Demo of going from an igraph dendrogram to a simplified view as a ggraph circlepack as in https://twitter.com/clairemcwhite/status/1079895521446293505

simplified_dendrogram_to_circlepack.R

library(tidygraph)
library(tidyverse)
library(ggraph)
library(dendextend)
library(igraph)

palette_OkabeIto <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999")

cut_df <- function(dendrogram, height, c){
    #Function to cut a dendrogram
    cd <- cutree(dendrogram, h = height) %>% as.data.frame()
    cd$ID <- row.names(cd)
    cd <- cd %>% as_tibble()
    colname <- paste("cut", as.character(round(c, 2)), sep = "_")
    names(cd) <- c(colname, "ID")
    return(cd)
}

cut_dend <- function(dendrogram, cuts){
    #Function to cut the dendrogram at particular heights
    ht <- max(get_nodes_attr(dendrogram, "height"))
    cut_clusters <- data.frame(ID = as.character())
    for (c in cuts){
        cut_clusters <- merge(cut_clusters, cut_df(dendrogram, c*ht, c), all=TRUE)
        
    }
    return(cut_clusters)
}

#The point of this code is to threshold a dendrogram at various points, and plot clusters as subsets of their parent cluster



d_iris <- as.dendrogram(hclust(dist(iris))) 
plot(d_iris, vertex.label = NA)

#Choose points up the dendrogram at which to cut into clusters, 1.0 is stem and 0.0 is tips
#In this iris dendrogram, 0.6 cuts into two clusters, 0.4 into 4 clusters, and 0.2 into 12 clusters
cut_clusters <- cut_dend(d_iris, c(0.6,0.4,0.2))

#Each cluster needs a unique ID, not a plain number
#Borrow unique identifier for each column from its column header 
clusters_uniqued <- cut_clusters  %>%
    gather(clusterset, clusternum, -ID) %>%
    mutate(clusterid = paste0(clusternum, clusterset)) %>%
    select(-clusternum) %>%
    spread( clusterset, clusterid) 

#All nodes need to be connected back to single origin point
#This can be called anything, just not the same thing as the other cluster ids
clusters_uniqued$link <- "origin" 

#create edges in "from", "to" format 
#https://www.r-graph-gallery.com/334-basic-dendrogram-with-ggraph/
edges_level0_1 = clusters_uniqued %>% select(link, cut_0.6) %>% unique %>% rename(from = link, to = cut_0.6)
edges_level1_2 = clusters_uniqued %>% select( cut_0.6, cut_0.4) %>% unique %>% rename(from = cut_0.6, to = cut_0.4)
edges_level2_3 = clusters_uniqued %>% select( cut_0.4, cut_0.2) %>% unique %>% rename(from = cut_0.4, to = cut_0.2)
#The final level is the individual tips of the dendrogram as the smallest dots in the circlepack
edges_level3_final = clusters_uniqued %>% select( cut_0.2, ID) %>% unique %>% rename(from = cut_0.2, to = ID)

edge_list=rbind(edges_level0_1, edges_level1_2, edges_level2_3, edges_level3_final )

mygraph <- as_tbl_graph(edge_list) 

# Get the row names from the iris data set into a normal data column
iris_plain <- iris %>% mutate(name = rownames(iris))

# Add on species labels to the nodes
mygraph <- mygraph %>% activate("nodes") %>% 
    left_join(iris_plain, by = "name")

#If you want to hide the first large circle see: https://www.r-graph-gallery.com/315-hide-first-level-in-circle-packing/
ggraph(mygraph, layout = 'circlepack') + 
    geom_node_circle(aes(fill = Species )) +
    theme_void() + 
    scale_fill_manual(values = palette_OkabeIto, na.value = "white", limits = c("setosa", "versicolor", "virginica")) + 
    NULL

Now with species labels

Code updated to use tidygraph that makes adding annotations to nodes easier

Instead of using the igraph function "graph_from_data_frame(edge_list)"
use the tidygraph function "as_tbl_graph(edge_list)"

And also a very lazy MS paint schematic of how the dendrogram is simplified.