Faceting for subplots in R Programming

Introduction

Faceting helps you split one big plot into smaller plots based on categories. It makes it easier to compare groups side by side.

You want to compare sales data for different regions in separate small plots.

You have survey results for different age groups and want to see each group's pattern.

You want to show how a variable changes over time for different categories.

You want to display multiple subsets of data clearly without mixing them in one plot.

Syntax

R Programming

ggplot(data) +
  geom_point(mapping) +
  facet_wrap(~ variable)

# or

ggplot(data) +
  geom_point(mapping) +
  facet_grid(rows ~ cols)

facet_wrap() creates subplots wrapped into multiple rows or columns.

facet_grid() arranges subplots in a grid with rows and columns based on variables.

Examples

This makes small plots for each car class side by side.

R Programming

ggplot(mpg) +
  geom_point(aes(displ, hwy)) +
  facet_wrap(~ class)

This creates a grid of plots with drv as rows and cyl as columns.

R Programming

ggplot(mpg) +
  geom_point(aes(displ, hwy)) +
  facet_grid(drv ~ cyl)

Sample Program

This program shows how engine size relates to highway fuel efficiency for each car class in separate small plots.

R Programming

library(ggplot2)

# Use the built-in mpg dataset
# Plot engine displacement vs highway miles per gallon
# Facet by car class

ggplot(mpg) +
  geom_point(aes(displ, hwy)) +
  facet_wrap(~ class) +
  labs(title = "Engine Size vs Highway MPG by Car Class")

Important Notes

Faceting automatically shares axes by default, making comparison easier.

You can control the number of rows or columns in facet_wrap() with nrow or ncol.

Use scales = "free" inside faceting functions to allow axes to vary per subplot.

Summary

Faceting splits one plot into many small plots based on categories.

facet_wrap() arranges plots wrapped in rows or columns.

facet_grid() arranges plots in a grid by rows and columns.