🥧 R – Pie Charts and Bar Charts: Create Category Plots with Base R & ggplot2

Introduction – Visualizing Categorical Data in R

When working with categorical data, two common visualization tools are Pie Charts and Bar Charts. R provides both base plotting and ggplot2 options to create these visuals effectively. While pie charts show proportions, bar charts are better for comparing values across categories.

In this guide, you’ll learn:

• How to create pie and bar charts in base R and ggplot2
• Use color, labels, legends, and customization
• Choose between bar and pie charts based on best practices
• Understand chart creation line by line

🥧 1. Pie Charts in Base R

Basic Pie Chart

slices <- c(10, 20, 30, 40)
labels <- c("Q1", "Q2", "Q3", "Q4")
pie(slices, labels = labels, main = "Quarterly Revenue Share")

Explanation:

• slices: Numerical values representing each category
• labels: Category names shown on chart
• main: Title of the chart

Pie Chart with Percentages

slices <- c(25, 25, 30, 20)
labels <- c("A", "B", "C", "D")
pct <- round(slices / sum(slices) * 100)
labels_pct <- paste(labels, pct, "%")
pie(slices, labels = labels_pct, main = "Category Share")

Explanation:

• round(slices / sum(slices) * 100): Calculates percentage share
• paste(): Combines label with percentage
• Shows both label and % on the chart

2. Bar Charts in Base R

Vertical Bar Chart

counts <- table(mtcars$cyl)
barplot(counts, main = "Car Cylinders",
        xlab = "Cylinders", ylab = "Count",
        col = "lightblue", border = "black")

Explanation:

• table() counts frequency of cylinder values
• barplot() displays those counts as vertical bars
• col, border: Adds color and border styling

Horizontal Bar Chart

barplot(counts, horiz = TRUE,
        main = "Horizontal Bar Chart",
        xlab = "Count", col = "orange")

Stacked Bar Chart Example

data <- matrix(c(3, 2, 5, 4), nrow = 2)
barplot(data, main = "Stacked Bars", col = c("blue", "green"), legend = c("Set A", "Set B"))

3. Bar Charts with ggplot2

Simple Bar Plot

library(ggplot2)
ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar(fill = "skyblue") +
  labs(title = "Cylinder Distribution", x = "Cylinders", y = "Count")

Explanation:

• geom_bar() counts observations for each category
• factor(cyl) ensures categorical axis
• fill sets color

Bar Chart with Custom Data

df <- data.frame(
  category = c("A", "B", "C"),
  value = c(10, 20, 15)
)

ggplot(df, aes(x = category, y = value)) +
  geom_col(fill = "steelblue") +
  labs(title = "Category Values")

Explanation:

• geom_col() uses actual y-values (vs. geom_bar() which counts)
• Great for plotting custom category totals

When to Use Bar vs Pie Charts

🔔 Tip: Prefer bar charts for accurate comparison; pie charts are useful for quick, aesthetic proportion views.

Save Pie or Bar Charts

png("bar_chart.png", width = 600, height = 400)
barplot(counts, main = "Save Example")
dev.off()

Summary – Recap & Next Steps

Both pie and bar charts are effective tools for categorical data visualization. While bar charts provide clarity and comparison, pie charts are best for quick insights into proportions.

Key Takeaways:

• Use pie() for proportions, barplot() for frequency
• ggplot2 offers layered, customizable charting
• Prefer bar charts for comparison; pie charts for visual summaries
• Use geom_bar() (counts) or geom_col() (custom values) in ggplot2

Real-World Relevance:
Used in business dashboards, surveys, marketing reports, and presentation graphics where category comparison is vital.

FAQs – Pie and Bar Charts in R

Can I sort bars by height in R?
Yes, reorder the factor:

df$category <- factor(df$category, levels = df$category[order(df$value)])

How do I add labels to each bar in a bar chart?
Use text() in base R:

bp <- barplot(counts)
text(bp, counts, labels = counts, pos = 3)

What’s the difference between geom_bar() and geom_col()?
geom_bar() counts automatically; geom_col() uses given y-values.

How do I make a 3D pie chart?
Use plotrix::pie3D() from the plotrix package.

Can I use hex or RGB colors in plots?
Yes:

barplot(counts, col = "#1f77b4")