How to use geom_ribbon() in R

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

February 21, 2026

Introduction

The geom_ribbon() function in ggplot2 creates filled areas between two y-values, making it perfect for visualizing confidence intervals, error bands, or ranges in data. It’s commonly used in statistical plots to show uncertainty around predictions or to highlight areas between curves.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

We want to create a simple ribbon plot showing the range between minimum and maximum penguin body mass values across different flipper lengths. This will help us understand the spread of body mass measurements.

Step 1: Prepare the data

First, we’ll calculate summary statistics for body mass by flipper length.

penguin_summary <- penguins |>
  filter(!is.na(body_mass_g), !is.na(flipper_length_mm)) |>
  group_by(flipper_length_mm) |>
  summarise(
    min_mass = min(body_mass_g),
    max_mass = max(body_mass_g),
    .groups = 'drop'
  )

This creates a dataset with minimum and maximum body mass values for each flipper length.

Step 2: Create the basic ribbon plot

Now we’ll use geom_ribbon() to visualize the range between minimum and maximum values.

ggplot(penguin_summary, aes(x = flipper_length_mm)) +
  geom_ribbon(aes(ymin = min_mass, ymax = max_mass), 
              alpha = 0.3, fill = "blue") +
  labs(x = "Flipper Length (mm)", 
       y = "Body Mass (g)")

The ribbon shows the full range of body mass values, with the shaded area representing the spread between minimum and maximum values.

Step 3: Add a center line

Let’s enhance the plot by adding the mean body mass as a line through the ribbon.

penguin_summary <- penguin_summary |>
  left_join(
    penguins |>
      group_by(flipper_length_mm) |>
      summarise(mean_mass = mean(body_mass_g, na.rm = TRUE))
  )

This adds the mean body mass for each flipper length to our summary data.

Step 4: Complete the ribbon plot

Now we’ll create the final plot with both the ribbon and center line.

ggplot(penguin_summary, aes(x = flipper_length_mm)) +
  geom_ribbon(aes(ymin = min_mass, ymax = max_mass),
              alpha = 0.3, fill = "steelblue") +
  geom_line(aes(y = mean_mass), color = "darkblue", linewidth = 1) +
  labs(title = "Penguin Body Mass Range by Flipper Length",
       subtitle = "Ribbon shows min-max range, line shows mean",
       x = "Flipper Length (mm)", y = "Body Mass (g)") +
  theme_minimal()

Ribbon plot in R using geom_ribbon() in ggplot2 showing penguin body mass min-max range by flipper length with a mean line overlay from the palmerpenguins dataset

The completed plot shows both the range (ribbon) and average (line) of body mass across flipper lengths.

Example 2: Practical Application

The Problem

We need to create a confidence interval plot showing the relationship between car weight and fuel efficiency. This is a common scenario in data analysis where we want to show both the predicted line and the uncertainty around our predictions.

Step 1: Fit a model and generate predictions

First, we’ll create a linear model and generate predictions with confidence intervals.

model <- lm(mpg ~ wt, data = mtcars)
new_data <- data.frame(wt = seq(min(mtcars$wt), max(mtcars$wt), 
                               length.out = 100))
predictions <- predict(model, new_data, interval = "confidence")

This creates smooth predictions across the weight range with 95% confidence intervals.

Step 2: Combine predictions with original data

Next, we’ll combine our predictions into a single data frame for plotting.

plot_data <- data.frame(
  wt = new_data$wt,
  fit = predictions[,"fit"],
  lower = predictions[,"lwr"],
  upper = predictions[,"upr"]
)

Now we have all the necessary components: the fitted line and confidence bounds.

Step 3: Create the confidence interval plot

Finally, we’ll create a professional-looking plot with the ribbon showing confidence intervals.

ggplot() +
  geom_ribbon(data = plot_data, 
              aes(x = wt, ymin = lower, ymax = upper),
              alpha = 0.2, fill = "red") +
  geom_line(data = plot_data, aes(x = wt, y = fit), 
            color = "red", size = 1) +
  geom_point(data = mtcars, aes(x = wt, y = mpg))

The ribbon shows the confidence interval around our regression line, with actual data points overlaid.

Step 4: Polish the visualization

Let’s add proper labels and styling to make the plot publication-ready.

ggplot() +
  geom_ribbon(data = plot_data,
              aes(x = wt, ymin = lower, ymax = upper),
              alpha = 0.2, fill = "red") +
  geom_line(data = plot_data, aes(x = wt, y = fit),
            color = "red", linewidth = 1) +
  geom_point(data = mtcars, aes(x = wt, y = mpg)) +
  labs(title = "Car Weight vs Fuel Efficiency",
       subtitle = "Linear regression with 95% confidence ribbon",
       x = "Weight (1000 lbs)", y = "Miles per Gallon") +
  theme_classic()

Regression plot in R using geom_ribbon() in ggplot2 showing a linear model fit with a 95% confidence interval ribbon for mtcars weight versus miles per gallon

The final plot clearly shows the relationship between weight and fuel efficiency with confidence bounds.

Summary

  • geom_ribbon() requires ymin and ymax aesthetics to define the ribbon boundaries
  • Use alpha parameter to control transparency and avoid overwhelming other plot elements
  • Combine ribbons with geom_line() or geom_point() to show central tendencies alongside ranges
  • Common applications include confidence intervals, prediction bands, and min/max ranges

  • Always ensure your data is properly sorted by x-values for smooth ribbon appearance