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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.

Bu egzersiz

Dealing With Missing Data in R

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • 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).

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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() + 
  ___(~___)
Kodu Düzenle ve Çalıştır