Adapt layered R data frame so that values of variables match in rows (based on group and date)

Question

I want to research group A's effect on B regarding certain dependent variables I dubbed "target_n". Due to the way in which the data was generated I have "layers" of information in my dataset that are ordered by group. That means, in rows for which Group=="B" I have information on B's values on "target_n" and for rows where Group=="A", I have information on the A's values on "X_n". Group "C" is basically a "other"-category but I would need to have them in the same row as A and B as well to make sure that A's effects are on B and not on C. The following should add some clarity:

My data (df) are structured like this:

df<-data.frame(
"Date"=c(1990-03,2000-01,2010-09,1990-03,2000-01,2010-09,1990-03,2000-01,2010-09),
"Group"=c("A","A","A","B","B","B","C","C","C"),
"X_1_A"=c(9,4,7,NA,NA,NA,NA,NA,NA),
"X_2_A"=c(1,2,6,NA,NA,NA,NA,NA,NA),
"target_1_B"=c(NA,NA,NA,0,2,9,NA,NA,NA),
"target_2_B"=c(NA,NA,NA,9,2,1,NA,NA,NA),
"target_1_C"=c(NA,NA,NA,NA,NA,NA,5,3,1),
"target_2_C"=c(NA,NA,NA,NA,NA,NA,1,9,2)
)

What I want is to compute new variables both for group "A" and group "C" so that everything falls within the same rows. If I were to do that manually,I would take A's column "X_1" score at date "1990-03" and assign it to B's place in A's column for the same date.

So in the end, my data would look like this:

df<-data.frame(
"Date"=c(1990,2000,2010,1990,2000,2010,1990,2000,2010),
"Group"=c("A","A","A","B","B","B","C","C","C"),
"X_1_A"=c(9,4,7,NA,NA,NA,NA,NA,NA),
"X_2_A"=c(1,2,6,NA,NA,NA,NA,NA,NA),
"target_1_B"=c(NA,NA,NA,0,2,9,NA,NA,NA),
"target_2_B"=c(NA,NA,NA,9,2,1,NA,NA,NA),
"target_1_C"=c(NA,NA,NA,NA,NA,NA,5,3,1),
"target_2_C"=c(NA,NA,NA,NA,NA,NA,1,9,2),
"NEW_X_1_A"=c(NA,NA,NA,9,4,7,NA,NA,NA),
"NEW_X_2_A"=c(NA,NA,NA,1,2,6,NA,NA,NA),
"NEW_target_1_C"=c(NA,NA,NA,5,3,1,NA,NA,NA),
"NEW_target_2_C"=c(NA,NA,NA,1,9,2,NA,NA,NA)
)

(I have a number of these "X_"s and exactly the same number of "target_" variables. I also do not just have this group of A, B and C, but A1,A2,A3,C1,C2,C3 and even more Bs. For each set of A1,B1,C1 I also have a "set" of dates that does not match anothers "set". But that would be less of a problem as I could simply slice my dataset horizontally into sets, do the trick for all of them separately and merge them again.)

But how would I bring A's and C's values into B's rows based on Group=="B" and based on date?

Answer 1

Using data.table you can try

df<-data.frame(
  "Date"=c("1990-03","2000-01","2010-09","1990-03","2000-01","2010-09","1990-03","2000-01","2010-09"),
  "Group"=c("A","A","A","B","B","B","C","C","C"),
  "X1_A"=c(9,4,7,NA,NA,NA,NA,NA,NA),
  "X2_A"=c(1,2,6,NA,NA,NA,NA,NA,NA),
  "target_value_1_B"=c(NA,NA,NA,0,2,9,NA,NA,NA),
  "target_value_2_B"=c(NA,NA,NA,9,2,1,NA,NA,NA),
  "target_value_1_C"=c(NA,NA,NA,NA,NA,NA,5,3,1),
  "target_value_2_C"=c(NA,NA,NA,NA,NA,NA,1,9,2)
)

library(data.table)
setDT(df)[,`:=` (NEW_X1 = ifelse(Group=="B",X1_A[Group=="A"],NA),
                 NEW_X2 = ifelse(Group=="B",X2_A[Group=="A"],NA),
                 NEW_target_value_1_C =ifelse(Group=="B",target_value_1_C[Group=="C"],NA),
                 NEW_target_value_2_C =ifelse(Group=="B",target_value_2_C[Group=="C"],NA)
                 )]

Which results in:

df
      Date Group X1_A X2_A target_value_1_B target_value_2_B target_value_1_C target_value_2_C NEW_X1 NEW_X2 NEW_target_value_1_C NEW_target_value_2_C
1: 1990-03     A    9    1               NA               NA               NA               NA     NA     NA                   NA                   NA
2: 2000-01     A    4    2               NA               NA               NA               NA     NA     NA                   NA                   NA
3: 2010-09     A    7    6               NA               NA               NA               NA     NA     NA                   NA                   NA
4: 1990-03     B   NA   NA                0                9               NA               NA      9      1                    5                    1
5: 2000-01     B   NA   NA                2                2               NA               NA      4      2                    3                    9
6: 2010-09     B   NA   NA                9                1               NA               NA      7      6                    1                    2
7: 1990-03     C   NA   NA               NA               NA                5                1     NA     NA                   NA                   NA
8: 2000-01     C   NA   NA               NA               NA                3                9     NA     NA                   NA                   NA
9: 2010-09     C   NA   NA               NA               NA                1                2     NA     NA                   NA                   NA