Donut Chart vs Pie Chart: Design Rules, Fixes, And Alternatives

Understand the Donut Chart vs. Pie Chart Basics

Before diving into a detailed donut chart vs pie chart comparison, it’s essential to understand the foundation of these common, yet often misused, visualizations. Both are designed to illustrate a part-to-whole relationship, showing how individual segments contribute to a total. While they share a circular design, their differences in readability and data presentation are significant.

What is a pie chart used for?

A pie chart is a circular statistical graphic divided into slices to illustrate numerical proportion. Each slice's central angle, area, and arc length is proportional to the quantity it represents. The primary objective of a pie chart is to compare each group’s contribution to the whole, rather than comparing groups to each other. So, what are pie charts good for? They excel when you need to show, for example, the percentage of total revenue generated by a few key products.

What is a doughnut chart?

A doughnut chart (or donut chart) is a variation of the pie chart with the center cut out. It also shows the relationship of parts to a whole, but with a key advantage: the empty center can be used to display additional information, such as the total value of all segments combined or a key performance indicator. This makes the what is a doughnut chart question simple—it's a pie chart that offers a bit more contextual real estate.

Part-to-Whole Questions These Charts Answer

Both charts are best suited for answering simple compositional questions. Think of queries like:

• What is the market share distribution among our top four competitors?

• What percentage of our website traffic comes from different sources (e.g., Organic, Social, Direct)?

• How did survey respondents vote on a single-choice question?

Donut vs. Pie Chart at a Glance

To clarify the pie chart vs donut chart decision, here’s a quick comparison:

Deciding when to use pie chart visuals requires a quick check. Before building one, confirm:

• Your data segments add up to a meaningful 100% total.

• You are visualizing five or fewer categories to avoid a cluttered, unreadable chart.

• Your primary goal is to show each slice's proportion of the whole, not to make precise comparisons between slices.

While both charts have their critics, they remain effective for communicating a simple, clean part-to-whole narrative. The following sections will explore perceptual accuracy, a decision matrix for choosing better alternatives, and practical design rules to ensure your charts are clear and effective.

Perception and Accuracy That Drive the Right Choice

The debate over the donut chart vs pie chart isn't merely about aesthetics; it’s rooted in how our brains interpret visual information. The effectiveness of a chart depends on how accurately we can decode the data it presents. Seminal research in graphical perception provides a framework for understanding why one chart might be better than another in specific situations.

Why Angle and Area Can Mislead

Pioneering work by William S. Cleveland and Robert McGill in the 1980s established a hierarchy of perceptual tasks, ranking how accurately humans judge different visual encodings. They concluded that we are most accurate when judging position along a common scale, which is the core strength of a bar chart. This is followed by length, then angle, and finally area. Since pie charts rely on us comparing angles and areas, they are inherently more difficult to read precisely. This is why a direct pie chart vs bar graph comparison almost always favors the bar chart when you need to rank categories or spot small differences between them.

How Donut Charts Change Perception

A common misconception is that donut charts are worse than pie charts because they remove the central angles. However, research suggests we don't read pie charts by angle alone. Instead, we likely use a combination of angle, area, and arc length. The doughnut vs pie chart format shifts our focus away from area and more toward the length of the outer arc. Studies have consistently found that donut charts are no less accurate than pie charts for part-to-whole comparisons, debunking the idea that they are inherently inferior.

When Labels Trump Visual Decoding

For simple visualizations with few categories, the debate over perceptual accuracy can be secondary to one simple design element: labels. When a pie or donut chart is clearly labeled with its values or percentages, viewers rely on the text rather than trying to visually estimate the proportions. In these cases, the chart serves as a visual anchor for the data, and the small differences in perceptual error become less significant.

If precise comparisons matter, prefer bars; if showing a small number of parts of a whole, a labeled pie or donut can work.

This hierarchy of perception is a useful guide:

• Most Accurate: Position / Length (Bar charts)

• Less Accurate: Angle (Pie charts)

• Least Accurate: Area (Pie charts, Treemaps)

Ultimately, both pies and donuts can be acceptable when there are very few categories and the labels are clear. The key is knowing when to use pie charts effectively and when a different chart type is required for better clarity, a topic the next section covers with a detailed decision matrix.

Decision Matrix for Pies, Donuts, and Better Alternatives

Choosing the right chart shouldn’t be a matter of opinion. With a clear understanding of your analytical goals and data structure, you can select a visualization that communicates your message with precision. This decision matrix is designed to provide clear, prescriptive guidance on whether to use a pie chart, a donut chart, or a more effective alternative.

Decision Rules for Pie and Donut Selection

Use this table to map your specific data challenge to the most appropriate chart type. The right choice depends entirely on what you need your audience to understand.

When a Bar Chart Beats Both

Sometimes, the best choice is to move away from circular charts entirely. Certain red flags in your data should immediately point you toward alternatives to pie charts. Consider a bar chart or another format if you encounter any of the following situations:

• You have too many categories: A pie chart with more than five or six slices becomes a visual puzzle that is difficult to read.

• You need precise comparisons: If the audience needs to see that Category A is slightly larger than Category B, a bar chart makes this difference obvious.

• Categories have similar values: When slices are close in size, it is nearly impossible for the human eye to distinguish the difference in a pie chart.

• You are comparing multiple datasets: Never use a series of pie charts to compare data (e.g., market share in 2024 vs. 2025). A grouped or stacked bar chart is far superior.

Now that you have a framework for selecting the best chart for your data, the next step is to ensure it is designed for maximum readability and impact.

Design Rules That Make Circle Charts Readable

Once you’ve determined that a pie and donut chart is the right choice, applying a few simple design rules is critical for ensuring clarity and trust. These micro-rules are tool-agnostic and help your audience decode the information quickly and accurately, avoiding common visualization pitfalls.

Slice Ordering and Starting Position

A disorganized chart is a confusing chart. Bring a logical order to your data by following two key principles:

• Do: Sort your slices. The most effective method is to arrange segments in descending order by value.

• Do: Start the largest slice at 12 o’clock and proceed clockwise with the remaining slices. This provides a consistent starting point for the eye and makes the proportions easier to follow.

Label Placement That Reduces Eye Travel

The goal of labeling is to make data interpretation effortless. The best way to achieve this is by placing information directly where it's needed.

• Do: Use direct labels on or next to each slice, including the category name and its value or percentage. This practice minimizes cognitive load.

• Don't: Rely on a separate color legend. Forcing your audience to look back and forth between the legend and the chart increases the time and effort required to understand the data.

If you must add a legend for accessibility or formatting requirements, also provide direct labels for the largest slices to aid quick comprehension.

Donut Center Labels That Add Meaning

The empty center of a donut pie chart is valuable real estate. Use it to provide context and summarize the key takeaway of the doughnut diagram.

• Do: Place a summary value in the center. This is often the total sum of all categories or a key performance indicator.

• Do: Clearly format the center text, typically with a large number for the value and smaller text below for the descriptive label (e.g., "1,250 Total Users").

Color Contrast and Category Consistency

Color should be used to enhance clarity, not distract from it.

• Do: Use distinct colors with strong contrast to make each segment easily distinguishable.

• Don't: Use 3D effects, shadows, or exploded slices. These stylistic choices distort the proportions of the slices and can mislead the viewer.

Finally, frame your chart with a clear title and subtitle to set the context, such as a title of “Share of Total Revenue by Segment” and a subtitle of “Top 4 segments account for most of the total.” With these design principles in mind, you can move on to the practical steps of building and refining your charts in various tools.

How to Build and Fix Pies in Common Tools

Applying design theory is the first step; the next is implementing it in your tool of choice. While specific clicks vary, the core concepts for creating clear, effective pie and donut charts are universal. Below are three tool-agnostic mini-guides for common fixes, followed by a map to popular platforms.

How to Group Small Slices into 'Other'

When a pie chart has too many small, unreadable slices, grouping them improves clarity. This is a common practice when creating a bar of pie chart or simply cleaning up a cluttered visual.

1. Establish a Threshold: Decide on a rule for grouping. You can either group the smallest slices by a fixed number (e.g., the bottom four categories) or, more systematically, group all categories that fall below a certain percentage of the total (e.g., anything under 10%).

2. Create a Calculated Group: In most BI tools, you can create a group from a dimension. Select the small categories you identified and combine them into a single new category named 'Other.'

3. Style and Document: Assign a neutral gray color to the 'Other' slice to de-emphasize it. In the chart's tooltip or subtitle, briefly explain the grouping rule (e.g., "'Other' includes all segments under 10% of total sales").

How to Reorder and Label Slices

A logical order and clear labels are non-negotiable for readability.

1. Sort Your Data: Always sort the underlying data for the chart in descending order. This ensures the largest, most important slice appears first.

2. Anchor at 12 O'Clock: Configure your chart to place the largest slice starting at the 12 o’clock position, with the rest following clockwise in descending order.

3. Apply Direct Labels: Add labels directly to each slice that show both the category name and its percentage or value. This removes the need for a separate legend, making the chart easier to read.

When and How to Convert to a Bar Chart

Knowing when to abandon a pie chart is as important as knowing how to build one.

1. Recognize the Triggers: Convert to a bar chart if you have more than five categories, if you need to show precise comparisons, or if several slices are very close in value.

2. Change the Chart Type: Most tools allow you to switch a visualization from a pie to a bar chart with a single click.

3. Orient and Sort: Place categories on one axis and values on the other. Sort the bars in descending order to maintain a clear visual hierarchy.

Finally, always consider accessibility. Ensure your color choices have sufficient contrast, and provide a simple data table view as a fallback for screen reader users. Now that we've covered the 'how,' let's look at the 'what' by comparing how different data stories are told with each chart type.

Text-Only Examples Comparing Pie, Donut, and Bar Charts

The best chart choice depends on the story you want to tell. The same data can convey a different message depending on its visual representation. The following scenarios use text-only descriptions to illustrate the narrative trade-offs between these chart types, helping you recognize patterns in your own data.

Four Clear Categories Scenario

Imagine a dataset with four distinct segments, such as quarterly sales figures that are all significant but not identical.

• Pie Chart Narrative: The chart immediately shows how the year's total sales are divided among the four quarters. The viewer gets a quick sense of each quarter's contribution to the whole.

• Donut Chart Narrative: Similar to the pie, the arcs show the proportional contribution of each quarter. The center is used to display the total annual sales figure, adding valuable context.

• Bar Chart Narrative: The four bars make it easy to see that Q4 had the highest sales, followed by Q2, Q1, and then Q3. The focus shifts from part-to-whole composition to a precise ranking of each quarter's performance.

Editorial Choice: A pie or donut chart is acceptable here. Choose the donut if displaying a summary metric in the center adds value to the story.

Many Small Categories Scenario

Consider a dataset showing website traffic sources, where two sources dominate and a long tail of ten smaller sources contributes the rest.

• Pie Chart Narrative: The chart is dominated by two large slices, followed by a series of thin, unreadable slivers that are impossible to label or distinguish. The visual is cluttered and confusing.

• Donut Chart Narrative: The result is the same as the pie chart. The many small arcs create a jagged, hard-to-read visual that obscures the data's meaning.

• Bar Chart Narrative: The chart clearly shows the two dominant sources with long bars, while also giving each of the ten smaller sources its own distinct, readable bar. This highlights the significant difference between pie chart and bar chart for detailed comparisons.

Editorial Choice: Choose a bar chart for precise comparison across many categories. Alternatively, group the small slices into an 'Other' category if using a pie or donut.

Highlighting a Single Dominant Category

Think of a survey where one answer option received the vast majority of votes.

• Pie Chart Narrative: The chart features one massive slice that takes up most of the circle, instantly communicating its dominance over the few other small slices. This is a perfect example of when do you use a pie chart effectively.

• Donut Chart Narrative: Like the pie, a single large arc visually emphasizes the winning category. The center can be used to display the exact percentage or vote count for that dominant option, reinforcing the key message.

• Bar Chart Narrative: One bar is dramatically longer than the others, clearly showing the winning option. While effective, the part-to-whole impression is slightly less immediate than in a circle graph comparison.

Editorial Choice: All three can work, but the pie or donut chart naturally excels at emphasizing a single component's dominance over the whole.

Understanding these narrative trade-offs helps you make better choices, but it's also crucial to avoid common design mistakes that can mislead your audience, which we'll cover next.

Common Mistakes and How to Fix Them Fast

Even when you’ve decided when would you use a pie chart , common design mistakes can undermine its effectiveness and mislead your audience. These pitfalls distort data, create confusion, and erode trust. Fortunately, they are easy to identify and correct with a few simple rules.

Design Choices That Mislead

Certain stylistic choices actively harm data comprehension. Avoid these common errors to ensure your chart is clear and honest:

• 3D Effects and Exploded Slices: Adding a third dimension or pulling slices apart warps their perceived size and makes accurate comparisons impossible. The perspective effect makes slices closer to the viewer appear larger than they are.

• Too Many Categories: A pie chart with more than five or six slices becomes a cluttered, unreadable mess of thin slivers.

• Comparing Multiple Pies: Asking viewers to compare segments across separate pie charts forces their eyes to jump back and forth, making it difficult to spot trends or differences accurately.

• Inconsistent Totals: Pie charts must represent a complete whole. Using data that doesn't sum to 100% violates the chart's fundamental purpose and leads to confusion.

Exact Fixes You Can Apply Today

Correcting these mistakes is straightforward. Here are the most critical fixes:

• Fix for 3D/Exploded Slices: Always use a standard 2D chart. This simple change ensures that the proportions are represented accurately.

• Fix for Too Many Categories: Group the smallest slices into a single “Other” category. If every category must be displayed individually, switch to a bar chart—a much better pie chart alternative for detailed data.

• Fix for Comparing Multiple Pies: To show how composition changes over time or between groups, use a stacked or grouped bar chart instead.

Avoid comparing groups with multiple pies; use a grouped bar chart instead.

Title and Label Templates That Clarify Intent

A clear title is the final piece of the puzzle, explaining precisely what the chart represents. This helps define when is it appropriate to use a pie chart —when the story is simple enough to be summarized clearly. Use these templates:

• Title: “Share of Total by [Category]” (e.g., “Share of Total Website Traffic by Source”).

• Subtitle: “[Context or Grouping Rule]” (e.g., “Top four sources shown; remaining grouped as ‘Other’”).

Fixing these visual mistakes is the first step toward clarity; the next is ensuring your charts are understandable for all audiences.

Accessibility Templates for Inclusive Charts

Creating a visually effective chart is only half the job. To ensure your data is accessible to everyone, including people with disabilities, you must build inclusivity into your design from the start. These templates and guardrails will help make your pie and donut charts understandable for all users.

Alt Text Templates That Prioritize Meaning

Alternative text provides a non-visual means of understanding your chart and is a core requirement for web accessibility. For a simple doughnut graph , alt text can briefly describe the main takeaway. For a more complex pie graph illustration , the alt text should guide users to a more detailed description or an accompanying data table.

Donut chart showing the share of total revenue by marketing channel. The top three channels are Paid Search at 45%, Organic Search at 30%, and Direct at 15%. The remaining 10% is grouped into an ‘Other’ category. A full data table is provided below.

Data Table Fallbacks for Screen Readers

For any chart with more than a few slices, a data table is the best way to give users with visual impairments the ability to explore the data at their own pace. To be effective, tables must be properly structured with header cells (<th>) and a caption that describes their content, as this markup provides critical context for assistive technologies.

Here is a minimal HTML skeleton for a data fallback:

  <table>


  <caption>Share of Total Revenue by Marketing Channel</caption>


  <thead>


    <tr>


      <th scope="col">Category</th>


      <th scope="col">Value</th>


      <th scope="col">Percent of Total</th>


    </tr>


  </thead>


  <tbody>


    <tr>


      <td>Paid Search</td>


      <td>$1,800</td>


      <td>45%</td>


    </tr>


    <!-- Additional data rows -->


  </tbody>


</table>

Color and Contrast Guardrails

Visual design choices directly impact usability. Follow these guardrails to create a more accessible donut graphic.

• Check Color Contrast: Text labels on or near the chart should have a contrast ratio of at least 4.5:1 against their background to meet WCAG guidelines.

• Don't Rely on Color Alone: Differentiate slices with visual indicators other than color, such as patterns or textures, to assist users with colorblindness.

• Ensure Keyboard Navigation: If your chart is interactive, all elements like tooltips or clickable slices must be focusable and operable using only a keyboard.

Build Pies and Donuts Faster With Affine Edgeless

Choosing and refining the right chart often involves brainstorming, iteration, and comparing multiple versions side-by-side. Traditional tools can be rigid, making this creative process difficult. A modern solution like Affine Edgeless combines a professional document editor with a flexible whiteboard, streamlining the workflow from initial rules to final design for any donut chart or pie chart.

Build and Compare Pie and Donut Variations Fast

The core of this flexibility comes from an infinite canvas, a workspace without fixed page boundaries where you can spread out ideas. Instead of creating separate files, you can place a pie chart, a doughnut chart , and a bar chart version of your data next to each other. This direct visual comparison makes it easier to decide which format best tells your story before committing to a final design in a BI tool.

Turn Written Rules into Visual Checks

Affine’s ability to switch between a structured page mode and a visual edgeless mode allows you to turn design principles into actionable checklists. You can list the rules discussed in this article—like sorting slices, grouping small categories, and labeling conventions—and convert them into a visual diagram that sits next to your chart drafts, ensuring you don't miss a critical step.

Collaborate on Grouping and Labels

Deciding on details like the threshold for an 'Other' category or the wording for a donut chart’s center label often requires team input. With real-time collaboration, your team can use a shared canvas to discuss rules, add annotations with sticky notes, and finalize design choices together, creating a more efficient and aligned process for all types of pie chart visualizations.

To see how a flexible workspace can improve your data visualization workflow, explore how Affine Edgeless can streamline your part-to-whole design sprints without breaking your analysis flow.

Frequently Asked Questions

1. Why is a donut chart better than a pie chart?

A donut chart is often considered better than a pie chart because its hollow center provides space to display a total value or a key performance indicator, adding immediate context. It also shifts the viewer's focus from comparing areas to comparing arc lengths, which some studies suggest can be just as effective while offering a cleaner, more modern aesthetic.

2. What are the disadvantages of using a doughnut chart?

The primary disadvantages of a doughnut chart include difficulty in accurately comparing the sizes of different categories, especially when values are similar. They are less effective when displaying more than five or six categories, as the chart becomes cluttered. They are also not suitable for showing changes over time, where a stacked bar or area chart would be more appropriate.

3. When should you use a bar chart instead of a pie chart?

You should use a bar chart instead of a pie chart whenever precise comparison between categories is important. Bar charts excel at showing rankings or small differences in values because they rely on comparing lengths on a common baseline, which is perceptually easier for the human eye to decode than angles or areas. A bar chart is also the superior choice when you have more than five or six categories to display.

4. What is the main difference between a pie chart and a donut chart?

The main physical difference is that a donut chart is a pie chart with the center cut out. Functionally, this difference allows the donut chart to use its central space for displaying summary information, like a total value. While both show part-to-whole relationships, pie charts use area and angle for comparison, whereas donut charts emphasize arc length.

5. Can you compare multiple datasets using pie charts?

No, you should avoid using multiple pie charts to compare datasets, such as showing compositional changes over time or across different groups. This practice is ineffective because it requires viewers to compare non-adjacent slices and angles, which is difficult and often leads to inaccurate conclusions. A grouped or stacked bar chart is the correct alternative for this type of comparison.

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