Didzis Elferts showed how to plot a dendogram using ggplot2 and ggdendro:
horizontal dendrogram in R with labels
here is the code:
labs = pas
A short way to achieve a similar result is to use the package dendextend, taken from this overview.
df <- USArrests # really bad idea to muck up internal datasets
labs <- paste("sta_",1:50,sep="") # new labels
rownames(df) <- labs # set new row names
require(magrittr)
require(ggplot2)
require(dendextend)
dend <- df %>% dist %>%
hclust %>% as.dendrogram %>%
set("branches_k_color", k = 4) %>% set("branches_lwd", 0.7) %>%
set("labels_cex", 0.6) %>% set("labels_colors", k = 4) %>%
set("leaves_pch", 19) %>% set("leaves_cex", 0.5)
ggd1 <- as.ggdend(dend)
ggplot(ggd1, horiz = TRUE)
Note: The order of the states is slightly different compared to those above - not really changing interpretation though.
Workaround would be to plot cluster object with plot() and then use function rect.hclust() to draw borders around the clusters (nunber of clusters is set with argument k=). If result of rect.hclust() is saved as object it will make list of observation where each list element contains observations belonging to each cluster.
plot(hc)
gg<-rect.hclust(hc,k=2)
Now this list can be converted to dataframe where column clust contains names for clusters (in this example two groups) - names are repeated according to lengths of list elemets.
clust.gr<-data.frame(num=unlist(gg),
clust=rep(c("Clust1","Clust2"),times=sapply(gg,length)))
head(clust.gr)
num clust
sta_1 1 Clust1
sta_2 2 Clust1
sta_3 3 Clust1
sta_5 5 Clust1
sta_8 8 Clust1
sta_9 9 Clust1
New data frame is merged with label() information of dendr object (dendro_data() result).
text.df<-merge(label(dendr),clust.gr,by.x="label",by.y="row.names")
head(text.df)
label x y num clust
1 sta_1 8 0 1 Clust1
2 sta_10 28 0 10 Clust2
3 sta_11 41 0 11 Clust2
4 sta_12 31 0 12 Clust2
5 sta_13 10 0 13 Clust1
6 sta_14 37 0 14 Clust2
When plotting dendrogram use text.df to add labels with geom_text() and use column clust for colors.
ggplot() +
geom_segment(data=segment(dendr), aes(x=x, y=y, xend=xend, yend=yend)) +
geom_text(data=text.df, aes(x=x, y=y, label=label, hjust=0,color=clust), size=3) +
coord_flip() + scale_y_reverse(expand=c(0.2, 0)) +
theme(axis.line.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.y=element_blank(),
axis.title.y=element_blank(),
panel.background=element_rect(fill="white"),
panel.grid=element_blank())

Adding to @DidzisElferts' and @jlhoward's code, the dendrogram itself can be coloured.
library(ggplot2)
library(ggdendro)
library(plyr)
library(zoo)
df <- USArrests # really bad idea to muck up internal datasets
labs <- paste("sta_", 1:50, sep = "") # new labels
rownames(df) <- labs # set new row names
cut <- 4 # Number of clusters
hc <- hclust(dist(df), "ave") # hierarchical clustering
dendr <- dendro_data(hc, type = "rectangle")
clust <- cutree(hc, k = cut) # find 'cut' clusters
clust.df <- data.frame(label = names(clust), cluster = clust)
# Split dendrogram into upper grey section and lower coloured section
height <- unique(dendr$segments$y)[order(unique(dendr$segments$y), decreasing = TRUE)]
cut.height <- mean(c(height[cut], height[cut-1]))
dendr$segments$line <- ifelse(dendr$segments$y == dendr$segments$yend &
dendr$segments$y > cut.height, 1, 2)
dendr$segments$line <- ifelse(dendr$segments$yend > cut.height, 1, dendr$segments$line)
# Number the clusters
dendr$segments$cluster <- c(-1, diff(dendr$segments$line))
change <- which(dendr$segments$cluster == 1)
for (i in 1:cut) dendr$segments$cluster[change[i]] = i + 1
dendr$segments$cluster <- ifelse(dendr$segments$line == 1, 1,
ifelse(dendr$segments$cluster == 0, NA, dendr$segments$cluster))
dendr$segments$cluster <- na.locf(dendr$segments$cluster)
# Consistent numbering between segment$cluster and label$cluster
clust.df$label <- factor(clust.df$label, levels = levels(dendr$labels$label))
clust.df <- arrange(clust.df, label)
clust.df$cluster <- factor((clust.df$cluster), levels = unique(clust.df$cluster), labels = (1:cut) + 1)
dendr[["labels"]] <- merge(dendr[["labels"]], clust.df, by = "label")
# Positions for cluster labels
n.rle <- rle(dendr$segments$cluster)
N <- cumsum(n.rle$lengths)
N <- N[seq(1, length(N), 2)] + 1
N.df <- dendr$segments[N, ]
N.df$cluster <- N.df$cluster - 1
# Plot the dendrogram
ggplot() +
geom_segment(data = segment(dendr),
aes(x=x, y=y, xend=xend, yend=yend, size=factor(line), colour=factor(cluster)),
lineend = "square", show.legend = FALSE) +
scale_colour_manual(values = c("grey60", rainbow(cut))) +
scale_size_manual(values = c(.1, 1)) +
geom_text(data = N.df, aes(x = x, y = y, label = factor(cluster), colour = factor(cluster + 1)),
hjust = 1.5, show.legend = FALSE) +
geom_text(data = label(dendr), aes(x, y, label = label, colour = factor(cluster)),
hjust = -0.2, size = 3, show.legend = FALSE) +
scale_y_reverse(expand = c(0.2, 0)) +
labs(x = NULL, y = NULL) +
coord_flip() +
theme(axis.line.y = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
panel.background = element_rect(fill = "white"),
panel.grid = element_blank())
The 2-cluster and 4-cluster solutions:

This approach is very similar to @DidzisElferts', just a little simpler.
df <- USArrests # really bad idea to muck up internal datasets
labs <- paste("sta_",1:50,sep="") # new labels
rownames(df) <- labs # set new row names
library(ggplot2)
library(ggdendro)
hc <- hclust(dist(df), "ave") # heirarchal clustering
dendr <- dendro_data(hc, type="rectangle") # convert for ggplot
clust <- cutree(hc,k=2) # find 2 clusters
clust.df <- data.frame(label=names(clust), cluster=factor(clust))
# dendr[["labels"]] has the labels, merge with clust.df based on label column
dendr[["labels"]] <- merge(dendr[["labels"]],clust.df, by="label")
# plot the dendrogram; note use of color=cluster in geom_text(...)
ggplot() +
geom_segment(data=segment(dendr), aes(x=x, y=y, xend=xend, yend=yend)) +
geom_text(data=label(dendr), aes(x, y, label=label, hjust=0, color=cluster),
size=3) +
coord_flip() + scale_y_reverse(expand=c(0.2, 0)) +
theme(axis.line.y=element_blank(),
axis.ticks.y=element_blank(),
axis.text.y=element_blank(),
axis.title.y=element_blank(),
panel.background=element_rect(fill="white"),
panel.grid=element_blank())
