tapply within a data.frame: a simpler alternative?

On Wed, Dec 10, 2008 at 11:02 AM, baptiste auguie  
<ba208 at exeter.ac.uk> wrote:

Dear list,

I have a data.frame with x, y values and a 3-level factor "group",  
say. I
want to create a new column in this data.frame with the values of y  
scaled
to 1 by group. Perhaps the example below describes it best:

x <- seq(0, 10, len=100)
my.df <- data.frame(x = rep(x, 3), y=c(3*sin(x), 2*cos(x),  
cos(2*x)), #
note how the y values have a different maximum depending on the  
group
      group = factor(rep(c("sin", "cos", "cos2"), each=100)))
library(reshape)
df.melt <- melt(my.df, id=c("x","group")) # make a long format
df.melt <- df.melt[ order(df.melt$group) ,] # order the data.frame  
by the
group factor
df.melt$norm <- do.call(c, tapply(df.melt$value, df.melt$group,
function(.v) {.v / max(.v)})) # calculate the normalised value per  
group and
assign it to a new column
library(lattice)
xyplot(norm + value ~ x,groups=group,  data=df.melt, auto.key=T) #  
check
that it worked

This procedure works, but it feels like I'm reinventing the wheel  
using
hammer and saw. I tried to use aggregate, by, ddply (plyr package),  
but I
coudn't find anything straight-forward.

It's pretty easy with ddply:

df.melt <- ddply(df.melt, .(group), transform, norm = y / max(y))

Hadley

--
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