This text began as notes for a course in statistical computing for second year actuarial and statistical students at the University of Western Ontario. Both authors are interested in statistical computing, both as support for our other research and for its own sake. However, we have found that our students were not learning the right sort of programming basics before they took our classes. At every level from undergraduate through Ph.D., we found that students were not able to produce simple, reliable programs; that they didn’t understand enough about numerical computation to understand how rounding error could influence their results; and that they didn’t know how to begin a difficult computational project.

We looked into service courses from other departments, but we found that they emphasized languages and concepts that our students would not use again. Our students need to be comfortable with simple programming so that they can put together a simulation of a stochastic model; they also need to know enough about numerical analysis so that they can do numerical computations reliably.We were unable to find this mix in an existing course, so we designed our own.

We chose to base this text on R. R is an open-source computing package which has seen a huge growth in popularity in the last few years. Being open source, it is easily obtainable by students and economical to install in our computing lab. One of us (Murdoch) is a member of theRcore development team, and the other (Braun) is a co-author of a book on data analysis using R. These facts made it easy for us to choose R, but we are both strong believers in the idea that there are certain universals of programming, and in this text we try to emphasize those: it is not a manual about programming in R, it is a course in statistical programming that uses R.