On Mon, Sep 12, 2011 at 12:26 PM, Laurent Gautier <[email protected]> wrote:
> Probably not.
>
> R is doing a lot of things behind the hood. Sometimes it is good, sometimes
> it is bad.
> The code snippet given to you has a quadratic time-complexity ( O(nm) ). It
> can be make linearithmic ( O(n log(m) ) ) simply:
>
> from rpy2.robjects.vector import BoolVector
> ref = set(differential)
> select_b = BoolVector(tuple(x in ref for x in source.rx2('gene')))
> mysubset = source.rx(select_b, True)
If I reckon well BoolVector(...genexpr...) is not possible here due to
R API limitation - we need length of iterable, isn't it?
It seems like copying R on Python level is not always nice
and can be quite inefficient.
Above probably could be rewritten in more Pythonic way (it would
be more efficient I believe, as well)
mysubset = source.rx((x in ref for x in source.rx2('gene')), True)
or
mysubset = DataFrame(row for row in source if row['gene'] in ref)
but of course is not supported by rpy2.
Is there a chance to make rpy2 bit more Python integrated? :)
Best regards,
w
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