An R "meta" book. By Joseph Rickert I am a book person. I collect books on all sorts of subjects that interest me and consequently I have a fairly extensive collection of R books, many of which I find to be of great value. Nevertheless, when I am asked to recommend an R book to someone new to R I am usually flummoxed. R is growing at a fantastic rate, and people coming to R for the first time span I wide range of sophistication. Recently, however, while crawling around CRAN, it occurred to me that there is a tremendous amount of high quality material on a wide range of topics in the Contributed Documentation page that would make a perfect introduction to all sorts of people coming to R.
The content column lists the topics that I think ought to be included in a good introductory probability and statistics textbook. Finally, I don’t mean to imply that the documents in my table are the best assembled in the Contributed Documentation page. W. Andrew Barr's Paleoecology Blog: Getting staRted with R. As a PhD student and researcher, I often hear friends and colleagues say that they want to learn R, but that the learning curve is so steep that they can't seem to get started. It's true that learning any tool as powerful as R can be confusing at first, especially if you are not accustomed to typing commands in a terminal. That said, there are TONS of resources available for learning R. This post describes some of the resources that I have found most useful in my jouRney.
Online Resources O'Reilly Code School TryR - this is a truly fantastic online interactive introduction to learning basic skills in R. Books Below are links to five of the R books that I have gotten the most out of over the years. Shameless commerce disclosure: if you purchase one of these books through one of the above links, I will receive a small referral fee from amazon. Mirai Solutions - XLConnect. XLConnect is a powerful package that allows R users to read and write Excel files in a highly integrated manner from within R. It uses the Apache POI API (see as the underlying interface, so you may expect us to extend the current functionality of XLConnect accordingly in the future. One of the great things about XLConnect is its portability. You can use it across various platforms and operating systems - be it Windows 32 bit, Linux 64 bit or a Mac, you can run your favorite piece of XLConnect-code on all of them.
XLConnect works with both xls and the newer xml-based xlsx formats. It works with OpenOffice* and it even works if you don’t have Excel installed at all. Feature-wise XLConnect allows you to produce formatted Excel-reports (including Graphics) straight from within R. This enables automation of manual formatting and reporting processes. Comprehensive documentation is provided together with the package, including demos as well as unit tests.
Making Reproducible Research Enjoyable | Yihui Xie. Note: this is a contributed article for the ICSA Bulletin and the basic idea can be summarized in this picture. It is hard to convince people to think about reproducible research (RR). There are two parts of difficulties: (1) tools used to be for experts only and (2) it is still common practice to copy and paste. For some statisticians, RR is almost equivalent to Sweave (R + LaTeX). I love LaTeX, but LaTeX is still hell to many people. I had an experience of teaching Sweave in a stat-computing class at Iowa State University, and I can tell you their horrible faces after I taught them LaTeX in the first half of the class. I will never do that again. But RR is really important. The basic idea was the same: to mix code and text together, then compile the whole document with code being executed, and you get a report without copying/pasting anything since the code will faithfully give you results.
I use Github extensively and learned markdown there. Knitr: Elegant, flexible and fast dynamic report generation with R | knitr. Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more). This package is developed on GitHub; for installation instructions and FAQ's, see README.
This website serves as the full documentation of knitr, and you can find the main manual, the graphics manual and other demos / examples here. For a more organized reference, see the knitr book. Motivation One of the difficulties with extending Sweave is we have to copy a large amount of code from the utils package (the file SweaveDrivers.R has more than 700 lines of R code), and this is what the two packages mentioned above have done. Features Acknowledgements Misc. R site search. Package RcmdrPlugin.pointG. Rattle: A Graphical User Interface for Data Mining using R. Montreal R Users Group. See the general information page for workshop goals, format, what to expect, and what you need.
You can reserve your spot by signing-up to the Montreal R User Group and registering for the workshops you are interested in. The workshops are free to attend (!) And are open to everyone. We are now supported by: We can be found on. R-statistics blog. R: Monash University Statistics and Econometrics Consulting Service.