Visualize imputed values in a scatter plot
Now, let's recreate one of the previous plots we saw in chapter three that used geom_miss_point()
.
To do this, we need to impute the data below the range of the data. This is a special kind of imputation to explore the data. This imputation will illustrate what we need to practice: how to track missing values. To impute the data below the range of the data, we use the function impute_below_all()
.
This exercise is part of the course
Dealing With Missing Data in R
Exercise instructions
Using the oceanbuoys
data:
- Impute and track the missing values using
bind_shadow()
andimpute_below_all()
, andadd_label_shadow()
. - Visualize the missingness in wind and air temperature on the x and y-axis respectively, coloring missing air temp values with
air_temp_c_NA
. - Visualize humidity and air temp on the x and y-axis respectively, coloring any missing cases using the variable
any_missing
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Impute and track the missing values
ocean_imp_track <- bind_shadow(___) %>%
impute_below_all() %>%
add_label_shadow()
# Visualize the missingness in wind and air temperature,
# coloring missing air temp values with air_temp_c_NA
ggplot(___,
aes(x = ___, y = ___, color = ___)) +
geom_point()
# Visualize humidity and air temp, coloring any missing cases using the variable any_missing
ggplot(___,
aes(x = ___, y = ___, color = ___)) +
geom_point()