On Tue, Sep 29, 2009 at 5:28 PM, Gary Strangman
wrote:
rpy2.robjects.r.lm(f,myframe, na.action=NULL) # fails; "keyword can't be
an expression"
rpy2.robjects.r.lm(f,myframe, na_action=NULL) # fails; "extra arguments
na_action are just disregarded"
The underscore version wor
Hi again,
What is the proper syntax to set parameters like na.action in R's lm()
function? For example, if I create a dataframe with NAs and want them to
be dealt with via R's na.exclude, how do I formulate the call? Things
like the following fail:
>>> from rpy2.robjects import *
>>> f = r.fo
Very helpful, thanks!
As for having data in the "wrong" order, it's a little odd that a datafile
that's perfect for loading into R as a dataframe (via read.table), is
inherently in the "wrong" order for dataframe creation after reading it
into python (using numpy.genfromtext(), or f.readlines(
On Tue, Sep 29, 2009 at 4:21 AM, Gary Strangman
wrote:
> Without benchmarking, that seems mighty inefficient. Nathaniel Smith's
> rnumpy mostly allows the following:
>
> df = rnumpy.r.data_frame(numpy.array(d,np.object))
>
> ... which is 2 conversions (rather than 4), but I haven't been able to ge
On Mon, Sep 28, 2009 at 4:57 PM, Peng Yu wrote:
> Hi,
>
> In R, I can plot a 2 column matrix
>> x=1:3
>> y=1:3
>> plot(x,y)
>> plot(cbind(x,y))
>
> But I'm wondering why I can not do so with rpy. In the following
> python code, the last line does not work. Can somebody let me know
> why?
>
> impor
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That's the problem ... I don't have the data in R format to start, nor is
there a simple way of getting it there (except through python, of course,
in which case I have it in python, not R ;-) I did actually use the
read.table method for a while, but with several hundred thousand disk
hits eac
Great. Thanks for the jump-start!
On Tue, 29 Sep 2009, Laurent Gautier wrote:
> Gary Strangman wrote:
>>
>> Hi Laurent,
>>
>> The only way to reduce the number of transformations is to add an
>> equivalent number of columns to the dataframe (so that instead of several
>> hundred thousand con
Gary Strangman wrote:
>
> Hi Laurent,
>
> The only way to reduce the number of transformations is to add an
> equivalent number of columns to the dataframe (so that instead of
> several hundred thousand conversions, I need several hundred thousand
> columns), and then passing this beast back-a
On Tue, Sep 29, 2009 at 12:21 PM, Gary Strangman wrote:
>
> Hi Laurent,
>
> The only way to reduce the number of transformations is to add an
> equivalent number of columns to the dataframe (so that instead of several
> hundred thousand conversions, I need several hundred thousand columns),
> and t
Hi Laurent,
The only way to reduce the number of transformations is to add an
equivalent number of columns to the dataframe (so that instead of several
hundred thousand conversions, I need several hundred thousand columns),
and then passing this beast back-and-forth between python and R for
r
Gary,
Two things come to my mind:
- Try having an initial Python data structure that requires less
transformations than your current one in order to become a DataFrame.
- Use rpy2.rinterface when speed matters. This can already get you
faster than R.
http://rpy.sourceforge.net/rpy2/doc-dev/htm
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