R – Charts & Graphs Overview: Bar, Line, Pie, Box, Scatter Plots

Introduction – Visualizing Data in R

R is renowned for its powerful data visualization capabilities. From simple bar charts to complex multi-faceted plots, R allows you to visualize trends, distributions, and relationships using both base R functions and advanced libraries like ggplot2.

In this guide, you’ll learn:

• The most common types of charts and graphs in R
• Syntax using both base R and ggplot2
• Practical code examples with clear explanations
• When to use each type of chart effectively

1. Bar Charts – Compare Categories

Base R:

counts <- table(mtcars$cyl)
barplot(counts, main = "Cylinder Counts", xlab = "Cylinders", col = "steelblue")

Explanation:

• table() creates frequency counts of cyl.
• barplot() visualizes the count by category.
• main sets title, xlab sets x-axis label.

ggplot2:

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

2. Line Charts – Trend Over Time

plot(AirPassengers, type = "l", col = "blue", lwd = 2,
     main = "Monthly Airline Passengers", ylab = "Passengers")

Explanation:

• type = "l" specifies line chart.
• lwd adjusts line width.
• Useful for time series data.

3. Scatterplots – Show Relationships

plot(mtcars$wt, mtcars$mpg,
     main = "MPG vs Weight", xlab = "Weight", ylab = "MPG",
     pch = 19, col = "red")

Explanation:

• pch = 19 sets solid circle.
• Reveals correlation between weight and fuel efficiency.

4. Pie Charts – Part-to-Whole Comparison

slices <- c(10, 20, 30, 40)
labels <- c("A", "B", "C", "D")
pie(slices, labels = labels, main = "Pie Chart Example")

Explanation:

• Each slice shows percentage of total.
• Use sparingly (bar charts often more informative).

5. Boxplots – Summary of Distributions

boxplot(mpg ~ cyl, data = mtcars, 
        main = "MPG by Cylinder", xlab = "Cylinders", ylab = "MPG")

Explanation:

• Compares distribution of mpg across cylinder groups.
• Useful for spotting outliers and variability.

6. Histograms – Frequency Distribution

hist(mtcars$mpg, breaks = 10, col = "gray",
     main = "Histogram of MPG", xlab = "Miles Per Gallon")

Explanation:

• breaks = 10 defines bin count.
• Ideal for checking skewness, spread, and normality.

7. Advanced Plotting with ggplot2

Quick Overview:

ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(binwidth = 2, fill = "blue", color = "white")

Save Charts to File

png("myplot.png", width = 600, height = 400)
plot(mtcars$wt, mtcars$mpg)
dev.off()

Explanation:

• png() opens graphic device.
• dev.off() saves and closes the plot.

Summary – Recap & Next Steps

Charts and graphs are essential for communicating insights visually. R makes plotting fast, customizable, and powerful for any level of complexity.

Key Takeaways:

• Use base R (plot(), barplot(), hist()) for simple visuals
• Use ggplot2 for flexible and professional charts
• Choose chart types based on your data goal (compare, trend, relate)
• Always label axes, use color meaningfully, and explore data visually

Real-World Relevance:
Used in business dashboards, academic research, machine learning diagnostics, and automated reporting.

FAQs – R Charts & Graphs

What is the difference between plot() and ggplot() in R?
plot() is from base R—simple and quick. ggplot() from ggplot2 is more customizable and elegant.

Can I save a plot directly as a PDF or PNG?
Yes. Use pdf(), png(), jpeg(), and wrap your plot inside.

How do I add a legend to a plot?
Use legend() in base R or labs(color = "Group") in ggplot2.

Which chart is best for showing part-to-whole relationships?
Pie charts can show parts, but bar charts are more effective and accurate.

How do I plot multiple lines on the same chart?
Use lines() in base R or multiple geom_line() calls in ggplot2.