Merge pull request #6732 from mkriele/master

R: Explained differences of max, maximum , sum, etc.

doc/manual/noteworthy-differences.rst

@@ -104,6 +104,8 @@ One of Julia's goals is to provide an effective language for data analysis and s
 - In Julia, values are passed and assigned by reference. If a function modifies an array, the changes will be visible in the caller. This is very different from R and allows new functions to operate on large data structures much more efficiently.
 - Concatenation of vectors and matrices is done using ``hcat`` and ``vcat``, not ``c``, ``rbind`` and ``cbind``.
 - A Julia range object like ``a:b`` is not shorthand for a vector like in R, but is a specialized type of object that is used for iteration without high memory overhead. To convert a range into a vector, you need to wrap the range with brackets ``[a:b]``.
+- ``max``, ``min`` are the equivalent of ``pmax`` and ``pmin`` in R, but both arguments need to have the same dimensions.  While ``maximum``, ``minimum`` replace ``max`` and ``min`` in R, there are important differences.
+- The functions ``sum``, ``prod``, ``maximum``, ``minimum`` are different from their counterparts in R. They all accept one or two arguments. The first argument is an iterable collection such as an array.  If there is a second argument, then this argument indicates the dimensions, over which the operation is carried out.  For instance, let ``A=[[1 2],[3,4]]`` in Julia and ``B=rbind(c(1,2),c(3,4))`` be the same matrix in R.  Then ``sum(A)`` gives the same result as ``sum(B)``, but ``sum(A,1)`` is a row vector containing the sum over each column and ``sum(A,2)`` is a column vector containing the sum over each row.  This contrasts to the behavior of R, where ``sum(B,1)=11`` and ``sum(B,2)=12``.  If the second argument is a vector, then it specifies all the dimensions over which the sum is performed, e.g., ``sum(A,[1,2])=10``.  It should be noted that there is no error checking regarding the second argument. 
 - Julia has several functions that can mutate their arguments. For example, it has ``sort(v)`` and ``sort!(v)``.
 - ``colMeans()`` and ``rowMeans()``, ``size(m, 1)`` and ``size(m, 2)``
 - In R, performance requires vectorization. In Julia, almost the opposite is true: the best performing code is often achieved by using devectorized loops.