Nothing
## ---- 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)
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