create id variable from table of duplicates - r

Question

I have a dataframe where each row has a unique identifier, but some rows are actually duplicates.
fdf <- data.frame(name = c("fred", "ferd", "frad", 'eric', "eirc", "george"),
id = 1:6)
fdf
#> name id
#> 1 fred 1
#> 2 ferd 2
#> 3 frad 3
#> 4 eric 4
#> 5 eirc 5
#> 6 george 6
I have determined which rows are duplicated and this information is stored in a second dataframe as pairs of the unique id's. So the key tells me row 1 is the same individual as rows 2 and 3, etc.
key <- data.frame(id1 = c(1,1,2,4), id2 = c(2,3,3,5))
key
#> id1 id2
#> 1 1 2
#> 2 1 3
#> 3 2 3
#> 4 4 5
I'm struggling to think up a straightforward way to use the key to create an id variable in my original dataframe. Desired output would be:
fdf$realid <- c(1,1,1,2,2,3)
fdf
#> name id realid
#> 1 fred 1 1
#> 2 ferd 2 1
#> 3 frad 3 1
#> 4 eric 4 2
#> 5 eirc 5 2
#> 6 george 6 3
Edit for clarity
Keys here are the set of true connections between rows in the data.frame fdf. Thus you can imagine starting with the set of all feasible connections:
# id1 id2
# 1 2
# 1 3
# 1 4
# ...
# 6 4
# 6 5
determining which are true connections (based on the other variables in each observation).
# id1 id2 match
# 1 2 match
# 1 3 no match
# 1 4 match
# ...
# 6 4 no match
# 6 5 no match
and sub-setting to the cases that are matches.

Answer 1

The easiest way would be to recreate the key data frame to the following format (i.e. which id belongs to which realid)
key <- data.frame(id = c(1, 2, 3, 4, 5, 6),
realid = c(1, 1, 1, 2, 2, 3))
Then it is just a matter of merging fdf and key together with merge
fdf <- merge(fdf, key_table, by.x = "id")
fdf
id name realid
1 1 fred 1
2 2 ferd 1
3 3 frad 1
4 4 eric 2
5 5 eirc 2
6 6 george 3

Answer 2

I didn't find a 'straight forward way', but it seems to work well.
First you check which IDs are together in a group, by checking whether there's 'overlap', i.e. whether the intersection between two rows in key is non-empty:
check_overlap <- function(pair1, pair2){
newset <- intersect(pair1, pair2)
length(newset) != 0
}
Then we can apply this function to the rows in key against the other rows. If a row has been matched already, it is automatically removed from key, like this:
check_overlaps <- function(key){
cont <- data.frame()
i <- 1
while(nrow(key) > 0){
ids <- apply(key, 1, check_overlap, key[1, ])
vals <- unique(unlist(key[ids, ]))
key <- key[!ids, ]
cont <- rbind(cont, cbind(vals, rep(i, length(vals))))
i <- i+1
}
return(cont)
}
new_ids <- check_overlaps(key)
# vals V2
# 1 1 1
# 2 2 1
# 3 3 1
# 4 4 2
# 5 5 2
The problem with merging fdf and new_ids, however, is that some old IDs may not occur in key, but they should be mapped to a new ID according to the new order. You can manipulate key a bit a priori and do:
for(val in unique(fdf$id)){
if(!(val %in% unlist(key))){
key <- rbind(key, c(val, val))
}
}
new_ids2 <- check_overlaps(key)
vals V2
# 1 1 1
# 2 2 1
# 3 3 1
# 4 4 2
# 5 5 2
# 6 6 3
Which is easy to merge with fdf like:
merge(fdf, new_ids2, by.x = "id", by.y = "vals")
id name V2
# 1 1 fred 1
# 2 2 ferd 1
# 3 3 frad 1
# 4 4 eric 2
# 5 5 eirc 2
# 6 6 george 3

Answer 3

If I understand your question correctly it can be solved by creating groups of matching ids and creating a new (real) id out of these groups:
# determine the groups of ids
id_groups <- list()
i = 1
for (id in unique(key$id1)) {
if (!(id %in% unlist(id_groups))) {
id_groups[[i]] <- c(id, key$id2[key$id1 == id])
i = i + 1
}
}
# add ids without match
id_groups <- c(id_groups, setdiff(fdf$id, unlist(id_groups)))
# for every id in fdf, set real_id to index in id_groups to which id belongs
fdf$real_id <- sapply(fdf$id, function(id) {
which(sapply(id_groups, function(group) id %in% group))
})