From: Ian Bentley <ian.bentley_at_gmail.com>

Date: Sat, 12 Jun 2010 12:44:39 -0400

Date: Sat, 12 Jun 2010 12:44:39 -0400

Thanks Gabor - I was able to use that for my purposes.

On 11 June 2010 16:27, Bert Gunter <gunter.berton_at_gene.com> wrote:

> So two time series? Fair enough. But less is more. Plot them as separates

*> series of points connected by lines, different colors for the two different
**> series. Or as two trellises plots. You may also wish to overlay a smooth to
**> help the reader see the "trend"(e.g via a loess or other nonparametric
**> smooth, or perhaps just a fitted line).
**>
**> The only part of a bar that conveys information is the top. The rest of the
**> fill is "chartjunk" (Tufte's term) and distracts.
**>
**>
**> I'll keep this in mind. I am just using this chart for my own analysis
*

now, and probably won't include it later.

> Bert Gunter

*> Genentech Nonclinical Biostatistics
**>
**>
**>
**> -----Original Message-----
**> From: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org]
**> On
**> Behalf Of Ian Bentley
**> Sent: Friday, June 11, 2010 12:15 PM
**> To: Bert Gunter
**> Cc: r-help_at_r-project.org; Hadley Wickham
**> Subject: Re: [R] Transforming simulation data which is spread
**> acrossmanyfiles into a barplot
**>
**> 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.berton_at_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-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org]
**> > On
**> > Behalf Of Hadley Wickham
**> > Sent: Friday, June 11, 2010 11:53 AM
**> > To: Ian Bentley
**> > Cc: r-help_at_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.bentley_at_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_at_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_at_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_at_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.Received on Sat 12 Jun 2010 - 16:47:01 GMT

Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Australia.

Archive generated by hypermail 2.2.0, at Sat 12 Jun 2010 - 16:50:28 GMT.

*
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help.
Please read the posting
guide before posting to the list.
*