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Evaluating imputations: The scale

While the mean imputation might not look so bad when we compare it using a box plot, it is important to get a sense of the variation in the data. This is why it is important to explore how the scale and spread of imputed values changes compared to the data.

One way to evaluate the appropriateness of the scale of the imputations is to use a scatter plot to explore whether or not the values are appropriate.

Este ejercicio forma parte del curso

Dealing With Missing Data in R

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Instrucciones del ejercicio

Using the data with already imputed values, ocean_imp_mean:

  • Explore imputations in air temperature (on the x-axis) and humidity (on the y-axis) using a scatter plot, remembering to use color = any_missing.
  • Build upon this previous visualization by faceting by year.

Ejercicio interactivo práctico

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# Explore imputations in air temperature and humidity,  
# coloring by the variable, any_missing
ggplot(___, 
       aes(x = ___, y = ___, color = ___)) + 
  geom_point()

# Explore imputations in air temperature and humidity,  
# coloring by the variable, any_missing, and faceting by year
ggplot(___, 
       aes(x = ___, y = ___, color = ___)) + 
  ___() +  
  facet_wrap(~___)
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