[R] conditionally merging adjacent rows in a data frame
Nikhil Kaza
nikhil.list at gmail.com
Wed Dec 9 14:37:49 CET 2009
This is great!! Sqldf is exactly the kind of thing I was looking for,
other stuff.
I suppose you can speed up both functions 1 and 5 using aggregate and
tapply only once, as was suggested earlier. But it comes at the
expense of readability.
Nikhil
On 9 Dec 2009, at 7:59AM, Titus von der Malsburg wrote:
> On Wed, Dec 9, 2009 at 12:11 AM, Gabor Grothendieck
> <ggrothendieck at gmail.com> wrote:
>> Here are a couple of solutions. The first uses by and the second
>> sqldf:
>
> Brilliant! Now I have a whole collection of solutions. I did a
> simple
> performance comparison with a data frame that has 7929 lines.
>
> The results were as following (loading appropriate packages is not
> included in
> the measurements):
>
> times <- c(0.248, 0.551, 41.080, 0.16, 0.190)
> names(times) <- c("aggregate","summaryBy","by
> +transform","sqldf","tapply")
> barplot(times, log="y", ylab="log(s)")
>
> So sqldf clearly wins followed by tapply and aggregate. summaryBy
> is slower
> than necessary because it computes for x and dur both, mean /and/ sum.
> by+transform presumably suffers from the contruction of many
> intermediate data
> frames.
>
> Are there any canonical places where R-recipes are collected? If
> yes I would
> write-up a summary.
>
> These were the competitors:
>
> # Gary's and Nikhil's aggregate solution:
>
> aggregate.fixations1 <- function(d) {
>
> idx <- c(TRUE,diff(d$roi)!=0)
> d2 <- d[idx,]
>
> idx <- cumsum(idx)
> d2$dur <- aggregate(d$dur, list(idx), sum)[2]
> d2$x <- aggregate(d$x, list(idx), mean)[2]
>
> d2
> }
>
> # Marek's symmaryBy:
>
> library(doBy)
>
> aggregate.fixations2 <- function(d) {
>
> idx <- c(TRUE,diff(d$roi)!=0)
> d2 <- d[idx,]
>
> d$idx <- cumsum(idx)
> d2$r <- summaryBy(dur+x~idx, data=d, FUN=c(sum,
> mean))[c("dur.sum", "x.mean")]
> d2
> }
>
> # Gabor's by+transform solution:
>
> aggregate.fixations3 <- function(d) {
>
> idx <- cumsum(c(TRUE,diff(d$roi)!=0))
>
> d2 <- do.call(rbind, by(d, idx, function(x)
> transform(x, dur = sum(dur), x = mean(x))[1,,drop =
> FALSE ]))
>
> d2
> }
>
> # Gabor's sqldf solution:
>
> library(sqldf)
>
> aggregate.fixations4 <- function(d) {
>
> idx <- c(TRUE,diff(d$roi)!=0)
> d2 <- d[idx,]
>
> d$idx <- cumsum(idx)
> d2$r <- sqldf("select sum(dur), avg(x) x from d group by idx")
>
> d2
> }
>
> # Titus' solution using plain old tapply:
>
> aggregate.fixations5 <- function(d) {
>
> idx <- c(TRUE,diff(d$roi)!=0)
> d2 <- d[idx,]
>
> idx <- cumsum(idx)
> d2$dur <- tapply(d$dur, idx, sum)
> d2$x <- tapply(d$x, idx, mean)
>
> d2
> }
>
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