R help - Jun 2008 - Create Matrix from Loop of Vectors, Sort It and Pick Top-K

Hi,

I have the following dataset (simplified for example).

__DATA__
300.35  200.25 104.30
22.00 31.12 89.99
444.50 22.10 43.00
22.10 200.55 66.77

Now from that I wish to do the following:

1. Compute variance of each row
2. Pick top-2 row with highest variance
3. Store those selected rows for further processing

To achieve this, I tried to: a) read the table and compute
variance  for each row, b) append variance with its original
row in a vector, c) store a vector into multidimentional array (matrix),
d) sort that array. But I am stuck at the step (b).

Can anybody suggest what's the best way to achieve
my aim above?

This is the sample code I have so far (not working).

__BEGIN__

#data <- read.table("testdata.txt")


# Is this a right way to initialize?
all.arr = NULL

for (gi in 1:nofrow) {
   gex <- as.vector(data.matrix(data[gi,],rownames.force=FALSE))

   #compute variance
   gexvar <- var(gex)

   # join variance with its original vector
   nvec <- c(gexvar,gex)

   # I'm stuck here.....This doesn't seem to work
   all.arr <- data.frame(nvec)
}

print(all.arr)
__END__
--
 Gundala Viswanath
Jakarta - Indonesia

Dear Gundala,

Try this:

# Data set
DF=read.table(textConnection("300.35  200.25 104.30
22.00 31.12 89.99
444.50 22.10 43.00
22.10 200.55 66.77"),header=FALSE,sep="")

# Variances
VAR=apply(DF,1,var)

# Order
pos=order(VAR)

# Print VAR and pos
VAR
pos

# ordered VAR
VAR[pos]

# top-2 highest VAR
VAR[pos][3:4]


HTH,

Jorge




On Thu, Jun 19, 2008 at 10:59 AM, Gundala Viswanath <gundalav@gmail.com>
wrote:
> Hi,
>
> I have the following dataset (simplified for example).
>
> __DATA__
> 300.35  200.25 104.30
> 22.00 31.12 89.99
> 444.50 22.10 43.00
> 22.10 200.55 66.77
>
> Now from that I wish to do the following:
>
> 1. Compute variance of each row
> 2. Pick top-2 row with highest variance
> 3. Store those selected rows for further processing
>
> To achieve this, I tried to: a) read the table and compute
> variance  for each row, b) append variance with its original
> row in a vector, c) store a vector into multidimentional array (matrix),
> d) sort that array. But I am stuck at the step (b).
>
> Can anybody suggest what's the best way to achieve
> my aim above?
>
> This is the sample code I have so far (not working).
>
> __BEGIN__
>
> #data <- read.table("testdata.txt")
>
>
> # Is this a right way to initialize?
> all.arr = NULL
>
> for (gi in 1:nofrow) {
>   gex <- as.vector(data.matrix(data[gi,],rownames.force=FALSE))
>
>   #compute variance
>   gexvar <- var(gex)
>
>   # join variance with its original vector
>   nvec <- c(gexvar,gex)
>
>   # I'm stuck here.....This doesn't seem to work
>   all.arr <- data.frame(nvec)
> }
>
> print(all.arr)
> __END__
> --
>  Gundala Viswanath
> Jakarta - Indonesia
>
> ______________________________________________
> 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.
>
	[[alternative HTML version deleted]]

on 06/19/2008 09:59 AM Gundala Viswanath wrote:
> Hi,
> 
> I have the following dataset (simplified for example).
> 
> __DATA__
> 300.35  200.25 104.30
> 22.00 31.12 89.99
> 444.50 22.10 43.00
> 22.10 200.55 66.77
> 
> Now from that I wish to do the following:
> 
> 1. Compute variance of each row
> 2. Pick top-2 row with highest variance
> 3. Store those selected rows for further processing
> 
> To achieve this, I tried to: a) read the table and compute
> variance  for each row, b) append variance with its original
> row in a vector, c) store a vector into multidimentional array (matrix),
> d) sort that array. But I am stuck at the step (b).
> 
> Can anybody suggest what's the best way to achieve
> my aim above?
> 
> This is the sample code I have so far (not working).
> 
> __BEGIN__
> 
> #data <- read.table("testdata.txt")
> 
> 
> # Is this a right way to initialize?
> all.arr = NULL
> 
> for (gi in 1:nofrow) {
>    gex <- as.vector(data.matrix(data[gi,],rownames.force=FALSE))
> 
>    #compute variance
>    gexvar <- var(gex)
> 
>    # join variance with its original vector
>    nvec <- c(gexvar,gex)
> 
>    # I'm stuck here.....This doesn't seem to work
>    all.arr <- data.frame(nvec)
> }
> 
> print(all.arr)
> __END__
> --
If your data is contained in a data frame 'DF':

 > DF
       V1     V2     V3
1 300.35 200.25 104.30
2  22.00  31.12  89.99
3 444.50  22.10  43.00
4  22.10 200.55  66.77


# Get row-wise variances and cbind() them to DF
 > DF.var <- cbind(DF, var = apply(DF, 1, var, na.rm = TRUE))

 > DF.var
       V1     V2     V3       var
1 300.35 200.25 104.30  9610.336
2  22.00  31.12  89.99  1361.915
3 444.50  22.10  43.00 56676.803
4  22.10 200.55  66.77  8622.817


# Sort DF by 'var' using order()
 > DF.var[order(DF.var$var, decreasing = TRUE), ]
       V1     V2     V3       var
3 444.50  22.10  43.00 56676.803
1 300.35 200.25 104.30  9610.336
4  22.10 200.55  66.77  8622.817
2  22.00  31.12  89.99  1361.915


To get the top 2, you can take a couple of approaches:

 > DF.var[order(DF.var$var, decreasing = TRUE)[1:2], ]
       V1     V2    V3       var
3 444.50  22.10  43.0 56676.803
1 300.35 200.25 104.3  9610.336


or

 > head(DF.var[order(DF.var$var, decreasing = TRUE), ], 2)
       V1     V2    V3       var
3 444.50  22.10  43.0 56676.803
1 300.35 200.25 104.3  9610.336


See ?cbind, ?apply, ?order and ?head for more information.

HTH,

Marc Schwartz