-
Notifications
You must be signed in to change notification settings - Fork 0
/
2.1_Descriptive_stats.R
125 lines (101 loc) · 5.03 KB
/
2.1_Descriptive_stats.R
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
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
########################################################################################################################
## GOSSIP IN HUNGARIAN HIGH SCHOOLS
## Descriptive statistics (2.1)
## R script written by Jose Luis Estevez (Linkoping University)
## Date: October 19th, 2020
########################################################################################################################
# R PACKAGES REQUIRED
library(statnet)
########################################################################################################################
# DATA LOADING
rm(list=ls())
load('data.RData')
load('tidieddata.RData')
########################################################################################################################
# GENERAL DESCRIPTIVE STATS
# Number of unique students in the final sample
unique_students <- union(union(rownames(students$wave1),rownames(students$wave2)),rownames(students$wave3))
length(unique_students)
c(nrow(students$wave1),nrow(students$wave2),nrow(students$wave3)) # Students per wave
# Age of students
for(i in 1:nrow(std)){
if(is.na(std$yrbrth_1[i]) & is.na(std$yrbrth_2[i]) & is.na(std$yrbrth_3[i])){
std$yrbrth[i] <- std$yrbrth_4[i]
}else if(is.na(std$yrbrth_1[i]) & is.na(std$yrbrth_2[i])){
std$yrbrth[i] <- std$yrbrth_3[i]
}else if(is.na(std$yrbrth_1[i])){
std$yrbrth[i] <- std$yrbrth_2[i]
}else{
std$yrbrth[i] <- std$yrbrth_1[i]
}
}
for(i in 1:nrow(std)){
if(is.na(std$mobrth_1[i]) & is.na(std$mobrth_2[i]) & is.na(std$mobrth_3[i])){
std$mobrth[i] <- std$mobrth_4[i]
}else if(is.na(std$mobrth_1[i]) & is.na(std$mobrth_2[i])){
std$mobrth[i] <- std$mobrth_3[i]
}else if(is.na(std$mobrth_1[i])){
std$mobrth[i] <- std$mobrth_2[i]
}else{
std$mobrth[i] <- std$mobrth_1[i]
}
}
# Age in wave 1
2011.5 - mean(std[rownames(std) %in% rownames(students$wave1),]$yrbrth +
std[rownames(std) %in% rownames(students$wave1),]$mobrth/12,na.rm=TRUE)
# Age in wave 2
2012.5 - mean(std[rownames(std) %in% rownames(students$wave2),]$yrbrth +
std[rownames(std) %in% rownames(students$wave2),]$mobrth/12,na.rm=TRUE)
# Age in wave 3
2013.5 - mean(std[rownames(std) %in% rownames(students$wave3),]$yrbrth +
std[rownames(std) %in% rownames(students$wave3),]$mobrth/12,na.rm=TRUE)
# Proportion of women
students$wave1$ID <- rownames(students$wave1)
students$wave2$ID <- rownames(students$wave2)
students$wave3$ID <- rownames(students$wave3)
final_sample <- do.call(rbind,students)
final_sample <- final_sample[!duplicated(final_sample),]
sum(final_sample$female)/nrow(final_sample)
# Proportion of Roma
sum(final_sample$roma,na.rm=TRUE)/nrow(final_sample)
# Roma missing
sum(is.na(final_sample$roma))
########################################################################################################################
# DESCRIPTIVE RESULTS
# Gossip networks: descriptive statistics
gossip_ntw <- c(networks$wave1$gossip,networks$wave2$gossip,networks$wave3$gossip)
gossip_desc <- data.frame(matrix(NA,nrow=length(gossip_ntw),ncol=13))
names(gossip_desc) <- c('class','time','female','roma','ties',
'ties_missing','potential_ties','missing',
'ave_degree','density','recip','transitivity','isolates')
gossip_desc$class <- names(gossip_ntw)
gossip_desc$time <- c(rep(1,6),rep(2,6),rep(3,5))
# Extraction of information from the gossip networks
for(i in seq_along(gossip_ntw)){
gossip_desc$ties[i] <- sum(gossip_ntw[[i]],na.rm=TRUE)
gossip_desc$ties_missing[i] <- sum(is.na(gossip_ntw[[i]]))
gossip_desc$potential_ties[i] <- nrow(gossip_ntw[[i]]) * (nrow(gossip_ntw[[i]])-1)
gossip_desc$ave_degree[i] <- round(mean(degree(gossip_ntw[[i]])),1)
gossip_desc$density[i] <- round(gden(gossip_ntw[[i]])*100,1)
gossip_desc$recip[i] <- round(grecip(gossip_ntw[[i]],measure='edgewise')*100,1)
gossip_desc$transitivity[i] <- round(igraph::transitivity(igraph::graph_from_adjacency_matrix(gossip_ntw[[i]]))*100,1)
gossip_desc$isolates[i] <- length(isolates(gossip_ntw[[i]]))
}
gossip_desc$missing <- round(gossip_desc$ties_missing / gossip_desc$potential_ties*100,1)
# Addition of actor-attributes
specifications <- vector('list',length(networks))
names(specifications) <- names(networks)
for(wave in seq_along(specifications)){
specifications[[wave]] <- data.frame(row.names=names(networks[[wave]]$gossip))
}
for(wave in seq_along(specifications)){
for(room in rownames(specifications[[wave]])){
specifications[[wave]][room,'roma'] <- sum(students[[wave]][students[[wave]]$class==room,]$roma==1,na.rm=TRUE)
specifications[[wave]][room,'female'] <- sum(students[[wave]][students[[wave]]$class==room,]$female==1,na.rm=TRUE)
}
}
specifications <- as.data.frame(do.call('rbind',specifications))
gossip_desc$female <- specifications$female
gossip_desc$roma <- specifications$roma
# Print table
write.table(gossip_desc[order(gossip_desc$class),],'descriptive_table.csv',row.names=FALSE,sep=';')