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

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🧲 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

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🧰 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.

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✅ ggplot2:

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

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📈 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.

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📉 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.

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📊 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).

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📦 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.

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📚 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.

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📐 7. Advanced Plotting with ggplot2

✅ Quick Overview:

Function	Description
geom_bar()	Bar chart
geom_line()	Line plot
geom_point()	Scatterplot
geom_boxplot()	Boxplot
geom_histogram()	Histogram

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

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🖼️ 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.

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📌 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.

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❓ 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.

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