If you have a large datatable, you might consider using 'data.table'
which is better performing than 'plyr'
> x <- read.table(textConnection("Gene ProbeID
> Expression_Level
+ A 1 0.34
+ A 2 0.21
+ E 3 0.11
+ A 4 0.21
+ F 5 0.56
+ F 6 0.87"), header = TRUE)
> closeAllConnections()
> require(data.table)
> x <- data.table(x)
> x[,
+ list(nProbes = length(ProbeID)
+ , Mean_Level = mean(Expression_Level)
+ )
+ , by = Gene
+ ]
Gene nProbes Mean_Level
[1,] A 3 0.2533333
[2,] E 1 0.1100000
[3,] F 2 0.7150000
>
>
On Thu, Jun 30, 2011 at 3:28 AM, Max Mariasegaram
<max.mariasega...@qut.edu.au> wrote:
> Hi,
>
> I am interested in using the cast function in R to perform some aggregation.
> I did once manage to get it working, but have now forgotten how I did this.
> So here is my dilemma. I have several thousands of probes (about 180,000)
> corresponding to each gene; what I'd like to do is obtain is a frequency
> count of the various occurrences of each probes for each gene.
>
> The data would look something like this:
>
> Gene ProbeID Expression_Level
> A 1 0.34
> A 2 0.21
> E 3 0.11
> A 4 0.21
> F 5 0.56
> F 6 0.87
> .
> .
> .
> (180000 data points)
>
> In each case, the probeID is unique. The output I am looking for is something
> like this:
>
> Gene No.ofprobes Mean_expression
> A 3 0.25
>
> Is there an easy way to do this using "cast" or "melt"? Ideally, I would also
> like to see the unique probes corresponding to each gene in the wide format.
>
> Thanks in advance
> Max
>
> Maxy Mariasegaram| Reserach Fellow | Australian Prostate Cancer Research
> Centre| Level 1, Building 33 | Princess Alexandra Hospital | 199 Ipswich
> Road, Brisbane QLD 4102 Australia | t: 07 3176 3073| f: 07 3176 7440 | e:
> maria...@qut.edu.au
>
>
> [[alternative HTML version deleted]]
>
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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>
--
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
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