1. Florian Schwendinger
  2. PythonInR



PythonInR - Makes accessing Python from within R as easy as pie.

More documenation can be found at https://bitbucket.org/Floooo/pythoninr and http://pythoninr.bitbucket.org/.


Python >= 2.7.0 R >= 2.15.0

- pack


Python headers

On Debian and Debian-based Linux distributions (including Ubuntu and other derivatives) the "Python Development Headers" can be installed by typing the following into the terminal.

    apt-get install python-dev

For installation on Red Hat Enterprise Linux , Fedora, and other Red Hat Linux-based distributions, use the following:

    yum install python-devel


There are no additional dependencies on Windows. (One obviously needs to have R and Python installed.)


#   or via devtools

Windows Setup

Since the Windows version of PythonInR uses explicit linkage one can switch between different Python versions without recompiling the package. This flexibility comes at the price of additional configuration at the startup. Which results in a different behavior for the static (Linux default) and the explicit linked (Windows default) version. Where as the static linked version automatically connects, when the package get’s loaded, the explicitly linked version needs to be connected manually.

To enable automatic connection for the explicitly linked version the environment variable PYTHON_EXE has to be set. You can put your Python path into your .Renviron or .Rprofile file (Setting up a .Renviron file).


Python 3

Due to api changes in Python 3 the function execfile is no longer available. The PythonInR package provides a execfile function following the typical workaround.

def execfile(filename):
    exec(compile(open(filename, 'rb').read(), filename, 'exec'), globals())

Type Casting

R to Python (pySet)

To allow a nearly one to one conversion from R to Python, PythonInR provides Python classes for vectors, matrices and data.frames which allow an easy conversion from R to Python and back. The names of the classes are PrVector, PrMatrix and PrDataFrame.

Default Conversion

R length (n) Python
logical 1 boolean
integer 1 integer
numeric 1 double
character 1 unicode
logical n > 1 PrVector
integer n > 1 PrVector
numeric n > 1 PrVector
character n > 1 PrVector
list without names n > 0 list
list with names n > 0 dict
matrix n > 0 PrMatrix
data.frame n > 0 PrDataFrame

Change the predefined conversion of pySet

PythonInR is designed in way that the conversion of types can easily be added or changed. This is done by utilizing polymorphism: if pySet is called, pySet calls pySetPoly which can be easily modified by the user. The following example shows how pySetPoly can be used to modify the behavior of pySet on the example of integer vectors.

The predefined type casting for integer vectors at an R level looks like the following:

setMethod("pySetPoly", signature(key="character", value = "integer"),
          function(key, value){
    success <- pySetSimple(key, list(vector=unname(value), names=names(value), rClass=class(value)))
    cmd <- sprintf("%s = PythonInR.prVector(%s['vector'], %s['names'], %s['rClass'])", 
                   key, key, key, key)

To change the predefined behavior one can simply use setMethod again.

pySetPoly <- PythonInR:::pySetPoly

pySet("x", 1:3)

          signature(key="character", value = "integer"),
          function(key, value){
    PythonInR:::pySetSimple(key, value)

pySet("x", 1:3)

NOTE PythonInR:::pySetSimple
The functions pySetSimple and pySetPoly shouldn't be used outside the function pySet since they do not check if R is connected to Python. If R is not connected to Python this can yield to segfault !

NOTE (named lists):
When executing pySet("x", list(b=3, a=2)) and pyGet("x") the order of the elements in x will change. This is not a special behavior of PythonInR but the default behavior of Python for dictionaries.

NOTE (matrix):
Matrices are either transformed to an object of the class PrMatrix or to an numpy array (if the option useNumpy is set to TRUE).

NOTE (data.frame):
Data frames are either transformed to an object of the class PrDataFrame
or to a pandas DataFrame (if the option usePandas is set to TRUE).

R to Python (pyGet)

Python R simplify
boolean logical TRUE / FALSE
integer integer TRUE / FALSE
double numeric TRUE / FALSE
string character TRUE / FALSE
unicode character TRUE / FALSE
bytes character TRUE / FALSE
tuple list FALSE
tuple list or vector TRUE
list list FALSE
list list or vector TRUE
dict named list FALSE
dict named list or vector TRUE
PrVetor vector TRUE / FALSE
PrMatrix matrix TRUE
PrDataFrame data.frame TRUE

Change the predefined conversion of pyGet

Similar to pySet the behavior of pyGet can be changed by utilizing pyGetPoly. The predefined version of pyGetPoly for an object of class PrMatrix looks like the following:

setMethod("pyGetPoly", signature(key="character", autoTypecast = "logical", simplify = "logical", pyClass = "PrMatrix"),
          function(key, autoTypecast, simplify, pyClass){
    x <- pyExecg(sprintf("x = %s.toDict()", key), autoTypecast = autoTypecast, simplify = simplify)[['x']]
    M <- do.call(rbind, x[['matrix']])
    rownames(M) <- x[['rownames']]
    colnames(M) <- x[['colnames']]

For objects of type "type" no conversion is defined. Therefore, PythonInR doesn't know how to transform it into an R object so it will return a PythonInR_Object. This is kind of a nice example since the return value of type(x) is a function therefore PythonInR will return an object of type pyFunction.


One can define a new function to get elements of type "type" as follows.

pyGetPoly <- PythonInR:::pyGetPoly
setMethod("pyGetPoly", signature(key="character", autoTypecast = "logical", simplify = "logical", pyClass = "type"),
          function(key, autoTypecast, simplify, pyClass){
    pyExecg(sprintf("x = %s.__name__", key))[['x']]

NOTE pyGetPoly
The functions pyGetPoly should not be used outside the function pyGet since it does not check if R is connected to Python. If R is not connected to Python this will yield to segfault !

NOTE (bytes):
In short, in Python 3 the data type string was replaced by the data type bytes. More information can be found here.

Cheat Sheet

Command Short Description Example Usage
BEGIN.Python Start a Python read-eval-print loop BEGIN.Python() print("Hello" + " " + "R!") END.Python
pyAttach Attach a Python object to an R environment pyAttach("os.getcwd", .GlobalEnv)
pyCall Call a callable Python object pyCall("pow", list(2,3), namespace="math")
pyConnect Connect R to Python pyConnect()
pyDict Create a representation of a Python dict in R myNewDict = pyDict('myNewDict', list(p=2, y=9, r=1))
pyDir The Python function dir (similar to ls) pyDir()
pyExec Execute Python code pyExec('some_python_code = "executed"')
pyExecfile Execute a file (like source) pyExecfile("myPythonFile.py")
pyExecg Execute Python code and get all assigned variables pyExecg('some_python_code = "executed"')
pyExecp Execute and print Python Code pyExecp('"Hello" + " " + "R!"')
pyExit Close Python pyExit()
pyFunction Create a representation of a Python function in R pyFunction(key)
pyGet Get a Python variable pyGet('myPythonVariable')
pyGet0 Get a Python variable pyGet0('myPythonVariable')
pyHelp Python help pyHelp("help")
pyImport Import a Python module pyImport("numpy", "np")
pyIsConnected Check if R is connected to Python pyIsConnected()
pyList Create a representation of a Python list in R pyList(key)
pyObject Create a representation of a Python object in R pyObject(key)
pyOptions A function to get and set some package options pyOptions("numpyAlias", "np")
pyPrint Print a Python variable from within R pyPrint("somePythonVariable")
pySet Set a R variable in Python pySet("pi", pi)
pySource A modified BEGIN.Python aware version of source pySource("myFile.R")
pyTuple Create a representation of a Python tuple in R pyTuple(key)
pyType Get the type of a Python variable pyType("sys")
pyVersion Returns the version of Python pyVersion()

Usage Examples

Dynamic Documents

Data and Text Mining