R/RStudio

There are many compelling reasons why I use `R' for data analysis (the underlying statistical engine) and the R front-end `RStudio' (far too many to delve into here). Since RStudio automatically calls R when both are installed, I recommend using RStudio as your default interface to R. You can if you prefer run R in `stand alone' mode, but RStudio is an integrated development environment that provides a much more intuitive front end for the user (plus it is platform independent, so whether you use Linux, Mac OS X, MS Windows etc. we will all have the identical menus/options available). Below I discuss each in turn.

What is R? This is perhaps best answered by quoting from the R website (www.r-project.org) directly (see "What is R?" on the R website for even more details, and also see two New York Times articles for further background information (article 1 Jan 6 2009, article 2 Jan 8 2009)).

From the R website, we see that

"R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public Licensein source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS."

For an introduction to R you have a range of options. One popular source is titled "An Introduction to R" that some may find useful (R-intro.pdf). Or, having installed R, you can browse the help facilities that are available within R itself. Or you can see the page `Getting Help with R' on the RStudio website (www.rstudio.org/docs/help_with_r).

Here is a link to a set (90+) of `two minute tutorial' videos describing `how do do stuff in R in two minutes or less' (www.twotorials.com).

And here is a link to R code school (tryr.codeschool.com).

What is `RStudio'? RStudio is an IDE (Integrated Development Environment) for R (www.rstudio.org) that I highly recommend you install once you have first installed R on your system (RStudio is a platform-independent front-end for R is very user friendly but note you must first install R on your system prior to invoking RStudio). Though RStudio is not necessary for using R, it makes using R more seamless. I can certainly report that all who have experienced using R through the RStudio IDE seem to prefer it to the standard R interface (myself included).

For a variety of documents that will assist with using RStudio, kindly see the FAQ (www.rstudio.org/docs/faq) and the documents section of the RStudio website located at www.rstudio.org/docs.

Here are some additional references that may be of interest:

Racine, J.S. (2012), “RStudio: A Platform-Independent IDE for R and Sweave,” Journal of Applied Econometrics, Volume 27, Issue 1, 167-172.
Racine, J.S. and R. Hyndman (2002), “Using R to Teach Econometrics,” Journal of Applied Econometrics, March/April, Volume 17, Issue 2, 175-189.