I make a version for matrix. Because, it would be more efficient to
split each column of a matrix than to convert a matrix to a data.frame
then call split() on the data.frame. Note that the version for a
matrix and a data.frame is slightly different. Would somebody add this
in R as well?
split.matrix<-function(x,f) {
#print('processing matrix')
v=lapply(
1:dim(x)[[2]]
, function(i) {
base:::split.default(x[,i],f)#the difference is here
}
)
w=lapply(
seq(along=v[[1]])
, function(i) {
result=do.call(
cbind
, lapply(v,
function(vj) {
vj[[i]]
}
)
)
colnames(result)=colnames(x)
return(result)
}
)
names(w)=names(v[[1]])
return(w)
}
On Wed, Dec 9, 2009 at 5:44 PM, Charles C. Berry <cbe...@tajo.ucsd.edu> wrote:
> On Wed, 9 Dec 2009, William Dunlap wrote:
>
>> Here are some differences between the current and proposed
>> split.data.frame.
>
> Adding 'drop=FALSE' fixes this case. See in line correction below.
>
> Chuck
>
>>
>>> d<-data.frame(Matrix=I(matrix(1:10, ncol=2)),
>>
>> Named=c(one=1,two=2,three=3,four=4,five=5),
>> row.names=as.character(1001:1005))
>>>
>>> group<-c("A","B","A","A","B")
>>> split.data.frame(d,group)
>>
>> $A
>> Matrix.1 Matrix.2 Named
>> 1001 1 6 1
>> 1003 3 8 3
>> 1004 4 9 4
>>
>> $B
>> Matrix.1 Matrix.2 Named
>> 1002 2 7 2
>> 1005 5 10 5
>>
>>> mysplit.data.frame(d,group) # lost row.names and 2nd column of Matrix
>>
>> [1] "processing data.frame"
>> $A
>> Matrix Named
>> [1,] 1 1
>> [2,] 3 3
>> [3,] 4 4
>>
>> $B
>> Matrix Named
>> [1,] 2 2
>> [2,] 5 5
>>
>>
>> Bill Dunlap
>> Spotfire, TIBCO Software
>> wdunlap tibco.com
>>
>>> -----Original Message-----
>>> From: r-devel-boun...@r-project.org
>>> [mailto:r-devel-boun...@r-project.org] On Behalf Of
>>> pengyu...@gmail.com
>>> Sent: Wednesday, December 09, 2009 2:10 PM
>>> To: r-de...@stat.math.ethz.ch
>>> Cc: r-b...@r-project.org
>>> Subject: [Rd] split() is slow on data.frame (PR#14123)
>>>
>>> Please see the following code for the runtime comparison between
>>> split() and mysplit.data.frame() (they do the same thing
>>> semantically). mysplit.data.frame() is a fix of split() in term of
>>> performance. Could somebody include this fix (with possible checking
>>> for corner cases) in future version of R and let me know the inclusion
>>> of the fix?
>>>
>>> m=300000
>>> n=6
>>> k=30000
>>>
>>> set.seed(0)
>>> x=replicate(n,rnorm(m))
>>> f=sample(1:k, size=m, replace=T)
>>>
>>> mysplit.data.frame<-function(x,f) {
>>> print('processing data.frame')
>>> v=lapply(
>>> 1:dim(x)[[2]]
>>> , function(i) {
>>> split(x[,i],f)
>
> Change to:
>
> split(x[,i,drop=FALSE],f)
>
>
>>> }
>>> )
>>>
>>> w=lapply(
>>> seq(along=v[[1]])
>>> , function(i) {
>>> result=do.call(
>>> cbind
>>> , lapply(v,
>>> function(vj) {
>>> vj[[i]]
>>> }
>>> )
>>> )
>>> colnames(result)=colnames(x)
>>> return(result)
>>> }
>>> )
>>> names(w)=names(v[[1]])
>>> return(w)
>>> }
>>>
>>> system.time(split(as.data.frame(x),f))
>>> system.time(mysplit.data.frame(as.data.frame(x),f))
>>>
>>> ______________________________________________
>>> r-de...@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>>
>>
>> ______________________________________________
>> r-de...@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-devel
>>
>
> Charles C. Berry (858) 534-2098
> Dept of Family/Preventive
> Medicine
> E mailto:cbe...@tajo.ucsd.edu UC San Diego
> http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
>
>
>
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