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# -*- coding: utf-8 -*-
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"""
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======
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Rmagic
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======
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Magic command interface for interactive work with R via rpy2
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.. note::
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The ``rpy2`` package needs to be installed separately. It
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can be obtained using ``easy_install`` or ``pip``.
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You will also need a working copy of R.
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Usage
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=====
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To enable the magics below, execute ``%load_ext rmagic``.
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``%R``
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{R_DOC}
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``%Rpush``
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{RPUSH_DOC}
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``%Rpull``
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{RPULL_DOC}
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``%Rget``
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{RGET_DOC}
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"""
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#-----------------------------------------------------------------------------
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# Copyright (C) 2012 The IPython Development Team
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#
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# Distributed under the terms of the BSD License. The full license is in
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# the file COPYING, distributed as part of this software.
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#-----------------------------------------------------------------------------
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import sys
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import tempfile
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from glob import glob
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from shutil import rmtree
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# numpy and rpy2 imports
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import numpy as np
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import rpy2.rinterface as ri
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import rpy2.robjects as ro
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try:
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from rpy2.robjects import pandas2ri
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pandas2ri.activate()
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except ImportError:
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pandas2ri = None
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from rpy2.robjects import numpy2ri
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numpy2ri.activate()
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# IPython imports
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from IPython.core.displaypub import publish_display_data
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from IPython.core.magic import (Magics, magics_class, line_magic,
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line_cell_magic, needs_local_scope)
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from IPython.testing.skipdoctest import skip_doctest
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from IPython.core.magic_arguments import (
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argument, magic_arguments, parse_argstring
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)
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from IPython.external.simplegeneric import generic
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from IPython.utils.py3compat import str_to_unicode, unicode_to_str, PY3
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class RInterpreterError(ri.RRuntimeError):
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"""An error when running R code in a %%R magic cell."""
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def __init__(self, line, err, stdout):
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self.line = line
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self.err = err.rstrip()
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self.stdout = stdout.rstrip()
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def __unicode__(self):
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s = 'Failed to parse and evaluate line %r.\nR error message: %r' % \
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(self.line, self.err)
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if self.stdout and (self.stdout != self.err):
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s += '\nR stdout:\n' + self.stdout
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return s
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if PY3:
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__str__ = __unicode__
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else:
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def __str__(self):
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return unicode_to_str(unicode(self), 'utf-8')
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def Rconverter(Robj, dataframe=False):
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"""
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Convert an object in R's namespace to one suitable
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for ipython's namespace.
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For a data.frame, it tries to return a structured array.
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It first checks for colnames, then names.
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If all are NULL, it returns np.asarray(Robj), else
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it tries to construct a recarray
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Parameters
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----------
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Robj: an R object returned from rpy2
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"""
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is_data_frame = ro.r('is.data.frame')
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colnames = ro.r('colnames')
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rownames = ro.r('rownames') # with pandas, these could be used for the index
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names = ro.r('names')
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if dataframe:
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as_data_frame = ro.r('as.data.frame')
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cols = colnames(Robj)
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_names = names(Robj)
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if cols != ri.NULL:
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Robj = as_data_frame(Robj)
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names = tuple(np.array(cols))
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elif _names != ri.NULL:
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names = tuple(np.array(_names))
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else: # failed to find names
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return np.asarray(Robj)
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Robj = np.rec.fromarrays(Robj, names = names)
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return np.asarray(Robj)
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@generic
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def pyconverter(pyobj):
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"""Convert Python objects to R objects. Add types using the decorator:
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@pyconverter.when_type
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"""
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return pyobj
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# The default conversion for lists seems to make them a nested list. That has
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# some advantages, but is rarely convenient, so for interactive use, we convert
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# lists to a numpy array, which becomes an R vector.
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@pyconverter.when_type(list)
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def pyconverter_list(pyobj):
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return np.asarray(pyobj)
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if pandas2ri is None:
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# pandas2ri was new in rpy2 2.3.3, so for now we'll fallback to pandas'
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# conversion function.
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try:
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from pandas import DataFrame
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from pandas.rpy.common import convert_to_r_dataframe
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@pyconverter.when_type(DataFrame)
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def pyconverter_dataframe(pyobj):
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return convert_to_r_dataframe(pyobj, strings_as_factors=True)
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except ImportError:
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pass
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@magics_class
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class RMagics(Magics):
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"""A set of magics useful for interactive work with R via rpy2.
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"""
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def __init__(self, shell, Rconverter=Rconverter,
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pyconverter=pyconverter,
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cache_display_data=False):
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"""
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Parameters
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----------
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shell : IPython shell
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Rconverter : callable
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To be called on values taken from R before putting them in the
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IPython namespace.
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pyconverter : callable
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To be called on values in ipython namespace before
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assigning to variables in rpy2.
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cache_display_data : bool
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If True, the published results of the final call to R are
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cached in the variable 'display_cache'.
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"""
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super(RMagics, self).__init__(shell)
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self.cache_display_data = cache_display_data
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self.r = ro.R()
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self.Rstdout_cache = []
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self.pyconverter = pyconverter
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self.Rconverter = Rconverter
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def eval(self, line):
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'''
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Parse and evaluate a line of R code with rpy2.
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Returns the output to R's stdout() connection,
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the value generated by evaluating the code, and a
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boolean indicating whether the return value would be
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visible if the line of code were evaluated in an R REPL.
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R Code evaluation and visibility determination are
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done via an R call of the form withVisible({<code>})
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'''
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old_writeconsole = ri.get_writeconsole()
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ri.set_writeconsole(self.write_console)
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try:
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res = ro.r("withVisible({%s})" % line)
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value = res[0] #value (R object)
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visible = ro.conversion.ri2py(res[1])[0] #visible (boolean)
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except (ri.RRuntimeError, ValueError) as exception:
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warning_or_other_msg = self.flush() # otherwise next return seems to have copy of error
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raise RInterpreterError(line, str_to_unicode(str(exception)), warning_or_other_msg)
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text_output = self.flush()
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ri.set_writeconsole(old_writeconsole)
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return text_output, value, visible
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def write_console(self, output):
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'''
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A hook to capture R's stdout in a cache.
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'''
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self.Rstdout_cache.append(output)
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def flush(self):
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'''
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Flush R's stdout cache to a string, returning the string.
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'''
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value = ''.join([str_to_unicode(s, 'utf-8') for s in self.Rstdout_cache])
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self.Rstdout_cache = []
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return value
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@skip_doctest
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@needs_local_scope
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@line_magic
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def Rpush(self, line, local_ns=None):
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'''
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A line-level magic for R that pushes
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variables from python to rpy2. The line should be made up
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of whitespace separated variable names in the IPython
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namespace::
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In [7]: import numpy as np
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In [8]: X = np.array([4.5,6.3,7.9])
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In [9]: X.mean()
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Out[9]: 6.2333333333333343
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In [10]: %Rpush X
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In [11]: %R mean(X)
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Out[11]: array([ 6.23333333])
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'''
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if local_ns is None:
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local_ns = {}
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inputs = line.split(' ')
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for input in inputs:
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try:
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val = local_ns[input]
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except KeyError:
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try:
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val = self.shell.user_ns[input]
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except KeyError:
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# reraise the KeyError as a NameError so that it looks like
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# the standard python behavior when you use an unnamed
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# variable
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raise NameError("name '%s' is not defined" % input)
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self.r.assign(input, self.pyconverter(val))
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@skip_doctest
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@magic_arguments()
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@argument(
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'-d', '--as_dataframe', action='store_true',
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default=False,
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help='Convert objects to data.frames before returning to ipython.'
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)
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@argument(
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'outputs',
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nargs='*',
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)
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@line_magic
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def Rpull(self, line):
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'''
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A line-level magic for R that pulls
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variables from python to rpy2::
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In [18]: _ = %R x = c(3,4,6.7); y = c(4,6,7); z = c('a',3,4)
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In [19]: %Rpull x y z
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In [20]: x
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Out[20]: array([ 3. , 4. , 6.7])
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In [21]: y
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Out[21]: array([ 4., 6., 7.])
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In [22]: z
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Out[22]:
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array(['a', '3', '4'],
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dtype='|S1')
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If --as_dataframe, then each object is returned as a structured array
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after first passed through "as.data.frame" in R before
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being calling self.Rconverter.
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This is useful when a structured array is desired as output, or
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when the object in R has mixed data types.
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See the %%R docstring for more examples.
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Notes
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-----
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Beware that R names can have '.' so this is not fool proof.
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To avoid this, don't name your R objects with '.'s...
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'''
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args = parse_argstring(self.Rpull, line)
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outputs = args.outputs
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for output in outputs:
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self.shell.push({output:self.Rconverter(self.r(output),dataframe=args.as_dataframe)})
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@skip_doctest
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@magic_arguments()
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@argument(
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'-d', '--as_dataframe', action='store_true',
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default=False,
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help='Convert objects to data.frames before returning to ipython.'
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)
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@argument(
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'output',
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nargs=1,
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type=str,
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)
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@line_magic
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def Rget(self, line):
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'''
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Return an object from rpy2, possibly as a structured array (if possible).
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Similar to Rpull except only one argument is accepted and the value is
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returned rather than pushed to self.shell.user_ns::
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In [3]: dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')]
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In [4]: datapy = np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5, 'e')], dtype=dtype)
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In [5]: %R -i datapy
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In [6]: %Rget datapy
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Out[6]:
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array([['1', '2', '3', '4'],
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['2', '3', '2', '5'],
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['a', 'b', 'c', 'e']],
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dtype='|S1')
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In [7]: %Rget -d datapy
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Out[7]:
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array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5.0, 'e')],
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dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])
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'''
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args = parse_argstring(self.Rget, line)
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output = args.output
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return self.Rconverter(self.r(output[0]),dataframe=args.as_dataframe)
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@skip_doctest
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@magic_arguments()
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@argument(
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'-i', '--input', action='append',
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help='Names of input variable from shell.user_ns to be assigned to R variables of the same names after calling self.pyconverter. Multiple names can be passed separated only by commas with no whitespace.'
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)
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@argument(
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|
'-o', '--output', action='append',
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|
help='Names of variables to be pushed from rpy2 to shell.user_ns after executing cell body and applying self.Rconverter. Multiple names can be passed separated only by commas with no whitespace.'
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|
)
|
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@argument(
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|
'-w', '--width', type=int,
|
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|
help='Width of png plotting device sent as an argument to *png* in R.'
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|
)
|
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|
@argument(
|
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|
'-h', '--height', type=int,
|
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|
help='Height of png plotting device sent as an argument to *png* in R.'
|
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|
)
|
|
|
|
|
|
@argument(
|
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|
'-d', '--dataframe', action='append',
|
|
|
help='Convert these objects to data.frames and return as structured arrays.'
|
|
|
)
|
|
|
@argument(
|
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|
'-u', '--units', type=unicode, choices=["px", "in", "cm", "mm"],
|
|
|
help='Units of png plotting device sent as an argument to *png* in R. One of ["px", "in", "cm", "mm"].'
|
|
|
)
|
|
|
@argument(
|
|
|
'-r', '--res', type=int,
|
|
|
help='Resolution of png plotting device sent as an argument to *png* in R. Defaults to 72 if *units* is one of ["in", "cm", "mm"].'
|
|
|
)
|
|
|
@argument(
|
|
|
'-p', '--pointsize', type=int,
|
|
|
help='Pointsize of png plotting device sent as an argument to *png* in R.'
|
|
|
)
|
|
|
@argument(
|
|
|
'-b', '--bg',
|
|
|
help='Background of png plotting device sent as an argument to *png* in R.'
|
|
|
)
|
|
|
@argument(
|
|
|
'-n', '--noreturn',
|
|
|
help='Force the magic to not return anything.',
|
|
|
action='store_true',
|
|
|
default=False
|
|
|
)
|
|
|
@argument(
|
|
|
'code',
|
|
|
nargs='*',
|
|
|
)
|
|
|
@needs_local_scope
|
|
|
@line_cell_magic
|
|
|
def R(self, line, cell=None, local_ns=None):
|
|
|
'''
|
|
|
Execute code in R, and pull some of the results back into the Python namespace.
|
|
|
|
|
|
In line mode, this will evaluate an expression and convert the returned value to a Python object.
|
|
|
The return value is determined by rpy2's behaviour of returning the result of evaluating the
|
|
|
final line.
|
|
|
|
|
|
Multiple R lines can be executed by joining them with semicolons::
|
|
|
|
|
|
In [9]: %R X=c(1,4,5,7); sd(X); mean(X)
|
|
|
Out[9]: array([ 4.25])
|
|
|
|
|
|
In cell mode, this will run a block of R code. The resulting value
|
|
|
is printed if it would printed be when evaluating the same code
|
|
|
within a standard R REPL.
|
|
|
|
|
|
Nothing is returned to python by default in cell mode::
|
|
|
|
|
|
In [10]: %%R
|
|
|
....: Y = c(2,4,3,9)
|
|
|
....: summary(lm(Y~X))
|
|
|
|
|
|
Call:
|
|
|
lm(formula = Y ~ X)
|
|
|
|
|
|
Residuals:
|
|
|
1 2 3 4
|
|
|
0.88 -0.24 -2.28 1.64
|
|
|
|
|
|
Coefficients:
|
|
|
Estimate Std. Error t value Pr(>|t|)
|
|
|
(Intercept) 0.0800 2.3000 0.035 0.975
|
|
|
X 1.0400 0.4822 2.157 0.164
|
|
|
|
|
|
Residual standard error: 2.088 on 2 degrees of freedom
|
|
|
Multiple R-squared: 0.6993,Adjusted R-squared: 0.549
|
|
|
F-statistic: 4.651 on 1 and 2 DF, p-value: 0.1638
|
|
|
|
|
|
In the notebook, plots are published as the output of the cell::
|
|
|
|
|
|
%R plot(X, Y)
|
|
|
|
|
|
will create a scatter plot of X bs Y.
|
|
|
|
|
|
If cell is not None and line has some R code, it is prepended to
|
|
|
the R code in cell.
|
|
|
|
|
|
Objects can be passed back and forth between rpy2 and python via the -i -o flags in line::
|
|
|
|
|
|
In [14]: Z = np.array([1,4,5,10])
|
|
|
|
|
|
In [15]: %R -i Z mean(Z)
|
|
|
Out[15]: array([ 5.])
|
|
|
|
|
|
|
|
|
In [16]: %R -o W W=Z*mean(Z)
|
|
|
Out[16]: array([ 5., 20., 25., 50.])
|
|
|
|
|
|
In [17]: W
|
|
|
Out[17]: array([ 5., 20., 25., 50.])
|
|
|
|
|
|
The return value is determined by these rules:
|
|
|
|
|
|
* If the cell is not None, the magic returns None.
|
|
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|
|
* If the cell evaluates as False, the resulting value is returned
|
|
|
unless the final line prints something to the console, in
|
|
|
which case None is returned.
|
|
|
|
|
|
* If the final line results in a NULL value when evaluated
|
|
|
by rpy2, then None is returned.
|
|
|
|
|
|
* No attempt is made to convert the final value to a structured array.
|
|
|
Use the --dataframe flag or %Rget to push / return a structured array.
|
|
|
|
|
|
* If the -n flag is present, there is no return value.
|
|
|
|
|
|
* A trailing ';' will also result in no return value as the last
|
|
|
value in the line is an empty string.
|
|
|
|
|
|
The --dataframe argument will attempt to return structured arrays.
|
|
|
This is useful for dataframes with
|
|
|
mixed data types. Note also that for a data.frame,
|
|
|
if it is returned as an ndarray, it is transposed::
|
|
|
|
|
|
In [18]: dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')]
|
|
|
|
|
|
In [19]: datapy = np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5, 'e')], dtype=dtype)
|
|
|
|
|
|
In [20]: %%R -o datar
|
|
|
datar = datapy
|
|
|
....:
|
|
|
|
|
|
In [21]: datar
|
|
|
Out[21]:
|
|
|
array([['1', '2', '3', '4'],
|
|
|
['2', '3', '2', '5'],
|
|
|
['a', 'b', 'c', 'e']],
|
|
|
dtype='|S1')
|
|
|
|
|
|
In [22]: %%R -d datar
|
|
|
datar = datapy
|
|
|
....:
|
|
|
|
|
|
In [23]: datar
|
|
|
Out[23]:
|
|
|
array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c'), (4, 5.0, 'e')],
|
|
|
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])
|
|
|
|
|
|
The --dataframe argument first tries colnames, then names.
|
|
|
If both are NULL, it returns an ndarray (i.e. unstructured)::
|
|
|
|
|
|
In [1]: %R mydata=c(4,6,8.3); NULL
|
|
|
|
|
|
In [2]: %R -d mydata
|
|
|
|
|
|
In [3]: mydata
|
|
|
Out[3]: array([ 4. , 6. , 8.3])
|
|
|
|
|
|
In [4]: %R names(mydata) = c('a','b','c'); NULL
|
|
|
|
|
|
In [5]: %R -d mydata
|
|
|
|
|
|
In [6]: mydata
|
|
|
Out[6]:
|
|
|
array((4.0, 6.0, 8.3),
|
|
|
dtype=[('a', '<f8'), ('b', '<f8'), ('c', '<f8')])
|
|
|
|
|
|
In [7]: %R -o mydata
|
|
|
|
|
|
In [8]: mydata
|
|
|
Out[8]: array([ 4. , 6. , 8.3])
|
|
|
|
|
|
'''
|
|
|
|
|
|
args = parse_argstring(self.R, line)
|
|
|
|
|
|
# arguments 'code' in line are prepended to
|
|
|
# the cell lines
|
|
|
|
|
|
if cell is None:
|
|
|
code = ''
|
|
|
return_output = True
|
|
|
line_mode = True
|
|
|
else:
|
|
|
code = cell
|
|
|
return_output = False
|
|
|
line_mode = False
|
|
|
|
|
|
code = ' '.join(args.code) + code
|
|
|
|
|
|
# if there is no local namespace then default to an empty dict
|
|
|
if local_ns is None:
|
|
|
local_ns = {}
|
|
|
|
|
|
if args.input:
|
|
|
for input in ','.join(args.input).split(','):
|
|
|
try:
|
|
|
val = local_ns[input]
|
|
|
except KeyError:
|
|
|
try:
|
|
|
val = self.shell.user_ns[input]
|
|
|
except KeyError:
|
|
|
raise NameError("name '%s' is not defined" % input)
|
|
|
self.r.assign(input, self.pyconverter(val))
|
|
|
|
|
|
if getattr(args, 'units') is not None:
|
|
|
if args.units != "px" and getattr(args, 'res') is None:
|
|
|
args.res = 72
|
|
|
args.units = '"%s"' % args.units
|
|
|
|
|
|
png_argdict = dict([(n, getattr(args, n)) for n in ['units', 'res', 'height', 'width', 'bg', 'pointsize']])
|
|
|
png_args = ','.join(['%s=%s' % (o,v) for o, v in png_argdict.items() if v is not None])
|
|
|
# execute the R code in a temporary directory
|
|
|
|
|
|
tmpd = tempfile.mkdtemp()
|
|
|
self.r('png("%s/Rplots%%03d.png",%s)' % (tmpd.replace('\\', '/'), png_args))
|
|
|
|
|
|
text_output = ''
|
|
|
try:
|
|
|
if line_mode:
|
|
|
for line in code.split(';'):
|
|
|
text_result, result, visible = self.eval(line)
|
|
|
text_output += text_result
|
|
|
if text_result:
|
|
|
# the last line printed something to the console so we won't return it
|
|
|
return_output = False
|
|
|
else:
|
|
|
text_result, result, visible = self.eval(code)
|
|
|
text_output += text_result
|
|
|
if visible:
|
|
|
old_writeconsole = ri.get_writeconsole()
|
|
|
ri.set_writeconsole(self.write_console)
|
|
|
ro.r.show(result)
|
|
|
text_output += self.flush()
|
|
|
ri.set_writeconsole(old_writeconsole)
|
|
|
|
|
|
except RInterpreterError as e:
|
|
|
print(e.stdout)
|
|
|
if not e.stdout.endswith(e.err):
|
|
|
print(e.err)
|
|
|
rmtree(tmpd)
|
|
|
return
|
|
|
|
|
|
self.r('dev.off()')
|
|
|
|
|
|
# read out all the saved .png files
|
|
|
|
|
|
images = [open(imgfile, 'rb').read() for imgfile in glob("%s/Rplots*png" % tmpd)]
|
|
|
|
|
|
# now publish the images
|
|
|
# mimicking IPython/zmq/pylab/backend_inline.py
|
|
|
fmt = 'png'
|
|
|
mimetypes = { 'png' : 'image/png', 'svg' : 'image/svg+xml' }
|
|
|
mime = mimetypes[fmt]
|
|
|
|
|
|
# publish the printed R objects, if any
|
|
|
|
|
|
display_data = []
|
|
|
if text_output:
|
|
|
display_data.append(('RMagic.R', {'text/plain':text_output}))
|
|
|
|
|
|
# flush text streams before sending figures, helps a little with output
|
|
|
for image in images:
|
|
|
# synchronization in the console (though it's a bandaid, not a real sln)
|
|
|
sys.stdout.flush(); sys.stderr.flush()
|
|
|
display_data.append(('RMagic.R', {mime: image}))
|
|
|
|
|
|
# kill the temporary directory
|
|
|
rmtree(tmpd)
|
|
|
|
|
|
# try to turn every output into a numpy array
|
|
|
# this means that output are assumed to be castable
|
|
|
# as numpy arrays
|
|
|
|
|
|
if args.output:
|
|
|
for output in ','.join(args.output).split(','):
|
|
|
self.shell.push({output:self.Rconverter(self.r(output), dataframe=False)})
|
|
|
|
|
|
if args.dataframe:
|
|
|
for output in ','.join(args.dataframe).split(','):
|
|
|
self.shell.push({output:self.Rconverter(self.r(output), dataframe=True)})
|
|
|
|
|
|
for tag, disp_d in display_data:
|
|
|
publish_display_data(tag, disp_d)
|
|
|
|
|
|
# this will keep a reference to the display_data
|
|
|
# which might be useful to other objects who happen to use
|
|
|
# this method
|
|
|
|
|
|
if self.cache_display_data:
|
|
|
self.display_cache = display_data
|
|
|
|
|
|
# if in line mode and return_output, return the result as an ndarray
|
|
|
if return_output and not args.noreturn:
|
|
|
if result != ri.NULL:
|
|
|
return self.Rconverter(result, dataframe=False)
|
|
|
|
|
|
__doc__ = __doc__.format(
|
|
|
R_DOC = ' '*8 + RMagics.R.__doc__,
|
|
|
RPUSH_DOC = ' '*8 + RMagics.Rpush.__doc__,
|
|
|
RPULL_DOC = ' '*8 + RMagics.Rpull.__doc__,
|
|
|
RGET_DOC = ' '*8 + RMagics.Rget.__doc__
|
|
|
)
|
|
|
|
|
|
|
|
|
def load_ipython_extension(ip):
|
|
|
"""Load the extension in IPython."""
|
|
|
ip.register_magics(RMagics)
|
|
|
# Initialising rpy2 interferes with readline. Since, at this point, we've
|
|
|
# probably just loaded rpy2, we reset the delimiters. See issue gh-2759.
|
|
|
if ip.has_readline:
|
|
|
ip.readline.set_completer_delims(ip.readline_delims)
|
|
|
|
