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Exercise

How many missing values are there?

One of the first things that you will want to check with a new dataset is if there are any missing missing values, and how many there are.

You could use are_na() to and count up the missing values, but the most efficient way to count missings is to use the n_miss() function. This will tell you the total number of missing values in the data.

You can then find the percent of missing values in the data with the pct_miss function. This will tell you the percentage of missing values in the data.

You can also find the complement to these - how many complete values there are - using n_complete and pct_complete.

Instructions
100 XP

Using the example dataframe of heights and weights dat_hw:

  • Use n_miss() on the dataframe dat_hw to count the total number of missing values the dataframe.
  • Use n_miss() on the variable dat_hw$weight to count the total number of missing values it.
  • Similarly, use prop_miss(), n_complete(), and prop_complete() to get the proportion of missings, and the number and proportion of complete values for the dataframe and the variables.
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