The Elements of Choosing Colors for Great Data Visualization in R


Color is crucial for elegant data visualization. In our previous article we describe the list of color palettes available in R.

In this current article we define the basics for using the power of color and, we describe an R package and an online tool for generating beautiful color schemes.

Understanding color wheel

color wheel

(Image from Nancy Duarte, slide:ology)

The color wheel helps you to visualize the relationships between colors.

The wheel is divided into pie slices which have the following components:

  • hue (true color): On the wheel above, the hue is four rings out of the center.
  • tints: correspond to the colors toward the center of the wheel (= hue + white color)
  • shades: corresponds to the ring of colors on the outside of the hue ( = hue + black color)

Defining the basics for choosing colors

  1. Monochromatic: Variations of the same color.

  2. Analogous: colors that are touching in the wheel creates narrow harmonious color scheme.

  3. Complementary: Colors from the opposite ends of the wheel provide the most contrast.

color wheel basics

(Image from Nancy Duarte, slide:ology)

  1. Split Complementary: A variation of the complementary scheme that uses two colors on either side of a directly complementary color. These colors have high visual contrast but with less visual tension than purely complementary colors.

  2. Triadic: Three colors equally spaced around the color wheel create vivid visual interest.

  3. Tetradic: Two pairs of complementary colors. This scheme is popular because it offers strong visual contrast while retaining harmony.

color wheel basics

(Image from Nancy Duarte, slide:ology)

colortools: R package for creating easily color schemes in R

The excellent R package colortools developed by Gaston Sanchez is an easy to use solution for generating color schemes in R.

Install colortools

install.packages("colortools")

Load colortools

library(colortools)

Color wheel

The function wheel() can be used to generate a color wheel for a given color:

wheel("darkblue", num = 12)
##  [1] "#00008B" "#46008B" "#8B008B" "#8B0046" "#8B0000" "#8B4500" "#8B8B00"
##  [8] "#468B00" "#008B00" "#008B45" "#008B8B" "#00468B"

The Elements of Choosing Colors for Great Data Visualization in R.

Analogous color scheme

The function adjacent() or analogous() can be used:

analogous("darkblue")
## [1] "#00008B" "#46008B" "#00468B"

The Elements of Choosing Colors for Great Data Visualization in R.

Complementary color scheme

complementary("steelblue")
## [1] "#4682B4" "#B47846"

The Elements of Choosing Colors for Great Data Visualization in R.

Split Complementary Color Scheme

splitComp("steelblue")
## [1] "#4682B4" "#B4464B" "#B4AF46"

The Elements of Choosing Colors for Great Data Visualization in R.

Tetradic Color Scheme

tetradic("steelblue")
## [1] "#4682B4" "#7846B4" "#B47846" "#82B446"

The Elements of Choosing Colors for Great Data Visualization in R.

Square color scheme

square("steelblue")
## [1] "#4682B4" "#AF46B4" "#B47846" "#4BB446"

The Elements of Choosing Colors for Great Data Visualization in R.

Sequential colors

sequential("steelblue")
##  [1] "#B4B4B4FF" "#ABB0B4FF" "#A2ACB4FF" "#99A8B4FF" "#90A4B4FF"
##  [6] "#87A0B4FF" "#7E9BB4FF" "#7597B4FF" "#6C93B4FF" "#638FB4FF"
## [11] "#5A8BB4FF" "#5187B4FF" "#4883B4FF" "#3F7FB4FF" "#367BB4FF"
## [16] "#2D77B4FF" "#2473B4FF" "#1B6EB4FF" "#126AB4FF" "#0966B4FF"
## [21] "#0062B4FF"

The Elements of Choosing Colors for Great Data Visualization in R.

Design your color scheme online

The online tool Colors Scheme Designer can be used:

colors scheme designer

Read more

  • slide:ology: The Art and Science of Creating Great Presentations (by Nancy Duarte)
  • R package colortools by Gaston Sanchez.

Infos

This analysis has been performed using R software (ver. 3.2.3) and colortools (ver. 0.1.5)









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