Bdtyk

I try to find the "group" (id3) based on two variables (id1, id2):

df = data.frame(id1 = c(1,1,2,2,3,3,4,4,5,5),

            id2 = c('a','b','a','c','c','d','x','y','y','z'),

            id3 = c(rep('group1',6), rep('group2',4)))





   id1 id2      id3

1    1   a   group1

2    1   b   group1

3    2   a   group1

4    2   c   group1

5    3   c   group1

6    3   d   group1

7    4   x   group2

8    4   y   group2

9    5   y   group2

10   5   z   group2   

For example id1=1 is related to a and b of id2. But id1=2 is also related to a so both belong to one group (id3=group1). But since id1=2 and id1=3 share id2=c, also id1=3 belongs to that group (id3=1). The values of the tuple ((1,2),('a','b','c')) appear no where else, so no other row belongs to that group (which is labeled group1 generically).

My idea was to create a table based on id3 which would subsequently populated in a loop.

solution = data.frame(id3= c('group1', 'group2'),id1=NA, id2=NA)

group= 1 



for (step in c(1:1000)) { # run many steps to make sure to get all values

  solution$id1[group] = # populate  

  solution$id2[group] = # populate  



  if (fully populated) {

    group = group +1

  }} 

I am struggling to see how to populate.

Disclaimer: I asked a similar question here, but using names in id2 led a lot of people point me to fuzzy string procedures in R, which are not needed here, since there exist an exact solution. I also include all code I have tried since then in this post.

You can leverage on igraph to find the different clusters of networks

library(igraph)

g <- graph_from_data_frame(df, FALSE)

cg <- clusters(g)$membership

df$id3 <- cg[df$id1]

df

output:

   id1 id2 id3

1    1   a   1

2    1   b   1

3    2   a   1

4    2   c   1

5    3   c   1

6    3   d   1

7    4   x   2

8    4   y   2

9    5   y   2

10   5   z   2