# [R] Faster Subsetting

Weiser, Dr. Constantin constantin.weiser at uni-mainz.de
Wed Sep 28 19:00:57 CEST 2016

```I just modified the reproducible example a bit, so it's a bit more
realistic. The function "mean" could be "easily" replaced by your analysis.

And here are some possible solutions:

tmp <- data.frame(id = rep(1:2000, each = 100), foo = rnorm(200000))
tmp <- tmp[sample(dim(tmp)[1]),] # re-sampling the dataset

## with specialized packages
require(plyr)
system.time({
res1 <- ddply(tmp, .(id), summarize, mean=mean(foo))
})

require(dplyr)
system.time({
res2 <- tmp %>%
group_by(id) %>%
summarise(mean = mean(foo))
})

library(data.table)
system.time({
res3 <- data.table(tmp)[, list(mean=mean(foo)), by=id]
})

## build-in R-methods
system.time({
res4 <- aggregate(tmp\$foo, by = list(id=tmp\$id), FUN = mean)
})

system.time({
res5 <- sapply(unique(tmp\$id), simplify = TRUE,
FUN = function(x){
c(id=x, mean=mean(tmp[which(tmp\$id == x), "foo"]))
})
})
res5 <- t(res5)

system.time({
res5 <- sapply(unique(tmp\$id), simplify = TRUE,
FUN = function(x){
sub.tmp <- subset(tmp, tmp\$id == x)
c(x,mean=mean(sub.tmp[, "foo"]))
})
})
res5 <- t(res5)

Yours
Constantin

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Am 28.09.2016 um 18:28 schrieb Doran, Harold:
> Thank you very much. I don’t know tidyverse, I’ll look at that now. I did some tests with data.table package, but it was much slower on my machine, see examples below
>
> tmp <- data.frame(id = rep(1:200, each = 10), foo = rnorm(2000))
>
> idList <- unique(tmp\$id)
>
> system.time(replicate(500, tmp[which(tmp\$id == idList[1]),]))
>
> system.time(replicate(500, subset(tmp, id == idList[1])))
>
>
> library(data.table)
>
> tmp2 <- as.data.table(tmp)     # data.table
>
> system.time(replicate(500, tmp2[which(tmp\$id == idList[1]),]))
>
> system.time(replicate(500, subset(tmp2, id == idList[1])))
>
> From: Dominik Schneider [mailto:dosc3612 at colorado.edu]
> Sent: Wednesday, September 28, 2016 12:27 PM
> To: Doran, Harold <HDoran at air.org>
> Cc: r-help at r-project.org
> Subject: Re: [R] Faster Subsetting
>
> I regularly crunch through this amount of data with tidyverse. You can also try the data.table package. They are optimized for speed, as long as you have the memory.
> Dominik
>
> On Wed, Sep 28, 2016 at 10:09 AM, Doran, Harold <HDoran at air.org<mailto:HDoran at air.org>> wrote:
> I have an extremely large data frame (~13 million rows) that resembles the structure of the object tmp below in the reproducible code. In my real data, the variable, 'id' may or may not be ordered, but I think that is irrelevant.
>
> I have a process that requires subsetting the data by id and then running each smaller data frame through a set of functions. One example below uses indexing and the other uses an explicit call to subset(), both return the same result, but indexing is faster.
>
> Problem is in my real data, indexing must parse through millions of rows to evaluate the condition and this is expensive and a bottleneck in my code.  I'm curious if anyone can recommend an improvement that would somehow be less expensive and faster?
>
> Thank you
> Harold
>
>
> tmp <- data.frame(id = rep(1:200, each = 10), foo = rnorm(2000))
>
> idList <- unique(tmp\$id)
>
> ### Fast, but not fast enough
> system.time(replicate(500, tmp[which(tmp\$id == idList[1]),]))
>
> ### Not fast at all, a big bottleneck
> system.time(replicate(500, subset(tmp, id == idList[1])))
>
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