R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
Good basic treatment of a number of topics (especially linear regression and Sweave), but in some of the later chapters it became painfully apparent that this is, as advertised, a graduate-level text. Unless you're a statistician or an economist, a lot of content will sail over your head, as it assumes you're already familiar with the statistical tests being discussed, why you'd want to do them, and what the results mean.
At the end, I decided it was time to head back and do a really thorough reading of Venables and Ripley's classic Modern Applied Statistics with S.
An easy to read, easy to follow, practical guide to R learners with econometric orientation. It explains the basic econometric analysis briefly followed by practical hands-on exercises.