Correlation node attribute edge type

Question

I'm trying to find out correlation between node attributes and edge type. I only manage to compute a correlation matrix between node attributes, but my expected output would be a correlation matrix between node attributes and edge type using

nodes %>% correlate() %>% fashion()

Can I use this function for correlating node attributes and edge type?

Example data:

nodes <- structure(list(Age = c(96L, 45L, 34L, 42L, 43L, 22L, 29L, 24L, 
37L, 36L, 30L, 29L, 30L, 44L, 41L, 38L, 41L, 31L, 44L, 45L, 39L, 
36L, 46L, 53L, 32L, 34L, 41L, 34L, 47L, 50L, 39L), Gender = c(2, 
3, 2, 3, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 3, 3, 2, 1, 2, 3, 2, 3, 
2, 1, 2, 2, 3, 2, 2, 2, 1), Main.activity = c(2, 2, 2, 1, 2, 
2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 
2, 2, 1, 2, 2), Groups = c(3, 5, 6, 5, 6, 6, 6, 6, 5, 4, 4, 5, 
6, 5, 1, 6, 2, 6, 5, 6, 2, 6, 2, 4, 1, 5, 6, 2, 1, 2, 5), Religions = c(2, 
3, 2, 3, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 3, 3, 2, 1, 2, 3, 2, 3, 
2, 1, 2, 2, 3, 2, 2, 2, 1), Elder = c(1, 6, 2, 7, 6, 2, 2, 2, 
5, 5, 6, 4, 5, 5, 6, 6, 4, 3, 2, 4, 4, 6, 2, 6, 4, 4, 6, 6, 3, 
3, 4)), row.names = c("BePil01", "Be0101", "Be0102", "Be0103", 
"Be0104", "Be0105", "Be0106", "Be0107", "BePil02", "Be0201", 
"Be0202", "Be0203", "Be0204", "Be0205", "Be0206", "Be0207", "BePil03", 
"Be0301", "Be0302", "Be0303", "Be0304", "Be0305", "Be030201", 
"BePil04", "Be0401", "Be0402", "Be0403", "Be0404", "Be0405", 
"Be0406", "Be0407"), class = "data.frame")

links <- structure(list(Source = c("BePil01", "BePil01", "BePil01", "BePil01", 
"BePil02", "BePil02", "BePil02", "BePil02", "BePil02", "BePil02", 
"BePil02", "BePil03", "BePil03", "BePil03", "BePil03", "BePil03", 
"Be0302", "BePil04", "BePil04", "BePil04", "BePil04", "BePil04"
), Target = c("Be0102", "Be0104", "Be0105", "Be0106", "Be0201", 
"Be0202", "Be0203", "Be0204", "Be0205", "Be0206", "Be0207", "Be0301", 
"Be0302", "Be0303", "Be0304", "Be0305", "Be030201", "Be0403", 
"Be0404", "Be0405", "Be0406", "Be0407"), Type = c("Directed", 
"Directed", "Directed", "Directed", "Directed", "Directed", "Directed", 
"Directed", "Directed", "Directed", "Directed", "Directed", "Directed", 
"Directed", "Directed", "Directed", "Directed", "Directed", "Directed", 
"Directed", "Directed", "Directed"), Weight = c(1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), Learning.type = c(4, 2, 4, 1, 4, 4, 2, 2, 1, 2, 4, 4, 
4, 4, 2, 4, 3, 2, 5, 2, 4, 2)), row.names = c(NA, -22L), class = "data.frame")