Create Artworks with aRtsy in R

The aRtsy package by Koen Derks offers a user-friendly and ggplot2-based platform for creating generative artworks. It leverages algorithms that incorporate randomness to produce unique drawings. The above demo graphics are generated by the script below.

# install.packages("aRtsy")library(aRtsy)library(RColorBrewer)
set.seed(123)
p1 <- canvas_strokes(  colors = c("white", "black", "steelblue", "firebrick") )
p2 <- canvas_blacklight(  colors = brewer.pal(11, "Spectral"))
p3 <- canvas_flow(  colors = colorPalette("dark2"),   lwd = 1, polar = T)
p4 <- canvas_function(  colors = colorPalette("tuscany1"),  background = "black")
p5 <- canvas_gemstone(  colors = colorPalette("dark3"))
p6 <- canvas_circlemap(  colors = colorPalette("neon2"))
p7 <- canvas_recaman(  colors = colorPalette("random", n = 10))
p8 <- canvas_turmite(  colors = colorPalette("dark2"))
p9 <- canvas_watercolors(  colors = colorPalette("tuscany2"))
library(patchwork)
( p1 | p2 | p3 ) /   ( p4 | p5 | p6 ) /  ( p7 | p8 | p9 )

Continue Exploring — 🚀 one level up!

Apart from the fantastic aRtsy package, you can create beautiful patterns using self-defined math functions as in the following article.

In addition to abstract artworks created by randomization algorithms, heatmaps are another powerful tool that not only offers unique aesthetic appeal, but also provides valuable insights into data patterns – check out the following amazing heatmap visualizing the population distribution in Africa.

And check out these beautiful heatmaps highlighting the power of vaccines in disease control throughout the U.S. history.

Last but not the least, check out this awesome 2D histogram with a world map overlay that visualizes the hurricane activities in North Atlantic Ocean.