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Exercise

Summarizing missingness

Now that you understand the behavior of missing values in R, and how to count them, let's scale up our summaries for cases (rows) and variables, using miss_var_summary() and miss_case_summary(), and also explore how they can be applied for groups in a dataframe, using the group_by function from dplyr.

Instructions 1/3
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  • 1

    Calculate summaries of missingness in the airquality dataset for variables using the miss_var_summary() function.

    • 2

      Calculate summaries of missingness in the airquality dataset for the cases using the miss_case_summary() function.

    • 3

      Using the airquality dataset, use group_by() to create summaries for each variable and case, by each Month.

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