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!
Это упражнение является частью курса
Handling Missing Data with Imputations in R
Интерактивное практическое упражнение
Попробуйте выполнить это упражнение, дополнив этот пример кода.
# Load VIM
___
# Draw a combined aggregation plot of africa
africa %>%
___(___ = ___, ___ = ___)