Remove duplicates where values are swapped across

Question

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pair-wise duplicate removal from dataframe [duplicate] (4 answers)

Closed 3 years ago.

I have a simple dataframe like this:

| id1 | id2 | location   | comment   |
|-----|-----|------------|-----------|
| 1   | 2   | Alaska     | cold      |
| 2   | 1   | Alaska     | freezing! |
| 3   | 4   | California | nice      |
| 4   | 5   | Kansas     | boring    |
| 9   | 10  | Alaska     | cold      |

The first two rows are duplicates because id1 and id2 both went to Alaska. It doesn't matter that their comment are different.

How can I remove one of these duplicates -- either one would be fine to remove.

I was first trying to sort id1 and id2, then get the index where they are duplicated, then go back and use the index to subset the original df. But I can't seem to pull this off.

df <- data.frame(id1 = c(1,2,3,4,9), id2 = c(2,1,4,5,10), location=c('Alaska', 'Alaska', 'California', 'Kansas', 'Alaska'), comment=c('cold', 'freezing!', 'nice', 'boring', 'cold'))

Answer 1

We can use apply with MARGIN=1 to sort by row for the 'id' columns, cbind with 'location' and then use duplicated to get a logical index that can be used for removing/keeping the rows.

df[!duplicated(data.frame(t(apply(df[1:2], 1, sort)), df$location)),]
#   id1 id2   location comment
#1   1   2     Alaska    cold
#3   3   4 California    nice
#4   4   5     Kansas  boring
#5   9  10     Alaska    cold