Understanding the Pitfalls of Popular Data Visualization Trends in Data Science

In the world of data science, effective communication of results is paramount. The right visualization can bring insights to life, while the wrong one can mislead and confuse. This article delves into some of the popular data visualization trends that data scientists often critique and highlights the reasons behind these criticisms.

A Critique of Radar Charts

Radar charts, also known as spider charts or web charts, have gained popularity in recent years. However, they are often criticized for their incomprehensibility and lack of clarity. These charts plot data points across two or more quantitative variables, forming a polygonal curve. While they can be useful in certain contexts, such as comparing attributes of different entities, their complex and overlapping nature can make them misleading.

The Worst Chart In The World - Pie Charts

Pie charts are among the most hated data visualization trends. Despite their widespread use, they are often criticized for their inaccuracy and lack of precision. Pie charts are best used for showing partial components of a whole, but when there are many categories or the values are close to each other, it becomes difficult to compare the segments accurately. Misinterpretation and confusion are common, making pie charts a one of the worst charts in the world according to many experts.

Understanding Stacked Bar Charts: The Best or the Worst?

Stacked bar charts, which display the total amount for each category and the individual contribution of each category to the total within that category, are widely used. However, they can be misleading when the data distribution is uneven among categories. Overlapping segments can obscure the true representation of data, leading to misinterpretation. While they can be useful for certain types of data, it’s crucial to use them judiciously to avoid confusion.

A Histogram is NOT a Bar Chart

Often, the terms 'histogram' and 'bar chart' are used interchangeably, but they serve different purposes. A histogram is a graphical representation of the distribution of numerical data, where the data is grouped into intervals. In contrast, a bar chart is used to compare quantities across different categories. Misusing these charts can lead to incorrect conclusions about the data distribution, making it essential to understand the difference between the two.

Why Word Clouds Harm Insights

Word clouds are visually appealing and can effectively communicate the frequency of words. However, they are often criticized for their lack of quantitative significance. The size of the words in a word cloud is often based on the frequency, but this can be misleading. It’s crucial to provide context and other visual elements to ensure that insights derived from the word cloud are accurate.

Spaghetti Plots and the Complexity of Data Visualization

Spaghetti plots, which display individual data points of a time series for each category, can be highly complex and confusing. Overlapping lines can make it difficult to understand the individual trends and patterns. While they can be useful in certain contexts, such as comparing multiple time series, they can be overwhelming and misleading if not designed carefully.

Key Considerations in Effective Data Visualization

Effective data visualization involves more than just choosing the right chart. Here are some key considerations:

Clean Data: Ensure your data is clean and free from errors such as duplicates, missing values, and NA (Not Applicable) values. Appropriate Visualization Choice: Use the right type of visualization for your data and purpose. Different visualizations are suited for different types of data and insights. Color Use: Use a limited number of colors (ideally 5 or fewer) to avoid overwhelming the viewer and help in quicker data interpretation. Inconsistent Scales: Represent multiple variables on a single scale to avoid confusion. Consistency is key in data visualization. Use Tools Wisely: While tools like Tableau are excellent, they should not be used as a sole means of data analysis. Combine them with sound data science practices.

Conclusion

Data visualization is a powerful tool when used effectively. However, it is essential to be aware of the pitfalls and to use best practices to ensure that the data is accurately and effectively communicated. By understanding the trends and their limitations, data scientists can create more meaningful and insightful visualizations.

For those interested in learning more about the frustrations with data visualization and ineffective communication, data visualization experts and data journalists often tweet about these topics. Click here for a list of the most famous data journalists to stay updated.