Faceting is a powerful way to break a complex plot into multiple simpler ones, allowing you to compare data across different categories. Instead of creating separate plots manually for each group (e.g., each fuel type), ggplot2 offers functions like facet_wrap() and facet_grid() to automatically generate subplots for categorical variables. This makes trends, outliers, and patterns easier to detect across subgroups.

Example: Faceting by Fuel Type

Faceting allows you to split a plot into multiple panels based on the values of a categorical variable. In this example, the scatter plot of selling price vs. kilometers driven is divided into separate panels for each fuel type.

ggplot(df, aes(x = km_driven, y = selling_price)) +
  geom_point() +
  facet_wrap(~ fuel) +
  labs(title = "Selling Price vs Kilometers Driven (by Fuel Type)")

This makes it easier to compare relationships across categories, since each fuel type is shown in its own plot. Patterns that might be hidden in a combined scatter plot become clearer when separated.

Example: Facet Grid for Fuel and Transmission

A facet grid can be used to split a plot into multiple panels based on two categorical variables. In this example, the scatter plot of selling price vs. kilometers driven is divided by fuel type (rows) and transmission type (columns).

ggplot(df, aes(x = km_driven, y = selling_price)) +
  geom_point() +
  facet_grid(fuel ~ transmission) +
  labs(title = "Selling Price vs Kilometers Driven (by Fuel and Transmission)")

This visualization makes it easy to compare how the relationship between mileage and price differs not just by fuel type but also by transmission. It provides a clear side-by-side view of patterns across multiple categories.

Plot Types Summary