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
_____________________________
Baptiste Auguié
School of Physics
University of Exeter
Stocker Road,
Exeter, Devon,
EX4 4QL, UK
Phone: +44 1392 264187
http://newton.ex.ac.uk/research/emag
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