Thanks to Jean Adams, Bill Dunlap, Gerald Jean, Carlisle Thacker, Andrzej
Galecki and Nick Ellis, Alec Stephenson and Jean Adams for quick replies.
The question was how to convert a data frame from raw, repeated data, to
counts (or weights).
Many people suggestd tapply() and table() but these require some
reconstruction on your own.
aggregate() was also suggested, but seemed to fail on a large (>95000) row
dataset.
Bill Dunlap provided code for a(his?) function agg.length() that did the trick:
agg.length <- function(by, sort.it = T) {
if(!is.data.frame(by))
by <- data.frame(by)
if(sort.it) {
ord <- do.call("order", by)
by <- by[ord, ]
}
logical.diff <- function(group)
group[-1] != group[ - length(group)]
change <- logical.diff(by[[1]])
for(i in seq(along = by)[-1])
change <- change | logical.diff(by[[i]])
by <- by[c(T, change), , drop = F]
by$length <- diff(c(0, seq(len = length(change) + 1)[c(change, T)]))
by
}
as did the function from Gerald Jean by Bill Dunlap and Phil Corbett?
"TableRows" <- function(dum)
{
### Author : Bill Dunlap and Phil Corbett(I think?)
### Date : 29 Nov 2000, 09:02
### Purpose: will return the unique rows of the data.frame "dum" with an added
### column giving the number of times the unique rows were repeated in
### the original data.frame.
### ----------------------------------------------------------------------
### Arguments:
### dum : input data.frame containing duplicated rows.
### ----------------------------------------------------------------------
dum.list <- as.list(data.frame(dum))
names(dum.list) <- NULL
dum.strings <- do.call("paste", c(list(sep = "\001"), dum.list))
tab <- table(factor(dum.strings, levels =
unique(dum.strings)))
cbind(dum[match(unique(dum.strings), dum.strings), ], count = tab)
}
The fucntion summarize() provided by Jean Adams summarize() [if you use
hmisc, you may want to change the name of this], may work, but was taking
an inordinate amount of time on a large data set.
summarize <- function(sumry, index, fcn = "mean", ..., newnames =
dimnames(sumry)[[2]]) {
# sumry is a matrix of variables that will be summarized
# index is a matrix or data frame of discrete variables for which data is
summarized
# fcn is the summary function
ind <- apply(index, 1, function(x)
paste(format(x), collapse = " "))
res <- apply(sumry, 2, tapply, ind, fcn, ...)
matchup <- tapply(sumry[, 1], ind)
rows <- match(1:num.uniq(ind), matchup)
df <- data.frame(index[rows, ], res)
names(df)[ - (1:dim(index)[2])] <- newnames
df
}
and Alec Stephenson pointed out an example from S-Programming:
data.frame(expand.grid(lapply(tmp,levels)),
weight=as.vector(do.call("table",tmp)))
Dr. Andrew Beckerman
Institute of Biological Science
University of Stirling, Stirling FK9 4LA, Scotland, UK
phone:+44 (0)1786 467808 fax: +44 (0)1786 464994
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