Pokémon Go has made the whole world gone wild on the hunt for those cute little creatures. After catching hundreds of Weedles, Rattatas and Pidgeys, I got a bit tired and thought it is time to do some Pokémon science.
Naturally, the whole Pokémon hype has already led to several interesting analyses with available data mainly from the awesome PokeAPI. For instance, this blog post about a cluster analysis of the original 151 Pokémon or this extended analysis of all 721 available Pokémon.
Since clustering is boring, I will do something more exciting and try to rank Pokemon according to their strength with a little bit of help from my own research.
Continue reading Gotta rank’em all:
What is the best Pokémon?
This post is inspired by the star wars social network.
I created the interaction networks of Lord of the Rings characters for all three movies based on the scripts I found online. The networks capture the story line of all movies surprisingly well and might be a nice gimmick for all Lord of the Rings enthusiasts. Code and network files can be found on github.
I also created interactive versions of the networks where you can drag, click and hover and generally play around a bit. The links to the those versions are below the respective plots.
For those who are interested in technical details of the data extraction and analysis, head down to the Making of section. But let’s start with some visualizations of the networks for the three movies.
Continue reading The Lord of the Rings: The Three Networks
If you are regularly reading this blog, you might have noticed a few things. First, I migrated the blog from blogger to my own domain and I am now using wordpress. I also changed the design a bit (might still be changed a bit in the future).
Again if you regularly read this blog you might have noticed that my plots are all rather inconsistent when it comes to formatting. I am working on a generic style I want to use from now on, which is hopefully a bit distinctive for my blog. I also would like to add more tutorial-like entries, such that you can redo the analysis yourself. I will also start uploading my code to github. This should be seen as a try to become a little more “professional”, yet still be easy to digest.
The next (mildly) exciting posts are currently being written and I hope you stay tuned and keep reading my blog.