Pie and Donut Charts, Dendrograms, Circular Packing, Sunburst, & WordCloud

Pie Charts

Pie charts show how a whole is divided into parts.

Each slice represents the proportion of a category relative to the total.

They are commonly used when:

1. there are few categories
2. proportions are easy to compare

However, pie charts can become difficult to interpret when there are many slices.

We will use the mpg dataset and count the number of vehicles in each class.

library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

data("mpg")

class_counts <- mpg %>%
  count(class)

ggplot(class_counts, aes(x = "", y = n, fill = class)) +
  geom_col(width = 1) +
  coord_polar("y") +
  theme_void() +
  labs(title = "Vehicle Class Distribution")

Explanation

• geom_col() creates a stacked bar chart
• coord_polar(“y”) converts the bar chart into a circular pie chart
• theme_void() removes unnecessary axes and grid lines

Prop <- c(3,7,9,1,2)
pie(Prop , labels = c("Gr-A","Gr-B","Gr-C","Gr-D","Gr-E")) #add labels to slices

pie(Prop , labels = c("Gr-A","Gr-B","Gr-C","Gr-D","Gr-E") , density=10 , angle=c(20,90,30,10,0)) # Add dashed lines by using density, and control the angles.

Donut Charts

Donut charts are similar to pie charts but include a hole in the center. This can make labels easier to read and reduce visual clutter.

ggplot(class_counts, aes(x = 2, y = n, fill = class)) +
  geom_col(width = 1) +
  coord_polar("y") +
  xlim(0.5, 2.5) +
  theme_void() +
  labs(title = "Vehicle Class Distribution (Donut Chart)")

Explanation

The donut hole is created by manipulating the x-axis limits.

• the bars are drawn away from the center
• the middle space becomes empty

To get percentage labels positioned around the donut like in your example, you need to:

1. Calculate proportions
2. Compute label positions
3. Add labels with geom_text()

library(ggplot2)
library(dplyr)
library(scales)

df <- data.frame(
  category = c("A","B","C"),
  value = c(10,60,30)
)

# Compute percentages and label positions
df <- df %>%
  mutate(
    prop = value / sum(value), #calculate proportions
    ymax = cumsum(prop), #calculate slice boundaries to see where they begin and end
    ymin = lag(ymax, default = 0),
    label_pos = (ymax + ymin) / 2, #place text in the center of each slice arc
    label = percent(prop) 
  )

ggplot(df, aes(ymax = ymax, ymin = ymin, xmax = 4, xmin = 3, fill = category)) +
  geom_rect() +
  coord_polar(theta = "y") +
  
  # percentage labels
  geom_text(aes(x = 4.2, y = label_pos, label = label), size = 4) + #add percentage labels
  
  xlim(2, 4.5) +
  theme_void()

data <- data.frame(
  category=c("A", "B", "C"),
  count=c(10, 60, 30)
)
 
# Compute percentages
data$fraction <- data$count / sum(data$count)

# Compute the cumulative percentages (top of each rectangle)
data$ymax <- cumsum(data$fraction)

# Compute the bottom of each rectangle
data$ymin <- c(0, head(data$ymax, n=-1))

# Compute label position
data$labelPosition <- (data$ymax + data$ymin) / 2

# Compute a good label
data$label <- paste0(data$category, "\n value: ", data$count)

# Make the plot
ggplot(data, aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=category)) +
  geom_rect() +
  geom_label( x=3.5, aes(y=labelPosition, label=label), size=6) +
  scale_fill_brewer(palette=4) +
  coord_polar(theta="y") +
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "none")

Sunburst Plots

install.packages("sunburstR")

## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)

library(sunburstR)
library(dplyr)

sunburst_data <- data.frame(
  sequence = c(
    "Fruit-Apples",
    "Fruit-Bananas",
    "Fruit-Oranges",
    "Vegetable-Carrots",
    "Vegetable-Broccoli",
    "Vegetable-Peppers"
  ),
  value = c(30, 20, 25, 15, 18, 12)
)

sunburst(sunburst_data)

Legend

Think of this like a nested dout plot, that can show a category within a category. They are not ideal when:

• students need precise comparisons
• there are many small slices
• labels become crowded

Treemap

library(treemap)
library(dplyr)

sales_data <- data.frame(
  category = c("Fruit", "Fruit", "Fruit", "Vegetable", "Vegetable", "Vegetable"),
  subcategory = c("Apples", "Bananas", "Oranges", "Carrots", "Broccoli", "Peppers"),
  value = c(30, 20, 25, 15, 18, 12)
)

treemap(
  sales_data,
  index = c("category", "subcategory"),
  vSize = "value",
  title = "Treemap of Category and Subcategory Counts"
)

They can become hard to read if:

• labels are too long
• there are too many tiny categories
• viewers need exact comparisons

Circular Packing

Circular packing visualizes nested hierarchical structures.

Large circles represent categories, while smaller circles inside represent subcategories.

This is useful for:

• hierarchical datasets
• taxonomies
• organizational structures

library(packcircles)
library(ggforce)

sizes <- c(50, 30, 20, 10, 5)

circle_data <- circleProgressiveLayout(sizes)

library(ggplot2)

ggplot(circle_data) +
  geom_circle(aes(x0 = x, y0 = y, r = radius),
              fill = "skyblue",
              color = "black") +
  coord_equal() +
  theme_void()

Explanation

• each circle size represents a category
• circles are packed efficiently to avoid overlap

library(ggraph)
library(igraph)

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:dplyr':
## 
##     as_data_frame, groups, union

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ lubridate 1.9.5     ✔ tibble    3.3.1
## ✔ purrr     1.2.1     ✔ tidyr     1.3.2
## ✔ readr     2.2.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ lubridate::%--%()       masks igraph::%--%()
## ✖ tibble::as_data_frame() masks igraph::as_data_frame(), dplyr::as_data_frame()
## ✖ readr::col_factor()     masks scales::col_factor()
## ✖ purrr::compose()        masks igraph::compose()
## ✖ tidyr::crossing()       masks igraph::crossing()
## ✖ purrr::discard()        masks scales::discard()
## ✖ dplyr::filter()         masks stats::filter()
## ✖ dplyr::lag()            masks stats::lag()
## ✖ purrr::simplify()       masks igraph::simplify()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(viridis)

## Loading required package: viridisLite
## 
## Attaching package: 'viridis'
## 
## The following object is masked from 'package:scales':
## 
##     viridis_pal

# We need a data frame giving a hierarchical structure. Let's consider the flare dataset:
edges <- flare$edges
vertices <- flare$vertices
mygraph <- graph_from_data_frame( edges, vertices=vertices )
 
# Control the size of each circle: (use the size column of the vertices data frame)
ggraph(mygraph, layout = 'circlepack', weight=size) + 
  geom_node_circle() +
  theme_void()

ggraph(mygraph, layout = 'circlepack', weight=size) + 
  geom_node_circle(aes(fill = depth)) + #add the fill designation
  theme_void() + 
  theme(legend.position="FALSE") +
   scale_fill_viridis() #choose a color pallette 

ggraph(mygraph, layout = 'circlepack', weight=size ) + 
  geom_node_circle(aes(fill = depth)) +
  geom_node_text( aes(label=shortName, filter=leaf, fill=depth, size=size)) +
  theme_void() + 
  theme(legend.position="FALSE") + 
  scale_fill_viridis()

## Warning in geom_node_text(aes(label = shortName, filter = leaf, fill = depth, :
## Ignoring unknown aesthetics: fill

Dendrograms

Dendrograms visualize hierarchical clustering.

They show how observations group together based on similarity.

Common uses include:

• gene expression analysis
• species similarity
• document clustering

# Load packages
library(ggraph)
library(igraph)
library(tidyverse)

# Step 1: Create the top level of the hierarchy
# One starting point called "origin" branches into 5 groups
level1 <- data.frame(
  from = "origin",
  to = paste0("group_", 1:5)
)

# Step 2: Create the second level of the hierarchy
# Each group branches into 5 subgroups
level2 <- data.frame(
  from = rep(level1$to, each = 5),
  to = paste0("subgroup_", 1:25)
)

# Step 3: Combine all connections into one edge list
edges <- bind_rows(level1, level2)

# Step 4: Turn the edge list into a graph object
graph <- graph_from_data_frame(edges)

# Step 5: Plot the hierarchy as a dendrogram
ggraph(graph, layout = "dendrogram", circular = FALSE) +
  geom_edge_diagonal() +
  geom_node_point() +
  theme_void()

# Adding Labels 
ggraph(graph, layout = 'dendrogram') + 
  geom_edge_diagonal() +
  geom_node_text(aes( label=name, filter=leaf) , angle=90 , hjust=1, nudge_y = -0.01) + #add a text label to each of your nodes
  ylim(-.4, NA)

ggraph(graph, layout = 'dendrogram') + 
  geom_edge_diagonal() +
  geom_node_text(aes( label=name, filter=leaf) , angle=90 , hjust=1, nudge_y = -0.04) +
  geom_node_point(aes(filter=leaf) , alpha=0.6) + #add points at the end of each line
  ylim(-.5, NA)

Interpretation

• each branch represents a cluster
• observations that join earlier are more similar

Circular Dendrograms

ggraph(graph, layout = "dendrogram", circular = TRUE) +
  geom_edge_diagonal() +
  geom_node_point() +
  theme_void()

ggraph(mygraph, layout = "dendrogram", circular = TRUE) +
  geom_edge_link() + #change from curved lines to straight linkages
  geom_node_point() + 
  theme_void()

#adding labels to circular plots is quite difficult. 

Word Clouds

Word clouds visualize text frequency.

Words that appear more often are displayed larger.

They are commonly used in:

• text analysis
• social media analysis
• document exploration

library(wordcloud2)

wordcloud2(data=demoFreq, size=1.6)

# Gives a proposed palette
wordcloud2(demoFreq, size=1.6, color='random-dark')

# or a vector of colors. vector must be same length than input data
wordcloud2(demoFreq, size=1.6, color=rep_len( c("green","blue"), nrow(demoFreq) ) )

# Change the background color
wordcloud2(demoFreq, size=1.6, color='random-light', backgroundColor="black")

Explanation

• word size reflects frequency
• color adds visual variation

You can custom the wordcloud shape using the shape argument. Available shapes are:

• circle
• cardioid
• diamond
• triangle-forward
• triangle
• pentagon
• star

# Change the shape:
wordcloud2(demoFreq, size = 0.7, shape = 'diamond')

Homework

Task 1. Pie chart with ggplot

Use the mpg dataset to create a table counting the number of vehicles in each class. Then create a pie chart showing the distribution of vehicle classes.

Create a simple pie chart in base R using the following values: 3, 7, 9, 1, 2

Label the slices:Gr-A, Gr-B, Gr-C, Gr-D, Gr-E

Requirements:

1. use count(class) from dplyr
2. use geom_col()
3. convert the plot to a pie chart with coord_polar(“y”)
4. use theme_void()
5. add a title

v <- c(3,7,9, 1,2)
pie(v , labels = c("Gr-A","Gr-B","Gr-C","Gr-D","Gr-E"))

ggplot(class_counts, aes(x = "", y = n, fill = class)) +
  geom_col() +
  coord_polar("y") +
  theme_void() +
  labs(title = "Vehicle Class Distribution")

Questions

• Which vehicle class appears most common?

SUV

• Why can pie charts become difficult to interpret when there are many categories?

You can’t easily tell what the proportions are without labels

Task 2: Create a simple pie chart in base R using the following values:

Create a donut chart using this small dataset:

data <- data.frame(
  category = c("A", "B", "C"),
  count = c(10, 60, 30)
)

ggplot(data, aes(x = 2, y = count, fill = category)) +
  geom_col(width = 1) +
  coord_polar("y") +
  xlim(0.5, 2.5) +
  theme_void() +
  labs(title = "Donut Chart")

Add labels that show either:

• the percentages, or
• the category names and values

data <- data %>%
  mutate(
    prop = count / sum(count), #calculate proportions
    ymax = cumsum(prop), #calculate slice boundaries to see where they begin and end
    ymin = lag(ymax, default = 0),
    label_pos = (ymax + ymin) / 2, #place text in the center of each slice arc
    label = percent(prop) 
  )

ggplot(data, aes(ymax = ymax, ymin = ymin, xmax = 4, xmin = 3, fill = category)) +
  geom_rect() +
  coord_polar(theta = "y") +
  
  # percentage labels
  geom_text(aes(x = 4.2, y = label_pos, label = label), size = 4) + #add percentage labels
  
  xlim(2, 4.5) +
  theme_void()

Questions

• Why do we need to calculate label positions manually for this plot?

Because it’s made by making a sqaure graph circular so the labels will automatically go where they’d go for a square graph

• Which label type is easier to read: percentages only, or category plus value?

Category plus value

Task 3: Sunburst Plot

Create the following dataset:

sunburst_data <- data.frame(
  sequence = c(
    "Fruit-Apples",
    "Fruit-Bananas",
    "Fruit-Oranges",
    "Vegetable-Carrots",
    "Vegetable-Broccoli",
    "Vegetable-Peppers"
  ),
  value = c(30, 20, 25, 15, 18, 12)
)

Use the sunburstR package to create a sunburst plot.

sunburst(sunburst_data)

Legend

Task 4: Treemap

Using this dataset:

sales_data <- data.frame(
  category = c("Fruit", "Fruit", "Fruit", "Vegetable", "Vegetable", "Vegetable"),
  subcategory = c("Apples", "Bananas", "Oranges", "Carrots", "Broccoli", "Peppers"),
  value = c(30, 20, 25, 15, 18, 12)
)

Create a treemap.

Requirements

1. use index = c(“category”, “subcategory”)
2. use vSize = “value”
3. include a title

treemap(
  sales_data,
  index = c("category", "subcategory"),
  vSize = "value",
  title = "Treemap of Category and Subcategory Counts"
)

• How is a treemap similar to a sunburst chart?

It has categories and subcategories

• How is it different?

It’s made of rectangles instead of a circle, and it’s not interactive

• Which one do you think is easier to read?

Sunburst chart

Task 5: Dendrogram

Create a simple hierarchy with:

• one origin
• four groups
• three subgroups per group

Then convert it into a graph and plot it as a dendrogram. Add labels and points to the ends of the branches.

level1 <- data.frame(
  from = "origin",
  to = paste0("group_", 1:5)
)

level2 <- data.frame(
  from = rep(level1$to, each = 4),
  to = paste0("subgroup_", 1:20)
)

edges <- bind_rows(level1, level2)
graph <- graph_from_data_frame(edges)
ggraph(graph, layout = 'dendrogram') + 
  geom_edge_diagonal() +
  geom_node_text(aes( label=name, filter=leaf) , angle=90 , hjust=1, nudge_y = -0.04) +
  geom_node_point(aes(filter=leaf) , alpha=0.6) + #add points at the end of each line
  ylim(-.5, NA)

Task 6: Word Cloud

Run the following code to create a word based dataset from The State of the Union text. Annotate the lines to ensure you know how it was built. This uses a real speech and produces meaningful words.

library(janeaustenr) # loading the word dataset
library(tidytext) # loading tidytext 
library(dplyr) # loading dplyr
library(wordcloud2) # loading wordcloud2

text <- austen_books() # making dataset with words as individual tokens, counting how many times they appear
word_counts <- text %>%
  unnest_tokens(word, text) %>%
  count(word, sort = TRUE)

data("stop_words") # loading  the stop words dataset, which is commonly used words

#Pay special attention to this. This is important for word clouds. 
word_counts <- word_counts %>%
  anti_join(stop_words) # removing really common words from word cloud dataset

## Joining with `by = join_by(word)`

Create a word cloud of the most commonly used words in the speech.

wordcloud2(data=word_counts, size=1.6)