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copied!<p>In case anyone is also interested in dendrogram export, here is my solution. Most probably, it's not the best one as I started using R only recently, but at least it works. So suggestions on how to improve the code are welcome. </p> <p>So, if<code>hr</code>is my hclust object and <code>df</code> is my data, the first column of which contains a simple index starting from 0, and the row names are the names of the clustered items:</p> <pre><code># Retrieve the leaf order (row name and its position within the leaves) leaf.order <- matrix(data=NA, ncol=2, nrow=nrow(df), dimnames=list(c(), c("row.num", "row.name"))) leaf.order[,2] <- hr$labels[hr$order] for (i in 1:nrow(leaf.order)) { leaf.order[which(leaf.order[,2] %in% rownames(df[i,])),1] <- df[i,1] } leaf.order <- as.data.frame(leaf.order) hr.merge <- hr$merge n <- max(df[,1]) # Re-index all clustered leaves and nodes. First, all leaves are indexed starting from 0. # Next, all nodes are indexed starting from max. index leave + 1. for (i in 1:length(hr.merge)) { if (hr.merge[i]<0) {hr.merge[i] <- abs(hr.merge[i])-1} else { hr.merge[i] <- (hr.merge[i]+n) } } node.id <- c(0:length(hr.merge)) # Generate dendrogram matrix with node index in the first column. dend <- matrix(data=NA, nrow=length(node.id), ncol=6, dimnames=list(c(0:(length(node.id)-1)), c("node.id", "parent.id", "pruning.level", "height", "leaf.order", "row.name")) ) dend[,1] <- c(0:((2*nrow(df))-2)) # Insert a leaf/node index # Calculate parent ID for each leaf/node: # 1) For each leaf/node index, find the corresponding row number within the merge-table. # 2) Add the maximum leaf index to the row number as indexing the nodes starts after indexing all the leaves. for (i in 1:(nrow(dend)-1)) { dend[i,2] <- row(hr.merge)[which(hr.merge %in% dend[i,1])]+n } # Generate table with indexing of all leaves (1st column) and inserting the corresponding row names into the 3rd column. hr.order <- matrix(data=NA, nrow=length(hr$labels), ncol=3, dimnames=list(c(), c("order.number", "leaf.id", "row.name"))) hr.order[,1] <- c(0:(nrow(hr.order)-1)) hr.order[,3] <- t(hr$labels[hr$order]) hr.order <- data.frame(hr.order) hr.order[,1] <- as.numeric(hr.order[,1]) # Assign the row name to each leaf. dend <- as.data.frame(dend) for (i in 1:nrow(df)) { dend[which(dend[,1] %in% df[i,1]),6] <- rownames(df[i,]) } # Assign the position on the dendrogram (from left to right) to each leaf. for (i in 1:nrow(hr.order)) { dend[which(dend[,6] %in% hr.order[i,3]),5] <- hr.order[i,1]-1 } # Insert height for each node. dend[c((n+2):nrow(dend)),4] <- hr$height # All leaves get the highest possible pruning level dend[which(dend[,1] <= n),3] <- nrow(hr.merge) # The nodes get a decreasing index starting from the pruning level of the # leaves minus 1 and up to 0 for (i in (n+2):nrow(dend)) { if ((dend[i,4] != dend[(i-1),4]) || is.na(dend[(i-1),4])){ dend[i,3] <- dend[(i-1),3]-1} else { dend[i,3] <- dend[(i-1),3] } } dend[,3] <- dend[,3]-min(dend[,3]) dend <- dend[order(-node.id),] # Write results table. write.table(dend, file="path", sep=";", row.names=F) </code></pre>

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