I have network data that looks like this:
data <- data.frame(id = as.character(1:20),
cluster = sample(c("a", "b", "c"), 20, replace = T))
head(data)
# id cluster
# 1 b
# 2 b
# 3 c
# 4 a
# 5 c
# 6 a
# 7 a
In the past, I've successfully gotten cluster data from links, but not the other way around. I tried using the igraph package for network analysis but couldn't find a function that gave me a satisfactory result, and I didn't want to rely on a loop since they're inefficient and I in general try to stay away from them.
Expected Result
I'm wondering if there's an efficient way to create a new data.frame with all possible "links" within a cluster, so that I end up with a data.frame that looks like:
id1 id2 cluster
4 6 a
7 4 a
7 6 a
1 2 b
3 5 c
You can do this in data.table:
set.seed(123)
data <- data.frame(id = as.character(1:20),
cluster = sample(c("a", "b", "c"), 20, replace = T))
library(data.table)
setDT(data)[, id:=as.character(id)][, t(combn(id, 2, FUN = list)), by = cluster]
#> cluster V1 V2 #> cluster V1 V2 #> cluster V1 V2
#> 1: a 1 17 #> 21: c 12 8 #> 41: c 5 8
#> 2: a 1 7 #> 22: c 12 13 #> 42: c 5 13
#> 3: a 1 9 #> 23: c 15 16 #> 43: c 8 13
#> 4: a 1 10 #> 24: c 15 19 #> 44: b 14 18
#> 5: a 1 11 #> 25: c 15 3 #> 45: b 14 20
#> 6: a 17 7 #> 26: c 15 5 #> 46: b 14 2
#> 7: a 17 9 #> 27: c 15 8 #> 47: b 14 4
#> 8: a 17 10 #> 28: c 15 13 #> 48: b 14 6
#> 9: a 17 11 #> 29: c 16 19 #> 49: b 18 20
#> 10: a 7 9 #> 30: c 16 3 #> 50: b 18 2
#> 11: a 7 10 #> 31: c 16 5 #> 51: b 18 4
#> 12: a 7 11 #> 32: c 16 8 #> 52: b 18 6
#> 13: a 9 10 #> 33: c 16 13 #> 53: b 20 2
#> 14: a 9 11 #> 34: c 19 3 #> 54: b 20 4
#> 15: a 10 11 #> 35: c 19 5 #> 55: b 20 6
#> 16: c 12 15 #> 36: c 19 8 #> 56: b 2 4
#> 17: c 12 16 #> 37: c 19 13 #> 57: b 2 6
#> 18: c 12 19 #> 38: c 3 5 #> 58: b 4 6
#> 19: c 12 3 #> 39: c 3 8
#> 20: c 12 5 #> 40: c 3 13
or in dplyr:
set.seed(123)
data <- data.frame(id = as.character(1:20),
cluster = sample(c("a", "b", "c"), 20, replace = T))
library(dplyr)
data %>% group_by(cluster) %>% mutate(id=as.character(id)) %>%
do(data.frame(t(combn(.$id, 2))))
#> # A tibble: 58 x 3
#> # Groups: cluster [3]
#> cluster X1 X2
#> <fct> <dbl> <dbl>
#> 1 a 1 17
#> 2 a 1 7
#> 3 a 1 9
#> 4 a 1 10
#> 5 a 1 11
#> 6 a 17 7
#> 7 a 17 9
#> 8 a 17 10
#> 9 a 17 11
#> 10 a 7 9
#> # ... with 48 more rows
Created on 2019-05-16 by the reprex package (v0.2.1)