I am using R2.6.0 on Windows Small Business Server 2003. I apologize if the
answer to my question is available
I have searched but have not found anything
that I thought helped me.
I have a dataframe that contains ~4.5 million rows and 5 columns. (see
memory and df details below). I am trying to save the dataframe to a MS SQL
Server database, using the sqlSave function. The code below seems to work,
but takes several hours.
sqlSave(channel, dat=idxdata, varTypes=c(ddates="datetime") )
Any tips how I can speed things up? Or is my dataframe so large that it is
going to take a while? (I have ~20 dataframes that I need to save to SQL, so
speed is somewhat important.) Is there an altogether different approach I
should consider taking?
FYI, here is information re: the dataframe and memory on my system. Please
let me know if there is any further information I should provide.
> memory.size(max = F) #reports amount of memory currently in use
[1] 131.8365
> str(idxdata)
'data.frame': 4474553 obs. of 5 variables:
$ idkey : int 1003 1003 1003 1003 1003 1003 1003 1003 1003 1003 ...
$ nnd : Factor w/ 25 levels "01","01C","02",..: 1 1 1 1 1 1 1 1 1 1 ...
$ curcdd : Factor w/ 2 levels "CAD","USD": 2 2 2 2 2 2 2 2 2 2 ...
$ ddates:Class 'Date' num [1:4474553] 6942 6943 6944 6945 6948 ...
$ idx : num 100 100 100 100 100 100 100 100 100 100 ...
> object.size(idxdata)
[1] 125289688
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