# 33 SWIG and R

- Bugs
- Using R and SWIG
- Precompiling large R files
- General policy
- Language conventions
- C++ classes
- Enumerations

R is a GPL'ed open source statistical and plotting environment. Information about R can be found at www.r-project.org. The R bindings are under active development. They have been used to compile and run an R interface to QuantLib running on Mandriva Linux with gcc. The R bindings also work on Microsoft Windows using Visual C++.

## 33.1 Bugs

Currently the following features are not implemented or broken:

- Garbage collection of created objects
- C Array wrappings

## 33.2 Using R and SWIG

To use R and SWIG in C mode, execute the following commands where example.c is the name of the file with the functions in them

    swig -r example.i
    R CMD SHLIB example_wrap.c example.c

The corresponding options for C++ mode are

    swig -c++ -r -o example_wrap.cpp example.i
    R CMD SHLIB example_wrap.cpp example.cpp

Note that R is sensitive to the names of the files. The name of the wrapper file must be the name of the library unless you use the -o option to R when building the library, for example:

    swig -c++ -r -o example_wrap.cpp example.i
    R CMD SHLIB -o example.so example_wrap.cpp example.cpp

R is also sensitive to the name of the file extension in C and C++ mode. In C++ mode, the file extension must be .cpp rather than .cxx for the R compile command to recognize it. If your C++ code is in a file using something other than a .cpp extension, then it may still work using PKG_LIBS:

    swig -c++ -r -o example_wrap.cpp example.i
    PKG_LIBS="example.cxx" R CMD SHLIB -o example example_wrap.cpp

The commands produces two files. A dynamic shared object file called example.so, or example.dll, and an R wrapper file called example.R. To load these files, start up R and type in the following commands

    dyn.load(paste("example", .Platform$dynlib.ext, sep=""))
    source("example.R")
    cacheMetaData(1)

The cacheMetaData(1) will cause R to refresh its object tables. Without it, inheritance of wrapped objects may fail.

These two files can be loaded in any order

If you are compiling code yourself (not using R itself), there are a few things to watch out for:

- The output shared library name (to the left of the file extension) MUST match the module name, or alternatively, you can also set the -package NAME command line argument. See swig -r -help for more information
- If you do not set the output file name appropriately, you might see errors like

      > fact(4)
      Error in .Call("R_swig_fact", s_arg1, as.logical(.copy), PACKAGE = "example") :
        "R_swig_fact" not available for .Call() for package "example"

- Make sure the architecture of the shared library(x64 for instance), matches the architecture of the R program you want to load your shared library into

## 33.3 Precompiling large R files

In cases where the R file is large, one make save a lot of loading time by precompiling the R wrapper. This can be done by creating the file makeRData.R which contains the following

    source('BigFile.R')
    save(list=ls(all=TRUE), file="BigFile.RData", compress=TRUE)
    q(save="no")

This will generate a compiled R file called BigFile.RData that will save a large amount of loading time.

## 33.4 General policy

The general policy of the module is to treat the C/C++ as a basic wrapping over the underlying functions and rely on the R type system to provide R syntax.

## 33.5 Language conventions

getitem and setitem use C++ conventions (i.e. zero based indices). [<- and [ are overloaded to allow for R syntax (one based indices and slices)

## 33.6 C++ classes

C++ objects are implemented as external pointer objects with the class being the mangled name of the class. The C++ classes are encapsulated as an SEXP with an external pointer type. The class is the mangled name of the class. The nice thing about R is that is allows you to keep track of the pointer object which removes the necessity for a lot of the proxy class baggage you see in other languages.

## 33.7 Enumerations

R doesn't have a native enumeration type. Enumerations are represented as character strings in R, with calls to R functions that convert back and forth between integers. The details of enumeration names and contents are stored in hidden R environments, which are named according the the enumeration name - for example, an enumeration colour:

    enum colour { red=-1, blue, green = 10 };

will be initialized by the following call in R:

    defineEnumeration("_colour",
     .values=c("red" = .Call('R_swig_colour_red_get',FALSE, PACKAGE='enum_thorough'),
    "blue" = .Call('R_swig_colour_blue_get',FALSE, PACKAGE='enum_thorough'),
    "green" = .Call('R_swig_colour_green_get',FALSE, PACKAGE='enum_thorough')))

which will create an environment named `.__E___colour`. The enumeration values are initialised via calls to C/C++ code, allowing complex values for enumerations to be used. Calls to the C/C++ code require the compiled library to be loaded, so a `delayedAssign` is employed within `defineEnumeration` in order to allow the code to be easily used in R packages. The user typically does not need to access the enumeration lookup functions or know the name of the enumeration type used by R. Attributes containing the type information are attached by swig to functions requiring enumeration arguments or returning enumeration values, and those attributes are used to identify and access the appropriate environments and thus translate between characters and integers. The relevant functions, for debugging purposes, are `enumToInteger` and `enumFromInteger`. Anonymous enumerations are ignored by the binding generation process, leaving no way of accessing the value of anonymous enumerations from R code.