For me, 2015 was the year of R. The year I finally started to use R productively and on an almost daily basis (after years of learning and forgetting and learning all over again). In this post, I share my experiences and tell you why you should start using it for your next data journalism project in 2016.
Back when I was working at Tages-Anzeiger, I was asked to find a way to condense the content of several hundred PDF files into one spreadsheet. These PDFs contained indicator variables about the performance of nursing and retirement homes, and for some strange reason, they were only available as individual PDFs. I took it as an opportunity to learn new features of Node.js and it turned out to be a really good solution. In this post, I explain what I came up with.
As georeferenced data from social media, be it in the form of Tweets, Foursquare Check-Ins, Instragram photos, Flickr pictures, etc., are increasingly available, so do (geospatial) analyses and visualizations done with them become more and more popular. Often, such studies and applications claim to be able to infer social, cultural, and even political insights from these data, spatially fine-grained and referenced down to the level of countries and cities.
I haven’t seen a single one which actually succeeded in plausibly explaining the how to me.