# Note this is a bad example for our purposes as the graph is nearly complete # Thus you should replace this example with a different network. ################################################ ## Download data from web footballdata <- read.csv("http://www.repole.com/sun4cast/stats/cfb20140906.csv") # To simplify things, I took 2 columns from this dataset data <- cbind(footballdata\$ScoreOff, footballdata\$RushAttOff) plot(data, asp=1) # Create adjacency matrix by calculating distance between points # Note I probably should have normalized the data first Adj <- as.matrix(dist(data)) g <- graph_from_adjacency_matrix(Adj, mode="undirected", weighted = TRUE) tkplot(g) gsize(g) # number of edges gorder(g) # number of vertices deg_2 <- neighbors(g, '2') # Neighbors of vertex 2 length(deg_2) # degree of vertex 2 deg_3 <- neighbors(g, '3') intersection(deg_2, deg_3) # vertices that are neighbors of vertex 2 and 3 farthest_vertices(g) # find the length of the longest path in the network Degrees <- degree(g) which.max(Degrees) table(Degrees) hist(Degrees, breaks=10) # Betweeness measures how frequently the vertex lies # on shortest paths between any two vertices in the network. bet<- betweenness(g) hist(bet, breaks = 20) plot(bet)