An analysis of Pokémon Go types, created with R

As anyone who has tried Pokémon Go recently is probably aware, Pokémon come in different types. A Pokémon's type affects where and when it appears, and the types of attacks it is vulnerable to. Some types, like Normal, Water and Grass are common; others, like Fairy and Dragon are rare. Many Pokémon have two or more types.

To get a sense of the distribution of Pokémon types, Joshua Kunst used R to download data from the Pokémon API and created a treemap of all the Pokémon types (and for those with more than 1 type, the secondary type). Johnathon's original used the 800+ Pokémon from the modern universe, but I used his R code to recreate the map for the 151 original Pokémon used in Pokémon Go.

Pokémon have many other attributes as well, including: weight, height, attack and defense ratings, hit points, and speed. It's hard to visualize so many variables in a 2-dimensional cart, so Joshua used a technique called t-Distributed Stochastic Neighbor Embedding (implemented in the tsne package for R) to cluster similar Pokémon in a two-dimensional chart, and used R's image-handling capbilities to include avatars for each of the Pokémon. 

This chart, which includes modern Pokémon along with the 151 originals in Pokémon Go, is colored according to each Pokémon's primary type. As you can see, the TSNE algorithm is super effective at clustering Pokémon according to type.

For more details on Joshua's analysis, including interactive versions of these charts and the R code that created them, follow the link below.

Joshua Kunst: Pokémon: Visualize 'em all! (via Matthew Bashton)