I’ve been using R for a couple of years now. This post is aimed at me a couple of years ago, or you if you’re just starting to use R and are pressed for time. Here’s some things I wish I’d known in early 2009. Use a naming convention read.csv is a great function, but 
R is great for prototyping models. Not so great if those models have to run a business. Here’s some tips to help with that: Validate, alert, and monitor Sink Use 64-bit Linux Write your own functions tryCatch Validate, alert, and monitor: Sooner or later something is going to go wrong with your model. Maybe some 
What languages and tools should you learn as a math/stat consultant? To jump to the answer: Excel/VBA, SQL, R, Java, and Python. Spreadsheets have many problems with verifiability and scalability, so why Excel? Excel is: Useful for prototyping ideas quickly, either for your own use or to show to other team members Well-known and understood 
My fellow bloggers John and Scott have posted recently about the free statistical programming language R. How does it compare to an expensive language like SAS? If you’ve done any statistical analysis, then you’ll know that getting and cleaning the data is a major step in any project. SAS does a pretty good job at 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
