R: igraph quick introduction

A quick introduction to igraph functions. You may have to install igraph package if you have not done so earliar

install.packages("igraph")

Making a graph using a vector

library(igraph)
# define the relationships (described by pairs in the vector - delibrately shown with gaps) while defining the nodes.
g <- graph(c(1,2,   2,3,   3,4,   5,6), directed=FALSE)
# plot the graph
plot(g)

# display the structure
str(g)

## IGRAPH U--- 6 4 -- 
## + edges:
## [1] 1--2 2--3 3--4 5--6

You will observe that the relationships are created between node 1,2; 2,3; 3,4 and so on. You may use plot to create empty graphs, or graphs with the given edges, adjacency matrices, etc. Specifically, this is an undirected graph.

Controlling Layouts

# 50 vertices and 3 children per vertex
g <- graph.tree(50, 3)
# print the structure
str(g)

## IGRAPH D--- 50 49 -- Tree
## + attr: name (g/c), children (g/n), mode (g/c)
## + edges:
##  [1]  1-> 2  1-> 3  1-> 4  2-> 5  2-> 6  2-> 7  3-> 8  3-> 9  3->10  4->11
## [11]  4->12  4->13  5->14  5->15  5->16  6->17  6->18  6->19  7->20  7->21
## [21]  7->22  8->23  8->24  8->25  9->26  9->27  9->28 10->29 10->30 10->31
## [31] 11->32 11->33 11->34 12->35 12->36 12->37 13->38 13->39 13->40 14->41
## [41] 14->42 14->43 15->44 15->45 15->46 16->47 16->48 16->49 17->50

# use a circular layout or other layout algorithms to position the vertices
# example http://en.wikipedia.org/wiki/Force-directed_graph_drawing
plot(g, layout=layout.circle)

plot(g, layout=layout.fruchterman.reingold)

Interactive Tools

You may have to install rgl package if you have not done so earliar

install.packages("rgl")

# launch interactive viewer
tkplot(g, layout=layout.kamada.kawai)

## Loading required package: tcltk

## [1] 1

# or an OpenGL graph plotter
library(rgl)
coords <- layout.fruchterman.reingold(g, dim=3)
rglplot(g, layout=coords)

few more methods to define and plot graphs

g <- graph.formula(Jon-Jane-Nate-Jon, Jack-Nate-Jill, Nate-Holly)
plot(g)

Observe that the relationships are created between node Jon and Jane; Jane and Nate; Nate and Jon, and so on.

Reading a adjacency matrix from a CSV file

Download CSV file here

# Change the working directory
setwd("/path/to/folder/where/csv/file/is/saved/");
# read the csv file with adjacency matrix into memory
# ** NOTICE** row.names=1, header=TRUE
# Steps involved 
# Read the CSV -> Convert to Matrix -> Force it to be numeric -> Create the graph object -> plot
myData<- read.csv("1.01.RiGraph.csv", row.names=1, header=TRUE)
# The data
myData

##    A1 A2 A3 A4 A5
## A1  0  1  1  0  1
## A2  1  0  1  0  0
## A3  1  1  0  1  1
## A4  0  0  1  0  0
## A5  1  0  1  0  0

myMatrix=as.matrix(myData)
mode(myMatrix) <- "numeric"
g=graph.adjacency(adjmatrix=myMatrix,mode="undirected",weighted=TRUE,diag=FALSE)
plot(g)

Interesting metrics

# calculate the degree
degree(g)

## A1 A2 A3 A4 A5 
##  3  2  4  1  2

# lets plot the histogram of degrees
hist(degree(g), breaks=c(0:4))

# calculate degree distribution of individual vertices
degree.distribution(g)

## [1] 0.0 0.2 0.4 0.2 0.2

plot(degree.distribution(g))
lines(degree.distribution(g))

# Calculate graph/edge density
graph.density(g)

## [1] 0.6

#  number of vertices
length(V(g))

## [1] 5

# number of edges
length(E(g))

## [1] 6

# diameter
diameter(g)

## [1] 2