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  3. Dealing With Missing Data in R

Exercise

Visualize imputed values in a scatter plot

Now, let's recreate one of the previous plots we saw in chapter three that used geom_miss_point().

To do this, we need to impute the data below the range of the data. This is a special kind of imputation to explore the data. This imputation will illustrate what we need to practice: how to track missing values. To impute the data below the range of the data, we use the function impute_below_all().

Instructions

100 XP

Using the oceanbuoys data:

  • Impute and track the missing values using bind_shadow() and impute_below_all(), and add_label_shadow().
  • Visualize the missingness in wind and air temperature on the x and y-axis respectively, coloring missing air temp values with air_temp_c_NA.
  • Visualize humidity and air temp on the x and y-axis respectively, coloring any missing cases using the variable any_missing.