Eventhough I highly recommend using C++ together with R, in some cases fortran can be faster. For simple problems that do not require additional libraries and when the intel compiler is avaibalbe, f90 can be a very good option.

In this note we will see how one can write some f90 code and call it from R. You will notice however how this procedure is less convenient than when using Rcpp.

## Overview of the process

What needs to be done is:

• write a Fortran subroutine
• compile the subroutine
• load the generated library in R
• call the function in R

## the f90 subroutine

let's write a very simple matrix multiplication

subroutine fmult(A,X,Y,n,m)
implicit none

integer :: n,i,j
double precision,dimension(n,m) :: A
double precision,dimension(m) :: X
double precision,dimension(n) :: Y

do i = 1,n
Y(i)=0
do j = 1,m
Y(i) = Y(i) + A(i,j,1) * X(j)
end do
end do

end

save this code to fmult.f90 , we then compile it into a library using the following command:

R CMD SHLIB fmult.f90

at this point we can start R, load the library and call the function:

dyn.load('fmult.so')

A = array(runif(100),c(10,10))
X = array(runif(10),c(10))
res = .Fortran('fmult', A=A, X=X, Y=double(length(X)),as.integer(dim(A)),as.integer(dim(A)))
print(res$Y) ## R wrapper function Now we want to write a wrapper that will call the f90 function. Rcpp and inline are packages that do this automatically. Unfortunately, at the moment, neither seems to work properly with f90 so we will do it by hand. matmult <- function(A,X) { if (!is.loaded(symbol.For('fmult'))) { dyn.load('fmult.so') } res = .Fortran('fmult', A=A, X=X, Y=double(length(X)),as.integer(dim(A)),as.integer(dim(A))) return(res$Y)
}

and we are done!

## Tricks to remember

• you need to wrap integer with as.integer or they will be set to 0
• sometimes the routine will be renamed, you can list the internal names using nm -g in the terminal
• it is painful, but constraint yourself to using implicit none