Advanced ggplot2

In this lesson, you will explore advanced features of ggplot2 that help you create more informative and professional-quality visualizations.

You will learn how to customize plot appearance, use themes, split data into multiple panels, and improve readability for real-world data analysis.

Adding Multiple Layers

One of the biggest strengths of ggplot2 is its layered structure.

You can combine multiple geometries in a single plot to show different aspects of the data.

data <- data.frame(
  month = c("Jan", "Feb", "Mar", "Apr", "May"),
  sales = c(200, 250, 300, 280, 350)
)

ggplot(data, aes(x = month, y = sales)) +
  geom_col(fill = "skyblue") +
  geom_point(color = "darkblue", size = 3)

Using Themes

Themes control the overall look and feel of a plot.

ggplot2 provides built-in themes that can be applied easily.

ggplot(data, aes(x = month, y = sales)) +
  geom_col(fill = "steelblue") +
  theme_minimal()

Themes help create clean and consistent visual styles across reports.

Commonly Used Themes

• theme_minimal() – clean and modern
• theme_classic() – simple with axis lines
• theme_bw() – black and white style

Customizing Axes

Axis labels and text can be customized for better readability.

This is especially useful when working with long labels.

ggplot(data, aes(x = month, y = sales)) +
  geom_col(fill = "orange") +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Faceting (Multiple Panels)

Faceting allows you to split a dataset into multiple panels based on a variable.

This helps compare patterns across categories.

data2 <- data.frame(
  month = rep(c("Jan", "Feb", "Mar"), 2),
  sales = c(200, 250, 300, 180, 220, 260),
  region = c("East", "East", "East", "West", "West", "West")
)

ggplot(data2, aes(x = month, y = sales)) +
  geom_col(fill = "lightgreen") +
  facet_wrap(~ region)

Adding Labels to Bars

Data labels make plots easier to interpret by showing exact values.

This is commonly used in dashboards and reports.

ggplot(data, aes(x = month, y = sales)) +
  geom_col(fill = "purple") +
  geom_text(aes(label = sales), vjust = -0.5)

Why Advanced ggplot2 Matters

• Creates publication-ready visuals
• Improves clarity and storytelling
• Supports complex data comparisons
• Widely used in analytics and reporting

📝 Practice Exercises

Exercise 1

Create a bar chart with a minimal theme.

Exercise 2

Add rotated x-axis labels to a plot.

Exercise 3

Use faceting to compare data across groups.

Exercise 4

Add value labels to bars in a bar chart.

✅ Practice Answers

Answer 1

ggplot(data, aes(x = month, y = sales)) +
  geom_col() +
  theme_minimal()

Answer 2

ggplot(data, aes(x = month, y = sales)) +
  geom_col() +
  theme(axis.text.x = element_text(angle = 45))

Answer 3

ggplot(data2, aes(x = month, y = sales)) +
  geom_col() +
  facet_wrap(~ region)

Answer 4

ggplot(data, aes(x = month, y = sales)) +
  geom_col() +
  geom_text(aes(label = sales), vjust = -0.5)

What’s Next?

In the next lesson, you will move into Statistics Basics, where you will start applying R to statistical analysis.