Understanding Color Palettes
The color palette is a range of colors used to implement the graphical representation. They are mainly used for enriching data categories and advanced tweaking of the processed data. The right combination of colors helps with the readability, and moreover, interpretation of information presented in visual form.
Types of Color Palettes
There are several types of color palettes, each suited for different types of data and visualization goals. Here are some common types of color palettes:
- Qualitative Palettes: Qualitative palettes are used when dealing with categorical data where it’s important to account for the differences between the categories. These palettes adopt many different shades without any sense of structure.
- Sequential Palettes: Sequential palettes are used on ordered data that progress from low to high or in a sequential manner. These palettes apply a range of colors to represent a scale or degree of emphasis.
- Diverging Palettes: They could be used with data ranging around a mean value. These palettes employ one high value, one low value and a medium value for displaying variances.
Main R Packages for Color Palettes
In R, there are several packages dedicated to providing a wide range of color palettes for data visualization. Here are some of the main R packages for color palettes:
- RColorBrewer: The RColorBrewer package contains numerous color sets that are pre-selected with references to the theory of data visualization. It comprises Qualitative, Sequential, and Diverging palettes.
- viridis: The viridis package contains colorblind safe palettes with good uniformity in perception which makes it very useful especially when using graphics on scientific data.
- colorspace: The colorspace package provides comprehensive functions to alter and analyze color palettes. They include functions that can generate qualitative, sequential and diverging palettes.
- ggplot2: The ggplot2 package is a very useful package that can be used to create graphics in the context of R. It has several in-built functions that help to apply color scale as well to integrate with other color packages.
Commonly Used Color Palettes in R
In R, several commonly used color palettes are popular among data analysts and visualization due to their effectiveness in conveying information and their aesthetic appeal.
1. Base R Color Palettes
The language provides a bunch of built-in color palettes including functions such as rainbow(), heat.colors(), terrain.colors(), topo.colors(), and cm.colors().
palette <- rainbow(5)
barplot(1:5, col = palette)
Output:
base R color palette
2. RColorBrewer Palettes in R
RColorBrewer library is extensively used as it provides a set of visually effective colors for data visualization.
#Install and load RColorBrewer
install.packages("RColorBrewer")
library(RColorBrewer)
#Display all available palettes
display.brewer.all()
Output:
RColorBrewer
3. viridis Palettes in R
viridis package is a colorblind-friendly palette with uniformity levels that are usually higher.
#Install and load viridis
install.packages("viridis")
library(viridis)
#Example of viridis palette
barplot(1:5, col = viridis(5))
Output:
viridis Palettes in R
4. ggplot2 Scale Palettes in R
One of the first ggplot2 package features, provides pre-built color scales that match the plotting system’s default theme.
#Install and load ggplot2
install.packages("ggplot2")
library(ggplot2)
#Example using ggplot2 with a color scale
ggplot(mpg, aes(x = class, fill = class)) + geom_bar() +
scale_fill_brewer(palette = "Set3")
Output:
ggplot2 Scale Palettes in R
Color Palettes in R
The art of data visualization involves the use of color palettes as a key ingredient to have a uniform set of colors that will separate our data elements clearly. R guides the creation of simple and attractive charts through its color palettes. This article covers the color palettes (different types), how to apply them, and walks through the practical examples in R Programming Language.
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