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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!

This exercise is part of the course

Handling Missing Data with Imputations in R

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load VIM
___

# Draw a combined aggregation plot of africa
africa %>%
  ___(___ = ___, ___ = ___)
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