Add unique rows to dataframe (opposite of intersect) - r

Question

I am relatively new to R but slowly finding my way. I encountered a problem, however, and hope someone can help me.
Let's say I two dataframes (lets call them A and B), both containing survey responses. A contains all responses from the first set of people. B contains the responses of the second set of people, plus the people of the first set but with their responses set to NA. An example:
Dataframe A:
Household Individual Answer_A Answer_b
1 2 5 6
1 3 6 6
2 1 2 3
Dataframe B:
Household Individual Answer_A Answer_b
1 1 3 6
1 2 NA NA
1 3 NA NA
2 1 NA NA
2 2 4 7
I want to get one dataframe with all individuals and their responses:
Dataframe C:
Household Individual Answer_A Answer_b
1 1 3 6
1 2 5 6
1 3 6 6
2 1 2 3
2 2 4 7
If I only have two datasets I can use rbind.fill, with rbind.fill(B, A) to get dataframe C, as then the NAs in B are overwritten with answers in A.
But... if I would have to add a third dataset, D, that would consist of NAs for people in A and B, I would not be able to use this solution. What would I be able to do then? I've looked at intersect, outersect, different forms of join, but can't seem to think of a good solution.
Any thoughts?

Answer 1

Maybe you can left_join and then use coalesce
library(dplyr)
left_join(B, A, by = c("Household", "Individual")) %>%
mutate(Answer_A = coalesce(Answer_A.x, Answer_A.y),
Answer_B = coalesce(Answer_b.x, Answer_b.y)) %>%
select(-matches("\\.x|\\.y"))
# Household Individual Answer_A Answer_B
#1 1 1 3 6
#2 1 2 5 6
#3 1 3 6 6
#4 2 1 2 3
#5 2 2 4 7
data
A <- structure(list(Household = c(1L, 1L, 2L), Individual = c(2L,
3L, 1L), Answer_A = c(5L, 6L, 2L), Answer_b = c(6L, 6L, 3L)), class = "data.frame",
row.names = c(NA, -3L))
B <- structure(list(Household = c(1L, 1L, 1L, 2L, 2L), Individual = c(1L,
2L, 3L, 1L, 2L), Answer_A = c(3L, NA, NA, NA, 4L), Answer_b = c(6L,
NA, NA, NA, 7L)), class = "data.frame", row.names = c(NA, -5L))