In the last two posts we saw how to download posts from R-bloggers, and then extract the title, author and date of each post and write that information to a csv file. Since we now have a nice data set from r-bloggers, we can start to examine the develo...
Introduction R-bloggers.com is a blog aggregator maintained by Tal Galili. It is a great website for both learning about R and keeping up-to-date with the latest developments (because someone will probably, and very kindly, post about the status of some R related feature). There is also an R-bloggers facebook page where a number of 
20 years of data is nowhere near enough to satisfy my insatiable appetite for bigger datasets. While I showed Record Long Term Treasury Returns with Vanguard’s US Long Treasury mutual fund, its 20 year life is not sufficient to give me comfort ...
Recology has moved to Github, using Jekyll.I just finished moving the Recology blog content, etc. to Github. This move is intended to make it easy to do exactly what I want with this blog without the constrictions of Blogger. Thanks for co...
Unfortunately, due to the vexing complexities of academic style guides and the limitations of associated software packages, citing a non-standard name, such as Cher, Prince, or R Development Core Team can be problematic. Thankfully, I have discovered a...
If you've been following the presidential primary process here in the US, you've probably seen many maps of the results of the Iowa caucuses by now (such as this infamous one from Fox News). But you might be interested to learn how such maps can be made using the R language. The Offensive Politics blog explains the process in...
In 1854 there was a massive cholera outbreak in Soho, London – in three days over 120 people died from the disease. Famously, John Snow plotted the locations of the deaths on a map and found they clustered around a pump in Broad Street – he suggested that the pump be taken out of service
There are only really two ways to preserve your statistical analyses. You either save the variables that you create, or you save the code that you used to create them. In general the latter is much preferred because at some point you’ll realise that your model was wrong, or your dataset has changed, and you 
Quantum Financier wrote an interesting article Regime Switching System Using Volatility Forecast. The article presents an elegant algorithm to switch between mean-reversion and trend-following strategies based on the market volatility. Two model are examined: one using the historical volatility and another using the Garch(1,1) Volatility Forecast. The mean-reversion strategy is modeled with RSI(2): Long when 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
