I had heard about this amazing language for statistical analysis and finally decided to give it a shot after reading a sample first chapter preview from “**R in Action”** by Robert Kabacoff (this looks like a great book by the way and is on my list of books to buy). The first thing was to download and install the software on my Mac, which was a quick and entirely painless process. You fire up R by clicking on the “R” icon in the Mac applications folder.

The initial window looks pretty much like a unix terminal. To see what R can do, I decided to start with a simple linear regression example. The set of R statements below create a series of (x,y) coordinates and then plot each point on a simple plot.

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> x <- c(0,1,2,3,4,5,6,7,8,9,10) > y <- c(0,1,2,2,4,4,5,6,7,10,11) > plot(x,y) |

Here is the result:

Next, we create a least-squares fit to the data and plot the resulting line on top of the data:

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> model <- lm(y~x) > yfit <- model$fitted.values > lines(x,yfit) |

And this is what we get:

You want the actual equation of the line? The slope and Y-intercept can be obtained using:

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> lm(y~x) Call: lm(formula = y ~ x) Coefficients: (Intercept) x -0.500 1.045 |

Now this was a really simple example, but it was enough to illustrate what R can do. And going by first impressions, it looks like I will be exploring a lot of R in the future.