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Faceting to explore missingness (multiple plots)

Another useful technique with geommisspoint() is to explore the missingness by creating multiple plots.

Just as we have done in the previous exercises, we can use the nabular data to help us create additional faceted plots.

We can even create multiple faceted plots according to values in the data, such as year, and features of the data, such as missingness.

This exercise is part of the course

Dealing With Missing Data in R

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Exercise instructions

  • Use geom_miss_point() and facet_wrap() to explore how the missingness in wind_ew and air_temp_c is different for missingness of humidity.
  • Use geom_miss_point() and facet_grid() to explore how the missingness in wind_ew and air_temp_c is different for missingness of humidity and by year.

Hands-on interactive exercise

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

# Use geom_miss_point() and facet_wrap to explore how the missingness  
# in wind_ew and air_temp_c is different for missingness of humidity
bind_shadow(oceanbuoys) %>%
  ggplot(aes(x = ___,
           y = ___)) + 
  geom_miss_point() + 
  facet_wrap(~___)

# Use geom_miss_point() and facet_grid to explore how the missingness in wind_ew and air_temp_c 
# is different for missingness of humidity AND by year - by using `facet_grid(humidity_NA ~ year)`
bind_shadow(oceanbuoys) %>%
  ggplot(aes(x = ___,
             y = ___)) + 
  geom_miss_point() + 
  facet_grid(humidity_NA~year)
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