Another way that Matthew Dowle showed me for this type of problem is
to reshape frame to a long format. It makes it easier to manipulate
and can be faster.
> longdt <- with(frame, data.table(group = unlist(rep(group, each=7)), x =
> c(a,b,c,d,e,f,g)))
>
> system.time(new.frame4 <- longdt[, x/mean(x, na.rm = TRUE), by = "group"])
user system elapsed
0.54 0.04 0.61
>
> # Or, remove the NAs ahead of time for more speed:
>
> longdt2 <- longdt[!is.na(longdt$x),]
> system.time(new.frame4 <- longdt2[, x/mean(x), by = "group"])
user system elapsed
0.17 0.00 0.17
- Tom
On Wed, Apr 7, 2010 at 3:46 PM, Tom Short <tshort.rli...@gmail.com> wrote:
> Here's how I would have done the data.table method. It's a bit faster
> than the ave approach on my machine:
>
>> # install.packages("data.table",repos="http://R-Forge.R-project.org";)
>> library(data.table)
>>
>> f3 <- function(frame) {
> + frame <- as.data.table(frame)
> + frame[, lapply(.SD[,2:ncol(.SD), with = FALSE],
> + function(x) x / mean(x, na.rm = TRUE)),
> + by = "group"]
> + }
>>
>> system.time(new.frame2 <- f2(frame)) # ave
> user system elapsed
> 0.50 0.08 1.24
>> system.time(new.frame3 <- f3(frame)) # data.table
> user system elapsed
> 0.25 0.01 0.30
>
> - Tom
>
> Tom Short
>
>
> On Wed, Apr 7, 2010 at 12:46 PM, Dimitri Liakhovitski <ld7...@gmail.com>
> wrote:
>> I would like to thank once more everyone who helped me with this question.
>> I compared the speed for different approaches. Below are the results
>> of my comparisons - in case anyone is interested:
>>
>> ### Building an EXAMPLE FRAME with N rows - with groups and a lot of NAs:
>> N<-100000
>> set.seed(1234)
>> frame<-data.frame(group=rep(paste("group",1:10),N/10),a=rnorm(1:N),b=rnorm(1:N),c=rnorm(1:N),d=rnorm(1:N),e=rnorm(1:N),f=rnorm(1:N),g=rnorm(1:N))
>> frame<-frame[order(frame$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
>>
>> ______________________________________________
>> 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.
>>
>
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