1 Color Gradients

1.1 Introduction

Color choice is one of the most critical—and most commonly mishandled—aspects of data visualization. The rainbow color scale, despite its popularity, is fundamentally flawed and can mislead readers. This chapter explains why and shows you better alternatives.

Learning Objectives

By the end of this chapter, you will:

Understand why rainbow color scales are problematic
Learn about perceptually uniform color scales
Know when to use viridis, ColorBrewer, and diverging scales
Be able to test your color choices for accessibility

1.2 What is the Rainbow Scale?

Interactive Challenge: Can you order these colors?

1.3 The Correct Rainbow Order

Colors in order: Red → Orange → Yellow → Green → Cyan → Blue → Purple/Magenta

Figure from Gołębiowska and Çöltekin (2022)

1.4 Comparing Rainbow Implementations

Why This Order?

This follows the visible light spectrum by wavelength:

Red: ~700 nm (longest)
Violet: ~400 nm (shortest)

But wavelength order ≠ perceptual order!

1.5 A Tale of Two Colormaps

Which one shows the data more accurately?

The data is smooth, yet Colormap A creates false boundaries!

1.6 Perceptual Non-Uniformity: Demonstrated

The data is perfectly smooth, yet rainbow creates artificial edges!

1.7 Real world consequences

Image from Heron, Crameri, and Shephard (2021)

1.8 Not just spatial data

Figure from Haseneyer et al. (2011)

1.9 Medical Consequences

Lives at Stake

Borkin et al. (2011) - IEEE Visualization

Studied physicians diagnosing heart disease using medical imaging:

Physicians using jet colormap: More errors, slower diagnosis
Physicians using perceptually uniform colormaps: Fewer errors, faster

Why?

Bright yellow appears more “intense” than dark red
But dark red represents higher values (more critical condition)
Perceptual bias leads to misdiagnosis

Reference: Borkin et al. (2011)

Figure from Borkin et al. (2011)

1.10 Comparison: Rainbow vs Better Alternatives

Notice how rainbow and heat have sharp transitions while viridis/magma are smooth!

1.11 Desaturated

Rainbow loses all information when desaturated! Viridis/magma remain readable.

1.12 The Viridis Color Scales

All viridis scales are perceptually uniform

1.13 The Viridis Color Scales, desaturated

1.15 Color Scale Comparison: Pros and Cons

	Rainbow	Jet	Turbo	Heat	ggplot default	Brewer Blues	Viridis	Magma	Cividis
Perceptually uniform	❌	❌	✅	❌	✅	✅	✅	✅	✅
Colorblind safe	❌	❌	❌	❌	✅	✅	✅	✅	✅
B&W/grayscale safe	❌	❌	❌	❌	✅	✅	✅	✅	✅
Good on projectors	⚠️	⚠️	✅	⚠️	✅	✅	✅	✅	✅
Print friendly	❌	❌	✅	❌	✅	✅	✅	✅	✅
Engaging colors	✅	✅	✅	⚠️	⚠️	⚠️	✅	✅	⚠️
Wide color range	✅	✅	✅	⚠️	⚠️	⚠️	✅	✅	⚠️
Recommendation	❌ AVOID	❌ AVOID	⚠️ Careful	❌ AVOID	OK	Good	✅ DEFAULT	✅ Great	✅ Great

1.16 Viridis Family: Show All Options

All are perceptually uniform and colorblind-friendly!

1.17 Recommendation for continous scales

For most use cases: Use Viridis (or Magma/Plasma variants)

When to use something else:

Diverging data (has meaningful center): ColorBrewer diverging (RdBu, RdYlBu)
High colorblind audience: Cividis
Print-only publication: Brewer Blues or Greens

Never use: Rainbow or Jet

2 Colors for qualitative data

2.1 ColorBrewer: All Palettes

Explore all palettes at: colorbrewer2.org

2.2 ColorBrewer Website

Screenshot from colorbrewer2.org. Used under fair use for educational purposes.

2.3 Yellow Color Warning

The Yellow Problem

Even though ColorBrewer includes yellow in some palettes (e.g., “YlOrRd”, “RdYlBu”, “Set1”):

Yellow has serious issues:

Poor printing: Yellow can be nearly invisible on white paper
Projection problems: On projected slides, yellow often washes out
Low contrast: Yellow text on white background is unreadable
Photocopying: Disappears when photocopied in B&W

Recommendation:

For presentations: Avoid yellow-heavy palettes
For print: Use darker yellows or oranges instead
For text: NEVER use yellow text on light backgrounds

Removing yellow from Set1:

library(RColorBrewer)

# Set1 has yellow as the 6th color
set1_colors <- brewer.pal(9, "Set1")
set1_colors
# [1] "#E41A1C" "#377EB8" "#4DAF4A" "#984EA3" "#FF7F00" "#FFFF33" "#A65628" "#F781BF" "#999999"

# Remove yellow (position 6)
set1_no_yellow <- set1_colors[-6]

# Use in ggplot2
scale_color_manual(values = set1_no_yellow)

2.4 Recommendations Summary

Best Practices for Color

Continuous data: Viridis family
- scale_fill_viridis_c() or scale_color_viridis_c()
- Options: “viridis”, “magma”, “plasma”, “inferno”, “cividis”
Diverging data (meaningful center): ColorBrewer
- scale_fill_distiller(palette = "RdBu") for continuous
- scale_fill_gradient2() for custom diverging
Categorical/qualitative data:
- Viridis: scale_fill_viridis_d() or scale_color_viridis_d()
- ColorBrewer: scale_fill_brewer(palette = "Set2") (up to 8-12 categories)
AVOID:
- ❌ Rainbow/jet colormaps
- ❌ Red-green combinations (colorblind issue)
- ❌ Yellow text or yellow-heavy palettes (visibility issue)
ALWAYS TEST:
- ✅ Grayscale conversion
- ✅ Colorblind simulation
- ✅ Print preview

2.5 Quick Reference: Code Examples

library(ggplot2)

# --- CONTINUOUS DATA ---

# Viridis continuous (best default)
ggplot(data, aes(x, y, fill = continuous_var)) +
  geom_raster() +
  scale_fill_viridis_c(option = "viridis")  # or "magma", "plasma", "cividis"

# ColorBrewer sequential continuous
ggplot(data, aes(x, y, fill = continuous_var)) +
  geom_raster() +
  scale_fill_distiller(palette = "Blues")  # or "YlOrRd", "Greens", etc.

# --- DIVERGING DATA (meaningful center) ---

# ColorBrewer diverging
ggplot(data, aes(x, y, fill = fold_change)) +
  geom_raster() +
  scale_fill_distiller(palette = "RdBu", direction = 1)

# Custom diverging
ggplot(data, aes(x, y, fill = fold_change)) +
  geom_raster() +
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0)

# --- CATEGORICAL/QUALITATIVE DATA ---

# Viridis discrete
ggplot(data, aes(x, y, color = category)) +
  geom_point() +
  scale_color_viridis_d(option = "viridis")

# ColorBrewer qualitative
ggplot(data, aes(x, y, color = category)) +
  geom_point() +
  scale_color_brewer(palette = "Set2")  # or "Dark2", "Paired", etc.

2.6 Summary

Key Takeaways

Never use rainbow or jet for scientific figures - they create false boundaries
Use viridis family as your default for continuous data (perceptually uniform, colorblind-safe)
Use diverging scales when data has a meaningful center point (zero, neutral)
Test for colorblindness using simulation tools before publishing
Check grayscale conversion - your figures may be printed in black and white
Avoid yellow in palettes for print and presentations (low contrast)

2.7 Exercises

Try It Yourself

Take one of your existing plots that uses rainbow colors
Replace it with scale_fill_viridis_c()
Use colorspace::desaturate() to check how it looks in grayscale
Use Coblis to simulate colorblindness
Compare the results - can you see the difference?

2.8 Further Reading

Crameri, Shephard, and Heron (2020) - The misuse of colour in science communication
Gołębiowska and Çöltekin (2022) - What’s wrong with the rainbow?
Viridis package documentation
ColorBrewer website

1.1 Introduction

Learning Objectives

1.2 What is the Rainbow Scale?

1.3 The Correct Rainbow Order

1.4 Comparing Rainbow Implementations

1.5 A Tale of Two Colormaps

1.6 Perceptual Non-Uniformity: Demonstrated

1.7 Real world consequences

1.8 Not just spatial data

1.9 Medical Consequences

1.10 Comparison: Rainbow vs Better Alternatives

1.11 Desaturated

1.12 The Viridis Color Scales

1.13 The Viridis Color Scales, desaturated

1.14 Green-Blind Vision (Deuteranopia)

1.15 Color Scale Comparison: Pros and Cons

1.16 Viridis Family: Show All Options

1.17 Recommendation for continous scales

2 Colors for qualitative data

2.1 ColorBrewer: All Palettes

2.2 ColorBrewer Website

2.3 Yellow Color Warning

2.4 Recommendations Summary

2.5 Quick Reference: Code Examples

2.6 Summary

2.7 Exercises

2.8 Further Reading