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.
This exercise is part of the course
Dealing With Missing Data in R
Exercise instructions
- Explore the distribution of wind east west (
wind_ew) for the missingness of air temperature usinggeom_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).
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# 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() +
___(~___)