I'm having trouble using split on a very large data-set with ~1400 levels of
the factor to be split. Unfortunately, I can't reproduce it with the simple
self-contained example below. As you can see, splitting the artificial
dataframe of size ~13MB results in a split dataframe of ~ 144MB, with an
increase memory allocation of ~10 fold for the split object. If split scales
linearly, then my actual 52MB dataframe should be easily handled by my 12GB
of RAM, but it is not. instead, when I try to split selectSubAct.df on one
of its factors with 1473 levels, my memory is slowly gobbled up (plus 3 GB
of swap) until I cancel the operation.
Any ideas on what might be happening? Thanks, Mark
myDataFrame <- data.frame(matrix(LETTERS, ncol = 7, nrow = 399000))
mySplitVar <- factor(as.character(1:1400))
myDataFrame <- cbind(myDataFrame, mySplitVar)
object.size(myDataFrame)
## 12860880 bytes # ~ 13MB
myDataFrame.split <- split(myDataFrame, myDataFrame$mySplitVar)
object.size(myDataFrame.split)
## 144524992 bytes # ~ 144MB
object.size(selectSubAct.df)
## 52,348,272 bytes # ~ 52MB
> sessionInfo()
R version 2.10.0 Patched (2009-10-27 r50222)
x86_64-unknown-linux-gnu
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices datasets utils methods base
loaded via a namespace (and not attached):
[1] tools_2.10.0
Mark W. Kimpel MD ** Neuroinformatics ** Dept. of Psychiatry
Indiana University School of Medicine
15032 Hunter Court, Westfield, IN 46074
(317) 490-5129 Work, & Mobile & VoiceMail
(317) 399-1219 Skype No Voicemail please
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