How to use geom_smooth() in R

ggplot2

ggplot2 geom_smooth()

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

Published

February 21, 2026

Introduction

The geom_smooth() function in ggplot2 adds smooth trend lines to scatter plots, helping reveal underlying patterns in your data. Use it when you want to visualize relationships between variables without being distracted by individual data point variations.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

You have a scatter plot of penguin bill length versus bill depth, but the individual points make it hard to see the overall relationship pattern.

Step 1: Create a basic scatter plot

First, let’s create a simple scatter plot to see our data.

penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point()

This shows all the individual penguin measurements as points, but the trend isn’t immediately clear.

Step 2: Add a smooth trend line

Now we’ll add a smooth line to reveal the underlying pattern.

penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point() +
  geom_smooth()

Scatter plot in R using geom_smooth() in ggplot2 with a LOESS trend line and confidence band showing penguin bill length vs bill depth from the palmerpenguins dataset

The gray ribbon shows the confidence interval around the smooth blue trend line, revealing a slight negative relationship.

Step 3: Remove confidence bands

Sometimes the confidence interval can be distracting, so we can remove it.

penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point() +
  geom_smooth(se = FALSE)

Setting se = FALSE removes the gray confidence band, leaving just the clean trend line.

Example 2: Practical Application

The Problem

You’re analyzing car performance data and want to understand how engine displacement affects fuel efficiency. You also want to compare different smoothing methods to see which reveals the pattern most clearly.

Step 1: Explore the basic relationship

Let’s start by examining the relationship between engine size and miles per gallon.

mtcars |>
  ggplot(aes(x = disp, y = mpg)) +
  geom_point() +
  geom_smooth()

This reveals a clear negative relationship - larger engines tend to have lower fuel efficiency.

Step 2: Try different smoothing methods

The default uses LOESS smoothing, but we can try a linear model instead.

mtcars |>
  ggplot(aes(x = disp, y = mpg)) +
  geom_point() +
  geom_smooth(method = "lm")

Using method = "lm" fits a straight line, which might be more appropriate for this linear-looking relationship.

Step 3: Compare multiple methods

We can overlay different smoothing methods to compare their approaches.

mtcars |>
  ggplot(aes(x = disp, y = mpg)) +
  geom_point() +
  geom_smooth(method = "lm", color = "red", se = FALSE) +
  geom_smooth(method = "loess", color = "blue", se = FALSE)

The red linear line and blue LOESS curve show similar patterns, helping confirm the relationship’s nature.

Step 4: Add grouping by categories

Let’s see how transmission type affects the relationship pattern.

mtcars |>
  ggplot(aes(x = disp, y = mpg, color = factor(am))) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

Scatter plot in R using geom_smooth() in ggplot2 with linear regression lines grouped by transmission type showing mtcars engine displacement vs fuel efficiency

This creates separate trend lines for automatic (0) and manual (1) transmissions, revealing different efficiency patterns.

Step 5: Customize the appearance

Finally, let’s make the plot more presentation-ready with better styling.

mtcars |>
  ggplot(aes(x = disp, y = mpg)) +
  geom_point(alpha = 0.6) +
  geom_smooth(color = "darkblue", fill = "lightblue") +
  labs(x = "Engine Displacement", y = "Miles per Gallon")

Polished scatter plot in R using geom_smooth() in ggplot2 with custom dark blue line and light blue confidence band showing engine displacement vs miles per gallon

This adds transparency to points and custom colors to the smooth line and confidence interval.

Summary

geom_smooth() adds trend lines to help identify patterns in scatter plots
Use se = FALSE to remove confidence intervals when they’re distracting
The method parameter lets you choose between LOESS (default), linear models (“lm”), and other smoothing approaches
Group-wise smoothing with color aesthetics reveals how relationships vary across categories
Combine with customized colors and transparency for publication-ready visualizations

--- title: "How to use geom_smooth() in R" description: "Learn how to use geom_smooth() 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_smooth()'] format: html: code-fold: false code-tools: true --- ## Introduction The `geom_smooth()` function in ggplot2 adds smooth trend lines to scatter plots, helping reveal underlying patterns in your data. Use it when you want to visualize relationships between variables without being distracted by individual data point variations. ## Getting Started ```r library(tidyverse) library(palmerpenguins) ``` ## Example 1: Basic Usage ### The Problem You have a scatter plot of penguin bill length versus bill depth, but the individual points make it hard to see the overall relationship pattern. ### Step 1: Create a basic scatter plot First, let's create a simple scatter plot to see our data. ```r penguins |> ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + geom_point() ``` This shows all the individual penguin measurements as points, but the trend isn't immediately clear. ### Step 2: Add a smooth trend line Now we'll add a smooth line to reveal the underlying pattern. ```r penguins |> ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + geom_point() + geom_smooth() ``` ![Scatter plot in R using geom_smooth() in ggplot2 with a LOESS trend line and confidence band showing penguin bill length vs bill depth from the palmerpenguins dataset](/images/ggplot2/geom-smooth-in-r-loess-trend-line-penguins-ggplot.png) The gray ribbon shows the confidence interval around the smooth blue trend line, revealing a slight negative relationship. ### Step 3: Remove confidence bands Sometimes the confidence interval can be distracting, so we can remove it. ```r penguins |> ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + geom_point() + geom_smooth(se = FALSE) ``` Setting `se = FALSE` removes the gray confidence band, leaving just the clean trend line. ## Example 2: Practical Application ### The Problem You're analyzing car performance data and want to understand how engine displacement affects fuel efficiency. You also want to compare different smoothing methods to see which reveals the pattern most clearly. ### Step 1: Explore the basic relationship Let's start by examining the relationship between engine size and miles per gallon. ```r mtcars |> ggplot(aes(x = disp, y = mpg)) + geom_point() + geom_smooth() ``` This reveals a clear negative relationship - larger engines tend to have lower fuel efficiency. ### Step 2: Try different smoothing methods The default uses LOESS smoothing, but we can try a linear model instead. ```r mtcars |> ggplot(aes(x = disp, y = mpg)) + geom_point() + geom_smooth(method = "lm") ``` Using `method = "lm"` fits a straight line, which might be more appropriate for this linear-looking relationship. ### Step 3: Compare multiple methods We can overlay different smoothing methods to compare their approaches. ```r mtcars |> ggplot(aes(x = disp, y = mpg)) + geom_point() + geom_smooth(method = "lm", color = "red", se = FALSE) + geom_smooth(method = "loess", color = "blue", se = FALSE) ``` The red linear line and blue LOESS curve show similar patterns, helping confirm the relationship's nature. ### Step 4: Add grouping by categories Let's see how transmission type affects the relationship pattern. ```r mtcars |> ggplot(aes(x = disp, y = mpg, color = factor(am))) + geom_point() + geom_smooth(method = "lm", se = FALSE) ``` ![Scatter plot in R using geom_smooth() in ggplot2 with linear regression lines grouped by transmission type showing mtcars engine displacement vs fuel efficiency](/images/ggplot2/geom-smooth-in-r-linear-fit-by-transmission-ggplot.png) This creates separate trend lines for automatic (0) and manual (1) transmissions, revealing different efficiency patterns. ### Step 5: Customize the appearance Finally, let's make the plot more presentation-ready with better styling. ```r mtcars |> ggplot(aes(x = disp, y = mpg)) + geom_point(alpha = 0.6) + geom_smooth(color = "darkblue", fill = "lightblue") + labs(x = "Engine Displacement", y = "Miles per Gallon") ``` ![Polished scatter plot in R using geom_smooth() in ggplot2 with custom dark blue line and light blue confidence band showing engine displacement vs miles per gallon](/images/ggplot2/geom-smooth-in-r-custom-colors-confidence-band-ggplot.png) This adds transparency to points and custom colors to the smooth line and confidence interval. ## Summary - `geom_smooth()` adds trend lines to help identify patterns in scatter plots - Use `se = FALSE` to remove confidence intervals when they're distracting - The `method` parameter lets you choose between LOESS (default), linear models ("lm"), and other smoothing approaches - Group-wise smoothing with color aesthetics reveals how relationships vary across categories - Combine with customized colors and transparency for publication-ready visualizations --- ## 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)