## ---- echo=FALSE, results="hide"---------------------------------------------- library(rnetcarto) require("igraph") ## ---- echo=TRUE--------------------------------------------------------------- # Generate a simple random network a = matrix(as.integer(runif(100)<.3), ncol=10) a[lower.tri(a)] = 0 rownames(a) = c('a','b','b','c','d','e','f','g','h','i') colnames(a) = rownames(a) # Find an optimal partition for modularity using netcarto. # The output consists in a table containing node properties, # and the modularity value of the partition. netcarto(a) ## ---- echo=TRUE--------------------------------------------------------------- input = matrix(0,3,3) input[1,2] = 1 input[2,3] = 1 input[3,1] = 1 input[2,1] = 1 input[3,2] = 1 input[1,3] = 1 rownames(input) = c("A","B","C") colnames(input) = rownames(input) print(input) ## ---- echo=TRUE--------------------------------------------------------------- # import from rnetcarto matrix format to igraph: G = igraph::graph.adjacency(input,weighted=TRUE,mode="undirected") # Export to a matrix compatible with netcarto: input = igraph::get.adjacency(G,sparse=FALSE) ## ---- echo=FALSE-------------------------------------------------------------- plot(G, layout = igraph::layout.circle, , vertex.size = 60, vertex.color="red", vertex.frame.color= "white", vertex.label.color = "white", vertex.label.family = "sans", edge.width=1, edge.color="black") ## ---- echo=TRUE--------------------------------------------------------------- input = matrix(0,7,7) input[1,2] = 10 input[2,3] = 10 input[3,1] = 10 input[4,5] = 10 input[5,6] = 10 input[6,4] = 10 rownames(input) = c("A","B","C","D","E","F","G") colnames(input) = rownames(input) ## ---- echo=FALSE-------------------------------------------------------------- input = matrix(0,6,6) input[1,2] = 10 input[2,3] = 10 input[3,1] = 10 input[4,5] = 10 input[5,6] = 10 input[6,4] = 10 input = input+t(input)-diag(input) rownames(input) = c("A","B","C","D","E","F") colnames(input) = rownames(input) print(input) ## ---- echo=FALSE-------------------------------------------------------------- G = igraph::graph.adjacency(input,weighted=TRUE,mode="undirected") plot(G, layout = layout.circle, , vertex.size = 60, vertex.color="red", vertex.frame.color= "white", vertex.label.color = "white", vertex.label.family = "sans", edge.width=1, edge.color="black") ## ---- echo=TRUE--------------------------------------------------------------- input = matrix(0,6,2) input[1,1] = 1 input[2,1] = 1 input[3,1] = 1 input[4,2] = 1 input[5,2] = 1 input[6,2] = 1 rownames(input) = c("A","B","C","D","E","F") colnames(input) = c("Team 1", "Team 2") print(input) ## ---- echo=TRUE--------------------------------------------------------------- nd1 = c("A","B","C","D","E","F","C") nd2 = c("B","C","A","E","F","D","D") web = list(nd1,nd2,weights) print(list(nd1,nd2)) ## ---- echo=TRUE--------------------------------------------------------------- nd1 = c("A","B","C","D","E","F","C","A") nd2 = c("B","C","A","E","F","D","D","D") weights = c(10,10,10,10,10,10,10,10,1) web = list(nd1,nd2,weights) print(web) ## ---- echo=TRUE--------------------------------------------------------------- nd1 = c("A","B","C","D","E","F","C","A") nd2 = c("Team1","Team2","Team1","Team1","Team2","Team1","Team1","Team2") bipartite = list(nd1,nd2) print(bipartite) ## ---- echo=TRUE--------------------------------------------------------------- netcarto(igraph::get.adjacency(G,sparse=FALSE)) ## ---- echo=TRUE--------------------------------------------------------------- netcarto(bipartite, bipartite=TRUE)