Hi,
Probably you might have got the solution.
Anyway, you can try this:
Dat <- read.table(text="
ID TIME DV
1 0 0.880146038
1 1 0.88669051
1 3 0.610784702
1 5 0.756046666
2 0 0.456263368
2 1 0.369991537
2 3 0.508798346
2 5 0.441037014
3 0 0.854905349
3 1 0.960457553
3 3 0.609434409
3 5 0.655006334
",sep="", header = TRUE)
SpDV<-function(x,n) split(x,sort(rank(x) %%n))
DVlist<-SpDV(Dat$DV,length(unique(Dat$ID)))
Dat$DVn<-cbind(unlist(lapply(DVlist,FUN=function(x) x/x[1])))
> Dat
ID TIME DV DVn
1 1 0 0.8801460 1.0000000
2 1 1 0.8866905 1.0074357
3 1 3 0.6107847 0.6939584
4 1 5 0.7560467 0.8590014
5 2 0 0.4562634 1.0000000
6 2 1 0.3699915 0.8109166
7 2 3 0.5087983 1.1151418
8 2 5 0.4410370 0.9666281
9 3 0 0.8549053 1.0000000
10 3 1 0.9604576 1.1234665
11 3 3 0.6094344 0.7128677
12 3 5 0.6550063 0.7661741
A.K.
----- Original Message -----
From: york8866 <yu_y...@hotmail.com>
To: r-help@r-project.org
Cc:
Sent: Tuesday, June 19, 2012 8:08 PM
Subject: [R] data normalization
I have a dataframe such like the following:
ID TIME DV
1 0 0.880146038
1 1 0.88669051
1 3 0.610784702
1 5 0.756046666
2 0 0.456263368
2 1 0.369991537
2 3 0.508798346
2 5 0.441037014
3 0 0.854905349
3 1 0.960457553
3 3 0.609434409
3 5 0.655006334
. . .
. . .
I would like to generate another column with the normalized values of DV.
for each ID, normalize to the value at TIME 0.
I was able to use 2 loops to do the normalization, however, as 2 loops took
a long time for the calculation, I don't know whether there's a better way
to do it.
I have the following code with only 1 loop, but it did not work. Can anyone
help with this? Thanks,
IDS <- unique(data.frame$ID)
for(WhichID in IDS)
{
subset <- data.frame[,"ID"] == WhichID
DVREAL <- data.frame[subset,"DV"]
DVNORM[WhichID] <- DVREAL/DVREAL[1]
}
--
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and provide commented, minimal, self-contained, reproducible code.
