Just a short post to celebrate that I learned today how incredibly easy it is to make a heatmap of correlations with ggplot2 (and reshape2, of course). So, what is going on in that short passage? cor makes a correlation matrix with all the pairwise correlations between variables (twice; plus a diagonal of ones). melt 
It is time for the NCAA Basketball Tournament. Sixty-four teams dream big (er…I mean 68…well actually by now, 64) and schools like Iona and Florida Gulf Coast University (go Eagles!) are hoping that Robert Morris astounding victory in the N.I.T. … Continue reading →
tl;dr: Play with the app here
When you live in Paris, chances are you are (home or work) very close to a metro station, so when you want to meet with some friends, you usually end up picking another metro station as a meeting point. Yet, finding the optimal place to meet can easily become a complex problem considering...
My research has allowed me to implement techniques for visualizing multivariate models in R and I wanted to share some additional techniques I’ve developed, in addition to my previous post. For example, I think a primary obstacle towards developing a useful neural network model is an under-appreciation of the effects model parameters have on model 
A great. recent enhancement for the Shiny App is the ability to upload local files. Now, in addition to users being able to interact with data provided on the host e.g. Soccer Tables or via the web, Wikipedia Search Rates they can use apps to view and analyse their own data I have knocked up
During the last half century the European Union has adopted more than 100 000 pieces of legislation. In this presentation I look into the patterns of legislative adoption over time. I tried to create clear and engaging graphs that provide … Continue reading →
Making available replication materials for the research you do is A Good Thing. It’s also work, and it’s quite easy to never get around to. Certainly I claim no special virtue in this department so I am always happy when there’s an institutional stick to prod my better nature in the right direction. One such institutional
Second part on logistic regression (first one here). We used in the previous post a likelihood ratio test to compare a full and null model. The same can be done to compare a full and nested model to test the contribution of any subset of parameters: Interpretation of coefficients Note: Dohoo do not report the 
Scholarly metadata - the meta-information surrounding articles - can be super useful. Although metadata does not contain the full content of articles, it contains a lot of useful information, including title, authors, abstract, URL to the article, etc.
One of the largest sources of metadata is provided via the Open Archives Initiative Protocol for Metadata Harvesting or OAI-PMH....
Someone who wishes to remain anonymous writes: I’ve been following your blog a long time and enjoy your posts on visualization/statistical graphics matters. I don’t recall however you ever describing the details of your setup for plotting. I’m a new R user (convert from matplotlib) and would love to know your thoughts on the ideal
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
