Hi All, It is possible to establish conversion functions so that R dataframe, lists, and vector objects are better translated into python equivalents. I've made several aborted stabs at this, but my time has been extremely limited. The basic task is to create a functionally equivalent python class [The tricky bit here is that R list and vector objects have both order and names. It is possible to emulate this in python by creating a base object that maintains a dictionary of names in along side the data vector/matrix data.] See the example in the rpu documentation at http://rpy.sourceforge.net/rpy/doc/manual_html/DataFrame-class.html#DataFram e%20class. This shouldn't be very hard if someone can dedicate a bit of time to it. -Greg (Current RPy maintainer)

-----Original Message----- From: rpy-list-admin@lists.sourceforge.net [mailto:rpy-list-admin@lists.sourceforge.net]On Behalf Of Tim Churches Sent: Wednesday, April 06, 2005 4:22 PM To: rpy-list@lists.sourceforge.net Subject: [Rpy] [Fwd: Re: [Numpy-discussion] Possible example application of the array interface]

The following discussion occured on the Numeric Python mailing list. Others may wish to enjoin the conversation.

Tim C

-------- Original Message -------- Subject: Re: [Numpy-discussion] Possible example application of the array interface Date: Thu, 7 Apr 2005 03:10:08 +1000 (EST) From: Michael Sorich <mike_lists@yahoo.com.au> To: numpy-discussion@lists.sourceforge.net

I think that this is a great idea! While I have a strong preference for python, I generally use R for statistical analyses due to the large number of mature libraries available. There are also some aspects of the R data types (eg data-frames and column/row names for 2D arrays) that are really nice for spreadsheet like data. I hope that scipy.base record arrays will be as easily manipulated as data-frames are.

While RPy works well for small simple problems, there are data conversion limitations between R and Python. If one could efficiently convert between the major R data types and python scipy.base data types without loss of data, it would become possible to do most of the data manipulation in python and freely mix in R functions when required. This may encourage the use of python for the development of statistical routines.

...

From my meager understanding of RPy:

R vectors are converted to python lists. It may make more sense to convert them to an array (either stdlib or scipy.base version) - without copying data if possible.

R arrays and matrices are converted to Numeric arrays. Eg

In [8]: r.array([1,2,3,4,5,6],dim=[2,3]) Out[8]: array([[1, 3, 5], [2, 4, 6]])

However, column and row names (or dimnames for arrays with >2 dimensions) are lost in R->Py conversion. I do not know whether these conversions require copying of the data.

R data-frames are currently converted to python dictionaries and I don’t think that there is any simple way to convert a python object to an R data frame. This is the biggest limitation of rpy in my opinion.

In [16]: r.data_frame(col1=[1,2,3,4],col2=['one','two','three','four']) Out[16]: {'col2': ['one', 'two', 'three', 'four'], 'col1': [1, 2, 3, 4]}

If it were possible to convert between an R data-frame and a scipy.base record array without copying or losing data, RPy would become more useful.

I wish I understood C, scipy.base and R well enough to give this a go. However, this is Way over my head!

Mike

--- Magnus Lie Hetland <magnus@hetland.org> wrote:

...

I was just thinking about some experimental designs, and whether I could, perhaps, do the statistics in Python. I remembered having used RPy [1] briefly at some time (there may be other similar bindings out there -- I don't remember) and started thinking about whether I could, perhaps, combine it with numpy in some way. My first thought was to reimplement the relevant statistical functions; then I thought about how to convert data back and forth -- but then it occurred to me that R also uses arrays extensively, and that it could, perhaps, be possible to expose those (through something like RPy) through the array interface/protocol!

This would be (IMO) a good example of the benefits of the array protocol; it's not a matter of "getting yet another array module". RPy is an external library/language with *lots* of features that might be useful to numpy users, many of which aren't likely to be implemented in Python for quite a while, I'd guess (unless, perhaps, someone writes a translator from R, which I'm sure is doable).

I don't know enough (at least yet ;) about the implementation of RPy and the R library to say for sure whether this would even be possible, but it does seem like it could be really useful...

[1] rpy.sf.net

-- Magnus Lie Hetland Fall seven times, stand up eight http://hetland.org [Japanese proverb]

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