In my attempts to avoid creating intermediate objects to generate R DataFrames (to reduce the memory footprint), I've been trying many approaches. Unfortunately some of them fail using rpy2.
As an example:
matrix = robjects.baseenv.get("matrix")
# create an empty matrix
random = matrix(nrow=5, ncol=5, dimnames=robjects.ListVector({1:
robjects.StrVector(["a", "b", "c", "d", "e"]), 2:
robjects.StrVector(["first","second", "third", "fourth", "fifth")}))
#Try to set first row, first column to something else
random.rx[1,1] = 5
/usr/lib64/python2.7/site-packages/rpy2/robjects/vectors.pyc in
__setitem__(self, item, value)
94 res = fun(*args)
95 #FIXME: check refcount and copying
---> 96 self._parent.__sexp__ = res.__sexp__
97
98
ValueError: Mismatch in SEXP type (as returned by typeof)
Likewise, if I convert this to a data.frame, setting with rx2 accessors fails
with an R error
as_data_frame = robjects.baseenv.get("as.data.frame")
random = as_data_frame(random)
random.rx2["first"] = [1,2,3,4,5]
/usr/lib64/python2.7/site-packages/rpy2/robjects/vectors.pyc in
__setitem__(self, item, value)
92 conversion.py2ro(item),
93 conversion.py2ro(value)]
---> 94 res = fun(*args)
95 #FIXME: check refcount and copying
96 self._parent.__sexp__ = res.__sexp__
RRuntimeError: Error in [[<-.data.frame`(list(src = c(NA, NA, NA, NA, NA, NA,
NA, NA, : argument "values" is missing, with no default
It looks like a limitation on rpy2's part, as both approaches work in pure R.
--
Luca Beltrame, Ph.D. - Translational Genomics Unit
Oncology Department, Mario Negri Institute
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