I'm still new to rpy/R and came across a behavior that get's me confused
and that in my opinion is a bit inconsistent.
I've been trying understand how I can manipulate different R objects as
python objects and came across something that I'm not sure if it's
supposed to happen.
>>> a = r.array([[1],[2]])
>>> b = r.array([[1,2],[2,3]])
>>> a
array([ 1., 2.])
>>> b
[[1, 2], [2, 3]]
>>> type(a)
<type 'numpy.ndarray'>
>>> type(b)
<type 'list'>
Both data structures are lists of lists in python but are returned as
array (as I would expect) but also as lists of lists as if untouched.
This became a problem when checking the converted object in R:
>>> r.is_array(a)
True
>>> r.is_array(b)
False
I'm not sure if this is to be considered a problem, but the fact of not
being consistent got me lost during the last couple of hours.
Is there any clean way to work around this problem?
I also tried using the array() function, but that got me an even more
confused since:
>>> c=array(b)
>>> d=r.array(c)
>>> r.is_array(c)
True
>>> r.is_array(d)
True
>>> r.print_(c)
[,1] [,2]
[1,] 1 2
[2,] 2 3
array([[ 1., 2.],
[ 2., 3.]])
>>> r.print_(d)
[1] 1 2 2 3
array([ 1., 2., 2., 3.])
>>> r.library("gplots")
>>> r.is_what(c)
['is.array', 'is.atomic', 'is.double', 'is.matrix', 'is.numeric', 'is.real']
>>> r.is_what(d)
['is.array', 'is.atomic', 'is.double', 'is.numeric', 'is.real']
c and d are now both arrays, but have different structures, and now c is
also matrix.
...
But this is probably my superficial knowledge on R
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