Colours

Palettes from Famous Paintings, GoT, Harry Potter, and Wes Anderson

Author

Arvind V.

Published

May 22, 2021

Modified

September 30, 2025

1 Setting up R Packages

library(tidyverse) # Manage data
library(ggformula) # ggplot2 with a formula interface
library(scales) # Create special ( % or $ ) scales
library(RColorBrewer) # Colour Palettes
library(paletteer) # Colour Palettes Super Package
library(colorspace) # Colour Palettes
library(wesanderson) # Colour Palettes
library(grDevices) # Colour Palettes and fonts and...;-O
####
# devtools::install_github("EdwinTh/dutchmasters")
library(dutchmasters) # Dutch Masters Palettes
####
# devtools::install_github("aljrico/gameofthrones")
library(gameofthrones) # You all know this!
library(harrypotter) # Harry Potter Palettes
library(ggthemes) # Extra ggplot2 themes and scales

library(patchwork) # arranges plots on Row-Col
library(reactable) # Interactive tables ( for choosing palettes)

Plot Fonts and Theme

Show the Code

library(systemfonts)
library(showtext)
library(ggrepel)
library(marquee)
## Clean the slate
systemfonts::clear_local_fonts()
systemfonts::clear_registry()
##
showtext_opts(dpi = 96) # set DPI for showtext
sysfonts::font_add(
  family = "Alegreya",
  regular = "../../../../../../fonts/Alegreya-Regular.ttf",
  bold = "../../../../../../fonts/Alegreya-Bold.ttf",
  italic = "../../../../../../fonts/Alegreya-Italic.ttf",
  bolditalic = "../../../../../../fonts/Alegreya-BoldItalic.ttf"
)

sysfonts::font_add(
  family = "Roboto Condensed",
  regular = "../../../../../../fonts/RobotoCondensed-Regular.ttf",
  bold = "../../../../../../fonts/RobotoCondensed-Bold.ttf",
  italic = "../../../../../../fonts/RobotoCondensed-Italic.ttf",
  bolditalic = "../../../../../../fonts/RobotoCondensed-BoldItalic.ttf"
)
showtext_auto(enable = TRUE) # enable showtext
##
theme_custom <- function() {
  theme_bw(base_size = 10) +

    theme_sub_axis(
      title = element_text(
        family = "Roboto Condensed",
        size = 8
      ),
      text = element_text(
        family = "Roboto Condensed",
        size = 6
      )
    ) +

    theme_sub_legend(
      text = element_text(
        family = "Roboto Condensed",
        size = 6
      ),
      title = element_text(
        family = "Alegreya",
        size = 8
      )
    ) +

    theme_sub_plot(
      title = element_text(
        family = "Alegreya",
        size = 14, face = "bold"
      ),
      title.position = "plot",
      subtitle = element_text(
        family = "Alegreya",
        size = 10
      ),
      caption = element_text(
        family = "Alegreya",
        size = 6
      ),
      caption.position = "plot"
    )
}

## Use available fonts in ggplot text geoms too!
ggplot2::update_geom_defaults(geom = "text", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "label", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))

ggplot2::update_geom_defaults(geom = "marquee", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "text_repel", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))
ggplot2::update_geom_defaults(geom = "label_repel", new = list(
  family = "Roboto Condensed",
  face = "plain",
  size = 3.5,
  color = "#2b2b2b"
))

## Set the theme
ggplot2::theme_set(new = theme_custom())

## tinytable options
options("tinytable_tt_digits" = 2)
options("tinytable_format_num_fmt" = "significant_cell")
options(tinytable_html_mathjax = TRUE)


## Set defaults for flextable
flextable::set_flextable_defaults(font.family = "Roboto Condensed")

2 Introduction

We will look at how to use colour scales and palettes to embellish our plots in ggplot2.

3 Examine the Data

We will use the penguins dataset built into R itself. Your should try other datasets too!

Here is a glimpse of the data:

data(penguins, package = "datasets") # Load the penguins dataset)
glimpse(penguins)

Rows: 344
Columns: 8
$ species     <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Ad…
$ island      <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgersen, Tor…
$ bill_len    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, 42.0, …
$ bill_dep    <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, 20.2, …
$ flipper_len <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186, 180,…
$ body_mass   <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, 4250, …
$ sex         <fct> male, female, female, NA, female, male, female, male, NA, …
$ year        <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

3.1 Data Dictionary

Quantitative Data

bill_len - length of the penguin’s bill in millimeters (continuous)
bill_dep - depth of the penguin’s bill in millimeters (continuous)
flipper_len - length of the penguin’s flipper in millimeters (continuous)
body_mass - mass of the penguin in grams (continuous)
year - year of observation (discrete, integer)

Qualitative Data

species - the species of penguin (categorical)
island - the island where the penguin was observed (categorical)

Figure 1: Penguin Species and Measurements

4 Colour vs Fill aesthetic

Fill and colour scales in ggplot2 can use the same palettes. Some shapes such as lines only accept the colour aesthetic, while others, such as polygons, accept both colour and fill aesthetics. In the latter case, the colour refers to the border of the shape, and the fill to the interior.

All symbols have a foreground colour, so if we add color = "navy", they all are affected.

Figure 3: Shapes in ggplot2 have the colour aesthetic.

While all symbols have a foreground colour, symbols 21-25 also take a background colour (fill). So if we add fill = "orchid", only the last row of symbols are affected.

Figure 4: Some Shapes in ggplot2 also have the fill aesthetic.

5 Discrete vs continuous variables

Please revisit:

WHAT IS THE DIFFERENCE BETWEEN CATEGORICAL, ORDINAL AND INTERVAL VARIABLES?

In order to use color with your data, most importantly, you need to know if you’re dealing with discrete or continuous variables. Discrete variables are categorical variables that can take on a limited number of values. For example, the species of penguins in the penguins dataset is a discrete variable because it can only take on a limited number of values (Adelie, Chinstrap, Gentoo).

Continuous variables, on the other hand, can take on any value within a range. For example, the body_mass of penguins in the penguins dataset is a continuous variable because it can take on any value within a range (e.g., 3000g to 6000g).

6 Colour Palettes

Colour palettes are a set of colours that can be used to represent data in a visual way. They are used to make plots more visually appealing and to help convey information more effectively. In R, there are many packages that provide colour palettes, and they can be used in ggplot2 to create beautiful and informative plots. Colour palettes can be used to represent different types of data, such as categorical, ordinal, and continuous data.

There are three main types of colour palettes in R:

For Quantitative Variables: Sequential (type = “seq”) 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. (for numerical data, that are ordered)
For Quantitative Variables: Diverging (type = “div”) 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.(for numerical data that can be positive or negative, often representing deviations from some norm or baseline)
For Qualitative Variables (type = “qual”) 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. (for qualitative unordered data)

Let us now create plots that show the effect of these palettes on the penguins dataset. We will use different colour packages from R to achieve this.

6.1 Create a simple set of scatter plots

We will create simple base plots in ggplot2 and see how we may alter the colour scales using palettes.

names(penguins)

[1] "species"     "island"      "bill_len"    "bill_dep"    "flipper_len"
[6] "body_mass"   "sex"         "year"

p1 <- penguins %>%
  drop_na() %>%
  # pipe data into ggplot
  # after removing data rows that have missing ( NA ) values
  gf_point(body_mass ~ flipper_len,
    color = ~species
  ) %>% # COLOUR = DISCRETE/QUAL VARIABLE

  gf_labs(
    title = "Default QUAL Colours",
    x = "Flipper Length (mm)", y = "Body Mass (g)"
  ) # Add labels


p2 <-
  penguins %>%
  drop_na() %>%
  # pipe the data into ggplot,
  # after removing data rows that have missing ( NA ) values
  gf_point(body_mass ~ flipper_len,
    color = ~bill_len
  ) %>% # COLOUR = CONT/QUANT VARIABLE
  gf_labs(
    title = "Default QUANT Colours",
    x = "Flipper Length (mm)", y = "Body Mass (g)"
  ) # Add labels

patchwork::wrap_plots(p1, p2, ncol = 2) + plot_annotation(tag_levels = "1", tag_prefix = "P")

Figure 5: Default colours in ggplot2 for discrete and continuous variables

Note that these use the default colours in R. We did not specify any colour scales, so the default colours are used. The first plot uses a qualitative variable (species) and the second plot uses a quantitative variable (bill length).

Note the colour legends next to either of the plots: this shows the default colours used by ggplot2 for the discrete and continuous variables.

7 Changing Colour Scales with chameleon ease

P1: For Discrete (QUAL) Variables The commands below are used to fill colours based on Qualitative Variables:

scale_colour/fill_discrete (Default ggplot2 colours)
scale_colour/fill_brewer # RColorBrewer
scale_colour/fill_manual # Specify colours manually
scale_colour/fill_paletteer_d # Use palettes from paletteer package

P2: For Continuous (QUANT) Variables

The commands below are used to fill colours based on Quantitative Variables:

scale_colour/fill_continuous() (Default ggplot2 colours)
scale_colour/fill_gradient (Two colour gradient)
scale_colour/fill_gradient2 (Three colour gradient)
scale_colour/fill_gradientn (Specify Palette, from other packages also, like wesanderson )
scale_colour/fill_distiller (Palettes from RColorBrewer)
scale_colour/fill_paletteer_c (Use palettes from paletteer package)

Now to use these!

7.1 Using `ggplot2` colour scales

Let us just assure ourselves of the “default-ness” of ggplot2 colours, when we do not specify a palette or colour scale.

p10 <- p1 %>%
  gf_refine(scale_color_discrete())

p20 <- p2 %>%
  gf_refine(scale_color_continuous())

patchwork::wrap_plots(p10, p20, ncol = 2) +
  plot_annotation(tag_levels = "1", tag_prefix = "P")

No change, as we are using the default colours in ggplot2. we expected this, of course.

7.2 Using `RColorBrewer`

Let us examine the palettes available in the {RColorBrewer} package.

Show the Code

RColorBrewer::brewer.pal.info %>% reactable::reactable(data = ., filterable = TRUE, minRows = 5, searchable = TRUE, resizable = TRUE, striped = TRUE, compact = TRUE, bordered = TRUE, highlight = TRUE, showSortable = TRUE, showSortIcon = TRUE)
RColorBrewer::display.brewer.all(type = "all")

Figure 7: Colour Palettes in RColorBrewer

Figure 8: Colour Palettes in RColorBrewer

Note the palette category: div / seq / qual !!

p11 <- p1 +
  scale_color_brewer(palette = "Set2") +
  labs(
    title = "",
    subtitle = "QUAL Palette = Set2"
  )

p21 <- p2 +
  scale_color_distiller(palette = "YlOrRd") +
  labs(
    title = "",
    subtitle = "QUANT Palette = YlOrRd"
  )

patchwork::wrap_plots(p11, p21, ncol = 2) + plot_annotation(title = "Using the RColorBrewer package", tag_levels = "1", tag_prefix = "P")

Figure 9: RColorBrewer colours in ggplot2 for discrete and continuous variables

Note the legend changes again! The colours are now from the RColorBrewer package, and the legend shows the palette used.

7.3 Using `paletteer`

The paletteer package by Emil Hvitfeldt contains over 2000 palettes and seems to have everything accessible in a simple way!

Note

NOTE: In order to access some palettes in paletteer, you may be asked to install other packages. E.g. harrypotter or scico. These need not be brought into your session using library() but are accessed directly by paletteer which is very convenient!!

Show the Code

# What discrete palettes are there in paletteer?
palettes_d_names %>% reactable::reactable(data = ., filterable = TRUE, minRows = 5, searchable = TRUE, resizable = TRUE, striped = TRUE, compact = TRUE, bordered = TRUE, highlight = TRUE, showSortable = TRUE, showSortIcon = TRUE)

Table 1: Discrete Colour Palettes in paletteer

Show the Code

# What continuous palettes are there in paletteer?
palettes_c_names %>% reactable::reactable(data = ., filterable = TRUE, minRows = 5, searchable = TRUE, resizable = TRUE, striped = TRUE, compact = TRUE, bordered = TRUE, highlight = TRUE, showSortable = TRUE, showSortIcon = TRUE)

Table 2: Continuous Colour Palettes in paletteer

Dynamic Palettes from paletteer

Dynamic palettes are those that can change based on the data they are applied to, where the colors of the palette depend on the number of colors you need, like the green.pal palette from the cartography package. They are useful for creating plots that adapt to the data being visualized.

Dynamic palettes are available for both discrete and continuous variables in paletteer.

Show the Code

# What dynamic palettes are there in paletteer?
palettes_dynamic_names %>% reactable::reactable(data = ., filterable = TRUE, minRows = 5, searchable = TRUE, resizable = TRUE, striped = TRUE, compact = TRUE, bordered = TRUE, highlight = TRUE, showSortable = TRUE, showSortIcon = TRUE)

Table 3: Dynamic Palettes in paletteer

Show the Code

p12 <- p1 +
  scale_colour_paletteer_d("ggthemes_ptol::qualitative",
    dynamic = TRUE
  ) +
  labs(
    title = "",
    subtitle = "ggThemes Palette: Qualitative"
  )

# I like Vermeer's "Girl with the Pearl Earring"!
p13 <- p1 +
  scale_colour_paletteer_d("dutchmasters::pearl_earring") +

  labs(
    title = "",
    subtitle = " Vermeer Palette: Qualitative",
    caption = "Girl with the Pearl Earring"
  )

patchwork::wrap_plots(p12, p13, ncol = 2) + plot_annotation(title = "Using the paletteer package")

OK, one of the Games of Thrones Palettes, and Harry Potter palette, both for QUANT data!

Show the Code

p22 <- p2 +
  scale_color_got(option = "jon_snow") +
  labs(
    title = "",
    subtitle = "Game of Thrones::Jon Snow",
    caption = "Oh you awful Srishti people..."
  )

# Harry Potter Gryffindor Palette.
# Will ask for `harrypotter` package to be installed. Say yes!
p23 <- p2 +
  scale_colour_paletteer_c("harrypotter::gryffindor") +
  labs(
    title = "",
    subtitle = "Harry Potter::Gryffindor"
  )

patchwork::wrap_plots(p22, p23, ncol = 2) + plot_annotation(title = "Using the got and paletteer packages")

7.4 Using Manual Scales

There are a good few ways in which we can manually specify colour scales for both Qual and Quant variables in ggplot. There are built in functions for developing colour gradients and palettes, for Quant variables, such as scale_colour_gradient(), scale_colour_gradient2(), and scale_colour_gradientn().

For Qual variables, we can use scale_colour_manual() or scale_fill_manual() alog with a vector of colours, or a named vector of colours.

Continuous Two-Colour Gradients

Creates a pallete containing continuous shades between two colours:

Show the Code

p2 +
  scale_color_gradient(
    low = "dodgerblue", # Play with this setting
    high = "firebrick"
  ) + # Play with this setting

  labs(
    title = "Two Colour Gradients",
    subtitle = "P2: Continuous 2-Colour Pallete"
  )

Continuous Three-Colour Gradients

Sometimes we want a palette this way: a midpoint colour, and colours for the two extremes of a continuous variable:

Show the Code

colour_midpoint <- mean(penguins$bill_len,
  na.rm = TRUE
) # remove missing values
# Struggled all morning on 22 Aug 2020 to get at this ;-D

# Play with the function: 0/mean/median/mode/max/min

p2 +
  scale_colour_gradient2(
    low = "brown", # Play with this in the chunk below
    mid = "white", # Play with this in the chunk below
    high = "purple", # Play with this in the chunk below
    midpoint = colour_midpoint, # see above
    space = "Lab", # don't mess with this!
    na.value = "grey50"
  ) +
  labs(
    title = "Three colour continuous gradient",
    subtitle = "Mid Colour mapped to midpoint of data variable",
    caption = "Colours inspired by my favourite cocker spaniel, Lord Chestnut"
  ) # Play with these

Continuous n-Colour Gradients - grDevices package

Show the Code

# grDevices Palettes
p2 +
  scale_colour_gradientn(
    colours = terrain.colors(10)
  ) +
  # Try these:
  # heat.colors() / topo.colors() / cm.colors() / rainbow()

  labs(
    title = "N-colour continuous gradients",
    subtitle = "Palettes from grDevices",
    caption = "Palette: terrain.colors"
  )

Continuous n-Colour Gradients - `wesanderson` Palettes

Let us look at the wesanderson package, which has some beautiful colour palettes inspired by Wes Anderson’s movies.

Show the Code

p2 +
  scale_colour_gradientn(
    colors = wes_palette(
      name = "Zissou1Continuous",
      n = 11
    ), # Keep an eye on "n" for the chosen palette!
    na.value = "grey"
  ) +

  labs(
    title = "N-colour continuous gradients",
    subtitle = "Palettes from wesanderson",
    caption = "Palette: Zissou1Continuous"
  ) # Change this caption based on palette choice

Discrete `wesanderson` Palettes

Show the Code

p1 +
  scale_colour_discrete(type = wes_palette(
    name = "GrandBudapest1",
    n = 3
  )) +
  labs(title = "Wes Anderson Palette: GrandBudapest")

We can also specify colour codes ourselves with scale_colour_manual(). Use argument values instead of type. These colours could be chosen manually, or based on a palette from another package, or even from a website like Coolors.

manual_colours <- c("#fba4b2", "#af14b8", "#1fb1e1")
manual_colours

[1] "#fba4b2" "#af14b8" "#1fb1e1"

p1 +
  scale_colour_manual(values = manual_colours) +
  labs(
    title = "Manual Colours for Qual variable",
    subtitle = "Using scale_colour_manual()",
    caption = "Colours: afc4b8, f1a4b2, 1fb1e1"
  ) # Change this caption based on palette choice

8 Wait, But Why?

Why do we need all these colour palettes? Why not just use the default colours in ggplot2? The default colours in ggplot2 are not always suitable for all types of data. For example, if you have a categorical variable with many levels, the default colours may not be distinct enough to differentiate between the levels. Similarly, if you have a continuous variable with a wide range of values, the default colours may not be able to represent the data effectively.

Using colour palettes allows you to choose colours that are more suitable for your data, and that can help convey information more effectively. It also allows you to create more visually appealing plots that are easier to interpret.

Finally, colours in charts also reflect your brand! Websites like The Economist and FiveThirtyEight have their own branded palettes. Color Selection websites like Coolors can help you create colour palettes that match your brand colours.

9 Conclusion

We have seen how to use colour palettes in ggplot2 to enhance our plots. We have used the RColorBrewer, paletteer, and wesanderson packages to create beautiful and informative plots. We have also seen how to use manual colour scales to create custom colour palettes.

We have also seen how to use colour scales for both discrete and continuous variables, and how to use different types of colour palettes for different types of data.

10 References

Lisa Charlotte Muth. How to pick more beautiful colors for your data visualizations. https://www.datawrapper.de/blog/beautifulcolors
Albert Rapp. (2022). 4 Ways to use colors in ggplot more efficiently https://albert-rapp.de/posts/ggplot2-tips/07_four_ways_colors_more_efficiently/07_four_ways_colors_more_efficiently
Emil Hvitfeldt. (2023). paletteer: A Palette Manager for R. https://emilhvitfeldt.github.io/paletteer/
David Keyes.(2019). Themes to Improve Your ggplot Figures. https://rfortherestofus.com/2019/08/themes-to-improve-your-ggplot-figures/
https://coolors.co/ - A website to generate colour palettes.

R Package Citations

Package	Version	Citation
gameofthrones	1.0.2	Jimenez Rico (2025)
ggthemes	5.1.0	Arnold (2024)
grDevices	4.5.1	R Core Team (2025)
harrypotter	2.1.1	Jimenez Rico (2020)
paletteer	1.6.0	Hvitfeldt (2021)
patchwork	1.3.2	Pedersen (2025)
reactable	0.4.4	Lin (2023)
wesanderson	0.3.7	Ram and Wickham (2023)

Arnold, Jeffrey B. 2024. ggthemes: Extra Themes, Scales and Geoms for “ggplot2”. https://doi.org/10.32614/CRAN.package.ggthemes.

Hvitfeldt, Emil. 2021. paletteer: Comprehensive Collection of Color Palettes. https://github.com/EmilHvitfeldt/paletteer.

Jimenez Rico, Alejandro. 2020. harrypotter: Palettes Generated from All “Harry Potter” Movies. https://doi.org/10.32614/CRAN.package.harrypotter.

———. 2025. gameofthrones: Palettes Inspired in the TV Show “Game of Thrones”. https://github.com/aljrico/gameofthrones.

Lin, Greg. 2023. reactable: Interactive Data Tables for r. https://doi.org/10.32614/CRAN.package.reactable.

Pedersen, Thomas Lin. 2025. patchwork: The Composer of Plots. https://doi.org/10.32614/CRAN.package.patchwork.

R Core Team. 2025. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Ram, Karthik, and Hadley Wickham. 2023. wesanderson: A Wes Anderson Palette Generator. https://doi.org/10.32614/CRAN.package.wesanderson.

1 Setting up R Packages

Plot Fonts and Theme

2 Introduction

3 Examine the Data

3.1 Data Dictionary

4 Colour vs Fill aesthetic

5 Discrete vs continuous variables

6 Colour Palettes

6.1 Create a simple set of scatter plots

7 Changing Colour Scales with chameleon ease

7.1 Using ggplot2 colour scales

7.2 Using RColorBrewer

7.3 Using paletteer

7.4 Using Manual Scales

Continuous Two-Colour Gradients

Continuous Three-Colour Gradients

Continuous n-Colour Gradients - grDevices package

Continuous n-Colour Gradients - wesanderson Palettes

Discrete wesanderson Palettes

8 Wait, But Why?

9 Conclusion

10 References

R Package Citations

7.1 Using `ggplot2` colour scales

7.2 Using `RColorBrewer`

7.3 Using `paletteer`

Continuous n-Colour Gradients - `wesanderson` Palettes

Discrete `wesanderson` Palettes