Background Last June I did a blog post about building dot-denisty maps in R using UK Census data. It has proven to be a fairly popular post, most likely due to the maps looking like something you’re more likely to see in the Tate Modern… Not only do these maps looks beautiful, but there is a strong argument that they do a better job of representing data compared to the more common choropleth methods of filling geographical regions with one colour based on one variable.
Twitter Analytics There has been a surge in a lot of great twitter analytics recently in the #rstats world, in part due to Michael Kearney’s excellent rtweet package. It makes wrangling with the twitter API like water off a duck’s back (twitter, bird, duck? sorry). You almost feel like some sort of Zuckerberg-esque data tycoon when you realise the amount of data you can access with a few lines of code.
TL;DR - check the tracker out here. As a recent cryptocurrency ‘Investor’ (0.13 ETH baby) I wanted to build a light tracker tool that could help me keep up with the mad market volatility in a more personalised manner. Of course, there’s no better tool for this task than the open-source programming language R, and the multitude of packages built for it that allows programmers with very modest levels of front-end development and sys-admin knowledge to go from nada to fully deployed, production quality web-application in a few hours.
A Dive Into Some Global Flooding Data I always like to keep a look out for interesting open data sets. One great resource for such things is Jeremy Singer-Vine’s Data is Plural weekly newsletter that brings together a collection of “useful, curious datasets” for us all to enjoy and wrangle with. One that cropped up last week was The Dartmouth Flood Observatory’s Global Archive of Large Flood Events.