How about creating an index using multiple columns.
a <- with(d, aggregate(dur, list(rt=rt,tid=tid,mood=mood,roi=roi),
sum))
b <- with(d, aggregate(x, list(rt=rt,tid=tid,mood=mood,roi=roi),
mean))
c <- merge(a, b, by=c("rt","tid","mood", "roi"))
I suppose one could save some time by not running aggregate twice on
the same dataset, but I am not sure how.
Nikhil
On 8 Dec 2009, at 7:50AM, Titus von der Malsburg wrote:
Hi, I have a data frame and want to merge adjacent rows if some
condition is
met. There's an obvious solution using a loop but it is
prohibitively slow
because my data frame is large. Is there an efficient canonical
solution for
that?
head(d)
rt dur tid mood roi x
55 5523 200 4 subj 9 5
56 5523 52 4 subj 7 31
57 5523 209 4 subj 4 9
58 5523 188 4 subj 4 7
70 4016 264 5 indic 9 51
71 4016 195 5 indic 4 14
The desired result would have consecutive rows with the same roi
value merged.
dur values should be added and x values averaged, other values don't
differ in
these rows and should stay the same.
head(result)
rt dur tid mood roi x
55 5523 200 4 subj 9 5
56 5523 52 4 subj 7 31
57 5523 397 4 subj 4 8
70 4016 264 5 indic 9 51
71 4016 195 5 indic 4 14
There's also a solution using reshape. It uses an index for blocks
d$index <- cumsum(c(TRUE,diff(d$roi)!=0))
melts and then casts for every column using an appropriate
fun.aggregate.
However, this is a bit cumbersome and also I'm not sure how to make
sure that
I get the original order of rows.
Thanks for any suggestion.
Titus
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