I'm using rpy2 to access quantmod functions like this:
>>> from rpy2.robjects import r
>>> r('library(quantmod)')
>>> r('getSymbols("GOOG")')
>>> g = r('quarterlyReturn(GOOG)')
When I print g, I see it has the dates in the leftmost column:
>>> print g
quarterly.returns
2007-03-30 -0.016824034
2007-06-29 0.140867819
2007-09-28 0.085268797
2007-12-31 0.218960989
2008-03-31 -0.363003991
2008-06-30 0.195132472
2008-09-30 -0.239162646
2008-12-31 -0.231873564
2009-03-31 0.131350561
2009-06-30 0.211256680
2009-09-30 0.176142698
2009-12-31 0.250337804
2010-03-31 -0.085260815
2010-06-30 -0.215421780
2010-09-30 0.181683335
2010-12-31 0.129671542
2011-03-31 -0.012138660
2011-06-30 -0.136989570
2011-09-30 0.017101781
2011-12-30 0.254077353
2012-03-30 -0.007214739
2012-06-29 -0.095393300
2012-09-28 0.300705087
2012-12-31 -0.062451955
2013-03-28 0.122720461
2013-06-28 0.108513076
2013-08-02 0.029760214
but when I try to access those dates using native python or R matrix access
methods, the date data is missing:
>>> g.rx(1, True)
<Matrix - Python:0x3e237a0 / R:0x483b278>
[-0.016824]
>>> g[0]
-0.01682403433476387
>>> g[1]
0.14086781910249702
>>> g.rx(1, True)
<Matrix - Python:0x3e237a0 / R:0x483b278>
[-0.016824]
I also tried explicitly converting g (which *seems* to be a R matrix object
already) to an R matrix object like this:
>>> import rpy2.robjects as ro
>>> m = ro.r.matrix(g)
but that stripped out the left column completely:
>>> print m
[,1]
[1,] -0.016824034
[2,] 0.140867819
[3,] 0.085268797
[4,] 0.218960989
[5,] -0.363003991
[6,] 0.195132472
[7,] -0.239162646
[8,] -0.231873564
[9,] 0.131350561
[10,] 0.211256680
[11,] 0.176142698
[12,] 0.250337804
[13,] -0.085260815
[14,] -0.215421780
[15,] 0.181683335
[16,] 0.129671542
[17,] -0.012138660
[18,] -0.136989570
[19,] 0.017101781
[20,] 0.254077353
[21,] -0.007214739
[22,] -0.095393300
[23,] 0.300705087
[24,] -0.062451955
[25,] 0.122720461
[26,] 0.108513076
[27,] 0.029760214
While I can print g to a file object to preserve all the data:
>>> f=open('/tmp/goog.dat', 'w'); print >>f, g; f.close()
Then read it back in and split on two columns to get back a simulation of
the original matrix:
>>> f=open('/tmp/goog.dat', 'r'); goog_matrix = map(lambda x: x.split(),
f.read().splitlines()); f.close()
That seems like a huge kludge, considering g has the data somewhere...
Any ideas on what I might be doing wrong?
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