Create an Index of a combination of data.frame columns in R - r

Question

this question is kind of related to this one, however i want to create an Index using a unique combination of two data.frame columns.
So my data structure looks for example like this (dput):
structure(list(avg = c(0.246985988921473, 0.481522354272779,
0.575400762275067, 0.14651009243539, 0.489308880181752, 0.523678968337178
), i_ID = c("H", "H", "C", "C", "H", "S"), j_ID = c("P", "P",
"P", "P", "P", "P")), .Names = c("avg", "i_ID", "j_ID"), row.names = 7:12, class = "data.frame")
The created Index for the above structure should therefore look like this
1
1
2
2
1
3
In the example data the column j_ID always has the value P, but this isn't always the case. Furthermore vice-versa (S-P or P-S) combinations should result in the same index.
Someone knows a nice way to accomplish that? I can do it with a lot of for-loops and if-else commands, but thats not really elegant.

Answer 1

The interaction function will work well:
foo = structure(list(avg = c(0.246985988921473, 0.481522354272779, 0.575400762275067, 0.14651009243539, 0.489308880181752, 0.523678968337178), i_ID = c("H", "H", "C", "C", "H", "S"), j_ID = c("P", "P", "P", "P", "P", "P")), .Names = c("avg", "i_ID", "j_ID"), row.names = 7:12, class = "data.frame")
foo$idx <- as.integer(interaction(foo$i_ID, foo$j_ID))
> foo
avg i_ID j_ID idx
7 0.2469860 H P 2
8 0.4815224 H P 2
9 0.5754008 C P 1
10 0.1465101 C P 1
11 0.4893089 H P 2
12 0.5236790 S P 3
Ah, I didn't read carefully enough. There is probably a more elegant solution, but you can use outer function and upper and lower triangles:
# lets assign some test values
x <- c('a', 'b', 'c')
foo$idx <- c('a b', 'b a', 'b c', 'c b', 'a a', 'b a')
mat <- outer(x, x, FUN = 'paste') # gives all possible combinations
uppr_ok <- mat[upper.tri(mat, diag=TRUE)]
mat_ok <- mat
mat_ok[lower.tri(mat)] <- mat[upper.tri(mat)]
Then you can match indexes found in mat with those found in mat_ok:
foo$idx <- mat_ok[match(foo$idx, mat)]

Answer 2

To add to Justin's answer, if you would like the indexes to preserve the order of the original of the i_ID, you can assign the interaction() results to a variable and then order the levels.
x <- interaction(foo$i_ID, foo$j_ID)
x <- factor(x, levels=levels(x)[order(unique(foo$i_ID))])
foo$idx <- as.integer(x)
which gives:
> foo
avg i_ID j_ID idx
7 0.2469860 H P 1
8 0.4815224 H P 1
9 0.5754008 C P 2
10 0.1465101 C P 2
11 0.4893089 H P 1
12 0.5236790 S P 3