Thanks Jonathan. I'm already using RMySQL to load data for couple of days. I wanted to know what are the relevant R capabilities if I want to process much bigger tables.
R always reads the whole set into memory and this might be a limitation in case of big tables, correct? Doesn't it use temporary files or something similar to deal such amount of data? As an example I know that SAS handles sas7bdat files up to 1TB on a box with 76GB memory, without noticeable issues. --Roman ----- Original Message ----- > 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. [[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.
