Last year I sat on a couple of panels organised by I’m a Scientist’s Shane McCracken at various science communication conferences. A couple of days ago, I noticed Shane had popped up a post asking Who are you Twitter?, a quick review of a social media mapping exercise carried out on the followers of the 
Just over a year or so ago, Mat Morrison/@mediaczar introduced me to a visualisation he’d been working on (How should Page Admins deal with Flame Wars?) that I started to refer to as an accession chart (Visualising Activity Around a Twitter Hashtag or Search Term Using R). The idea is that we provide each entrant 
One of the problems with working with data files containing tens of thousands (or more) rows is that they can become unwieldy, if not impossible, to use with “everyday” desktop tools. When I was Revisiting MPs’ Expenses, the expenses data I downloaded from IPSA (the Independent Parliamentary Standards Authority) came in one large CSV file 
I couldn’t but notice the chatter about Iain Duncan Smith claiming he’d have no problem “living on 53 pounds a dayweek“, which made me wonder not only how many meal catered events he attends each week (and how many of his scheduled meeting also have complementary tea and biscuits (a bellweather of the extent of 
How can stats and data publishers, from NGOs and (inter)national statistics agencies to scientific researchers, publish their data in a way that supports its analysis directly, as well as in combination with other datasets? Here’s one approach I learned about from Michael Kao of the UN Food and Agriculture Organisation statistics division, FAOStat. At first 
Following a chat with @andypryke, I thought I’d try out a simple bit of feature detection around approximated follower acquisition charts (e.g. Estimated Follower Accession Charts for Twitter) to see if I could detect dates around which there were spikes in follower acquisition. So for example, here’s the follower acquistion chart for Seem Malhotra: We 
I’ve been doodling… Following a query about the possible purchase of Twitter followers for various public figure accounts (I need to get my head round what the problem is with that exactly?!), I thought I’d have a quick look at some stats around follower groupings… I started off with a data grab, pulling down the 
As the food labeling and substituted horsemeat saga rolls on, I’ve been surprised at how little use has been made of “data” to put the structure of the food chain into some sort of context* (or maybe I’ve just missed those stories?). One place that can almost always be guaranteed to post a few related 
Following directly on from F1Stats – Visually Comparing Qualifying and Grid Positions with Race Classification, and continuing in my attempt to replicate some of the methodology and results used in A Tale of Two Motorsports: A Graphical-Statistical Analysis of How Practice, Qualifying, and Past SuccessRelate to Finish Position in NASCAR and Formula One Racing, here’s 
A handful of open Linked Data have appeared through my feeds in the last couple of days, including (via RBloggers) SPARQL with R in less than 5 minutes, which shows how to query US data.gov Linked Data and then Leigh Dodds’ Brief Review of the Land Registry Linked Data. I was going to post a 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
