[R] collapsing a data frame
Ben Bolker
bolker at ufl.edu
Fri Oct 12 19:14:31 CEST 2007
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?
cheers
Ben Bolker
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