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Create Distinct Color Palette in R

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  • Last Updated : 23 May, 2021
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In this article, we will discuss how to create a distinct color palette in R Programming Language. There are a number of ways to create color palettes in R programming, all such methods are discussed below. 

Method 1: Using grDevices package

We’ll be using a package named grDevices, stands for graphics Devices, for making distinct color palettes in the first method. Here we’ll be using the colors function from the grDevices to make our palette.

  • Using colors() function: 

In this example we will make a pie chart using this very package. So, first we’ll be mentioning the number of colors, which we want in the palette. Then we’ll use the colors() function of this package, which contains a wide range of HCL(hue-chroma-luminance) based colors.

Example:

R




# number of colors in the palette
no_of_colors <- 10 
  
# applying the colors function from 
# grDevices package
color_palette <- grDevices::colors()  
  
# optional, for printing the hex 
# color codes
color_palette                         
  
# pie function for drawing a pie chart
pie(rep(1, no_of_colors), radius = 0.8, 
    col = color_palette, 
    main = "Color Palette using colors function")


Output:

Now, let’s say we wish to create a coloraturas’ palette of only Green color and its shades. We shall be following the above steps as usual for making this palette too, but the only change is that, we’ll be using the grep command here. grep is used to search or matches to argument pattern within each element of a character vector: they differ in the format of and amount of detail in the results.

Syntax:

grep(pattern, x, ignore.case = FALSE, perl = FALSE, value = FALSE,   fixed = FALSE, useBytes = FALSE, invert = FALSE)

Example:

R




# number of colors in the palette
no_of_colors <- 20      
  
# applying the colors function
color_palette <- grDevices::colors()                          
  
# prints all hex color codes
color_palette                                                 
  
# grep command for matching the 
# pattern(character string --> green), 
palette <- color_palette[grep(
  "green", grDevices::colors())]
  
# sample colours
green_palette <- sample(palette, no_of_colors)
  
# the list of the colours, which will 
# show up in the palette
green_palette
  
# shows the created palette
pie(rep(1, no_of_colors), col = green_palette, 
    main = "Green and its shades")


Output:

  • Using rainbow()

Here we’ll be making the color palette using the rainbow() function from grDevices package. rainbow() function is an in-built color palette which can be used in order to instantly generate some color vectors of desired length as per the parameter, and it returns the hexadecimal code of the colo available.

Syntax:

rainbow(variable name which stores the number of colors)

The entire process is just the same as before, only that we are using rainbow function over here.

Example:

R




# no. of colours in the palette
no_of_colors <- 15
  
# applying the rainbow function
colorful_palette <- rainbow(no_of_colors)
  
# prints the hex color codes
colorful_palette
  
# creates a pie chart of rainbow colours
pie(rep(1, no_of_colors), col = colorful_palette, 
    main = "Rainbow Palette")


Output

Method 2: Using viridis package.

Viridis package is a default color maps. It’s a series of color maps that are specially designed to improve graph readability for someone with common forms of color vision deficiency or color blindness.

This particular package consists of 8 color scales: “viridis”, being the topic name, and other such options with similar properties, which are listed below.

-> “magma”, –> option A

->“inferno”, –> option B

->“plasma”, –> option C

-> “viridis” –> default option D

->“cividis”, –> option E

-> “rocket”, –> option F

-> “mako”, –> option G

-> “turbo” –> option H

This viridis scale is for better understanding the color types, mentioned with their options.

Syntax:

viridis_pal(option = “option”)(variable storing the number of colors)

Example:

R




# installation of the viridis package
install.packages("viridis")
  
# loading of the library
library("viridis")
  
# it may happen that the above 2 
# lines of code pops out an error
# saying viridisLite has to loaded
# so to avoid that do load that 
# package too
library("viridisLite")
  
# number of colors in the palette
no_of_colors <- 10
  
# options represent the color types, 
# there are altogether 8 options.
palette <- viridis_pal(option = "D")(no_of_colors)
  
# hex color codes
palette
  
# represents colors in a pie chart manner
pie(rep(1, no_of_colors), col = palette, main = "viridis palette")


Output:

Also, if we wish to change the color pattern type of this palette, then we need to change the option(anything within A to H). Every part of the above code will remain the same except the option.

Example:

R




# loading of the library
library("viridis")
  
# it may happen that the above
# 2 lines of code pops out an
# error saying viridisLite has 
# to loaded so to avoid that do
# load that package too
library("viridisLite")
  
# number of colors in the palette
no_of_colors <- 10
  
# option A --> magma
palette <- viridis_pal(option = "A")(no_of_colors)
  
# hex color codes
palette
  
# represents colors in a pie chart manner
pie(rep(1, no_of_colors), col = palette, main = "magma palette")


Output:

Method 3: Using randomcoloR package

randomcoloR is an R language package for generating attractive and distinctive colors. The function distictColorPalette() generates optimally recognizable colors.

Syntax:

distictColorPalette(variable having the number of colors stored)

Example:

R




# installation
install.packages("randomcoloR")
library("randomcoloR")
  
# no. of colours in the palette
no_of_colors <- 15
  
# sample colors
palette <- distinctColorPalette(no_of_colors)     
  
# hex color codes
palette
  
# colors in the pie chart
pie(rep(1, no_of_colors), col = palette, 
    main = "palette using randomcoloR package")


Output:

Method 4: Using RColorBrewer package

This package creates nice looking color palettes, especially for thematic maps. 

  • brewer.pal() makes the color palettes from ColorBrewer available as R palettes.

Syntax:

brewer.pal(n, name)

Parameter:

  • n: Number of different colors in the palette, minimum 3, maximum depending on palette.
  • name: A palette name from the lists below.
  • display.brewer.pal() displays the selected palette in a graphics window.

Syntax:

display.brewer.pal(n, name)

Parameter:

  • n: Number of different colors in the palette, minimum 3, maximum depending on palette.
  • name: A palette name from the lists below.
  • display.brewer.all() displays a few palettes simultaneously in a graphics window.

Syntax:

display.brewer.all(n=NULL, type=”all”, select=NULL, exact.n=TRUE, colorblindFriendly=FALSE)

Parameter:

  • n: Number of different colors in the palette, minimum 3, maximum depending on palette.
  • name: A palette name from the lists below.
  • type: One of the string “div”, “qual”, “seq”, or “all”.
  • select: A list of names of existing palettes
  • exact.n: If TRUE, only display palettes with a color number given by n.
  • colorblindFriendly: if TRUE, display only colorblind friendly palettes.
  • brewer.pal.info returns information about the available palettes as a dataframe. brewer.pal.info is not a function, it is a variable. This might change in the future.
  •  rownames Retrieve or set the row or column names of a matrix-like object.

Syntax:

rownames(x) <- value

There are 3 types of palettes namely: sequential, diverging, and qualitative.

  • Sequential palettes are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values.

R




display.brewer.all(type="seq")


Output:

  • Diverging palettes put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues. 

R




display.brewer.all(type="div")


Output:

  • Qualitative palettes do not imply magnitude differences between legend classes, and hues are used to create the primary visual differences between classes. Qualitative schemes are best suited to representing nominal or categorical data.

R




display.brewer.all(type="qual")


Output:

Example:

R




# installation of the package RColorBrewer
install.packages("RColorBrewer")
  
# loading of the package
library("RColorBrewer")
  
# extraction of color info
# qual --> Qualitative color palette
palette5 <- brewer.pal.info[brewer.pal.info$category == "qual",]
  
# create vector with all colors
palette <- unlist(mapply(brewer.pal, palette5$maxcolors, 
                         rownames(palette5)))
palette
  
# sample colors
palette_palette <- sample(palette, no_of_colors)
  
# colors with their hex codes
palette_palette
  
# prints the pie chart
pie(rep(1, no_of_colors), col = palette_palette, 
    main = "RColorBrewer Palette")


Output:

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