[R] Calculation of group summaries

[Søren Højsgaard]

Perhaps I lost track of what the original question was, but on my homepage http://genetics.agrsci.dk/~sorenh/misc/ there is a package called doBy in which there is a function called summaryBy (which mimics proc summary from sas). For example
	summaryBy(cbind(Weight,Feed)~Evit+Cu,  data=dietox12, FUN=c(mean,myvar))  
Søren


-----Oprindelig meddelelse-----
Fra: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] På vegne af Gabor Grothendieck
Sendt: 15. juli 2005 04:43
Til: Seeliger.Curt at epamail.epa.gov
Cc: R-Help
Emne: Re: [R] Calculation of group summaries

1. Try using more spaces so your code is easier to read.

2. Use data.frame to define your data frame (since the method in your post creates data frames of factors rather than the desired classes).  

3. Given the appropriate function, f, a single 'by' statement rbind'ed together, as shown, will create the result.

nsites <- 6
yearList <- 1999:2001
fakesub <- data.frame(
	year = rep(yearList, nsites/length(yearList), each = 11),
	site_id  = rep(c('site1','site2'), each = 11*nsites),
	visit_no = rep(1, 11*2*nsites),
	transect = rep(LETTERS[1:11], nsites, each = 2),
	transdir = rep(c('LF','RT'), 11*nsites),
	undercut = abs(rnorm(11*2*nsites, 10)),
	angle    = runif(11*2*nsites, 0, 180)
)


f <- function(x) cbind(year = x[1,1], site_id = x[1,2], visit_no = x[1,3], 
	mean = mean(x[,6]), sd = sd(x[,6]), length = length(x[,6])) do.call("rbind", by(fakesub, fakesub[,1:3], f))





On 7/14/05, Seeliger.Curt at epamail.epa.gov <Seeliger.Curt at epamail.epa.gov> wrote:
> Several people suggested specific functions (by, tapply, sapply and 
> others); thanks for not blowing off a simple question regarding how to 
> do the following SQL in R:
> >   select year,
> >          site_id,
> >          visit_no,
> >          mean(undercut) AS meanUndercut,
> >          count(undercut) AS nUndercut,
> >          std(undercut) AS stdUndercut
> >   from channelMorphology
> >   group by year, site_id, visit_no
> >   ;
> 
> I'd spent quite a bit of time with the suggested functions earlier but 
> had no luck as I'd misread the docs and put the entire dataframe where 
> it only wants the columns to be processed.  Sometimes it's the 
> simplest of things.
> 
> This has lead to another confoundment-- sd() acts differently than
> mean() for some reason, at least with R 1.9.0.  For some reason, means 
> generate NA results and a warning message for each group:
> 
>  argument is not numeric or logical: returning NA in:
> mean.default(data[x, ], ...)
> 
> Of course, the argument is numeric, or there'd be no sd value.  Or 
> more likely, I'm still missing something really basic. If I wrap the 
> value in
> as.numeric() things work fine.  Why should I have to do this for mean 
> and median, but not sd? The code below should reproduce this error
> 
>  # Fake data for demo:
>  nsites<-6
>  yearList<-1999:2001
>  fakesub<-as.data.frame(cbind(
>                 year     =rep(yearList,nsites/length(yearList),each=11)
>                ,site_id  =rep(c('site1','site2'),each=11*nsites)
>                ,visit_no =rep(1,11*2*nsites)
>                ,transect =rep(LETTERS[1:11],nsites,each=2)
>                ,transdir =rep(c('LF','RT'),11*nsites)
>                ,undercut =abs(rnorm(11*2*nsites,10))
>                ,angle    =runif(11*2*nsites,0,180)
>                ))
> 
>  # Create group summaries:
>  sdmets<-by(fakesub$undercut
>            ,list(fakesub$year,fakesub$site_id,fakesub$visit_no)
>            ,sd
>            )
>  nmets<-by(fakesub$undercut
>           ,list(fakesub$year,fakesub$site_id,fakesub$visit_no)
>           ,length
>           )
>  xmets<-by(fakesub$undercut
>           ,list(fakesub$year,fakesub$site_id,fakesub$visit_no)
>           ,mean
>           )
>   xmets<-by(as.numeric(fakesub$undercut)
>           ,list(fakesub$year,fakesub$site_id,fakesub$visit_no)
>           ,mean
>           )
> 
>  # Put site id values (year, site_id and visit_no) into results:
>  # List unique id combinations as a list of lists.  Then  # reorganize 
> that into 3 vectors for final results.
>  # Certainly, there MUST be a better way...
>  foo<-strsplit(unique(paste(fakesub$year
>                            ,fakesub$site_id
>                            ,fakesub$visit_no
>                            ,sep='#'))
>               ,split='#'
>               )
>  year<-list()
>  for(i in 1:length(foo)) {year<-rbind(year,foo[[i]][1])}
>  site_id<-list()
>  for(i in 1:length(foo)) {site_id<-rbind(site_id,foo[[i]][2])}
>  visit_no<-list()
>  for(i in 1:length(foo)) {visit_no<-rbind(visit_no,foo[[i]][3])}
> 
>  # Final result, more or less
>  data.frame(cbind(a=year,b=site_id,c=visit_no,sdmets,nmets,xmets))
> 
> 
> cur
> 
> --
> Curt Seeliger, Data Ranger
> CSC, EPA/WED contractor
> 541/754-4638
> seeliger.curt at epa.gov
> 
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