Interfaces to R#

This is the reference to the Sagemath R interface, usable from any Sage program.

The %r interface creating an R cell in the sage notebook is decribed in the Notebook manual.

The %R and %%R interface creating an R line or an R cell in the Jupyter notebook are briefly decribed at the end of this page. This documentation will be expanded and placed in the Jupyter notebook manual when this manual exists.

The following examples try to follow “An Introduction to R” which can be found at http://cran.r-project.org/doc/manuals/R-intro.html .

EXAMPLES:

Simple manipulations; numbers and vectors

The simplest data structure in R is the numeric vector which consists of an ordered collection of numbers. To create a vector named \(x\) using the R interface in Sage, you pass the R interpreter object a list or tuple of numbers:

sage: x = r([10.4,5.6,3.1,6.4,21.7]); x
[1] 10.4  5.6  3.1  6.4 21.7

>>> from sage.all import *
>>> x = r([RealNumber('10.4'),RealNumber('5.6'),RealNumber('3.1'),RealNumber('6.4'),RealNumber('21.7')]); x
[1] 10.4  5.6  3.1  6.4 21.7

You can invert elements of a vector x in R by using the invert operator or by doing 1/x:

sage: ~x
[1] 0.09615385 0.17857143 0.32258065 0.15625000 0.04608295
sage: 1/x
[1] 0.09615385 0.17857143 0.32258065 0.15625000 0.04608295

>>> from sage.all import *
>>> ~x
[1] 0.09615385 0.17857143 0.32258065 0.15625000 0.04608295
>>> Integer(1)/x
[1] 0.09615385 0.17857143 0.32258065 0.15625000 0.04608295

The following assignment creates a vector \(y\) with 11 entries which consists of two copies of \(x\) with a 0 in between:

sage: y = r([x,0,x]); y
[1] 10.4  5.6  3.1  6.4 21.7  0.0 10.4  5.6  3.1  6.4 21.7

>>> from sage.all import *
>>> y = r([x,Integer(0),x]); y
[1] 10.4  5.6  3.1  6.4 21.7  0.0 10.4  5.6  3.1  6.4 21.7

Vector Arithmetic

The following command generates a new vector \(v\) of length 11 constructed by adding together (element by element) \(2x\) repeated 2.2 times, \(y\) repeated just once, and 1 repeated 11 times:

sage: v = 2*x+y+1; v
[1] 32.2 17.8 10.3 20.2 66.1 21.8 22.6 12.8 16.9 50.8 43.5

>>> from sage.all import *
>>> v = Integer(2)*x+y+Integer(1); v
[1] 32.2 17.8 10.3 20.2 66.1 21.8 22.6 12.8 16.9 50.8 43.5

One can compute the sum of the elements of an R vector in the following two ways:

sage: sum(x)
[1] 47.2
sage: x.sum()
[1] 47.2

>>> from sage.all import *
>>> sum(x)
[1] 47.2
>>> x.sum()
[1] 47.2

One can calculate the sample variance of a list of numbers:

sage: ((x-x.mean())^2/(x.length()-1)).sum()
[1] 53.853
sage: x.var()
[1] 53.853

sage: x.sort()
[1] 3.1  5.6  6.4 10.4 21.7
sage: x.min()
[1] 3.1
sage: x.max()
[1] 21.7
sage: x
[1] 10.4  5.6  3.1  6.4 21.7

sage: r(-17).sqrt()
[1] NaN
sage: r('-17+0i').sqrt()
[1] 0+4.123106i

>>> from sage.all import *
>>> ((x-x.mean())**Integer(2)/(x.length()-Integer(1))).sum()
[1] 53.853
>>> x.var()
[1] 53.853

>>> x.sort()
[1] 3.1  5.6  6.4 10.4 21.7
>>> x.min()
[1] 3.1
>>> x.max()
[1] 21.7
>>> x
[1] 10.4  5.6  3.1  6.4 21.7

>>> r(-Integer(17)).sqrt()
[1] NaN
>>> r('-17+0i').sqrt()
[1] 0+4.123106i

Generating an arithmetic sequence:

sage: r('1:10')
[1] 1  2  3  4  5  6  7  8  9 10

>>> from sage.all import *
>>> r('1:10')
[1] 1  2  3  4  5  6  7  8  9 10

Because from is a keyword in Python, it can’t be used as a keyword argument. Instead, from_ can be passed, and R will recognize it as the correct thing:

sage: r.seq(length=10, from_=-1, by=.2)
[1] -1.0 -0.8 -0.6 -0.4 -0.2  0.0  0.2  0.4  0.6  0.8

sage: x = r([10.4,5.6,3.1,6.4,21.7])
sage: x.rep(2)
[1] 10.4  5.6  3.1  6.4 21.7 10.4  5.6  3.1  6.4 21.7
sage: x.rep(times=2)
[1] 10.4  5.6  3.1  6.4 21.7 10.4  5.6  3.1  6.4 21.7
sage: x.rep(each=2)
[1] 10.4 10.4  5.6  5.6  3.1  3.1  6.4  6.4 21.7 21.7

>>> from sage.all import *
>>> r.seq(length=Integer(10), from_=-Integer(1), by=RealNumber('.2'))
[1] -1.0 -0.8 -0.6 -0.4 -0.2  0.0  0.2  0.4  0.6  0.8

>>> x = r([RealNumber('10.4'),RealNumber('5.6'),RealNumber('3.1'),RealNumber('6.4'),RealNumber('21.7')])
>>> x.rep(Integer(2))
[1] 10.4  5.6  3.1  6.4 21.7 10.4  5.6  3.1  6.4 21.7
>>> x.rep(times=Integer(2))
[1] 10.4  5.6  3.1  6.4 21.7 10.4  5.6  3.1  6.4 21.7
>>> x.rep(each=Integer(2))
[1] 10.4 10.4  5.6  5.6  3.1  3.1  6.4  6.4 21.7 21.7

Missing Values:

sage: na = r('NA')
sage: z = r([1,2,3,na])
sage: z
[1]  1  2  3 NA
sage: ind = r.is_na(z)
sage: ind
[1] FALSE FALSE FALSE  TRUE
sage: zero = r(0)
sage: zero / zero
[1] NaN
sage: inf = r('Inf')
sage: inf-inf
[1] NaN
sage: r.is_na(inf)
[1] FALSE
sage: r.is_na(inf-inf)
[1] TRUE
sage: r.is_na(zero/zero)
[1] TRUE
sage: r.is_na(na)
[1] TRUE
sage: r.is_nan(inf-inf)
[1] TRUE
sage: r.is_nan(zero/zero)
[1] TRUE
sage: r.is_nan(na)
[1] FALSE

>>> from sage.all import *
>>> na = r('NA')
>>> z = r([Integer(1),Integer(2),Integer(3),na])
>>> z
[1]  1  2  3 NA
>>> ind = r.is_na(z)
>>> ind
[1] FALSE FALSE FALSE  TRUE
>>> zero = r(Integer(0))
>>> zero / zero
[1] NaN
>>> inf = r('Inf')
>>> inf-inf
[1] NaN
>>> r.is_na(inf)
[1] FALSE
>>> r.is_na(inf-inf)
[1] TRUE
>>> r.is_na(zero/zero)
[1] TRUE
>>> r.is_na(na)
[1] TRUE
>>> r.is_nan(inf-inf)
[1] TRUE
>>> r.is_nan(zero/zero)
[1] TRUE
>>> r.is_nan(na)
[1] FALSE

Character Vectors:

sage: labs = r.paste('c("X","Y")', '1:10', sep='""'); labs
[1] "X1"  "Y2"  "X3"  "Y4"  "X5"  "Y6"  "X7"  "Y8"  "X9"  "Y10"

>>> from sage.all import *
>>> labs = r.paste('c("X","Y")', '1:10', sep='""'); labs
[1] "X1"  "Y2"  "X3"  "Y4"  "X5"  "Y6"  "X7"  "Y8"  "X9"  "Y10"

Index vectors; selecting and modifying subsets of a data set:

sage: na = r('NA')
sage: x = r([10.4,5.6,3.1,6.4,21.7,na]); x
[1] 10.4  5.6  3.1  6.4 21.7   NA
sage: x['!is.na(self)']
[1] 10.4  5.6  3.1  6.4 21.7

sage: x = r([10.4,5.6,3.1,6.4,21.7,na]); x
[1] 10.4  5.6  3.1  6.4 21.7   NA
sage: (x+1)['(!is.na(self)) & self>0']
[1] 11.4  6.6  4.1  7.4 22.7
sage: x = r([10.4,-2,3.1,-0.5,21.7,na]); x
[1] 10.4 -2.0  3.1 -0.5 21.7   NA
sage: (x+1)['(!is.na(self)) & self>0']
[1] 11.4  4.1  0.5 22.7

>>> from sage.all import *
>>> na = r('NA')
>>> x = r([RealNumber('10.4'),RealNumber('5.6'),RealNumber('3.1'),RealNumber('6.4'),RealNumber('21.7'),na]); x
[1] 10.4  5.6  3.1  6.4 21.7   NA
>>> x['!is.na(self)']
[1] 10.4  5.6  3.1  6.4 21.7

>>> x = r([RealNumber('10.4'),RealNumber('5.6'),RealNumber('3.1'),RealNumber('6.4'),RealNumber('21.7'),na]); x
[1] 10.4  5.6  3.1  6.4 21.7   NA
>>> (x+Integer(1))['(!is.na(self)) & self>0']
[1] 11.4  6.6  4.1  7.4 22.7
>>> x = r([RealNumber('10.4'),-Integer(2),RealNumber('3.1'),-RealNumber('0.5'),RealNumber('21.7'),na]); x
[1] 10.4 -2.0  3.1 -0.5 21.7   NA
>>> (x+Integer(1))['(!is.na(self)) & self>0']
[1] 11.4  4.1  0.5 22.7

Distributions:

sage: r.options(width="60")
$width
[1] 80

sage: rr = r.dnorm(r.seq(-3,3,0.1))
sage: rr
 [1] 0.004431848 0.005952532 0.007915452 0.010420935
 [5] 0.013582969 0.017528300 0.022394530 0.028327038
 [9] 0.035474593 0.043983596 0.053990967 0.065615815
[13] 0.078950158 0.094049077 0.110920835 0.129517596
[17] 0.149727466 0.171368592 0.194186055 0.217852177
[21] 0.241970725 0.266085250 0.289691553 0.312253933
[25] 0.333224603 0.352065327 0.368270140 0.381387815
[29] 0.391042694 0.396952547 0.398942280 0.396952547
[33] 0.391042694 0.381387815 0.368270140 0.352065327
[37] 0.333224603 0.312253933 0.289691553 0.266085250
[41] 0.241970725 0.217852177 0.194186055 0.171368592
[45] 0.149727466 0.129517596 0.110920835 0.094049077
[49] 0.078950158 0.065615815 0.053990967 0.043983596
[53] 0.035474593 0.028327038 0.022394530 0.017528300
[57] 0.013582969 0.010420935 0.007915452 0.005952532
[61] 0.004431848

>>> from sage.all import *
>>> r.options(width="60")
$width
[1] 80

>>> rr = r.dnorm(r.seq(-Integer(3),Integer(3),RealNumber('0.1')))
>>> rr
 [1] 0.004431848 0.005952532 0.007915452 0.010420935
 [5] 0.013582969 0.017528300 0.022394530 0.028327038
 [9] 0.035474593 0.043983596 0.053990967 0.065615815
[13] 0.078950158 0.094049077 0.110920835 0.129517596
[17] 0.149727466 0.171368592 0.194186055 0.217852177
[21] 0.241970725 0.266085250 0.289691553 0.312253933
[25] 0.333224603 0.352065327 0.368270140 0.381387815
[29] 0.391042694 0.396952547 0.398942280 0.396952547
[33] 0.391042694 0.381387815 0.368270140 0.352065327
[37] 0.333224603 0.312253933 0.289691553 0.266085250
[41] 0.241970725 0.217852177 0.194186055 0.171368592
[45] 0.149727466 0.129517596 0.110920835 0.094049077
[49] 0.078950158 0.065615815 0.053990967 0.043983596
[53] 0.035474593 0.028327038 0.022394530 0.017528300
[57] 0.013582969 0.010420935 0.007915452 0.005952532
[61] 0.004431848

Convert R Data Structures to Python/Sage:

sage: rr = r.dnorm(r.seq(-3,3,0.1))
sage: sum(rr._sage_())
9.9772125168981...

>>> from sage.all import *
>>> rr = r.dnorm(r.seq(-Integer(3),Integer(3),RealNumber('0.1')))
>>> sum(rr._sage_())
9.9772125168981...

Or you get a dictionary to be able to access all the information:

sage: rs = r.summary(r.c(1,4,3,4,3,2,5,1))
sage: rs
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
  1.000   1.750   3.000   2.875   4.000   5.000
  sage: d = rs._sage_()
  sage: d['DATA']
  [1, 1.75, 3, 2.875, 4, 5]
  sage: d['_Names']
  ['Min.', '1st Qu.', 'Median', 'Mean', '3rd Qu.', 'Max.']
  sage: d['_r_class']
  ['summaryDefault', 'table']

>>> from sage.all import *
>>> rs = r.summary(r.c(Integer(1),Integer(4),Integer(3),Integer(4),Integer(3),Integer(2),Integer(5),Integer(1)))
>>> rs
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
  1.000   1.750   3.000   2.875   4.000   5.000
>>> d = rs._sage_()
>>> d['DATA']
  [1, 1.75, 3, 2.875, 4, 5]
>>> d['_Names']
  ['Min.', '1st Qu.', 'Median', 'Mean', '3rd Qu.', 'Max.']
>>> d['_r_class']
  ['summaryDefault', 'table']

It is also possible to access the plotting capabilities of R through Sage. For more information see the documentation of r.plot() or r.png().

THE JUPYTER NOTEBOOK INTERFACE (work in progress).

The %r interface described in the Sage notebook manual is not useful in the Jupyter notebook : it creates a inferior R interpreter which cannot be escaped.

The RPy2 library allows the creation of an R cell in the Jupyter notebook analogous to the %r escape in command line or %r cell in a Sage notebook.

The interface is loaded by a cell containing the sole code:

“%load_ext rpy2.ipython”

After execution of this code, the %R and %%R magics are available:

  • %R allows the execution of a single line of R code. Data exchange is

    possible via the -i and -o options. Do “%R?” in a standalone cell to get the documentation.

  • %%R allows the execution in R of the whole text of a cell, with

    similar options (do “%%R?” in a standalone cell for documentation).

A few important points must be noted:

  • The R interpreter launched by this interface IS (currently) DIFFERENT from the R interpreter used br other r… functions.

  • Data exchanged via the -i and -o options have a format DIFFERENT from the format used by the r… functions (RPy2 mostly uses arrays, and bugs the user to use the pandas Python package).

  • R graphics are (beautifully) displayed in output cells, but are not directly importable. You have to save them as .png, .pdf or .svg files and import them in Sage for further use.

In its current incarnation, this interface is mostly useful to statisticians needing Sage for a few symbolic computations but mostly using R for applied work.

AUTHORS:

  • Mike Hansen (2007-11-01)

  • William Stein (2008-04-19)

  • Harald Schilly (2008-03-20)

  • Mike Hansen (2008-04-19)

  • Emmanuel Charpentier (2015-12-12, RPy2 interface)

class sage.interfaces.r.HelpExpression[source]#

Bases: str

Used to improve printing of output of r.help.

class sage.interfaces.r.R(maxread=None, logfile=None, init_list_length=1024, seed=None)[source]#

Bases: ExtraTabCompletion, Interface

An interface to the R interpreter.

R is a comprehensive collection of methods for statistics, modelling, bioinformatics, data analysis and much more. For more details, see http://www.r-project.org/about.html

Resources:

EXAMPLES:

sage: r.summary(r.c(1,2,3,111,2,3,2,3,2,5,4))
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
1.00    2.00    3.00   12.55    3.50  111.00

>>> from sage.all import *
>>> r.summary(r.c(Integer(1),Integer(2),Integer(3),Integer(111),Integer(2),Integer(3),Integer(2),Integer(3),Integer(2),Integer(5),Integer(4)))
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
1.00    2.00    3.00   12.55    3.50  111.00
available_packages()[source]#

Returns a list of all available R package names.

This list is not necessarily sorted.

OUTPUT: list of strings

Note

This requires an internet connection. The CRAN server is that is checked is defined at the top of sage/interfaces/r.py.

EXAMPLES:

sage: ap = r.available_packages()                   # optional - internet
sage: len(ap) > 20                                  # optional - internet
True

>>> from sage.all import *
>>> ap = r.available_packages()                   # optional - internet
>>> len(ap) > Integer(20)                                  # optional - internet
True
call(function_name, *args, **kwds)[source]#

This is an alias for function_call().

EXAMPLES:

sage: r.call('length', [1,2,3])
[1] 3

>>> from sage.all import *
>>> r.call('length', [Integer(1),Integer(2),Integer(3)])
[1] 3
chdir(dir)[source]#

Changes the working directory to dir

INPUT:

  • dir – the directory to change to.

EXAMPLES:

sage: import tempfile
sage: tmpdir = tempfile.mkdtemp()
sage: r.chdir(tmpdir)

>>> from sage.all import *
>>> import tempfile
>>> tmpdir = tempfile.mkdtemp()
>>> r.chdir(tmpdir)

Check that tmpdir and r.getwd() refer to the same directory. We need to use realpath() in case $TMPDIR (by default /tmp) is a symbolic link (see Issue #10264).

sage: os.path.realpath(tmpdir) == sageobj(r.getwd())  # known bug (issue #9970)
True

>>> from sage.all import *
>>> os.path.realpath(tmpdir) == sageobj(r.getwd())  # known bug (issue #9970)
True
completions(s)[source]#

Return all commands names that complete the command starting with the string s. This is like typing s[Ctrl-T] in the R interpreter.

INPUT:

  • s – string

OUTPUT: list – a list of strings

EXAMPLES:

sage: dummy = r._tab_completion(use_disk_cache=False)  # clean doctest
sage: 'testInheritedMethods' in r.completions('tes')
True

>>> from sage.all import *
>>> dummy = r._tab_completion(use_disk_cache=False)  # clean doctest
>>> 'testInheritedMethods' in r.completions('tes')
True
console()[source]#

Runs the R console as a separate new R process.

EXAMPLES:

sage: r.console()                    # not tested
    R version 2.6.1 (2007-11-26)
    Copyright (C) 2007 The R Foundation for Statistical Computing
    ISBN 3-900051-07-0
    ...

>>> from sage.all import *
>>> r.console()                    # not tested
    R version 2.6.1 (2007-11-26)
    Copyright (C) 2007 The R Foundation for Statistical Computing
    ISBN 3-900051-07-0
    ...
convert_r_list(l)[source]#

Converts an R list to a Python list.

EXAMPLES:

sage: s = 'c(".GlobalEnv", "package:stats", "package:graphics", "package:grDevices", \n"package:utils", "package:datasets", "package:methods", "Autoloads", \n"package:base")'
sage: r.convert_r_list(s)
['.GlobalEnv',
 'package:stats',
 'package:graphics',
 'package:grDevices',
 'package:utils',
 'package:datasets',
 'package:methods',
 'Autoloads',
 'package:base']

>>> from sage.all import *
>>> s = 'c(".GlobalEnv", "package:stats", "package:graphics", "package:grDevices", \n"package:utils", "package:datasets", "package:methods", "Autoloads", \n"package:base")'
>>> r.convert_r_list(s)
['.GlobalEnv',
 'package:stats',
 'package:graphics',
 'package:grDevices',
 'package:utils',
 'package:datasets',
 'package:methods',
 'Autoloads',
 'package:base']
eval(code, *args, **kwds)[source]#

Evaluates a command inside the R interpreter and returns the output as a string.

EXAMPLES:

sage: r.eval('1+1')
'[1] 2'

>>> from sage.all import *
>>> r.eval('1+1')
'[1] 2'
function_call(function, args=None, kwds=None)[source]#

Return the result of calling an R function, with given args and keyword args.

OUTPUT: RElement – an object in R

EXAMPLES:

sage: r.function_call('length', args=[ [1,2,3] ])
[1] 3

>>> from sage.all import *
>>> r.function_call('length', args=[ [Integer(1),Integer(2),Integer(3)] ])
[1] 3
get(var)[source]#

Returns the string representation of the variable var.

INPUT:

  • var – a string

OUTPUT: string

EXAMPLES:

sage: r.set('a', 2)
sage: r.get('a')
'[1] 2'

>>> from sage.all import *
>>> r.set('a', Integer(2))
>>> r.get('a')
'[1] 2'
help(command)[source]#

Returns help string for a given command.

INPUT: - command – a string

OUTPUT: HelpExpression – a subclass of string whose __repr__ method is __str__, so it prints nicely

EXAMPLES:

sage: r.help('c')
title
-----

Combine Values into a Vector or List

name
----

c
...

>>> from sage.all import *
>>> r.help('c')
title
-----
<BLANKLINE>
Combine Values into a Vector or List
<BLANKLINE>
name
----
<BLANKLINE>
c
...
install_packages(package_name)[source]#

Install an R package into Sage’s R installation.

EXAMPLES:

sage: r.install_packages('aaMI')       # not tested
...
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
...
Please restart Sage in order to use 'aaMI'.

>>> from sage.all import *
>>> r.install_packages('aaMI')       # not tested
...
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
...
Please restart Sage in order to use 'aaMI'.
library(library_name)[source]#

Load the library library_name into the R interpreter.

This function raises an ImportError if the given library is not known.

INPUT:

  • library_name – string

EXAMPLES:

sage: r.library('grid')
sage: 'grid' in r.eval('(.packages())')
True
sage: r.library('foobar')
Traceback (most recent call last):
...
ImportError: ...

>>> from sage.all import *
>>> r.library('grid')
>>> 'grid' in r.eval('(.packages())')
True
>>> r.library('foobar')
Traceback (most recent call last):
...
ImportError: ...
na()[source]#

Returns the NA in R.

OUTPUT: RElement – an element of R

EXAMPLES:

sage: r.na()
[1] NA

>>> from sage.all import *
>>> r.na()
[1] NA
plot(*args, **kwds)[source]#

The R plot function. Type r.help(‘plot’) for much more extensive documentation about this function. See also below for a brief introduction to more plotting with R.

If one simply wants to view an R graphic, using this function is is sufficient (because it calls dev.off() to turn off the device).

However, if one wants to save the graphic to a specific file, it should be used as in the example below to write the output.

EXAMPLES:

This example saves a plot to the standard R output, usually a filename like Rplot001.png - from the command line, in the current directory, and in the cell directory in the notebook. We use a temporary directory in this example while doctesting this example, but you should use something persistent in your own code:

sage: from tempfile import TemporaryDirectory
sage: with TemporaryDirectory() as d:               # optional - rgraphics
....:     _ = r.setwd(d)
....:     r.plot("1:10")
null device
          1

>>> from sage.all import *
>>> from tempfile import TemporaryDirectory
>>> with TemporaryDirectory() as d:               # optional - rgraphics
...     _ = r.setwd(d)
...     r.plot("1:10")
null device
          1

To save to a specific file name, one should use png() to set the output device to that file. If this is done in the notebook, it must be done in the same cell as the plot itself:

sage: filename = tmp_filename() + '.png'
sage: r.png(filename='"%s"'%filename)               # optional - rgraphics
NULL
sage: x = r([1,2,3])
sage: y = r([4,5,6])
sage: r.plot(x,y)                                   # optional - rgraphics
null device
          1
sage: import os; os.unlink(filename)                # optional - rgraphics

>>> from sage.all import *
>>> filename = tmp_filename() + '.png'
>>> r.png(filename='"%s"'%filename)               # optional - rgraphics
NULL
>>> x = r([Integer(1),Integer(2),Integer(3)])
>>> y = r([Integer(4),Integer(5),Integer(6)])
>>> r.plot(x,y)                                   # optional - rgraphics
null device
          1
>>> import os; os.unlink(filename)                # optional - rgraphics

Please note that for more extensive use of R’s plotting capabilities (such as the lattices package), it is advisable to either use an interactive plotting device or to use the notebook. The following examples are not tested, because they differ depending on operating system:

sage: # not tested
sage: r.X11()
sage: r.quartz()
sage: r.hist("rnorm(100)")
sage: r.library("lattice")
sage: r.histogram(x = "~ wt | cyl", data="mtcars")
sage: r.dev_off()

>>> from sage.all import *
>>> # not tested
>>> r.X11()
>>> r.quartz()
>>> r.hist("rnorm(100)")
>>> r.library("lattice")
>>> r.histogram(x = "~ wt | cyl", data="mtcars")
>>> r.dev_off()

In the notebook, one can use r.png() to open the device, but would need to use the following since R lattice graphics do not automatically print away from the command line:

sage: filename = tmp_filename() + '.png'  # not needed in notebook, used for doctesting
sage: r.png(filename='"%s"'%filename)               # optional - rgraphics
NULL
sage: r.library("lattice")
sage: r("print(histogram(~wt | cyl, data=mtcars))")         # optional - rgraphics
sage: import os; os.unlink(filename)                # optional - rgraphics

>>> from sage.all import *
>>> filename = tmp_filename() + '.png'  # not needed in notebook, used for doctesting
>>> r.png(filename='"%s"'%filename)               # optional - rgraphics
NULL
>>> r.library("lattice")
>>> r("print(histogram(~wt | cyl, data=mtcars))")         # optional - rgraphics
>>> import os; os.unlink(filename)                # optional - rgraphics
png(*args, **kwds)[source]#

Creates an R PNG device.

This should primarily be used to save an R graphic to a custom file. Note that when using this in the notebook, one must plot in the same cell that one creates the device. See r.plot() documentation for more information about plotting via R in Sage.

These examples won’t work on the many platforms where R still gets built without graphics support.

EXAMPLES:

sage: filename = tmp_filename() + '.png'
sage: r.png(filename='"%s"'%filename)               # optional - rgraphics
NULL
sage: x = r([1,2,3])
sage: y = r([4,5,6])
sage: r.plot(x,y)                                   # optional - rgraphics
null device
          1
sage: import os; os.unlink(filename)                # optional - rgraphics

>>> from sage.all import *
>>> filename = tmp_filename() + '.png'
>>> r.png(filename='"%s"'%filename)               # optional - rgraphics
NULL
>>> x = r([Integer(1),Integer(2),Integer(3)])
>>> y = r([Integer(4),Integer(5),Integer(6)])
>>> r.plot(x,y)                                   # optional - rgraphics
null device
          1
>>> import os; os.unlink(filename)                # optional - rgraphics

We want to make sure that we actually can view R graphics, which happens differently on different platforms:

sage: s = r.eval('capabilities("png")')   # should be on Linux and Solaris
sage: t = r.eval('capabilities("aqua")')  # should be on all supported Mac versions
sage: "TRUE" in s+t                                 # optional - rgraphics
True

>>> from sage.all import *
>>> s = r.eval('capabilities("png")')   # should be on Linux and Solaris
>>> t = r.eval('capabilities("aqua")')  # should be on all supported Mac versions
>>> "TRUE" in s+t                                 # optional - rgraphics
True
read(filename)[source]#

Read filename into the R interpreter by calling R’s source function on a read-only file connection.

EXAMPLES:

sage: filename = tmp_filename()
sage: f = open(filename, 'w')
sage: _ = f.write('a <- 2+2\n')
sage: f.close()
sage: r.read(filename)
sage: r.get('a')
'[1] 4'

>>> from sage.all import *
>>> filename = tmp_filename()
>>> f = open(filename, 'w')
>>> _ = f.write('a <- 2+2\n')
>>> f.close()
>>> r.read(filename)
>>> r.get('a')
'[1] 4'
require(library_name)[source]#

Load the library library_name into the R interpreter.

This function raises an ImportError if the given library is not known.

INPUT:

  • library_name – string

EXAMPLES:

sage: r.library('grid')
sage: 'grid' in r.eval('(.packages())')
True
sage: r.library('foobar')
Traceback (most recent call last):
...
ImportError: ...

>>> from sage.all import *
>>> r.library('grid')
>>> 'grid' in r.eval('(.packages())')
True
>>> r.library('foobar')
Traceback (most recent call last):
...
ImportError: ...
set(var, value)[source]#

Set the variable var in R to what the string value evaluates to in R.

INPUT:

  • var – a string

  • value – a string

EXAMPLES:

sage: r.set('a', '2 + 3')
sage: r.get('a')
'[1] 5'

>>> from sage.all import *
>>> r.set('a', '2 + 3')
>>> r.get('a')
'[1] 5'
set_seed(seed=None)[source]#

Set the seed for R interpreter.

The seed should be an integer.

EXAMPLES:

sage: r = R()
sage: r.set_seed(1)
1
sage: r.sample("1:10", 5) # random
[1] 3 4 5 7 2

>>> from sage.all import *
>>> r = R()
>>> r.set_seed(Integer(1))
1
>>> r.sample("1:10", Integer(5)) # random
[1] 3 4 5 7 2
source(s)[source]#

Display the R source (if possible) about the function named s.

INPUT:

  • s – a string representing the function whose source code you want to see

OUTPUT: string – source code

EXAMPLES:

sage: print(r.source("c"))
function (...)  .Primitive("c")

>>> from sage.all import *
>>> print(r.source("c"))
function (...)  .Primitive("c")
version()[source]#

Return the version of R currently running.

OUTPUT: tuple of ints; string

EXAMPLES:

sage: r.version()                   # not tested
((3, 0, 1), 'R version 3.0.1 (2013-05-16)')
sage: rint, rstr = r.version()
sage: rint[0] >= 3
True
sage: rstr.startswith('R version')
True

>>> from sage.all import *
>>> r.version()                   # not tested
((3, 0, 1), 'R version 3.0.1 (2013-05-16)')
>>> rint, rstr = r.version()
>>> rint[Integer(0)] >= Integer(3)
True
>>> rstr.startswith('R version')
True
class sage.interfaces.r.RElement(parent, value, is_name=False, name=None)[source]#

Bases: ExtraTabCompletion, InterfaceElement

dot_product(other)[source]#

Implements the notation self . other.

INPUT:

  • self, other – R elements

OUTPUT: R element

EXAMPLES:

sage: c = r.c(1,2,3,4)
sage: c.dot_product(c.t())
     [,1] [,2] [,3] [,4]
[1,]    1    2    3    4
[2,]    2    4    6    8
[3,]    3    6    9   12
[4,]    4    8   12   16

sage: v = r([3,-1,8])
sage: v.dot_product(v)
     [,1]
[1,]   74

>>> from sage.all import *
>>> c = r.c(Integer(1),Integer(2),Integer(3),Integer(4))
>>> c.dot_product(c.t())
     [,1] [,2] [,3] [,4]
[1,]    1    2    3    4
[2,]    2    4    6    8
[3,]    3    6    9   12
[4,]    4    8   12   16

>>> v = r([Integer(3),-Integer(1),Integer(8)])
>>> v.dot_product(v)
     [,1]
[1,]   74
is_string()[source]#

Tell whether this element is a string.

EXAMPLES:

sage: r('"abc"').is_string()
True
sage: r([1,2,3]).is_string()
False

>>> from sage.all import *
>>> r('"abc"').is_string()
True
>>> r([Integer(1),Integer(2),Integer(3)]).is_string()
False
stat_model(x)[source]#

The tilde regression operator in R.

EXAMPLES:

sage: x = r([1,2,3,4,5])
sage: y = r([3,5,7,9,11])
sage: a = r.lm( y.tilde(x) ) # lm( y ~ x )
sage: d = a._sage_()
sage: d['DATA']['coefficients']['DATA'][1]
2

>>> from sage.all import *
>>> x = r([Integer(1),Integer(2),Integer(3),Integer(4),Integer(5)])
>>> y = r([Integer(3),Integer(5),Integer(7),Integer(9),Integer(11)])
>>> a = r.lm( y.tilde(x) ) # lm( y ~ x )
>>> d = a._sage_()
>>> d['DATA']['coefficients']['DATA'][Integer(1)]
2
tilde(x)[source]#

The tilde regression operator in R.

EXAMPLES:

sage: x = r([1,2,3,4,5])
sage: y = r([3,5,7,9,11])
sage: a = r.lm( y.tilde(x) ) # lm( y ~ x )
sage: d = a._sage_()
sage: d['DATA']['coefficients']['DATA'][1]
2

>>> from sage.all import *
>>> x = r([Integer(1),Integer(2),Integer(3),Integer(4),Integer(5)])
>>> y = r([Integer(3),Integer(5),Integer(7),Integer(9),Integer(11)])
>>> a = r.lm( y.tilde(x) ) # lm( y ~ x )
>>> d = a._sage_()
>>> d['DATA']['coefficients']['DATA'][Integer(1)]
2
class sage.interfaces.r.RFunction(parent, name, r_name=None)[source]#

Bases: InterfaceFunction

A Function in the R interface.

INPUT:

  • parent – the R interface

  • name – the name of the function for Python

  • r_name – the name of the function in R itself (which can have dots in it)

EXAMPLES:

sage: length = r.length
sage: type(length)
<class 'sage.interfaces.r.RFunction'>
sage: loads(dumps(length))
length

>>> from sage.all import *
>>> length = r.length
>>> type(length)
<class 'sage.interfaces.r.RFunction'>
>>> loads(dumps(length))
length
class sage.interfaces.r.RFunctionElement(obj, name)[source]#

Bases: InterfaceFunctionElement

sage.interfaces.r.is_RElement(x)[source]#

Return True if x is an element in an R interface.

INPUT:

  • x – object

OUTPUT: bool

EXAMPLES:

sage: from sage.interfaces.r import is_RElement
sage: is_RElement(2)
doctest:...: DeprecationWarning: the function is_RElement is deprecated; use isinstance(x, sage.interfaces.abc.RElement) instead
See https://github.com/sagemath/sage/issues/34804 for details.
False
sage: is_RElement(r(2))
True

>>> from sage.all import *
>>> from sage.interfaces.r import is_RElement
>>> is_RElement(Integer(2))
doctest:...: DeprecationWarning: the function is_RElement is deprecated; use isinstance(x, sage.interfaces.abc.RElement) instead
See https://github.com/sagemath/sage/issues/34804 for details.
False
>>> is_RElement(r(Integer(2)))
True
sage.interfaces.r.r_console()[source]#

Spawn a new R command-line session.

EXAMPLES:

sage: r.console()                    # not tested
    R version 2.6.1 (2007-11-26)
    Copyright (C) 2007 The R Foundation for Statistical Computing
    ISBN 3-900051-07-0
    ...

>>> from sage.all import *
>>> r.console()                    # not tested
    R version 2.6.1 (2007-11-26)
    Copyright (C) 2007 The R Foundation for Statistical Computing
    ISBN 3-900051-07-0
    ...
sage.interfaces.r.r_version()[source]#

Return the R version.

EXAMPLES:

sage: r_version()                       # not tested
((3, 0, 1), 'R version 3.0.1 (2013-05-16)')
sage: rint, rstr = r_version()
sage: rint[0] >= 3
True
sage: rstr.startswith('R version')
True

>>> from sage.all import *
>>> r_version()                       # not tested
((3, 0, 1), 'R version 3.0.1 (2013-05-16)')
>>> rint, rstr = r_version()
>>> rint[Integer(0)] >= Integer(3)
True
>>> rstr.startswith('R version')
True
sage.interfaces.r.reduce_load_R()[source]#

Used for reconstructing a copy of the R interpreter from a pickle.

EXAMPLES:

sage: from sage.interfaces.r import reduce_load_R
sage: reduce_load_R()
R Interpreter

>>> from sage.all import *
>>> from sage.interfaces.r import reduce_load_R
>>> reduce_load_R()
R Interpreter