If you do not need all the variables in the SPSS files, use package 'memisc'.
spss.system.file() and it's subset() allow you to just load the
variables needed.
You will need to transform into data.frame as the memisc data.set
includes the SPSS attributes, user-missings etc.
Paul Bivand
Centre for
This won't help with large memory issues, but just a pointer:
When you start to construct data_all with these commands
data_all = vector("list", 17);
data_all[[1993]] = data1993;
The first pre-allocates a list of length 17, but the second adds the
data to the 1993rd slot requiring a complete rea
Le lundi 30 janvier 2012 à 09:54 +0100, Petr Kurtin a écrit :
> Hi,
>
> I have got a lot of SPSS data for years 1993-2010. I load all data into
> lists so I can easily index the values over the years. Unfortunately loaded
> data occupy quite a lot of memory (10Gb) - so my question is, what's the
>
Hi,
I have got a lot of SPSS data for years 1993-2010. I load all data into
lists so I can easily index the values over the years. Unfortunately loaded
data occupy quite a lot of memory (10Gb) - so my question is, what's the
best approach to work with big data files? Can R get a value from the fil
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