An Introduction to R's ggplot2 for Beautiful Visualizations
🎨 An Introduction to R's ggplot2 for Beautiful Visualizations
📦 Install and Load ggplot2
install.packages("ggplot2") # Install once
library(ggplot2) # Load in each session
📊 Using a Sample Dataset
We’ll use the built-in mtcars and diamonds datasets:
data(mtcars)
head(mtcars)
data(diamonds)
head(diamonds)
🧠 The Grammar of Graphics
ggplot2 is built using layers:
ggplot(data) +
aes(mapping) +
geom_*() +
theme_*() +
labs()
Each component adds something to the plot.
🔹 1. Basic Scatter Plot
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point()
✅ Shows the relationship between weight (wt) and miles per gallon (mpg).
🔹 2. Add Color and Size
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl), size = hp)) +
geom_point()
✅ Use color for cylinder count and size for horsepower.
🔹 3. Add Trend Line (Linear Regression)
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
✅ Adds a linear trend line without the confidence interval.
🔹 4. Bar Plot
ggplot(diamonds, aes(x = cut)) +
geom_bar(fill = "skyblue")
✅ Shows count of diamonds by cut.
🔹 5. Box Plot
ggplot(diamonds, aes(x = cut, y = price)) +
geom_boxplot(fill = "orange")
✅ Compare price distribution by cut quality.
🔹 6. Histogram
ggplot(diamonds, aes(x = price)) +
geom_histogram(binwidth = 500, fill = "steelblue")
✅ Visualize distribution of diamond prices.
🔹 7. Density Plot
ggplot(diamonds, aes(x = price, fill = cut)) +
geom_density(alpha = 0.4)
✅ Smoothed distribution, grouped by cut.
🔹 8. Faceting: Multiple Plots by Category
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
facet_wrap(~cyl)
✅ Creates separate scatter plots for each cylinder type.
🔹 9. Customizing Labels and Themes
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "darkgreen") +
labs(
title = "Fuel Efficiency by Car Weight",
x = "Weight (1000 lbs)",
y = "Miles per Gallon"
) +
theme_minimal()
✅ Improves readability and adds a clean theme.
🔹 10. Save Your Plot
ggsave("my_plot.png", width = 8, height = 6, dpi = 300)
✅ Save your chart as an image file.
🎯 Quick Reference: Common ggplot2 Geoms
Geom Description
geom_point() Scatter plots
geom_bar() Bar charts (categorical)
geom_col() Bar charts with values
geom_histogram() Histograms
geom_boxplot() Box plots
geom_line() Line plots
geom_density() Smoothed distributions
geom_smooth() Trend lines
📌 Pro Tips
Use theme_minimal(), theme_bw(), or theme_classic() to improve plot aesthetics.
Use aes() inside ggplot() to set global mappings.
Use aes() inside geom_*() for layer-specific mappings.
Always add labels and titles for clarity.
🧪 Mini Project Idea
Dataset: diamonds
Goals:
Bar plot of diamond cuts
Box plot of price by cut
Scatter plot of price vs. carat with color by clarity
Facet by cut or clarity
✅ Summary
ggplot2 allows you to:
Build plots layer-by-layer
Create stunning, professional-quality charts
Customize every detail with ease
Once you get comfortable with ggplot2, you’ll be able to communicate insights visually with far more impact.
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