# Re: [R] Calculation of group summaries

From: <Seeliger.Curt_at_epamail.epa.gov>
Date: Fri 15 Jul 2005 - 10:31:51 EST

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@epa.gov

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