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Exercise

Exploring and summarizing missing data

The first step when dealing with missing data is to make sure your dataset indeed has missing values. Sometimes this is quite obvious as you clearly see NA values in some of the columns. There are times, however, when other values are used to denote missingness and your job is to explicitly turn these into NAs.

In this exercise, you will explore the cylinder bands dataset to first identify and then summarize the missing values in it using a few handy functions from the naniar package, which is already loaded in your workspace. This dataset uses several numeric attributes to determine whether a piece of roto printing is a cylinder band or not.

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  • Use two base R functions in tandem to check if any of the values in the bands dataset is NA. After that, call the corresponding function in the naniar package to achieve the same result.
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