Generate unique dyad identifiers for unorder pairs - r

Question

The dataframe I am working on is coded in dyadic format where each observation (i.e., row) contains a source node (from) and a target node (to) along with other some dyadic covariates (such as dyadic correlation, corr).
For simplicity sake, I want to treat each dyad as un-ordered and generate a unique identifier for each dyad like the one (i.e., df1) elow:
# original data
df <- data.frame(
from = c("A", "A", "A", "B", "C", "A", "D", "E", "F", "B"),
to = c("B", "C", "D", "C", "B", "B", "A", "A", "A", "A"),
corr = c(0.5, 0.7, 0.2, 0.15, 0.15, 0.5, 0.2, 0.45, 0.54, 0.5))
from to corr
1 A B 0.50
2 A C 0.70
3 A D 0.20
4 B C 0.15
5 C B 0.15
6 A B 0.50
7 D A 0.20
8 E A 0.45
9 F A 0.54
10 B A 0.50
# desired format
df1 <- data.frame(
from = c("A", "A", "A", "B", "C", "A", "D", "E", "F", "B"),
to = c("B", "C", "D", "C", "B", "B", "A", "A", "A", "A"),
corr = c(0.5, 0.7, 0.2, 0.15, 0.15, 0.5, 0.2, 0.45, 0.54, 0.5),
dyad = c(1, 2, 3, 4, 4, 1, 3, 5, 6, 1))
from to corr dyad
1 A B 0.50 1
2 A C 0.70 2
3 A D 0.20 3
4 B C 0.15 4
5 C B 0.15 4
6 A B 0.50 1
7 D A 0.20 3
8 E A 0.45 5
9 F A 0.54 6
10 B A 0.50 1
where dyad A-B/B-A, A-D/D-A are treated as identical pairs and are assigned with the same dyad identifiers.
While it's easy to extract a list of un-ordered pairs from the original data, it's hard to map them onto the original dataframe to generate un-ordered dyad identifiers. Could anyone offer some insights on this?

Answer 1

One dplyr option could be:
df %>%
mutate(dyad = group_indices(., paste0(pmax(from, to), pmin(from, to))))
from to corr dyad
1 A B 0.50 1
2 A C 0.70 2
3 A D 0.20 4
4 B C 0.15 3
5 C B 0.15 3
6 A B 0.50 1
7 D A 0.20 4
8 E A 0.45 5
9 F A 0.54 6
10 B A 0.50 1
Or:
df %>%
mutate(dyad = dense_rank(paste0(pmax(from, to), pmin(from, to))))
However, if you need to assign the identifiers in a specific order (meaning that the identifiers hold some information on their own), then the solution from #Ronak Shah could be better for you.

Answer 2

One way using apply could be to sort and paste the value in two column, convert them to factor and then integer to get a unique number for each combination.
df$temp <- apply(df[1:2], 1, function(x) paste(sort(x), collapse = "_"))
df$dyad <- as.integer(factor(df$temp, levels = unique(df$temp)))
df$temp <- NULL
df
# from to corr dyad
#1 A B 0.50 1
#2 A C 0.70 2
#3 A D 0.20 3
#4 B C 0.15 4
#5 C B 0.15 4
#6 A B 0.50 1
#7 D A 0.20 3
#8 E A 0.45 5
#9 F A 0.54 6
#10 B A 0.50 1