I’ve largely avoided “time” in R to date, but following a chat with @mhawksey at #dev8d yesterday, I went down a rathole last night exploring a few ways of visualising a Twitter user timeline and as a result also had a quick initial play with some time handling features of R, such as timeseries...
A couple of weeks ago I saw a great example of an open learning blogpost from @katy_bird: Generating a word cloud (or not) from a Twitter hashtag. It described the trials and tribulations associated with trying to satisfy a request for the generation of a wordcloud based on tweets associated with a specific Twitter...
Selecting subsets of a data.frame is easy in R if you define the predicates manually. But if you need to define many conditions the standard slicing and subsetting methods are cumbersome. For this illustration I want to pick some large number of numeri...
Following on from the sketched ideas relating to estimating the Potential Audience Size for a Hashtag Community?, here are a few quick doodles around the graph representation of the tag users – followers graph that explore the extent to which we can use quite simple counts and analyses to get a feel for how...
In a previous post, I demonstrated how to use my R package MHadapive to do general MCMC to estimate Bayesian models. The functions in this package are an implementation of the Metropolis-Hastings algorithm. In this post, I want to provide an intuitive way to picture what is going on ‘under the hood’in this algorithm....
What’s the potential audience size around a Twitter hashtag? Way back when, in the early days of webs stats, reported figures tended to centre around the notion of hits, the number of calls made to a server via website activity. I forget the details, but the metric was presumably generated from server logs. This...
Some of us over at McGill’s Biology Graduate Student Association have been developing and delivering R/Statistics workshops over the last few years. Through invited graduate students and faculty, we have tackled everything from multi-part introductory workshops to get your feet wet, to special topics such as GLMs, GAMs, Multi-model inference, Phylogenetic analysis, Bayesian modeling,...
If you can write the likelihood function for your model, MHadaptive will take care of the rest (ie. all that MCMC business). I wrote this R package to simplify the estimation of posterior distributions of arbitrary models. Here’s how it works: 1) Define your model (ie the likelihood * prior). In this example, lets...
I think one of valid criticisms around a lot of the visualisations I post here and on my various #f1datajunkie blogs is that I often don’t post any explanatory context around the visualisations. This is partly a result of the way I use my blog posts in a selfish way to document the evolution...
(Almost) every introductory course in probability introduces conditional probability using the famous Monte Hall problem. In a nutshell, the problem is one of deciding on a best strategy in a simple game. In the game, the contestant is asked to select one of three doors. Behind one of the doors is a great prize...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).