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Nabular data and summarising by missingness

In this exercise, we are going to explore how to use nabular data to explore the variation in a variable by the missingness of another.

We are going to use the oceanbuoys dataset from naniar, and then create multiple plots of the data using facets.

This allows you to explore different layers of missingness.

Este exercício faz parte do curso

Dealing With Missing Data in R

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Instruções do exercício

  • Explore the distribution of wind east west (wind_ew) for the missingness of air temperature using geom_density() and faceting by the missingness of air temperature (air_temp_c_NA).
  • Build upon this visualization by filling by the missingness of humidity (humidity_NA).

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Explore the distribution of wind east west (wind_ew) for the missingness of air temperature 
# using geom_density() and faceting by the missingness of air temperature (air_temp_c_NA).
___ %>%
  bind_shadow(___) %>%
  ggplot(aes(x = ___)) + 
  geom_density() + 
  facet_wrap(~___)

# Build upon this visualization by coloring by the missingness of humidity (humidity_NA).
___ %>%
  ___(___) %>%
  ggplot(aes(x = ___,
             color = ___)) + 
  geom_density() + 
  ___(~___)
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