I saw an example online of taking hclust dendrogram and plotting it using ggplot2 and thought I would give it a try to see what it would look like. I get an error when trying to use ggplot; Error: ggplot2 doesn't know how to deal with data of class phylo. Regular plot works fine but I can't get ggplot2 to work.
see code below.... rows=100 columns=100 #create matrix all=matrix(nrow=rows, ncol=columns) #initialize first column all[,1]=rbinom(rows,1,.5) #set probability probv=.9 for (j in 2:columns) { for (i in 1:rows) { #set the probability based on the values in the columns to the left if (all[i,j-1]==1) {all[i,j]=rbinom(1,1,1-probv)} else {all[i,j]=rbinom(1,1,probv)} } } #make the first row an outlier all[5,]=rbinom(columns,1,.1) all[4,]=rbinom(columns,1,.1) all[3,]=rbinom(columns,1,.1) all[2,]=rbinom(columns,1,.1) all[1,]=rbinom(columns,1,.1) all[6:9,]=rbinom(columns,1,.1) all[10:19,]=rbinom(columns,1,.3) all[20:29,]=rbinom(columns,1,.7) #1 to 10 .5 11 to 89 .9 .1 90 columns #calculate the distances for clustering rand1=dist(all) #cluster on those distances and plot hc1=hclust(rand1,"average") plot(hc1) ## now plot using ggplot and polar coordinates. Line starting p<-ggplot(data=x) yields the error. This is the online code I found. library(ape) library(cluster) library(ggplot2) x <- as.phylo(hc1) p <- ggplot(data=x) p <- p + geom_segment(aes(y=x,x=y,yend=xend,xend=yend), colour="blue",alpha=1) p <- p + geom_text(data=label.phylo(x), aes(x=y, y=x, label=label),family=3, size=3) + xlim(0, xlim) + coord_polar() theme <- theme_update( axis.text.x = theme_blank(), axis.ticks = theme_blank(), axis.title.x = theme_blank(), axis.title.y = theme_blank(), legend.position = "none" ) p <- p + theme_set(theme) print(p) -- View this message in context: http://r.789695.n4.nabble.com/hclust-and-ggplot2-tp4193353p4193353.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.