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4. Undervisning.r
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4. Undervisning.r
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#### 4. Undervisning - Degrees og density!
setwd("~/My Dropbox/R/interlocks/")
library(ggplot2)
library(igraph)
memory.size(4000)
# Download data om indsomhederne
# Windows
download.file("https://raw.github.com/antongrau/soc.sna/master/Organisation_BIQ_top.csv", destfile="Organisation_BIQ_top.csv")
download.file("https://raw.github.com/antongrau/soc.sna/master/Relation_BIQ_top.csv", destfile="Relation_BIQ_top.csv")
download.file("https://raw.github.com/antongrau/soc.sna/master/delbeskrivelse.r", destfile="delbeskrivelse.r")
download.file("https://raw.github.com/antongrau/soc.sna/master/gplot2.R", destfile="gplot2.r")
# Mac/Linux
# Hvis du har en gammel mac så må du hente det ned manuelt og ligge filerne direkte i dit working directory
download.file("https://raw.github.com/antongrau/soc.sna/master/Organisation_BIQ_top.csv", destfile="Organisation_BIQ_top.csv", method="curl")
download.file("http://raw.github.com/antongrau/soc.sna/master/Relation_BIQ_top.csv", destfile="Relation_BIQ_top.csv", method="curl")
download.file("https://raw.github.com/antongrau/soc.sna/master/delbeskrivelse.r", destfile="delbeskrivelse.r", method="curl")
download.file("https://raw.github.com/antongrau/soc.sna/master/gplot2.R", destfile="gplot2.r", method="curl")
# Indlæs data
rel <- read.csv("Relation_BIQ_top.csv", sep="|", encoding="UTF-8", strip.white=TRUE)
virk <- read.csv(file="Organisation_BIQ_top.csv", sep="|", fileEncoding="UTF-8", dec = ",")
edges <- data.frame(as.character(rel$NAVN), as.character(rel$ORG_NAVN))
edge.table <- table(edges)
tabnet <- as.matrix(edge.table)
adj.ind <- tabnet %*% t(tabnet)
diag(adj.ind) <- 0
table(diag(adj.ind))
net.ind <- graph.adjacency(adj.ind, mode="undirected", weighted=TRUE)
##################################################
############### Plot #############################
source("gplot2.r")
layout <- layout.fruchterman.reingold(net.ind)
gplot(net.ind, vertex.coord=layout, text.alpha=0)
##################################################
############### Degrees ##########################
degree.ind <- sort(degree(net.ind), decreasing=TRUE)
plot(degree.ind)
table(degree.ind)
as.matrix(head(degree.ind, 20))
deg <- degree(net.ind)
n <- gplot(net.ind, vertex.coord=layout, vertex.fill=deg ,text.alpha=0, vertex.size=4, edge.alpha=0.1)
n + scale_fill_gradient2(high = "red")
totaldegree <- sum(degree.ind)
totaldegree
average.degree <- totaldegree/nrow(adj.ind)
average.degree
######################################
######## Neighborhoods ###############
n1 <- neighborhood.size(net.ind, 1)-1
n1
n2 <- neighborhood.size(net.ind, 2)-1
plot(sort(n2))
grannis.g.factor <- sum(n2-n1)/sum(n1)
grannis.g.factor
names(n2) <- rownames(adj.ind)
as.matrix(head(sort(n2, decreasing=TRUE), 20))
as.matrix(head(sort(n2-n1, decreasing=TRUE), 20))
n3 <- neighborhood.size(net.ind, 3)-1
names(n3) <- rownames(adj.ind)
as.matrix(head(sort(n3, decreasing=TRUE), 20))
plot(sort(n2, decreasing=TRUE))
plot(sort(n3, decreasing=TRUE))
n6 <- neighborhood.size(net.ind, 6)-1
plot(sort(n6, decreasing=TRUE))
#######################################################
################# Density #############################
# Average path
plot(path.length.hist(net.ind)$res) # Wauv den er normalfordelt!
average.path.length(net.ind)
# Density
L <- ecount(net.ind) # Antallet af edges
L
n <- vcount(net.ind) # Antallet af vertices
n
L/(n*(n-1)/2)
graph.density(net.ind) # Det samme uden matematik!
# Den største component
com <- clusters(net.ind)
largest.cluster <- which.max(com$csize)
largest.component <- com$membership == largest.cluster
table(com$csize)
adj.ind <- adj.ind[largest.component, largest.component]
net.ind <- graph.adjacency(adj.ind, mode="undirected", weighted=TRUE)
save(adj.ind, file="adj.ind")
save(net.ind, file="net.ind")
#################################################################################
################################# Centralitet ###################################
# Den relative centralitet
cent.deg <- centralization.degree(net.ind)
str(cent.deg)
cent.deg$centralization # Antallet af degrees i det bedst forbundne punkt sat i forhold til alle forbindelser
cent.deg$theoretical_max # Det teoretiske maximale antal degrees for hele netværket
## Closeness
close <- closeness(net.ind)
str(close)
as.matrix(head(sort(close, decreasing=TRUE), 40))
as.matrix(tail(sort(close, decreasing=TRUE), 40))
plot(sort(close, decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=1), decreasing=TRUE))
as.matrix(head(sort(closeness.estimate(net.ind, cutoff=1), decreasing=TRUE), 20))
plot(sort(closeness.estimate(net.ind, cutoff=2), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=3), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=4), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=5), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=6), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=7), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=8), decreasing=TRUE))
as.matrix(head(sort(closeness.estimate(net.ind, cutoff=8), decreasing=TRUE), 20))
plot(sort(closeness.estimate(net.ind, cutoff=9), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=10), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=11), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=12), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=13), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=14), decreasing=TRUE))
plot(sort(closeness.estimate(net.ind, cutoff=15), decreasing=TRUE))
## betweenness
between <- betweenness(net.ind)
str(between)
as.matrix(head(sort(between, decreasing=TRUE), 40))
as.matrix(tail(sort(between, decreasing=TRUE), 40))
plot(sort(between, decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=1), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=2), decreasing=TRUE))
as.matrix(head(sort(betweenness.estimate(net.ind, cutoff=2), decreasing=TRUE), 20))
plot(sort(betweenness.estimate(net.ind, cutoff=3), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=4), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=5), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=6), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=7), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=8), decreasing=TRUE))
as.matrix(head(sort(betweenness.estimate(net.ind, cutoff=8), decreasing=TRUE), 20))
plot(sort(betweenness.estimate(net.ind, cutoff=9), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=10), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=11), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=12), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=13), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=14), decreasing=TRUE))
plot(sort(betweenness.estimate(net.ind, cutoff=15), decreasing=TRUE))
### Eigenvector centrality
eig <- evcent(net.ind)
str(eig)
as.matrix(head(sort(eig$vector, decreasing=TRUE), 30))
plot(sort(eig$vector, decreasing=TRUE))
### Closeness og Betweenness
set <- data.frame(close, between)
ggplot(set, aes(x=close, y=between)) + geom_point(aes(color=eig$vector)) + geom_smooth()
###################################################################
########################## Regeringen #############################
# Download datasættet
# Windows
download.file("https://raw.github.com/antongrau/soc.sna/master/edgelist.csv", destfile="edgelist.csv")
# Mac/Linux
download.file("https://raw.github.com/antongrau/soc.sna/master/edgelist.csv", destfile="edgelist.csv", method="curl")
data <- read.csv(file="edgelist.csv", sep=",", fileEncoding="latin1")
# Her fjerner vi tomme rækker i data
navn <- data$NAVN
org <- data$ORG
a <- navn==""
navn <- navn[a==FALSE, drop=TRUE]
org <- org[a==FALSE, drop=TRUE]
ind.org <- data.frame(navn, org)
colnames(ind.org) <- c("navn", "org")
affiliation <- as.matrix(affiliation)
# Så laves adjacency matricen for individer
adjacency.individ <- affiliation%*%t(affiliation)
# Her slettes vi diagonalen i begge matricer
diag(adjacency.individ) <- 0
# Så laver vi et netværksobjekt for individer
graph.individ <- graph.adjacency(adjacency.individ, mode="undirected", weighted=TRUE)
# Degree centralitet
cent.deg <- centralization.degree(graph.individ)
str(cent.deg)
cent.deg$centralization # Antallet af degrees i det bedst forbundne punkt sat i forhold til alle forbindelser
cent.deg$theoretical_max # Det teoretiske maximale antal degrees for hele netværket
sort(degree(graph.individ), decreasing=TRUE)
## Closeness
close <- closeness(graph.individ)
as.matrix(sort(close, decreasing=TRUE))
plot(sort(close, decreasing=TRUE))
## Betweenness
between <- betweenness(graph.individ, weights=E(graph.individ)$weight)
as.matrix(sort(between, decreasing=TRUE))
plot(sort(close, decreasing=TRUE))
layout <- layout.fruchterman.reingold(graph.individ)
gplot(graph.individ, layout, vertex.size=close, vertex.fill=close) + scale_fill_gradient2(high = "red")
gplot(graph.individ, layout, vertex.size=between, vertex.fill=between) + scale_fill_gradient2(high = "red")