Reputation: 63
I have two large dataframes. A minimum, reproducible example of them looks like this:
A <- data.frame(A=c("a","b","c","d"), B=c(1,2,3,4), C=c(1,2,NA,NA), D=c(1,2,3,4))
A
A B C D
1 a 1 1 1
2 b 2 2 2
3 c 3 NA 3
4 d 4 NA 4
B <- data.frame(A=c("c","d"), B=c(3,4), C=c(3,4))
B
A B C
1 c 3 3
2 d 4 4
For every row with a NA in A, I have a corresponding row in B with the replacement of the missing value. I would like to merge the two dataframes A and B to a "common" dataframe AB in a way that the NA's in dataframe A, column C are replaced by their corrsponding value in dataframe B, column C. The result should look like this:
AB <- data.frame(A=c("a","b","c","d"), B=c(1,2,3,4), C=c(1,2,3,4), D=c(1,2,3,4))
AB
A B C D
1 a 1 1 1
2 b 2 2 2
3 c 3 3 3
4 d 4 4 4
The "closest" (not so close either) I got to the solution was with the following code:
AB <- merge(A,B, all.x = TRUE)
AB
A B C D
1 a 1 1 1
2 b 2 2 2
3 c 3 NA 3
4 d 4 NA 4
Which, obviously, just uses the variables from A. I have already consulted the follwing questions:
Please consider that the real dataframes are much larger. If you need any further information, please let me know. Thanks in advance!
Upvotes: 1
Views: 1689
Reputation: 2987
You could do something like this in base
:
index <- match(B$A, A$A)
A$C[index] <- B$C
# A B C D
#1 a 1 1 1
#2 b 2 2 2
#3 c 3 3 3
#4 d 4 4 4
Upvotes: 1
Reputation: 27732
Using the data.table
-package, you can perform an update-join, which should run fast on large datasets.
library(data.table)
#set A and B as data.table
setDT(A);setDT(B)
#update col C in data.table A with col C from data.table B, join by cols A and B
A[ B, C := i.C, on = .( A, B) ]
output
# A B C D
# 1: a 1 1 1
# 2: b 2 2 2
# 3: c 3 3 3
# 4: d 4 4 4
Upvotes: 1