tapply within a data.frame: a simpler alternative?

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.

I'll appreciate any input,

Baptiste