I would like to thank once more everyone who helped me with this question.
I compared the speed for different approaches. Below are the results
of my comparisons - in case anyone is interested:
### Building an EXAMPLE FRAME with N rows - with groups and a lot of NAs:
N<-100000
set.seed(1234)
frame<-data.frame(group=rep(paste("group",1:10),N/10),a=rnorm(1:N),b=rnorm(1:N),c=rnorm(1:N),d=rnorm(1:N),e=rnorm(1:N),f=rnorm(1:N),g=rnorm(1:N))
frame<-frame[order(frame$group),]
## Introducing 60% NAs:
names.used<-names(frame)[2:length(frame)]
set.seed(1234)
for(i in names.used){
i.for.NA<-sample(1:N,round((N*.6),0))
frame[[i]][i.for.NA]<-NA
}
lapply(frame[2:8], function(x) length(x[is.na(x)])) # Checking that it worked
ORIGframe<-frame ## placeholder for the unchanged original frame
####### Objective of the code - divide each value by its group mean ####
### METHOD 1 - the FASTEST - using ave():##############################
frame<-ORIGframe
f2 <- function(frame) {
for(i in 2:ncol(frame)) {
frame[,i] <- ave(frame[,i], frame[,1], FUN=function(x)x/mean(x,na.rm=TRUE))
}
frame
}
system.time({new.frame<-f2(frame)})
# Took me 0.23-0.27 sec
#######################################
### METHOD 2 - fast, just a bit slower - using data.table:
##############################
# If you don't have it - install the package - NOT from CRAN:
install.packages("data.table",repos="http://R-Forge.R-project.org";)
library(data.table)
frame<-ORIGframe
system.time({
table<-data.table(frame)
colMeanFunction<-function(data,key){
data[[key]]=NULL
ret=as.matrix(data)/matrix(rep(as.numeric(colMeans(as.data.frame(data),na.rm=T)),nrow(data)),nrow=nrow(data),ncol=ncol(data),byrow=T)
return(ret)
}
groupedMeans = table[,colMeanFunction(.SD, "group"), by="group"]
names.to.use<-names(groupedMeans)
for(i in
1:length(groupedMeans)){groupedMeans[[i]]<-as.data.frame(groupedMeans[[i]])}
groupedMeans<-do.call(cbind, groupedMeans)
names(groupedMeans)<-names.to.use
})
# Took me 0.37-.45 sec
#######################################
### METHOD 3 - fast, a tad slower (using model.matrix & matrix
multiplication):##############################
frame<-ORIGframe
system.time({
mat <- as.matrix(frame[,-1])
mm <- model.matrix(~0+group,frame)
col.grp.N <- crossprod( !is.na(mat), mm ) # Use this line if don't
want to use NAs for mean calculations
# col.grp.N <- crossprod( mat != 0 , mm ) # Use this line if don't
want to use zeros for mean calculations
mat[is.na(mat)] <- 0.0
col.grp.sum <- crossprod( mat, mm )
mat <- mat / ( t(col.grp.sum/col.grp.N)[ frame$group,] )
is.na(mat) <- is.na(frame[,-1])
mat<-as.data.frame(mat)
})
# Took me 0.44-0.50 sec
#######################################
### METHOD 5- much slower - it's the one I started
with:##############################
frame<-ORIGframe
system.time({
frame <- do.call(cbind, lapply(names.used, function(x){
unlist(by(frame, frame$group, function(y) y[,x] / mean(y[,x],na.rm=T)))
}))
})
# Took me 1.25-1.32 min
#######################################
### METHOD 6 - the slowest; using "plyr" and
"ddply":##############################
frame<-ORIGframe
library(plyr)
function3 <- function(x) x / mean(x, na.rm = TRUE)
system.time({
grouping.factor<-"group"
myvariables<-names(frame)[2:8]
frame3<-ddply(frame, grouping.factor, colwise(function3, myvariables))
})
# Took me 1.36-1.47 min
#######################################
Thanks again!
Dimitri
On Wed, Mar 31, 2010 at 8:29 PM, William Dunlap <wdun...@tibco.com> wrote:
> Dimitri,
>
> You might try applying ave() to each column. E.g., use
>
> f2 <- function(frame) {
> for(i in 2:ncol(frame)) {
> frame[,i] <- ave(frame[,i], frame[,1],
> FUN=function(x)x/mean(x,na.rm=TRUE))
> }
> frame
> }
>
> Note that this returns a data.frame and retains the
> grouping column (the first) while your original
> code returns a matrix without the grouping column.
>
> Bill Dunlap
> Spotfire, TIBCO Software
> wdunlap tibco.com
>
>> -----Original Message-----
>> From: r-help-boun...@r-project.org
>> [mailto:r-help-boun...@r-project.org] On Behalf Of Bert Gunter
>> Sent: Tuesday, March 30, 2010 10:52 AM
>> To: 'Dimitri Liakhovitski'; 'r-help'
>> Subject: Re: [R] Code is too slow: mean-centering variables
>> in a data framebysubgroup
>>
>> ?scale
>>
>> Bert Gunter
>> Genentech Nonclinical Biostatistics
>>
>>
>>
>> -----Original Message-----
>> From: r-help-boun...@r-project.org
>> [mailto:r-help-boun...@r-project.org] On
>> Behalf Of Dimitri Liakhovitski
>> Sent: Tuesday, March 30, 2010 8:05 AM
>> To: r-help
>> Subject: [R] Code is too slow: mean-centering variables in a
>> data frame
>> bysubgroup
>>
>> Dear R-ers,
>>
>> I have a large data frame (several thousands of rows and about 2.5
>> thousand columns). One variable ("group") is a grouping variable with
>> over 30 levels. And I have a lot of NAs.
>> For each variable, I need to divide each value by variable mean - by
>> subgroup. I have the code but it's way too slow - takes me about 1.5
>> hours.
>> Below is a data example and my code that is too slow. Is there a
>> different, faster way of doing the same thing?
>> Thanks a lot for your advice!
>>
>> Dimitri
>>
>>
>> # Building an example frame - with groups and a lot of NAs:
>> set.seed(1234)
>> frame<-data.frame(group=rep(paste("group",1:10),10),a=rnorm(1:
> 100),b=rnorm(1
>> :100),c=rnorm(1:100),d=rnorm(1:100),e=rnorm(1:100),f=rnorm(1:1
>> 00),g=rnorm(1:
>> 100))
>> frame<-frame[order(frame$group),]
>> names.used<-names(frame)[2:length(frame)]
>> set.seed(1234)
>> for(i in names.used){
>> i.for.NA<-sample(1:100,60)
>> frame[[i]][i.for.NA]<-NA
>> }
>> frame
>>
>> ### Code that does what's needed but is too slow:
>> Start<-Sys.time()
>> frame <- do.call(cbind, lapply(names.used, function(x){
>> unlist(by(frame, frame$group, function(y) y[,x] /
>> mean(y[,x],na.rm=T)))
>> }))
>> Finish<-Sys.time()
>> print(Finish-Start) # Takes too long
>>
>> --
>> Dimitri Liakhovitski
>> Ninah.com
>> dimitri.liakhovit...@ninah.com
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
--
Dimitri Liakhovitski
Ninah.com
dimitri.liakhovit...@ninah.com
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
