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  3. Handling Missing Data with Imputations in R

Exercise

Analyzing missing data patterns

The first step in working with incomplete data is to gain some insights into the missingness patterns, and a good way to do it is with visualizations. You will start your analysis of the africa data with employing the VIM package to create two visualizations: the aggregation plot and the spine plot. They will tell you how many data are missing, in which variables and configurations, and whether we can say something about the missing data mechanism. Let's kick off with some plotting!

Instructions 1/4

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  • Load the VIM package.
  • Draw a combined aggregation plot of the africa data set, while displaying the numbers on the plot.