-
Notifications
You must be signed in to change notification settings - Fork 1
/
graph.R
135 lines (109 loc) · 7.6 KB
/
graph.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
126
127
128
129
130
131
132
133
134
135
## install.packages("networkD3")
library("networkD3")
library("igraph")
library("plyr")
# Calculate degree for all nodes. Degree of a node tells us the number of edges incident to the node
# Calculate betweenness for all nodes. Betweenness centrality tells us how central
# the node is, in terms of the number of shortest paths from all nodes to all others
# that pass through that node
# Calculate Dice similarities between all pairs of nodes
# Dice similarity represents the similarity between two nodes’ first neighborhoods
# D(node1, node2) = 2 * number of mutual neighbours of node1 and node2 /
# (number of neighbours of node1 + number of neighbours of node2)
#####################---------------------------------------------###################
# Data format: dataframe with 3 variables; variables 1 & 2 correspond to interactions; variable 3 is weight of interaction
edgeList<- read.csv(file.path("data", "co_occurence_complaints.csv"),
stringsAsFactors = F)
colnames(edgeList) <- c("SourceName", "TargetName", "Weight")
## Filter the edges
mean(edgeList$Weight)
sd(edgeList$Weight)
cut.off <- mean(edgeList$Weight)
flag <- edgeList["Weight"]>cut.off
edgeList <- edgeList[flag,]
# Create a graph. Simplify ensures that there are no duplicated edges or self loops
gD <- igraph::simplify(igraph::graph.data.frame(edgeList, directed=FALSE))
vcount(gD) #no of nodes
ecount(gD) # no of edges
# Create a node list object (actually a data frame object) that will contain information about nodes
nodeList <- data.frame(ID = c(0:(igraph::vcount(gD) - 1)), # because networkD3 library requires IDs to start at 0
nName = igraph::V(gD)$name) # V() - vertices of the graph
## Join in officer information to nodeList
officerFile <- file.path("data", "toy.officer_data_cleaned.csv")
officers <- read.csv(officerFile)
officers$ID <- officers$officer_id - 1
officers$secondary <- ifelse(officers$secondary == 1, officers$secondary + 1,
officers$secondary)
officers$tertiary <- ifelse(officers$tertiary == 1, officers$tertiary + 2,
officers$tertiary)
officers$class <- rowSums(officers[,c("primary", "secondary", "tertiary")])
nodeList <- join(nodeList, officers, by = "ID")
# Map node names from the edge list to node IDs
getNodeID <- function(x){
which(x == igraph::V(gD)$name) - 1 # to ensure that IDs start at 0
}
# And add them to the edge list
edgeList <- plyr::ddply(edgeList, .variables = c("SourceName", "TargetName", "Weight"),
function (x) data.frame(SourceID = getNodeID(x$SourceName),
TargetID = getNodeID(x$TargetName)))
############################################################################################
# Calculate some node properties and node similarities that will be used to illustrate
# different plotting abilities and add them to the edge and node lists
# Calculate degree for all nodes
nodeList <- cbind(nodeList, nodeDegree=igraph::degree(gD, v = igraph::V(gD), mode = "all"))
# Calculate betweenness for all nodes
betAll <- igraph::betweenness(gD, v = igraph::V(gD), directed = FALSE) / (((igraph::vcount(gD) - 1) * (igraph::vcount(gD)-2)) / 2)
betAll.norm <- (betAll - min(betAll))/(max(betAll) - min(betAll))
nodeList <- cbind(nodeList, nodeBetweenness=100*betAll.norm) # We are scaling the value by multiplying it by 100 for visualization purposes only (to create larger nodes)
rm(betAll, betAll.norm)
#Calculate Dice similarities between all pairs of nodes
dsAll <- igraph::similarity.dice(gD, vids = igraph::V(gD), mode = "all")
F1 <- function(x) {data.frame(diceSim = dsAll[x$SourceID +1, x$TargetID + 1])}
edgeList <- plyr::ddply(edgeList, .variables=c("SourceName", "TargetName", "Weight", "SourceID", "TargetID"),
function(x) data.frame(F1(x)))
rm(dsAll, F1, getNodeID, gD)
############################################################################################
# We will also create a set of colors for each edge, based on their dice similarity values
# We'll interpolate edge colors based on the using the "colorRampPalette" function, that
# returns a function corresponding to a collor palete of "bias" number of elements (in our case, that
# will be a total number of edges, i.e., number of rows in the edgeList data frame)
F2 <- colorRampPalette(c("#FFFF00", "#FF0000"), bias = nrow(edgeList), space = "rgb", interpolate = "linear")
colCodes <- F2(length(unique(edgeList$diceSim)))
edges_col <- sapply(edgeList$diceSim, function(x) colCodes[which(sort(unique(edgeList$diceSim)) == x)])
rm(colCodes, F2)
# update edgeList after this
############################################################################################
## ## Try to add age group category for coloring nodes
## nodeList <- nodeList %>%
## mutate(ageGroup = c("thirties", "forties", "fifties", "sixtyPlus")[(cut(age, breaks = c(0, 40, 50, 60, 100),
## include.lowest = TRUE))])
## ######################
## ## Load in officers ##
## ######################
## load(file.path("data", "ID.Rdata")
D3_network_LM <- networkD3::forceNetwork(Links = edgeList, # data frame that contains info about edges
Nodes = nodeList, # data frame that contains info about nodes
Source = "SourceID", # ID of source node
Target = "TargetID", # ID of target node
Value = "Weight", # value from the edge list (data frame) that will be used to value/weight relationship amongst nodes
NodeID = "nName", # value from the node list (data frame) that contains node description we want to use (e.g., node name)
Nodesize = "nodeBetweenness", # value from the node list (data frame) that contains value we want to use for a node size
Group = "nodeDegree", # value from the node list (data frame) that contains value we want to use for node color
colourScale = JS("d3.scaleOrdinal(d3.schemeCategory10);"), # color scale to use
## colourScale = JS("var o = d3.scale.ordinal()
## .domain([\"WHITE\", \"HISPANIC\", \"BLACK\", \"A_OTHER\"])
## .range(colorbrewer.RdBu[9]);"),
height = 1000, # Size of the plot (vertical)
width = 1000, # Size of the plot (horizontal)
fontSize = 20, # Font size
linkDistance = networkD3::JS("function(d) { return 10*d.value; }"), # Function to determine distance between any two nodes, uses variables already defined in forceNetwork function (not variables from a data frame)
linkWidth = networkD3::JS("function(d) { return d.value/5; }"),# Function to determine link/edge thickness, uses variables already defined in forceNetwork function (not variables from a data frame)
opacity = 0.85, # opacity
zoom = TRUE, # ability to zoom when click on the node
opacityNoHover = 0.1, # opacity of labels when static
linkColour = edges_col) # edge colors
# Plot network
D3_network_LM
# Save network as html file
networkD3::saveNetwork(D3_network_LM, file.path("graphs", "D3_LM.html"),
selfcontained = TRUE)