- Load the dataset called studentNetwork.RData. Read the datasets negative-words.txt and positive-words.txt into R. Here is a link: studentNetwork.RDataView in a new window It is also in the Files section
#dir <- "C:/Users/Bhupendra Mishra/Desktop/donotbackup/"
#setwd(dir)
load("C:/Users/Bhupendra Mishra/Desktop/donotbackup/studentNetwork.RData")
#load("studentNetwork.RData")
- Make a plot of the network
library(igraph)
## Warning: package 'igraph' was built under R version 3.2.2
##
## Attaching package: 'igraph'
##
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
##
## The following object is masked from 'package:base':
##
## union
library(statnet)
## Warning: package 'statnet' was built under R version 3.2.2
## Loading required package: network
## Warning: package 'network' was built under R version 3.2.2
## network: Classes for Relational Data
## Version 1.13.0 created on 2015-08-31.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Martina Morris, University of Washington
## Skye Bender-deMoll, University of Washington
## For citation information, type citation("network").
## Type help("network-package") to get started.
##
##
## Attaching package: 'network'
##
## The following objects are masked from 'package:igraph':
##
## %c%, %s%, add.edges, add.vertices, delete.edges,
## delete.vertices, get.edge.attribute, get.edges,
## get.vertex.attribute, is.bipartite, is.directed,
## list.edge.attributes, list.vertex.attributes,
## set.edge.attribute, set.vertex.attribute
##
## Loading required package: networkDynamic
## Warning: package 'networkDynamic' was built under R version 3.2.2
##
## networkDynamic: version 0.8.1, created on 2015-10-06
## Copyright (c) 2015, Carter T. Butts, University of California -- Irvine
## Ayn Leslie-Cook, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Skye Bender-deMoll, University of Washington
## with contributions from
## Zack Almquist, University of California -- Irvine
## David R. Hunter, Penn State University
## Li Wang
## Kirk Li, University of Washington
## Steven M. Goodreau, University of Washington
## Jeffrey Horner
## Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("networkDynamic").
##
## Loading required package: ergm
## Warning: package 'ergm' was built under R version 3.2.2
## Loading required package: statnet.common
## Warning: package 'statnet.common' was built under R version 3.2.2
##
## ergm: version 3.5.1, created on 2015-10-18
## Copyright (c) 2015, Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Carter T. Butts, University of California -- Irvine
## Steven M. Goodreau, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Martina Morris, University of Washington
## with contributions from
## Li Wang
## Kirk Li, University of Washington
## Skye Bender-deMoll, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("ergm").
##
## NOTE: If you use custom ERGM terms based on 'ergm.userterms'
## version prior to 3.1, you will need to perform a one-time update
## of the package boilerplate files (the files that you did not write
## or modify) from 'ergm.userterms' 3.1 or later. See
## help('eut-upgrade') for instructions.
##
## Loading required package: sna
## Warning: package 'sna' was built under R version 3.2.2
## sna: Tools for Social Network Analysis
## Version 2.3-2 created on 2014-01-13.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## For citation information, type citation("sna").
## Type help(package="sna") to get started.
##
##
## Attaching package: 'sna'
##
## The following object is masked from 'package:network':
##
## %c%
##
## The following objects are masked from 'package:igraph':
##
## %c%, betweenness, bonpow, closeness, components, degree,
## dyad.census, evcent, hierarchy, is.connected, neighborhood,
## triad.census
##
## Loading required package: tergm
## Warning: package 'tergm' was built under R version 3.2.2
##
## tergm: version 3.3.1, created on 2015-10-25
## Copyright (c) 2015, Pavel N. Krivitsky, University of Wollongong
## Mark S. Handcock, University of California -- Los Angeles
## with contributions from
## David R. Hunter, Penn State University
## Steven M. Goodreau, University of Washington
## Martina Morris, University of Washington
## Nicole Bohme Carnegie, New York University
## Carter T. Butts, University of California -- Irvine
## Ayn Leslie-Cook, University of Washington
## Skye Bender-deMoll
## Li Wang
## Kirk Li, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("tergm").
##
## Loading required package: ergm.count
## Warning: package 'ergm.count' was built under R version 3.2.2
##
## ergm.count: version 3.2.0, created on 2015-06-18
## Copyright (c) 2015, Pavel N. Krivitsky, University of Wollongong
## with contributions from
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("ergm.count").
##
## NOTE: The form of the term 'CMP' has been changed in version 3.2
## of 'ergm.count'. See the news or help('CMP') for more information.
##
##
## statnet: version 2015.11.0, created on 2015-11-04
## Copyright (c) 2015, Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Carter T. Butts, University of California -- Irvine
## Steven M. Goodreau, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Skye Bender-deMoll
## Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("statnet").
##
## unable to reach CRAN
plot(studentNetwork, main = "Student NEtwork")
- How many nodes and edges are there in studentNetwork?
summary(studentNetwork)
## Network attributes:
## vertices = 205
## directed = FALSE
## hyper = FALSE
## loops = FALSE
## multiple = FALSE
## bipartite = FALSE
## total edges = 203
## missing edges = 0
## non-missing edges = 203
## density = 0.009708274
##
## Vertex attributes:
##
## Course_of_Study:
## character valued attribute
## attribute summary:
## Business Fine_Arts Liberal_Arts Physical_Sciences
## 109 4 68 6
## Technology
## 18
##
## Sex:
## character valued attribute
## attribute summary:
## F M
## 99 106
##
## StudentID:
## integer valued attribute
## 205 values
##
## Tweets:
## character valued attribute
## attribute summary:
## the 10 most common values are:
## abnormal|arbitrary|better-than-expected|dirt-cheap|foolish|lawful|lonesome|pretty|supremely|trump|unconditional|unthinkable
## 1
## abominably|affably|benefit|enchant|enraptured|finagle|fugitive|gleeful|ingenious|nourish|premier|priceless|rapturously|vexingly|wasteful
## 1
## abominate|adventuresome|affluent|blatantly|conveniently|dummy-proof|hedonistic|idol|improvement|irking|laudable|refresh|rumbling|silent|sweetness|titillatingly
## 1
## abort|altruistically|barbarously|disgruntle|faith|imaginative|indebted|ingenious|unwatchable
## 1
## abound|amenable|anomalous|baffling|dominates|drab|enjoy|flawlessly|happily|humorous|illness|reaffirm|shiny|stupendously|taboo|thoughtfulness|treasure|well-being|well-educated
## 1
## abound|clouding|comfortable|expansive|glorious|impartial|principled|reforming|statuesque|troubled|woo
## 1
## absurdly|aspiration|brainwash|clear|ergonomical|eye-catch|immaculate|inevitable|nurturing|punk|rumbling
## 1
## abundant|entranced|hoodwink|outperforms|regress|solemn|thriving|upseting
## 1
## acclaimed|accomplishment|believable|boisterous|breach|flawlessly|fondness|frail|hooray|idolized|peerless|randomly|spew|temptingly
## 1
## accolade|calming|calumniation|cure|effusively|offending|saint|stupendous
## 1
##
## Year:
## numeric valued attribute
## attribute summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 3.000 2.732 4.000 6.000
##
## No edge attributes
##
## Network edgelist matrix:
## [,1] [,2]
## [1,] 1 25
## [2,] 1 52
## [3,] 1 58
## [4,] 1 70
## [5,] 1 87
## [6,] 1 92
## [7,] 1 96
## [8,] 1 100
## [9,] 1 110
## [10,] 1 127
## [11,] 1 151
## [12,] 1 161
## [13,] 1 174
## [14,] 2 52
## [15,] 2 100
## [16,] 2 134
## [17,] 2 190
## [18,] 5 204
## [19,] 8 30
## [20,] 8 104
## [21,] 8 160
## [22,] 9 19
## [23,] 9 54
## [24,] 9 115
## [25,] 9 205
## [26,] 11 44
## [27,] 11 74
## [28,] 13 61
## [29,] 13 153
## [30,] 14 182
## [31,] 15 22
## [32,] 15 55
## [33,] 15 76
## [34,] 15 189
## [35,] 16 40
## [36,] 16 140
## [37,] 17 122
## [38,] 18 63
## [39,] 18 129
## [40,] 18 158
## [41,] 18 195
## [42,] 21 59
## [43,] 21 102
## [44,] 21 140
## [45,] 22 55
## [46,] 22 64
## [47,] 22 76
## [48,] 22 123
## [49,] 22 189
## [50,] 23 36
## [51,] 25 43
## [52,] 25 60
## [53,] 25 77
## [54,] 25 87
## [55,] 25 92
## [56,] 25 147
## [57,] 27 68
## [58,] 27 93
## [59,] 29 51
## [60,] 29 55
## [61,] 30 54
## [62,] 30 104
## [63,] 30 160
## [64,] 31 114
## [65,] 31 185
## [66,] 32 178
## [67,] 33 140
## [68,] 34 187
## [69,] 34 200
## [70,] 36 97
## [71,] 36 167
## [72,] 38 155
## [73,] 43 60
## [74,] 43 77
## [75,] 44 74
## [76,] 44 136
## [77,] 47 74
## [78,] 47 79
## [79,] 47 102
## [80,] 47 105
## [81,] 47 139
## [82,] 47 153
## [83,] 47 189
## [84,] 47 191
## [85,] 47 201
## [86,] 51 55
## [87,] 51 61
## [88,] 52 127
## [89,] 52 190
## [90,] 53 83
## [91,] 53 136
## [92,] 54 115
## [93,] 55 61
## [94,] 55 66
## [95,] 55 86
## [96,] 55 123
## [97,] 55 157
## [98,] 56 71
## [99,] 56 129
## [100,] 57 133
## [101,] 58 149
## [102,] 59 65
## [103,] 59 104
## [104,] 63 198
## [105,] 64 66
## [106,] 64 123
## [107,] 64 139
## [108,] 64 157
## [109,] 65 104
## [110,] 66 82
## [111,] 66 157
## [112,] 70 158
## [113,] 70 195
## [114,] 74 136
## [115,] 74 176
## [116,] 75 187
## [117,] 75 204
## [118,] 78 137
## [119,] 79 99
## [120,] 79 108
## [121,] 79 164
## [122,] 79 173
## [123,] 81 131
## [124,] 83 136
## [125,] 87 88
## [126,] 87 92
## [127,] 87 96
## [128,] 87 110
## [129,] 87 127
## [130,] 87 156
## [131,] 87 179
## [132,] 87 183
## [133,] 88 183
## [134,] 89 111
## [135,] 89 131
## [136,] 90 117
## [137,] 91 187
## [138,] 92 110
## [139,] 96 100
## [140,] 96 110
## [141,] 96 137
## [142,] 96 150
## [143,] 96 179
## [144,] 98 192
## [145,] 99 164
## [146,] 99 173
## [147,] 100 137
## [148,] 100 150
## [149,] 101 108
## [150,] 102 189
## [151,] 102 201
## [152,] 103 128
## [153,] 103 141
## [154,] 104 160
## [155,] 105 139
## [156,] 108 173
## [157,] 109 121
## [158,] 109 142
## [159,] 110 134
## [160,] 112 185
## [161,] 114 138
## [162,] 114 185
## [163,] 115 144
## [164,] 123 139
## [165,] 123 157
## [166,] 123 178
## [167,] 123 189
## [168,] 124 142
## [169,] 124 160
## [170,] 124 161
## [171,] 125 204
## [172,] 127 150
## [173,] 127 151
## [174,] 129 158
## [175,] 132 150
## [176,] 132 185
## [177,] 134 196
## [178,] 136 202
## [179,] 137 179
## [180,] 138 180
## [181,] 138 185
## [182,] 139 189
## [183,] 139 193
## [184,] 140 160
## [185,] 140 194
## [186,] 142 160
## [187,] 146 192
## [188,] 148 194
## [189,] 149 165
## [190,] 149 186
## [191,] 153 170
## [192,] 158 195
## [193,] 160 166
## [194,] 160 194
## [195,] 161 190
## [196,] 164 173
## [197,] 165 178
## [198,] 165 187
## [199,] 165 199
## [200,] 179 196
## [201,] 181 182
## [202,] 183 190
## [203,] 189 191
Answer: We have total 205 nodes and 203 edges
- What are the attributes of this network object? What do they each contain?
Answer: There are four attributes and they contains as follows: 1. Course_of_study(Business, Fine_Arts, Liberal_Arts, Physical_Science, Technology) 2. Sex(F, M) 3. StudentID(205 integer value) 4. Tweets(character values)
5, What proportion of the students are Studying Business? What proportion are in their 2nd Year?
symbol = c(6,5,3,7,4,2)
student.sex = studentNetwork%v%"Sex"
summary(student.sex)
## F M
## 99 106
barplot(table(student.sex), main = "Sex of student", col=symbol)
student.course_study=studentNetwork%v%"Course_of_Study"
summary(student.course_study)
## Business Fine_Arts Liberal_Arts Physical_Sciences
## 109 4 68 6
## Technology
## 18
barplot(table(student.course_study), main="course of study of student", col=symbol)
student.year=studentNetwork%v%"Year"
#View(student.year)
summary(as.character(student.year))
## 1 2 3 4 5 6
## 62 40 42 25 24 12
barplot(table(student.year), main="Year of study of student", col=symbol)
Write a function that splits the pipe delimited string into a vector of single words, counts which ones are positive and which ones are negative, assigns a score of +1 for each positive word and -1 for each negative word, and sums them for a total score.
#Ceate legend for network graph
symbol.sex=c(4,12) [match(student.sex, c("M","F"))]
symbol.course_study=c(1,2,3,4,5)[match(student.course_study,c("Business", "Fine_Arts", "Liberal_Arts", "Physical_Sciences", "Technology"))]
plot(studentNetwork, vertex.sides = symbol.sex, vertex.rot = 45, vertex.cex = 2, vertex.col = symbol[student.year], edge.lwd = 2, cex.main = 1, displayisolates = TRUE, main = "Network Diagram - Student Year")
legend("bottomright", c("Year1", "Year2", "Year3", "Year4", "Year5", "Year6"), fill = symbol, cex=0.6)
Adjacency matrix: Netword of Nodes and their interconnection can be represented with adjacency matrix The Adjacency matrix of a finete graph G on n vertices is the n x n matrix where the non-diagonal entry a(ij) is the number #of edges from vertex i to vertex j, and the diagonal entry a(ij), depending on convention, is either once or twice the number of edges (loops) from vertex i to itself. Undirected graphs often use the latter convention of counting loops twrice, whereas directed graphs typically use the former convention. There exists a unique adjacency matrix for each isomorphism class of graphs and it is not the adjacency matrix of any other isomorphism class of graphs. In the special case of finite simple graph. The adjacency matrix is a (0,1)-matrix with zeroz on its diagonal. If the graph is undirected, the adjacency matrix is symmetric
Reference:
https://en.wikipedia.org/wiki/Adjacency_matrix
#Creating Adjacency Matrix
student.matrix=studentNetwork[,]
Degree: A Node’s degree in an undirected network is defined as its number of edges to other nodes
student.degree<-degree(student.matrix)
student.degree
## [1] 26 8 0 0 2 0 0 6 8 0 4 0 4 2 8 4 2 8 2 0 6 12 2
## [24] 0 14 0 4 0 4 8 4 2 2 4 0 6 0 2 0 2 0 0 6 6 0 0
## [47] 18 0 0 0 6 8 4 6 18 4 2 4 6 4 6 0 4 10 4 8 0 2 0
## [70] 6 2 0 0 10 4 4 4 2 10 0 2 2 4 0 0 2 20 4 4 2 2 8
## [93] 2 0 0 14 2 2 6 10 2 8 4 10 4 0 0 6 4 10 2 2 0 6 6
## [116] 0 2 0 0 0 2 2 14 6 2 0 10 2 6 0 4 4 2 6 0 10 8 6
## [139] 12 10 2 6 0 2 0 2 2 2 6 8 4 0 6 0 2 2 8 8 0 16 6
## [162] 0 0 6 8 2 2 0 0 2 0 0 8 2 0 2 0 6 8 2 2 4 6 0
## [185] 10 2 8 0 14 8 4 4 2 6 6 4 0 2 2 2 4 2 0 6 2
- Plot a histogram of the scores. What does it indicate?
hist(student.degree, col=symbol, main="Distribution of Nodes' Degree", ylab="Number of Students", xlab="Numbder of Connections")
Betweenness: A deeper measure of network structure is obtained through betweenness. Betweenness is a centrality measure of a node/vertex within a graph Nodes that occur on many shortest paths between other nodes have heigher betweenness than those that do not
student.betweenness <- betweenness(student.matrix)
plot(student.betweenness, col="green", main="Betweenness Centrality", ylab="Betweenness")
n.words <- read.table("C:/Users/Bhupendra Mishra/Desktop/donotbackup//negative-words.txt", header=TRUE, quote="\"")
#View(n.words)
p.words <-read.table("C:/Users/Bhupendra Mishra/Desktop/donotbackup//positive-words.txt", header=TRUE, quote="\"")
#View(p.words)
library(parallel)
library(foreach)
## Warning: package 'foreach' was built under R version 3.2.2
student.tweets=studentNetwork%v%"Tweets"
#student.tweets
tweet.score <- foreach(i=1:205, .combine='rbind') %dopar%
{
words<-unlist(strsplit(student.tweets[i], split ='\\|'))
# View(words)
pos.matches = match(words, unlist(p.words))
# View(pos.matches)
neg.matches = match(words, unlist(n.words))
#View(neg.matches)
pos.matches = !is.na(pos.matches)
neg.matches = !is.na(neg.matches)
score = sum(pos.matches) - sum(neg.matches)
}
## Warning: executing %dopar% sequentially: no parallel backend registered
hist(tweet.score, main="Sentiment analysis of the Students",xlab="Tweet Score",col=symbol)
#View(tweet.score)
- Do the distribution between 2nd Year and 4th Year students look different? How about between those studying Business and those studying Technology?
#Histogram of Tweet Scores for year 1 and year 2 respectively
par(mfrow = c(1,2))
hist(tweet.score[student.year==2], main="Sentiment - Year 2", xlab="Tweet Score", col=symbol)
hist(tweet.score[student.year==4], main="Sentiment - Year 4", xlab="Tweet Score", col=symbol)
#Histogram of Tweets Scorces for Business and Technolgy Students respectively
hist(tweet.score[student.course_study=="Business"], main="Sentiment - Business", xlab="Tweet Score", col=symbol)
hist(tweet.score[student.course_study=="Technology"], main= "Sentiment - Technology", xlab="Tweet Score", col=symbol)
