Thanks, that's helpful because I can see the individuals and how many times
they accessed:
The 'plyr' solution of Karl Ove Hufthammer gives me the exact summary
statistics that I'm looking for.
Jab
--- On Fri, 13/11/09, markle...@verizon.net <markle...@verizon.net> wrote:
From: markle...@verizon.net <markle...@verizon.net>
Subject: Re: Re: [R] processing log file
To: jabez...@yahoo.co.uk
Date: Friday, 13 November, 2009, 16:36
Hi: I think below does what you want but it doesn't come out formatted very
nicely. Maybe someone can show you
the formatting ? Good luck.
table.users <- read.table(textConnection("Date UserName Machine
2008-11-25 John 641
2008-11-25 Clive 611
2008-11-25 Jeremy 641
2008-11-25 Walt 722
2008-11-25 Tony 645
2008-11-26 Tony 645
2008-11-26 Tony 641
2008-11-26 Tony 641
2008-11-26 Walt 641
2008-11-26 Walt 645
2008-11-30 John 641
2008-11-30 Clive 611
2008-11-30 Tony 641
2008-11-30 John 641
2008-11-30 John 641"),header=TRUE,as.is=TRUE)
print(table.users)
print(str(table.users))
lapply(split(table.users,table.users$Date),function(.df) {
table(.df$Machine)
})
lapply(split(table.users,table.users$Date),function(.df) {
table(.df$UserName)
})
On Nov 13, 2009, Karl Ove Hufthammer <k...@huftis.org> wrote:
On Fri, 13 Nov 2009 11:03:31 +0000 (GMT) Jabez Wilson
<jabez...@yahoo.co.uk> wrote:
> What I want to do is to find out how many unique users logged
> on each day, and how many individual machines where accessed per day.
Use the 'plyr' package:
library(plyr)
ddply(table.users, .(Date), summarise,
users=length(unique(Username)),
machines=length(unique(Machine)))
--
Karl Ove Hufthammer
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