Aggregating data (with more than one function)

agg.dat <- do.call("rbind", by(dat$Salary, dat$Department, 

agg.dat <- data.frame(dept=rownames(agg.dat), agg.dat)
agg.dat

From: Robin Schroeder 

Dear list & Andy, 

I am hopelessly stumped, how would one add the department 
names as a variable?

Robin

Robin Tori Schroeder
International Institute for Sustainability 
P.O. Box 873211
Arizona State University
Tempe, Arizona 85287-3211
Phone: (480) 727-7290

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Liaw, Andy
Sent: Monday, March 28, 2005 6:45 PM
To: 'Sivakumaran Raman'; r-help at stat.math.ethz.ch
Subject: RE: [R] Aggregating data (with more than one function)


Here's one possible way, using the data you supplied:

dat <- read.table("clipboard", header=T, row=1)
do.call("rbind",by(dat$Salary, dat$Department, function(x) 

c(mean=mean(x),
total=sum(x))))
            mean  total
Finance 83925.67 251777
HR      63333.33 190000
IT      59928.67 179786
Sales   62481.67 187445

If you need the department names as a variable, you can add 
that easily.

HTH,
Andy

From: Sivakumaran Raman

I have the data similar to the following in a data frame:
    LastName   Department  Salary
1   Johnson    IT          56000
2   James      HR          54223
3   Howe       Finance     80000
4   Jones      Finance     82000
5   Norwood    IT          67000
6   Benson     Sales       76000
7   Smith      Sales       65778
8   Baker      HR          56778
9   Dempsey    HR          78999
10  Nolan      Sales       45667
11  Garth      Finance     89777
12  Jameson    IT          56786

I want to calculate both the mean salary broken down by 
Department and 
also the
total amount paid out per department i.e. I want both 

sum(Salary) and

mean(Salary) for each Department. Right now, I am using 
aggregate.data.frame
twice, creating two data frames, and then combining them 
using data.frame.
However, this seems to be very memory and processor 

intensive and is 

taking a
very long time on my data set. Is there a quicker way to do this?

Thanks in advance,
Siv Raman