I enjoy using R in my personal projects primarily for three semi-interrelated reasons. First the similarity between R and S-plus makes R easy to learn and comfortable to use. Second of course is R's unbelievably low price. And third, because of the first two reasons, there is a large and robust community of support. There are a tremendous number of modules, plugins and examples available on the web for such things as time-series analysis, advanced statistics, and good object-oriented design techniques.
One of the best repositories of good R-related news, help, tips, and other resources is R-bloggers. For example, I re-used a piece of code given in this post titled "R-Code Yahoo Finance Data Loading." Of course, I had to modify it in order to use it as part of my staging proces for downloading data from some of the various emerging markets in which I am interested. (I am finding that one of the nice changes in today's information-driven world is that some of the markets I am building models for are very interested in attracting investors by providing a lot of free data.) Similarly useful, was a post on how to Pull Yahoo Finance Key-Statistics Instantaneously Using XML and XPath in R.
These examples obviously were not a perfect match for my needs, but they did jumpstart my process. I was much faster at planning and executing the code because of R-bloggers. I have also gleaned a number of other good ideas on R coding techniques from them. So R-bloggers has now become a site I monitor fairly regularly.