Transforming simulation data which is spread across many files into a barplot

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)
}

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

to do is to use a loop, to read in 50 or so of these files, and then

a stacked barplot. ?Ideally, the stacked barplot would have 1 bar per

and two stacks per bar. ?The first stack would be the mean of the sent,

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)
}

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)
}

--- But I really don't know how to handle massaging this data into the
matrix I need.

I hope this makes sense, I find it a little hard to describe.

Can anyone give me some help jumping into this one?

Thanks

--
Ian Bentley
M.Sc. Candidate
Queen's University
Kingston, Ontario

? ? ? ?[[alternative HTML version deleted]]

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,

putting it in a nicer format.  Essentially, the python script reduces

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/