What is R Good For? Applications and Advantages

What R is good at

Statistics for relatively advanced users: R has thousands of packages, designed, maintained, and widely used by statisticians. Statistical graphics: try doing some of our plots in Stata and you won’t have much fun.
Flexible code: R has a rather liberal syntax, and variables don’t need to be declared as they would in (for example) C++, which makes it very easy to code in. This also has disadvantages in terms of how safe the code is.
Vectorization: R is designed to make it very easy to write functions which are applied pointwise to every element of a vector. This is extremely useful in statistics.
R is powerful: if a command doesn’t exist already, you can code it yourself.

What R is not so good at

Statistics for non-statisticians: there is a steep learning curve, which puts some people off. Try Stata, SAS or SPSS (if you must).
Numerical methods, such as solving partial differential equations; try Matlab.
Analytical methods, such as algebraically integrating a function. Try Mathematica or Maple.
Precision graphics, such as might be useful in psychology experiments. Try Matlab.
Optimization. Though it does have some very easy to use methods built-in.
Low-level, high-speed or critical code; use C, C++, Java or similar. (However, note that such code can be called from R to give the best of both worlds.