In cases where I have to parse through large datasets that will not fit into R's memory, I will grab relevant data using SQL and then analyze said data using R. There are several packages designed to do this, like [1] and [2] below, that allow you to query a database using SQL and end up with that data in an R data.frame.
[1] http://cran.cnr.berkeley.edu/web/packages/RMySQL/index.html [2] http://cran.cnr.berkeley.edu/web/packages/RSQLite/index.html On Wed, May 25, 2011 at 12:29 AM, Roman Naumenko <ro...@bestroman.com> wrote: > Hi R list, > > I'm new to R software, so I'd like to ask about it is capabilities. > What I'm looking to do is to run some statistical tests on quite big > tables which are aggregated quotes from a market feed. > > This is a typical set of data. > Each day contains millions of records (up to 10 non filtered). > > 2011-05-24 750 Bid DELL 14130770 400 > 15.4800 BATS 35482391 Y 1 1 0 0 > 2011-05-24 904 Bid DELL 14130772 300 > 15.4800 BATS 35482391 Y 1 0 0 0 > 2011-05-24 904 Bid DELL 14130773 135 > 15.4800 BATS 35482391 Y 1 0 0 0 > > I'll need to filter it out first based on some criteria. > Since I keep it mysql database, it can be done through by query. Not > super efficient, checked it already. > > Then I need to aggregate dataset into different time frames (time is > represented in ms from midnight, like 35482391). > Again, can be done through a databases query, not sure what gonna be faster. > Aggregated tables going to be much smaller, like thousands rows per > observation day. > > Then calculate basic statistic: mean, standard deviation, sums etc. > After stats are calculated, I need to perform some statistical > hypothesis tests. > > So, my question is: what tool faster for data aggregation and filtration > on big datasets: mysql or R? > > Thanks, > --Roman N. > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > -- =============================================== Jon Daily Technician =============================================== #!/usr/bin/env outside # It's great, trust me. ______________________________________________ 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.
