mercy!!! ;-)
thanks, everyone. sure beats me trying to reinvent a slower version of the
wheel. came in very handy.
I think it would be nice to see some of these pointers in the "?by" manual
page. not sure who to ask to do this, but maybe this person reads r-help.
/iaw
----
Ivo Welch (ivo.we...@brown.edu, ivo.we...@gmail.com)
On Mon, Aug 30, 2010 at 5:23 PM, Gabor Grothendieck <ggrothendi...@gmail.com
> wrote:
> On Mon, Aug 30, 2010 at 3:54 PM, Dennis Murphy <djmu...@gmail.com> wrote:
> > Hi:
> >
> > You've already gotten some good replies re aggregate() and plyr; here are
> > two more choices, from packages doBy and data.table, plus the others for
> > a contained summary:
> >
> > key <- c(1,1,1,2,2,2)
> > val1 <- rnorm(6)
> > indf <- data.frame( key, val1)
> > outdf <- by(indf, indf$key, function(x) c(m=mean(x), s=sd(x)) )
> > outdf
> >
> > # Alternatives:
> >
> > # aggregate (base) with new formula interface
> >
> > # write a small function to return multiple outputs
> > f <- function(x) c(mean = mean(x, na.rm = TRUE), sd = sd(x, na.rm =
> TRUE))
> >
> > aggregate(val1 ~ key, data = indf, FUN = f)
> > key val1.mean val1.sd
> > 1 1 -0.9783589 0.6378922
> > 2 2 0.2816016 1.4490699
> >
> > # package doBy (get the same output)
> >
> > library(doBy)
> > summaryBy(val1 ~ key, data = indf, FUN = f)
> > key val1.mean val1.sd
> > 1 1 -0.9783589 0.6378922
> > 2 2 0.2816016 1.4490699
> >
> > # package plyr
> >
> > library(plyr)
> > ddply(indf, .(key), summarise, mean = mean(val1), sd = sd(val1))
> > key mean sd
> > 1 1 -0.9783589 0.6378922
> > 2 2 0.2816016 1.4490699
> >
> > # package data.table
> >
> > library(data.table)
> > indt <- data.table(indf)
> > indt[, list(mean = mean(val1), sd = sd(val1)), by =
> list(as.integer(key))]
> > key mean sd
> > [1,] 1 -0.9783589 0.6378922
> > [2,] 2 0.2816016 1.4490699
> >
> > It's a cornucopia! :) Multiple grouping variables are no problem with
> these
> > functions, BTW.
> >
> > HTH,
>
>
> And here are yet four more:
>
> >
> > f.by <- function(x) c(key = x$key[1], mean = mean(x$val), sd =
> sd(x$val))
> > do.call(rbind, by(indf, indf["key"], f.by))
> key mean sd
> 1 1 0.006794852 0.3779713
> 2 2 0.251890650 0.4379315
> >
> > library(sqldf)
> > sqldf("select key, avg(val1) mean, stdev(val1) sd from indf group by
> key")
> key mean sd
> 1 1 0.006794852 0.3779713
> 2 2 0.251890650 0.4379315
> >
> > library(remix)
> > remix(val1 ~ key, transform(indf, key = factor(key)), funs = c(mean, sd))
> val1 ~ key
> ==========
>
> +-----+---+------+-------+------+
> | | mean | sd |
> +=====+===+======+=======+======+
> | key | 1 | val1 | 0.01 | 0.38 |
> + +---+------+-------+------+
> | | 2 | val1 | 0.25 | 0.44 |
> +-----+---+------+-------+------+
> >
> > library(Hmisc)
> > summary(val1 ~ key, indf, fun = function(x) c(mean = mean(x), sd =
> sd(x)))
> val1 N=6
>
> +-------+-+-+-----------+---------+
> | | |N|mean |sd.val1 |
> +-------+-+-+-----------+---------+
> |key |1|3|0.006794852|0.3779713|
> | |2|3|0.251890650|0.4379315|
> +-------+-+-+-----------+---------+
> |Overall| |6|0.129342751|0.3897180|
> +-------+-+-+-----------+---------+
>
> --
> Statistics & Software Consulting
> GKX Group, GKX Associates Inc.
> tel: 1-877-GKX-GROUP
> email: ggrothendieck at gmail.com
>
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