u'##library \xe0 charger\r\nlibrary(bitops)\r\nlibrary(gdata)\r\nlibrary(caTools)\r\nlibrary(tools)\r\nlibrary(gplots)\r\nlibrary(timeSeries)\r\nlibrary(timeDate)\r\nlibrary(fBasics)\r\nlibrary(igraph)\r\nlibrary(tcltk )\r\nlibrary(ade4)\r\nlibrary(Cairo)\r\n\r\n\r\n\r\n#netoyage de la base reconstitu\xe9e\r\n##Sur ces mots, selection en CSV\r\nsetwd(\'C:\\\\Users\\\\alaure\\\\Desktop\\\\NICO\\\\Version octobre\\\\Datas\')\r\nC=read.csv("REST.csv",sep=\',\',header=TRUE)\r\n\r\nC=subset(C,X.1==1,select=c(-X.1))\r\nmots=C$X\r\nC=subset(C,select=c(-X))\r\nvecteurArtistes=colnames(C)\r\n\r\n## de nouveau, m\xe9nage pour les lignes ou moins de X personnes\r\ndi=dim(C)\r\ngarder=rep(1,di[1])\r\nfor (i in 1:di[1])           {\r\nmarg=as.data.frame(table(C[i,]))\r\nmargtot=0\r\n  for (j in 2:di[2])  {if (marg[j]==0) {margtot=margtot+1} else {margtot=margtot}  }\r\n  if (margtot>(di[2]-9)) {garder[i]<-0}     }\r\nE=subset(C, garder==1)\r\nmots2=subset(mots, garder==1)\r\n                   di=dim(E)\r\nNBArtist=rep(1,di[1])\r\nfor (i in 1:di[1])           {\r\n  marg=as.data.frame(table(E[i,]))\r\n  NBArtist[i]=0\r\n  for (j in 2:di[2])  {if (marg[j]==0) {NBArtist[i]=NBArtist[i]} else {NBArtist[i]=NBArtist[i]+1}  }  }\r\n  \r\n  \r\n\r\n## piechart                                         \r\nu=apply ( E,1,sum)\r\nu=as.matrix(u)\r\npie.u <- u/(apply(u,2,sum))\r\nnames(pie.u) <- mots2\r\npar(mai=c(.3,0.3,.3,.3))\r\npie(pie.u,main="Piechart", col=c("#00FF00","#FF0080","#4B0082","#FFD700","#4682B4","#FFEEEE","#BC81FF","#F0E68C","#DC143C","#FFFF00","#FF0000","#00BFFF","#DA70D6","#40E0D0","#FF1493","#FFFFFF","#6A5ACD"))\r\n\r\n\r\n ##### barplot\r\nu=apply ( E,1,sum)\r\nu=as.matrix(u)\r\nu=cbind(NBArtist,u)\r\nrownames(u)<-mots2\r\ncolnames(u)<-c("Number of artist using each word","Number of occurence of each word")\r\npar(mai=c(.5,2,.4,.5))\r\nbarplot(height=u[,2],ylab="",horiz=TRUE,beside=TRUE,names=mots2,las=1,main="Nombre de mots par artistes")\r\nbarplot(height=u[,1],ylab="",horiz=TRUE,beside=TRUE,names=mots2,las=1,main="Nombre d\'artistes qui utilisent un mot")\r\n\r\n\r\nbarplot2(height=t(u),ylab="",horiz=TRUE,beside=TRUE,names=mots2,las=1,legend=colnames(u))\r\n\r\n\r\n##Assocplot : words / artists\r\n#rownames(E)<-mots2\r\n#assoc(E[1:5,1:5])\r\n\r\n## plot with two dimensions : link between two words and the artists\r\ndim(E )\r\napply(E,2,sum)\r\n\r\nMosa=subset(E,mots2=="work"|mots2=="Woman-women"|mots2=="video"|mots2=="politics"|mots2=="feminist",\r\n      select=c(Anne.Mie.Van.Kerckhoven , Jo.Spence, Ewa.Partum , Girls.Who.Like.Porno ,   Maria.Ruido )   )\r\n    mosaicplot(Mosa,main="",shade = TRUE)\r\n\r\n #Plot with dimension : the way the artist are using two words \r\npar(mai=c(1,1,.4,.5))\r\n plot(t(subset(E,mots2=="work")),t(subset(E,mots2=="Woman-women")),xlab="Work",ylab="Woman-Women",ylim=c(0,75),xlim=c(0,150) )\r\n   text(t(subset(E,mots2=="Woman-women"))~t(subset(E,mots2=="work")), labels=colnames(E),pos = 4, pch=3,cex=0.8)\r\n cor(t(subset(E,mots2=="work")),t(subset(E,mots2=="Woman-women")))\r\n\r\n plot(t(subset(E,mots2=="work")),t(subset(E,mots2=="politics")),xlab="Work",ylab="politics",ylim=c(0,75),xlim=c(0,150) )\r\ncor(t(subset(E,mots2=="work")),t(subset(E,mots2=="politics")))\r\n cor(t(subset(E,mots2=="Woman-women")),t(subset(E,mots2=="power")))\r\n plot(t(subset(E,mots2=="Woman-women")),t(subset(E,mots2=="power")),xlab="Work",ylab="politics",ylim=c(0,75),xlim=c(0,150) )\r\n\r\n###### Working with the proportion represented by each word in place of the number of time###\r\n\r\n  a<-dim(E)\r\nEprop<-matrix(0, nrow = a[1], ncol = a[2])\r\nEprop2=as.data.frame(Eprop)\r\ntotper=rep(0,a[1])\r\nfor (i in 1:a[1])  { (totper[i]<-sum(E[i,1:a[2]]))}\r\nfor (i in 1:a[1]) (Eprop2[i,]<-E[i,]/totper[i])\r\ncolnames(Eprop2)<-colnames(E)   \r\n## Graphique 3 D\r\nsymbols(x=t(subset(Eprop2,mots2=="work")),y=t(subset(Eprop2,mots2=="Woman-women")),circles=t(subset(Eprop2, mots2=="feminist")), \r\ninches=.3,xlab="Work",ylab="Woman-Women",bg = 1:10,fg="gray30", main = "Circles Plot - Work, Woman-Women, and size = feminist") \r\ntext(t(subset(Eprop2,mots2=="Woman-women"))~t(subset(Eprop2,mots2=="work")), labels=colnames(Eprop2),pos = 4, pch=3,cex=0.8)\r\n\r\n\r\nsymbols(x=t(subset(Eprop2,mots2=="politics")),y=t(subset(Eprop2,mots2=="gender")),circles=t(subset(Eprop2, mots2=="feminist")), \r\ninches=.3,xlab="Work",ylab="Woman-Women",bg = 1:10,fg="gray30", main = "Circles Plot - Politics, gender, and size = feminist") \r\n   text(t(subset(Eprop2,mots2=="gender"))~t(subset(Eprop2,mots2=="politics")), labels=colnames(Eprop2),pos = 4, pch=3,cex=0.8)\r\n \r\n symbols(x=t(subset(Eprop2,mots2=="politics")),y=t(subset(Eprop2,mots2=="gender")),circles=t(subset(Eprop2, mots2=="feminist")),xlim=c(0,.1),ylim=c(0,.1), \r\ninches=.3,xlab="Work",ylab="Woman-Women",bg = 1:10,fg="gray30", main = "Circles Plot - Politics, gender, and size = feminist") \r\n   text(t(subset(Eprop2,mots2=="gender"))~t(subset(Eprop2,mots2=="politics")), labels=colnames(Eprop2),pos = 4, pch=3,cex=0.8)\r\n\r\n\r\n   ##################### Expliquer ici les PCA ######################\r\n\r\n##Sur ces mots, selection en CSV\r\nC=read.csv("REST.csv",sep=\',\',header=TRUE) \r\nmots=C$X\r\nmots=subset(mots,C$X.1==1)\r\nC=subset(C,X.1==1,select=c(-X,-X.1))\r\nvecteurArtistes=colnames(C)\r\n\r\n## de nouvea m\xe9nage pour les lignes ou moins de X personnes\r\ndi=dim(C)\r\ngarder=rep(\'\',di[1])    \r\nfor (i in 1:di[1])           {\r\nmarg=as.data.frame(table(C[i,]))\r\nmargtot=0\r\n  for (j in 2:di[2])  {if (marg[j]==0) {margtot=margtot+1} else {margtot=margtot}  }\r\n  if (margtot>(di[2]-9)) {garder[i]<-\'NON\'}     }\r\nE=subset(C, garder==\'\')\r\nmots=subset(mots, garder==\'\')\r\ndi=dim(E)\r\n\r\n## deuxi\xe8me \xe9tape :a partir de l\xe0, on transpose\r\n##Total pour celles qui ont plus de 8 mots\r\ndC=dim(E)\r\ntot=rep(0,dC[2])\r\npc=t(E)\r\n\r\nfor (i in 1:dC[2])  { (tot[i]<-sum(pc[i,]))}\r\npc1=rep(0,dC[1])\r\nvectorshort=rep("blank",1)\r\nfor (i in 1:dC[2]) (if (tot[i]<5) {cat("out ")} \r\n      else \r\n     { pc1=rbind(pc1,pc[i,])\r\n      vectorshort=rbind(vectorshort,vecteurArtistes[i])}        )\r\nrownames(pc1)<-vectorshort  \r\nd1=dim(pc1)\r\n\r\n## troisi\xe8me \xe9tape : homog\xe9n\xe9iser le nombre de mots\r\n##par personne, pour celles qui restent\r\n\r\na<-dim(pc1)\r\npc2<-matrix(0, nrow = a[1], ncol = a[2])\r\ntotper=rep(0,a[1])\r\nfor (i in 1:a[1])  { (totper[i]<-sum(pc1[i,1:a[2]]))}\r\nfor (i in 1:a[1]) (pc2[i,]<-pc1[i,]/totper[i])\r\ncolnames(pc2)<- mots\r\nrownames(pc2)<-rownames(pc1)\r\n\r\n\r\n\r\n##et voil\xe0 la PCA\r\ndimpc2=dim(pc2)\r\npc3=pc2[2:dimpc2[1],]\r\nuD3=dudi.pca(pc3,scannf=FALSE,nf=4)\r\n\r\n   \r\npar(col=\'red\', col.main=\'blue\', font=1, family="courrier")\r\n\r\npar(col=\'red\')\r\nscatter(uD3, xax = 1, yax = 2,clab.row = 0.8,clab.col=0.8,permute=TRUE)\r\n\r\n\r\npar(col=\'red\')\r\nscatter(uD3, xax = 3, yax = 4,clab.row = 0.8,clab.col=0.8,permute=TRUE)\r\n\r\nz=length(uD3$eig)\r\nsut=uD3$eig/sum(  uD3$eig)\r\n\r\nbarplot(sut, name=rep(1:z,1), main="Eigen Values"  )\r\n\r\n\r\n\r\npar(col=\'black\')\r\naxe1=uD3$co\r\naxea=cbind(rep(1,length(axe1[,1])),axe1[,1] )\r\naxeb=cbind(rep(1,length(axe1[,1])),axe1[,2] )\r\n\r\nrownames(axea)<-rownames(axe1)  \r\nrownames(axeb)<-rownames(axe1)  \r\n\r\n\r\nplot(axea[,2]~axea[,1],ylim=c(-1,1), pch=19)\r\ntext(axea[,2]~axea[,1], labels=rownames(axea),pos = 4, pch=19)\r\n\r\n\r\nplot(axeb[,2]~axeb[,1],ylim=c(-1,1), pch=19)\r\ntext(axeb[,2]~axeb[,1], labels=rownames(axeb),pos = 4)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n## a totaly different way of looking \xe0 this data\r\n## i graph##\r\n\r\nEprime=as.matrix(t(E))%*%as.matrix(E)\r\n\r\nF=graph.adjacency(Eprime,diag=FALSE,weighted=TRUE,mode="undirected")\r\nnames.Vertex=rownames(Eprime)                  \r\nplot.igraph(F,layout=layout.fruchterman.reingold,vertex.size=10,vertex.label=names.Vertex)\r\n           clusters(F)\r\n            \r\nEConnect=subset(E, select=c(-Klub.Zwei,-Migrantas,-Elssie.Ansareo))\r\nEprimeconnect0=as.matrix(t(EConnect))%*%as.matrix(EConnect)\r\nEprimeConnect=as.matrix(Eprimeconnect0)\r\nFconnect=graph.adjacency(Eprimeconnect,diag=FALSE,weighted=TRUE,mode="undirected")\r\nnames.Vertex.Connect=substring(rownames(EprimeConnect),1,10)\r\n\r\nclusters(Fconnect)                                        \r\nplot.igraph(Fconnect,layout=layout.fruchterman.reingold,vertex.size=15,vertex.label=names.Vertex.Connect)\r\nplot.igraph(Fconnect,layout=layout.kamada.kawai,vertex.size=30,vertex.label=names.Vertex.Connect)\r\n\r\n        \r\n                                                  \r\n                                       \r\n##Eliminating some week links to see the stronger one  \r\nEprimeconnectResum<-Eprimeconnect\r\ndEprimeconnect=dim(Eprimeconnect)\r\nfor (i in 1:dEprimeconnect[1])    \r\n     (for (j in 1:dEprimeconnect[2]) \r\n        (if (Eprimeconnect[i,j]<200) {EprimeconnectResum[i,j]<-0}\r\n            else {EprimeconnectResum[i,j]<-Eprimeconnect[i,j]}  ))\r\n            \r\nFconnectResum=graph.adjacency(EprimeconnectResum,diag=FALSE,weighted=TRUE,mode="undirected")\r\nnames.Vertex.Connect.resum=substring(rownames(EprimeconnectResum),1,10)\r\n                                        \r\nplot.igraph(FconnectResum,layout=layout.fruchterman.reingold,vertex.size=15,vertex.label=names.Vertex.Connect.resum)\r\n           clusters(FconnectResum)\r\nplot.igraph(FconnectResum,layout=layout.kamada.kawai,vertex.size=30,vertex.label=names.Vertex.Connect)\r\n                                \r\n\r\n### moiiie !\r\ntkplot(F, canvas.width=600, canvas.height=500,vertex.label=names.Vertex)\r\ntkplot(Fconnect, canvas.width=600, canvas.height=500,vertex.label=names.Vertex.Connect)\r\ntkplot(FconnectResum, canvas.width=600, canvas.height=500,vertex.label=names.Vertex.Connect.resum)\r\n\r\n\r\n### pourquoi pas tant qu\'on y est... \r\n pagerank=page.rank(F)\r\n pagerank2=as.data.frame(pagerank$vector )\r\nrownames(pagerank2)<-rownames(Eprime)\r\npar(mai=c(.5,2,.4,.5))\r\nbarplot(height=pagerank2[,1],ylab="",horiz=TRUE,beside=TRUE,names=rownames(Eprime),col="#4682B4",las=1,main="Page rank of the artists")\r\n'
<span id="c_0001" class="chunk">##library </span><span id="c_0002" class="chunk">à </span><span id="c_0003" class="chunk">charger </span><span id="c_0004" class="chunk">library(bitops) </span><span id="c_0005" class="chunk">library(gdata) </span><span id="c_0006" class="chunk">library(caTools) </span><span id="c_0007" class="chunk">library(tools) </span><span id="c_0008" class="chunk">library(gplots) </span><span id="c_0009" class="chunk">library(timeSeries) </span><span id="c_0010" class="chunk">library(timeDate) </span><span id="c_0011" class="chunk">library(fBasics) </span><span id="c_0012" class="chunk">library(igraph) </span><span id="c_0013" class="chunk">library(tcltk </span><span id="c_0014" class="chunk">) </span><span id="c_0015" class="chunk">library(ade4) </span><span id="c_0016" class="chunk">library(Cairo) </span><span id="c_0017" class="chunk">#netoyage </span><span id="c_0018" class="chunk">de </span><span id="c_0019" class="chunk">la </span><span id="c_0020" class="chunk">base </span><span id="c_0021" class="chunk">reconstituée </span><span id="c_0022" class="chunk">##Sur </span><span id="c_0023" class="chunk">ces </span><span id="c_0024" class="chunk">mots, </span><span id="c_0025" class="chunk">selection </span><span id="c_0026" class="chunk">en </span><span id="c_0027" class="chunk">CSV </span><span id="c_0028" class="chunk">setwd('C:\\Users\\alaure\\Desktop\\NICO\\Version </span><span id="c_0029" class="chunk">octobre\\Datas') </span><span id="c_0030" class="chunk">C=read.csv("REST.csv",sep=',',header=TRUE) </span><span id="c_0031" class="chunk">C=subset(C,X.1==1,select=c(-X.1)) </span><span id="c_0032" class="chunk">mots=C$X </span><span id="c_0033" class="chunk">C=subset(C,select=c(-X)) </span><span id="c_0034" class="chunk">vecteurArtistes=colnames(C) </span><span id="c_0035" class="chunk">## </span><span id="c_0036" class="chunk">de </span><span id="c_0037" class="chunk">nouveau, </span><span id="c_0038" class="chunk">ménage </span><span id="c_0039" class="chunk">pour </span><span id="c_0040" class="chunk">les </span><span id="c_0041" class="chunk">lignes </span><span id="c_0042" class="chunk">ou </span><span id="c_0043" class="chunk">moins </span><span id="c_0044" class="chunk">de </span><span id="c_0045" class="chunk">X </span><span id="c_0046" class="chunk">personnes </span><span id="c_0047" class="chunk">di=dim(C) </span><span id="c_0048" class="chunk">garder=rep(1,di[1]) </span><span id="c_0049" class="chunk">for </span><span id="c_0050" class="chunk">(i </span><span id="c_0051" class="chunk">in </span><span id="c_0052" class="chunk">1:di[1]) </span><span id="c_0053" class="chunk">{ </span><span id="c_0054" class="chunk">marg=as.data.frame(table(C[i,])) </span><span id="c_0055" class="chunk">margtot=0 </span><span id="c_0056" class="chunk">for </span><span id="c_0057" class="chunk">(j </span><span id="c_0058" class="chunk">in </span><span id="c_0059" class="chunk">2:di[2]) </span><span id="c_0060" class="chunk">{if </span><span id="c_0061" class="chunk">(marg[j]==0) </span><span id="c_0062" class="chunk">{margtot=margtot+1} </span><span id="c_0063" class="chunk">else </span><span id="c_0064" class="chunk">{margtot=margtot} </span><span id="c_0065" class="chunk">} </span><span id="c_0066" class="chunk">if </span><span id="c_0067" class="chunk">(margtot>(di[2]-9)) </span><span id="c_0068" class="chunk">{garder[i]<-0} </span><span id="c_0069" class="chunk">} </span><span id="c_0070" class="chunk">E=subset(C, </span><span id="c_0071" class="chunk">garder==1) </span><span id="c_0072" class="chunk">mots2=subset(mots, </span><span id="c_0073" class="chunk">garder==1) </span><span id="c_0074" class="chunk">di=dim(E) </span><span id="c_0075" class="chunk">NBArtist=rep(1,di[1]) </span><span id="c_0076" class="chunk">for </span><span id="c_0077" class="chunk">(i </span><span id="c_0078" class="chunk">in </span><span id="c_0079" class="chunk">1:di[1]) </span><span id="c_0080" class="chunk">{ </span><span id="c_0081" class="chunk">marg=as.data.frame(table(E[i,])) </span><span id="c_0082" class="chunk">NBArtist[i]=0 </span><span id="c_0083" class="chunk">for </span><span id="c_0084" class="chunk">(j </span><span id="c_0085" class="chunk">in </span><span id="c_0086" class="chunk">2:di[2]) </span><span id="c_0087" class="chunk">{if </span><span id="c_0088" class="chunk">(marg[j]==0) </span><span id="c_0089" class="chunk">{NBArtist[i]=NBArtist[i]} </span><span id="c_0090" class="chunk">else </span><span id="c_0091" class="chunk">{NBArtist[i]=NBArtist[i]+1} </span><span id="c_0092" class="chunk">} </span><span id="c_0093" class="chunk">} </span><span id="c_0094" class="chunk">## </span><span id="c_0095" class="chunk">piechart </span><span id="c_0096" class="chunk">u=apply </span><span id="c_0097" class="chunk">( </span><span id="c_0098" class="chunk">E,1,sum) </span><span id="c_0099" class="chunk">u=as.matrix(u) </span><span id="c_0100" class="chunk">pie.u </span><span id="c_0101" class="chunk"><- </span><span id="c_0102" class="chunk">u/(apply(u,2,sum)) </span><span id="c_0103" class="chunk">names(pie.u) </span><span id="c_0104" class="chunk"><- </span><span id="c_0105" class="chunk">mots2 </span><span id="c_0106" class="chunk">par(mai=c(.3,0.3,.3,.3)) </span><span id="c_0107" class="chunk">pie(pie.u,main="Piechart", </span><span id="c_0108" class="chunk">col=c("#00FF00","#FF0080","#4B0082","#FFD700","#4682B4","#FFEEEE","#BC81FF","#F0E68C","#DC143C","#FFFF00","#FF0000","#00BFFF","#DA70D6","#40E0D0","#FF1493","#FFFFFF","#6A5ACD")) </span><span id="c_0109" class="chunk">##### </span><span id="c_0110" class="chunk">barplot </span><span id="c_0111" class="chunk">u=apply </span><span id="c_0112" class="chunk">( </span><span id="c_0113" class="chunk">E,1,sum) </span><span id="c_0114" class="chunk">u=as.matrix(u) </span><span id="c_0115" class="chunk">u=cbind(NBArtist,u) </span><span id="c_0116" class="chunk">rownames(u)<-mots2 </span><span id="c_0117" class="chunk">colnames(u)<-c("Number </span><span id="c_0118" class="chunk">of </span><span id="c_0119" class="chunk">artist </span><span id="c_0120" class="chunk">using </span><span id="c_0121" class="chunk">each </span><span id="c_0122" class="chunk">word","Number </span><span id="c_0123" class="chunk">of </span><span id="c_0124" class="chunk">occurence </span><span id="c_0125" class="chunk">of </span><span id="c_0126" class="chunk">each </span><span id="c_0127" class="chunk">word") </span><span id="c_0128" class="chunk">par(mai=c(.5,2,.4,.5)) </span><span id="c_0129" class="chunk">barplot(height=u[,2],ylab="",horiz=TRUE,beside=TRUE,names=mots2,las=1,main="Nombre </span><span id="c_0130" class="chunk">de </span><span id="c_0131" class="chunk">mots </span><span id="c_0132" class="chunk">par </span><span id="c_0133" class="chunk">artistes") </span><span id="c_0134" class="chunk">barplot(height=u[,1],ylab="",horiz=TRUE,beside=TRUE,names=mots2,las=1,main="Nombre </span><span id="c_0135" class="chunk">d'artistes </span><span id="c_0136" class="chunk">qui </span><span id="c_0137" class="chunk">utilisent </span><span id="c_0138" class="chunk">un </span><span id="c_0139" class="chunk">mot") </span><span id="c_0140" class="chunk">barplot2(height=t(u),ylab="",horiz=TRUE,beside=TRUE,names=mots2,las=1,legend=colnames(u)) </span><span id="c_0141" class="chunk">##Assocplot </span><span id="c_0142" class="chunk">: </span><span id="c_0143" class="chunk">words </span><span id="c_0144" class="chunk">/ </span><span id="c_0145" class="chunk">artists </span><span id="c_0146" class="chunk">#rownames(E)<-mots2 </span><span id="c_0147" class="chunk">#assoc(E[1:5,1:5]) </span><span id="c_0148" class="chunk">## </span><span id="c_0149" class="chunk">plot </span><span id="c_0150" class="chunk">with </span><span id="c_0151" class="chunk">two </span><span id="c_0152" class="chunk">dimensions </span><span id="c_0153" class="chunk">: </span><span id="c_0154" class="chunk">link </span><span id="c_0155" class="chunk">between </span><span id="c_0156" class="chunk">two </span><span id="c_0157" class="chunk">words </span><span id="c_0158" class="chunk">and </span><span id="c_0159" class="chunk">the </span><span id="c_0160" class="chunk">artists </span><span id="c_0161" class="chunk">dim(E </span><span id="c_0162" class="chunk">) </span><span id="c_0163" class="chunk">apply(E,2,sum) </span><span id="c_0164" class="chunk">Mosa=subset(E,mots2=="work"|mots2=="Woman-women"|mots2=="video"|mots2=="politics"|mots2=="feminist", </span><span id="c_0165" class="chunk">select=c(Anne.Mie.Van.Kerckhoven </span><span id="c_0166" class="chunk">, </span><span id="c_0167" class="chunk">Jo.Spence, </span><span id="c_0168" class="chunk">Ewa.Partum </span><span id="c_0169" class="chunk">, </span><span id="c_0170" class="chunk">Girls.Who.Like.Porno </span><span id="c_0171" class="chunk">, </span><span id="c_0172" class="chunk">Maria.Ruido </span><span id="c_0173" class="chunk">) </span><span id="c_0174" class="chunk">) </span><span id="c_0175" class="chunk">mosaicplot(Mosa,main="",shade </span><span id="c_0176" class="chunk">= </span><span id="c_0177" class="chunk">TRUE) </span><span id="c_0178" class="chunk">#Plot </span><span id="c_0179" class="chunk">with </span><span id="c_0180" class="chunk">dimension </span><span id="c_0181" class="chunk">: </span><span id="c_0182" class="chunk">the </span><span id="c_0183" class="chunk">way </span><span id="c_0184" class="chunk">the </span><span id="c_0185" class="chunk">artist </span><span id="c_0186" class="chunk">are </span><span id="c_0187" class="chunk">using </span><span id="c_0188" class="chunk">two </span><span id="c_0189" class="chunk">words </span><span id="c_0190" class="chunk">par(mai=c(1,1,.4,.5)) </span><span id="c_0191" class="chunk">plot(t(subset(E,mots2=="work")),t(subset(E,mots2=="Woman-women")),xlab="Work",ylab="Woman-Women",ylim=c(0,75),xlim=c(0,150) </span><span id="c_0192" class="chunk">) </span><span id="c_0193" class="chunk">text(t(subset(E,mots2=="Woman-women"))~t(subset(E,mots2=="work")), </span><span id="c_0194" class="chunk">labels=colnames(E),pos </span><span id="c_0195" class="chunk">= </span><span id="c_0196" class="chunk">4, </span><span id="c_0197" class="chunk">pch=3,cex=0.8) </span><span id="c_0198" class="chunk">cor(t(subset(E,mots2=="work")),t(subset(E,mots2=="Woman-women"))) </span><span id="c_0199" class="chunk">plot(t(subset(E,mots2=="work")),t(subset(E,mots2=="politics")),xlab="Work",ylab="politics",ylim=c(0,75),xlim=c(0,150) </span><span id="c_0200" class="chunk">) </span><span id="c_0201" class="chunk">cor(t(subset(E,mots2=="work")),t(subset(E,mots2=="politics"))) </span><span id="c_0202" class="chunk">cor(t(subset(E,mots2=="Woman-women")),t(subset(E,mots2=="power"))) </span><span id="c_0203" class="chunk">plot(t(subset(E,mots2=="Woman-women")),t(subset(E,mots2=="power")),xlab="Work",ylab="politics",ylim=c(0,75),xlim=c(0,150) </span><span id="c_0204" class="chunk">) </span><span id="c_0205" class="chunk">###### </span><span id="c_0206" class="chunk">Working </span><span id="c_0207" class="chunk">with </span><span id="c_0208" class="chunk">the </span><span id="c_0209" class="chunk">proportion </span><span id="c_0210" class="chunk">represented </span><span id="c_0211" class="chunk">by </span><span id="c_0212" class="chunk">each </span><span id="c_0213" class="chunk">word </span><span id="c_0214" class="chunk">in </span><span id="c_0215" class="chunk">place </span><span id="c_0216" class="chunk">of </span><span id="c_0217" class="chunk">the </span><span id="c_0218" class="chunk">number </span><span id="c_0219" class="chunk">of </span><span id="c_0220" class="chunk">time### </span><span id="c_0221" class="chunk">a<-dim(E) </span><span id="c_0222" class="chunk">Eprop<-matrix(0, </span><span id="c_0223" class="chunk">nrow </span><span id="c_0224" class="chunk">= </span><span id="c_0225" class="chunk">a[1], </span><span id="c_0226" class="chunk">ncol </span><span id="c_0227" class="chunk">= </span><span id="c_0228" class="chunk">a[2]) </span><span id="c_0229" class="chunk">Eprop2=as.data.frame(Eprop) </span><span id="c_0230" class="chunk">totper=rep(0,a[1]) </span><span id="c_0231" class="chunk">for </span><span id="c_0232" class="chunk">(i </span><span id="c_0233" class="chunk">in </span><span id="c_0234" class="chunk">1:a[1]) </span><span id="c_0235" class="chunk">{ </span><span id="c_0236" class="chunk">(totper[i]<-sum(E[i,1:a[2]]))} </span><span id="c_0237" class="chunk">for </span><span id="c_0238" class="chunk">(i </span><span id="c_0239" class="chunk">in </span><span id="c_0240" class="chunk">1:a[1]) </span><span id="c_0241" class="chunk">(Eprop2[i,]<-E[i,]/totper[i]) </span><span id="c_0242" class="chunk">colnames(Eprop2)<-colnames(E) </span><span id="c_0243" class="chunk">## </span><span id="c_0244" class="chunk">Graphique </span><span id="c_0245" class="chunk">3 </span><span id="c_0246" class="chunk">D </span><span id="c_0247" class="chunk">symbols(x=t(subset(Eprop2,mots2=="work")),y=t(subset(Eprop2,mots2=="Woman-women")),circles=t(subset(Eprop2, </span><span id="c_0248" class="chunk">mots2=="feminist")), </span><span id="c_0249" class="chunk">inches=.3,xlab="Work",ylab="Woman-Women",bg </span><span id="c_0250" class="chunk">= </span><span id="c_0251" class="chunk">1:10,fg="gray30", </span><span id="c_0252" class="chunk">main </span><span id="c_0253" class="chunk">= </span><span id="c_0254" class="chunk">"Circles </span><span id="c_0255" class="chunk">Plot </span><span id="c_0256" class="chunk">- </span><span id="c_0257" class="chunk">Work, </span><span id="c_0258" class="chunk">Woman-Women, </span><span id="c_0259" class="chunk">and </span><span id="c_0260" class="chunk">size </span><span id="c_0261" class="chunk">= </span><span id="c_0262" class="chunk">feminist") </span><span id="c_0263" class="chunk">text(t(subset(Eprop2,mots2=="Woman-women"))~t(subset(Eprop2,mots2=="work")), </span><span id="c_0264" class="chunk">labels=colnames(Eprop2),pos </span><span id="c_0265" class="chunk">= </span><span id="c_0266" class="chunk">4, </span><span id="c_0267" class="chunk">pch=3,cex=0.8) </span><span id="c_0268" class="chunk">symbols(x=t(subset(Eprop2,mots2=="politics")),y=t(subset(Eprop2,mots2=="gender")),circles=t(subset(Eprop2, </span><span id="c_0269" class="chunk">mots2=="feminist")), </span><span id="c_0270" class="chunk">inches=.3,xlab="Work",ylab="Woman-Women",bg </span><span id="c_0271" class="chunk">= </span><span id="c_0272" class="chunk">1:10,fg="gray30", </span><span id="c_0273" class="chunk">main </span><span id="c_0274" class="chunk">= </span><span id="c_0275" class="chunk">"Circles </span><span id="c_0276" class="chunk">Plot </span><span id="c_0277" class="chunk">- </span><span id="c_0278" class="chunk">Politics, </span><span id="c_0279" class="chunk">gender, </span><span id="c_0280" class="chunk">and </span><span id="c_0281" class="chunk">size </span><span id="c_0282" class="chunk">= </span><span id="c_0283" class="chunk">feminist") </span><span id="c_0284" class="chunk">text(t(subset(Eprop2,mots2=="gender"))~t(subset(Eprop2,mots2=="politics")), </span><span id="c_0285" class="chunk">labels=colnames(Eprop2),pos </span><span id="c_0286" class="chunk">= </span><span id="c_0287" class="chunk">4, </span><span id="c_0288" class="chunk">pch=3,cex=0.8) </span><span id="c_0289" class="chunk">symbols(x=t(subset(Eprop2,mots2=="politics")),y=t(subset(Eprop2,mots2=="gender")),circles=t(subset(Eprop2, </span><span id="c_0290" class="chunk">mots2=="feminist")),xlim=c(0,.1),ylim=c(0,.1), </span><span id="c_0291" class="chunk">inches=.3,xlab="Work",ylab="Woman-Women",bg </span><span id="c_0292" class="chunk">= </span><span id="c_0293" class="chunk">1:10,fg="gray30", </span><span id="c_0294" class="chunk">main </span><span id="c_0295" class="chunk">= </span><span id="c_0296" class="chunk">"Circles </span><span id="c_0297" class="chunk">Plot </span><span id="c_0298" class="chunk">- </span><span id="c_0299" class="chunk">Politics, </span><span id="c_0300" class="chunk">gender, </span><span id="c_0301" class="chunk">and </span><span id="c_0302" class="chunk">size </span><span id="c_0303" class="chunk">= </span><span id="c_0304" class="chunk">feminist") </span><span id="c_0305" class="chunk">text(t(subset(Eprop2,mots2=="gender"))~t(subset(Eprop2,mots2=="politics")), </span><span id="c_0306" class="chunk">labels=colnames(Eprop2),pos </span><span id="c_0307" class="chunk">= </span><span id="c_0308" class="chunk">4, </span><span id="c_0309" class="chunk">pch=3,cex=0.8) </span><span id="c_0310" class="chunk">##################### </span><span id="c_0311" class="chunk">Expliquer </span><span id="c_0312" class="chunk">ici </span><span id="c_0313" class="chunk">les </span><span id="c_0314" class="chunk">PCA </span><span id="c_0315" class="chunk">###################### </span><span id="c_0316" class="chunk">##Sur </span><span id="c_0317" class="chunk">ces </span><span id="c_0318" class="chunk">mots, </span><span id="c_0319" class="chunk">selection </span><span id="c_0320" class="chunk">en </span><span id="c_0321" class="chunk">CSV </span><span id="c_0322" class="chunk">C=read.csv("REST.csv",sep=',',header=TRUE) </span><span id="c_0323" class="chunk">mots=C$X </span><span id="c_0324" class="chunk">mots=subset(mots,C$X.1==1) </span><span id="c_0325" class="chunk">C=subset(C,X.1==1,select=c(-X,-X.1)) </span><span id="c_0326" class="chunk">vecteurArtistes=colnames(C) </span><span id="c_0327" class="chunk">## </span><span id="c_0328" class="chunk">de </span><span id="c_0329" class="chunk">nouvea </span><span id="c_0330" class="chunk">ménage </span><span id="c_0331" class="chunk">pour </span><span id="c_0332" class="chunk">les </span><span id="c_0333" class="chunk">lignes </span><span id="c_0334" class="chunk">ou </span><span id="c_0335" class="chunk">moins </span><span id="c_0336" class="chunk">de </span><span id="c_0337" class="chunk">X </span><span id="c_0338" class="chunk">personnes </span><span id="c_0339" class="chunk">di=dim(C) </span><span id="c_0340" class="chunk">garder=rep('',di[1]) </span><span id="c_0341" class="chunk">for </span><span id="c_0342" class="chunk">(i </span><span id="c_0343" class="chunk">in </span><span id="c_0344" class="chunk">1:di[1]) </span><span id="c_0345" class="chunk">{ </span><span id="c_0346" class="chunk">marg=as.data.frame(table(C[i,])) </span><span id="c_0347" class="chunk">margtot=0 </span><span id="c_0348" class="chunk">for </span><span id="c_0349" class="chunk">(j </span><span id="c_0350" class="chunk">in </span><span id="c_0351" class="chunk">2:di[2]) </span><span id="c_0352" class="chunk">{if </span><span id="c_0353" class="chunk">(marg[j]==0) </span><span id="c_0354" class="chunk">{margtot=margtot+1} </span><span id="c_0355" class="chunk">else </span><span id="c_0356" class="chunk">{margtot=margtot} </span><span id="c_0357" class="chunk">} </span><span id="c_0358" class="chunk">if </span><span id="c_0359" class="chunk">(margtot>(di[2]-9)) </span><span id="c_0360" class="chunk">{garder[i]<-'NON'} </span><span id="c_0361" class="chunk">} </span><span id="c_0362" class="chunk">E=subset(C, </span><span id="c_0363" class="chunk">garder=='') </span><span id="c_0364" class="chunk">mots=subset(mots, </span><span id="c_0365" class="chunk">garder=='') </span><span id="c_0366" class="chunk">di=dim(E) </span><span id="c_0367" class="chunk">## </span><span id="c_0368" class="chunk">deuxième </span><span id="c_0369" class="chunk">étape </span><span id="c_0370" class="chunk">:a </span><span id="c_0371" class="chunk">partir </span><span id="c_0372" class="chunk">de </span><span id="c_0373" class="chunk">là, </span><span id="c_0374" class="chunk">on </span><span id="c_0375" class="chunk">transpose </span><span id="c_0376" class="chunk">##Total </span><span id="c_0377" class="chunk">pour </span><span id="c_0378" class="chunk">celles </span><span id="c_0379" class="chunk">qui </span><span id="c_0380" class="chunk">ont </span><span id="c_0381" class="chunk">plus </span><span id="c_0382" class="chunk">de </span><span id="c_0383" class="chunk">8 </span><span id="c_0384" class="chunk">mots </span><span id="c_0385" class="chunk">dC=dim(E) </span><span id="c_0386" class="chunk">tot=rep(0,dC[2]) </span><span id="c_0387" class="chunk">pc=t(E) </span><span id="c_0388" class="chunk">for </span><span id="c_0389" class="chunk">(i </span><span id="c_0390" class="chunk">in </span><span id="c_0391" class="chunk">1:dC[2]) </span><span id="c_0392" class="chunk">{ </span><span id="c_0393" class="chunk">(tot[i]<-sum(pc[i,]))} </span><span id="c_0394" class="chunk">pc1=rep(0,dC[1]) </span><span id="c_0395" class="chunk">vectorshort=rep("blank",1) </span><span id="c_0396" class="chunk">for </span><span id="c_0397" class="chunk">(i </span><span id="c_0398" class="chunk">in </span><span id="c_0399" class="chunk">1:dC[2]) </span><span id="c_0400" class="chunk">(if </span><span id="c_0401" class="chunk">(tot[i]<5) </span><span id="c_0402" class="chunk">{cat("out </span><span id="c_0403" class="chunk">")} </span><span id="c_0404" class="chunk">else </span><span id="c_0405" class="chunk">{ </span><span id="c_0406" class="chunk">pc1=rbind(pc1,pc[i,]) </span><span id="c_0407" class="chunk">vectorshort=rbind(vectorshort,vecteurArtistes[i])} </span><span id="c_0408" class="chunk">) </span><span id="c_0409" class="chunk">rownames(pc1)<-vectorshort </span><span id="c_0410" class="chunk">d1=dim(pc1) </span><span id="c_0411" class="chunk">## </span><span id="c_0412" class="chunk">troisième </span><span id="c_0413" class="chunk">étape </span><span id="c_0414" class="chunk">: </span><span id="c_0415" class="chunk">homogénéiser </span><span id="c_0416" class="chunk">le </span><span id="c_0417" class="chunk">nombre </span><span id="c_0418" class="chunk">de </span><span id="c_0419" class="chunk">mots </span><span id="c_0420" class="chunk">##par </span><span id="c_0421" class="chunk">personne, </span><span id="c_0422" class="chunk">pour </span><span id="c_0423" class="chunk">celles </span><span id="c_0424" class="chunk">qui </span><span id="c_0425" class="chunk">restent </span><span id="c_0426" class="chunk">a<-dim(pc1) </span><span id="c_0427" class="chunk">pc2<-matrix(0, </span><span id="c_0428" class="chunk">nrow </span><span id="c_0429" class="chunk">= </span><span id="c_0430" class="chunk">a[1], </span><span id="c_0431" class="chunk">ncol </span><span id="c_0432" class="chunk">= </span><span id="c_0433" class="chunk">a[2]) </span><span id="c_0434" class="chunk">totper=rep(0,a[1]) </span><span id="c_0435" class="chunk">for </span><span id="c_0436" class="chunk">(i </span><span id="c_0437" class="chunk">in </span><span id="c_0438" class="chunk">1:a[1]) </span><span id="c_0439" class="chunk">{ </span><span id="c_0440" class="chunk">(totper[i]<-sum(pc1[i,1:a[2]]))} </span><span id="c_0441" class="chunk">for </span><span id="c_0442" class="chunk">(i </span><span id="c_0443" class="chunk">in </span><span id="c_0444" class="chunk">1:a[1]) </span><span id="c_0445" class="chunk">(pc2[i,]<-pc1[i,]/totper[i]) </span><span id="c_0446" class="chunk">colnames(pc2)<- </span><span id="c_0447" class="chunk">mots </span><span id="c_0448" class="chunk">rownames(pc2)<-rownames(pc1) </span><span id="c_0449" class="chunk">##et </span><span id="c_0450" class="chunk">voilà </span><span id="c_0451" class="chunk">la </span><span id="c_0452" class="chunk">PCA </span><span id="c_0453" class="chunk">dimpc2=dim(pc2) </span><span id="c_0454" class="chunk">pc3=pc2[2:dimpc2[1],] </span><span id="c_0455" class="chunk">uD3=dudi.pca(pc3,scannf=FALSE,nf=4) </span><span id="c_0456" class="chunk">par(col='red', </span><span id="c_0457" class="chunk">col.main='blue', </span><span id="c_0458" class="chunk">font=1, </span><span id="c_0459" class="chunk">family="courrier") </span><span id="c_0460" class="chunk">par(col='red') </span><span id="c_0461" class="chunk">scatter(uD3, </span><span id="c_0462" class="chunk">xax </span><span id="c_0463" class="chunk">= </span><span id="c_0464" class="chunk">1, </span><span id="c_0465" class="chunk">yax </span><span id="c_0466" class="chunk">= </span><span id="c_0467" class="chunk">2,clab.row </span><span id="c_0468" class="chunk">= </span><span id="c_0469" class="chunk">0.8,clab.col=0.8,permute=TRUE) </span><span id="c_0470" class="chunk">par(col='red') </span><span id="c_0471" class="chunk">scatter(uD3, </span><span id="c_0472" class="chunk">xax </span><span id="c_0473" class="chunk">= </span><span id="c_0474" class="chunk">3, </span><span id="c_0475" class="chunk">yax </span><span id="c_0476" class="chunk">= </span><span id="c_0477" class="chunk">4,clab.row </span><span id="c_0478" class="chunk">= </span><span id="c_0479" class="chunk">0.8,clab.col=0.8,permute=TRUE) </span><span id="c_0480" class="chunk">z=length(uD3$eig) </span><span id="c_0481" class="chunk">sut=uD3$eig/sum( </span><span id="c_0482" class="chunk">uD3$eig) </span><span id="c_0483" class="chunk">barplot(sut, </span><span id="c_0484" class="chunk">name=rep(1:z,1), </span><span id="c_0485" class="chunk">main="Eigen </span><span id="c_0486" class="chunk">Values" </span><span id="c_0487" class="chunk">) </span><span id="c_0488" class="chunk">par(col='black') </span><span id="c_0489" class="chunk">axe1=uD3$co </span><span id="c_0490" class="chunk">axea=cbind(rep(1,length(axe1[,1])),axe1[,1] </span><span id="c_0491" class="chunk">) </span><span id="c_0492" class="chunk">axeb=cbind(rep(1,length(axe1[,1])),axe1[,2] </span><span id="c_0493" class="chunk">) </span><span id="c_0494" class="chunk">rownames(axea)<-rownames(axe1) </span><span id="c_0495" class="chunk">rownames(axeb)<-rownames(axe1) </span><span id="c_0496" class="chunk">plot(axea[,2]~axea[,1],ylim=c(-1,1), </span><span id="c_0497" class="chunk">pch=19) </span><span id="c_0498" class="chunk">text(axea[,2]~axea[,1], </span><span id="c_0499" class="chunk">labels=rownames(axea),pos </span><span id="c_0500" class="chunk">= </span><span id="c_0501" class="chunk">4, </span><span id="c_0502" class="chunk">pch=19) </span><span id="c_0503" class="chunk">plot(axeb[,2]~axeb[,1],ylim=c(-1,1), </span><span id="c_0504" class="chunk">pch=19) </span><span id="c_0505" class="chunk">text(axeb[,2]~axeb[,1], </span><span id="c_0506" class="chunk">labels=rownames(axeb),pos </span><span id="c_0507" class="chunk">= </span><span id="c_0508" class="chunk">4) </span><span id="c_0509" class="chunk">## </span><span id="c_0510" class="chunk">a </span><span id="c_0511" class="chunk">totaly </span><span id="c_0512" class="chunk">different </span><span id="c_0513" class="chunk">way </span><span id="c_0514" class="chunk">of </span><span id="c_0515" class="chunk">looking </span><span id="c_0516" class="chunk">à </span><span id="c_0517" class="chunk">this </span><span id="c_0518" class="chunk">data </span><span id="c_0519" class="chunk">## </span><span id="c_0520" class="chunk">i </span><span id="c_0521" class="chunk">graph## </span><span id="c_0522" class="chunk">Eprime=as.matrix(t(E))%*%as.matrix(E) </span><span id="c_0523" class="chunk">F=graph.adjacency(Eprime,diag=FALSE,weighted=TRUE,mode="undirected") </span><span id="c_0524" class="chunk">names.Vertex=rownames(Eprime) </span><span id="c_0525" class="chunk">plot.igraph(F,layout=layout.fruchterman.reingold,vertex.size=10,vertex.label=names.Vertex) </span><span id="c_0526" class="chunk">clusters(F) </span><span id="c_0527" class="chunk">EConnect=subset(E, </span><span id="c_0528" class="chunk">select=c(-Klub.Zwei,-Migrantas,-Elssie.Ansareo)) </span><span id="c_0529" class="chunk">Eprimeconnect0=as.matrix(t(EConnect))%*%as.matrix(EConnect) </span><span id="c_0530" class="chunk">EprimeConnect=as.matrix(Eprimeconnect0) </span><span id="c_0531" class="chunk">Fconnect=graph.adjacency(Eprimeconnect,diag=FALSE,weighted=TRUE,mode="undirected") </span><span id="c_0532" class="chunk">names.Vertex.Connect=substring(rownames(EprimeConnect),1,10) </span><span id="c_0533" class="chunk">clusters(Fconnect) </span><span id="c_0534" class="chunk">plot.igraph(Fconnect,layout=layout.fruchterman.reingold,vertex.size=15,vertex.label=names.Vertex.Connect) </span><span id="c_0535" class="chunk">plot.igraph(Fconnect,layout=layout.kamada.kawai,vertex.size=30,vertex.label=names.Vertex.Connect) </span><span id="c_0536" class="chunk">##Eliminating </span><span id="c_0537" class="chunk">some </span><span id="c_0538" class="chunk">week </span><span id="c_0539" class="chunk">links </span><span id="c_0540" class="chunk">to </span><span id="c_0541" class="chunk">see </span><span id="c_0542" class="chunk">the </span><span id="c_0543" class="chunk">stronger </span><span id="c_0544" class="chunk">one </span><span id="c_0545" class="chunk">EprimeconnectResum<-Eprimeconnect </span><span id="c_0546" class="chunk">dEprimeconnect=dim(Eprimeconnect) </span><span id="c_0547" class="chunk">for </span><span id="c_0548" class="chunk">(i </span><span id="c_0549" class="chunk">in </span><span id="c_0550" class="chunk">1:dEprimeconnect[1]) </span><span id="c_0551" class="chunk">(for </span><span id="c_0552" class="chunk">(j </span><span id="c_0553" class="chunk">in </span><span id="c_0554" class="chunk">1:dEprimeconnect[2]) </span><span id="c_0555" class="chunk">(if </span><span id="c_0556" class="chunk">(Eprimeconnect[i,j]<200) </span><span id="c_0557" class="chunk">{EprimeconnectResum[i,j]<-0} </span><span id="c_0558" class="chunk">else </span><span id="c_0559" class="chunk">{EprimeconnectResum[i,j]<-Eprimeconnect[i,j]} </span><span id="c_0560" class="chunk">)) </span><span id="c_0561" class="chunk">FconnectResum=graph.adjacency(EprimeconnectResum,diag=FALSE,weighted=TRUE,mode="undirected") </span><span id="c_0562" class="chunk">names.Vertex.Connect.resum=substring(rownames(EprimeconnectResum),1,10) </span><span id="c_0563" class="chunk">plot.igraph(FconnectResum,layout=layout.fruchterman.reingold,vertex.size=15,vertex.label=names.Vertex.Connect.resum) </span><span id="c_0564" class="chunk">clusters(FconnectResum) </span><span id="c_0565" class="chunk">plot.igraph(FconnectResum,layout=layout.kamada.kawai,vertex.size=30,vertex.label=names.Vertex.Connect) </span><span id="c_0566" class="chunk">### </span><span id="c_0567" class="chunk">moiiie </span><span id="c_0568" class="chunk">! </span><span id="c_0569" class="chunk">tkplot(F, </span><span id="c_0570" class="chunk">canvas.width=600, </span><span id="c_0571" class="chunk">canvas.height=500,vertex.label=names.Vertex) </span><span id="c_0572" class="chunk">tkplot(Fconnect, </span><span id="c_0573" class="chunk">canvas.width=600, </span><span id="c_0574" class="chunk">canvas.height=500,vertex.label=names.Vertex.Connect) </span><span id="c_0575" class="chunk">tkplot(FconnectResum, </span><span id="c_0576" class="chunk">canvas.width=600, </span><span id="c_0577" class="chunk">canvas.height=500,vertex.label=names.Vertex.Connect.resum) </span><span id="c_0578" class="chunk">### </span><span id="c_0579" class="chunk">pourquoi </span><span id="c_0580" class="chunk">pas </span><span id="c_0581" class="chunk">tant </span><span id="c_0582" class="chunk">qu'on </span><span id="c_0583" class="chunk">y </span><span id="c_0584" class="chunk">est... </span><span id="c_0585" class="chunk">pagerank=page.rank(F) </span><span id="c_0586" class="chunk">pagerank2=as.data.frame(pagerank$vector </span><span id="c_0587" class="chunk">) </span><span id="c_0588" class="chunk">rownames(pagerank2)<-rownames(Eprime) </span><span id="c_0589" class="chunk">par(mai=c(.5,2,.4,.5)) </span><span id="c_0590" class="chunk">barplot(height=pagerank2[,1],ylab="",horiz=TRUE,beside=TRUE,names=rownames(Eprime),col="#4682B4",las=1,main="Page </span><span id="c_0591" class="chunk">rank </span><span id="c_0592" class="chunk">of </span><span id="c_0593" class="chunk">the </span><span id="c_0594" class="chunk">artists") </span>