TL;DR: Let’s see how to use a Pie Chart for effective data visualization. This blog covers best practices for creating clear, insightful charts, including data binding, slice optimization, and label customization. Learn how to group minor categories, sort slices, and avoid common pitfalls like 3D distortions. Improve your charts’ readability and impact with these expert tips!
Welcome to our latest post in the Weekly Data Visualization blog series!
Pie charts are common, appearing in everything from business reports to school projects. Despite their widespread use, pie charts are often misused or misunderstood, leading to ineffective or even misleading representations of data. In this blog, we’ll explore the best practices for using pie charts with the help of the Syncfusion Blazor Accumulation Charts.
Pie Charts display data in slices, each representing a proportional part of the total. Syncfusion Blazor Accumulation Charts offer comprehensive tools to efficiently create these visuals, ensuring clarity and impact. Let’s dive into the specific steps involved in using pie charts effectively in such contexts.
What data to use for Pie Charts?
Pie charts are most effective for illustrating how various categories constitute a whole. For example, data such as the distribution of market shares among smartphone brands and statistics on mobile browser usage require pie charts. They excel at conveying proportions and making it easy to compare the size of each part relative to the entire dataset.
Let’s visualize smartphone market share data using the Blazor Pie Chart. This tool can depict smartphone market shares clearly and effectively, emphasizing the straightforward communication of data through pie charts.
To achieve accurate data visualization, bind the dataset to the chart’s DataSource field. Each smartphone company’s name is mapped to the XName property, while the corresponding market share values are assigned to the YName property.
Here’s an example of how to create a pie chart.
@using Syncfusion.Blazor.Charts
<SfAccumulationChart Title="Smartphone Market Share Distribution">
<AccumulationChartSeriesCollection>
<AccumulationChartSeries DataSource="@SmartphoneMarketShare" XName="Company" YName="MarketSharePercentage" Radius="70%">
<AccumulationDataLabelSettings Visible="true" Position="AccumulationLabelPosition.Outside" Format="{value}%"></AccumulationDataLabelSettings>
</AccumulationChartSeries>
</AccumulationChartSeriesCollection>
</SfAccumulationChart>
Now, define the data model and the list of data.
@code {
public class MarketShareData
{
public string Company { get; set; }
public double MarketSharePercentage { get; set; }
public string Label { get; set; }
}
Private List<MarketShareData> SmartphoneMarketShare = new List<MarketShareData>
{
new MarketShareData { Company = "Xiaomi", MarketSharePercentage = 12.1 },
new MarketShareData { Company = "Oppo", MarketSharePercentage = 6.8 },
new MarketShareData { Company = "Apple", MarketSharePercentage = 31.6 },
new MarketShareData { Company = "Samsung", MarketSharePercentage = 27.1 },
new MarketShareData { Company = "Vivo", MarketSharePercentage = 5.4 },
new MarketShareData { Company = "OnePlus", MarketSharePercentage = 3.0 },
new MarketShareData { Company = "Tecno", MarketSharePercentage = 4.8 },
new MarketShareData { Company = "Huawei", MarketSharePercentage = 2.5 },
new MarketShareData { Company = "Realme", MarketSharePercentage = 1.5 },
new MarketShareData { Company = "Motorola", MarketSharePercentage = 1.0 },
new MarketShareData { Company = "Nokia", MarketSharePercentage = 0.9 },
new MarketShareData { Company = "Sony", MarketSharePercentage = 0.8 },
new MarketShareData { Company = "Google", MarketSharePercentage = 1.2 },
new MarketShareData { Company = "LG", MarketSharePercentage = 0.7 },
new MarketShareData { Company = "ZTE", MarketSharePercentage = 0.6 }
};
}
Refer to the following image.
In the above chart, each slice represents a company’s proportion of total sales. Apple dominates the chart with a 31.6% market share, showcasing its strong brand loyalty and innovative product lineup, particularly the iPhone. Samsung follows with a substantial 27.1% share, attributed to its wide range of products catering to various consumer needs. Xiaomi secured a 12.1% share which is recognized for offering high-quality and affordable smartphones, especially in emerging markets. The remaining companies have smaller market shares, indicating a more limited influence in the broader market, but they still contribute to the industry’s competitive landscape.
Determine the number of slices
When using pie charts, it’s essential to maintain clarity by limiting the number of slices to 5 to 6 significant data points. This approach ensures a more focused and impactful visualization. For instance, if we’re examining the smartphone market share, we can highlight the key players while minimizing clutter. Using the Blazor Pie Chart, you can emphasize key players such as Xiaomi, Oppo, Apple, Samsung, and Vivo.
Here’s what a simplified dataset with five significant slices might look like.
The above chart emphasizes the dominant players: Xiaomi, Oppo, Apple, Samsung, and Vivo. Apple and Samsung lead the market with 31.6% and 27.1% shares, respectively, reflecting their strong brand loyalty and extensive product offerings. Xiaomi follows with 12.1%, with Oppo and Vivo rounding out the visual representation by focusing on innovative features and competitive pricing, ensuring a clear and concise understanding of the market landscape.
Group minor categories
Grouping is essential in data visualization to simplify complex datasets. By combining minor data points into an Others category, this technique reduces visual clutter and highlights the principal elements, especially in pie charts where too many small slices can confuse viewers.
Through the GroupTo property, we can group less significant data into an Others category. For example, we can set the GroupTo property to 6 for smartphone companies with less than 6% market shares in the Others category. This approach simplifies the chart, emphasizing major players and resulting in a cleaner, more intuitive visual. By streamlining the presentation, the chart enhances data communication and makes it easier for users to derive valuable insights.
Here’s how you might aggregate smaller data points.
<AccumulationChartSeries DataSource="@SmartphoneMarketShare"
XName="Company"
YName="MarketSharePercentage"
Radius="70%"
PointColorMapping="Fill"
GroupTo="6">
</AccumulationChartSeries>
Refer to the following image.
The above chart aggregates the smaller companies in the Others category, representing a combined market share of 22.4%. This aggregation simplifies the visual by reducing clutter from minor contributions, allowing viewers to easily perceive key market trends and dominant players, thereby enhancing the chart’s clarity and impact.
Use clear labels
To enhance the clarity of data labels in pie charts, it is essential to customize them to effectively present company names and their market shares. This customization is achieved through data label mapping, allowing for a more tailored information display. Implementing percentage labels further improves data comprehension by providing precise insights into each segment’s proportion.
The Name API within the AccumulationDataLabelSettings is crucial for customizing label presentation. It enables you to map custom labels from your dataset onto the chart. By adding a Label property with the default X and Y values, you can map this property to the Name field in the AccumulationDataLabelSettings, displaying custom labels that feature the company name and market share.
Here’s how you might apply the customized labels.
<AccumulationDataLabelSettings Visible="true"
Name="Label"
Position="AccumulationLabelPosition.Outside">
</AccumulationDataLabelSettings>
@code {
List<MarketShareData> SmartphoneMarketShare = new List<MarketShareData>
{
new MarketShareData { Company = "Xiaomi", MarketSharePercentage = 12.1, Label = "Xiaomi: 12.1%" },
new MarketShareData { Company = "Oppo", MarketSharePercentage = 6.8, Label = "Oppo: 6.8%" },
new MarketShareData { Company = "Apple", MarketSharePercentage = 31.6, Label = "Apple: 31.6%" },
new MarketShareData { Company = "Samsung", MarketSharePercentage = 27.1, Label = "Samsung: 27.1%" },
new MarketShareData { Company = "Others", MarketSharePercentage = 22.4, Label = "Others: 22.4%" },
};
}
Refer to the following image.
The labels on the chart slices correspond to the Label property in the data model, ensuring that each slice is marked with clear, pre-formatted information like Apple: 31.6%. This improves data comprehension and lets the chart dynamically display the smartphone company name and its shares.
Avoid legends
The legend becomes unnecessary when data labels are prominently displayed and provide all necessary details. Removing the legend can streamline the chart’s appearance, making it cleaner and more focused. This is especially useful when each segment is clearly labeled, making it easy for viewers to associate the data without additional guidance.
<AccumulationChartLegendSettings Visible="false">
</AccumulationChartLegendSettings>
Refer to the following image.
In this example, the legend is excluded because the data labels show percentage values and directly provide all the necessary information on the chart. This approach leaves the chart cleaner and enhances direct data comprehension.
Sort slices for readability
Sorting slices for readability involves organizing the data points on a chart or graph in a specific order to enhance the viewer’s understanding and interpretation.
When dealing with data such as smartphone market shares, sorting the data points from the largest to the smallest share while placing the Others category at the end helps create a more intuitive and visually appealing representation. This approach allows viewers to quickly grasp the companies that hold the largest market shares.
Refer to the following image.
In the above chart, the data points are ordered from the largest to the smallest share: Apple, Samsung, Xiaomi, and Oppo, with the Others category at the end. Arranging data this way allows for easier comparison and a clearer understanding of the market distribution among smartphone companies.
Avoid 3D Pie Charts
When presenting data visually, it’s essential to consider the information’s clarity and accuracy. While 3D charts may seem visually appealing, they can often distort the perception of data, making it harder for viewers to interpret the proportions and relationships between data points accurately. The depth and angles in 3D charts can confuse and mislead the audience. For example, slices in a 3D pie chart might appear larger or smaller than their actual size due to perspective distortion.
It’s advisable to use 2D charts instead to enhance readability and ensure accurate data interpretation. 2D charts provide a straightforward, clear representation of data, allowing viewers to easily compare and understand the information without the visual complications that can arise with 3D models. Sticking to 2D charts ensures that your data is communicated effectively and accurately.
Best practices for Pie Charts
Simplify the chart
When creating a pie chart, it’s important to keep it simple by limiting the number of slices. Ideally, a pie chart should be used when representing 3 to 6 categories. Having too many slices can clutter the chart, making it challenging for viewers to interpret the data accurately. By restricting the number of slices, you ensure each category is distinct, and the chart remains clear and easy to understand. This simplicity effectively conveys the proportions and compares the different segments.
Choose clear colors
Use different colors for each segment of a pie chart to ensure each slice is easily distinguishable. This helps differentiate the slices and prevents confusion that can arise from using similar shades. Clear color choices enhance readability and make the chart more effective in conveying the data.
Provide clear labels
Each slice of a pie chart should be labeled with the category name and its corresponding percentage or value. This helps viewers immediately understand what each slice represents and its significance.
Stick to a flat design
Avoid using 3D effects or extrusions in your pie chart, as these can distort the data and make it challenging to assess the proportions of each slice accurately. A flat, 2D pie chart provides a straightforward, clear view of the data, making it easier for viewers to interpret and compare the sizes of each segment.
Use percentages wisely
Incorporate percentage labels or data values on each slice to clearly convey the proportion each segment represents. Ensure that the total of all percentages adds up to 100% (or close) to accurately reflect the overall distribution. This helps viewers easily understand the relative sizes of each category.
Sort slices
Sorting slices in a pie chart involves organizing them according to their size, typically from largest to smallest. This arrangement enhances readability and allows viewers to compare categories quickly and easily. By presenting the data in this order, you facilitate a clearer understanding of which segments hold the most significant shares, providing immediate insights into the distribution.
Ensure data accuracy
Always double-check that the data depicted in the pie chart is precise and correctly represents the proportions. Inaccurate data can lead to misleading interpretations and incorrect conclusions. Verifying the accuracy of the data ensures that the chart provides a truthful and reliable visual representation.
Common mistakes with Pie Charts
To ensure the effectiveness, it’s essential to avoid the following common mistakes in pie charts:
Too many slices
When a pie chart includes too many slices, it becomes cluttered and challenging to interpret. For example, a pie chart displaying sales data for 15 different products can overwhelm viewers. In such cases, a bar chart or stacked column chart can offer clearer insights.
Similar slice sizes
If the slices of a pie chart are too similar in size, it can be difficult to differentiate between them. For instance, a pie chart showing the percentage distribution of 10 similar-sized categories might confuse the audience. Ensure there are noticeable differences between slice sizes for better clarity.
Misleading representations
Pie charts should only be used when the data represents parts of a whole. Using a pie chart to show data such as customer satisfaction ratings on a scale from 1 to 5 can be misleading, as the ratings are discrete rather than proportional. For such data, consider using a bar chart or histogram instead.
Inconsistent data
Accurate and consistent data is crucial for pie charts. If a pie chart shows incorrect percentages or the total doesn’t add up to 100% (or close), it can mislead the audience. Always verify data accuracy and consistency before finalizing the chart.
References
For more details, refer to the Blazor Pie Chart demo.
Conclusion
Thanks for reading! In this blog, we’ve explored the best practices for using Pie Charts and avoiding mistakes for better data visualization. Try them out and leave your feedback in the comments section below!
The latest version of the Blazor Charts component is available for current customers from the License and Downloads page. If you are not a Syncfusion customer, try our 30-day free trial to check it out.
You can also contact us through our support forums, support portal, orfeedback portal. We are always happy to assist you!
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