structural_missing.R

n <- 1000
people <- tibble(
	children = rpois(n, 1.4),
	# for childless people, sat with childcare is not applicable
	sat_with_child_care = if_else(children > 0, rnorm(n), NA_real_),
	sat_with_housing = rnorm(n),
	happiness = rnorm(n) + sat_with_housing + 
		if_else(children > 0, sat_with_child_care, 0),
)

# I can kick out the childless subsample
summary(lm(happiness ~ sat_with_housing + sat_with_child_care, data = people))

people %>% 
	# I can discretize it and add dummies, but I lose information this way
	mutate(sat_with_child_care = factor(case_when(
		children == 0 ~ "none",
		sat_with_child_care < -1 ~ "low",
		sat_with_child_care > 1 ~ "high",
		TRUE ~ "medium"), levels = c("none", "low", "medium", "high"))) %>% 
lm(happiness ~ sat_with_housing + sat_with_child_care, data = .) %>% 
	summary()