GGPlot Colors Best Tricks You Will Love



GGPlot Colors Best Tricks You Will Love

This article presents multiple great solutions you should know for changing ggplot colors.

When creating graphs with the ggplot2 R package, colors can be specified either by name (e.g.: “red”) or by hexadecimal code (e.g. : “#FF1234”).

It is also possible to use pre-made color palettes available in different R packages, such as: viridis, RColorBrewer and ggsci packages.

In this tutorial, you’ll learn how to:

  • Change ggplot colors by assigning a single color value to the geometry functions (geom_point, geom_bar, geom_line, etc). You can use R color names or hex color codes.
  • Set a ggplot color by groups (i.e. by a factor variable). This is done by mapping a grouping variable to the color or to the fill arguments. In this case, we’ll show how to change manually the default ggplot2 colors by using the functions scale_color_manual() and scale_fill_manual(). These functions makes it possible to set a custom color palette for each group level.
  • Use a list of colors that are color-blind friendly. R packages such as viridis and RColorBrewer provide different color scales that are robust to color-blindness.
  • Use predefined ggplot color palettes.
  • Change a ggplot gradient color (also known as continuous color). To create a gradient color in ggplot2, a continuous variable is mapped to the options color or fill. There are three different types of function to modify the default ggplot2 gradient color, including scale_color_gradient(),scale_color_gradient2(), scale_color_gradientn(). The same scale functions exist for the fill arguments: scale_fill_gradient(), scale_fill_gradient2(), scale_fill_gradientn(). We’ll describe step by step how to use them.

Contents:

Key ggplot2 R functions

This section presents the key ggplot2 R function for changing a plot color.

Set ggplot color manually:

  • scale_fill_manual() for box plot, bar plot, violin plot, dot plot, etc
  • scale_color_manual() or scale_colour_manual() for lines and points

Use colorbrewer palettes:

  • scale_fill_brewer() for box plot, bar plot, violin plot, dot plot, etc
  • scale_color_brewer() or scale_colour_brewer() for lines and points

Use grey color scales:

  • scale_fill_grey() for box plot, bar plot, violin plot, dot plot, etc
  • scale_colour_grey() or scale_colour_brewer() for points, lines, etc

Change the default ggplot gradient color:

  • scale_color_gradient(), scale_fill_gradient() for sequential gradients between two colors
  • scale_color_gradient2(), scale_fill_gradient2() for diverging gradients
  • scale_color_gradientn(), scale_fill_gradientn() for gradient between n colors

Prerequisites

  1. Load ggplot2 package and set the default theme:
library(ggplot2)
theme_set(
  theme_minimal() +
    theme(legend.position = "right")
  )
  1. Initialize ggplots using the iris data set. Create a box plot (bp) and a scatter plot (sp) that we’ll customize in the next section:
# Box plot
bp <- ggplot(iris, aes(Species, Sepal.Length))

# Scatter plot
sp <- ggplot(iris, aes(Sepal.Length, Sepal.Width))

Specify a single color

Colors in R can be specified either by name (e.g.: “red”) or by hexadecimal color codes, such as “#FF1234”. You can find many examples of color names and codes at:

The following R script changes the fill color (in box plots) and points color (in scatter plots).

  • Using hexadecimal color codes:
# Box plot
bp + geom_boxplot(fill = "#FFDB6D", color = "#C4961A")

# Scatter plot
sp + geom_point(color = "#00AFBB") 

  • Using color names
# Box plot
bp + geom_boxplot(fill = "lightgray", color = "black")

# Scatter plot
sp + geom_point(color = "steelblue") 

Change colors by groups: ggplot default colors

You can set colors according to the levels of a grouping variable by:

  • Mapping the argument color to the variable of interest. This will be applied to points, lines and texts
  • Mapping the argument fill to the variable of interest. This will change the fill color of areas, such as in box plot, bar plot, histogram, density plots, etc.

In the following example, we’ll map the options color and fill to the grouping variable Species, for scatter plot and box plot, respectively.

Changes colors by groups using the levels of Species variable:

# Box plot
bp <- bp + geom_boxplot(aes(fill = Species)) +
  theme(legend.position = "top")
bp

# Scatter plot
sp <- sp + geom_point(aes(color = Species)) +
  theme(legend.position = "top")
sp

Set custom color palettes

It’s possible to set manually the color palettes by using the functions:

  • scale_fill_manual() for box plot, bar plot, violin plot, dot plot, etc
  • scale_color_manual() or scale_colour_manual() for lines and points
# Box plot
bp + scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Scatter plot
sp + scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

You might also find interesting, the following list of colors:

custom.col <- c("#FFDB6D", "#C4961A", "#F4EDCA", 
                "#D16103", "#C3D7A4", "#52854C", "#4E84C4", "#293352")
show_pal(custom.col)

Use a colorblind-friendly palette

When selecting a set of colors, it’s recommended to make sure that you choose color palettes that are robust to colorblindness. In the next sections, we’ll show you to check that your production figures are colorblind-friendly.

Here, we present two color-blind-friendly palettes, one with gray, and one with black (palettes source: http://jfly.iam.u-tokyo.ac.jp/color/).

# The palette with grey:
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# The palette with black:
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

You can use these palettes as follow:

# To use for fills, add
bp + scale_fill_manual(values = cbp1)

# To use for line and point colors, add
sp + scale_colour_manual(values=cbp1)

Predefined ggplot color palettes

You can modify the default ggplot colors by using predefined color palettes available in different R packages. The most commonly used color scales, include:

  • Viridis color scales [viridis package].
  • Colorbrewer palettes [RColorBrewer package]
  • Grey color palettes [ggplot2 package]
  • Scientific journal color palettes [ggsci package]
  • Wes Anderson color palettes [wesanderson package]

Learn more at: Top R Color Palettes to Know for Great Data Visualization

Viridis color palettes

The viridis R package provides color palettes to make beautiful plots that are: printer-friendly, perceptually uniform and easy to read by those with colorblindness. Key functions scale_color_viridis() and scale_fill_viridis()

library(viridis)
# Gradient color
ggplot(iris, aes(Sepal.Length, Sepal.Width))+
  geom_point(aes(color = Sepal.Length)) +
  scale_color_viridis(option = "D")

# Discrete color. use the argument discrete = TRUE
ggplot(iris, aes(Sepal.Length, Sepal.Width))+
  geom_point(aes(color = Species)) +
  geom_smooth(aes(color = Species, fill = Species), method = "lm") + 
  scale_color_viridis(discrete = TRUE, option = "D")+
  scale_fill_viridis(discrete = TRUE) 

RColorBrewer palettes

The RColorBrewer package creates a nice looking color palettes. Read more at: The A – Z Of Rcolorbrewer Palette

Two color scale functions are available in ggplot2 for using the colorbrewer palettes:

  • scale_fill_brewer() for box plot, bar plot, violin plot, dot plot, etc
  • scale_color_brewer() for lines and points

For example:

# Box plot
bp + scale_fill_brewer(palette = "Dark2")

# Scatter plot
sp + scale_color_brewer(palette = "Dark2")

To display colorblind-friendly brewer palettes, use this R code:

library(RColorBrewer)
display.brewer.all(colorblindFriendly = TRUE)

Grey color palettes

Key functions available in ggplot2:

  • scale_fill_grey() for box plot, bar plot, violin plot, dot plot, etc
  • scale_colour_grey() for points, lines, etc
# Box plot
bp + scale_fill_grey(start = 0.8, end = 0.2) 

# Scatter plot
sp + scale_color_grey(start = 0.8, end = 0.2) 

Scientific journal color palettes

The R package ggsci contains a collection of high-quality color palettes inspired by colors used in scientific journals, data visualization libraries, and more.

The color palettes are provided as ggplot2 scale functions:

  • scale_color_npg() and scale_fill_npg(): Nature Publishing Group color palettes
  • scale_color_aaas() and scale_fill_aaas(): American Association for the Advancement of Science color palettes
  • scale_color_lancet() and scale_fill_lancet(): Lancet journal color palettes
  • scale_color_jco() and scale_fill_jco(): Journal of Clinical Oncology color palettes
  • scale_color_tron() and scale_fill_tron(): This palette is inspired by the colors used in Tron Legacy. It is suitable for displaying data when using a dark theme.

You can find more examples in the ggsci package vignettes.

  1. Usage in ggplot2. We’ll use JCO and the Tron Legacy color palettes.
library("ggsci")
# Change area fill color using jco palette
bp + scale_fill_jco()


# Change point color 
sp + scale_color_jco()

Learn more at: Top R Color Palettes to Know for Great Data Visualization

Wes Anderson color palettes

It contains 16 color palettes from Wes Anderson movies.

library(wesanderson)
names(wes_palettes)
##  [1] "BottleRocket1"  "BottleRocket2"  "Rushmore1"      "Royal1"        
##  [5] "Royal2"         "Zissou1"        "Darjeeling1"    "Darjeeling2"   
##  [9] "Chevalier1"     "FantasticFox1"  "Moonrise1"      "Moonrise2"     
## [13] "Moonrise3"      "Cavalcanti1"    "GrandBudapest1" "GrandBudapest2"

The available color palettes are illustrated in the chart below :

You can use the different palettes as discrete or continuous color in ggplot2, as follow:

library(wesanderson)
# Discrete color
bp + scale_fill_manual(values = wes_palette("GrandBudapest1", n = 3))

# Gradient color
pal <- wes_palette("Zissou1", 100, type = "continuous")
ggplot(heatmap, aes(x = X2, y = X1, fill = value)) +
  geom_tile() + 
  scale_fill_gradientn(colours = pal) + 
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) + 
  coord_equal() 

Learn more at: Top R Color Palettes to Know for Great Data Visualization

Gradient or continuous colors

Default ggplot gradient colors

For gradient colors, you should map the map the argument color and/or fill to a continuous variable. The default ggplot2 setting for gradient colors is a continuous blue color.

In the following example, we color points according to the variable: Sepal.Length.

sp2 <- ggplot(iris, aes(Sepal.Length, Sepal.Width))+
  geom_point(aes(color = Sepal.Length))

Key functions to change default gradient colors

The default gradient color can be modified using the following ggplot2 functions:

  • scale_color_gradient(), scale_fill_gradient() for sequential gradients between two colors
  • scale_color_gradient2(), scale_fill_gradient2() for diverging gradients
  • scale_color_gradientn(), scale_fill_gradientn() for gradient between n colors

Set gradient between two colors

Change the colors for low and high ends of the gradient:

# Sequential color scheme. 
# Specify the colors for low and high ends of gradient
sp2 + scale_color_gradient(low = "blue", high = "red")

# Diverging color scheme
# Specify also the colour for mid point
mid <- mean(iris$Sepal.Length)
sp2 + scale_color_gradient2(midpoint = mid, low = "blue", mid = "white",
                            high = "red", space = "Lab" )

Note that, the functions scale_color_continuous() and scale_fill_continuous() can be also used to set gradient colors.

Set gradient between n colors

In the example below, we’ll use the R base function rainbow() to generate a vector of 5 colors, which will be used to set the gradient colors.

sp2 + scale_color_gradientn(colours = rainbow(5))

Conclusion

This article presents how to customize ggplot colors. The main points are summarized as follow.

  • Create a basic ggplot. Map the color argument to a factor or grouping variable.
p <- ggplot(iris, aes(Sepal.Length, Sepal.Width))+
  geom_point(aes(color = Species))
p
  • Set the color palette manually using a custom color scale:
p + scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
  • Use color blind-friendly palette:
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
p + scale_color_manual(values = cbp1)
  • Use RColorBrewer palettes:
p + scale_color_brewer(palette = "Dark2")
  • Use viridis color scales:
library(viridis)
p + scale_color_viridis(discrete = TRUE)





Comments ( 9 )

  • Inti

    Very nice tips. Thank you!

    • Thank you for your positive feedback, highly appreciated!

  • David_Rowie

    Excellent post! Thank you very much Kassambra!

    • Hi David Rowie, I appreciate your positive feedback, thank you!

  • Mehrad

    Awesome tips! Thanks so much for the post!

  • Dave

    Fantastic guide, super helpful, thank you!

    • Thank you for the positive feedback, highly appreciated!

  • Thanks a lot, but do you know how to color each point a different colour manually ? My dataframe is given below, the colour of the points is in col column.

    head(df)
    x1 x2 z col
    1 0.72 2757 86 #2DFE89
    2 0.72 2757 86 #2DFE89
    3 0.72 2757 86 #2DFE89
    4 0.70 2757 82 #28FE97
    5 0.86 2757 26 #007C7D
    6 0.75 2757 79 #24FEA1

    • Change point color by a grouping variable, then set the color manually:

      
      library(ggplot2)
      ggplot(iris, aes(Sepal.Length, Petal.Width)) +
        geom_point(aes(color = Species)) + 
        scale_color_manual(values = c("lightgray", "darkgray", "black"))
      

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