Comparing each element in subsets of a large data - r

Question

I have a large data with raw responses and wanted to compare each element for subject 1 in group 1 with its corresponding element for subject 1 in group 2. Of course, the comparison needs to be kept between subject 2 in group 1 and subject 2 in group 2, and between subject 3 in group 1 and subject 3 in group 2, and so on. What makes the problem even complex is that there are 100 groups, which in turn are 50 paired groups.
The output needs to keep the original raw response if they are the same. If they are different, the raw response needs to be replaced with '9'.
I'm pretty sure I could do it with for-loop, but wondering if there is anything better than for-loop in r, such as ifelse or apply?
As making my data simple, it would look like below.
df<-as.data.frame(matrix(sample(c(1:5),60,replace=T),nrow=12))
df$subject<-rep(1:3)
df$group<-rep(1:4, each=3)
Thanks for any help.

Answer 1

#Initialization of data
df<-as.data.frame(matrix(sample(c(1:5),60,replace=T),nrow=12))
df$subject<-rep(1:3)
df$group<-rep(1:4, each=3)
>df
V1 V2 V3 V4 V5 subject group
1 3 3 3 4 5 1 1
2 4 4 3 1 3 2 1
3 3 2 2 4 2 3 1
4 4 4 3 5 3 1 2
5 3 2 1 5 1 2 2
6 2 5 4 4 1 3 2
7 3 2 3 2 2 1 3
8 1 2 3 3 3 2 3
9 2 2 2 2 5 3 3
10 3 3 3 5 4 1 4
11 5 3 5 4 2 2 4
12 5 3 1 1 3 3 4
Processing without for loop
#processing without for loop
# assumption: initial data is sorted by group (can be easily done)
coloumns<-!dimnames(x)[[2]] %in% c('group','subject');
subjects<-df[, 'subject']
tabl<-table(subjects)
rows<-order(subjects)
rows2<-cumsum(tabl)
rows1<-rows2-tabl+1
df[rows[-rows1],coloumns][df[rows[-rows1],coloumns]!=df[rows[-rows2],coloumns]]<-9
>df
V1 V2 V3 V4 V5 subject group
1 3 3 3 4 5 1 1
2 4 4 3 1 3 2 1
3 3 2 2 4 2 3 1
4 9 9 3 9 9 1 2
5 9 9 9 9 9 2 2
6 9 9 9 4 9 3 2
7 9 9 3 9 9 1 3
8 9 2 9 9 9 2 3
9 2 9 9 9 9 3 3
10 3 9 3 9 9 1 4
11 9 9 9 9 9 2 4
12 9 9 9 9 9 3 4

Answer 2

Below is what I did to get the output. Again, thanks to Stanislav
df<-as.data.frame(matrix(sample(c(1:5),60,replace=T),nrow=12))
df$subject<-rep(1:3)
df$group<-rep(1:4, each=3)
> df
V1 V2 V3 V4 V5 subject group
1 1 4 3 1 5 1 1
2 2 1 4 1 5 2 1
3 1 2 5 4 5 3 1
4 5 4 1 4 3 1 2
5 5 1 3 2 2 2 2
6 1 2 2 4 5 3 2
7 5 4 2 3 1 1 3
8 2 3 4 3 5 2 3
9 2 5 3 5 3 3 3
10 4 2 1 4 1 1 4
11 2 3 3 5 5 2 4
12 5 3 3 4 5 3 4
col<-!dimnames(df)[[2]] %in% c('subject','group')
n<-length(df[,1])
temp<-table(df$group)
n.sub<-temp[1]
temp<-seq(1,n,by=2*n.sub)
s1<-c(sapply(temp, function(x) seq.int(x, length.out=n.sub)))
temp<-seq(n.sub+1,n,by=2*n.sub)
s2<-c(sapply(temp, function(x) seq.int(x, length.out=n.sub)))
df[s2,col][df[s1,col]!=df[s2,col]]<-9
> df
V1 V2 V3 V4 V5 subject group
1 1 4 3 1 5 1 1
2 2 1 4 1 5 2 1
3 1 2 5 4 5 3 1
4 9 4 9 9 9 1 2
5 9 1 9 9 9 2 2
6 1 2 9 4 5 3 2
7 5 4 2 3 1 1 3
8 2 3 4 3 5 2 3
9 2 5 3 5 3 3 3
10 9 9 9 9 1 1 4
11 2 3 9 9 5 2 4
12 9 9 3 9 9 3 4