Although I am most familiar with R for statistical analysis and programming, I also use a fair amount of SAS at work.

I found it a huge transition at first, but one thing that helped make SAS “click” for me is that it was designed around those (now-ancient) computers that used punch cards. So the DATA step processes one observation at a time, as if you were feeding it punch cards one after another, and never loads the whole dataset into memory at once. I think this is also why many SAS procedures require you to sort your dataset first. It makes some things awkward to do, and often it takes more code than the equivalent in R, but on the other hand it means you can process huge datasets without worrying about whether they will fit into memory. (Well… memory size should be a non-issue for the DATA step, but not for all procedures. We’ve run into serious memory issues on large datasets when using PROC MIXED and PROC MCMC, so using SAS does not guarantee that you never have to fear large data.)

The Little SAS Book (by Delwiche and Slaughter) and Learning SAS by Example (by Cody) are two good resources for learning SAS. If you’re able to take a class directly from the SAS Institute, they tend to be taught well, and you get a book of class notes with a very handy cheat sheet.

Today I ran into a double question that might be relevant to other R users:
Why can’t I assign a dataframe row into a matrix row?
And why won’t my function accept this dataframe row as an input argument?

A single row of a dataframe is a one-row dataframe, i.e. a list, not a vector. R won’t automatically treat dataframe rows as vectors, because a dataframe’s columns can be of different types. So converting them to a vector (which must be all of a single type) would be tricky to generalize.

But if in your case you know all your columns are numeric (no characters, factors, etc), you can convert it to a numeric matrix yourself, using the as.matrix() function, and then treat its rows as vectors.

> # Create a simple dataframe
> # and an empty matrix of the same size
> my.df <- data.frame(x=1:2, y=3:4)
> my.df
  x y
1 1 3
2 2 4
> dim(my.df)
[1] 2 2
> my.matrix <- matrix(0, nrow=2, ncol=2)
> my.matrix
     [,1] [,2]
[1,]    0    0
[2,]    0    0
> dim(my.matrix)
[1] 2 2
>
> # Try assigning a row of my.df into a row of my.matrix
> my.matrix[1,] <- my.df[1,]
> my.matrix
[[1]]
[1] 1

[[2]]
[1] 0

[[3]]
[1] 3

[[4]]
[1] 0

> dim(my.matrix)
NULL
> # my.matrix became a list!
>
> # Convert my.df to a matrix first
> # before assigning its rows into my.matrix
> my.matrix <- matrix(0, nrow=2, ncol=2)
> my.matrix[1,] <- as.matrix(my.df)[1,]
> my.matrix
     [,1] [,2]
[1,]    1    3
[2,]    0    0
> dim(my.matrix)
[1] 2 2
> # Now it works.
>
> # Try using a row of my.df as input argument
> # into a function that requires a vector,
> # for example stem-and-leaf-plot:
> stem(my.df[1,])
Error in stem(my.df[1, ]) : 'x' must be numeric
> # Fails because my.df[1,] is a list, not a vector.
> # Convert to matrix before taking the row:
> stem(as.matrix(my.df)[1,])

  The decimal point is at the |

  1 | 0
  1 |
  2 |
  2 |
  3 | 0

> # Now it works.

For clarifying dataframes vs matrices vs arrays, I found this link quite useful:
http://faculty.nps.edu/sebuttre/home/R/matrices.html#DataFrames

I’m preparing “R101,” an introductory workshop on the statistical software R. Perhaps other beginners might find some use in the following summary and resources. (See also the post on resources for teaching yourself introductory statistics.)

Do you have obligatory screenshots of nifty graphics that R can produce? Yes, we do.

Nice. So what exactly is R? It is an open-source software tool for statistics, data processing, data visualization, etc. (Technically there’s a programming language called S, and R is just one open-source software tool that implements the S language. But you’ll often hear people just say “the R language.” Beginners can worry about the nuances later.)
Open source means it is free to download and use; this is great for academics and others with low budgets. It also means you can inspect the code of any algorithm if you want to double-check it or just to see how it’s done; this is great for validating and building on each others’ ideas. And it is easy to share code in user-defined “packages,” of which there are thousands, all helping people use cutting-edge statistical tools as soon as they are invented.

How do I get started? Download and install R from CRAN, the Comprehensive R Archive Network. There are Windows, Mac, and Linux versions.
In Windows at least, when you open the program there is a big window containing a smaller window, the R Console. You can type and submit commands in the Console window at the prompts (the “>” signs). Try typing 3+5 and hit Enter, and you should see the output [1] 8 which is good. The output of 3+5 is a 1-item vector (hence the [1]) with the value 8 as it should be.
Great, now you know how to use R as a desktop calculator!
Or you can type your commands in a script, so that you can save your code easily. Go to “File -> New script” and it will open the R Editor window. Type 3+5 in there, highlight it, and then either click the “Run line or selection” icon on the top menu bar or just hit Ctrl+R on the keyboard. It should copy the command into the Console window and run it, with the same result as before.
Sweet, now you can save the code you used to do your calculations.
Quick-R has more details on using the R interface.
Next, try A Sample Session from the R manual to see examples of other things R can do.

What are the key concepts? Basically, everything is a function or an object. Objects are where your data and results are stored: data frames, matrices, vectors, lists, etc. Functions take objects in, think about them, and spit new objects out. Functions sometimes also have side effects (like displaying a table of output or a graph, or changing a display setting).
If you want to save the results or output of a function, use <- which is the assignment operator (think of an arrow pointing left). For example, to save the natural log of 10 into a variable called x, type the command x <- log(10). Then you can use x as the input to another function.
Note that functions create new output rather than affecting the input variable. If you have a vector called y that you need sorted, sort(y) will print out a sorted copy of y but will not changed y itself. If you actually want y to be sorted, you have to reassign it: y <- sort(y).
Functions always take their input in parentheses: (). So if you see a word followed by parentheses, you know it’s a function in R. You will also see square brackets: []. These are used for locating or extracting data in objects. For example, if you have a vector called y, then y[3] gives you the 3rd element of that vector. If y is a matrix, then y[4,7] is the element in the 4th row, 7th column.

How do I get help? If you know you want to use a function named foo, you can learn more about it by typing ?foo which will bring up the help file for that function. The “Usage” section tells you the arguments, their default order, and their default values. (If no default value is given, it is a required argument.) “Arguments” gives more details about each argument. “Value” gives the structure of the output. “Examples” shows an example of the function in use.
If you know what you want to do but don’t know what the function is called, I suggest looking through the R Reference Card. If that does not answer your question, you can try searching using RSeek.org or search.r-project.org, search engine tuned to the R sites and mailing lists… since just typing the letter R into Google is not always helpful 🙂

Where do I read more?
Online resources for general beginners:
R for Beginners
Simple R
Official Introduction to R
R Fundamentals
Kickstarting R
Let’s Use R Now
UCLA R Class Notes
A Quick and (Very) Dirty Intro to Doing Your Statistics in R
Hints for the R Beginner
R Tutorials from Universities Around the World (88 as of last count)

For statisticians used to other packages:
Quick-R
R for SAS and SPSS Users

For programmers:
R’s unconventional features
Google’s R code style guide

Good books (as suggested by Cosma Shalizi):
Paul Teetor, The R Cookbook: “explains how to use R to do many, many common tasks”
Norman Matloff, The Art of R Programming: “Good introduction to programming for complete novices using R.”