198 lines
5.3 KiB
HTML
198 lines
5.3 KiB
HTML
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
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<html>
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<head>
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<title>SWIG and R</title>
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<link rel="stylesheet" type="text/css" href="style.css">
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<meta http-equiv="content-type" content="text/html; charset=UTF-8">
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</head>
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<body bgcolor="#ffffff">
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<H1><a name="R">39 SWIG and R</a></H1>
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<!-- INDEX -->
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<div class="sectiontoc">
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<ul>
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<li><a href="#R_nn2">Bugs</a>
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<li><a href="#R_nn3">Using R and SWIG</a>
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<li><a href="#R_nn4">Precompiling large R files</a>
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<li><a href="#R_nn5">General policy</a>
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<li><a href="#R_language_conventions">Language conventions</a>
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<li><a href="#R_nn6">C++ classes</a>
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<li><a href="#R_nn7">Enumerations</a>
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</ul>
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</div>
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<!-- INDEX -->
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<p>
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R is a GPL'ed open source statistical and plotting environment.
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Information about R can be found at <a
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href="http://www.r-project.org/">www.r-project.org</a>.
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The R bindings are under active development. They have been used to
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compile and run an R interface to QuantLib running on Mandriva Linux
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with gcc. The R bindings also work on Microsoft Windows using Visual C++.
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</p>
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<H2><a name="R_nn2">39.1 Bugs</a></H2>
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<p>
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Currently the following features are not implemented or broken:
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</p>
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<ul>
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<li>Garbage collection of created objects
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<li>C Array wrappings
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</ul>
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<H2><a name="R_nn3">39.2 Using R and SWIG</a></H2>
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<p>
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To use R and SWIG in C mode, execute the following commands where
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example.c is the name of the file with the functions in them
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</p>
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<div class="shell">
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<pre>
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swig -r example.i
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R CMD SHLIB example_wrap.c example.c
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</pre>
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</div>
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<p>
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The corresponding options for C++ mode are
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</p>
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<div class="shell">
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<pre>
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swig -c++ -r -o example_wrap.cpp example.i
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R CMD SHLIB example_wrap.cpp example.cpp
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</pre>
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</div>
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<p>
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Note that R is sensitive to the names of the files.
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The name of the wrapper file must be the
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name of the library unless you use the -o option to R when building the library, for example:
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</p>
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<div class="shell">
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<pre>
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swig -c++ -r -o example_wrap.cpp example.i
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R CMD SHLIB -o example.so example_wrap.cpp example.cpp
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</pre>
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</div>
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<p>
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R is also sensitive to the name of the file
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extension in C and C++ mode. In C++ mode, the file extension must be .cpp
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rather than .cxx for the R compile command to recognize it. If your C++ code is
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in a file using something other than a .cpp extension, then it may still work using PKG_LIBS:
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</p>
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<div class="shell">
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<pre>
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swig -c++ -r -o example_wrap.cpp example.i
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PKG_LIBS="example.cxx" R CMD SHLIB -o example example_wrap.cpp
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</pre>
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</div>
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<p>
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The commands produces two files. A dynamic shared object file called
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example.so, or example.dll, and an R wrapper file called example.R. To load these
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files, start up R and type in the following commands
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</p>
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<div class="shell">
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<pre>
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dyn.load(paste("example", .Platform$dynlib.ext, sep=""))
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source("example.R")
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cacheMetaData(1)
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</pre>
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</div>
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The cacheMetaData(1) will cause R to refresh its object tables.
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Without it, inheritance of wrapped objects may fail.
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<p>
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These two files can be loaded in any order
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</p>
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<p>
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If you are compiling code yourself (not using R itself), there are a few things to watch out for:
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</p>
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<ul>
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<li>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
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<li>If you do not set the output file name appropriately, you might see errors like
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<div class="shell">
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<pre>
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> fact(4)
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Error in .Call("R_swig_fact", s_arg1, as.logical(.copy), PACKAGE = "example") :
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"R_swig_fact" not available for .Call() for package "example"
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</pre>
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</div>
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<li>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
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</ul>
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<H2><a name="R_nn4">39.3 Precompiling large R files</a></H2>
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In cases where the R file is large, one make save a lot of loading
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time by precompiling the R wrapper. This can be done by creating the
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file makeRData.R which contains the following
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<pre>
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source('BigFile.R')
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save(list=ls(all=TRUE), file="BigFile.RData", compress=TRUE)
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q(save="no")
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</pre>
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This will generate a compiled R file called BigFile.RData that
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will save a large amount of loading time.
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<H2><a name="R_nn5">39.4 General policy</a></H2>
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<p>
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The general policy of the module is to treat the C/C++ as a basic
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wrapping over the underlying functions and rely on the R type system
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to provide R syntax.
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</p>
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<H2><a name="R_language_conventions">39.5 Language conventions</a></H2>
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<p>
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getitem and setitem use C++ conventions (i.e. zero based indices). [<-
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and [ are overloaded to allow for R syntax (one based indices and
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slices)
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</p>
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<H2><a name="R_nn6">39.6 C++ classes</a></H2>
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<p>
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C++ objects are implemented as external pointer objects with the class
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being the mangled name of the class. The C++ classes are encapsulated
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as an SEXP with an external pointer type. The class is the mangled
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name of the class. The nice thing about R is that is allows you to
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keep track of the pointer object which removes the necessity for a lot
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of the proxy class baggage you see in other languages.
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</p>
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<H2><a name="R_nn7">39.7 Enumerations</a></H2>
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<p>
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enumerations are characters which are then converted back and forth to
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ints before calling the C routines. All of the enumeration code is
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done in R.
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</p>
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</body>
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</html>
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