I'm relatively new to using R, and I am trying to find a decent solution for my current dilemma.
Right now, I am currently trying to parse second data from a 7 months of CSV data. This is over 10GB of data, and I've run into some "memory issues" loading them all into a single dataset to be plotted. If possible, I'd really like to keep both the one second resolution, and all 100 or so columns intact to make things easier on myself. The problem I have is that the machine that is running this script only has 8GB of RAM. I've had issues parsing files with lapply, and some sort of csv reader. So far I've tried read.csv, readr.read_table, and data.table.fread with only fread having any sort of memory management (fread seems to crash on me however). The basic approach I am using is as follows: # Get the data files = list.files(pattern="*.csv") set <- lapply(files, function(x) fread(x, header = T, sep = ',')) #replace fread with something that can parse csv data # Handle the data (Do my plotting down here) ... These processes work with smaller data sets, but I would like to in a worse case scenario be able to parse through 1 year data which would be around 20GB. Thank you for your time, Robert Dupuis ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
