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#Let's charge the Igraph package library("igraph") #Simulating Data : Mark of 9 students. 3 are known to be strong in Letters, 3 in Art and 3 in Math. a=c(6,8,11,14,15,14,19,18,20) b=c(20,20,19,18,18,18,2,4,8) c=c(3,4,3,15,18,19,2,1,8) data = matrix(c( a,a+1,a-sample(seq(1,3),9,replace=T) ,a-sample(seq(1,3),9,replace=T), b,b-sample(seq(1,3),9,replace=T),b+sample(seq(1,3),9,replace=T),c, c-sample(seq(1,3),9,replace=T),(b+c)/2) , 9 , 9) colnames(data)=c("charline", "JP", "Kath", "Gary", "Conor", "Marion", "JB", "gui", "Vincent") rownames(data)=c("french","phylo","latin","musique","art","sport","math","stat","R") #Let's have a look to the rough data data #I calculte correlation between student's Marks. I Want to see who has the same kind of Mark structure dist=cor(data) dist[dist<0.5]=0 diag(dist)=0 #Let's make the I-Graph. All the students with a correlation > 0.5 will be linked one to each other on the graph. Students with strong correlation will be closer. People with strong skills in letters, art and math will be Highlighted. # First I ask to Igraph to calculate the best position of each individuals, doing iterations g1<-graph.adjacency(dist,weighted="TRUE",mode="undirected") lay<-layout.fruchterman.reingold(grap=g1,niter=6000) # Then I make the plot. I ask to show the know structure of the population par(mar=c(0.5,0.5,0.5,0.5)) plot.igraph(g1, mark.shape=1 , mark.groups=list(c(1:3) , c(4:6) , c(7:9) ) , layout=lay,vertex.label=rownames(dist) , vertex.color="green" , vertex.size=0.5 , edge.arrow.size=13 , main="") #Finally I add a legend legend("bottomleft", legend = c("Letter lovers" , "art lovers" , "math lovers"), col = rainbow(3, alpha = 0.3) , pch = 15, bty = "n", pt.cex = 1.5, cex = 0.8 , text.col = "black", horiz = FALSE, inset = c(0.1, 0.1)) #There we can see that the known structure is relevant. Nevertheless, it looks like the student JB, who is supposed to be strong in Math, is really close to people that are strong in Art. Actually he is strong in both ! |

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