[R] conditionally merging adjacent rows in a data frame

[Nikhil Kaza]

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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