R Notebook: Social Network Analysis with igraph

• rm(list=ls())
• getwd()

pre.sourceCode { color: inherit; background-color: transparent; }

Load the the given data file :

library(sna)
library(igraph)
library(network)
(sort(mydata))

##  [1] "alc.att" "alc.beh" "fem"     "fri"     "hob"     "res.act" "res.pas"
##  [8] "sch.att" "sch.beh" "spo.att" "spo.beh" "str.abs" "tob.att" "tob.beh"

• Explore the given dataset such as its class, dataframe, and format

• Notes
  • V = August/September 2003
  • W = November/December 2003
  • X = February/March 2004
  • Y = May/June 2004
  • NA = non-response

• To make a table for descriptive network statistics over time

  • average degree
  • standard deviation of outdegrees and of indegrees
  • reciprocity index
  • transitivity index
  • % non-respondents(absence of participation) at all four time points.

• “fri” : adjacency matrix

  • 0 = no friendship reported
  • 1 = friendship reported
  • NA = person did not respond
  • 10 = structurally absent student

Social Network Graph at Wave V

fri_mat = matrix(fri)
fri_V = fri_mat[,1][[1]]
omitted_V_nodes = c(-23, -24,-25) 
fri_V_final  = fri_V[omitted_V_nodes , omitted_V_nodes ] 

igraph_fV_directed = graph.adjacency(as.matrix(fri_V_final),mode = "directed",weighted = NULL)
mynet_V = igraph_fV_directed
mean(degree(mynet_V ))
sd(degree(mynet_V, mode = "in"))
sd(degree(mynet_V, mode = "out"))
reciprocity(mynet_V)
transitivity(mynet_V)

Raw data : Adjacency matrix

1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25
0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	10	10	10
0	0	1	0	1	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	1	10	10	10
0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	10	10	10
1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	10	10	10
0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	10	10	10
1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	10	10	10
0	1	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	10	10	10
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	10	10	10
0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	10	10
0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	10	10	10
1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	10	10	10
0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	10	10
0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	10	10	10
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	10	10	10
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	10	10	10
0	1	0	0	0	0	0	1	1	0	0	1	0	1	0	0	0	0	0	0	0	0	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10

Remove isolated : Adjacency matrix

1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22
0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0
0	0	1	0	1	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	1
0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0
0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0
1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1
0	1	1	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1
1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1
0	1	0	0	0	0	0	1	1	0	0	1	0	1	0	0	0	0	0	0	0	0

• plot a network diagram with color node for gender and size change with alc.att
• male (0) = yellow in color
• female(1) = green in color
• node size changed with alc.att

Social Network Graph at Wave W

fri_W =fri_mat[,1][[2]]
fri_W[is.na(fri_W )] = 0
omitted_W_nodes = c(-15, -23, -24,-25)
fri_W_final = fri_W[omitted_W_nodes , omitted_W_nodes]
igraph_fW_directed = graph.adjacency(as.matrix(fri_W_final),mode = "directed",weighted = NULL)
mynet_W = igraph_fW_directed
mean(degree(mynet_W ))
sd(degree(mynet_W, mode = "in"))
sd(degree(mynet_W, mode = "out"))
reciprocity(mynet_W)
transitivity(mynet_W)

Raw data : Adjacency matrix

1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25
0	0	0	0	0	0	0	0	0	0	0	1	1	0	10	1	0	0	0	0	0	0	10	10	10
0	0	1	0	1	0	1	0	1	0	0	0	0	1	10	0	0	0	0	0	0	1	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	0	0	0	0	0	0	0	10	10	10
0	1	1	0	1	0	1	0	1	0	0	1	0	1	10	0	0	0	0	0	0	1	10	10	10
0	0	0	0	0	0	0	0	1	0	0	0	0	0	10	0	0	0	0	0	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	0	0	1	0	1	1	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	1	0	1	10	0	0	0	0	0	0	1	10	10	10
0	0	0	0	0	0	0	0	0	0	1	0	0	0	10	0	0	0	1	1	0	0	10	10	10
0	0	0	0	1	0	0	0	0	0	0	1	0	1	10	0	0	0	0	0	0	1	10	10	10
0	0	1	1	0	0	1	0	0	0	0	0	0	0	10	0	1	0	0	0	0	0	10	10	10
0	0	0	0	0	0	0	1	0	0	0	0	0	0	10	0	0	0	0	1	0	0	10	10	10
0	0	0	0	0	0	1	0	1	0	0	0	0	1	10	0	0	0	0	0	0	1	10	10	10
1	0	0	0	0	0	0	0	0	0	0	0	0	0	10	1	0	0	0	0	0	0	10	10	10
0	0	0	0	0	0	1	0	0	0	0	1	0	0	10	0	0	0	0	0	0	1	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
1	0	0	0	0	0	0	0	0	0	0	0	1	0	10	0	0	0	0	0	0	0	10	10	10
0	0	0	1	1	0	0	0	0	0	0	0	0	0	10	0	0	0	0	0	0	0	10	10	10
0	0	0	0	0	1	0	0	0	0	0	0	0	0	10	0	0	0	0	1	1	0	10	10	10
0	0	0	0	0	0	0	1	0	0	0	0	0	0	10	0	0	0	0	0	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	0	0	0	0	0	0	0	10	10	10
0	0	0	0	0	1	0	0	0	0	0	0	0	0	10	0	0	1	0	0	0	0	10	10	10
0	1	0	0	0	0	1	0	0	0	0	1	0	1	10	0	0	0	0	0	0	0	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10

Remove isolated : Adjacency matrix

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	16	17	18	19	20	21	22
1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0
2	0	0	1	0	1	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	1
3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	0	1	1	0	1	0	1	0	1	0	0	1	0	1	0	0	0	0	0	0	1
5	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0
7	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1
8	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0
9	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1
10	0	0	1	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0
11	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0
12	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	1
13	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
14	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1
16	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
17	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
18	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
19	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
21	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
22	0	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0

Social Network Graph at Wave X

fri_X =fri_mat[,1][[3]]
omitted_X_nodes = c(-15,-17,-18, -23, -24,-25)
fri_X_final = fri_X[omitted_X_nodes , omitted_X_nodes]
igraph_fX_directed = graph.adjacency(as.matrix(fri_X_final),mode = "directed",weighted = NULL)
mynet_X = igraph_fX_directed

mean(degree(mynet_X ))
sd(degree(mynet_X, mode = "in"))
sd(degree(mynet_X, mode = "out"))
reciprocity(mynet_X)
transitivity(mynet_X)

Raw data : Adjacency matrix

1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25
0	0	0	0	0	0	0	0	0	0	0	0	1	0	10	1	0	10	0	0	0	0	10	10	10
0	0	1	0	0	0	0	0	0	0	0	0	0	1	10	0	0	10	0	0	0	1	10	10	10
0	1	0	1	1	0	1	0	1	0	0	0	0	1	10	0	0	10	1	0	0	1	10	10	10
0	1	1	0	0	0	0	0	1	0	0	0	0	1	10	0	0	10	0	0	0	1	10	10	10
0	1	1	1	0	0	0	0	1	0	0	0	0	1	10	0	0	10	0	0	0	1	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	0	0	10	0	0	1	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	1	0	0	10	0	0	10	0	0	0	1	10	10	10
0	0	0	0	0	0	0	0	0	0	1	0	0	0	10	0	0	10	0	1	0	1	10	10	10
0	0	0	0	1	0	0	0	0	0	0	0	0	1	10	0	0	10	0	0	0	1	10	10	10
0	0	1	1	0	0	0	0	0	0	0	0	0	0	10	0	0	10	0	0	0	0	10	10	10
0	0	0	0	0	0	0	1	0	0	0	0	0	0	10	0	0	10	1	1	0	0	10	10	10
1	0	0	0	0	0	1	0	1	0	0	0	0	1	10	0	0	10	0	0	0	1	10	10	10
1	0	0	0	0	0	0	0	0	0	0	0	0	0	10	1	0	10	0	0	0	0	10	10	10
0	1	1	1	1	0	1	0	1	0	0	1	0	0	10	0	0	10	0	0	0	1	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
1	0	0	0	0	0	0	0	0	0	0	0	1	0	10	0	0	10	0	0	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	0	0	0	0	10	0	0	10	0	0	0	0	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
0	0	0	0	0	0	0	0	0	0	1	0	0	0	10	0	0	10	0	1	0	0	10	10	10
0	0	0	0	0	0	0	0	0	0	1	0	0	0	10	0	0	10	0	0	0	0	10	10	10
0	0	0	0	0	1	0	0	0	0	0	0	0	0	10	0	0	10	0	0	0	0	10	10	10
0	0	0	1	0	0	0	0	0	0	0	0	0	1	10	0	0	10	0	0	0	0	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10

Remove isolated : Adjacency matrix

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	16	19	20	21	22
1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0
2	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1
3	0	1	0	1	1	0	1	0	1	0	0	0	0	1	0	1	0	0	1
4	0	1	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1
5	0	1	1	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1
6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
7	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1
8	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1
9	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	1
10	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
11	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0
12	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1
13	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
14	0	1	1	1	1	0	1	0	1	0	0	1	0	0	0	0	0	0	1
16	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
19	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
20	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
21	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
22	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0

Social Network Graph at Wave Y

fri_Y =fri_mat[,1][[4]]
omitted_Y_nodes = c(-1, -14,-15, -18)
fri_Y_final = fri_Y[omitted_Y_nodes  , omitted_Y_nodes  ]
igraph_fY_directed = graph.adjacency(as.matrix(fri_Y_final),mode = "directed",weighted = NULL)
mynet_Y = igraph_fY_directed
mean(degree(mynet_Y ))
sd(degree(mynet_Y, mode = "in"))
sd(degree(mynet_Y, mode = "out"))
reciprocity(mynet_Y)
transitivity(mynet_Y)

Raw data : Adjacency matrix

1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	0	1	0	0	0	1	0	1	0	0	1	0	10	10	0	0	10	0	0	0	1	0	0	0
10	1	0	0	0	0	1	1	1	1	0	0	0	10	10	0	0	10	0	0	0	1	0	0	0
10	1	1	0	0	0	0	0	0	0	0	0	0	10	10	0	0	10	0	0	0	1	0	0	0
10	1	1	1	0	0	0	0	1	0	0	0	0	10	10	0	1	10	0	0	0	1	0	0	0
10	0	0	0	0	0	0	0	0	0	0	0	0	10	10	0	0	10	0	0	1	0	1	1	1
10	0	0	0	0	0	0	0	0	0	0	1	1	10	10	1	0	10	0	0	0	1	0	0	0
10	0	0	0	0	0	0	0	0	0	1	0	0	10	10	0	0	10	1	1	0	1	0	0	0
10	0	0	0	1	0	0	0	0	0	0	0	0	10	10	0	1	10	0	0	0	0	0	0	0
10	0	1	1	0	0	1	0	0	0	0	0	0	10	10	0	1	10	0	0	0	0	0	0	0
10	0	0	0	0	0	0	1	0	0	0	0	0	10	10	0	0	10	0	1	0	0	0	0	0
10	0	0	0	0	0	1	0	0	0	0	0	0	10	10	1	0	10	0	0	0	0	0	0	0
10	0	0	0	0	0	0	0	0	0	0	0	0	10	10	1	0	10	0	0	0	0	0	0	0
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	0	0	0	0	0	0	0	0	0	0	0	1	10	10	0	0	10	0	0	0	0	0	0	0
10	0	0	0	1	0	0	0	0	0	0	0	0	10	10	0	0	10	0	0	0	1	0	0	0
10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10	10
10	0	0	0	0	0	0	1	0	0	0	0	0	10	10	0	0	10	0	0	0	0	0	0	0
10	0	0	0	0	0	0	0	0	0	1	0	0	10	10	0	0	10	0	0	0	0	0	0	0
10	0	0	0	0	1	0	0	0	0	0	0	0	10	10	0	0	10	0	0	0	0	1	1	0
10	1	0	1	0	0	0	0	0	0	0	0	0	10	10	0	0	10	0	0	0	0	0	0	0
10	0	0	0	0	1	0	0	0	0	0	0	0	10	10	0	0	10	0	0	1	0	0	1	1
10	0	0	0	0	1	0	0	0	0	0	0	0	10	10	0	0	10	0	0	0	0	1	0	1
10	0	0	0	0	1	0	0	0	0	0	0	0	10	10	0	0	10	0	0	1	0	1	1	0

Remove isolated : Adjacency matrix

	2	3	4	5	6	7	8	9	10	11	12	13	16	17	19	20	21	22	23	24	25
2	0	1	0	0	0	1	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0
3	1	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0	0	1	0	0	0
4	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
5	1	1	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0
6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	1
7	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	1	0	0	0
8	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	1	0	0	0
9	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
10	0	1	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
11	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0
12	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
13	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
16	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
17	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
19	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
20	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
21	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
22	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
23	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1
24	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1
25	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0

+ Make a table containing descriptive information for individual level variables

	V	W	X	Y
mean_active_response	0.880	0.800	0.800	0.840
mean_passive_response	0.880	0.840	0.840	0.920
avg_deg	5.182	5.714	6.421	6.095
sd_deg_In	1.869	1.852	2.226	1.322
sd_deg_Out	1.469	1.852	2.175	1.564
reciprocal	0.561	0.567	0.656	0.594
transitive	0.422	0.613	0.646	0.574

+ percentage of females
+  means and standard deviation of 
    + alcohol consumption
    + tobacco consumption
    + school engagement
    + sportivity

Social Network Structure at Wave V, W,X,Y

	V	W	X	Y
me_alc_att	1.880	1.600	1.680	2.000
sd_alc_att	1.130	1.190	1.215	1.384
me_alc_beh	1.636	1.158	1.200	1.524
sd_alc_beh	0.902	0.501	0.523	0.928
me_tob_att	1.273	1.517	1.250	1.667
sd_tob_att	0.420	0.721	0.388	0.624
me_tob_beh	1.136	1.158	1.050	1.286
sd_tob_beh	0.468	0.501	0.224	0.956
me_sch_att	4.709	4.730	4.640	4.562
sd_sch_att	0.379	0.345	0.572	0.450
me_sch_beh	4.352	3.992	4.150	3.964
sd_sch_beh	0.315	0.388	0.440	0.463
me_spo_att	3.773	3.750	3.900	3.619
sd_spo_att	1.020	0.910	0.912	1.024
me_spo_beh	2.905	3.211	3.400	3.143
sd_spo_beh	1.261	1.228	1.095	1.236
Female_Prop	0.600	0.600	0.600	0.600
Male_Prop	0.400	0.400	0.400	0.400

Run A Simple Model: Logit Regression Modeling at Wave V (Social Network)

• with glm to test any relationship among Indegree centrality , fem and alc.att
  
• glm(formula = factor(indeg_V) ~ factor(femV) + factor(alcatt) + (tobcatt), family = “binomial”)

indeg_V = degree(mynet_V, mode = "in")

alc.att[is.na(alc.att)] = 0
alc.beh[is.na(alc.beh)] = 0

alcatt_V = alc.att[omitted_V_nodes ,"V"]
alcbeh_V = alc.beh[omitted_V_nodes , "V"]
fem_V = fem[omitted_V_nodes]
model_V =  glm(factor(indeg_V) ~  factor(fem_V)+ factor(alcatt_V)  + factor(alcbeh_V),  family= "binomial"   )
summary(model_V)

## 
## Call:
## glm(formula = factor(indeg_V) ~ factor(fem_V) + factor(alcatt_V) + 
##     factor(alcbeh_V), family = "binomial")
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -1.79412   0.00000   0.00001   0.50106   1.17741  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)
## (Intercept)          40.089  13750.027   0.003    0.998
## factor(fem_V)1      -38.703  13750.027  -0.003    0.998
## factor(alcatt_V)2    -1.386      1.500  -0.924    0.355
## factor(alcatt_V)3   -20.899  10472.027  -0.002    0.998
## factor(alcatt_V)4   -38.379  34613.728  -0.001    0.999
## factor(alcbeh_V)2    19.856  12430.268   0.002    0.999
## factor(alcbeh_V)3    39.591  17398.971   0.002    0.998
## factor(alcbeh_V)4    41.079  31051.551   0.001    0.999
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 20.862  on 21  degrees of freedom
## Residual deviance: 10.549  on 14  degrees of freedom
## AIC: 26.549
## 
## Number of Fisher Scoring iterations: 20