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.
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.
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.
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.
Sometimes it’s worth making New Year resolutions… A year ago I made one for 2017 to start an R blog using RMarkdown and Jekyll static sites. At the time, I didn’t even know git that well, had no clue what static sites are and was mostly oblivious to the rich and vibrant R community on Twitter. Fast-forward one year and… the picture couldn’t be any more different! I’d like to share my thoughts on writing this blog (and data science blog in general) and how it taught me about getting stuff done.