Introduction to rpy2

This introduction aims at making a gentle start to rpy2, either when coming from R to Python/rpy2, from Python to rpy2/R, or from elsewhere to Python/rpy2/R.

Getting started

It is assumed here that the rpy2 package has been properly installed. In python, making a package or module available is achieved by importing it:

import rpy2.robjects as robjects

The r instance

The object r in rpy2.robjects represents the running embedded R process.

If familiar with R and the R console, r is a little like a communication channel from Python to R.

Getting R objects

In Python the [ operator is an alias for the ethod __getitem__().

The __getitem__() method of rpy2.robjects.r, evaluates a variable from the R console.

Example in R:

> pi
[1] 3.141593

With rpy2:

>>> pi = robjects.r['pi']
>>> pi[0]


Under the hood, the variable pi is gotten by default from the R base package, unless an other variable with the name pi was created in R’s .globalEnv.

Whenever one wishes to be specific about where the symbol should be looked for (which should be most of the time), it possible to wrap R packages in Python namespace objects (see R packages).

For more details on environments, see Section Environments.

Also, note that pi is not a scalar but a vector of length 1

Evaluating R code

The r object is also callable, and the string passed to it evaluated as R code.

This can be used to get variables, and provide an alternative to the method presented above.

Example in R:

> pi
[1] 3.141593

With rpy2:

>>> pi = robjects.r('pi')
>>> pi[0]


The result is an R vector. The Section R vectors below will provide explanation for the following behavior:

>>> piplus2 = robjects.r('pi') + 2
>>> piplus2.r_repr()
c(3.14159265358979, 2)
>>> pi0plus2 = robjects.r('pi')[0] + 2
>>> print(pi0plus2)

The evaluation is performed in what is known to R users as the Global Environment, that is the place one starts at when starting the R console. Whenever the R code creates variables, those variables are “located” in that Global Environment by default.


        f <- function(r, verbose=FALSE) {
            if (verbose) {
                cat("I am calling f().\n")
            2 * pi * r

The expression above returns the value 18.85, but first creates an R function f. That function f is present in the R Global Environement, and can be accessed with the __getitem__ mechanism outlined above:

>>> r_f = robjects.globalenv['f']
>>> print(r_f.r_repr())
function (r, verbose = FALSE)
    if (verbose) {
        cat("I am calling f().\n")
    2 * pi * r


As shown earlier, an alternative way to get the function is to get it from the R singleton

>>> r_f = robjects.r['f']

The function r_f is callable, and can be used like a regular Python function.

>>> res = r_f(3)

Jump to Section Calling R functions for more on calling functions.

Interpolating R objects into R code strings

Against the first impression one may get from the title of this section, simple and handy features of rpy2 are presented here.

An R object has a string representation that can be used directly into R code to be evaluated.

Simple example:

>>> letters = robjects.r['letters']
>>> rcode = 'paste(%s, collapse="-")' %(letters.r_repr())
>>> res = robjects.r(rcode)
>>> print(res)

R vectors

In R, data are mostly represented by vectors, even when looking like scalars.

When looking closely at the R object pi used previously, we can observe that this is in fact a vector of length 1.

>>> len(robjects.r['pi'])

As such, the python method add() will result in a concatenation (function c() in R), as this is the case for regular python lists.

Accessing the one value in that vector has to be stated explicitly:

>>> robjects.r['pi'][0]

There is much that can be achieved with vectors, having them to behave more like Python lists or R vectors. A comprehensive description of the behavior of vectors is found in robjects.vector.

Creating rpy2 vectors

Creating R vectors can be achieved simply:

>>> res = robjects.StrVector(['abc', 'def'])
>>> print(res.r_repr())
c("abc", "def")
>>> res = robjects.IntVector([1, 2, 3])
>>> print(res.r_repr())
>>> res = robjects.FloatVector([1.1, 2.2, 3.3])
>>> print(res.r_repr())
c(1.1, 2.2, 3.3)

R matrixes and arrays are just vectors with a dim attribute.

The easiest way to create such objects is to do it through R functions:

>>> v = robjects.FloatVector([1.1, 2.2, 3.3, 4.4, 5.5, 6.6])
>>> m = robjects.r['matrix'](v, nrow = 2)
>>> print(m)
     [,1] [,2] [,3]
[1,]  1.1  3.3  5.5
[2,]  2.2  4.4  6.6

Calling R functions

Calling R functions is disappointingly similar to calling Python functions:

>>> rsum = robjects.r['sum']
>>> rsum(robjects.IntVector([1,2,3]))[0]

Keywords are also working:

>>> rsort = robjects.r['sort']
>>> res = rsort(robjects.IntVector([1,2,3]), decreasing=True)
>>> print(res.r_repr())
c(3L, 2L, 1L)


By default, calling R functions return R objects.

More information on functions is in Section Functions.

Getting help

R has a builtin help system that, just like the pydoc strings are used frequently in python during interactive sessions, is used very frequently by R programmmers. This help system is accessible from an R function, therefore accessible from rpy2.

Help on a topic within a given package, or currently loaded packages

>>> from rpy2.robjects.packages import importr
>>> utils = importr("utils")
>>> help_doc ="help")
>>> help_doc[0]

Converting the object returned to a string produces the full help text on the topic:

>>> str(help_doc)
[...long output...]


The help message so produced is not a string returned to the console but is directly printed by R to the standard output. The call to str() only returns an empty string, and the reason for this is somewhat involved for an introductory documentation. This behaviour is rooted in R itself and in rpy2 the string representation of R objects is the string representation as given by the R console, which in that case takes a singular route.

For a Python friendly help to the R help system, consider the module

Locate topics among available packages

>>> help_where = utils.help_search("help")

As before with help, the result can be printed / converted to a string, giving a similar result to what is obtained from an R session.


The data structure returned can otherwise be used to access the information returned in details.

>>> tuple(help_where)
(<StrVector - Python:0x1f9a968 / R:0x247f908>,
 <StrVector - Python:0x1f9a990 / R:0x25079d0>,
 <StrVector - Python:0x1f9a9b8 / R:0x247f928>,
 <Matrix - Python:0x1f9a850 / R:0x1ec0390>)
>>> tuple(help_where[3].colnames)
('topic', 'title', 'Package', 'LibPath')

However, this is beyond the scope of an introduction, and one should master the content of the module robjects.vector before anything else.


This section demonstrates some of the features of rpy2.

Graphics and plots

import rpy2.robjects as robjects

r = robjects.r

x = robjects.IntVector(range(10))
y = r.rnorm(10)


r.layout(r.matrix(robjects.IntVector([1,2,3,2]), nrow=2, ncol=2))
r.plot(r.runif(10), y, xlab="runif", ylab="foo/bar", col="red")

Setting dynamically the number of arguments in a function call can be done the usual way in python.

There are several ways to plot data in R, some of which are presented in this documentation:

The general setup is repeated here:

from rpy2 import robjects
from rpy2.robjects import Formula, Environment
from rpy2.robjects.vectors import IntVector, FloatVector
from rpy2.robjects.lib import grid
from rpy2.robjects.packages import importr, data
from rpy2.rinterface import RRuntimeError
import warnings

# The R 'print' function
rprint = robjects.globalenv.get("print")
stats = importr('stats')
grdevices = importr('grDevices')
base = importr('base')
datasets = importr('datasets')


The setup specific to ggplot2 is:

import math, datetime
import rpy2.robjects.lib.ggplot2 as ggplot2
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
base = importr('base')

mtcars = data(datasets).fetch('mtcars')['mtcars']
pp = ggplot2.ggplot(mtcars) + \
     ggplot2.aes_string(x='wt', y='mpg', col='factor(cyl)') + \
     ggplot2.geom_point() + \
     ggplot2.geom_smooth(ggplot2.aes_string(group = 'cyl'),
                         method = 'lm')

More about plots and graphics in R, as well as more advanced plots are presented in Section Graphics.


By default, the embedded R open an interactive plotting device, that is a window in which the plot is located. Processing interactive events on that devices, such as resizing or closing the window must be explicitly required (see Section Processing interactive events).

Linear models

The R code is:

ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)

anova(lm.D9 <- lm(weight ~ group))

summary(lm.D90 <- lm(weight ~ group - 1))# omitting intercept

One way to achieve the same with rpy2.robjects is

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

ctl = FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14])
trt = FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69])
group =, 10, 20, labels = ["Ctl","Trt"])
weight = ctl + trt

robjects.globalenv["weight"] = weight
robjects.globalenv["group"] = group
lm_D9 = stats.lm("weight ~ group")

# omitting the intercept
lm_D90 = stats.lm("weight ~ group - 1")

This way to perform a linear fit it matching precisely the way in R presented above, but there are other ways (see Section Formulae for storing the variables directly in the lookup environment of the formula).

Q: Now how to extract data from the resulting objects ?

A: Well, it all depends on the object. R is very much designed for interactive sessions, and users often inspect what a function is returning in order to know how to extract information.

When taking the results from the code above, one could go like:

>>> print(lm_D9.rclass)
[1] "lm"

Here the resulting object is a list structure, as either inspecting the data structure or reading the R man pages for lm would tell us. Checking its element names is then trivial:

>>> print(lm_D9.names)
 [1] "coefficients"  "residuals"     "effects"       "rank"
 [5] "fitted.values" "assign"        "qr"            "df.residual"
 [9] "contrasts"     "xlevels"       "call"          "terms"
[13] "model"

And so is extracting a particular element:

>>> print(lm_D9.rx2('coefficients'))
(Intercept)    groupTrt
      5.032      -0.371


>>> print(lm_D9.rx('coefficients'))
(Intercept)    groupTrt
      5.032      -0.371

More about extracting elements from vectors is available at Extracting items.

Principal component analysis

The R code is

m <- matrix(rnorm(100), ncol=5)
pca <- princomp(m)
plot(pca, main="Eigen values")
biplot(pca, main="biplot")

The rpy2.robjects code can be as close to the R code as possible:

import rpy2.robjects as robjects

r = robjects.r

m = r.matrix(r.rnorm(100), ncol=5)
pca = r.princomp(m)
r.plot(pca, main="Eigen values")
r.biplot(pca, main="biplot")

However, the same example can be made a little tidier (with respect to being specific about R functions used)

from rpy2.robjects.packages import importr

base     = importr('base')
stats    = importr('stats')
graphics = importr('graphics')

m = base.matrix(stats.rnorm(100), ncol = 5)
pca = stats.princomp(m)
graphics.plot(pca, main = "Eigen values")
stats.biplot(pca, main = "biplot")

Creating an R vector or matrix, and filling its cells using Python code

from rpy2.robjects import NA_Real
from rpy2.rlike.container import TaggedList
from rpy2.robjects.packages import importr

base = importr('base')

# create a numerical matrix of size 100x10 filled with NAs
m = base.matrix(NA_Real, nrow=100, ncol=10)

# fill the matrix
for row_i in xrange(1, 100+1):
    for col_i in xrange(1, 10+1):
        m.rx[TaggedList((row_i, ), (col_i, ))] = row_i + col_i * 100

One more example

short demo.


from rpy2.robjects.packages import importr
graphics = importr('graphics')
grdevices = importr('grDevices')
base = importr('base')
stats = importr('stats')

import array

x = array.array('i', range(10))
y = stats.rnorm(10)


graphics.par(mfrow = array.array('i', [2,2]))
graphics.plot(x, y, ylab = "foo/bar", col = "red")

kwargs = {'ylab':"foo/bar", 'type':"b", 'col':"blue", 'log':"x"}
graphics.plot(x, y, **kwargs)

m = base.matrix(stats.rnorm(100), ncol=5)
pca = stats.princomp(m)
graphics.plot(pca, main="Eigen values")
stats.biplot(pca, main="biplot")