Call Python from R through Rcpp

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Introduction

This post provides a brief introduction to calling Python from R through Rcpp. The official Python documentation explains how to embed python into C/C++ applications. Moreover, the Boost.Python library provides seamless interoperability between C++ and the Python programming language. Similarlly, Rcpp provides interoperability between C++ and R. Therefore, it is not hard to call Python from R through Rcpp and Boost.Python.

Although there is a package rPython which provides an interface to Python from R through Java, it is interesting to try to connect R and Python via C++.

In this article, we show how to call Python 2.7 from R on Ubuntu.

Hello World

The most difficult thing is to establish a development environment. On Ubuntu, we need to install the following packages to build via embeded Python:

sudo apt-get install python2.7 python2.7-dev libboost-python-dev

Then, we pass the following flags to the compiler:

py_cflags <- system("python2.7-config --cflags", intern=TRUE)
Sys.setenv("PKG_CFLAGS"=sprintf("%s %s", Sys.getenv("PKG_CFLAGS"), py_cflags))
Sys.setenv("PKG_CXXFLAGS"=sprintf("%s %s", Sys.getenv("PKG_CXXFLAGS"), py_cflags))
py_ldflags <- system("python2.7-config --ldflags", intern=TRUE)
Sys.setenv("PKG_LIBS"=sprintf("%s %s %s", Sys.getenv("PKG_CFLAGS"), "-lboost_python-py27", py_ldflags))

The following hello world should then work:

#include 
#include 

using namespace Rcpp;

//[[Rcpp::export]]
void initialize_python() {
    Py_SetProgramName("");  /* optional but recommended */
    Py_Initialize();
}

//[[Rcpp::export]]
void finalize_python() {
    Py_Finalize();
}

//[[Rcpp::export]]
void hello_python() {
    PyRun_SimpleString("from time import time,ctimen"
                       "print 'Today is',ctime(time())n");
}

Let’s call them in R:

initialize_python()
hello_python()

Today is Sun Apr  6 09:03:02 2014

It shows that the hello_python function successfully initializes the Python engine and runs the Python script through PyRun_SimpleString.

Type Conversion

With Boost.Python and Rcpp, we can easily transfer the data between R and Python. The following C codes transfer the R IntegerVector to Python List:

#include 
#include 

namespace py = boost::python;

typedef Rcpp::XPtr PyList;

using namespace Rcpp;

//[[Rcpp::export]]
SEXP IntVec_to_py_list(IntegerVector src) {
    PyList pretval(new py::list());
    int glue;
    for(int i = 0;i < src.size();i++) {
        glue = src[i];
        pretval->append(glue);
    }
    return pretval;
}
IntVec_to_py_list(1:10)


The pointer refers to the memory of the transformed Python object.

Call Python Function

The following example shows how to define a function in Python and expose it in R.

#include 
#include 
#include 

namespace py = boost::python;

typedef Rcpp::XPtr PyList;

using namespace Rcpp;

//[[Rcpp::export]]
void pycall(std::string py_script) {
    PyRun_SimpleString(py_script.c_str());
}

//[[Rcpp::export]]
void pyfun(std::string fun_name, SEXP fun_argument) {
    // create the module of python which is similar to the R_GlobalEnv
    py::object module((py::handle<>(py::borrowed(PyImport_AddModule("__main__")))));
    // look up and retrieve the function of the given name in the module
    py::object pyfun = module.attr("__dict__")[fun_name.c_str()];
    // call the function with the API of boost::python
    py::list argv(*PyList(fun_argument));
    pyfun(argv);
}
pycall("
def print_list(src):
    for i in src:
        print i
")
a <- IntVec_to_py_list(1:10)
pyfun("print_list", a)

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Error Handling

Errors in the Python engine can be handled easily by the C++ try/catch idiom as the following example shows:

#include 
#include 
#include 

namespace py = boost::python;

typedef Rcpp::XPtr PyList;

//[[Rcpp::export]]
void pyfun(std::string fun_name, SEXP fun_argument) {
    try {
        // create the module of python which is similar to the R_GlobalEnv
        py::object module((py::handle<>(py::borrowed(PyImport_AddModule("__main__")))));
        // look up and retrieve the function of the given name in the module
        py::object pyfun = module.attr("__dict__")[fun_name.c_str()];
        // call the function with the API of boost::python
        py::list argv(*PyList(fun_argument));
        pyfun(argv);
    }
    catch (py::error_already_set) {
        PyErr_Print();
    }
}
pycall("
def print_list(src):
    for i in src:
        print i
")
a <- IntVec_to_py_list(1:10)
pyfun("print_lists", a) # a typo of the function name

KeyError: 'print_lists'
Error in sys.excepthook:
Traceback (most recent call last):
  File "/usr/lib/python2.7/dist-packages/apport_python_hook.py", line 64, in apport_excepthook
    from apport.fileutils import likely_packaged, get_recent_crashes
  File "/usr/lib/python2.7/dist-packages/apport/__init__.py", line 5, in 
    from apport.report import Report
  File "/usr/lib/python2.7/dist-packages/apport/report.py", line 16, in 
    from xml.parsers.expat import ExpatError
  File "/usr/lib/python2.7/xml/parsers/expat.py", line 4, in 
    from pyexpat import *
ImportError: /usr/lib/python2.7/lib-dynload/pyexpat.x86_64-linux-gnu.so: undefined symbol: _Py_ZeroStruct

Original exception was:
KeyError: 'print_lists'

Summary

These examples show how to integrate Python and R with Rcpp and Boost.Python. It relied on two C++ libraries which ease the integration work greatly: Rcpp for R, and Boost.Python for Python. The core steps discussed above are initializing the engine (Hello World), transforming the data (Type Conversion), exposing functions (Call Python Function), and handling errors properly (Error Handling).

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