How to use scale_color_manual() in R

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

February 21, 2026

Introduction

The scale_color_manual() function in ggplot2 allows you to manually specify colors for different groups in your plots. This gives you complete control over your color scheme, making it perfect when you need specific brand colors, want to ensure accessibility, or simply prefer custom aesthetics over ggplot2’s default color palette.

Getting Started

library(tidyverse)
library(palmerpenguins)

Example 1: Basic Usage

The Problem

By default, ggplot2 automatically assigns colors to different groups in your data. However, these default colors might not match your preferences or requirements for a professional presentation.

Step 1: Create a basic plot with default colors

First, let’s create a scatter plot to see ggplot2’s default color scheme.

basic_plot <- penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, 
             color = species)) +
  geom_point(size = 3)

basic_plot

This creates a scatter plot where each penguin species gets a different default color automatically.

Step 2: Apply custom colors manually

Now we’ll override the default colors with our own choices using scale_color_manual().

custom_plot <- penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, 
             color = species)) +
  geom_point(size = 3) +
  scale_color_manual(values = c("red", "blue", "green"))

custom_plot

The plot now uses our specified colors: red, blue, and green for the three penguin species.

Step 3: Use better color choices

Let’s improve our color selection with more professional hex codes.

professional_plot <- penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, 
             color = species)) +
  geom_point(size = 3) +
  scale_color_manual(values = c("#E74C3C", "#3498DB", "#2ECC71"))

professional_plot

These hex codes provide more sophisticated colors that work well together and maintain good contrast.

Scatter plot in R using scale_color_manual() in ggplot2 showing penguin bill length versus bill depth colored by species with professional hex color codes from the palmerpenguins dataset

Example 2: Practical Application

The Problem

You’re creating a report for stakeholders about car performance data, and you need the colors to match your company’s brand guidelines. Additionally, you want to ensure the legend shows meaningful labels rather than the raw data values.

Step 1: Examine the data structure

Let’s look at our car data and create a basic plot to understand what we’re working with.

mtcars |>
  ggplot(aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 4) +
  labs(x = "Weight (1000 lbs)", y = "Miles per Gallon")

This shows the relationship between car weight and fuel efficiency, colored by number of cylinders.

Step 2: Apply brand colors with custom labels

Now we’ll apply specific brand colors and improve the legend labels for better presentation.

branded_plot <- mtcars |>
  ggplot(aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 4) +
  scale_color_manual(
    values = c("4" = "#1f4e79", "6" = "#70ad47", "8" = "#c55a5a"),
    labels = c("4 Cylinder", "6 Cylinder", "8 Cylinder"),
    name = "Engine Type"
  )

branded_plot

The plot now uses brand-specific colors and displays professional labels in the legend.

Step 3: Add finishing touches

Let’s complete the visualization with proper titles and theme adjustments.

final_plot <- mtcars |>
  ggplot(aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 4) +
  scale_color_manual(
    values = c("4" = "#1f4e79", "6" = "#70ad47", "8" = "#c55a5a"),
    labels = c("4 Cylinder", "6 Cylinder", "8 Cylinder"),
    name = "Engine Type"
  ) +
  labs(title = "Fuel Efficiency by Vehicle Weight",
       x = "Weight (1000 lbs)", y = "Miles per Gallon") +
  theme_minimal()

final_plot

The final visualization is now publication-ready with custom colors, clear labels, and professional styling.

Scatter plot in R using scale_color_manual() in ggplot2 showing mtcars weight versus miles per gallon with custom brand hex colors by cylinder count and minimal theme styling

Summary

  • scale_color_manual() gives you complete control over color choices in ggplot2 visualizations
  • Use the values parameter to specify colors either by name (“red”) or hex codes (“#E74C3C”)
  • The labels parameter lets you customize legend text for better readability
  • The name parameter changes the legend title to something more descriptive

  • Combine with other ggplot2 elements like labs() and theme_minimal() for professional-looking plots