Dear Young,
I am sorry, my bad, it should have been
with(X, tapply(x, list(id, t), function(y) y ))
to get exactly what you asked for.
HTH,
Jorge
On Wed, Jul 1, 2009 at 4:59 PM, Jorge Ivan Velez
<jorgeivanve...@gmail.com>wrote:
> Dear Young,
> Try this:
>
> with(X, tapply(x, list(t,id), function(y) y ))
>
> HTH,
>
> Jorge
>
>
> On Wed, Jul 1, 2009 at 1:35 PM, Young Cho <young.s...@gmail.com> wrote:
>
>> Hi, thanks everyone for any help in advance.
>>
>> I found myself dealing with a tabular time-series data formatted each row
>> like [ time stamp, ID, values]. I made a small examples:
>>
>> X = data.frame(t=c(1,1,1,2,2,2,2,3,3,3,4,4,4,5,5),id =
>> c('a','b','c','c','b','d','e','b','a','e','a','b','d','b','c'))
>> X$x = rnorm(15)
>>
>> 't' is time stamp, 'id' is identifier, 'x' is time series values. They are
>> not necessarily ordered and have sometimes missing values. In order to do
>> any analysis, I used to convert this type of data into a matrix form :
>>
>> Y = matrix(NA,length(unique(X$id)),length(unique(X$t)))
>> rownames(Y) = sort(unique(X$id))
>> colnames(Y) = sort(unique(X$t))
>> for(i in 1:nrow(Y)){
>> xi = X[ X$id == rownames(Y)[i], ]
>> Y[i, match(xi$t, colnames(Y)) ] = xi$x
>> }
>>
>> Then, run any R operations on Y. Now, this conversion gets very painfully
>> slow as my data gets substantially larger. I was wondering if there is
>> some
>> better ways to convert a table like 'X' into a matrix like 'Y', or even
>> better ways to re-format data, not necessarily matrix form.
>>
>> Young
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
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>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
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