I have a dataframe that stores adjacency relations. I want to divide numbers into different groups according to this dataframe. The dataframe are as follows:
df = data.frame(from=c(1,1,2,2,2,3,3,3,4,4,4,5,5), to=c(1,3,2,3,4,1,2,3,2,4,5,4,5))
df
from to
1 1 1
2 1 3
3 2 2
4 2 3
5 2 4
6 3 1
7 3 2
8 3 3
9 4 2
10 4 4
11 4 5
12 5 4
13 5 5
In above dataframe, number 1 has links with number 1 and 3, number 2 has links with number 2, 3, 4, so number 1 can not be in same group with number 3 and number 2 can not be in same group with number 3 and number 4. In the end, groups can be c(1, 2, 5) and c(3, 4).
I wonder how to program it?
First replace the values of to with NA when from and to are equal.
df2 <- transform(df, to = replace(to, from == to, NA))
Then recursively bind each row of the data if from of the latter row has not appeared in to of the former rows.
Reduce(function(x, y) {
if(y$from %in% x$to) x else rbind(x, y)
}, split(df2, 1:nrow(df2)))
# from to
# 1 1 NA
# 2 1 3
# 3 2 NA
# 4 2 3
# 5 2 4
# 12 5 4
# 13 5 NA
Finally, you could extract unique elements for the both columns to get the two groups.
The overall pipeline should be
df |>
transform(to = replace(to, from == to, NA)) |>
(\(dat) split(dat, 1:nrow(dat)))() |>
Reduce(f = \(x, y) if(y$from %in% x$to) x else rbind(x, y))