Finding group with distinct (non-overlapping) elements - r

Question

I have a simple dataframe with group IDs and elements of each group, like this:
x <- data.frame("ID" = c(1,1,1,2,2,2,3,3,3), "Values" = c(3,5,7,2,4,5,2,4,6))
Each ID may have a different number of elements. Now I want to find all IDs that have distinct elements with other IDs. In this example, ID1 and ID3 will be selected because they have distinct elements (3,5,7 vs 2,4,6). I also want to copy these unique IDs and their elements into a new dataframe, similar to the original.
How would I do that in R? My skills with R is quite limited.
Thank you very much!
Bests,

Answer 1

Seems like a good question for igraph cliques with one edge to another clique but I cant seem to wrap my head on how to use it.
Anyway, here is an option applying join to identify IDs with same Values and then anti-join to remove those IDs using data.table:
library(data.table)
DT <- as.data.table(x)
for (i in DT[, unique(ID)]) {
dupeID <- DT[DT[ID==i], on=.(Values), .(ID=unique(x.ID[x.ID!=i.ID]))]
DT <- DT[!dupeID , on=.(ID)]
}
output:
ID Values
1: 1 3
2: 1 5
3: 1 7
4: 3 2
5: 3 4
6: 3 6

Answer 2

x <- data.frame("ID" = c(1,1,1,2,2,2,3,3,3), "Values" = c(3,5,7,2,4,5,2,4,6))
gps = split(x, x$ID)
nGroups = length(gps)
k = 1
results = data.frame(ID = NULL, Values = NULL)
for(i in 1:(nGroups - 1)){
j = i + 1
while(j <= nGroups){
if(length(intersect(gps[[i]]$Values, gps[[j]]$Values)) == 0){
print(c(i,j))
results = rbind(results, gps[[i]], gps[[j]])
}
j = j + 1
}
}
results
> results
ID Values
1 1 3
2 1 5
3 1 7
7 3 2
8 3 4
9 3 6

Answer 3

You can try the following code, where the y is the list of data frames (including all data frames that have exclusive Value)
xs <- split(x,x$ID)
id <- names(xs)
y <- list()
ids <- seq_along(xs)
repeat {
if (length(ids)==0) break;
y[[length(y)+1]] <- xs[[ids[1]]]
p <- ids[[1]]
qs <- p
for (q in ids[-1]) {
if (length(intersect(xs[[p]]$Value,xs[[q]]$Value))==0) {
y[[length(y)]] <- rbind(y[[length(y)]],xs[[q]])
qs <- c(qs,q)
}
}
ids <- setdiff(ids,qs)
}
Example
x <- data.frame("ID" = c(1,1,1,2,2,2,3,3,3,4,4),
"Values" = c(3,5,7,2,4,5,2,4,6,1,3))
> x
ID Values
1 1 3
2 1 5
3 1 7
4 2 2
5 2 4
6 2 5
7 3 2
8 3 4
9 3 6
10 4 1
11 4 3
then you will get
> y
[[1]]
ID Values
1 1 3
2 1 5
3 1 7
7 3 2
8 3 4
9 3 6
[[2]]
ID Values
4 2 2
5 2 4
6 2 5
10 4 1
11 4 3