HI A.k,
I need one more question, if you can answer it please
M <- matrix(sample(1:8000),nrow=100)
colnames(M)<- paste("Col",1:ncol(M),sep="")
apply(M,2,function(x) c(Min=min(x),"1st Qu" =quantile(x, 0.25,names=FALSE),
Range = range(x),
Median = quantile(x, 0.5, names=FALSE),
Mean= mean(x),Std=sd(x),
"3rd Qu" = quantile(x,0.75,names=FALSE),
IQR=IQR(x),Max = max(x)))
why I get two range . isn't range mean the different between the max and min
Thanks
Date: Fri, 23 Nov 2012 16:08:12 -0800
From: ml-node+s789695n4650613...@n4.nabble.com
To: frespi...@hotmail.com
Subject: Re: Summary statistics for matrix columns
Hi,
No problem.
There are a couple of other libraries which deal with summary statistics:
library(pastecs)
?stat.desc() #
library(matrixStats)
#Using the functions from package: matrixStats
fun1<-function(x){
res<-rbind(colMins(x),colQuantiles(x)[,2],colMedians(x),colMeans(x),colSds(x),colQuantiles(x)[,4],colIQRs(x),colMaxs(x))
row.names(res)<-c("Min.","1st Qu.","Median","Mean","sd","3rd Qu.","IQR","Max.")
res}
set.seed(125)
x <- matrix(sample(1:80),nrow=8)
colnames(x)<- paste("Col",1:ncol(x),sep="")
fun1(x)
# 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 44.87500 26.87500 44.75000 50.12500
#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 34.87500 40.75000
#sd 24.39811 28.21727
#3rd Qu. 45.25000 63.00000
#IQR 20.75000 50.50000
#Max. 71.00000 72.00000
I thought this could be faster than the previous methods. But, it was the
slowest.
set.seed(125)
x1 <- matrix(sample(1:800000),nrow=1000)
colnames(x)<- paste("Col",1:ncol(x1),sep="")
system.time(fun1(x1))
# user system elapsed
# 0.968 0.000 0.956
A.K.
________________________________
From: Fares Said <[hidden email]>
To: arun <[hidden email]>
Cc: Pete Brecknock <[hidden email]>; R help <[hidden email]>
Sent: Friday, November 23, 2012 10:23 AM
Subject: Re: [R] Summary statistics for matrix columns
Thank you all
Sent from my iPhone
On 2012-11-23, at 10:19, "arun" <[hidden email]> wrote:
> 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 <[hidden email]>
> To: [hidden email]
> 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
>>
[[elided Hotmail spam]]
>>
>> 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
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>> 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
>
>
>
> --
> View this message in context:
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