Fill in missing rows in R - r

Question

Suppose I have a data frame which looks like this
ID A B C D Month
1 X M 5 1 3
1 X K 4 2 4
1 X K 3 7 5
1 X K 2 6 6
2 Y L 5 8 1
2 Y L 2 3 2
2 Y M 5 1 3
2 Y K 2 7 5
2 Y M 2 8 6
3 Z K 5 3 1
3 Z M 6 3 2
3 Z M 5 8 3
3 Z K 4 2 4
In this data ID and A are unique variables,
while B,C,D,Month can change their value
Month has 6 factor values from 1 to 6
B have 3 factor value from K,L,M
C,D can have any value.
I want this data to become like this
ID A B C D Month
1 X 0 0 0 1
1 X 0 0 0 2
1 X M 5 1 3
1 X K 4 2 4
1 X K 3 7 5
1 X K 2 6 6
2 Y L 5 8 1
2 Y L 2 3 2
2 Y M 5 1 3
2 Y 0 0 0 4
2 Y K 2 7 5
2 Y M 2 8 6
3 Z K 5 3 1
3 Z M 6 3 2
3 Z M 5 8 3
3 Z K 4 2 4
3 Z 0 0 0 5
3 Z 0 0 0 6
It should fill in the missing rows by keeping the unique variables values same and filling in the varying ones with zero.
I can use zoo library to fill in the missing values but how to fill in the complete missing rows?

Answer 1

Maybe something like this would work for your needs:
library(dplyr)
mydf %>%
full_join(expand.grid(ID = unique(mydf$ID), Month = 1:6)) %>%
group_by(ID) %>%
mutate(A = replace(A, is.na(A), unique(na.omit(A)))) %>%
arrange(ID, A, Month) %>%
replace(., is.na(.), 0)
# Joining by: c("ID", "Month")
# Source: local data frame [18 x 6]
# Groups: ID
#
# ID A B C D Month
# 1 1 X 0 0 0 1
# 2 1 X 0 0 0 2
# 3 1 X M 5 1 3
# 4 1 X K 4 2 4
# 5 1 X K 3 7 5
# 6 1 X K 2 6 6
# 7 2 Y L 5 8 1
# 8 2 Y L 2 3 2
# 9 2 Y M 5 1 3
# 10 2 Y 0 0 0 4
# 11 2 Y K 2 7 5
# 12 2 Y M 2 8 6
# 13 3 Z K 5 3 1
# 14 3 Z M 6 3 2
# 15 3 Z M 5 8 3
# 16 3 Z K 4 2 4
# 17 3 Z 0 0 0 5
# 18 3 Z 0 0 0 6

Answer 2

Here's a way using base R
frame <- expand.grid(ID = unique(dat$ID), Month = 1:6)
dat2 <- merge(dat, frame, by=c("ID", "Month"), all=TRUE)[, union(names(dat), names(frame))]
levels(dat2$B) <- c(levels(dat2$B), 0)
res <- lapply(split(dat2, dat2$ID), function(x) {
x$A[which(is.na(x$A))] <- unique(x$A)[!is.na(unique(x$A))]
x[is.na(x)] <- 0
x
})
do.call(rbind, res)
ID A B C D Month
1.1 1 X 0 0 0 1
1.2 1 X 0 0 0 2
1.3 1 X M 5 1 3
1.4 1 X K 4 2 4
1.5 1 X K 3 7 5
1.6 1 X K 2 6 6
2.7 2 Y L 5 8 1
2.8 2 Y L 2 3 2
2.9 2 Y M 5 1 3
2.10 2 Y 0 0 0 4
2.11 2 Y K 2 7 5
2.12 2 Y M 2 8 6
3.13 3 Z K 5 3 1
3.14 3 Z M 6 3 2
3.15 3 Z M 5 8 3
3.16 3 Z K 4 2 4
3.17 3 Z 0 0 0 5
3.18 3 Z 0 0 0 6