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