scatter map that can help with the latter’s potential issues with overplotting. If a scatter map would have so many points in a region that their number would not be easily visible, we might swap them out with a single bubble that reports the total number of points within the region.

While this example from Craigslist’s rental search doesn’t have the standard bubble scaling, it still demonstrates how bubbles can help summarize potentially densely-packed points.

Packed circle chart

Packed circle charts (aka circular packing, bubble cloud) are a chart type that can look like a bubble chart on its surface. While bubbles in a packed circle chart indicate numeric values or frequencies like before, this is the only variable present: the bubbles are clustered together in a dense arrangement without any real positional axes.

In a way, you can think of a packed circle chart as a  bar chart  made of discs. However, this exposes the packed circle chart’s weaknesses: like the bubble chart, it is difficult to get exact values or a ranking from the unordered bubble sizes. You’ll usually be better off sticking with a bar chart, lollipop chart, or dot plot to convey information due to their use of position to encode value. The one advantage that packed circles have is that, if there are lots of groups to plot, the circular packing can be much more compact than showing each category in a long line. However, you could also lump smaller values into an “other” group to reduce space in a more conventional chart.

Most often, circular packing tends to appear in a hierarchical context, where smaller circles are placed inside larger circles to show how a whole is divided into parts at multiple levels of division. Even here, the circular form for proportions is somewhat inefficient compared to other chart types like the treemap, so the circular packing chart’s advantage is firmly in aesthetics rather than practicality.

Visualization tools

Depending on the visualization method, bubble charts are sometimes considered their own chart type, but at other times are created through an overarching scatter plot option. Be careful about how certain tools interpret values to be encoded by point size: if they interpret values as dot radii or diameters, you will need to perform transformations in order to make sure point sizes are representative of true values.

The bubble chart is one of many different chart types that can be used for visualizing data. Learn more from our articles on  essential chart types ,  how to choose a type of data visualization , or by browsing the full collection of  articles in the charts category .

A Complete Guide to Bubble Charts

A fundamental way of interpreting the stories hidden in data is by using data visualization. Using charts, plots, and graphs, data analysts can spot visual clues that they might otherwise overlook. From pie charts to heat maps and line graphs, there are many data visualization approaches. But one of our personal favorites is the humble bubble chart .

So what exactly is a bubble chart? And how would you go about creating one? In this post, we’ll answer all your pressing questions. When you’re finished reading you should be well-versed in the power of the bubble chart! 

You can use the clickable menu to jump to the section of your choosing.

• What is a bubble chart?
• When to use a bubble chart
• How to create a bubble chart in Excel
• Bubble chart best practices
• Alternatives to the bubble chart

Ready to burst the bubble chart? Then let’s go.

1. What is a bubble chart?

A bubble chart, or bubble plot, is a type of data visualization used by data analysts who want to plot three distinct variables. It is similar to a scatter plot, which plots two data points along two axes. On a scatter plot, the pattern of points reveals if there is any correlation between the values. We show this correlation using a line of best fit.

Meanwhile, on a bubble chart, the single points of the scatter plot are replaced by bubbles representing a third dimension of data, shown by each bubble’s size. A smaller size means a smaller value, while a larger size means a greater one. Bubbles can also be labeled or color-coded, allowing us to categorize the values.

Scatter plot vs. bubble chart example

To illustrate, the following image shows a real comparison of a scatter plot and a bubble chart:

This scatter plot shows temperature in degrees celsius on the horizontal x-axis against sales of a product in dollars on the vertical y-axis degrees. The correlation between warmer weather and increased sales is marked using a line of best fit.

Meanwhile, this bubble chart plots the manufacturer suggested retail price (MSRP) of various automobiles against their horsepower. The size of the different bubbles shows how many units of each model the manufacturer has sold (using different colors and name labels to categorize the models).

Although this makes it look simple, you’ll learn that, without care, bubble charts can easily be over complicated or used for the wrong type of data. If your bubble chart is too overcrowded, there might be a better type of visualization to use. We’ll explore this further along in the article.

2. When to use a bubble chart

Now we understand what a bubble chart is, when might you use one?

As we’ve established, the primary purpose of a bubble chart is to display and compare three dimensions of data simultaneously. The chart’s x and y-axes represent two variables of the data (typically numeric), while the bubble’s size represents a third variable (also numeric). However, having three data points doesn’t automatically mean a bubble chart is the best solution. You’ll have to learn to use your judgment here.

If you want to compare the cost, efficiency, and emissions of different types of transportation, a bubble chart would be a good choice. Similarly, if you want to visualize the results of a survey with multiple questions or compare the heights, weights, and income of different groups of people—a bubble chart could work well. However, if the third variable does not correlate well with the first two (or if there are many categories within your dataset), consider avoiding a bubble chart and choosing a simpler visualization in its place.

3. How to create a bubble chart in Excel

If you’re relatively new to data analytics (in fact, even if you’re not) one of the best ways to create a bubble chart is using Microsoft Excel . Excel has a bit of an unfair reputation for being complicated to use. But once you’ve got to grips with it, it’s a powerful data visualization and wrangling tool.

Excel comes with a ton of inbuilt functionality for creating charts and graphs that effectively visualize data. On top of that, it has many built-in formulas to automate complex calculations. Let’s look at the steps you need to take to create a basic bubble chart in Excel.

1. Get your data ready and open it in Excel

Before you get started creating a bubble chart in Excel, you need a clean dataset . This should include at least three columns of numerical data, one for each axis of the chart; the horizontal x-axis, the vertical y-axis, and the z-axis (represented by the size of the bubbles) Import this data into Excel, making sure the columns of data, from left to right, are in the order that you want them on the final chart, i.e. ‘x, y, z’.

2. Select the data in the table that you want to use for your bubble chart

Next, select the data you want to use for the bubble chart. If your dataset has more than three columns, select the three you want to represent in your bubble chart. Don’t select column or row titles.

3. Click the Insert tab > Charts Group > Scatter Charts

Now, click on the Insert tab or ribbon. This might vary depending on whether you’re using Microsoft 365 or an older version of Excel. Once you’ve found the Insert tab, navigate to the bubble chart option under Chart Groups > Scatter Charts .

This is how the image tab looks on Excel 365.

4. Select the “Bubble” chart type

Next, select the bubble chart type . You’ll find two options available: 2D or 3D. Depending on your version of Excel, you might have to click OK , or it might automatically generate the chart for you. 

Either way, your chart should now appear in your worksheet. Sense check the chart, ensuring it accurately represents your data. As you can see, our chart matches our data table. However, it lacks detail, so we’re not done yet!

5. Customize your chart

Once you’ve generated your basic chart, there are several ways to customize it. In Microsoft 365, navigate to Chart Tools , look for the Design tab and then find the Chart Styles group to choose the style of chart you want.

You can also edit the chart directly using the Format tab or by selecting individual elements of your chart and adjusting them as needed. This comprehensive tutorial from Microsoft explores all the elements you can customize on your bubble chart.

However, to illustrate the sorts of changes available here are some of the customizations we’ve made to our chart:

• We have added a title to our chart, including labels and axis titles. 
• You can also consider deleting your chart’s legend if it doesn’t serve a useful purpose. Legends (or keys) are great for explaining symbols on complex datasets. However, for a dataset as small as this, we’ve opted to label the individual bubbles instead.
• Finally, the bubbles were very crowded on the original render, making them hard to interpret. To fix this, we’ve adjusted the minimum and maximum values on the x and y-axes to give the bubbles a little more breathing room. 

While our chart isn’t perfect, even with a few tweaks, it is a vast improvement on the original version generated by Excel.

Creating a bubble chart in Excel is surprisingly straightforward. You probably didn’t expect it to be so simple! The best way to learn all the elements, of course, is to experiment and play around. For more Excel tips, check out these 10 essential Excel features for data analysts .

4. Bubble chart best practices

While ‘pretty’ data visualizations are great, they should also be functional. In short, data visualization should represent data in a meaningful way that tells you something new. If it doesn’t, then there’s not much point in spending time on it. 

Like any other data visualization, bubble charts follow best practice rules to ensure they clearly represent useful information. A few best practices for bubble charts include:

• Using appropriate bubble sizes
• Using colors to encode data
• Using a legend or key to annotate the data for larger datasets
• Avoiding clutter by hiding gridlines and using limited data points
• Being sure to maintain appropriate titles and/or labels

Let’s take a look at each of these in more depth

Bubble size

The size of the bubbles can have a significant impact on the way data is represented and understood. If the bubbles are too small it may be difficult to see the patterns or relationships between data points. Meanwhile, if the bubbles are too large, the chart may become hard to interpret. It’s a good idea to experiment with different data types on the z-axis to see which reveals new information most clearly. For instance, data with a broad range of values might be better suited to this aspect of the chart, as opposed to data where the values are all close together (meaning the bubble sizes will be similar and won’t tell you anything new).

When using colors to encode data in a bubble chart, it’s necessary to use a palette that’s easy to distinguish. For example, you might use a light color for low values and a dark color for high values. At the same time, it’s vital to consider accessibility. Avoid using colors that are hard for those with impaired vision to see.

A legend, or key, is a graphical representation of the meaning of symbols on a chart. It usually appears as a box or table beside the chart. A bubble chart’s legend might include a reference bubble demonstrating the value of a given bubble’s size, allowing you to estimate values at a glance.

Legends aren’t always necessary but can be useful, especially for charts with more plot points. If you’ve ever come across a chart without one (you’ll often find poor data visualization on news websites, for example) you’ll know how frustrating they can be, as they don’t always make it clear what you are looking at.

Title and labels

A title and labels are useful for annotating a bubble chart. The title should briefly explain what the chart represents. For smaller datasets, consider using labels instead of a legend. Alternatively, use a legend and dispense with labels. If your legend explains the color coding, for example, labels achieving the same goal might not be necessary. 

Always use labels on your axes, though—these are a must-have as they help readers understand the chart at a glance. There are exceptions to this rule, but it’s rare.

General clutter

Clutter is the enemy of any data visualization, and a bubble chart is no exception. It’s not always easy to balance the right amount of information with the right amount of clean space. One way to avoid clutter is to hide gridlines or unnecessary labels.

Since bubble charts usually require more real estate on the page than a scatter plot, you can also consider plotting fewer points than you would for a scatter chart. While many points help reinforce a scatter graph, too many on a bubble chart quickly become cumbersome to interpret. Managing clutter is a bit of an art form!

5. Alternatives to the bubble chart

There are many alternatives to the bubble chart. Remember: even with three data points, there may be better options for drawing insights from your data. Three alternatives to the bubble chart include:

The scatter plot

While we’ve explored the similarities and differences between bubble charts and scatter plots, we haven’t explored when you would use a scatter plot for a dataset with more than two data points, which is sometimes the case. If you have three or more data points with little correlation between them, savvy application of scatter plots can often reveal more information than a bubble chart would.

For instance, consider a bubble chart that shows a strong correlation between two variables (such as temperature and ice cream sales) but little correlation with the third variable (the customer’s age). In this case, plotting the third data point merely overcomplicates the graph and obscures any useful insights. Instead, using a scatter plot with the two correlated data points would be a more sensible option. 

Similarly, if you have several data points, let’s say six, with three pairs of correlating data, you will get more information from three scatter plots (mapping the correlating pairs) than from two bubble plots that clutter the correlating pairs with an unrelated third data point.

Geographical bubble map

A geographical bubble map is a data visualization that shows the distribution of a numeric variable by geographical location. On a bubble map, bubbles are placed at specific locations, with their size corresponding to the value of the variable being visualized.

Take this example of active Covid cases in the US—it’s immediately clear which areas of the country are experiencing the highest rates of infection. In general, bubble maps are great for comparing values against geography and are often much clearer than a standard bubble chart.

Clustered bar chart

A clustered bar chart is a type of bar chart used to compare two or more variables. Unlike a standard bar chart—with one bar for each data point—a clustered bar chart has several bars for each data point. A key distinction between a clustered bar chart and a bubble chart is that the former emphasizes categorical data, while a bubble chart emphasizes numeric values.

While this doesn’t exclude numerical data from a bar chart, as our example shows, bar charts allow you to represent data in clusters, in this case, financial data across annual quarters. The categories in this chart are the different locations, shown by the different colors of the bars. This approach makes it very easy to make a like-for-like comparison, which may not be as clear on a bubble chart.

It’s easy to lose yourself in the fascinating world of data visualization. For more, check out the most common types of data visualization or explore some of the most interesting data visualization examples .

6. Next steps

You should now have a strong understanding of when and how to use bubble charts for data analysis. In this post, we’ve explored how bubble charts are used, when you might use one, and even how to create a basic bubble chart using Microsoft Excel. We’ve also touched on some best practices and have explored alternatives to bubble charts and when you might use them. 

It’s now time for you to put your new skills to the test. Why not play around in Microsoft Excel, or see what data plotting tools you can find? You’ll soon see that data visualization can be one of the most creative and enjoyable aspects of any data analytics project. And the best way to learn is to dive in and get your hands dirty!

Looking for more data analytics tips and tricks? Sign up for this free, five-day data analytics short course , or check out the following guides:

• Advanced SQL for data analytics
• What are CRUD operations?  
• CASE statements in SQL

Bubble Graphs

What is a bubble graph.

A bubble graph, also known as a bubble chart or a bubble plot, is a type of data visualization that displays three dimensions of data: the horizontal axis, the vertical axis, and the size of the bubbles. This graphical representation allows us to present complex data sets in a visually appealing and understandable manner.

How does it work?

In a bubble graph, each bubble represents a data point and is positioned according to its values on the horizontal and vertical axes. The bubbles can vary in size, which represents the magnitude or importance of the data point being represented.

By using different colors or shades within the bubbles, we can incorporate a fourth dimension of data, further enhancing the information conveyed. This allows for the exploration of correlations, patterns, and trends that may exist between the variables being plotted.

Key Features and Benefits

Bubble graphs have several key features and benefits:

Data Comparison : Bubble graphs enable the comparison of three variables simultaneously, providing a comprehensive overview of the relationships and differences between data points.

Identification of Patterns : By visually representing data points as bubbles, bubble graphs make it easier to identify patterns, clusters, and outliers within the dataset.

Visual Appeal : The use of bubbles and colors in a bubble graph makes it visually appealing and engaging, capturing the viewer's attention and facilitating effective communication of complex data.

Simplicity : Bubble graphs simplify complex data sets by condensing multiple variables into a single visualization, making it easier to interpret and understand the data at a glance.

Flexibility : Bubble graphs can be customized to represent different types of data and accommodate variations in the number of dimensions being analyzed.

Bubble graphs find applications in various fields, including:

• Market Research: Analyzing customer preferences, market trends, and product comparisons.
• Financial Analysis: Visualizing the relationships between variables such as revenue, profitability, and market share.
• Geographical Analysis: Mapping population density, housing prices, or other geographic data.
• Scientific Exploration: Plotting scientific data to identify correlations or anomalies.
• Project Management: Tracking project progress, resource allocation, and risk assessment.

Why Assess a Candidate's Knowledge of Bubble Graphs?

Assessing a candidate's knowledge of bubble graphs is crucial for several reasons:

Effective Data Visualization: Bubble graphs provide a powerful way to visually represent complex data. Assessing a candidate's understanding of bubble graphs ensures they can effectively communicate data insights and trends to stakeholders.

Data Analysis Skills: Proficiency in bubble graphs demonstrates a candidate's ability to analyze data and identify relationships between variables. The assessment allows you to gauge their analytical thinking and problem-solving abilities.

Decision Making Support: Bubble graphs can assist in making informed decisions based on data trends. Assessing a candidate's ability to interpret bubble graphs enables you to identify individuals who can contribute to data-driven decision making within your organization.

Communication and Presentation Skills: Assessing a candidate's knowledge of bubble graphs tests their ability to present data in a clear and concise manner. This skill is essential for effectively conveying information to team members and stakeholders.

Identifying Patterns and Trends: Bubble graphs can reveal patterns, clusters, and outliers within data sets. Assessing a candidate's skills in this area helps identify individuals who possess a keen eye for detail and can uncover valuable insights.

By assessing a candidate's understanding of bubble graphs, you can ensure that you are selecting individuals who have the necessary skills to analyze and present data effectively, contributing to informed decision making and successful data-driven initiatives within your organization.

Assessing Candidates on Bubble Graphs with Alooba

Alooba offers effective ways to assess candidates on their understanding of bubble graphs. With our comprehensive assessment platform, you can evaluate candidates' knowledge and skills in this area through the following test types:

Concepts & Knowledge Test : Our customizable multi-choice test allows you to assess candidates' theoretical understanding of bubble graphs. This test evaluates their knowledge of the basic principles, components, and applications of bubble graphs.

Diagramming Test : Through our in-browser diagram tool, candidates can create bubble graphs and demonstrate their practical skills in constructing and interpreting these visualizations. This in-depth test provides a subjective evaluation of candidates' ability to effectively design and communicate information using bubble graphs.

By utilizing Alooba's assessment platform, you can accurately and efficiently evaluate candidates on their knowledge and application of bubble graphs. The tests we offer provide a comprehensive assessment of candidates' theoretical understanding and practical skills in utilizing bubble graphs for effective data visualization and analysis.

Exploring the Elements of Bubble Graphs

Bubble graphs encompass various elements that contribute to their effectiveness in visualizing data. Here are some key topics that are included in the study of bubble graphs:

Data Points: Bubble graphs represent data through individual points or bubbles. These points are positioned based on their values on the horizontal and vertical axes, while the size of each bubble indicates the magnitude or importance of the data point being represented.

Horizontal and Vertical Axes: The horizontal and vertical axes provide the framework for positioning data points in a bubble graph. These axes represent different variables or dimensions, allowing for the comparison and analysis of data across these dimensions.

Bubble Size: The size of each bubble in a bubble graph provides an additional dimension of information. It can represent various metrics such as frequency, volume, or impact, depending on the data being visualized.

Color and Shading: Incorporating colors and shading within the bubbles of a bubble graph can introduce a fourth dimension of data. By assigning different colors or shades to the bubbles, you can represent additional variables or categories, bringing more context and insights to the visualization.

Patterns and Trends: Bubble graphs enable the identification of patterns, clusters, and trends within the data. By analyzing the positions, sizes, and colors of the bubbles, you can uncover relationships and correlations that may exist between the variables being plotted.

Labeling and Titles: Proper labeling of the axes, bubbles, and other elements in a bubble graph is essential for effective communication. Clear labeling helps viewers understand the information being conveyed and provides context for interpreting the visualization.

Understanding these elements of bubble graphs allows for the creation of impactful visualizations that effectively communicate insights hidden within complex datasets. By mastering these topics, individuals can utilize bubble graphs to visually analyze and present data in compelling ways.

Practical Applications of Bubble Graphs

Bubble graphs find widespread utility across industries and disciplines due to their ability to present complex data in a visually engaging manner. Here are some practical applications of bubble graphs:

Market Analysis: Bubble graphs can be used to analyze market data, such as customer preferences, product comparisons, and market trends. By visually representing data points as bubbles, market analysts can identify patterns and correlations, aiding in strategic decision-making.

Financial Analysis: In the realm of finance, bubble graphs are valuable for visualizing relationships between variables like revenue, profitability, and market share. Financial analysts can use bubble graphs to identify trends, outliers, and potential investment opportunities.

Geographical Mapping: Bubble graphs can be employed to map geographic data, such as population density, housing prices, or demographic information. By visualizing this data on a map, patterns and variations can be observed, assisting in urban planning, resource allocation, and policy-making.

Scientific Research: Scientists utilize bubble graphs to represent complex scientific data, identify correlations, and visualize patterns. This aids in various fields, including environmental studies, biology, chemistry, and physics, enabling researchers to communicate their findings effectively.

Project Management: Project managers employ bubble graphs to track project progress, resource allocation, and risk assessment. By visualizing project-related data in a bubble graph, managers can identify bottlenecks, analyze resource utilization, and make informed decisions to ensure successful project outcomes.

By leveraging the power of bubble graphs, professionals from diverse domains can gain valuable insights from their data. The simplicity and effectiveness of bubble graphs make them a versatile tool for data analysis, visualization, and decision-making processes across industries.

Roles That Require Proficiency in Bubble Graphs

Proficiency in bubble graphs is essential for individuals in various roles that involve data analysis and visualization. Here are some examples of roles that benefit from strong bubble graph skills:

Data Analyst : Data analysts rely on bubble graphs to visualize and communicate insights derived from datasets. A good understanding of bubble graphs enables them to present complex data in a clear and concise manner.

Data Scientist : Data scientists utilize bubble graphs as part of their toolkit to analyze and explore large datasets. Proficiency in bubble graphs allows them to identify patterns, trends, and correlations that contribute to actionable insights.

Data Engineer : Data engineers incorporate bubble graphs into their data visualization practices. They leverage their understanding of bubble graphs to design and implement data pipelines that facilitate the creation and utilization of bubble graph visualizations.

Analytics Engineer : Analytics engineers work with bubble graphs to develop sophisticated visualizations that convey analytical findings effectively. They utilize bubble graphs to highlight insights and trends derived from complex datasets.

Visualization Analyst : Visualization analysts specialize in designing and creating visual representations of data. Mastery of bubble graphs is crucial for their role as they harness the power of bubble graphs to demonstrate relationships and patterns in data visualizations.

Data Architect : Data architects utilize bubble graphs as part of their data modeling toolkit. Their knowledge of bubble graphs allows them to incorporate visual representations of data relationships into their architectural designs.

It is important for professionals in these roles to possess strong bubble graph skills to effectively analyze, visualize, and present data-driven insights. The ability to leverage the capabilities of bubble graphs contributes to their success in unraveling complex data patterns and communicating impactful findings to stakeholders.

Associated Roles

Analytics engineer.

Analytics Engineers are responsible for preparing data for analytical or operational uses. These professionals bridge the gap between data engineering and data analysis, ensuring data is not only available but also accessible, reliable, and well-organized. They typically work with data warehousing tools, ETL (Extract, Transform, Load) processes, and data modeling, often using SQL, Python, and various data visualization tools. Their role is crucial in enabling data-driven decision making across all functions of an organization.

Data Architect

Data Architects are responsible for designing, creating, deploying, and managing an organization's data architecture. They define how data is stored, consumed, integrated, and managed by different data entities and IT systems, as well as any applications using or processing that data. Data Architects ensure data solutions are built for performance and design analytics applications for various platforms. Their role is pivotal in aligning data management and digital transformation initiatives with business objectives.

Data Engineer

Data Engineers are responsible for moving data from A to B, ensuring data is always quickly accessible, correct and in the hands of those who need it. Data Engineers are the data pipeline builders and maintainers.

Data Migration Engineer

Data Migration Engineers are responsible for the safe, accurate, and efficient transfer of data from one system to another. They design and implement data migration strategies, often involving large and complex datasets, and work with a variety of database management systems. Their expertise includes data extraction, transformation, and loading (ETL), as well as ensuring data integrity and compliance with data standards. Data Migration Engineers often collaborate with cross-functional teams to align data migration with business goals and technical requirements.

Data Pipeline Engineer

Data Pipeline Engineers are responsible for developing and maintaining the systems that allow for the smooth and efficient movement of data within an organization. They work with large and complex data sets, building scalable and reliable pipelines that facilitate data collection, storage, processing, and analysis. Proficient in a range of programming languages and tools, they collaborate with data scientists and analysts to ensure that data is accessible and usable for business insights. Key technologies often include cloud platforms, big data processing frameworks, and ETL (Extract, Transform, Load) tools.

Data Warehouse Engineer

Data Warehouse Engineers specialize in designing, developing, and maintaining data warehouse systems that allow for the efficient integration, storage, and retrieval of large volumes of data. They ensure data accuracy, reliability, and accessibility for business intelligence and data analytics purposes. Their role often involves working with various database technologies, ETL tools, and data modeling techniques. They collaborate with data analysts, IT teams, and business stakeholders to understand data needs and deliver scalable data solutions.

ELT Developer

ELT Developers specialize in the process of extracting data from various sources, transforming it to fit operational needs, and loading it into the end target databases or data warehouses. They play a crucial role in data integration and warehousing, ensuring that data is accurate, consistent, and accessible for analysis and decision-making. Their expertise spans across various ELT tools and databases, and they work closely with data analysts, engineers, and business stakeholders to support data-driven initiatives.

ETL Developer

ETL Developers specialize in the process of extracting data from various sources, transforming it to fit operational needs, and loading it into the end target databases or data warehouses. They play a crucial role in data integration and warehousing, ensuring that data is accurate, consistent, and accessible for analysis and decision-making. Their expertise spans across various ETL tools and databases, and they work closely with data analysts, engineers, and business stakeholders to support data-driven initiatives.

Front-End Developer

Front-End Developers focus on creating and optimizing user interfaces to provide users with a seamless, engaging experience. They are skilled in various front-end technologies like HTML, CSS, JavaScript, and frameworks such as React, Angular, or Vue.js. Their work includes developing responsive designs, integrating with back-end services, and ensuring website performance and accessibility. Collaborating closely with designers and back-end developers, they turn conceptual designs into functioning websites or applications.

Pricing Analyst

Pricing Analysts play a crucial role in optimizing pricing strategies to balance profitability and market competitiveness. They analyze market trends, customer behaviors, and internal data to make informed pricing decisions. With skills in data analysis, statistical modeling, and business acumen, they collaborate across functions such as sales, marketing, and finance to develop pricing models that align with business objectives and customer needs.

Revenue Analyst

Revenue Analysts specialize in analyzing financial data to aid in optimizing the revenue-generating processes of an organization. They play a pivotal role in forecasting revenue, identifying revenue leakage, and suggesting areas for financial improvement and growth. Their expertise encompasses a wide range of skills, including data analysis, financial modeling, and market trend analysis, ensuring that the organization maximizes its revenue potential. Working across departments like sales, finance, and marketing, they provide valuable insights that help in strategic decision-making and revenue optimization.

Software Engineer

Software Engineers are responsible for the design, development, and maintenance of software systems. They work across various stages of the software development lifecycle, from concept to deployment, ensuring high-quality and efficient software solutions. Software Engineers often specialize in areas such as web development, mobile applications, cloud computing, or embedded systems, and are proficient in programming languages like C#, Java, or Python. Collaboration with cross-functional teams, problem-solving skills, and a strong understanding of user needs are key aspects of the role.

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Another name for Bubble Graphs is Bubble Charts .

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Enhancing Data Visualization: The Power of Bubble Charts

By   STC

July 15, 2023

Introduction

In the world of data visualization , there are numerous techniques to effectively communicate information. One such technique that has gained popularity in recent years is the bubble chart. In this article, we will delve into the intricacies of bubble charts, exploring their purpose, benefits, and how they can be leveraged to convey complex data in an engaging and intuitive manner.

Understanding Bubble Charts

A bubble chart is a type of chart that displays three dimensions of data points: the x-axis, the y-axis, and the size of the bubble itself. Each bubble represents a data point, where the x and y coordinates denote two variables, and the size of the bubble reflects a third variable. By visualizing this multidimensional data, bubble charts offer a unique way to uncover patterns, relationships, and outliers within datasets.

Advantages of Bubble Charts

1. Visualizing Multivariate Data

Bubble charts are particularly useful when dealing with datasets that contain multiple variables. Unlike traditional scatter plots that only represent two dimensions, bubble charts add a third dimension through the size of the bubbles. This additional dimension allows for the representation of complex relationships and trends that might be missed in simpler charts.

2. Highlighting Differences in Magnitude

The size of each bubble in a chart can be used to represent the magnitude of a specific variable. This enables viewers to quickly compare and contrast different data points based on their relative sizes. Using visual cues, bubble charts make it easier to identify significant differences and outliers in the data, leading to better insights and decision-making.

3. Encouraging Interactivity

Bubble charts can be made interactive, allowing users to explore the data more engagingly. By incorporating features such as tooltips or hover effects, viewers can access additional information about each data point by simply interacting with the chart. This interactivity promotes a deeper understanding of the underlying data and encourages users to explore various angles and perspectives.

Use Cases for Bubble Charts

Bubble charts find applications in a wide range of fields and industries. Here are a few notable examples:

Financial Analysis

Bubble charts can be employed to analyze financial data, such as stock prices and market capitalization. By plotting different stocks on the chart, investors can quickly identify companies with similar performance metrics or spot outliers that require further investigation.

Social Sciences

In social sciences, bubble charts can help illustrate relationships between variables. For instance, a researcher studying education could use a bubble chart to visualize the relationship between student enrollment, education spending, and test scores across different schools or regions.

Product Development

Bubble charts can aid product development teams in prioritizing features or assessing customer feedback. By mapping user satisfaction ratings, development efforts, and potential revenue impact, teams can make informed decisions on feature enhancements or bug fixes.

Tips for Creating Effective Bubble Charts

To maximize the impact of your bubble charts and ensure they resonate with your audience, consider the following guidelines:

• Choose Appropriate Scaling: Pay attention to the scaling of both the x and y axes to accurately represent the data. Improper scaling can distort the relationships between variables and lead to misinterpretation.
• Use Clear Labels: Clearly label each bubble to provide context and enable easy identification. Avoid cluttering the chart with excessive labels, opting for a clean and uncluttered design instead.
• Color and Contrast : Utilize color and contrast effectively to distinguish different categories or groups within the data. This visual distinction aids comprehension and makes it easier to identify patterns or outliers.
• Consider Animation: If the dataset is large or complex, consider adding animation to reveal the data gradually. This technique can enhance storytelling and guide the viewer’s attention to specific elements.

Bubble charts offer a powerful means of visualizing multidimensional data, providing valuable insights, and facilitating data-driven decision-making. By understanding the advantages of bubble charts, exploring their use cases, and following best practices for their creation, you can harness their potential to create compelling and informative visualizations. Incorporate bubble charts into your data visualization arsenal and elevate the impact of your analyses and presentations.

Remember, effective data visualization is not just about the tool—it’s about the story you tell with your data. So, go ahead and experiment with bubble charts to unlock new perspectives and take your data storytelling to the next level.

Now that you have a solid understanding of bubble charts, it’s time to leverage this knowledge and outrank other articles on Google with your insightful content. Good luck in your pursuit of SEO success!

To learn more and purchase the book, visit StorytellingwithCharts.com . Unleash the true power of your data and communicate insights with clarity and impact using bubble charts.

About the author

We are passionate about the power of visual storytelling and believe that charts can convey complex information in a captivating and easily understandable way. Whether you're a data enthusiast, a business professional, or simply curious about the world around you, this page is your gateway to the world of data visualization.

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Ultimate Guide to Bubble Charts

Sometimes visualizations are a much better way to convey information, show trends or really drive home a point. Bubble charts are one valuable type of data visualization tool that can help businesses analyze data and make a point in a concise, visual and effective way.

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.

Key Takeaways

• 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.

Variations of Bubble Charts

Bubble charts have been around for centuries. Research indicates that Swedish global health expert and data visionary Hans Rosling was the first to use this chart style to visualize health globally more than 200 years ago. Many variations have popped up since. However, the best type of bubble chart to use depends on your data set.

Labeled Bubble Charts

The only difference between a basic bubble chart and a labeled one is that the bubbles are labeled instead of relying on a legend. A labeled bubble chart is typically used when there is a small number of data points.

Bubble Maps

In a bubble map, a bubble or set of bubbles is added on top of an image on a map, which is also called a cartogram. The bubbles’ horizontal and vertical positioning are latitude and longitude coordinates. Examples might include population density or the size of a target market in specific cities or countries.

Color Variables Bubble Chart

Bubble charts can help you visualize more than three variables by introducing different colored bubbles. Each color could signify a particular trait that differentiates it from other data points included in the chart.

Bubble Clouds

These charts are also called packed circle charts or circular packing. Bubble clouds feature numerous bubbles packed together, usually depicting only one variable, such as population or dollar amount.

Scatter Plot

A scatter plot chart is similar to a bubble chart and serves as the foundation for the latter. But a scatter plot generally only compares two variables since they use dots that are all the same size. These charts can illustrate a third variable, often by using color.

Benefits of Bubble Charts

People use bubble charts for a variety of reasons. They’re a way to tell a story visually and can provide a quick assessment of the relationship between different data sets. Well-designed bubble charts effectively display three fields of data, using bubble position and proportion.

Other benefits of bubble charts include:

• Explaining a complex data set easily.
• Analyzing data sets with multiple inputs more easily.
• Helping to visualize patterns and uncover trends using data analysis.
• Helping to correlate data as part of a data series.
• Being dynamic enough to analyze finance, sales and marketing scenarios beyond time-series or whole-to-whole comparisons using line and bar charts.
• Effectively displaying a relatively large amount of information.
• Depicting the relationship between three or even four variables that can change over time without having to use 3D graphs.

Limitations of Bubble Charts

Bubble charts are not ideal if it’s important to depict exact values. Additionally, if data is too complex, or a lot of bubbles are adjacent to each other, the following can happen:

• A bubble chart can become difficult to understand.
• The overlapping of bubbles can make it difficult to distinguish them.
• Users may find it hard to depict zero or negative values.
• It can be hard to ascertain exact values using circle sizes.

How to Create a Bubble Chart

The most common way to create a bubble chart is in Excel or another spreadsheet application, even though some business intelligence analytics platforms are more efficient. But how do you create a bubble chart in this software? The following will help you create a bubble chart using an Excel worksheet.

• Create a blank worksheet or workbook.
• Enter in the data set, making sure there are at least four columns of data.
• In the worksheet, select cell A1, then press CTRL+V or command+V. You've now selected the data set.
• Use the insert tab and select bubble chart from the list of available charts.
• To change the bubble chart style, click on the chart, then choose the design tab. Here, you can change the appearance of the bubbles, including height or width, font size and other features.

Bubble Chart Tips & Best Practices

Charts should be intuitive and easy to understand. Here are a few guidelines to follow to ensure people aren’t confused.

Make sure the x-axis, y-axis and the value depicted by the bubbles are all clearly labeled.

It can be tempting to use shapes other than bubbles in this type of chart, but be wary of straying too far from circular shapes. Using non-circular shapes can lead to inaccuracies.

The size of all bubbles in a chart should be based on the size of the largest one. Be cautious of changing their diameter, as it could misrepresent the data (see the next tip).

Scale by Value

The size or area of the bubble should reflect the value. One common mistake is to scale up the bubbles by reflecting its value based on the diameter or radii. But if you do that, a bubble with twice the value of another will end up with four times the area, making the bubble chart misleading.

Limit Number of Points to Plot

Sometimes less is more, and too many data points is overwhelming. Limiting the number of data points keeps the chart easier to read.

Just as a map has a legend or key, your bubble chart should include one to help viewers compare and understand the different bubble sizes, identifying which colors and bubbles represent which data points.

Show Trends

Your chart should ideally illustrate a trend, with the third variable depicted in the bubble sizes, making a clear point. If the bubble's size doesn't make any difference to your story, consider another type of chart — a bubble chart is probably not the best option.

Incorporate Negative Values

Try to avoid encoding negative values with bubbles unless it truly adds value to the plot. A negative value cannot be directly assigned to a point size as an encoding. Additional information will need to be encoded into shape size to indicate negative values.

At this point, you should be ready to create some bubble charts. Bubble charts and their many variations are excellent tools for visualizing data and can help you represent key data points in a more digestible way. Once you start using them, you’ll see their value and figure out what business information they’re most useful for conveying.

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Bubble Chart FAQs

What is a bubble chart used for?

A bubble chart is primarily used to depict and show relationships between numeric variables. They are a great tool to establish the relationship between variables and examine relationships between key business indicators, such as cost, value and risk.

What are examples of bubble charts?

Bubble charts can be used to compare any number of variables — for instance, consumer satisfaction ratings, revenue and the production cost of different products.

Can I create a bubble chart in Excel?

Yes, Excel can help you create bubble charts of your own. See above for step-by-step directions for how to create this type of chart in Excel.

Funnel Charts: An Expert Guide for Businesses

When you’re choosing a chart to illustrate data, you might start with one of the more common formats, like a bar, pie or line chart. Another type — funnel charts — are a great choice for depicting certain processes that are…

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• Arc Diagram
• Box & Whisker Plot

Bubble Chart

• Bullet Graph
• Calendar Chart
• Candlestick Chart
• Chord Diagram
• Circle Packing
• Connection Map
• Correlogram
• Density Plot
• Donut Chart
• Dot Matrix Chart
• Gantt Chart
• Hierarchical Edge Bundling
• Illustration Diagram
• Marimekko Chart
• Mind Map – Brainstorm
• Multi-Set Bar Chart
• Network Diagram
• Nightingale Rose Chart
• Non-Ribbon Chord Diagram
• Open-High-Low-Close Chart
• Parallel Coordinates Plot
• Parallel Sets Plot
• Pictogram Chart
• Point & Figure Chart
• Population Pyramid
• Proportional Area Chart
• Radar Chart
• Radial Bar Chart
• Radial Column Chart
• Sankey Diagram
• Scatter Plot
• Spiral Plot
• Stacked Area Chart
• Stacked Bar Chart
• Stem And Leaf Plot
• Stream Chart
• Sunburst Diagram
• Tally Chart
• Tree Diagram
• Venn Diagram
• Violin Plot

A bubble chart uses three variables to represent data points in the form of bubbles. The first two variables help to plot the data on the X-axis and the Y-axis, respectively. The third variable indicates the size of the bubble. Each point in this chart is a category disparate to a scatterplot .

Why do you need it?

We need a bubble plot to visualize our irregular and untidy data, making it effective in grasping, each data category in just sight. The area of bubbles in this chart is proportional to the data of that category, which the bubble represents. We should always use bubble charts only when there are fewer categories, or else the plot will become bulky.

What kind of data you can visualize with it?

A bubble plot is good for three variable data. Assume you have a dataset in which two variables are GDP and life expectancy at birth, respectively, which we use to plot the bubbles on the X-Y plane. A third variable that is population gives size to the bubble, and additionally, the color of the bubble can visualize the continents.

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5 Types of Bubble Chart/Graph: Examples + [Excel Usage]

by Formplus | Last updated: Apr 20, 2020

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Data visualization using colorful dots is no doubt visually appealing and a creative statistical graphing method. But do you know something that is more visually appealing? Bubbles.

Bubble charts use multiple bubbles (or circles) to display data points in a 3-dimensional space. However, the interesting thing about this 3-dimensional display is that the third dimension is represented through the size of the bubble.

Although quite different from other dot-like graphing methods, like the others, it is of various types, has real-life applications and can also be plotted using Excel. The rest of this article will see us trying to dissect the intricacies of a bubble chart and guiding you through how to make one yourself using Excel.

What is a Bubble Chart? 

A bubble chart is a multivariable graph that uses bubbles to represent data points in 3 dimensions. Also known as a bubble graph, it has various applications in social sciences, economics, medicine, and other mathematical sciences.

As the name suggests, it represents data points with bubbles. Just like the dot plot , it is also a variation of the scatter plots, but replaces the dots with bubbles and also displays the values of 3 quantitative variables.

The 3 variables are displayed with 2 of the variables determining the data point that corresponds to the value on the horizontal and vertical axis. The third variable, on the other hand, corresponds with the size of the bubble. 

Types of Bubble Charts

Bubble charts have various uses in different aspects of applied mathematics and statistics. These uses happen to vary not only with the kind of problems they solve, but also give rise to different variants of bubble charts.

This is due to the fact that the representation of the bubble chart needed to solve problems in each field differs from the other. Discussions in this section will cover each of these variants, including a diagram showing how they look.

To further aid our discussion on Bubble Chart variations, let us consider the table below which displays some information about the students of a school.

Simple Bubble Chart

Simple Bubble Chart is the most basic bubble chart variation there is. In the real sense, the simple bubble chart is not a variation of the bubble chart.

It is the original version of the bubble chart generated using Excel. This illustration shows only the first three numerical columns of the data in the table above.  

In this case, we are dealing with only 3 variables, which is the simplest a bubble chart can have. The 2nd column (Number of Students) represents the horizontal axis, 3rd column (Average Score) represents the y-axis, while the size of the circle was determined by the 4th column (Math Average).

NOTE: The values used in generating this chart, is slightly different from the ones shown in the table above. You can also create different variations using the random number generator function in Excel.

Labeled Bubble Chart

The labeled bubble chart is also very simple but has little variations. Very similar to the simple bubble chart, the difference is that the bubbles on this chart have a label.

These labels are substitutes for legends. They are used in identifying the variable each bubble represents.

Bubble labeling is only feasible when dealing with a 3- variable bubble chart with very few data sets, just like in the illustration below. If the dataset is large, it is usually time-consuming and the chart becomes cluttered. 

Multivariable Bubble Chart

This bubble chart variation is used when dealing with multiple variables that are more than 3. It helps to visualize different groups of variables with the help of colors.

You will notice that in the first 2 graphs illustrated above, the bubbles have only one color. But in the illustration below, there are three colors, with each color representing the group of the variable under consideration. 

Here, we illustrated the different Sex, namely; Male, Female, and Others. This graph was obtained by plotting the data in all the 10 columns present in the dataset.

The graph was also obtained from a randomly generated value different from the one in the table above.

Map Bubble Chart

As the name implies, this bubble chart variation is illustrated on maps. It is usually used in geographical sciences, and are sometimes called cartograms. 

The bubbles represent a geographical location on the map. For example, when plotting a map that requires you to identify places with crude oil, gold, and some other mineral resources in the world, you use the Map Bubble Chart.

The x and y axes will be plotted using the latitude and longitude of these places, then the landmass will represent the size of the circle. 

The size of the bubble should tally with the size of the graph on which it is being plotted. This will ensure that each location is well recorded

3D Bubble Chart

This is a 3-dimensional variation of the bubble chart. In this case, we no longer have circle-like bubbles but have sphere-like bubbles.

The radius of the sphere is determined by the 3rd parameter in the dataset. This 3-dimensional bubble chart can also exhibit all the variations mentioned above. 

However, it is not always advisable to use a 3-D bubble chart to visualize data. This will affect the visual appeal of the bubble chart.

Uses of a Bubble Chart

• Search Engine Marketing

Search Engine Marketers can easily determine the effect of a high Cost Per Click rate on the ad position, clicks, and website conversions. A bubble chart will help determine if there are significant improvements in these factors.

By plotting the number of clicks on the x-axis, the cost per ad on the y-axis, and the increase in conversion rate will determine the size of the bubble.

• Keyword Density

When working on Word Graphs, keyword researchers can use it to determine the density of a particular keyword or hashtag. An example is the Google yearly report of the most searched words. 

This report can be made into a bubble chart, with the most searched occupying the biggest space on the map. This will make it easily identifiable.

• Promotional Effect

You can study the effect of promotions and adverts on your business using a bubble chart. The x-axis can have the number of adverts created, y-axis should have the cost spent on each ad, while the circle radius will translate to the revenue generated.

Bubble Chart Examples  

Bubble Chart Question Example: Consider the table below, representing the birth rate, death rate, and GDP of 5 countries. Use this information to construct a bubble chart. Hence, use this chart to determine the country with the highest GDP and explain how you arrive at this conclusion.

Solution: The corresponding bubble graph for this data is illustrated below, and it was constructed using Excel.

The country with the highest GDP is Australia. We arrive at this conclusion from the size of the bubbles.

The graph was plotted with the Birth rate on the horizontal axis, the Death rate on the vertical axis, and the GDP determines the size of the bubbles. Hence, the country with the biggest bubble has the highest GDP.

It is clear from the graph that our answer is Australia.

Example 2:  Use the graph generated from Example 1 above, to determine the country with the lowest GDP.

Solution:  The country with the lowest GDP is Ireland. This is clear from our observation that the circle representing Ireland is the smallest on the Chart.

How to Create a Bubble Chart with Excel  

Follow these steps to create your own Bubble Chart with Excel.

We will be using the table in Example 1 above to create our own bubble Chart in Excel.

• Step 1: Enter your data into the Excel worksheet.
• Step 2: Highlight the cells containing the data as shown in the diagram below. Go to Insert>Charts>Other Charts|Bubble

• Step 3:  Double-click on the first bubble and a dialog box like the one shown below will pop up. Go to solid fill, then edit the color and transparency of the circle, (See Diagram)

Do this for each of the circles by changing the color as you please. You should, however, try to make sure the transparency is uniform.

• Step 4:  Edit the layout of the graph, and add Chart titles, axis titles, changing the legend to the name of each country, etc.
• Step 5: Right-click on the graph, then go to Select Data.  A dialog box similar to the one below will show up. Click on Series1 , then Edit . Change the series name to Australia.

Click on the  Add  Button to add another series. This time, name it China. Do this for all the countries, and you will see your Legend change automatically.

In the solution we created earlier, the scale of the circles was reduced. You can also decide to reduce or even increase the scale for this graph by right-clicking on the graph, then going to Form Data Series. 

Go to Series Options in the resulting dialog box, then scale bubble size as you please.

Tips for Creating a Bubble Chart

Here are a few tips for you to follow when creating a bubble chart.

• Limit Plot Data Points

Most times, bubble plots tend to overlap when it is being plotted on the graph. Therefore, it is good practice to always increase the transparency of the bubbles.

This will help ensure that some bubbles are not hidden, making it difficult for the reader to interpret. However, this does not completely solve the problem of cluttering and the inability to read data points. 

The bubbles may still end up overlapping multiple times, till the transparency of the circles become ineffective. Transparency is great for readability, but it still has limitations.

• Include a Legend

Legends are to a bubble chart as the key is to the ignition. Just like, you can't start a car without the key, you can't read a multivariable chart without the legend.

The legend is what tells you the meaning of each color on the graph. In the example we treated earlier, if we didn't have a key, there was no way we could have been able to identify Australia as the country with the highest GDP.

Alternatively, you can label each bubble with their names to make it readable. This may, however, be infeasible when dealing with a large dataset.

• Present a Clear Trend

One of the most important parts of creating any graph is making sure it is readable. Your bubble chart should be interpretable by readers without putting in too much effort.

It should be able to present a clear trend with the bubble size as an indicator. Before finalizing your chart, try to experiment with the order in which the variables are implemented.

That is, if the variables are p 1 , p 2 , p 3 , you can experiment by plotting the chart (x,y,z) as (p 1, p 2, p 3 ), (p 2, p 1, p 3 ), …, making all the 6 permutations. You can now choose the best fit from these charts.

• Dealing With Negative Values

Variables that take negative values cannot directly translate to point size on the bubble chart. Therefore, one of the simplest hacks around this is to generate a pattern for the bubble display.

For example, data points with positive values can be colored, while negative-valued data points are left uncolored. Its size will now correspond to the equivalent positive value. 

That is, the size of -10.5 will be 10.5, but uncolored.

Why Use Formplus To Collect Bubble Chart Data?

With the amount of data available today, the process of collecting relevant data for your Bubble Chart Data can be highly demanding. Therefore, you need a tool that will help you collect relevant data without doing any hard work.

Do you need a tool to help optimize your data collection process? Here are a few reasons why you should choose Formplus.

• Real-time updates: Receive real-time updates on your data without stress using Formplus. When you collect your Bubble Chart Data with Formplus surveys, you receive an email notification whenever a response has been added to your data. 

The Google Sheet integration also ensures that your worksheet is updated immediately an entry is added to the dataset. You do not have to manually import the data into your worksheet each time there is an update.

Formplus automates this process for you each time, without fail.

• Security: Get rid of irrelevant responses on your surveys with the Captcha feature on Formplus. This feature prevents your form from being attacked by bots who leave spam messages.

Therefore, you will have little to no formatting to carry out on your generated Bubble chart data.

The data collected is also kept in secure cloud storage, away from third-parties. Formplus also supports Google Drive, Microsoft OneDrive, and Dropbox integration.

• Flexibility: You can use one of the existing Formplus survey templates or create your own from scratch using the Formplus builder. The builder offers you over 30+ form fields to use in collecting your data.

These form fields are capable of collecting any version of data, including images, documents, and even signatures. This feature is very applicable to job applications.

• Response Rate: Generate enough data for your bubble chart with an online survey that increases response rate. Some of these features include a save and resume feature, form validation, and the offline feature.

With form validation, you can eliminate no response biases from your survey. Respondents can also save their responses and resume whenever they please. 

Formplus surveys can also be filled offline by respondents who stay in a remote area with an unstable internet connection. 

• Generate Web Traffic: Generate more traffic to your website and social media pages with a submission button that links respondents to your website. Formplus allows you to add a link to your submission button in the form settings menu. 

Disadvantages of Bubble Charts

• The circle sizes can make it difficult to ascertain specific values at a glance.
• It cannot be used to display a lot of data. The circles will overlap and the plot will become cluttered. Therefore, making the chart unreadable.
• It is almost impossible to determine the value of the third variable from the plot alone. This is because circle sizes cannot be visually measured. This can only be possible in instances where the bubbles are labeled with the sizes.

Conclusion 

One of the common features of column charts and line charts is that they both have a numeric (y-axis) and categorical (x-axis) axis. But this is quite different from Bubble Graphs whose two axes are both numeric. 

Just like Dot Plots , Bubble Charts can be said to be a variation of scatter plots but defined with 3 dimensions. The third dimension in the Bubble plot is a size indicator that determines the area of each bubble on the chart.

The dynamism of the bubble size is the distinguishing factor that makes it a popular visualization technique. With Bubble Chart, you can create a 3-dimensional plot in 2 dimensions. 

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Bubble Charts

Bubble charts are a type of data visualization tool which combines elements of a scatter plot and a bubble map. They are used to display and compare three sets of data simultaneously. 

 A bubble on the chart represents each data point. The bubble’s position on the X and Y axes corresponds to the values of the first and second variables, while the bubble size corresponds to the third variable. Larger bubbles indicate larger values for the third variable.

Bubble charts are helpful when visualizing and comparing data with three quantitative variables. They can reveal patterns, trends, and relationships that may not be immediately apparent in other charts.

Some common uses of bubble charts are:

• Economic data: Displaying economic data, such as GDP (Gross Domestic Product) on one axis, population on another, and the size of the bubbles representing something like government spending or exports.
• Scientific data**: Visualizing scientific data where the X and Y axes represent two independent variables, and the size of the bubbles might represent a third measurement, like temperature or concentration.
• Market analysis: Analyzing market data, with price on one axis, volume on another, and the bubble size indicating market capitalization.
• Geospatial data: Creating geospatial bubble maps where the X and Y coordinates represent geographical locations, and the bubble size represents some attribute, such as the number of COVID-19 cases in different regions.

Bubble charts are effective in conveying the relationships between multiple variables but can become cluttered with a large number of bubbles. Providing clear labels and a legend is vital to help viewers interpret the data accurately.

List of recommended resources #

For a broad overview #.

A Complete Guide to Bubble Charts

This Chartio data tutorial by Mike Yi gives a broad overview of bubble charts used for representing the relationship between three numeric variables. 

Present your data in a bubble chart

This Microsoft Support blog gives an easy-to-follow guide on how to create bubble charts in Excel. 

For in depth understanding #

Data Visualization: A Practical Introduction

This accessible primer by Kieran Healy explains how to create effective graphics from data. It explains what makes some graphs succeed while others fail and how to think about data visualization in an honest and effective way.

Effective Data Visualization: The Right Chart for the Right Data

Written by Stephanie D. H. Evergreen, this comprehensive how-to guide shows readers how to create Excel charts and graphs that best communicate their data findings

Case study  #

Everyone does it—differently: A window into emerging adults’ smartphone use

This paper uses a bubble chart to depict social media platform use. The bubbles in this chart depict which social media platforms were used by how many participants in the study, and in which combinations. 

Learning Analytics Integrating Student Attendance Data

This doctoral thesis by Cameron Gray makes use of bubble charts to show the difference in price variation and sales volume variation for two store types.

References #

Bubble chart  

Data Visualization Resources: Types of Charts and Graphs for Data Viz

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2.1.1.2 - visual representations.

Frequency tables, pie charts, and bar charts can all be used to display data concerning one categorical (i.e., nominal- or ordinal-level) variable. Below are descriptions for each along with some examples. At the end of this lesson you will learn how to construct each of these using Minitab.

Frequency Tables Section  

A  frequency table  contains the counts of how often each value occurs in the dataset. Some statistical software, such as Minitab, will use the term  tally  to describe a frequency table. Frequency tables are most commonly used with nominal- and ordinal-level variables, though they may also be used with interval- or ratio-level variables if there are a limited number of possible outcomes. 

In addition to containing counts, some frequency tables may also include the percent of the dataset that falls into each category, and some may include cumulative values. A cumulative count is the number of cases in that category and all previous categories. A cumulative percent is the percent in that category and all previous categories. Cumulative counts and cumulative percentages should only be presented when the data are at least ordinal-level. 

The first example is a frequency table displaying the counts and percentages for Penn State undergraduate student enrollment by campus. Because this is a nominal-level variable, cumulative values were not included.

Penn State Fall 2019 Undergraduate Enrollments

The next example is a frequency table for an ordinal-level variable: class standing. Because ordinal-level variables have a meaningful order, we sometimes want to look at the cumulative counts or cumulative percents, which tell us the number or percent of cases at or below that level.

As an example, let's interpret the values in the "Sophomore" row. There are 22 sophomore students in this sample. There are 27 students who are sophomore or below (i.e., first-year or sophomore). In terms of percentages, 34.4% of students are sophomores and 42.2% of students are sophomores or below.

Pie Charts Section  

A pie chart displays data concerning one categorical variable by partitioning a circle into "slices" that represent the proportion in each category. When constructing a pie chart, pay special attention to the colors being used to ensure that it is accessible to individuals with different types of colorblindness. 

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Bar Charts Section  

A bar chart is a graph that can be used to display data concerning one nominal- or ordinal-level variable. The bars, which may be vertical or horizontal, symbolize the number of cases in each category. Note that the bars on a bar chart are separated by spaces; this communicates that this a categorical variable. 

The first example below is a bar chart with vertical bars. The second example is a bar chart with horizontal bars. Both examples are displaying the same data. On both charts, the size of the bar represents the number of cases in that category. 

Penn State Fall 2019 Undergraduate Enrollments

Considerations Section  

Pie charts tend to work best when there are only a few categories. If a variable has many categories, a pie chart may be difficult to read. In those cases, a frequency table or bar chart may be more appropriate. Each visual display has its own strengths and weaknesses. When first starting out, you may need to make a few different types of displays to determine which most clearly communicates your data.

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Bubble Chart

A bubble chart represents data points replaced with bubbles, adding an additional dimension of the data represented through the size of the bubbles. As a variation of a scatter chart, a bubble chart similarly does not use a category axis — both horizontal and vertical axes are value axes.

Quick details

What: Discover change, rank

Why: Communicate a people-centered story by condensing large datasets

History of Bubble Chart

Bubble charts were introduced by Fernanda Viegas and Martin Wattenberg as charts which represented items by labeled circles, whose areas are proportional to the displayed quantity. These charts effectively perform a visual square-root transformation and have since become a popular method of displaying data. Bubble charts are included in popular visualization toolkits such as D3 and have also been used by the New York Times Visualization Lab.

When to Use a Bubble Chart?

1 when you need to help users navigate complexity.

Use bubble charts to effectively display three fields of data using the position and proportion of the bubbles plotted. This can help in analyzing data sets with multiple inputs, visualizing patterns, and finding trends using data analysis.  As a detailed bubble chart with multiple data inputs conveys a lot of information at once it can help users navigate complexity with a clear introduction to the data and intent.

2 When required to compare and show the relationships between categories

Use a bubble chart when required to present four dimensions of data to show relationships by the use of positioning and proportions. In such a scenario the color temperature of the data bubble serves as the fourth dimension used to differentiate data points. The size of the bubble is also critical to a user interpreting the data efficiently and accurately — particularly when the range of data is spread out over a specific axis. When using the total area (instead of radius or diameter) of individual bubble plots to convey meaning, it is important to clearly document the scale.

3 When you need to combine the functionality of both pie charts and line charts for showing composition and correlation together

Use a bubble chart for analyzing finance scenarios beyond standard time-series or whole-to-whole comparisons using line and bar charts. It can effectively combine these different functionalities coherently through one visual chart giving an overall picture.

Types of Bubble Charts

1. labeled bubble charts.

Each individual bubble is labeled useful when there is a relatively small number of data points. 

2. 3D Bubble charts

3-D Bubble chart shows the data in 3-D format without using a depth axis

3. Cartograms

Bubbles drawn on top of maps are known as cartograms. From a conceptual perspective, the horizontal and vertical positions are represented as latitudes and longitudes, using an image in the background.

When Not to Use Bubble Charts?

1 when you do not have at least three dimensions of data.

The idea of using colorful bubbles to display data can be tempting because line and bar charts are ubiquitous and lack the unique visual appeal that colorful discs provide. However, if you do not have at least three dimensions of data, consider using a different chart for data visualization. Data visualizations like the bubble charts benefit from carefully thought out design. 

2 When you have a comprehensive overlap in data

If the two or more data points have similar X & Y values, the bubbles may overlap or one may be hidden behind another. It reduces the understandability of the chart reducing the ease of its readability, so Bubble Charts have a limited data size capacity. This can be remedied by introducing interactivity: adding clicking or hovering functionality over bubbles to display hidden information along with having an option to reorganize or filter out grouped categories.

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