Further exploring more combinations of missingness
It can be useful to get a bit of extra information about the number of cases in each missing condition.
In this exercise, we are going to add information about the number of observed cases using n() inside the summarize() function.
We will then add an additional level of grouping by looking at the combination of humidity being missing (humidity_NA) and air temperature being missing (air_temp_c_NA).
This exercise is part of the course
Dealing With Missing Data in R
Exercise instructions
Using group_by() and summarize() on wind_ew:
- Summarize by the missingness of
air_temp_c_NA. - Summarize by missingness of
air_temp_c_NAandhumidity_NA.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Summarize wind_ew by the missingness of `air_temp_c_NA`
oceanbuoys %>%
bind_shadow() %>%
group_by(___) %>%
summarize(wind_ew_mean = mean(___),
wind_ew_sd = sd(___),
n_obs = ___)
# Summarize wind_ew by missingness of `air_temp_c_NA` and `humidity_NA`
oceanbuoys %>%
bind_shadow() %>%
group_by(___, ___) %>%
summarize(wind_ew_mean = mean(___),
wind_ew_sd = sd(___),
n_obs = ___)