What are effective ways to handle missing data in visualizations?

1 Understand the missingness mechanism

The first step to handle missing data in visualizations is to understand the missingness mechanism, or the reason why the data is missing. There are three main types of missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). MCAR means that the missingness is independent of both the observed and unobserved data, MAR means that the missingness is independent of the unobserved data but may depend on the observed data, and MNAR means that the missingness depends on the unobserved data. Knowing the missingness mechanism can help you choose the appropriate method to handle the missing data, as well as assess the potential impact and bias of the missingness on your analysis and visualization.

2 Choose a suitable method to handle missing data

The second step to handle missing data in visualizations is to choose a suitable method to handle the missing data, depending on the missingness mechanism, the amount of missing data, and the type of visualization you want to create. There are three main methods to handle missing data: deletion, imputation, and modeling. Deletion means removing the cases or variables that have missing data, imputation means replacing the missing values with estimated values, and modeling means using statistical techniques to account for the uncertainty and variability of the missing data. Each method has its advantages and disadvantages, and you should consider the trade-offs between simplicity, accuracy, and validity when choosing a method.

3 Apply the chosen method to handle missing data

The third step to handle missing data in visualizations is to apply the chosen method to handle the missing data, using appropriate tools and techniques. Depending on the method you choose, you may need to use different software, functions, or packages to perform the deletion, imputation, or modeling of the missing data. For example, if you choose to delete the cases with missing data, you can use the dropna() function in pandas or the na.omit() function in R to remove the rows with missing values. If you choose to impute the missing values, you can use the SimpleImputer() class in scikit-learn or the mice() function in R to perform various types of imputation, such as mean, median, mode, or regression imputation. If you choose to model the missing data, you can use the MultipleImputation() class in statsmodels or the mice() function in R to perform multiple imputation, which creates multiple datasets with imputed values and then combines them to produce a single analysis and visualization.

4 Create the visualization with the handled data

The fourth step to handle missing data in visualizations is to create the visualization with the handled data, using appropriate tools and techniques. Depending on the type of visualization you want to create, you may need to use different software, functions, or packages to create the visualization, such as matplotlib, seaborn, plotly, or ggplot2. You should also consider the best practices and principles of data visualization, such as choosing the right chart type, using clear and consistent labels, axes, and legends, applying appropriate colors and scales, and avoiding clutter and distortion. You should also check the quality and accuracy of your visualization, and compare it with the original data and the missing data to see if there are any significant differences or anomalies.

5 Communicate the results and limitations of the visualization

The fifth step to handle missing data in visualizations is to communicate the results and limitations of the visualization, using appropriate tools and techniques. Depending on the audience and purpose of your communication, you may need to use different tools and techniques to communicate the results and limitations of your visualization, such as reports, dashboards, presentations, or interactive web applications. You should also consider the best practices and principles of data communication, such as telling a clear and compelling story, using simple and concise language, providing context and background, and highlighting the key insights and recommendations. You should also be transparent and honest about the missing data and how you handled it, and acknowledge the potential sources of error, bias, and uncertainty in your analysis and visualization.

6 Evaluate and improve the visualization based on feedback

The sixth and final step to handle missing data in visualizations is to evaluate and improve the visualization based on feedback, using appropriate tools and techniques. Depending on the source and type of feedback, you may need to use different tools and techniques to evaluate and improve the visualization, such as surveys, interviews, tests, or metrics. You should also consider the best practices and principles of data evaluation and improvement, such as defining the goals and criteria of your evaluation, collecting and analyzing the feedback, identifying the strengths and weaknesses of your visualization, and implementing the changes and enhancements. You should also monitor and measure the impact and outcomes of your visualization, and update it as needed based on new data or requirements.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?