The ggplot2 package provides an excellent platform for data visualization. One (minor) drawback of this package is that combining ggplot images into one plot, like the par() function does for regular plots, is not a straightforward procedure. Fortunately, R user Stephen Turner has kindly provided a function called “arrange” that does exactly this. The function, 
Following the Irish rugby team’s humiliating 60-0 defeat to New Zealand, an interesting question was posed on Twitter: what does a 60-0 result convert to in football/soccer? Intrigued, I decided to gather some data from both the English premier league (this season, more data collected and future blog posts to come!) and the equivalent English 
Time for an update of the plots. Here are the teams still left in the competition. This is the group stratification. Finally, the busy plot.
The second round of group games ended last night (sadly with Sweden’s elimination). Here is what the last number of days has done to the plots.
Where do these come from? Since most statistical packages calculate these estimates automatically, it is not unreasonable to think that many researchers using applied econometrics are unfamiliar with the exact details of their computation. For the purposes of illustration, I am going to estimate different standard errors from a basic linear regression model: , using the 
Now that every team has played a match it will be interesting to see how this has affected the (inverse) odds of victory. Since the plot in my last post was a bit ‘busy’, I have decided to use the facet_wrap function in gglplot2 to stratify by group. Also, re-producing the ‘busy’ plot from the 
After scanning this paper by Zeileis, Leitner & Hornik, I thought it would be interesting to see how the victory odds for each team changes as Euro 2012 progresses. To do this, I am going to collect the daily inverse odds of a tournament victory offered by a popular betting site for each team. Here 
Exploring whether regression coefficients differ between groups is an important part of applied econometric research, and particularly for research with a policy based objective. For example, a government in a developing country may decide to introduce free school lunches in an effort to improve childhood health. However, if this treatment is known to only improve 
Call me uncouth, but I like my TV loud, my beer cold and my optimization functions as simple as possible. Therefore, what I write in this blog post is very much from a layman’s perspective, and I am happy to be corrected on any fundamental errors. I have recently become interested in writing my own 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
