Donut Charts in Data Visualization

Highlighting part-to-whole relationships with donut charts

Data visualization turns complex numbers into clear, actionable insights. Among the many tools available, the donut chart has emerged as a popular choice for modern dashboards and reports. Its clean design and efficient use of space make it appealing, but using it effectively requires understanding its strengths and weaknesses.

This guide will walk you through everything you need to know about the donut chart. We will define what it is, explore its variations, and discuss its benefits and limitations. You’ll learn how to create one, see examples, and discover design best practices to ensure your visualizations are both beautiful and insightful.

What is a donut chart?

A donut chart is a variation of a pie chart, with a blank center that makes it resemble a donut. Like a pie chart, it is used to show the proportions of categorical data, where the total of all segments equals 100 percent. Each category is represented by an arc or “slice,” and the size of that arc corresponds to its percentage of the whole.

The key difference lies in how we interpret it. While a pie chart encourages viewers to compare the area of each slice, a donut chart shifts the focus to the length of the arcs. This can make it easier to compare the proportions of different categories, especially when they are close in value. The central hole isn’t just for aesthetics; it provides valuable space for a title, a summary metric (such as a total), or an icon, adding context directly to the visualization.

Designers often use donut charts in modern dashboard design because of their minimalist aesthetic and space-saving nature. They effectively communicate a simple part-to-whole relationship without overwhelming the viewer.

Types and variants of donut charts

Donut charts come in several forms, each suited for different visualization needs. Understanding these variants helps you choose the right one for your data story.

Simple donut chart

This is the most common type. It consists of a single ring divided into segments, each representing a different category. The simple donut chart is ideal for showing the composition of a single data series, such as the market share of different products or the demographic breakdown of a customer base.

Exploded donut chart

In an exploded donut chart, one or more segments are pulled away from the center to draw attention to them. This technique is useful for highlighting a specific category, such as your company’s market share versus competitors’ or a top-performing sales region. Use this feature sparingly, as exploding too many slices can make the chart look cluttered and defeat the purpose of emphasizing one key piece of data.

Nested donut chart (also called a multi-ring donut)

A nested donut chart, also known as a multi-ring donut, displays multiple data series using concentric rings. Each ring functions as a separate donut chart, allowing for the comparison of part-to-whole relationships across different dimensions. For example, the inner ring could show total sales by region, while the outer ring breaks down sales by product category within those regions. While powerful, a nested donut chart can become complex quickly. It’s best used with a very limited number of series and categories to maintain readability.

Half-donut chart (also called a semi-donut)

A semi-donut or half-donut chart is exactly what it sounds like: a 180-degree arc instead of a full 360-degree circle. This variant is often used to show progress toward a goal, where the filled portion represents the current value and the empty portion represents the remainder. For instance, you could use it to visualize campaign fundraising progress or the percentage of project tasks completed. Its shape inherently focuses on a 0-to-100 percent scale.

Anatomy of a donut chart

To build an effective donut chart, you need to understand its core components.

• Outer ring(s): This is the main visual element, composed of the arcs that represent your data. In a nested donut chart, you will have multiple concentric rings.
• Data segments (the slices): Each segment, or arc, of the ring represents a specific category. The length of the arc is proportional to its value relative to the total.
• Labels and legend: Labels are crucial for clarity. They can show the category name, the absolute value, the percentage, or a combination. Labels can be placed directly on or near the segments. Alternatively, a separate legend can be used to explain what each color represents, which is a good choice for cleaning up a crowded chart.
• Title: A clear, concise title tells the viewer exactly what the chart is about. Your title should answer the question, “What data am I looking at?”
• The center (the hole): This is the donut chart’s unique feature. It’s prime real estate for adding a small piece of context. You can use it to display the total sum of all categories, the primary metric being measured, a relevant icon, or a short summary.

Benefits of using a donut chart

Donut charts offer several advantages that have contributed to their widespread use in business intelligence and data analytics.

• Modern look and feel: Donut charts have a clean, minimalist design that fits well in contemporary dashboards.
• Efficient use of center space: The central hole is a functional design element. Displaying a total value or key KPI in the center provides immediate context without taking up extra space.
• Clear part-to-whole comparisons: They are excellent at showing how individual parts contribute to a whole, such as how different departments contribute to total company spending.
• Focus on arc length over area: By removing the center, donut charts encourage viewers to compare the lengths of the arcs rather than the areas of slices. Some research suggests this can lead to more accurate comparisons between segments.
• Ideal for limited dashboard space: Their compact, circular shape allows them to fit neatly into dashboards alongside other visualizations without dominating the layout.

Challenges and limitations

Despite their benefits, donut charts have limitations and are not suitable for every situation. Understanding when not to use one is just as important as knowing when to use one.

• Too many segments can clutter the visual: A donut chart becomes unreadable with too many categories. The arcs become too thin to compare effectively, and labels will overlap. As a rule, limit your chart to four-to-six segments for clarity.
• You might struggle comparing non-adjacent slices: It’s hard for the human eye to accurately compare the sizes of segments that are not next to each other. Ordering segments logically can help, but a bar chart is often a better choice for precise comparisons.
• You can’t include negative or zero values: Donut charts represent parts of a whole, so they cannot display negative or zero values. A category must have a positive value to be represented as a slice.
• They are poor at showing changes over time: Donut charts show a static snapshot of proportions at a single point in time. For visualizing trends or changes across multiple time periods, line charts or column charts are far more effective.
• Small categories are hard to read: Very small percentages are often lost in a donut chart. If a category represents less than 2-3 percent of the total, its arc may be too thin to be easily seen or labeled. Consider grouping these small categories into a single “Other” slice.

How to create a donut chart

Creating a donut chart is straightforward in most spreadsheet programs and business intelligence tools. The process generally follows these steps.

Step 1: Prepare your data

First, organize your data into two columns. One column should contain your distinct categories (e.g., Sales Channels, Survey Responses), and the second should contain their corresponding numerical values (e.g., Revenue, Number of Votes). Ensure the values are parts of a whole and can be summed to a meaningful total.

Step 2: Insert the chart

In a tool like Excel or a BI platform like Domo, select your data and choose the donut chart option from the chart library. The software will automatically generate a basic donut chart based on your selected data.

Step 3: Customize and refine

The default chart is just a starting point. Customization is key to making it effective.

• Adjust the Hole Size: Most tools allow you to adjust the size of the central hole. A common recommendation is to set the hole's radius to between 40 percent and 60 percent of the chart’s total radius. A smaller hole looks more like a pie chart, while a larger one creates thinner rings.
• Order the segments: Don't leave segments in a random or alphabetical order. Arrange them logically, such as from largest to smallest (descending), to make comparisons easier. Start the largest segment at the 12 o’clock position and proceed clockwise.
• Label wisely: Decide between using a legend or direct labels. Direct labels (placing the category name and percentage next to the slice) are often clearer, as they don’t require the viewer to look back and forth. Avoid clutter by only labeling what is necessary.
• Choose your colors: Use a color palette that is easy to distinguish. Avoid using too many colors that are very similar. Ensure your colors are accessible to users with color vision deficiencies.

Examples and use cases

Donut charts are versatile for showing composition. Here are a few real-world examples.

Use case 1: Sales revenue by product category

A business wants to see which product categories are driving the most revenue. A donut chart can quickly show that “Electronics” makes up 45% of sales, “Apparel” 30%, “Home Goods” 20%, and “Accessories” 5%. The total revenue figure can be placed in the center for immediate context.

Use case 2: Survey responses

A marketing team surveys customers about their satisfaction levels. A donut chart can visualize the responses: 60% “Very Satisfied,” 25% “Satisfied,” 10% “Neutral,” and 5% “Dissatisfied.” This provides a clear, at-a-glance summary of customer sentiment.

Use case 3: Website traffic sources

A digital analyst can use a donut chart to show the breakdown of website traffic: 50% from Organic Search, 25% from Direct Traffic, 15% from Social Media, and 10% from Referrals.

When is a bar chart better?
In all the examples above, a donut chart works well because the goal is to see the composition of a whole. However, if the primary goal were to precisely compare the performance of each category, a bar chart would be superior. 

For instance, if you wanted to see not just the proportion but the exact difference in revenue between “Home Goods” and “Accessories,” the length of two bars on a common baseline would make that comparison much easier than judging the arc lengths on a donut chart.

Donut chart best practices

To sum up, follow these design tips to create donut charts that are clear, accurate, and visually appealing.

• Limit your categories: Stick to four-to-six categories at most. If you have more, group the smallest ones into an “Other” category.
• Order segments logically: Arrange slices from largest to smallest, starting at the 12 o’clock position, to aid comparison. The only exception is if there is another inherent order, like survey responses (“Very Satisfied” to “Very Dissatisfied”).
• Use color with intention: Use a distinct and harmonious color palette. You can use a darker shade or a contrasting accent color to highlight a key segment. Always check for color contrast to ensure accessibility.
• Label clearly: Place labels close to their corresponding segments. Include the category name and percentage. Avoid a separate legend if the chart is not too crowded.
• Leverage the center space: Display the grand total, a primary KPI, or a title in the middle. This adds valuable context without cluttering the visualization.
• Avoid 3D and distortions: 3D effects, shadows, and other stylistic flourishes can distort the proportions of the segments, making the chart misleading. Keep the design flat and simple.
• Ensure accessibility: Besides color contrast, consider using patterns or textures to differentiate slices for users with color vision deficiencies. Include descriptive alt text for screen readers.
• Consider mobile responsiveness: Ensure that your chart and its labels are readable on smaller screens. This may require simplifying labels or increasing the font size for mobile views.

Alternatives and comparisons

A donut chart is just one tool in your visualization toolkit. Knowing the alternatives helps you choose the best chart for your data.

• Pie chart: This chart is the donut chart’s direct predecessor. Use a pie chart if you don’t need the central space for extra information and want to emphasize area-based comparisons. The donut vs pie chart debate often comes down to design preference, but the donut’s use of central space is a clear functional advantage.
• Bar chart or column chart: This chart is the best choice for precisely comparing values across different categories. The common baseline makes it easy to see small differences. Use a bar chart instead of a donut chart when you have many categories or when the primary goal is comparison, not composition.
• Stacked bar chart: This type of chart is a great alternative for showing part-to-whole relationships across multiple primary categories. For example, you could show sales for each region (the primary categories) and have each bar segmented by product category.
• Treemap: These are ideal for displaying hierarchical data and part-to-whole relationships, especially when you have a large number of categories that wouldn’t fit in a donut chart. The size of each rectangle represents its proportion of the whole.
• Waffle chart: This is a grid of squares (usually 10x10) where each square represents 1 percent. It's an excellent way to show percentages in a very intuitive, visually appealing format, and it works well for communicating progress toward a target.

Key takeaways from Domo’s guide to donut charts

The donut chart is a simple yet powerful tool for visualizing part-to-whole relationships. Its modern aesthetic and clever use of central space make it a favorite for dashboard design. By emphasizing arc length over area, it provides a clean way to represent proportions.

Remember the key principles of effective donut chart design: keep it simple, limit your categories, order them logically, and use labels and colors intentionally. Always be mindful of its limitations. Try to avoid it for scenarios such as time-series data or when you need to make precise comparisons between many categories.

When used correctly, the donut chart can be a valuable asset in your data storytelling toolkit. For those looking to build and share dashboards filled with clear and compelling visualizations, platforms like Domo can help you assemble on-brand reports and share data-driven insights across your organization.

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