Here's one way...
Not tested, so there maybe typos and such, but I've used this approach successfully quite a few times.
It can get kind of slow if dat1 has many, many rows.
The coding assumes no missing data, though that could be handled by adding the na.rm argument in apppropriate places, and changing the nrow() to something that counts only non-missing data.
myfun <- function(dfr) {
data.frame(
wshed=dfr$wshed[1],
site=dfr$site[1],
species=dfr$species[1],
mBda=mean(dfr$BdA),
cBda=sd(dfr$Bda)/mean(dfr$Bda),
sBda=sd(dfr$Bda)/sqrt(nrow(dfr)),
nBda=nrow(dfr),
msla=mean(dfr$BdA),
csla=sd(dfr$sla)/mean(dfr$sla),
ssla=sd(dfr$sla)/sqrt(nrow(dfr)),
nsla=nrow(dfr))
}tmp1 <- split(dat1,paste(dat1$wshed,dat1$site,dat1$species))
tmp2 <- lapply(tmp1,myfun)
dat2 <- do.call('rbind',tmp2)-Don
At 6:18 PM -0400 7/16/04, Greg Adkison wrote:
I would be incredibly grateful to anyone who'll help me translate some SAS code into R code.
Say for example that I have a dataset named "dat1" that includes five variables: wshed, site, species, bda, and sla. I can calculate with the following SAS code the mean, CV, se, and number of observations of "bda" and "sla" for each combination of "wshed," "species," and "site," restricting the species considered to only three of several species in dat1 (b, c, and p). Moreover, I can output these calculations and grouping variables to a dataset named "dat2" that will reside in RAM and include the variables wshed, site, species, mBdA, msla, cBda, sBdA, ssla, nBda, and nsla.
proc sort data=dat1; by wshed site species; proc means data=dat1 noprint mean cv stderr n; by wshed site species; where species in ('b', 'c', 'p'); var BdA sla; output out=dat2 mean=mBdA msla cv=cBdA csla stderr=sBdA ssla n=nBdA nsla;
Thanks, Greg
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-- -------------------------------------- Don MacQueen Environmental Protection Department Lawrence Livermore National Laboratory Livermore, CA, USA
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