How to use geom_area() in R

ggplot2
ggplot2 geom_area()
Learn how to use geom_area() in R with practical examples. Step-by-step guide with code you can copy and run immediately.
Published

February 21, 2026

Introduction

The geom_area() function in ggplot2 creates area plots by filling the area between a line and the x-axis. It’s particularly useful for visualizing cumulative data, showing proportions over time, or creating stacked area charts. Area plots help emphasize the magnitude of values and make it easy to see trends in continuous data.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Area Plot

The Problem

We want to create a simple area plot showing the cumulative distribution of penguin body masses. This will help us visualize how the data accumulates across different weight ranges.

Step 1: Prepare the data

First, we’ll create a density distribution of penguin body masses.

penguin_density <- penguins |>
  filter(!is.na(body_mass_g)) |>
  pull(body_mass_g) |>
  density()

This creates a density object that we can convert into a data frame for plotting.

Step 2: Convert to data frame

We need to transform the density data into a format ggplot2 can work with.

density_df <- data.frame(
  x = penguin_density$x,
  y = penguin_density$y
)

Now we have x and y coordinates representing the density curve of body masses.

Step 3: Create the basic area plot

Let’s create our first area plot using the density data.

ggplot(density_df, aes(x = x, y = y)) +
  geom_area(fill = "skyblue", alpha = 0.7) +
  labs(title = "Distribution of Penguin Body Mass",
       x = "Body Mass (g)", y = "Density")

Basic area plot in R using geom_area() in ggplot2 showing the kernel density distribution of penguin body mass from the palmerpenguins dataset

The area under the curve is now filled, making it easy to see the overall distribution shape.

Example 2: Stacked Area Chart with Multiple Groups

The Problem

We want to compare how penguin body mass distributions vary by species over time. A stacked area chart will show both individual species contributions and the total combined distribution, which is perfect for understanding composition and trends.

Step 1: Create sample time series data

We’ll simulate monthly data showing penguin populations by species.

time_data <- expand.grid(
  month = 1:12,
  species = c("Adelie", "Chinstrap", "Gentoo")
) |>
  mutate(population = case_when(
    species == "Adelie" ~ 100 + month * 5 + rnorm(36, 0, 10),
    species == "Chinstrap" ~ 60 + month * 3 + rnorm(36, 0, 8),
    species == "Gentoo" ~ 80 + month * 4 + rnorm(36, 0, 12)
  ))

This creates realistic population data that varies by month and species.

Step 2: Create the stacked area plot

Now we’ll build a stacked area chart to show population changes over time.

ggplot(time_data, aes(x = month, y = population, fill = species)) +
  geom_area(position = "stack", alpha = 0.8) +
  scale_fill_manual(values = c("Adelie" = "#FF6B6B", 
                               "Chinstrap" = "#4ECDC4", 
                               "Gentoo" = "#45B7D1"))

The stacked areas show both individual species trends and total population growth.

Step 3: Enhance with better formatting

Let’s improve the plot with better labels and theme.

time_data |>
  ggplot(aes(x = month, y = population, fill = species)) +
  geom_area(position = "stack", alpha = 0.8) +
  scale_fill_manual(values = c("Adelie" = "#FF6B6B", 
                               "Chinstrap" = "#4ECDC4", 
                               "Gentoo" = "#45B7D1")) +
  labs(title = "Penguin Population by Species Over Time",
       x = "Month", y = "Population", fill = "Species") +
  theme_minimal()

Stacked area chart in R using geom_area() in ggplot2 showing simulated monthly penguin population by species with custom colors

The final plot clearly shows how each species contributes to the total population throughout the year.

Step 4: Create proportional area chart

For better comparison, we can show relative proportions instead of absolute numbers.

time_data |>
  ggplot(aes(x = month, y = population, fill = species)) +
  geom_area(position = "fill", alpha = 0.8) +
  scale_fill_manual(values = c("Adelie" = "#FF6B6B", 
                               "Chinstrap" = "#4ECDC4", 
                               "Gentoo" = "#45B7D1")) +
  labs(title = "Relative Penguin Population Proportions",
       x = "Month", y = "Proportion", fill = "Species")

Proportional area chart in R using geom_area() with position = “fill” in ggplot2 showing relative penguin population proportions by species over time

Using position = "fill" shows each species as a proportion of the total, making relative changes more apparent.

Summary

  • Basic area plots use geom_area() to fill the area under a curve, perfect for showing distributions or cumulative data
  • Stacked area charts with position = "stack" display multiple groups while showing both individual and total values
  • Proportional area charts with position = "fill" emphasize relative contributions rather than absolute values
  • Customize appearance using fill, alpha, and scale_fill_manual() to create visually appealing and informative plots

  • Area plots work best with continuous x-axis data and are particularly effective for time series or cumulative datasets