How to create a sunburst chart?

Sunburst charts are powerful data visualization tools that provide a visually appealing and insightful way to represent hierarchical data. With their radial layout and color-coded segments, sunburst charts allow viewers to grasp the hierarchical structure and proportions of data at a glance.

In this article, we will explore the step-by-step process of creating a sunburst chart, enabling you to effectively communicate complex data patterns.

Components of a sunburst chart

Rings: A sunburst chart is a circular visualization that effectively portrays the hierarchical relationship between nodes. It is constructed using concentric rings, where each ring corresponds to a distinct level of the hierarchy.

The rings are divided into segments, providing insights into the number of children associated with each node. Each segment is labeled to indicate the specific child node it represents. Moreover, segments can be further divided into subsegments, facilitating the highlighting of specific relationships between two nodes.

Chart: Sunburst charts serve as radial visualizations for hierarchical data, offering a comprehensive overview of its structure.

Components of a sunburst chart include:

1. Core: The central point of the chart.
2. Outer ring: Represents all values in the dataset.
3. Arcs: Connect nodes and display their relationships.
4. Node labels: Display the names of each data element.

By utilizing a sunburst chart, you can effectively visualize hierarchical data and comprehensively understand its structure.

The process to create a sunburst chart:

Step 1: Define your data hierarchy

Before you begin creating a sunburst chart, determine the hierarchical structure of your data. Identify the main categories and subcategories that you want to represent in the chart. Each level of the hierarchy will be displayed as concentric circles in the chart.

Step 2: Choose the software

Select software that supports the creation of sunburst charts. Options include Microsoft Excel, Google Sheets, Tableau, or data visualization libraries like D3.js and Plotly. Choose the software that best suits your needs and is familiar to you.

Step 3: Prepare your data

Ensure your data is well-structured and organized in a hierarchical format. Each level of the hierarchy should be represented in separate columns or fields. For example, if you are visualizing sales data, you may have columns for regions, product categories, and subcategories.

Step 4: Create the sunburst chart

Open your chosen software and import or input your prepared data. Follow these general steps to create a sunburst chart:

a. Select the data range or columns containing your hierarchy.
b. Access the chart creation options, usually located in the toolbar or menu.
c. Choose the sunburst chart type or a similar radial chart variant.
d. Customize the chart appearance, including colors, labels, and formatting options.
e. Apply any necessary modifications based on your specific software’s requirements or desired visual style.

Step 5: Enhance the chart with interactivity

Consider adding interactive features to your sunburst chart to enhance the user experience. Some options include tooltips, drill-down functionality, or animation. These features allow users to explore different levels of the hierarchy and interact with the data dynamically.

Step 6: Analyze and interpret

the chart Once your sunburst chart is created, take the time to analyze and interpret the visualized data. Look for patterns, trends, or outliers within the hierarchical structure. Pay attention to the sizes of the segments, as they represent the relative proportions of each category.

Step 7: Communicate your findings

Effectively communicate your findings to your audience. Include a clear and concise caption or title for the chart, along with any relevant annotations or explanations. Provide a summary or key takeaways that highlight the most significant insights derived from the sunburst chart.

By following these steps, you’ll be able to create a visually appealing and informative sunburst chart that effectively presents hierarchical data. Remember to experiment with customization options and interactive features to make your chart more engaging and insightful.

Treemaps and Sunburst Charts:

Treemaps and sunburst charts are two powerful visualizations for depicting relationships among different data categories. While both have their advantages, they excel in different scenarios. Treemaps are particularly useful for presenting macro-level data, whereas sunburst charts allow for exploration of sub-categories.

However, when attempting to showcase relationships across multiple data levels in a treemap, it can become challenging to accommodate all the information on a single page. In contrast, sunburst charts are more space-efficient, enabling users to view the entirety of the data at once.

different use cases for sunburst charts:

1. Landing and navigation paths on a website:
   Sunburst charts can be used to visualize the paths users take when they land on a website and navigate through its pages. This helps analyze user behavior, identify popular paths, and optimize website design and content accordingly.
2. Visualizing file size of different modules in a software package:
   Sunburst charts can be employed to represent the file sizes of various modules within a software package. This allows users to understand the distribution of file sizes, identify modules that contribute the most to the overall size, and optimize them for improved efficiency.
3. Understanding revenue sources of a business:
   Sunburst charts are useful for visualizing the revenue sources of a business. By representing different revenue streams as segments, businesses can gain insights into the relative contribution of each source, identify areas of growth or decline, and make informed decisions to optimize revenue generation.
4. Analyzing world population breakdown:
   Sunburst charts can provide a comprehensive breakdown of world population across different geographical levels, such as continents, countries, regions, states, cities, and towns. This visualization allows for a clear understanding of population distribution, making it useful for demographic analysis and decision-making in various fields.
5. Salary expenses of employees across departments in an organization:
   Sunburst charts can be utilized to represent salary expenses across different departments within an organization. This visualization helps analyze the allocation of salary budgets, identify departments with higher or lower expenses, and make informed decisions regarding resource allocation and budget planning.

Advantages and Disadvantages of Sunburst Charts:

Advantages:

1. Effective visualization of hierarchical data: Sunburst charts provide a visually appealing representation of hierarchical data, allowing for easy comprehension of the relationships between different levels.
2. A comprehensive view of data: Sunburst charts enable the display of all data within a single view, making it convenient to observe and compare values across multiple dimensions simultaneously.
3. Efficient use of space: The radial orientation and concentric rings of sunburst charts optimize the utilization of space, facilitating the clear comparison of values and their interrelationships.

Disadvantages:

1. Limitations on nested levels: Sunburst charts have a limited capacity to represent numerous nested levels in a static picture. If the hierarchy contains a large number of levels, it may be challenging to visualize all of them simultaneously within the chart.
2. Difficulty in reading angular readings: Understanding angular readings can be challenging for the human eye. While sunburst charts are easy to read in their default left-to-right or top-down orientation, they can become more difficult to interpret when rotated by 90 degrees or displayed upside down, which may be necessary in certain cases.

It’s important to consider these advantages and disadvantages when deciding to use sunburst charts. They can be highly effective for representing hierarchical data, but the complexity of the data structure and the specific requirements of the analysis should be taken into account to ensure optimal visualization and interpretation.