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Evaluating bad imputations

In order to evaluate imputations, it helps to know what something bad looks like. To explore this, let's look at a typically bad imputation method: imputing using the mean value.

In this exercise we are going to explore how the mean imputation method works using a box plot, using the oceanbuoys dataset.

This exercise is part of the course

Dealing With Missing Data in R

View Course

Exercise instructions

For the oceanbuoys dataset:

  • Impute the mean value with impute_mean_all(), and track these imputations with add_label_shadow().
  • Explore the imputed values in humidity (humidity) using a box plot.
  • Explore the imputed values in air temperature (air_temp_c) using a box plot.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Impute the mean value and track the imputations 
ocean_imp_mean <- bind_shadow(___) %>% 
  ___() %>% 
  ___()

# Explore the mean values in humidity in the imputed dataset
ggplot(___, 
       aes(x = ___, y = ___)) + 
  geom_boxplot()

# Explore the values in air temperature in the imputed dataset
ggplot(___, 
       aes(x = ___, y = ___)) + 
  geom_boxplot()
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