How to use geom_line() in R

ggplot2

ggplot2 geom_line()

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

Published

February 21, 2026

Introduction

The geom_line() function in ggplot2 creates line graphs by connecting data points with straight lines. It’s perfect for visualizing trends over time, showing relationships between continuous variables, or displaying changes in values across ordered categories.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Line Plot

The Problem

We want to create a simple line plot showing how penguin body mass varies across different flipper lengths. This will help us understand the basic relationship between these two continuous variables.

Step 1: Prepare the data

We’ll start by examining our dataset and selecting the variables we need.

# Look at the penguins data structure
glimpse(penguins)

# Create a basic line plot
penguins |>
  filter(!is.na(flipper_length_mm), !is.na(body_mass_g))

This gives us a clean dataset with no missing values for our variables of interest.

Step 2: Create the basic line plot

Now we’ll create our first line plot using the default settings.

penguins |>
  filter(!is.na(flipper_length_mm), !is.na(body_mass_g)) |>
  ggplot(aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_line()

The plot connects all points in order of the x-axis values, creating a zigzag pattern that shows the data distribution.

Step 3: Improve the plot appearance

Let’s add proper labels and styling to make the plot more professional.

penguins |>
  filter(!is.na(flipper_length_mm), !is.na(body_mass_g)) |>
  ggplot(aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_line(color = "steelblue", linewidth = 0.8) +
  labs(title = "Penguin Body Mass vs Flipper Length",
       x = "Flipper Length (mm)",
       y = "Body Mass (g)")

Line plot in R using geom_line() showing penguin body mass versus flipper length with steelblue color in ggplot2

The result is a clean, styled line plot that clearly shows the relationship between flipper length and body mass.

Example 2: Time Series Analysis

The Problem

We want to analyze car performance trends over time using the mtcars dataset. Specifically, we’ll look at how average miles per gallon (mpg) varies across different numbers of cylinders, treating cylinders as a sequential variable to demonstrate time-series-like visualization.

Step 1: Aggregate the data

First, we need to calculate average mpg for each cylinder group.

mpg_by_cylinders <- mtcars |>
  group_by(cyl) |>
  summarise(avg_mpg = mean(mpg),
            count = n()) |>
  arrange(cyl)

print(mpg_by_cylinders)

This creates a summary table showing average fuel efficiency for 4, 6, and 8-cylinder cars.

Step 2: Create the trend line

Now we’ll visualize this trend using a line plot with enhanced styling.

mpg_by_cylinders |>
  ggplot(aes(x = cyl, y = avg_mpg)) +
  geom_line(color = "darkred", size = 1.2) +
  geom_point(size = 3, color = "darkred")

The combination of line and points clearly shows the declining trend in fuel efficiency as cylinder count increases.

Step 3: Add context and formatting

Let’s enhance the plot with better scaling and annotations.

mpg_by_cylinders |>
  ggplot(aes(x = cyl, y = avg_mpg)) +
  geom_line(color = "darkred", linewidth = 1.2) +
  geom_point(size = 3, color = "darkred") +
  scale_x_continuous(breaks = c(4, 6, 8)) +
  labs(title = "Average MPG by Number of Cylinders",
       x = "Number of Cylinders",
       y = "Average Miles per Gallon") +
  theme_minimal()

Trend line plot in R using geom_line() and geom_point() showing average miles per gallon by number of cylinders from the mtcars dataset in ggplot2

The final plot provides a clear, professional visualization of the relationship between engine size and fuel efficiency.

Step 4: Add multiple lines for comparison

We can extend this analysis by adding transmission type as a grouping variable.

mtcars |>
  mutate(transmission = ifelse(am == 0, "Automatic", "Manual")) |>
  group_by(cyl, transmission) |>
  summarise(avg_mpg = mean(mpg), .groups = "drop") |>
  ggplot(aes(x = cyl, y = avg_mpg, color = transmission)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 3)

Multi-line chart in R using geom_line() with color aesthetic showing average MPG by cylinders and transmission type (automatic vs manual) in ggplot2

This creates two separate trend lines, allowing us to compare fuel efficiency patterns between automatic and manual transmissions across different engine sizes.

Summary

geom_line() connects data points with straight lines, ideal for showing trends and relationships
Always filter out missing values before creating line plots to avoid gaps or errors
Combine geom_line() with geom_point() to highlight individual data points along the trend
Use aes(color = variable) to create multiple lines for different groups in your data
Enhance readability with proper labels, colors, and themes for professional-looking visualizations

--- title: "How to use geom_line() in R" description: "Learn how to use geom_line() 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_line()'] format: html: code-fold: false code-tools: true --- ## Introduction The `geom_line()` function in ggplot2 creates line graphs by connecting data points with straight lines. It's perfect for visualizing trends over time, showing relationships between continuous variables, or displaying changes in values across ordered categories. ## Getting Started ```r library(tidyverse) library(palmerpenguins) ``` ## Example 1: Basic Line Plot ### The Problem We want to create a simple line plot showing how penguin body mass varies across different flipper lengths. This will help us understand the basic relationship between these two continuous variables. ### Step 1: Prepare the data We'll start by examining our dataset and selecting the variables we need. ```r # Look at the penguins data structure glimpse(penguins) # Create a basic line plot penguins |> filter(!is.na(flipper_length_mm), !is.na(body_mass_g)) ``` This gives us a clean dataset with no missing values for our variables of interest. ### Step 2: Create the basic line plot Now we'll create our first line plot using the default settings. ```r penguins |> filter(!is.na(flipper_length_mm), !is.na(body_mass_g)) |> ggplot(aes(x = flipper_length_mm, y = body_mass_g)) + geom_line() ``` The plot connects all points in order of the x-axis values, creating a zigzag pattern that shows the data distribution. ### Step 3: Improve the plot appearance Let's add proper labels and styling to make the plot more professional. ```r penguins |> filter(!is.na(flipper_length_mm), !is.na(body_mass_g)) |> ggplot(aes(x = flipper_length_mm, y = body_mass_g)) + geom_line(color = "steelblue", linewidth = 0.8) + labs(title = "Penguin Body Mass vs Flipper Length", x = "Flipper Length (mm)", y = "Body Mass (g)") ``` ![Line plot in R using geom_line() showing penguin body mass versus flipper length with steelblue color in ggplot2](/images/ggplot2/geom-line-in-r-basic-line-plot-body-mass-flipper-ggplot.png) The result is a clean, styled line plot that clearly shows the relationship between flipper length and body mass. ## Example 2: Time Series Analysis ### The Problem We want to analyze car performance trends over time using the mtcars dataset. Specifically, we'll look at how average miles per gallon (mpg) varies across different numbers of cylinders, treating cylinders as a sequential variable to demonstrate time-series-like visualization. ### Step 1: Aggregate the data First, we need to calculate average mpg for each cylinder group. ```r mpg_by_cylinders <- mtcars |> group_by(cyl) |> summarise(avg_mpg = mean(mpg), count = n()) |> arrange(cyl) print(mpg_by_cylinders) ``` This creates a summary table showing average fuel efficiency for 4, 6, and 8-cylinder cars. ### Step 2: Create the trend line Now we'll visualize this trend using a line plot with enhanced styling. ```r mpg_by_cylinders |> ggplot(aes(x = cyl, y = avg_mpg)) + geom_line(color = "darkred", size = 1.2) + geom_point(size = 3, color = "darkred") ``` The combination of line and points clearly shows the declining trend in fuel efficiency as cylinder count increases. ### Step 3: Add context and formatting Let's enhance the plot with better scaling and annotations. ```r mpg_by_cylinders |> ggplot(aes(x = cyl, y = avg_mpg)) + geom_line(color = "darkred", linewidth = 1.2) + geom_point(size = 3, color = "darkred") + scale_x_continuous(breaks = c(4, 6, 8)) + labs(title = "Average MPG by Number of Cylinders", x = "Number of Cylinders", y = "Average Miles per Gallon") + theme_minimal() ``` ![Trend line plot in R using geom_line() and geom_point() showing average miles per gallon by number of cylinders from the mtcars dataset in ggplot2](/images/ggplot2/geom-line-in-r-trend-line-avg-mpg-by-cylinders-ggplot.png) The final plot provides a clear, professional visualization of the relationship between engine size and fuel efficiency. ### Step 4: Add multiple lines for comparison We can extend this analysis by adding transmission type as a grouping variable. ```r mtcars |> mutate(transmission = ifelse(am == 0, "Automatic", "Manual")) |> group_by(cyl, transmission) |> summarise(avg_mpg = mean(mpg), .groups = "drop") |> ggplot(aes(x = cyl, y = avg_mpg, color = transmission)) + geom_line(linewidth = 1.2) + geom_point(size = 3) ``` ![Multi-line chart in R using geom_line() with color aesthetic showing average MPG by cylinders and transmission type (automatic vs manual) in ggplot2](/images/ggplot2/geom-line-in-r-multi-line-mpg-by-transmission-ggplot.png) This creates two separate trend lines, allowing us to compare fuel efficiency patterns between automatic and manual transmissions across different engine sizes. ## Summary - `geom_line()` connects data points with straight lines, ideal for showing trends and relationships - Always filter out missing values before creating line plots to avoid gaps or errors - Combine `geom_line()` with [`geom_point()`](/ggplot2/how-to-use-geompoint-in-r.html) to highlight individual data points along the trend - Use `aes(color = variable)` to create multiple lines for different groups in your data - Enhance readability with proper labels, colors, and themes for professional-looking 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)