I have an edgelist of friendship nominations from 5 schools over 3 waves. I'd like to get each ego's indegree, outdegree, and constraint values into a dataframe. I think I need to create a list of graph objects and then use apply or a loop to go through them and calculate each measure, but I'm not sure how to do that and get a dataframe output with each ego's id number.
Here is some sample data:
edges<-read.table(text=" ego alter wave school
1 4 1 1
1 4 2 1
1 3 3 1
2 3 1 1
2 4 2 1
2 4 3 1
3 1 1 1
3 2 2 1
3 3 3 1
4 1 1 1
4 1 2 1
4 1 3 1
5 8 1 2
5 6 2 2
5 7 3 2
6 7 1 2
6 7 2 2
6 7 3 2
7 8 1 2
7 6 2 2
7 6 3 2
8 7 1 2
8 7 2 2
8 7 3 2
9 10 1 3
9 11 2 3
9 12 3 3
10 11 1 3
10 11 2 3
10 9 3 3
11 12 1 3
11 10 2 3
11 12 3 3
12 9 1 3
12 10 2 3
12 10 3 3
13 14 1 4
13 15 2 4
13 16 3 4
14 16 1 4
14 16 2 4
14 13 3 4
15 16 1 4
15 16 2 4
15 16 3 4
16 15 1 4
16 15 2 4
16 15 3 4
17 20 1 5
17 18 2 5
17 18 3 5
18 19 1 5
18 20 2 5
18 19 3 5
19 17 1 5
19 17 2 5
19 17 3 5
20 18 1 5
20 17 2 5
20 17 3 5", header = TRUE)
Here's the data I want:
df <-read.table(text="student_id wave indegree outdegree constraint
1 1 2 1 0.5555556
1 2 1 1 1.0000000
1 3 1 1 0.5000000
2 1 0 1 1.0000000
2 2 1 1 0.5000000
2 3 0 1 1.0000000
3 1 1 1 0.5000000
3 2 0 1 1.0000000
3 3 2 1 1.0000000
4 1 1 1 1.0000000
4 2 2 1 0.5555556
4 3 1 1 0.5000000
5 1 0 1 1.0000000
5 2 0 1 1.0000000
5 3 0 1 1.0000000
6 1 0 1 1.0000000
6 2 2 1 0.5555556
6 3 1 1 1.0000000
7 1 2 1 0.5555556
7 2 2 1 0.5555556
7 3 3 1 0.3750000
8 1 2 1 0.5555556
8 2 0 1 1.0000000
8 3 0 1 1.0000000
10 1 1 1 0.5000000
10 2 2 1 0.5555556
10 3 1 1 1.0069444
11 1 1 1 0.5000000
11 2 2 1 0.5555556
11 3 0 1 1.0000000
12 1 1 1 0.5000000
12 2 0 1 1.0000000
12 3 2 1 0.6111111
9 1 1 1 0.5000000
9 2 0 1 1.0000000
9 3 1 1 1.0069444
13 1 0 1 1.0000000
13 2 0 1 1.0000000
13 3 1 1 0.5000000
14 1 1 1 0.5000000
14 2 0 1 1.0000000
14 3 0 1 1.0000000
15 1 1 1 1.0000000
15 2 2 1 0.5555556
15 3 1 1 1.0000000
16 1 2 1 0.5555556
16 2 2 1 0.5555556
16 3 2 1 0.5555556
17 1 1 1 0.5000000
17 2 2 1 0.6111111
17 3 2 1 0.6111111
18 1 1 1 0.5000000
18 2 1 1 1.0069444
18 3 1 1 1.0069444
19 1 1 1 0.5000000
19 2 0 1 1.0000000
19 3 1 1 1.0069444
20 1 1 1 0.5000000
20 2 1 1 1.0069444
20 3 0 1 1.0000000", header = TRUE)
This is the really long code I used to get that data output above. Is there a way to do this will an apply or loop?
s1w1 <- graph_from_data_frame(d = filter(edges, school == 1 & wave == 1))
s1w2 <- graph_from_data_frame(d = filter(edges, school == 1 & wave == 2))
s1w3 <- graph_from_data_frame(d = filter(edges, school == 1 & wave == 3))
s2w1 <- graph_from_data_frame(d = filter(edges, school == 2 & wave == 1))
s2w2 <- graph_from_data_frame(d = filter(edges, school == 2 & wave == 2))
s2w3 <- graph_from_data_frame(d = filter(edges, school == 2 & wave == 3))
s3w1 <- graph_from_data_frame(d = filter(edges, school == 3 & wave == 1))
s3w2 <- graph_from_data_frame(d = filter(edges, school == 3 & wave == 2))
s3w3 <- graph_from_data_frame(d = filter(edges, school == 3 & wave == 3))
s4w1 <- graph_from_data_frame(d = filter(edges, school == 4 & wave == 1))
s4w2 <- graph_from_data_frame(d = filter(edges, school == 4 & wave == 2))
s4w3 <- graph_from_data_frame(d = filter(edges, school == 4 & wave == 3))
s5w1 <- graph_from_data_frame(d = filter(edges, school == 5 & wave == 1))
s5w2 <- graph_from_data_frame(d = filter(edges, school == 5 & wave == 2))
s5w3 <- graph_from_data_frame(d = filter(edges, school == 5 & wave == 3))
df1w1 <- data.frame(
student_id = names(V(s1w1)),
wave = 1,
indegree = degree(s1w1, mode = "in"),
outdegree = degree(s1w1, mode = "out"),
constraint = constraint(s1w1) %>% unlist())
df1w2 <- data.frame(
student_id = names(V(s1w2)),
wave = 2,
indegree = degree(s1w2, mode = "in"),
outdegree = degree(s1w2, mode = "out"),
constraint = constraint(s1w2) %>% unlist())
df1w3 <- data.frame(
student_id = names(V(s1w3)),
wave = 3,
indegree = degree(s1w3, mode = "in"),
outdegree = degree(s1w3, mode = "out"),
constraint = constraint(s1w3) %>% unlist())
df2w1 <- data.frame(
student_id = names(V(s2w1)),
wave = 1,
indegree = degree(s2w1, mode = "in"),
outdegree = degree(s2w1, mode = "out"),
constraint = constraint(s2w1) %>% unlist())
df2w2 <- data.frame(
student_id = names(V(s2w2)),
wave = 2,
indegree = degree(s2w2, mode = "in"),
outdegree = degree(s2w2, mode = "out"),
constraint = constraint(s2w2) %>% unlist())
df2w3 <- data.frame(
student_id = names(V(s2w3)),
wave = 3,
indegree = degree(s2w3, mode = "in"),
outdegree = degree(s2w3, mode = "out"),
constraint = constraint(s2w3) %>% unlist())
df3w1 <- data.frame(
student_id = names(V(s3w1)),
wave = 1,
indegree = degree(s3w1, mode = "in"),
outdegree = degree(s3w1, mode = "out"),
constraint = constraint(s3w1) %>% unlist())
df3w2 <- data.frame(
student_id = names(V(s3w2)),
wave = 2,
indegree = degree(s3w2, mode = "in"),
outdegree = degree(s3w2, mode = "out"),
constraint = constraint(s3w2) %>% unlist())
df3w3 <- data.frame(
student_id = names(V(s3w3)),
wave = 3,
indegree = degree(s3w3, mode = "in"),
outdegree = degree(s3w3, mode = "out"),
constraint = constraint(s3w3) %>% unlist())
df4w1 <- data.frame(
student_id = names(V(s4w1)),
wave = 1,
indegree = degree(s4w1, mode = "in"),
outdegree = degree(s4w1, mode = "out"),
constraint = constraint(s4w1) %>% unlist())
df4w2 <- data.frame(
student_id = names(V(s4w2)),
wave = 2,
indegree = degree(s4w2, mode = "in"),
outdegree = degree(s4w2, mode = "out"),
constraint = constraint(s4w2) %>% unlist())
df4w3 <- data.frame(
student_id = names(V(s4w3)),
wave = 3,
indegree = degree(s4w3, mode = "in"),
outdegree = degree(s4w3, mode = "out"),
constraint = constraint(s4w3) %>% unlist())
df5w1 <- data.frame(
student_id = names(V(s5w1)),
wave = 1,
indegree = degree(s5w1, mode = "in"),
outdegree = degree(s5w1, mode = "out"),
constraint = constraint(s5w1) %>% unlist())
df5w2 <- data.frame(
student_id = names(V(s5w2)),
wave = 2,
indegree = degree(s5w2, mode = "in"),
outdegree = degree(s5w2, mode = "out"),
constraint = constraint(s5w2) %>% unlist())
df5w3 <- data.frame(
student_id = names(V(s5w3)),
wave = 3,
indegree = degree(s5w3, mode = "in"),
outdegree = degree(s5w3, mode = "out"),
constraint = constraint(s5w3) %>% unlist())
df <- list(df1w1, df1w2, df1w3,
df2w1, df2w2, df2w3,
df3w1, df3w2, df3w3,
df4w1, df4w2, df4w3,
df5w1, df5w2, df5w3) %>%
reduce(full_join)
CodePudding user response：
Maybe you can try split lapply rbind like below
do.call(
rbind,
lapply(
split(edges, ~ school wave),
function(x) {
g <- graph_from_data_frame(x)
data.frame(
student_id = names(V(g)),
wave = E(g)$wave,
indegree = degree(g, mode = "in"),
outdegree = degree(g, mode = "out"),
constraint = constraint(g) %>% unlist()
)
}
)
)
which gives
student_id wave indegree outdegree constraint
1.1.1 1 1 2 1 0.5555556
1.1.2 2 1 0 1 1.0000000
1.1.3 3 1 1 1 0.5000000
1.1.4 4 1 1 1 1.0000000
2.1.5 5 1 0 1 1.0000000
2.1.6 6 1 0 1 1.0000000
2.1.7 7 1 2 1 0.5555556
2.1.8 8 1 2 1 0.5555556
3.1.9 9 1 1 1 0.5000000
3.1.10 10 1 1 1 0.5000000
3.1.11 11 1 1 1 0.5000000
3.1.12 12 1 1 1 0.5000000
4.1.13 13 1 0 1 1.0000000
4.1.14 14 1 1 1 0.5000000
4.1.15 15 1 1 1 1.0000000
4.1.16 16 1 2 1 0.5555556
5.1.17 17 1 1 1 0.5000000
5.1.18 18 1 1 1 0.5000000
5.1.19 19 1 1 1 0.5000000
5.1.20 20 1 1 1 0.5000000
1.2.1 1 2 1 1 1.0000000
1.2.2 2 2 1 1 0.5000000
1.2.3 3 2 0 1 1.0000000
1.2.4 4 2 2 1 0.5555556
2.2.5 5 2 0 1 1.0000000
2.2.6 6 2 2 1 0.5555556
2.2.7 7 2 2 1 0.5555556
2.2.8 8 2 0 1 1.0000000
3.2.9 9 2 0 1 1.0000000
3.2.10 10 2 2 1 0.5555556
3.2.11 11 2 2 1 0.5555556
3.2.12 12 2 0 1 1.0000000
4.2.13 13 2 0 1 1.0000000
4.2.14 14 2 0 1 1.0000000
4.2.15 15 2 2 1 0.5555556
4.2.16 16 2 2 1 0.5555556
5.2.17 17 2 2 1 0.6111111
5.2.18 18 2 1 1 1.0069444
5.2.19 19 2 0 1 1.0000000
5.2.20 20 2 1 1 1.0069444
1.3.1 1 3 1 1 0.5000000
1.3.2 2 3 0 1 1.0000000
1.3.3 3 3 2 1 1.0000000
1.3.4 4 3 1 1 0.5000000
2.3.5 5 3 0 1 1.0000000
2.3.6 6 3 1 1 1.0000000
2.3.7 7 3 3 1 0.3750000
2.3.8 8 3 0 1 1.0000000
3.3.9 9 3 1 1 1.0069444
3.3.10 10 3 1 1 1.0069444
3.3.11 11 3 0 1 1.0000000
3.3.12 12 3 2 1 0.6111111
4.3.13 13 3 1 1 0.5000000
4.3.14 14 3 0 1 1.0000000
4.3.15 15 3 1 1 1.0000000
4.3.16 16 3 2 1 0.5555556
5.3.17 17 3 2 1 0.6111111
5.3.18 18 3 1 1 1.0069444
5.3.19 19 3 1 1 1.0069444
5.3.20 20 3 0 1 1.0000000