r-tastic

Weird and wonderful exploration of data using R

My R take on Advent of Code - Day 1

Ho, ho, ho! It’s almost Christmas time and I don’t know about you, but I can’t wait for it! And what can be a better way of killing the waiting time (advent!) than participating in excellent Advent od Code. Big thanks to Colin Fay for telling me about it! It’s a series of coding riddles, one published every day between 1st and 25th of December. The riddles increase in difficulty level over time and they can be solved in any programming language, including R.

Exploring London Crime with R heat maps

Recently, I had a real pleasure to work with various types of data pulled from public APIs, one of them being data.police.uk API. Oh, those hours of pure intellectual exploration it’s given me! I have a soft spot for crime data and I explored it using heat maps in the past. Apart from checking and visualising stats for the new area in London we just moved to, it made me think more about good and better ways of presenting complex and multidimensional information.

First post in new r-tastic

OK, it had to happen and here it is: I moved my old r-tastic blog to blogdown and I’m not going to look back :) There are numerous resources that will: highlight the advantages of using blogdown with Hugo over other static site generators, such as Jekyll (my previous choice) explain how to set everything up (there are some excellent resources here, here or here) or deploy your site (e.

Prime hints for running a data project in R

I’ve been asked more and more for hints and best practices when working with R. It can be a daunting task, depending on how deep or specialised you want to be. So I tried to keep it as balanced as I could and mentioned point that definitely helped me in the last couple of years. Finally, there’s lots (and I mean, LOTS) of good advice out there that you should definitely check out - see some examples in the Quick Reference section below.

Trump VS Clinton Interpretable Text Classifier

I’ve been writing/talking a lot about LIME recently: in this blog/ at H20 meetup, or at coming AI Congress and I’m still sooo impressed by this tool for interpreting any, even black-box, algorithm! The part I love most is that LIME can be applied to both image and text data, that was well showcased in husky VS wolf (image) and Christian VS atheist (text) examples in the original publication. Thomas Lin Pedersen did an amazing job building lime package for R with excellent documentation and vignette.