Disentangling Data Science, every Tuesday
Tag: color
Could you guess that you are looking at Amsterdam?
Maybe you spotted the canals?
Bert Spaan colorcoded every building in the Netherlands according to their yaer of construction and visualized the resulting map of nearly 10 million buildings in a JavaScript leaflet webpage.
It resulted in this wonderful map, which my screenshots don’t do any honor. So have a look yourself!
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.
Let me know what you like about the package and do share any beautiful data visualizations you create with it!
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.
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.
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!
Looking for expert books on data visualization?
Have a look at these recommendations!
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")
What’s your favorite color palette among these 100?
