I'm not trying to see the relation between sent and received, but rather to show how these grow across the increasing complexity of the 50 data points.
On 11 June 2010 15:02, Bert Gunter <gunter.ber...@gene.com> wrote: > Ouch! Lousy plot. Instead, plot the 50 (mean sent, mean received)pairs as > a > y vs x scatterplot to see the relationship. > > Bert Gunter > Genentech Nonclinical Biostatistics > > > > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > On > Behalf Of Hadley Wickham > Sent: Friday, June 11, 2010 11:53 AM > To: Ian Bentley > Cc: r-help@r-project.org > Subject: Re: [R] Transforming simulation data which is spread across > manyfiles into a barplot > > On Fri, Jun 11, 2010 at 1:32 PM, Ian Bentley <ian.bent...@gmail.com> > wrote: > > I'm an R newbie, and I'm just trying to use some of it's graphing > > capabilities, but I'm a bit stuck - basically in massaging the already > > available data into a format R likes. > > > > I have a simulation environment which produces logs, which represent a > > number of different things. I then run a python script on this data, and > > putting it in a nicer format. Essentially, the python script reduces the > > number of files by two orders of magnitude. > > > > What I'm left with, is a number of files, which each have two columns of > > data in them. > > The files look something like this: > > --1000.log-- > > Sent Received > > 405.0 3832.0 > > 176.0 1742.0 > > 176.0 1766.0 > > 176.0 1240.0 > > 356.0 3396.0 > > ... > > > > This file - called 1000.log - represents a data point at 1000. What I'd > like > > to do is to use a loop, to read in 50 or so of these files, and then > produce > > a stacked barplot. Ideally, the stacked barplot would have 1 bar per > file, > > and two stacks per bar. The first stack would be the mean of the sent, > and > > the second would be the mean of the received. > > > > I've used a loop to read files in R before, something like this --- > > > > for (i in 1:50){ > > tmpFile <- paste(base, i*100, ".log", sep="") > > tmp <- read.table(tmpFile) > > } > > > > # Load data > library(plyr) > > paths <- dir(base, pattern = "\\.log", full = TRUE) > names(paths) <- basename(paths) > > df <- ddply(paths, read.table) > > # Compute averages: > avg <- ddply(df, ".id", summarise, > sent = mean(sent), > received = mean(received) > > You can read more about plyr at http://had.co.nz/plyr. > > Hadley > > -- > Assistant Professor / Dobelman Family Junior Chair > Department of Statistics / Rice University > http://had.co.nz/ > > ______________________________________________ > 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. > > -- Ian Bentley M.Sc. Candidate Queen's University Kingston, Ontario [[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.
