Passing Arrays Between R and Other Programming Languages

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A matrix or an array in R is essentially a vector with dimension attribute. For this reason, no matter you pass a vector, matrix or an array to an external call, you essentially pass a 1 dimensional array to the call. As a coinsequence, it's usually not conveneint to pass 2 dimensional arrays between R and other programming languages. To pass arrays from R to an external call, always use 1 dimensional array. If the 1-d array you pass to an external call stores data of a 2-d array, you have to pass extra arguments about the dimension information to the external call.

In C, an array name is an address. There's no way to figure out the length of the array from its address, so you have to pass information about the length of array to a C function if you want to call it from R. An array is an object in Java, and you can access its length attribute, so you don't have to pass information about the length to a Java method if you want to call it in R. Because of the way that R calls C functions, there's simple no way to return a 2-d array from a C function to R. However, since an 2-d array in Java is an object, you can return it to R. Applying function .jevalArray on the 2-d array object, you get a vector of addresses. Applying function .jevalArray on these addresses again (most conveniently with the help of function sapply or lapply), you get values of the 2-d array. For example, suppose x is an 2-d array object returned from a Java method to R, you can use the following code to get its content.

x = .jevalArray(x)
x = t(sapply(x,.jevalArray))

If a C function returns an array of unkown length, there is no directly way to return it to R. There are two ways to solve this problem. First, if you know the maximum length of the array to be returned, you can pass an array with this length from R to C to accept the returned result. Second, you can write data from C into a file and then read the data into R.

From these aspect, interfacing with Java in R is more convenient than interfacing with C in R. However, R offers APIs written in C for generating random numbers and manipulating states of random number generators. Another thing is that you probably have to debug and modify C code and Java code when you call them in R. For C, you have to unload and reload dynamic libraries; for Java, you have to restart the JVM. For some reason I'm not sure about (probably because of other loaded libraries requiring rJava package), restarting the JVM may not work. These are advantages and disadvantages of interfacing with C and Java in R. You can choose the most convenient one for you work.