Mastering Color Scales in ggplot2

Introduction

Choosing the right color scheme is vital for effective storytelling. This guide demonstrates four systemic ways to manage color in your ggplot2 visualizations, ranging from automated palettes to manual Hex code mapping.

The Art of Color in ggplot2: From Palettes to Hex Codes

Introduction Color is one of the most powerful tools in data visualization. It can be used to highlight patterns, group data, or evoke a specific professional tone. In this guide, we explore how to move from default colors to custom palettes using RColorBrewer and manual Hex codes.

1. RColorBrewer (Professional Palettes) The RColorBrewer package offers pre-designed, colorblind-friendly palettes.

Systemic Benefit: Using scale_color_brewer() ensures that your colors are aesthetically harmonious and accessible to a wider audience.

2. Manual Control (Hex Codes & Named Colors) For maximum precision—such as matching a brand’s identity—we use scale_color_manual().

Logic: You can map specific colors to specific data levels (e.g., “4” = “Blue”). Using Hex Codes (like #719AC9) provides millions of color possibilities, allowing for a highly specific and professional look.

1. Using Color Palettes (RColorBrewer)

The RColorBrewer library provides curated palettes that are scientifically designed for clarity.

Code

library(RColorBrewer)
library(tidyverse)

# Display colorblind-friendly palettes
display.brewer.all(colorblindFriendly = TRUE)

Type 1: Qualitative Palettes

Ideal for categorical data where there is no inherent ordering (e.g., drive types).

Code

mpg %>% 
  ggplot(aes(displ, hwy, color = drv)) +
  geom_jitter(size = 5) +
  scale_color_brewer(palette = "Set2") +
  theme_minimal() +
  labs(title = "Using RColorBrewer: Set2 Palette")

2. Taking Direct Control

Sometimes, you don’t need a scale; you just want a single, professional color for all your data points.

Code

mpg %>%
  ggplot(aes(displ, hwy)) +
  geom_jitter(color = "#97B3C6", size = 5) +
  theme_minimal() +
  labs(title = "Single Manual Color Mapping",
       subtitle = "Using Hex Code #97B3C6")

3. Specific Manual Mapping

scale_color_manual() allows you to assign specific colors to specific levels of your data. This is crucial for consistency across multiple charts in a report.

Code

mpg %>% 
  ggplot(aes(displ, hwy, color = drv)) +
  geom_jitter(size = 3) +
  scale_color_manual(values = c("4" = "blue",
                                "f" = "red",
                                "r" = "green")) +
  theme_minimal() +
  labs(title = "Manual Mapping: Basic Names")

Hex codes are the industry standard for professional design. They ensure your “blue” is exactly the shade you intended.

Code

mpg %>%
  ggplot(aes(displ, hwy, color = drv)) +
  geom_jitter(size = 5, alpha = 0.5) +
  scale_color_manual(values = c("4" = "#719AC9",
                                "f" = "#75B99C",
                                "r" = "#C98D71")) +
  theme_minimal() +
  labs(title = "Manual Mapping: Professional Hex Codes",
       subtitle = "Specific shades mapped to Drive Types")

Systemic Summary Toolkit

Method	Function	Best Use Case
RColorBrewer	`scale_color_brewer()`	Quick, professional, and accessible palettes.
Manual Hex	`scale_color_manual()`	Brand-specific colors or custom designs.
Direct Color	`geom_point(color = "...")`	When grouping is not required.
Transparency	`alpha`	Managing overlapping points in dense data.