Visualizing Data in R with Default Package:
Bar Plot and Pie Chart
Somsak Chanaim
International College of Digital Innovation, CMU
April 8, 2025
Example of Bar Plots
Bar Plot
A bar plot is a type of chart or graph that uses bars to represent data values.
It is essentially a visual display of information where individual bars or columns correspond to different categories or groups, and the length or height of each bar reflects the magnitude of the data it represents.
Bar plots are commonly used for visualizing categorical data and making comparisons between different groups.
The bar plot components:
Axes:
The horizontal axis typically represents categories or groups.
The vertical axis represents the values, frequencies, or percentages associated with each category.
Bars:
Each bar is a rectangular column that extends from the baseline (zero) to a height corresponding to the value it represents.
The width of the bars may vary, but they are usually uniform.
Spacing:
- There is often some space between adjacent bars to visually separate them.
bar plots are useful for showing the distribution of data, identifying patterns, and making comparisons between different categories.
Bar Plot Using R Programming
In R, you can create a bar plot using the barplot() function. We will create a basic bar plot with random data:
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# Create some example data
categories <- c("Category A", "Category B",
"Category C", "Category D")
values <- c(25, 40, 15, 30)
# Create a bar plot
barplot( height = values,
names.arg = categories,
col = "skyblue",
main = "Example bar plot",
ylab = "Values",
xlab = "Categories")
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Let’s break down the code:
vector:
valuesis a vector containing the corresponding values for each category.categoriesis a vector containing the names of the categories.
argument:
heightis the data you want to visualize. (numeric variable)names.argis used to specify the names of the categories. (character/factor variable)colsets the color of the bars (you can choose any color).mainsets the title of the plot.ylabandxlabset the labels for the y-axis and x-axis, respectively.
The table() Function
The table() function in R is used to create a contingency table, which shows the frequency distribution of a variable or the cross-tabulation of multiple variables.
It counts the number of occurrences of each unique value in a vector, factor, or set of factors.
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set.seed(1)
N <- 1000
faculty <- sample( x = c("ICDI", "SCI","ECON"),
size = N,
replace = TRUE,
prob = c(.5,.3,.2))
faculty
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table(faculty)
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Create the Bar Plot from table() Function
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barplot(table(faculty))
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Example with Pipe Operator
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faculty |>
table() |>
barplot(main = "The bar plot",
col = "green",
ylim = c(0, 540))
box() # add box
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Add More Color to each Bar
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faculty |>
table() |>
sort() |>
barplot(main = "The bar plot",
col = c("pink","yellow","blue"),
ylim = c(0, 540))
box() # add box
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name <- paste("student" ,1:5)
money <- c(100, 50, 150, 500, 20)
barplot( height = money,
names.arg = name,
ylim = c(0,520))
box(col ="red")
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Rotate Graph
Rotate graph by set horiz =TRUE
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barplot( height = money,
names.arg = name,
horiz = TRUE,
xlim = c(0,520)
)
box(col ="red")
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Ordering the variable ‘money’ from the minimum to the maximum value.
color: “#ffff00”
color: “#0000ff”
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library(dplyr)
Data <- data.frame(name, money)
Data <- Data |>
arrange(money)
barplot( height = Data$money,
names.arg = Data$name,
col = c(rep("#ffff00", 4) ,"#0000ff"),
ylab = "money")
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Transpose table
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set.seed(1)
N <- 1000
faculty <- sample( x = c("ICDI", "SCI","ECON"),
size = N,
replace = TRUE,
prob = c(.5,.3,.2))
gender <- sample( x = c("female","male"),
size = N,
replace = TRUE)
Data2 <- data.frame(faculty, gender)
table(Data2)
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t(table(Data2))
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Stacked bar plot
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Data2 |>
table() |>
barplot()
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Use t() to create another bar plot
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Data2 |>
table() |>
t() |>
barplot()
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Grouped Bar Plot
The argument beside = TRUE is used to create grouped bar plots, where bars corresponding to different categories are placed side by side, rather than stacked on top of each other.
color: “#ff0000”
color: “#00ff00”
color: “#0000ff”
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Data2 |>
table() |>
barplot(beside = TRUE,
legend.text = c(rownames(table(Data2))),
col = c("#ff0000", "#00ff00", "#0000ff"),
ylim = c(0,300)
)
box()
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Data2 |>
table() |>
t() |>
barplot( beside = TRUE,
legend.text = c(colnames(table(Data2 ))),
col = c("#ff0000", "#0000ff"),
ylim = c(0,300)
)
box()
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Exercise
Exercise 1: Basic Bar Plot
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Task:
Create a basic bar plot to visualize the frequency of different gear types in the mtcars dataset.
Instructions:
Load the
mtcarsdataset usingdata(mtcars).Use the
table()function to calculate the frequency of thegearvariable.Use the
barplot()function to create a bar plot.Add a title and labels to the axes using the
main,xlab, andylabarguments.
Exercise 2: Horizontal Bar Plot
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Task:
Create a horizontal bar plot of the frequency of cylinder types in the mtcars dataset.
Instructions:
Use the
table()function to calculate the frequency of thecylvariable.Use the
barplot()function with thehoriz = TRUEargument to create a horizontal bar plot.Customize the colors of the bars using the
colargument.
Exercise 3: Grouped Bar Plot
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Task:
Create a grouped bar plot to compare the frequency of cylinder types (cyl) by the number of gears (gear) in the mtcars dataset.
Instructions:
Use the
table()function to create a contingency table ofcylandgear.Use the
barplot()function to create a grouped bar plot by settingbeside = TRUE.Add a legend to the plot using the
legend.textandargs.legendarguments.
Solution:
# Create a contingency table of cyl and gear
cyl_gear_table <- table(mtcars$cyl, mtcars$gear)
# Create a grouped bar plot
barplot(cyl_gear_table,
beside = TRUE,
main = "Cylinder Types by Gear Types",
xlab = "Number of Gears",
ylab = "Frequency",
col = c("red", "green", "blue"),
legend.text = rownames(cyl_gear_table),
args.legend = list(title = "Cylinders", x = "topright"))Exercise 4: Stacked Bar Plot
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Task:
Create a stacked bar plot to show the distribution of cylinder types (cyl) within different gear types (gear) in the mtcars dataset.
Instructions:
Use the
table()function to create a contingency table ofcylandgear.Use the
barplot()function to create a stacked bar plot (default behavior).Customize the colors of the bars using a color palette.
Solution:
# Create a contingency table of cyl and gear
cyl_gear_table <- table(mtcars$cyl, mtcars$gear)
# Create a stacked bar plot
barplot(cyl_gear_table,
main = "Stacked Bar Plot of Cylinder Types by Gear Types",
xlab = "Number of Gears",
ylab = "Frequency",
col = c("red", "green", "blue"),
legend.text = rownames(cyl_gear_table),
args.legend = list(title = "Cylinders", x = "topright"))Exercise 5: Bar Plot with Custom Axis Labels and Colors
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Task:
Create a bar plot showing the counts of different species in the iris dataset, with custom axis labels, colors, and bar widths.
Instructions:
Use the
table()function to calculate the frequency of theSpeciesvariable in theirisdataset.Use the
barplot()function to create the bar plot.Customize the bar plot with the following:
Use custom colors for each bar.
Add custom names to the x-axis labels using the
names.argargument.Adjust the width of the bars using the
widthargument.
Solution:
# Calculate the frequency of species
species_freq <- table(iris$Species)
# Create the bar plot with customizations
barplot(species_freq,
main = "Frequency of Species in Iris Dataset",
xlab = "Species",
ylab = "Frequency",
col = c("purple", "orange", "cyan"),
names.arg = c("Setosa", "Versicolor", "Virginica"),
width = 0.7,
border = "black")Pie Chart
A pie chart is a circular statistical graphic that is divided into slices to illustrate numerical proportions. In R, you can create a pie chart using the pie() function.
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faculty |> table() |> pie()
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See Listing 1 for details.
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faculty |>
table() |>
pie( col = c("red","green","blue"))
box() # add box
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name <- paste("student" ,1:5)
money <- c(100, 50, 150, 500, 20)
pie(x = money,labels = name)
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pie() Function:
The
pie()function creates a pie chart.The first argument
xis the data you want to visualize, which in this case is the frequency of different cylinder types.The main argument specifies the title of the chart.
The
colargument is used to set custom colors for the slices of the pie chart.The
labelsargument customizes the labels that appear on the slices of the pie chart.
Why pie chart is not useful?
The pie chart is one of the most popular charts — but also one of the most criticized in data visualization.
❌ Why pie charts are often not recommended:
🎯 Humans are bad at comparing angles
It’s hard for people to accurately compare the size of pie slices
especially when values are close.
Bar charts allow for easier and more accurate comparison using length, not angle.
Example: Can you easily tell the difference between 23% and 27% in a pie chart? Not really.
🍩 Too many slices = confusion
Pie charts get messy with more than 4–5 categories.
Colors become harder to distinguish.
Labels may overlap or require a legend, which makes interpretation slower.
📉 No clear axis = no easy comparison
There’s no y-axis or consistent baseline.
We lose the advantage of alignment (as in bar plots) which helps the eye compare quantities.
📊 Bar chart does everything better
Bar charts:
Are easier to read
Handle small differences better
Are simpler to label
Are more scalable to many categories
✅ When pie charts might still be acceptable:
| Use Case | Okay? |
|---|---|
| Only 2–3 clear categories | ✅ Yes |
| Audience is general/public | ✅ Maybe |
| Style or storytelling focus | ✅ Yes (e.g. in infographics) |
| Detailed analysis or comparison | ❌ No |
🔁 Summary: Pie vs Bar
| Feature | Pie Chart | Bar Chart |
|---|---|---|
| Easy to compare | ❌ Hard | ✅ Easy |
| Best for small N | ✅ Yes (2–3 items) | ✅ Yes (scalable) |
| Labels/readability | ❌ Poor | ✅ Better |
| Style/visual appeal | ✅ Sometimes | ⚖️ Depends on use |
🧠 Expert Opinions:
Edward Tufte (data viz pioneer): “The only thing worse than a pie chart is several of them.”
Stephen Few: Recommends bar or dot plots for almost every case where pie charts are used.
Downloading webR
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Visualizing Data in R with Default Package: Bar Plot and Pie Chart Somsak Chanaim International College of Digital Innovation, CMU April 8, 2025