Data in R is stored in a data.frame, which is what's made when you do your read.csv(), and you access the columns (the variables) by using the $ operator.
If you have a data.frame called data1 and a variable called x1, you can access that variable by doing data1$x1.
You can also treat data.frames as matrices and manipulate the rows and columns directly using, say, data1[,1] to get the first column. To get, say, the first element in the first row of data1, do data1[1,1]. Notice before, we left out the first number, because we wanted the whole column. Similarly, data1[1,] will give you the first row. You can use the : operator to quickly ask for a range, so doing data1[1:10,1] will give you the first 10 observations in the first column. If you're dealing with variables, it's often easier to just use the $ operator like I mentioned above, though.
To get things like mean, standard deviation, etc., just use the functions on the variable (or the column vector) like so:
mean(data1$x1)
sd(data1$x1)
If x1 is the first variable (the first column) in data1, it would be equivalent to do the following:
mean(data1[,1])
sd(data1[,1])
But that's a little harder to read and understand immediately.
R is an imperative language (you tell the program what to do) while SAS is a declarative language (you ask the program what to do), and the latter can be a little easier for non-programmers. This makes R more powerful for generic statistical programming, but can sometimes require a little more work than SAS for certain functionality. That's why R is generally the choice for research, while SAS is generally the choice for industry.
SAS is nice and would probably be better for a non-programmer, non-statistician, but like you said, it's expensive as hell for an individual. For most things, there are ways to do what you want in both, though.
