Hi Jorge, Chuck and Kane,
thanks for your input!
The following code based on Jorge's answer did the trick to
standardize for subgroups within multiple columns:
# define a standardize function, but you could also define your custom
standardize function here
z.mean.sd <- function(data){
return.values <- (data - mean(data, na.rm = TRUE)) / (sd(data, na.rm
= TRUE))
return(return.values)
}
# assume there is some data.frame called sole.data with a group factor
sole.data$studie already read into R
sole.data <- read.csv2("SoLe.dat")
attach(sole.data)
# assume I have created a subset of the data.frame cor.vars with only
some of the vars needed to be standardized
cor.vars <- data.frame(var02, var04, var07, var10, var17, var24, var 36)
z.cor.vars <- apply(cor.vars, 2, tapply, sole.data$studie, z.mean.sd)
z.cor.vars <- sapply(z.cor.vars, unlist, USE.NAMES = FALSE)
z.cor.vars
BUT then Chuck's answer was much more elegant than my first woodpecker
solution
apply(iris[,1:4], 2, function(x){ave(x, iris$Species, FUN = scale)})
could be translated into
apply(sole.data[,c(2,4,7,10,17,24,36)], 2, function(x){ave(x,sole.data
$studie, FUN=scale)})
Thanks for the beauty of this code with an anonymous function call :)
-karsten
Am 29.11.2009 um 16:47 schrieb Jorge Ivan Velez:
Hi Karsten,
Let me assume your data is called d. If I understood what you are
trying to do, the following might help:
res <- apply(d, 2, tapply, d$group, scale)
res
See ?apply, ?tapply and ?scale for more information.
HTH,
Jorge
On Sun, Nov 29, 2009 at 10:41 AM, Karsten Wolf <> wrote:
Hi folks,
I have a dataframe df.vars with the follwing structure:
var1 var2 var3 group
Group is a factor.
Now I want to standardize the vars 1-3 (actually - there are many
more) by class, so I define
z.mean.sd <- function(data){
return.values <- (data - mean(data)) / (sd(data))
return(return.values)
}
now I can call for each var
z.var1 <- by(df.vars$var1, group, z.mean.sd)
which gives me the standardised data for each subgroup in a list
with the subgroups
z.var1 <- unlist(z.var1)
then gives me the z-standardised data for var1 in one vector. Great!
Now I would like to do this for the whole dataframe, but probably I
am not thinking vectorwise enough.
z.df.vars <- by(df.vars, group, z.mean.sd)
does not work. I banged my head on other solutions trying out sapply
and tapply, but did not succeed. Do I need to loop and put
everything together by hand? But I want to keep the columnnames in
the vector?
-karsten
---------------------------------------------------------------------------------------------
Karsten D. Wolf
Didactical Design of Interactive
Learning Environments
Universit?t Bremen - Fachbereich 12
web: http://www.ifeb.uni-bremen.de/wolf/