I've been using this setup:
> flist <- expression( list(mean.z = mean(z), sd.z = sd(z)) )
> dat[ , eval(flist), list(x)]
It works great, but there's one small catch. If I do something like
> flist <- expression(list(x.per.y = sum(x) / sum(y)))
> dat[, eval(flist), list(y)]
it does the wrong thing, because sum(y) in each group is just the common
value, rather than that value times the length. Is there any way around
this? Obviously I could rewrite the expression if I know I'm going to by
grouping by y, but I'd like it to be generic.
Thanks.
- Elliot
On Wed, Aug 8, 2012 at 9:17 AM, David Winsemius <dwinsem...@comcast.net>wrote:
>
> On Aug 7, 2012, at 9:28 PM, arun wrote:
>
> HI,
>>
>> Try this:
>>
>> fun1<-function(x,.expr){
>> .expr<-expression(list(mean.z=**mean(z),sd.z=sd(z)))
>> z1<-eval(.expr)
>> }
>>
>> #or
>> fun1<-function(x,.expr){
>> .expr<-expression(list(mean.z=**mean(z),sd.z=sd(z)))
>> z1<-.expr
>> }
>>
>>
>> dat[,eval(z1),list(x)]
>> dat[,eval(z1),list(y)]
>> dat[,eval(z1),list(x,y)]
>>
>>
> I'm not seeing the connection between those functions and the data.table
> call. (Running that code produces an error on my machine.) If the goal is
> to have an expression result then just create it with expression(). In the
> example:
>
> > flist <- expression( list(mean.z = mean(z), sd.z = sd(z)) )
> > dat[ , eval(flist), list(x)]
> x mean.z sd.z
> 1: 2 0.04436034 1.039615
> 2: 3 -0.06354504 1.077686
> 3: 1 -0.08879671 1.066916
>
> --
> David.
>
>
> A.K.
>>
>>
>>
>> ----- Original Message -----
>> From: Elliot Joel Bernstein
>> <elliot.bernstein@fdopartners.**com<elliot.bernst...@fdopartners.com>
>> >
>> To: r-help@r-project.org
>> Cc:
>> Sent: Tuesday, August 7, 2012 5:36 PM
>> Subject: [R] Repeated Aggregation with data.table
>>
>> I have been using ddply to do aggregation, and I frequently define a
>> single aggregation function that I use to aggregate over different
>> groups. For example,
>>
>> require(plyr)
>>
>> dat <- data.frame(x = sample(3, 100, replace=TRUE), y = sample(3, 100,
>> replace = TRUE), z = rnorm(100))
>>
>> f <- function(x) { data.frame(mean.z = mean(x$z), sd.z = sd(x$z)) }
>>
>> ddply(dat, "x", f)
>> ddply(dat, "y", f)
>> ddply(dat, c("x", "y"), f)
>>
>> I recently discovered the data.table package, which dramatically
>> speeds up the aggregation:
>>
>> require(data.table)
>> dat <- data.table(dat)
>>
>> dat[, list(mean.z = mean(z), sd.z = sd(z)), list(x)]
>> dat[, list(mean.z = mean(z), sd.z = sd(z)), list(y)]
>> dat[, list(mean.z = mean(z), sd.z = sd(z)), list(x,y)]
>>
>> But I can't figure out how to save the aggregation function
>> "list(mean.z = mean(z), sd.z = sd(z))" as a variable that I can reuse,
>> similar to the function "f" above. Can someone please explain how to
>> do that?
>>
>> Thanks.
>>
>> - Elliot
>>
>> --
>> Elliot Joel Bernstein, Ph.D. | Research Associate | FDO Partners, LLC
>> 134 Mount Auburn Street | Cambridge, MA | 02138
>> Phone: (617) 503-4619 | Email:
>> elliot.bernstein@fdopartners.**com<elliot.bernst...@fdopartners.com>
>>
>> ______________________________**________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help>
>> PLEASE do read the posting guide http://www.R-project.org/**
>> posting-guide.html <http://www.R-project.org/posting-guide.html>
>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>> ______________________________**________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help>
>> PLEASE do read the posting guide http://www.R-project.org/**
>> posting-guide.html <http://www.R-project.org/posting-guide.html>
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> David Winsemius, MD
> Alameda, CA, USA
>
>
--
Elliot Joel Bernstein, Ph.D. | Research Associate | FDO Partners, LLC
134 Mount Auburn Street | Cambridge, MA | 02138
Phone: (617) 503-4619 | Email: elliot.bernst...@fdopartners.com
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