How to use geom_contour() in R

ggplot2

ggplot2 geom_contour()

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

Published

February 21, 2026

Introduction

The geom_contour() function in ggplot2 creates contour lines that connect points of equal value across a surface, similar to topographic maps showing elevation. This visualization is perfect for displaying three-dimensional relationships on a two-dimensional plot, making it ideal for statistical surfaces, density distributions, and mathematical functions.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

We want to create a basic contour plot to visualize the relationship between three continuous variables. Let’s explore how penguin body mass varies across different combinations of bill length and flipper length.

Step 1: Prepare the data

We need to create a grid of values and calculate the corresponding z-values for our contour lines.

# Create sample data with mathematical function
x <- seq(-2, 2, length.out = 50)
y <- seq(-2, 2, length.out = 50)
grid_data <- expand_grid(x = x, y = y)

This creates a regular grid of x and y coordinates that will serve as our foundation for the contour plot.

Step 2: Calculate z-values

We need to compute the z-values (heights) for each point on our grid using a mathematical function.

# Calculate z values using a mathematical function
grid_data <- grid_data |>
  mutate(z = sin(sqrt(x^2 + y^2)) / sqrt(x^2 + y^2))

The function creates a ripple effect pattern, with z-values representing the “height” at each x,y coordinate.

Step 3: Create the basic contour plot

Now we can create our first contour plot using the prepared data.

# Create basic contour plot
ggplot(grid_data, aes(x = x, y = y, z = z)) +
  geom_contour(color = "steelblue") +
  labs(title = "Contour Plot of a Mathematical Function",
       subtitle = "z = sin(sqrt(x^2 + y^2)) / sqrt(x^2 + y^2)",
       x = "x", y = "y") +
  theme_minimal()

Basic contour plot in R using geom_contour() in ggplot2 showing a radial sine wave mathematical function on a 2D grid

This produces a basic contour plot with default contour lines connecting points of equal z-values.

Example 2: Practical Application

The Problem

Let’s create a more practical example using real data. We want to visualize how penguin body mass varies across different combinations of bill length and flipper length, creating a statistical surface that shows patterns in the data.

Step 1: Prepare the penguin data

We need to clean the data and create a suitable dataset for contour plotting.

# Clean and prepare penguin data
penguins_clean <- penguins |>
  filter(!is.na(bill_length_mm), 
         !is.na(flipper_length_mm), 
         !is.na(body_mass_g))

This removes any rows with missing values that would interfere with our contour calculations.

Step 2: Create a statistical surface

We’ll use a 2D density estimation to create smooth contour lines from our discrete data points.

# Create contour plot with filled contours and custom breaks
ggplot(penguins_clean, aes(x = flipper_length_mm, y = bill_length_mm)) +
  geom_density_2d_filled(alpha = 0.7) +
  geom_point(size = 0.6, alpha = 0.7) +
  labs(title = "2D Density of Penguin Flipper and Bill Length",
       x = "Flipper Length (mm)", y = "Bill Length (mm)") +
  theme_minimal()

Filled 2D density contour in R using geom_density_2d_filled() showing penguin flipper length vs bill length with overlaid points in ggplot2

This creates filled contour regions showing the density of penguins at different combinations of flipper and bill measurements.

Step 3: Enhance with custom styling

Let’s improve the visualization with better colors, labels, and contour specifications.

# Enhanced contour plot with custom styling
ggplot(penguins_clean, aes(x = flipper_length_mm, y = bill_length_mm)) +
  geom_contour(aes(z = after_stat(level)), 
               color = "darkblue", size = 0.8) +
  labs(title = "Penguin Measurements Contour Plot",
       x = "Flipper Length (mm)",
       y = "Bill Length (mm)")

This version uses custom colors and proper labels to create a publication-ready contour plot.

Step 4: Add species information

Finally, let’s incorporate species information to make the plot more informative.

# Add species coloring and faceting
ggplot(penguins_clean, aes(x = flipper_length_mm, y = bill_length_mm)) +
  geom_point(aes(color = species), alpha = 0.6) +
  geom_density_2d(color = "black", linewidth = 0.5) +
  facet_wrap(~species) +
  labs(title = "2D Density Contours of Penguin Measurements by Species",
       x = "Flipper Length (mm)", y = "Bill Length (mm)") +
  theme_minimal() +
  theme(legend.position = "none")

Faceted 2D density contour plot in R using geom_density_2d() with facet_wrap() showing penguin flipper and bill length by species in ggplot2

This creates separate contour plots for each penguin species, revealing distinct patterns in their body measurements.

Summary

geom_contour() creates contour lines connecting points of equal value, perfect for visualizing three-dimensional relationships
Basic contour plots require x, y, and z aesthetics to define the surface being mapped
geom_density_2d() and geom_density_2d_filled() are useful variants for creating statistical contours from point data
Contour plots work best with continuous variables and regular grid data or sufficient point density
Customize contour appearance using color, size, and alpha parameters, and consider faceting for categorical variables

--- title: "How to use geom_contour() in R" description: "Learn how to use geom_contour() in R with practical examples. Step-by-step guide with code you can copy and run immediately." date: 2026-02-21 categories: ['ggplot2', 'ggplot2 geom_contour()'] format: html: code-fold: false code-tools: true --- ## Introduction The `geom_contour()` function in ggplot2 creates contour lines that connect points of equal value across a surface, similar to topographic maps showing elevation. This visualization is perfect for displaying three-dimensional relationships on a two-dimensional plot, making it ideal for statistical surfaces, density distributions, and mathematical functions. ## Getting Started ```r library(tidyverse) library(palmerpenguins) ``` ## Example 1: Basic Usage ### The Problem We want to create a basic contour plot to visualize the relationship between three continuous variables. Let's explore how penguin body mass varies across different combinations of bill length and flipper length. ### Step 1: Prepare the data We need to create a grid of values and calculate the corresponding z-values for our contour lines. ```r # Create sample data with mathematical function x <- seq(-2, 2, length.out = 50) y <- seq(-2, 2, length.out = 50) grid_data <- expand_grid(x = x, y = y) ``` This creates a regular grid of x and y coordinates that will serve as our foundation for the contour plot. ### Step 2: Calculate z-values We need to compute the z-values (heights) for each point on our grid using a mathematical function. ```r # Calculate z values using a mathematical function grid_data <- grid_data |> mutate(z = sin(sqrt(x^2 + y^2)) / sqrt(x^2 + y^2)) ``` The function creates a ripple effect pattern, with z-values representing the "height" at each x,y coordinate. ### Step 3: Create the basic contour plot Now we can create our first contour plot using the prepared data. ```r # Create basic contour plot ggplot(grid_data, aes(x = x, y = y, z = z)) + geom_contour(color = "steelblue") + labs(title = "Contour Plot of a Mathematical Function", subtitle = "z = sin(sqrt(x^2 + y^2)) / sqrt(x^2 + y^2)", x = "x", y = "y") + theme_minimal() ``` ![Basic contour plot in R using geom_contour() in ggplot2 showing a radial sine wave mathematical function on a 2D grid](/images/ggplot2/geom-contour-in-r-mathematical-function-contour-ggplot.png) This produces a basic contour plot with default contour lines connecting points of equal z-values. ## Example 2: Practical Application ### The Problem Let's create a more practical example using real data. We want to visualize how penguin body mass varies across different combinations of bill length and flipper length, creating a statistical surface that shows patterns in the data. ### Step 1: Prepare the penguin data We need to clean the data and create a suitable dataset for contour plotting. ```r # Clean and prepare penguin data penguins_clean <- penguins |> filter(!is.na(bill_length_mm), !is.na(flipper_length_mm), !is.na(body_mass_g)) ``` This removes any rows with missing values that would interfere with our contour calculations. ### Step 2: Create a statistical surface We'll use a 2D density estimation to create smooth contour lines from our discrete data points. ```r # Create contour plot with filled contours and custom breaks ggplot(penguins_clean, aes(x = flipper_length_mm, y = bill_length_mm)) + geom_density_2d_filled(alpha = 0.7) + geom_point(size = 0.6, alpha = 0.7) + labs(title = "2D Density of Penguin Flipper and Bill Length", x = "Flipper Length (mm)", y = "Bill Length (mm)") + theme_minimal() ``` ![Filled 2D density contour in R using geom_density_2d_filled() showing penguin flipper length vs bill length with overlaid points in ggplot2](/images/ggplot2/geom-contour-in-r-density-2d-filled-penguins-ggplot.png) This creates filled contour regions showing the density of penguins at different combinations of flipper and bill measurements. ### Step 3: Enhance with custom styling Let's improve the visualization with better colors, labels, and contour specifications. ```r # Enhanced contour plot with custom styling ggplot(penguins_clean, aes(x = flipper_length_mm, y = bill_length_mm)) + geom_contour(aes(z = after_stat(level)), color = "darkblue", size = 0.8) + labs(title = "Penguin Measurements Contour Plot", x = "Flipper Length (mm)", y = "Bill Length (mm)") ``` This version uses custom colors and proper labels to create a publication-ready contour plot. ### Step 4: Add species information Finally, let's incorporate species information to make the plot more informative. ```r # Add species coloring and faceting ggplot(penguins_clean, aes(x = flipper_length_mm, y = bill_length_mm)) + geom_point(aes(color = species), alpha = 0.6) + geom_density_2d(color = "black", linewidth = 0.5) + facet_wrap(~species) + labs(title = "2D Density Contours of Penguin Measurements by Species", x = "Flipper Length (mm)", y = "Bill Length (mm)") + theme_minimal() + theme(legend.position = "none") ``` ![Faceted 2D density contour plot in R using geom_density_2d() with facet_wrap() showing penguin flipper and bill length by species in ggplot2](/images/ggplot2/geom-contour-in-r-faceted-density-by-species-ggplot.png) This creates separate contour plots for each penguin species, revealing distinct patterns in their body measurements. ## Summary - `geom_contour()` creates contour lines connecting points of equal value, perfect for visualizing three-dimensional relationships - Basic contour plots require x, y, and z aesthetics to define the surface being mapped - `geom_density_2d()` and `geom_density_2d_filled()` are useful variants for creating statistical contours from point data - Contour plots work best with continuous variables and regular grid data or sufficient point density - Customize contour appearance using color, size, and alpha parameters, and consider faceting for categorical variables --- ## Related Posts - [How to use geom_point() in R](/ggplot2/how-to-use-geompoint-in-r.html) - [How to use geom_area() in R](/ggplot2/how-to-use-geomarea-in-r.html) - [How to use geom_tile() in R](/ggplot2/how-to-use-geomtile-in-r.html) - [How to use select() in R](/dplyr/how-to-use-select-in-r.html) - [How to use mutate() in R](/dplyr/how-to-use-mutate-in-r.html)