# R integrating C functions#

## Purpose#

Although R is a nice and fairly complete software package for statistical analysis, there are nevertheless situations where it desirable to extend R. This may be either to add functionality that is implemented in some C library, or to eliminate performance bottlenecks in R code. In this how-to it is assumed that the users wants to call his own C functions from R.

## Prerequisites#

It is assumed that the reader is familiar with the use of R as well as R scripting, and is a reasonably proficient C programmer. Specifically the reader should be familiar with the use of pointers in C.

## Integration step by step#

Before all else, first load the appropriate R module to prepare your environment, e.g.,

```
$ module load R
```

If you want a specific version of R, you can first check which versions are available using

```
$ module av R
```

and then load the appropriate version of the module, e.g.,

```
$ module load R/3.1.1-intel-2014b
```

### A first example#

No tutorial is complete without the mandatory ‘hello world’ example. The C code in file ‘myRLib.c’ is shown below:

```
#include <R.h>
void sayHello(int *n) {
int i;
for (i = 0; i < *n; i++)
Rprintf(\"hello world!\\n\");
}
```

Three things should be noted at this point

the ‘R.h’ header file has to be included, this file is part of the R distribution, and R knows where to find it;

function parameters are

*always*pointers; andto print to the R console, ‘Rprintf’ rather than ‘printf’ should be used.

From this ‘myRLib.c’ file a shared library can be build in one convenient step:

```
$ R CMD SHLIB myRlib.c
```

If all goes well, i.e., if the source code has no syntax errors and all functions have been defined, this command will produce a shared library called ‘myRLib.so’.

To use this function from within R in a convenient way, a simple R wrapper can be defined in ‘myRLib.R’:

```
dyn.load(\"myRLib.so\");
sayHello <- function(n) {
.C(\"sayHello\", as.integer(n))
}
```

In this script, the first line loads the share library containing the ‘sayHello’ function. The second line defines a convenient wrapper to simplify calling the C function from R. The C function is called using the ‘.C’ function. The latter’s first parameter is the name of the C function to be called, i.e., ‘sayHello’, all other parameters will be passed to the C function, i.e., the number of times that ‘sayHello’ will say hello as an integer.

Now, R can be started to be used interactively as usual, i.e.,

```
$ R
```

In R, we first source the library’s definitions in ‘myRLib.R’, so that the wrapper functions can be used:

```
> source(\"myRLib.R\")
> sayHello(2)
hello world!
hello world!
[[1]]
[1] 2
```

Note that the ‘sayHello’ function is not particularly interesting since it does not return any value. The next example will illustrate how to accomplish this.

### A second, more engaging example#

Given R’s pervasive use of vectors, a simple example of a function that takes a vector of real numbers as input, and returns its components’ sum as output is shown next.

```
#include <R.h>
/* sayHello part not shown */
void mySum(double *a, int* n, double *s) {
int i;
*s = 0.0;
for (i = 0; i < *n; i++)
*s += a[i];
}
```

Note that both ‘a’ and ‘s’ are declared as pointers, the former being used as the address of the first array element, the second as an address to store a double value, i.e., the sum of array’s compoments.

To produce the shared library, it is build using the R appropriate command as before:

```
$ R CMD SHLIB myRLib.c
```

The wrapper code for this function is slightly more interesting since it will be programmed to provide a convenient \”function-feel".

```
dyn.load("myRLib.so");
# sayHello wrapper not shown
mySum <- function(a) {
n <- length(a);
result <- .C("mySum", as.double(a), as.integer(n), s = double(1));
result$s
}
```

Note that the wrapper functions is now used to do some more work:

it preprocesses the input by calculating the length of the input vector;

it initializes ‘s’, the parameter that will be used in the C function to store the result in; and

it captures the result from the call to the C function which contains all parameters passed to the function, in the last statement only extracting the actual result of the computation.

From R, ‘mySum’ can now easily be called:

```
> source("myRLib.R")
> mySum(c(1, 3, 8))
[1] 12
```

Note that ‘mySum’ will probably not be faster than R’s own ‘sum’ function.

### A last example#

Function can return vectors as well, so this last example illustrates how to accomplish this. The library is extended to:

```
#include <R.h>
/* sayHello and my_sum not shown */
void myMult(double *a, int *n, double *lambda, double *b) {
int i;
for (i = 0; i < *n; i++)
b[i] = (*lambda)*a[i];
}
```

The semantics of the function is simply to take a vector and a real number as input, and return a vector of which each component is the product of the corresponding component in the original vector with that real number.

After building the shared libary as before, we can extend the wrapper script for this new function as follows:

```
dyn.load("myRLib.so");
# sayHello and mySum wrapper not shown
myMult <- function(a, lambda) {
n <- length(a);
result <- .C("myMult", as.double(a), as.integer(n),
as.double(lambda), m = double(n));
result$m
}
```

From within R, ‘myMult’ can be used as expected.

```
> source("myRLib.R")
> myMult(c(1, 3, 8), 9)
[1] 9 27 72
> mySum(myMult(c(1, 3, 8), 9))
[1] 108
```

## Further reading#

Obviously, this text is just for the impatient. More in-depth documentation can be found on the nearest CRAN site.