Pie charts are among the most popular and widely used visual representation tools in the data analysis world. They’re simple, intuitive, and can instantly communicate a message about the proportionate distribution of data. However, like any tool, when used improperly, they can distort information, leading to misguided conclusions and decisions. To ensure clarity and accurate representation, here are some essential dos and don’ts when analyzing data with a pie chart.
Do: Use Pie Charts for Proportional Data
Pie charts excel at showing parts of a whole. When you have categorical data where you want to depict how different categories contribute to the total, a pie chart can be highly effective. For instance, if you want to display the market share of different companies in an industry or the distribution of respondents in a survey based on age groups, pie charts can paint a clear picture.
Don’t: Use Them for Comparing Individual Values
If your primary objective is to compare the size of individual categories, then a pie chart might not be the best choice. In such scenarios, a bar or column chart would be more effective. Why? Human eyes find it challenging to compare the size of angles or areas in a pie chart as opposed to the length of bars in a bar chart. The latter offers a more direct and accurate comparison between individual values.
Do: Limit the Number of Categories
To maintain clarity, it’s a good practice to keep the number of categories in a pie chart limited. Ideally, five to seven slices provide a balanced view without overwhelming the viewer. When there are too many slices, the chart becomes cluttered, making it difficult to distinguish between categories and understand their relative proportions.
Don’t: Use 3D Pie Charts Unnecessarily
The 3D pie charts may look fancy and can be tempting to use, especially in presentations where aesthetics are a priority. However, they often distort the proportion of slices, making them appear larger or smaller than they actually are. This distortion can lead to inaccurate interpretations of the data. Stick to 2D pie charts unless there’s a very specific reason to introduce a third dimension.
Do: Use a Consistent Color Scheme
Color plays a crucial role in pie charts. It helps differentiate between categories and can also be used to highlight specific sections of interest. When choosing colors, ensure there’s enough contrast between the slices. Also, it’s advisable to use a consistent color scheme, either based on category type or value. For instance, if you’re presenting quantitative data on global temperatures, warmer temperatures can be represented in varying shades of red, while cooler temperatures can be depicted in blues.
Don’t: Forget About the Legend
When using colors to represent different categories, it’s essential to include a legend that clearly indicates what each color stands for. While it might be tempting to label each slice directly, doing so can clutter the chart, especially if there are many categories. A well-placed legend makes the pie chart more readable and allows for a cleaner design.
Do: Emphasize Important Data Points
Sometimes, the objective of your pie chart is to emphasize a particular slice or category. You can do this by slightly exploding that slice (making it stand out from the pie) or using a bold or contrasting color. This helps draw the viewer’s attention to that particular data point, making the main message of the chart clear.
Don’t: Rely Solely on the Pie Chart
While pie charts are a powerful tool, they don’t always tell the complete story. Sometimes, you might need to complement them with other charts or textual data to give a fuller picture. For instance, a pie chart might show the proportion of sales from different regions, but a line chart can better show the sales trend over time.
Navigating the Charted Waters
In conclusion, while pie charts offer a visual feast that can simplify complex data, it’s imperative to use them judiciously. By following these dos and don’ts, you can create clear, effective, and accurate pie charts that communicate data insights effectively. Remember, the key is not just to make the data look good, but to ensure it tells the right story.
