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 ejercicio forma parte del curso
Dealing With Missing Data in R
Instrucciones del ejercicio
- 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
).
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# 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() +
___(~___)