On 10/12/07, Ben Bolker <[EMAIL PROTECTED]> wrote:
>
> Trying to find a quick/slick/easily interpretable way to
> collapse a data set.
>
> Suppose I have a data set that looks like this:
>
> h <- structure(list(INDEX = structure(1:6, .Label = c("1", "2", "3",
> "4", "5", "6"), class = "factor"), TICKS = c(0, 0, 0, 0, 0, 3
> ), BROOD = structure(c(1L, 1L, 2L, 3L, 3L, 3L), .Label = c("501",
> "502", "503"), class = "factor"), HEIGHT = c(465, 465, 472, 475,
> 475, 475), YEAR = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = c("95",
> "96", "97"), class = "factor"), LOCATION = structure(c(1L, 1L,
> 2L, 3L, 3L, 3L), .Label = c("32", "36", "37"), class = "factor")), .Names =
> c("INDEX",
> "TICKS", "BROOD", "HEIGHT", "YEAR", "LOCATION"), row.names = c(NA,
> 6L), class = "data.frame")
>
> i.e.,
> > h
> INDEX TICKS BROOD HEIGHT YEAR LOCATION
> 1 1 0 501 465 95 32
> 2 2 0 501 465 95 32
> 3 3 0 502 472 95 36
> 4 4 0 503 475 95 37
> 5 5 0 503 475 95 37
> 6 6 3 503 475 95 37
>
> I want a data set that looks like this:
> BROOD TICKS.mean HEIGHT YEAR LOCATION
> 501 0 465 95 32
> 502 0 472 95 36
> 503 1 475 95 37
>
> (for example). I.e., I want to collapse it to a dataset by brood,
> taking the mean of TICKS and reducing each of
> the other variables (would be nice to allow multiple summary
> statistics, e.g. TICKS.mean and TICKS.sd ...)
> In some ways, this is the opposite of a database join/merge
> operation -- I want to collapse the data frame back down.
> If I had the "unmerged" (i.e., the brood table) handy I could
> use it.
>
> I know I can construct this table a bit at a time,
> using tapply() or by() or aggregate() to get the means.
>
> Here's a solution that takes the first element of each factor
> and the mean of each numeric variable. I can imagine there
> are more general/flexible solutions. (One might want to
> specify more than one summary function, or specify that
> factors that vary within group should be dropped.)
>
> vtype = sapply(h,class) ## variable types [numeric or factor]
> vtypes = unique(vtype) ## possible types
> v2 = lapply(vtypes,function(z) which(vtype==z)) ## which are which?
> cfuns = list(factor=function(z)z[1],numeric=mean)## functions to apply
> m = mapply(function(w,f) { aggregate(h[w],list(h$BROOD),f) },
> v2,cfuns,SIMPLIFY=FALSE)
> data.frame(m[[1]],m[[2]][-1])
>
> My question is whether this is re-inventing the wheel. Is there
> some function or package that performs this task?
Maybe the reshape package? http://had.co.nz/reshape
hm <- melt(h, m = "TICKS")
cast(hm, BROOD + HEIGHT + YEAR + LOCATION ~ ., mean)
cast(hm, BROOD + HEIGHT + LOCATION ~ YEAR, mean)
cast(hm, BROOD ~ HEIGHT ~ YEAR, mean)
You should be able to create just about any data structure you need,
and if you can't let me know.
Hadley
--
http://had.co.nz/
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