Beautiful dendrogram visualizations in R: 5+ must known methods - Unsupervised Machine Learning


A variety of functions exists in R for visualizing and customizing dendrogram. The aim of this article is to describe 5+ methods for drawing a beautiful dendrogram using R software.

We start by computing hierarchical clustering using the data set USArrests:

# Load data
data(USArrests)
# Compute distances and hierarchical clustering
dd <- dist(scale(USArrests), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")

1 plot.hclust(): R base function

As you already know, the standard R function plot.hclust() can be used to draw a dendrogram from the results of hierarchical clustering analyses (computed using hclust() function).

A simplified format is:

plot(x, labels = NULL, hang = 0.1, 
     main = "Cluster dendrogram", sub = NULL,
     xlab = NULL, ylab = "Height", ...)

  • x: an object of the type produced by hclust()
  • labels: A character vector of labels for the leaves of the tree. The default value is row names. if labels = FALSE, no labels are drawn.
  • hang: The fraction of the plot height by which labels should hang below the rest of the plot. A negative value will cause the labels to hang down from 0.
  • main, sub, xlab, ylab: character strings for title.


# Default plot
plot(hc)

dendrogram visualization - Unsupervised Machine Learning

# Put the labels at the same height: hang = -1
plot(hc, hang = -1, cex = 0.6)

dendrogram visualization - Unsupervised Machine Learning

2 plot.dendrogram() function

In order to visualize the result of a hierarchical clustering analysis using the function plot.dendrogram(), we must firstly convert it as a dendrogram.

The format of the function plot.dendrogram() is:

plot(x, type = c("rectangle", "triangle"), horiz = FALSE)

  • x: an object of class dendrogram
  • type of plot. Possible values are “rectangle” or “triangle”
  • horiz: logical indicating if the dendrogram should be drawn horizontally or no


# Convert hclust into a dendrogram and plot
hcd <- as.dendrogram(hc)
# Default plot
plot(hcd, type = "rectangle", ylab = "Height")

dendrogram visualization - Unsupervised Machine Learning

# Triangle plot
plot(hcd, type = "triangle", ylab = "Height")

dendrogram visualization - Unsupervised Machine Learning

# Zoom in to the first dendrogram
plot(hcd, xlim = c(1, 20), ylim = c(1,8))

dendrogram visualization - Unsupervised Machine Learning

The above dendrogram can be customized using the arguments:

  • nodePar: a list of plotting parameters to use for the nodes (see ?points). Default value is NULL. The list may contain components named pch, cex, col, xpd, and/or bg each of which can have length two for specifying separate attributes for inner nodes and leaves.
  • edgePar: a list of plotting parameters to use for the edge segments (see ?segments). The list may contain components named col, lty and lwd (for the segments). As with nodePar, each can have length two for differentiating leaves and inner nodes.
  • leaflab: a string specifying how leaves are labeled. The default “perpendicular” write text vertically; “textlike” writes text horizontally (in a rectangle), and “none” suppresses leaf labels.
# Define nodePar
nodePar <- list(lab.cex = 0.6, pch = c(NA, 19), 
                cex = 0.7, col = "blue")
# Customized plot; remove labels
plot(hcd, ylab = "Height", nodePar = nodePar, leaflab = "none")

dendrogram visualization - Unsupervised Machine Learning

# Horizontal plot
plot(hcd,  xlab = "Height",
     nodePar = nodePar, horiz = TRUE)

dendrogram visualization - Unsupervised Machine Learning

# Change edge color
plot(hcd,  xlab = "Height", nodePar = nodePar, 
     edgePar = list(col = 2:3, lwd = 2:1))

dendrogram visualization - Unsupervised Machine Learning

3 Phylogenetic trees

The package ape (Analyses of Phylogenetics and Evolution) can be used to produce a more sophisticated dendrogram.

The function plot.phylo() can be used for plotting a dendrogram. A simplified format is:

plot(x, type = "phylogram", show.tip.label = TRUE,
     edge.color = "black", edge.width = 1, edge.lty = 1,
     tip.color = "black")

  • x: an object of class “phylo”
  • type: the type of phylogeny to be drawn. Possible values are: “phylogram” (the default), “cladogram”, “fan”, “unrooted” and “radial”
  • show.tip.label: if true labels are shown
  • edge.color, edge.width, edge.lty: line color, width and type to be used for edge
  • tip.color: color used for labels


# install.packages("ape")
library("ape")
# Default plot
plot(as.phylo(hc), cex = 0.6, label.offset = 0.5)

dendrogram visualization - Unsupervised Machine Learning

# Cladogram
plot(as.phylo(hc), type = "cladogram", cex = 0.6, 
     label.offset = 0.5)

dendrogram visualization - Unsupervised Machine Learning

# Unrooted
plot(as.phylo(hc), type = "unrooted", cex = 0.6,
     no.margin = TRUE)

dendrogram visualization - Unsupervised Machine Learning

# Fan
plot(as.phylo(hc), type = "fan")

dendrogram visualization - Unsupervised Machine Learning

# Radial
plot(as.phylo(hc), type = "radial")

dendrogram visualization - Unsupervised Machine Learning

# Cut the dendrogram into 4 clusters
colors = c("red", "blue", "green", "black")
clus4 = cutree(hc, 4)
plot(as.phylo(hc), type = "fan", tip.color = colors[clus4],
     label.offset = 1, cex = 0.7)

dendrogram visualization - Unsupervised Machine Learning

# Change the appearance
# change edge and label (tip)
plot(as.phylo(hc), type = "cladogram", cex = 0.6,
     edge.color = "steelblue", edge.width = 2, edge.lty = 2,
     tip.color = "steelblue")

dendrogram visualization - Unsupervised Machine Learning

4 ggdendro package : ggplot2 and dendrogram

The R package ggdendro can be used to extract the plot data from dendrogram and for drawing a dendrogram using ggplot2.

4.1 Installation and loading

ggdendro can be installed as follow:

install.packages("ggdendro")

ggdendro requires the package ggplot2. Make sure that ggplot2 is installed and loaded before using ggdendro.

Load ggdendro as follow:

library("ggplot2")
library("ggdendro")

4.2 Visualize dendrogram using ggdendrogram() function

The function ggdendrogram() creates dendrogram plot using ggplot2.

# Visualization using the default theme named theme_dendro()
ggdendrogram(hc)

dendrogram visualization - Unsupervised Machine Learning

# Rotate the plot and remove default theme
ggdendrogram(hc, rotate = TRUE, theme_dendro = FALSE)

dendrogram visualization - Unsupervised Machine Learning

4.3 Extract dendrogram plot data

The function dendro_data() can be used for extracting the data. It returns a list of data frames which can be extracted using the functions below:

  • segment(): To extract the data for dendrogram line segments
  • label(): To extract the labels
# Build dendrogram object from hclust results
dend <- as.dendrogram(hc)
# Extract the data (for rectangular lines)
# Type can be "rectangle" or "triangle"
dend_data <- dendro_data(dend, type = "rectangle")
# What contains dend_data
names(dend_data)
## [1] "segments"    "labels"      "leaf_labels" "class"
# Extract data for line segments
head(dend_data$segments)
##           x         y     xend      yend
## 1 19.771484 13.516242 8.867188 13.516242
## 2  8.867188 13.516242 8.867188  6.461866
## 3  8.867188  6.461866 4.125000  6.461866
## 4  4.125000  6.461866 4.125000  2.714554
## 5  4.125000  2.714554 2.500000  2.714554
## 6  2.500000  2.714554 2.500000  1.091092
# Extract data for labels
head(dend_data$labels)
##   x y          label
## 1 1 0        Alabama
## 2 2 0      Louisiana
## 3 3 0        Georgia
## 4 4 0      Tennessee
## 5 5 0 North Carolina
## 6 6 0    Mississippi

dend_data can be used to draw a customized dendrogram using ggplot2:

# Plot line segments and add labels
p <- ggplot(dend_data$segments) + 
  geom_segment(aes(x = x, y = y, xend = xend, yend = yend))+
  geom_text(data = dend_data$labels, aes(x, y, label = label),
            hjust = 1, angle = 90, size = 3)+
  ylim(-3, 15)
print(p)

dendrogram visualization - Unsupervised Machine Learning

5 dendextend package: Extending R’s dendrogram functionality

The package dendextend contains many functions for changing the appearance of a dendrogram and for comparing dendrograms.

In this section we’ll use the chaining operator (%>%) to simplify our code.

5.1 Chaining

The chaining operator (%>%) turns x %>% f(y) into f(x, y) so you can use it to rewrite multiple operations such that they can be read from left-to-right, top-to-bottom. For instance, the results of the two R codes below are equivalent.

Standard R code for creating a dendrogram:

data <- scale(USArrests)
dist.res <- dist(data)
hc <- hclust(dist.res, method = "ward.D2")
dend <- as.dendrogram(hc)
plot(dend)

R code for creating a dendrogram using chaining operator:

dend <- USArrests[1:5,] %>% # data
        scale %>% # Scale the data
        dist %>% # calculate a distance matrix, 
        hclust(method = "ward.D2") %>% # Hierarchical clustering 
        as.dendrogram # Turn the object into a dendrogram.
plot(dend)

5.2 Installation and loading

Install the stable version as follow:

install.packages('dendextend')

Loading:

library(dendextend)

5.3 How to change a dendrogram

The function set() can be used to change the parameters with dendextend.

The format is:

set(object, what, value)

  1. object: a dendrogram object
  2. what: a character indicating what is the property of the tree that should be set/updated
  3. value: a vector with the value to set in the tree (the type of the value depends on the “what”).


Possible values for the argument what include:

Value for the argument what Description
labels set the labels
labels_colors and labels_cex Set the color and the size of labels, respectively
leaves_pch, leaves_cex and leaves_col set the point type, size and color for leaves, respectively
nodes_pch, nodes_cex and nodes_col set the point type, size and color for nodes, respectively
hang_leaves hang the leaves
branches_k_color color the branches
branches_col, branches_lwd , branches_lty Set the color, the line width and the line type of branches, respectively
by_labels_branches_col, by_labels_branches_lwd and by_labels_branches_lty Set the color, the line width and the line type of branches with specific labels, respectively
clear_branches and clear_leaves Clear branches and leaves, respectively

5.4 Create a simple dendrogram

# Create a dendrogram and plot it
dend <- USArrests[1:5,] %>%  scale %>% 
        dist %>% hclust %>% as.dendrogram
dend %>% plot

dendrogram visualization - Unsupervised Machine Learning

# Get the labels of the tree
labels(dend)
## [1] "Alaska"     "Arizona"    "California" "Alabama"    "Arkansas"

5.5 Change labels

This section describes how to change label names as well as the color and the size for labels.

# Change the labels, and then plot:
dend %>% set("labels", c("a", "b", "c", "d", "e")) %>% plot

dendrogram visualization - Unsupervised Machine Learning

# Change color and size for labels
dend %>% set("labels_col", c("green", "blue")) %>% # change color
  set("labels_cex", 2) %>% # Change size
  plot(main = "Change the color \nand size") # plot

dendrogram visualization - Unsupervised Machine Learning

# Color labels by specifying the number of cluster (k)
dend %>% set("labels_col", value = c("green", "blue"), k=2) %>% 
          plot(main = "Color labels \nper cluster")
abline(h = 2, lty = 2)

dendrogram visualization - Unsupervised Machine Learning

In the R code above, the value of color vectors are too short. Hence, it’s recycled.

5.6 Change the points of a dendrogram nodes/leaves

# Change the type, the color and the size of node points
# +++++++++++++++++++++++++++++
dend %>% set("nodes_pch", 19) %>%  # node point type
  set("nodes_cex", 2) %>%  # node point size
  set("nodes_col", "blue") %>% # node point color
  plot(main = "Node points")

dendrogram visualization - Unsupervised Machine Learning

# Change the type, the color and the size of leave points
# +++++++++++++++++++++++++++++
dend %>% set("leaves_pch", 19) %>%  # node point type
  set("leaves_cex", 2) %>%  # node point size
  set("leaves_col", "blue") %>% # node point color
  plot(main = "Leaves points")

dendrogram visualization - Unsupervised Machine Learning

# Specify different point types and colors for each leave
dend %>% set("leaves_pch", c(17, 18, 19)) %>%  # node point type
  set("leaves_cex", 2) %>%  # node point size
  set("leaves_col", c("blue", "red", "green")) %>% #node point color
  plot(main = "Leaves points")

dendrogram visualization - Unsupervised Machine Learning

5.7 Change the color of branches

The color for branches can be controlled using k-means clustering:

# Default colors
dend %>% set("branches_k_color", k = 2) %>% 
  plot(main = "Default colors")
# Customized colors
dend %>% set("branches_k_color", 
             value = c("red", "blue"), k = 2) %>% 
   plot(main = "Customized colors")

dendrogram visualization - Unsupervised Machine Learningdendrogram visualization - Unsupervised Machine Learning

It’s also possible to use the function color_branches().

5.8 Adding colored rectangles

Clusters can be highlighted by adding colored rectangles. This is done using the rect.dendrogram() function (modeled based on the rect.hclust() function). One advantage of rect.dendrogram over rect.hclust, is that it also works on horizontally plotted trees:

# Vertical plot
dend %>% set("branches_k_color", k = 3) %>% plot
dend %>% rect.dendrogram(k=3, border = 8, lty = 5, lwd = 2)
# Horizontal plot
dend %>% set("branches_k_color", k = 3) %>% plot(horiz = TRUE)
dend %>% rect.dendrogram(k = 3, horiz = TRUE, border = 8, lty = 5, lwd = 2)

dendrogram visualization - Unsupervised Machine Learningdendrogram visualization - Unsupervised Machine Learning

5.9 Adding colored bars

This is useful for annotating the items in the clusters:

grp <- c(1,1,1, 2,2)
k_3 <- cutree(dend,k = 3, order_clusters_as_data = FALSE) 
# The FALSE above makes sure we get the clusters in the order of the
# dendrogram, and not in that of the original data. It is like:
# cutree(dend, k = 3)[order.dendrogram(dend)]
the_bars <- cbind(grp, k_3)
dend %>% set("labels", "") %>% plot
colored_bars(colors = the_bars, dend = dend)

dendrogram visualization - Unsupervised Machine Learning

5.10 ggplot2 integration

The following 2 steps are used:

  1. Transform a dendrogram into a ggdend object using as.ggdend() function
  2. Make the plot using the function ggplot()
dend <- iris[1:30,-5] %>% scale %>% dist %>% 
   hclust %>% as.dendrogram %>%
   set("branches_k_color", k=3) %>% set("branches_lwd", 1.2) %>%
   set("labels_colors") %>% set("labels_cex", c(.9,1.2)) %>% 
   set("leaves_pch", 19) %>% set("leaves_col", c("blue", "red"))
# plot the dend in usual "base" plotting engine:
plot(dend)

dendrogram visualization - Unsupervised Machine Learning

Produce the same plot in ggplot2 using the function:

library(ggplot2)
# Rectangle dendrogram using ggplot2
ggd1 <- as.ggdend(dend)
ggplot(ggd1) 

dendrogram visualization - Unsupervised Machine Learning

# Change the theme to the default ggplot2 theme
ggplot(ggd1, horiz = TRUE, theme = NULL) 

dendrogram visualization - Unsupervised Machine Learning

# Theme minimal
ggplot(ggd1, theme = theme_minimal()) 

dendrogram visualization - Unsupervised Machine Learning

# Create a radial plot and remove labels
ggplot(ggd1, labels = FALSE) + 
  scale_y_reverse(expand = c(0.2, 0)) +
  coord_polar(theta="x")

dendrogram visualization - Unsupervised Machine Learning

5.11 pvclust and dendextend

The package dendextend can be used to enhance many packages including pvclust. Recall that, pvclust is for calculating p-values for hierarchical clustering.

pvclust can be used as follow:

library(pvclust)
data(lung) # 916 genes for 73 subjects
set.seed(1234)
result <- pvclust(lung[1:100, 1:10], method.dist="cor", 
                  method.hclust="average", nboot=10)
## Bootstrap (r = 0.5)... Done.
## Bootstrap (r = 0.6)... Done.
## Bootstrap (r = 0.7)... Done.
## Bootstrap (r = 0.8)... Done.
## Bootstrap (r = 0.9)... Done.
## Bootstrap (r = 1.0)... Done.
## Bootstrap (r = 1.1)... Done.
## Bootstrap (r = 1.2)... Done.
## Bootstrap (r = 1.3)... Done.
## Bootstrap (r = 1.4)... Done.
# Default plot of the result
plot(result)
pvrect(result)

dendrogram visualization - Unsupervised Machine Learning

# pvclust and dendextend
result %>% as.dendrogram %>% 
  set("branches_k_color", k = 2, value = c("purple", "orange")) %>%
  plot
result %>% text
result %>% pvrect

dendrogram visualization - Unsupervised Machine Learning

6 Infos

This analysis has been performed using R software (ver. 3.2.1)









Want to Learn More on R Programming and Data Science?

Follow us by Email

by FeedBurner

On Social Networks:


 Get involved :
  Click to follow us on and Google+ :   
  Comment this article by clicking on "Discussion" button (top-right position of this page)
  Sign up as a member and post news and articles on STHDA web site.
This page has been seen 59123 times