On Tue, 6 May 2014 10:12:50 +0100 Dr Eberhard Lisse <e...@lisse.na> wrote
> Jeff > > It's in MySQL, at the moment roughly 1.8 GB, if I pull it into a > dataframe it saves to 180MB. I work from the dataframe. > > But, it's not only a size issue it's also a speed issue and hence I > don't care what I am going to use, as long as it is fast. > > sqldf is easy to understand for me but it takes ages. If > alternatives were roughly similar in speed I would remain with > sqldf. > > dplyr sounds faster, and promising, but the intrinsic stuff is > way beyond me (elderly Gynaecologist) on the learning curve... > > el > > on 2014-05-06, 09:41 Jeff Newmiller said the following: > > In what format is this "growing" data stored? CSV? SQL? Log > > textfile? You say you don't want to use sqldf, but you haven't > > said what you do want to use. > It seems like you are trying to extract a (relatively) small data set from a much larger SQL databaseWhy not do the SQL stiff in the database and the analysis *statsm graphics...) in R? Maybe use a make table query to grab the data of interest, and then import the whole table into R for the analysis? (Disclaimer: my ignorance of SQL is not far off total) HTH D. ____________________________________________________________ South Africas premier free email service - www.webmail.co.za Cheapest Insurance Quotes! https://www.outsurance.co.za/insurance-quote/personal/?source=msn&cr=Postit14_468x60_gif&cid=322 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
