Tag: color

paletteer: Hundreds of color palettes in R

paletteer: Hundreds of color palettes in R

Looking for just the right colors for your data visualization?

I often cover tools to pick color palettes on my website (e.g. here, here, or here) and also host a comprehensive list of color packages in my R programming resources overview.

However, paletteer is by far my favorite package for customizing your colors in R!

The paletteer package offers direct access to 1759 color palettes, from 50 different packages!

After installing and loading the package, paletteer works as easy as just adding one additional line of code to your ggplot:

install.packages("paletteer")
library(paletteer)

install.packages("ggplot2")
library(ggplot2)

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point() +
scale_color_paletteer_d("nord::aurora")

paletteer offers a combined collection of hundreds of other color palettes offered in the R programming environment, so you are sure you will find a palette that you like! Here’s the list copied below, but this github repo provides more detailed information about the package contents.

NameGithubCRAN
awtoolsawhstin/awtools – 0.2.1
basethemeKKPMW/basetheme – 0.1.20.1.2
calecopalan-bui/calecopal – 0.1.0
cartographyriatelab/cartography – 2.2.1.12.2.1
colorblindrclauswilke/colorblindr – 0.1.0
colRozjacintak/colRoz – 0.2.2
dichromat2.0-0
DresdenColorkatiesaund/DresdenColor – 0.0.0.9000
dutchmastersEdwinTh/dutchmasters – 0.1.0
fishualizenschiett/fishualize – 0.2.9990.1.0
gameofthronesaljrico/gameofthrones – 1.0.11.0.0
ggpomologicalgadenbuie/ggpomological – 0.1.2
ggsciroad2stat/ggsci – 2.92.9
ggthemesjrnold/ggthemes – 4.2.04.2.0
ggthemrcttobin/ggthemr – 1.1.0
ghibliewenme/ghibli – 0.3.0.90000.3.0
grDevices2.0-14
harrypotteraljrico/harrypotter – 2.1.02.1.0
IslamicArtlambdamoses/IslamicArt – 0.1.0
jcolorsjaredhuling/jcolors – 0.0.40.0.4
LaCroixColoRjohannesbjork/LaCroixColoR – 0.1.0
lisatyluRp/lisa – 0.1.1.90000.1.1
MapPalettesdisarm-platform/MapPalettes – 0.0.2
miscpalettesEmilHvitfeldt/miscpalettes – 0.0.0.9000
nationalparkcolorskatiejolly/nationalparkcolors – 0.1.0
NineteenEightyRm-clark/NineteenEightyR – 0.1.0
nordjkaupp/nord – 1.0.01.0.0
ochReropenscilabs/ochRe – 1.0.0
oompaBase3.2.9
palettesForRfrareb/palettesForR – 0.1.20.1.2
palettetowntimcdlucas/palettetown – 0.1.1.900000.1.1
palrAustralianAntarcticDivision/palr – 0.1.00.1.0
palskwstat/pals – 1.61.6
PNWColorsjakelawlor/PNWColors – 0.1.0
Polychrome1.2.3
rcartocolorNowosad/rcartocolor – 2.0.02.0.0
RColorBrewer1.1-2
Redmonder0.2.0
RSkittleBreweralyssafrazee/RSkittleBrewer – 1.1
scicothomasp85/scico – 1.1.01.1.0
tidyquantbusiness-science/tidyquant – 0.5.80.5.8
trekcolorsleonawicz/trekcolors – 0.1.20.1.1
tvthemesRyo-N7/tvthemes – 1.1.01.1.0
uniknhneth/unikn – 0.2.0.90030.2.0
vapeplotseasmith/vapeplot – 0.1.0
vapoRwavemoldach/vapoRwave – 0.0.0.9000
viridissjmgarnier/viridis – 0.5.10.5.1
visiblym-clark/visibly – 0.2.6
werpalssciencificity/werpals – 0.1.0
wesandersonkarthik/wesanderson – 0.3.6.90000.3.6
yarrrndphillips/yarrr – 0.1.60.1.5
Via the paletteer github page

Let me know what you like about the package and do share any beautiful data visualizations you create with it!

Overview of built-in colors in R

Overview of built-in colors in R

Most of my data visualizations I create using R programming — as you might have noticed from the content of my website.

Though I am colorblind myself, I love to work with colors and color palettes in my visualizations. And I’ve come across quite some neat tricks in my time.

For instance, did you it’s super easy to create a reproducible though custom color palette? Or that there’s a quick reference card for ggplot2’s built-in colors? Or, and this is this blog post’s main subject, that you can access all built-in base colors using colors()!

This last trick, I learned in this recent blog post I came across, by Chisato. She explored all colors() base R incorporates, using the new ggforce and ggraph packages (thank you Thomas Lin Petersen!). Her exploration resulted in some nice visual overviews, which you can view in more detail in the original blog here.

Colors() with no color family
Colors() that have at least 5 colors in their family
Colors() with similar names
Daily Art by Saskia Freeke

Daily Art by Saskia Freeke

Saskia Freeke (twitter) is a Dutch artist, creative coder, interaction designer, visual designer, and educator working from Amsterdam. She has been creating an awesome digital art piece for every day since January 1st 2015. Her ever-growing collection includes some animated, visual masterpieces.

My personal favorites are Saskia’s moving works, her GIFs:

Saskia uses Processing to create her art. Processing is a Java-based language, also used often by Daniel Shiffmann whom we know from the Coding Train.

Data Visualization Tools & Resources

There’s this amazing overview of helpful dataviz resources atwww.visualisingdata.com/resources!

Browse through hundreds of helpful data visualization tools, programs, and services. All neatly organized by Andy Kirk in categories: data handling, applications, programming, web-based, qualitative, mapping, specialist, and colour. What a great repository!

A snapshot of www.visualisingdata.com/resource

Looking for expert books on data visualization?
Have a look at these recommendations!

100 amazing color palettes including their Hex codes

100 amazing color palettes including their Hex codes

TJ Mahr hinted to this Canva webpage on Twitter. It contains 100 beautiful color palettes including their hexadecimal color codes. For instance, these three below.

The great thing is that these color palettes are include in the ggthemes package in R. Hence, the following code uses this Nightlife palette directly in an R script, resulting in the plot below.

library(ggplot2)
library(ggthemes)

ggplot(mtcars) +
  aes(x = disp, y = mpg, color = factor(cyl)) +
  geom_point(size = 6) +
  ggthemes::scale_color_canva(palette = "Nightlife")

Rplot

What’s your favorite color palette among these 100?

What to consider when choosing colors for data visualization, by DataWrapper.de

What to consider when choosing colors for data visualization, by DataWrapper.de

Lisa Charlotte Rost of DataWrapper often writes about data visualization and lately she has focused on the (im)proper use of color in visualization. In this recent blog, she gives a bunch of great tips and best practices, some of which I copied below:

color in data vis advice
Gradient colors can be great to show a pattern but, for categorical data, it is often easier to highlight the most important values with colored bars, positions (like in a dot plot) or even areas. [https://blog.datawrapper.de/colors/]
color in data vis advice
If you need more than seven colors in a chart, consider using another chart type or to group categories together. [https://blog.datawrapper.de/colors/]
color in data vis advice
Consider using the same color for the same variables, but do differentiate between categories, even across graphics. [https://blog.datawrapper.de/colors/]
color in data vis advice
Using grey for less important elements in your chart makes your highlight colors (which should be reserved for your most important data points) stick out even more.  [https://blog.datawrapper.de/colors/]
color in data vis advice
Consider color-blind people. There are many different types of color blindness: Use an online tool or Datawrapper’s automatic colorblind-check. [https://blog.datawrapper.de/colors/]
 You can find additional useful tips in the original DataWrapper blog.