HI,
You are right.
It is slower when compared to Pete's solution:
set.seed(125)
x <- matrix(sample(1:800000),nrow=1000)
colnames(x)<- paste("Col",1:ncol(x),sep="")
system.time({
res<-sapply(data.frame(x),function(x) c(summary(x),sd=sd(x),IQR=IQR(x)))
res1<-as.matrix(res)
res2<-res1[c(1:4,7,5,8,6),] })
# user system elapsed
# 0.596 0.000 0.597
system.time({
res<-apply(x,2,function(x) c(Min=min(x),
"1st Qu" =quantile(x, 0.25,names=FALSE),
Median = quantile(x, 0.5, names=FALSE),
Mean= mean(x),
Sd=sd(x),
"3rd Qu" = quantile(x,0.75,names=FALSE),
IQR=IQR(x),
Max = max(x))) })
# user system elapsed
# 0.384 0.000 0.384
A.K.
----- Original Message -----
From: Pete Brecknock <peter.breckn...@bp.com>
To: r-help@r-project.org
Cc:
Sent: Friday, November 23, 2012 8:42 AM
Subject: Re: [R] Summary statistics for matrix columns
frespider wrote
> Hi,
>
> it is possible. but don't you think it will slow the code if you convert
> to data.frame?
>
> Thanks
>
> Date: Thu, 22 Nov 2012 18:31:35 -0800
> From:
> ml-node+s789695n4650500h51@.nabble
> To:
> frespider@
> Subject: RE: Summary statistics for matrix columns
>
>
>
> HI,
>
> Is it possible to use as.matrix()?
>
> res<-sapply(data.frame(x),function(x) c(summary(x),sd=sd(x),IQR=IQR(x)))
>
> res1<-as.matrix(res)
>
> is.matrix(res1)
>
> #[1] TRUE
>
> res1[c(1:4,7,5,8,6),]
>
> # Col1 Col2 Col3 Col4 Col5 Col6 Col7
> Col8
>
> #Min. 10.00000 1.00000 17.00000 3.00000 18.00000 11.00000 13.00000
> 15.00000
>
> #1st Qu. 24.75000 29.50000 26.00000 7.75000 40.00000 17.25000 27.50000
> 34.75000
>
> #Median 34.00000 46.00000 42.50000 35.50000 49.50000 23.50000 51.50000
> 51.50000
>
> #Mean 42.50000 42.75000 41.75000 35.75000 44.88000 26.88000 44.75000
> 50.12000
>
> #sd 25.05993 27.77846 19.57221 28.40397 16.39196 16.60841 21.97239
> 25.51995
>
> #3rd Qu. 67.75000 58.50000 50.00000 63.25000 54.25000 30.25000 56.25000
> 70.50000
>
> #IQR 43.00000 29.00000 24.00000 55.50000 14.25000 13.00000 28.75000
> 35.75000
>
> #Max. 74.00000 77.00000 76.00000 70.00000 65.00000 63.00000 79.00000
> 80.00000
>
> # Col9 Col10
>
> #Min. 2.00000 6.00000
>
> #1st Qu. 24.50000 12.50000
>
> #Median 33.50000 48.00000
>
> #Mean 34.88000 40.75000
>
> #sd 24.39811 28.21727
>
> #3rd Qu. 45.25000 63.00000
>
> #IQR 20.75000 50.50000
>
> #Max. 71.00000 72.00000
>
> Solves the order and the matrix output!
>
> A.K.
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> If you reply to this email, your message will be added to the
> discussion
> below:
>
> http://r.789695.n4.nabble.com/Summary-statistics-for-matrix-columns-tp4650489p4650500.html
>
>
>
> To unsubscribe from Summary statistics for matrix columns, click here.
>
> NAML
Then maybe ....
x <- matrix(sample(1:8000),nrow=100)
colnames(x)<- paste("Col",1:ncol(x),sep="")
apply(x,2,function(x) c(Min=min(x),
"1st Qu" =quantile(x, 0.25,names=FALSE),
Median = quantile(x, 0.5, names=FALSE),
Mean= mean(x),
Sd=sd(x),
"3rd Qu" = quantile(x,0.75,names=FALSE),
IQR=IQR(x),
Max = max(x)))
HTH
Pete
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