The robjects package

Platforms: Unix, Windows


This module should be the right pick for casual and general use. Its aim is to abstract some of the details and provide an intuitive interface to both Python and R programmers.

>>> import rpy2.robjects as robjects

rpy2.robjects is written on the top of rpy2.rinterface, and one not satisfied with it could easily build one’s own flavor of a Python-R interface by modifying it (rpy2.rpy_classic is another example of a Python interface built on the top of rpy2.rinterface).

Visible differences with RPy-1.x are:

  • no CONVERSION mode in rpy2, the design has made this unnecessary
  • easy to modify or rewrite with an all-Python implementation

r: the instance of R

This class is currently a singleton, with its one representation instanciated when the module is loaded:

>>> robjects.r
>>> print(robjects.r)

The instance can be seen as the entry point to an embedded R process.

Being a singleton means that each time the constructor for R is called the same instance is returned; this is required by the fact that the embedded R is stateful.

The elements that would be accessible from an equivalent R environment are accessible as attributes of the instance. Readers familiar with the ctypes module for Python will note the similarity with it.

R vectors:

>>> pi = robjects.r.pi
>>> letters = robjects.r.letters

R functions:

>>> plot = robjects.r.plot
>>> dir = robjects.r.dir

This approach has limitation as:

  • The actual Python attributes for the object masks the R elements

  • ‘.’ (dot) is syntactically valid in names for R objects, but not for

    python objects.

That last limitation can partly be removed by using rpy2.rpy_classic if this feature matters most to you.

>>> robjects.r.as_null
# AttributeError raised
>>> import rpy2.rpy_classic as rpy
>>> rpy.set_default_mode(NO_CONVERSION)
>>> rpy.r.as_null
# R function as.null() returned


The section Partial use of rpy_classic outlines how to integrate rpy2.rpy_classic code.

Behind the scene, the steps for getting an attribute of r are rather straightforward:

  1. Check if the attribute is defined as such in the python definition for r
  2. Check if the attribute is can be accessed in R, starting from globalenv

When safety matters most, we recommend using __getitem__() to get a given R object.

>>> as_null = robjects.r['as.null']

Storing the object in a python variable will protect it from garbage collection, even if deleted from the objects visible to an R user.

>>> robjects.globalenv['foo'] = 1.2
>>> foo = robjects.r['foo']
>>> foo[0]

Here we remove the symbol foo from the R Global Environment.

>>> robjects.r['rm']('foo')
>>> robjects.r['foo']
LookupError: 'foo' not found

The object itself remains available, and protected from R’s garbage collection until foo is deleted from Python

>>> foo[0]

Evaluating a string as R code

Just like it is the case with RPy-1.x, on-the-fly evaluation of R code contained in a string can be performed by calling the r instance:

>>> print(robjects.r('1+2'))
[1] 3
>>> sqr = robjects.r('function(x) x^2')
>>> print(sqr)
function (x)
>>> print(sqr(2))
[1] 4

The astute reader will quickly realize that R objects named by python variables can be plugged into code through their R representation:

>>> x = robjects.r.rnorm(100)
>>> robjects.r('hist(%s, xlab="x", main="hist(x)")' %x.r_repr())


Doing this with large objects might not be the best use of your computing power.

R objects

The class rpy2.robjects.RObject can represent any arbitray R object, although it will often be used for objects without any more specific representation in Python/rpy2 (such as Vector, functions.Function, Environment).

The class inherits from the lower-level rpy2.rinterface.Sexp and from rpy2.robjects.robject.RObjectMixin, the later defining higher-level methods for R objects to be shared by other higher-level representations of R objects.

class rpy2.robjects.robject.RObjectMixin

Bases: object

Class to provide methods common to all RObject instances


String representation for an object that can be directly evaluated as R code.


R class for the object, stored an R string vector.

class rpy2.robjects.RObject

Bases: rpy2.robjects.robject.RObjectMixin, rpy2.rinterface.Sexp

Base class for all R objects.


Beside functions, and environments, most of the objects an R user is interacting with are vector-like. For example, this means that any scalar is in fact a vector of length one.

The class Vector has a constructor:

>>> x = robjects.Vector(3)
class rpy2.robjects.Vector(o)

Bases: rpy2.robjects.robject.RObjectMixin, rpy2.rinterface.SexpVector

R vector-like object. Items can be accessed with: - the method “__getitem__” (“[” operator) - the delegators rx or rx2


iterate over names and values

sample(n, replace=False, probabilities=None)

Draw a sample of size n from the vector. If ‘replace’ is True, the sampling is done with replacement. The optional argument ‘probabilities’ indicates sampling probabilities.

Creating vectors

Creating vectors can be achieved either from R or from Python.

When the vectors are created from R, one should not worry much as they will be exposed as they should by rpy2.robjects.

When one wants to create a vector from Python, either the class Vector or the convenience classes IntVector, FloatVector, BoolVector, StrVector can be used.

class rpy2.robjects.vectors.BoolVector(obj)

Bases: rpy2.robjects.vectors.Vector

Vector of boolean (logical) elements

class rpy2.robjects.vectors.IntVector(obj)

Bases: rpy2.robjects.vectors.Vector

Vector of integer elements


Count the number of times integer values are found

class rpy2.robjects.vectors.FloatVector(obj)

Bases: rpy2.robjects.vectors.Vector

Vector of float (double) elements

class rpy2.robjects.vectors.StrVector(obj)

Bases: rpy2.robjects.vectors.Vector

Vector of string elements


construct a factor vector from the vector of strings


R’s factors are somewhat peculiar: they aim at representing a memory-efficient vector of labels, and in order to achieve it are implemented as vectors of integers to which are associated a (presumably shorter) vector of labels. Each integer represents the position of the label in the associated vector of labels.

For example, the following vector of labels

a b a b b c

will become

1 2 1 2 2 3


a b c
>>> sv = ro.StrVector('ababbc')
>>> fac = ro.FactorVector(sv)
>>> print(fac)
[1] a b a b b c
Levels: a b c
>>> tuple(fac)
(1, 2, 1, 2, 2, 3)
>>> tuple(fac.levels)
('a', 'b', 'c')

Since a FactorVector is an IntVector with attached metadata (the levels), getting items Python-style was not changed from what happens when gettings items from a IntVector. A consequence to that is that information about the levels is then lost.

>>> item_i = 0
>>> fac[item_i]

Getting the level corresponding to an item requires using the levels,:

>>> fac.levels[fac[item_i] - 1]


Do not forget to subtract one to the value in the FactorVector. Indexing in Python starts at zero while indexing R starts at one, and recovering the level for an item requires an adjustment between the two.

When extracting elements from a FactorVector a sensible default might be to use R-style extracting (see Extracting items), as it preserves the integer/string duality.

class rpy2.robjects.vectors.FactorVector(obj)

Bases: rpy2.robjects.vectors.IntVector

Vector of ‘factors’


are the levels in the factor ordered ?


number of levels

Extracting items

Extracting elements of sequence/vector can become a thorny issue as Python and R differ on a number of points (index numbers starting at zero / starting at one, negative index number meaning index from the end / everything except, names cannot / can be used for subsettting).

In order to solve this, the Python way and the R way were made available through two different routes.

Extracting, Python-style

The python __getitem__() method behaves like a Python user would expect it for a vector (and indexing starts at zero).

>>> x = robjects.r.seq(1, 5)
>>> tuple(x)
(1, 2, 3, 4, 5)
>>> x.names = robjects.StrVector('abcde')
>>> print(x)
a b c d e
1 2 3 4 5
>>> x[0]
>>> x[4]
>>> x[-1]

Extracting, R-style

Access to R-style extracting/subsetting is granted though the two delegators rx and rx2, representing the R functions [ and [[ respectively.

In short, R-style extracting has the following characteristics:

  • indexing starts at one
  • the argument to subset on can be a vector of
    • integers (negative integers meaning exlusion of the elements)
    • booleans
    • strings (whenever the vector has names for its elements)
>>> print(x.rx(1))
[1] 1
>>> print(x.rx('a'))

R can extract several elements at once:

>>> i = robjects.IntVector((1, 3))
>>> print(x.rx(i))
[1] 1 3
>>> b = robjects.BoolVector((False, True, False, True, True))
>>> print(x.rx(b))
[1] 2 4 5

When a boolean extract vector is of smaller length than the vector, is expanded as necessary (this is know in R as the recycling rule):

>>> print(x.rx(True))
>>> b = robjects.BoolVector((False, True))
>>> print(x.rx(b))
[1] 2 4

In R, negative indices are understood as element exclusion.

>>> print(x.rx(-1))
>>> i = robjects.IntVector((-1, -3))
>>> print(x.rx(i))
[1] 2 4 5

That last example could also be written:

>>> i = - robjects.IntVector((1, 3)).ro
>>> print(x.rx(i))
[1] 2 4 5

R operators are vector operations, with the operator applyied to each element in the vector. This can be used to build extract indexes.

>>> i = > 3 # extract values > 3
>>> i = ( >= 2 ).ro & ( <= 4) # extract values between 2 and 4

(More on R operators in Section Operators).

R/S also have particularities, in which some see consistency issues. For example although the indexing starts at 1, indexing on 0 does not return an index out of bounds error but a vector of length 0:

>>> print(x.rx(0))

Missing values

Anyone with experience in the analysis of real data knows that some of the data might be missing. In S/Splus/R special NA values can be used in a data vector to indicate that fact, and rpy2.robjects makes aliases for those available as data objects NA_Logical, NA_Real, NA_Integer, NA_Character, NA_Complex.

>>> x = robjects.IntVector(range(3))
>>> x[0] <- robjects.NA_Integer
>>> print(x)
[1] NA  1  2

The translation of NA types is done at the item level, returning a pointer to the corresponding NA singleton class.

>>> xt = tuple(x)
>>> xt
(NA_integer, 1, 2)
>>> xt[0] is robjects.NA_Integer
>>> xt[0] == robjects.NA_Integer
>>> [y for y in x if y is not NA_Integer]
[1, 2]


NA_Logical is the alias for R’s NA.


The NA objects are imported from the corresponding rpy2.rinterface objects.


Mathematical operations on two vectors: the following operations are performed element-wise in R, recycling the shortest vector if, and as much as, necessary.

To expose that to Python, a delegating attribute ro is provided for vector-like objects.

Python R
+ +
- -
* *
/ /
** ** or ^
or |
and &
< <
<= <=
== ==
!= !=
>>> x = robjects.r.seq(1, 10)
>>> print( + 1)


In Python, using the operator + on two sequences concatenates them and this behavior has been conserved. >>> print(x + 1)


The boolean operator not cannot be redefined in Python (at least up to version 2.5), and its behavior could not be made to mimic R’s behavior


R vectors can have a name given to all or some of the elements. The property names can be used to get, or set, those names.

>>> x = robjects.r.seq(1, 5)
>>> x.names = robjects.StrVector('abcde')
>>> x.names[0]
>>> x.names[0] = 'z'
>>> tuple(x.names)
('z', 'b', 'c', 'd', 'e')


In R, arrays are simply vectors with a dimension attribute. That fact was reflected in the class hierarchy with robjects.Array inheriting from robjects.Vector.

class rpy2.robjects.vectors.Array(obj)

Bases: rpy2.robjects.vectors.Vector

An R array


names associated with the dimension.


names associated with the dimension.


A Matrix is a special case of Array. As with arrays, one must remember that this is just a vector with dimension attributes (number of rows, number of columns).

>>> m = robjects.r.matrix(robjects.IntVector(range(10)), nrow=5)
>>> print(m)
     [,1] [,2]
[1,]    0    5
[2,]    1    6
[3,]    2    7
[4,]    3    8
[5,]    4    9


In R, matrices are column-major ordered, although the constructor matrix() accepts a boolean argument byrow that, when true, will build the matrix as if row-major ordered.

Computing on matrices

Regular operators work element-wise on the underlying vector.

>>> m = robjects.r.matrix(robjects.IntVector(range(4)), nrow=2)
>>> print( + 1)
     [,1] [,2]
[1,]    1    3
[2,]    2    4

For more on operators, see Operators.

Matrix multiplication is available as, transposition as Matrix.transpose(). Common operations such as cross-product, eigen values computation , and singular value decomposition are also available through method with explicit names.

>>> print( m.crossprod(m) )
     [,1] [,2]
[1,]    1    3
[2,]    3   13
>>> print( m.transpose().dot(m) )
     [,1] [,2]
[1,]    1    3
[2,]    3   13
class rpy2.robjects.vectors.Matrix(obj)

Bases: rpy2.robjects.vectors.Array

An R matrix


Column names


crossproduct X’.Y


Matrix multiplication


Eigen values


Number of columns


Number of rows


Row names

svd(nu=None, nv=None, linpack=False)

SVD decomposition. If nu is None, it is given the default value min(tuple(self.dim)). If nv is None, it is given the default value min(tuple(self.dim)).


crossproduct X.Y’


transpose the matrix


Extracting can still be performed Python-style or R-style.

>>> m = ro.r.matrix(ro.IntVector(range(2, 8)), nrow=3)
>>> print(m)
     [,1] [,2]
[1,]    2    5
[2,]    3    6
[3,]    4    7
>>> m[0]
>>> m[5]
>>> print(m.rx(1))
[1] 2
>>> print(m.rx(6))
[1] 7

Matrixes are two-dimensional arrays, and elements can be extracted according to two indexes:

>>> print(m.rx(1, 1))
[1] 2
>>> print(m.rx(3, 2))
[1] 7

Data frames

Data frames are important data structures in R, as they are used to represent a data to analyze in a study in a relatively large number of cases.

A data frame can be thought of as a tabular representation of data, with one variable per column, and one data point per row. Each column is an R vector, which implies one type for all elements in one given column, and which allows for possibly different types across different columns.

If we take the example of data about the pharmacokinetics of theophylline in different subjects, the table of data could look like:

Subject Weight Dose Time conc
1 79.6 4.02 0.00 0.74
1 79.6 4.02 0.25 2.84
1 79.6 4.02 0.57 6.57
2 72.4 4.40 7.03 5.40
... ... ... ... ...

Such representation of the data shares similarities with a table in a relational database: the structure between the variables, or columns, is given by other column. In the example above, the grouping of the measures by subject is given by the column Subject.

In rpy2.robjects, DataFrame represents the R class data.frame.

Creating objects

Creating an DataFrame can be done by:

  • Using the constructor for the class
  • Create the data.frame through R
  • Read data from a file using the instance method from_csvfile()

The constructor for DataFrame accepts either a rinterface.SexpVector (with typeof equal to VECSXP, that is an R list) or any Python object implementing the method iteritems() (for example dict, or rpy2.rlike.container.OrdDict)

Empty data.frame:

>>> dataf = robjects.DataFrame({})

data.frame with 2 two columns (not that the order of the columns in the resulting DataFrame can be different from the order in which they are declared):

>>> d = {'a': robjects.IntVector((1,2,3)), 'b': robjects.IntVector((4,5,6))}
>>> dataf = robject.DataFrame(d)

To create a DataFrame and be certain of the order in which the columns are an ordered dictionary can be used:

>>> import rpy2.rlike.container as rlc
>>> od = rlc.OrdDict([('value', robjects.IntVector((1,2,3))),
                      ('letter', robjects.StrVector(('x', 'y', 'z')))])
>>> dataf = robjects.DataFrame(od)
>>> print(dataf.colnames)
[1] "letter" "value"

Creating the data.frame in R can otherwise be achieved in numerous ways, as many R functions do return a data.frame, such as the function data.frame().

Extracting elements

Here again, Python’s __getitem__() will work as a Python programmer will expect it to:

>>> len(dataf)
>>> dataf[0]
<Vector - Python:0x8a58c2c / R:0x8e7dd08>

The DataFrame is composed of columns, with each column being possibly of a different type:

>>> [column.rclass[0] for column in dataf]
['factor', 'integer']

Using R-style access to elements is a little more richer, with the rx2 accessor taking now more importance than earlier.

Like with Python’s __getitem__() above, extracting on one index selects columns:

>>> dataf.rx(1)
<DataFrame - Python:0x8a584ac / R:0x95a6fb8>
>>> print(dataf.rx(1))
1      x
2      y
3      z

It is important to notice that the result is itself of class DataFrame. Getting the column as a vector is requires the use of rx2.

>>> dataf.rx2(1)
<Vector - Python:0x8a4bfcc / R:0x8e7dd08>
>>> print(dataf.rx2(1))
[1] x y z
Levels: x y z

Since data frames are table-like structure, they can be thought of as two-dimensional arrays and can therefore be extracted on two indexes.

>>> subdataf = dataf.rx(1, True)
>>> print(subdataf)
  letter value
1      x     1
>>> rows_i <- robjects.IntVector((1,3))
>>> subdataf = dataf.rx(rows_i, True)
>>> print(subdataf)
  letter value
1      x     1
3      z     3

That last example is something extremely common in R. A vector of indices, here rows_i, is used to take a subset of the DataFrame.

Python docstrings

class rpy2.robjects.vectors.DataFrame(tlist)

Bases: rpy2.robjects.vectors.Vector

R ‘data.frame’.

cbind(*args, **kwargs)

bind objects as supplementary columns

static from_csvfile(path, header=True, sep=', ', quote='"', dec='.', row_names=rpy2.rinterface.MissingArg, col_names=rpy2.rinterface.MissingArg, fill=True, comment_char='', as_is=False)

Create an instance from data in a .csv file.


iterator across columns


iterator across rows


Number of columns. :rtype: integer


Number of rows. :rtype: integer

rbind(*args, **kwargs)

bind objects as supplementary rows


Row names

to_csvfile(path, quote=True, sep=', ', eol='n', na='NA', dec='.', row_names=True, col_names=True, qmethod='escape', append=False)

Save the data into a .csv file.


R environments can be described to the Python user as an hybrid of a dictionary and a scope.

The first of all environments is called the Global Environment, that can also be referred to as the R workspace.

An R environment in RPy2 can be seen as a kind of Python dictionnary.

Assigning a value to a symbol in an environment has been made as simple as assigning a value to a key in a Python dictionary:

>>> robjects.globalenv["a"] = 123
>>> print(

Care must be taken when assigning objects into an environment such as the Global Environment, as this can hide other objects with an identical name. The following example should make one measure that this can mean trouble if no care is taken:

>>> globalenv["pi"] = 123
>>> print(robjects.r.pi)
[1] 123
>>> robjects.r.rm("pi")
>>> print(robjects.r.pi)
[1] 3.1415926535897931

The class inherits from the class rpy2.rinterface.SexpEnvironment.

An environment is also iter-able, returning all the symbols (keys) it contains:

>>> env = robjects.r.baseenv()
>>> [x for x in env]
<a long list returned>


Although there is a natural link between environment and R packages, one should consider using the convenience wrapper dedicated to model R packages (see R packages).

class rpy2.robjects.Environment(o=None)

Bases: rpy2.robjects.robject.RObjectMixin, rpy2.rinterface.SexpEnvironment

An R environement.

get(item, wantfun=False)

Get a object from its R name/symol :param item: string (name/symbol) :rtype: object (as returned by conversion.ri2py())


R functions are callable objects, and be called almost like any regular Python function:

>>> plot = robjects.r.plot
>>> rnorm = robjects.r.rnorm
>>> plot(rnorm(100), ylab="random")

This is all looking fine and simple until R arguments with names such as na.rm are encountered. By default, this is addressed by having a translation of ‘.’ (dot) in the R argument name into a ‘_’ in the Python argument name.

Let’s take an example in R:

rank(0, na.last = TRUE)

In Python it can then write:

from rpy2.robjects.packages import importr
base = importr('base')

base.rank(0, na_last = True)


The object base.rank is an instance of functions.SignatureTranslatedFunction, a child class of functions.Function, and the translation of the argument names made during the creation of the instance. This saves the need to translate the names at each function call, and allow to perform sanity check regarding possible ambiguous translation with an acceptable cost (since this is only performed when the instance is created).

If translation is not desired, the class functions.Function can be used. With that class, using the special Python syntax **kwargs is one way to specify named arguments that contain a dot ‘.’

It is important to understand that the translation is done by inspecting the signature of the R function, and that not much can be guessed from the R ellipsis ‘...’ whenever present. Arguments falling in the ‘...’ will need to have their R names passes, as show in the example below:

>>> graphics = importr('graphics')
>>> graphics.par(cex_axis = 0.5)
Warning message:
In function (..., no.readonly = FALSE)  :
"cex_axis" is not a graphical parameter
<Vector - Python:0xa1688cc / R:0xab763b0>
>>> graphics.par(**{'cex.axis': 0.5})
<Vector - Python:0xae8fbec / R:0xaafb850>

There exists a way to specify manually a argument mapping:

from rpy2.robjects.functions import SignatureTranslatedFunction
from rpy2.robjects.packages import importr
graphics = importr('graphics')
graphics.par = SignatureTranslatedFunction(graphics.par,
                                           init_prm_translate = {'cex_axis': 'cex.axis'})
>>> graphics.par(cex_axis = 0.5)
<Vector - Python:0xa2cc90c / R:0xa5f7fd8>

Translating blindly each ‘.’ in argument names into ‘_’ currently appears to be a risky practice, and is left to one to decide for his own code. (Bad) example:

def iamfeelinglucky(**kwargs):
    res = {}
    for k, v in kwargs.iteritems:
        res[k.replace('_', '.')] = v
    return res

graphics.par(**(iamfeelinglucky(cex_axis = 0.5)))

Things are also not always that simple, as the use of a dictionary does not ensure that the order in which the arguments are passed is conserved.

R is capable of introspection, and can return the arguments accepted by a function through the function formals(), modelled as a method of functions.Function.

>>> from rpy2.robjects.packages import importr
>>> stats = importr('stats')
>>> rnorm = stats.rnorm
>>> rnorm.formals()
<Vector - Python:0x8790bcc / R:0x93db250>
>>> tuple(rnorm.formals().names)
('n', 'mean', 'sd')


Here again there is a twist coming from R, and some functions are “special”. rpy2 is exposing as rpy2.rinterface.SexpClosure R objects that can be either CLOSXP, BUILTINSXP, or SPECIALSXP. However, only CLOSXP objects will return non-null formals.

The R functions as defined in rpy2.robjects inherit from the class rpy2.rinterface.SexpClosure, and further documentation on the behavior of function can be found in Section Functions.

class rpy2.robjects.functions.Function(*args, **kwargs)

Bases: rpy2.robjects.robject.RObjectMixin, rpy2.rinterface.SexpClosure

Python representation of an R function.


Return the signature of the underlying R function (as the R function ‘formals()’ would).


Wrapper around the parent method rpy2.rinterface.SexpClosure.rcall().

class rpy2.robjects.functions.SignatureTranslatedFunction(*args, **kwargs)

Bases: rpy2.robjects.functions.Function

Python representation of an R function such as the character ‘.’ is replaced with ‘_’ whenever present in the R argument name.


For tasks such as modelling and plotting, an R formula can be a terse, yet readable, way of expressing what is wanted.

In R, it generally looks like:

x <- 1:10
y <- x + rnorm(10, sd=0.2)

fit <- lm(y ~ x)

In the call to lm, the argument is a formula, and it can read like model y using x. A formula is a R language object, and the terms in the formula are evaluated in the environment it was defined in. Without further specification, that environment is the environment in which the the formula is created.

The class robjects.Formula is representing an R formula.

x = robjects.Vector(array.array('i', range(1, 11)))
y = + rnorm(10, sd=0.2)

fmla = robjects.Formula('y ~ x')
env = fmla.environment
env['x'] = x
env['y'] = y

stats = importr('lm')
fit = stats.lm(fmla)

One drawback with that approach is that pretty printing of the fit object is note quite as clear as what one would expect when working in R. However, by evaluating R code on the fly, we can obtain a fit object that will display nicely:

fit = robjects.r('lm(%s)' %fmla.r_repr())
class rpy2.robjects.Formula(formula, environment = rinterface.globalenv)

Bases: rpy2.robjects.robject.RObjectMixin, rpy2.rinterface.Sexp


Get the environment in which the formula is finding its symbols.


Get the environment in which the formula is finding its symbols.


Set the environment in which a formula will find its symbols.

R packages

Importing R packages

In R, objects can be bundled into packages for distribution. In similar fashion to Python modules, the packages can be installed, and then loaded when their are needed. This is achieved by the R functions library() and require() (attaching the namespace of the package to the R search path).

from rpy2.robjects.packages import importr
utils = importr("utils")

The object utils is now a module-like object, in the sense that its __dict__ contains keys corresponding to the R symbols. For example the R function data() can be accessed like:

<SignatureTranslatedFunction - Python:0x913754c / R:0x943bdf8>

Unfortunately, accessing an R symbol can be a little less straightforward as R symbols can contain characters that are invalid in Python symbols. Anyone with experience in R can even add there is a predilection for the dot (.).

In an attempt to address this, during the import of the package a translation of the R symbols is attempted, with dots becoming underscores. This is not unlike what could be found in rpy, but with distinctive differences:

  • The translation is performed once, when the package is imported, and the results cached. The caching allows us to perform the check below.
  • A check that the translation is not masking other R symbols in the package is performed (e.g., both ‘print_me’ and ‘’ are present). Should it happen, a rpy2.robjects.packages.LibraryError is raised, the optional argument robject_translations to importr() shoud be used.
  • The translation is concerning one package, limiting the risk of masking when compared to rpy translating relatively blindly and retrieving the first match


The translation of ‘.’ into ‘_’ is clearly not sufficient, as R symbols can use a lot more characters illegal in Python symbols. Those more exotic symbols can be accessed through __dict__.


>>> utils.__dict__['?']
<Function - Python:0x913796c / R:0x9366fac>

In addition to the translation of robjects symbols, objects that are R functions see their named arguments translated as similar way (with ‘.’ becoming ‘_’ in Python).

>>> base = importr('base')
>>> base.scan._prm_translate
{'blank_lines_skip': 'blank.lines.skip',
 'comment_char': 'comment.char',
 'multi_line': 'multi.line',
 'na_strings': 'na.strings',
 'strip_white': 'strip.white'}
exception rpy2.robjects.packages.LibraryError

Error occuring when importing an R library

class rpy2.robjects.packages.Package(env, name, translation={})

Models an R package (and can do so from an arbitrary environment - with the caution that locked environments should mostly be considered).

rpy2.robjects.packages.importr(name, lib_loc=None, robject_translations={}, signature_translation=True, suppress_messages=True)

Import an R package (and return a module-like object).

rpy2.robjects.packages.wherefrom(symbol, startenv=<rpy2.rinterface.SexpEnvironment - Python:0x9ff4020 / R:0xa51dd38>)

For a given symbol, return the environment this symbol is first found in, starting from ‘startenv’

Finding where an R symbol is coming from

Knowing which object is effectively considered when a given symbol is resolved can be of much importance in R, as the number of packages attached grows and the use of the namespace accessors ”::” and ”:::” is not so frequent.

The function wherefrom() offers a way to find it:

>>> import rpy2.robjects.packages as rpacks
>>> env = rpacks.wherefrom('lm')
>>> env.do_slot('name')[0]


This does not generalize completely, and more details regarding environment, and packages as environment should be checked Section SexpEnvironment.

Installing/removing R packages

R is shipped with a set of recommended packages (the equivalent of a standard library), but there is a large (and growing) number of other packages available.

Installing those packages can done from within R, and one will consult an R-related documentation if unsure of how to do so.

Working with R’s OOPs

Object-Oriented Programming can a achieved in R, but in more than one way. Beside the official S3 and S4 systems, there is a rich ecosystem of alternative implementations of objects (aroma, or proto are two such systems).

S3 objects

S3 objects are default R objects (i.e., not S4 instances) for which an attribute “class” has been added.

>>> x = robjects.IntVector((1, 3))
>>> tuple(x.rclass)

Making the object x an instance of a class pair, itself inheriting from integer is only a matter of setting the attribute:

>>> x.rclass = robjects.StrVector(("pair", "integer"))
>>> tuple(x.rclass)
('pair', 'integer')

Methods for S3 classes are simply R functions with a name such as name.<class_name>, the dispatch being made at run-time from the first argument in the function call.

For example, the function plot.lm plots objects of class lm. The call plot(something) will see R extract the class name of the object something, and see if a function plot.<class_of_something> is in the search path.


This rule is not strict as there can exist functions with a dot in their name and the part after the dot not correspond to an S3 class name.

S4 objects

S4 objects are a little more formal regarding their class definition, and all instances belong to the low-level R type SEXPS4.

The definition of methods for a class can happen anytime after the class has been defined (a practice something referred to as monkey patching or duck punching in the Python world).

There are obviously many ways to try having a mapping between R classes and Python classes, and the one proposed here is to make Python classes that inherit rpy2.rinterface.methods.RS4.

Since the S4 system allows polymorphic definitions of methods, that is for a given method name there can exist several sets of possible arguments (and type for the arguments), it currently appears trickly to have an simple, automatic, and robust mapping of R methods to Python methods. For the time being, one will rely on human-written mappings, although some helpers are provided by rpy2.


More automation for reflecting S4 class definitions into Python is on the list of items to be worked on, so one may hope for more in a following release.

To make this a little more concrete, we take the R class lmList in the package lme4 and show how to write a Python wrapper for it.


The R package lme4 is not distributed with R, and will have to be installed for the example to work.

First, a bit of boilerplate code is needed. We obviously import the higher-level interface, as well the function rpy2.robjects.packages.importr(). The R class we want to represent is defined in the rpy2 modules and utilities.

import rpy2.robjects as robjects
import rpy2.rinterface as rinterface
from rpy2.robjects.packages import importr

lme4 = importr("lme4")
getmethod = robjects.baseenv.get("getMethod")

StrVector = robjects.StrVector

Once done, the Python class definition can be written. In the first part of that code, we choose a static mapping of the R-defined methods. The advantage for doing so is a bit of speed (as the S4 dispatch mechanism has a cost), and the disadvantage is that the a modification of the method at the R level would require a refresh of the mappings concerned. The second part of the code is wrapper to those mappings, where Python-to-R operations prior to calling the R method can be performed. In the last part of the class definition, a static methods is defined. This is one way to have polymorphic constructors implemented.

class LmList(robjects.methods.RS4):
    """ Reflection of the S4 class 'lmList'. """
    _coef = getmethod("coef", 
                      signature = StrVector(["lmList", ]),
                      where = "package:lme4")
    _confint = getmethod("confint", 
                         signature = StrVector(["lmList", ]),
                         where = "package:lme4")
    _formula = getmethod("formula", 
                         signature = StrVector(["lmList", ]),
                         where = "package:lme4")
    _lmfit_from_formula = getmethod("lmList",
                                    signature = StrVector(["formula", "data.frame"]),
                                    where = "package:lme4")

    def _call_get(self):
        return self.do_slot("call")
    def _call_set(self, value):
        return self.do_slot("call", value)
    call = property(_call_get, _call_set, None, "Get or set the RS4 slot 'call'.")

    def coef(self):
        """ fitted coefficients """
        return self._coef(self)
    def confint(self):
        """ confidence interval """
        return self._confint(self)
    def formula(self):
        """ formula used to fit the model """
        return self._formula(self)
    def from_formula(formula, 
                     data = rinterface.MissingArg,
                     family = rinterface.MissingArg,
                     subset = rinterface.MissingArg,
                     weights = rinterface.MissingArg):
        """ Build an LmList from a formula """
        res = LmList._lmfit_from_formula(formula, data,
                                         family = family,
                                         subset = subset,
                                         weights = weights)
        res = LmList(res)
        return res

Creating a instance of LmList can now be achieved by specifying a model as a Formula and a dataset.

sleepstudy = lme4.sleepstudy
formula = robjects.Formula('Reaction ~ Days | Subject')
lml1 = LmList.from_formula(formula, 

A drawback of the approach above is that the R “call” is stored, and as we are passing the DataFrame sleepstudy (and as it is believed to to be an anonymous structure by R) the call is verbose: it comprises the explicit structure of the data frame (try to print lml1). This becomes hardly acceptable as datasets grow bigger. An alternative to that is to store the columns of the data frame into the environment for the Formula, as shown below:

sleepstudy = lme4.sleepstudy
formula = robjects.Formula('Reaction ~ Days | Subject')
for varname in ('Reaction', 'Days', 'Subject'):
    formula.environment[varname] = sleepstudy.rx2(varname)

lml1 = LmList.from_formula(formula)
class rpy2.robjects.methods.RS4(sexp)

Bases: rpy2.robjects.robject.RObjectMixin, rpy2.rinterface.SexpS4

Python representation of an R instance of class ‘S4’.

static isclass(name)

Return whether the given name is a defined class.


Return the ‘slots’ defined for this object

validobject(test=False, complete=False)

Return whether the instance is ‘valid’ for its class.

Automated mapping of user-defined classes

Once a Python class mirroring an R classis defined, the mapping can be made automatic by adding new rules to the conversion system (see Section Mapping rpy2 objects to arbitrary python objects).

Object serialization

The python pickling system can be used to serialize objects to disk, and restore them from their serialized form.

import pickle
import rpy2.robjects as ro

x = ro.StrVector(('a', 'b', 'c'))

f = file('/tmp/foo.pso', 'w')
pickle.dump(x, f)

f = file('/tmp/foo.pso', 'r')
x_again = pickle.load(f)


Currently loading an object from a serialized form restores the object in its low-level form (as in rpy2.rinterface). Higher-level objects can be restored by calling the higher-level casting function rpy2.robjects.conversion.ri2py() (see Mapping rpy2 objects to arbitrary python objects).

Class diagram

Inheritance diagram of rpy2.robjects, rpy2.robjects.methods, rpy2.robjects.vectors, rpy2.robjects.functions