[datatable-help] How to speed up grouping time series, help please
Daniele Amberti
daniele.amberti at ors.it
Thu Apr 7 09:43:37 CEST 2011
Sorry for delayed answer Matthew and thanks for Your interest in the topic.
I can get the appropriate matrix shape from
> X
ID DATE VALUE
14 3 2000-01-01 00:00:03 0.5726334
4 1 2000-01-01 00:00:03 0.8830174
1 1 2000-01-01 00:00:00 0.2875775
15 3 2000-01-01 00:00:04 0.1029247
11 3 2000-01-01 00:00:00 0.9568333
9 2 2000-01-01 00:00:03 0.5514350
7 2 2000-01-01 00:00:01 0.5281055
6 2 2000-01-01 00:00:00 0.0455565
12 3 2000-01-01 00:00:01 0.4533342
8 2 2000-01-01 00:00:02 0.8924190
3 1 2000-01-01 00:00:02 0.4089769
13 3 2000-01-01 00:00:02 0.6775706
With
reshape(X, idvar = "DATE", timevar = "ID", direction = "wide")
DATE VALUE.3 VALUE.1 VALUE.2
14 2000-01-01 00:00:03 0.5726334 0.8830174 0.5514350
1 2000-01-01 00:00:00 0.9568333 0.2875775 0.0455565
15 2000-01-01 00:00:04 0.1029247 NA NA
7 2000-01-01 00:00:01 0.4533342 NA 0.5281055
8 2000-01-01 00:00:02 0.6775706 0.4089769 0.8924190
and then sorting by date.
What I was trying to do is to:
- DT <- data.table(X)
- setKey(DT, ID, DATE)
... than start the staff I'm not able to manage:
My idea was to have 3 data tables in a list and then merge, the code You refer to (do.call(merge ...)) is not working as described and so is not really relevant here. Also
Also data.table do not implement a merge method so this approach it's probably not optimal.
Another option can be data.table and reshape, a discussion already took place:
http://www.mail-archive.com/r-help@r-project.org/msg102833.html
but I didn't find a solution to the problem from that.
Since data.table is so fast in sorting and grouping I'm wondering if there is the possibility to reshape data as described in the example faster then what actually I'm able to do leveraging xts, split and merge functions.
X <- xts(X[,c("ID","VALUE")], as.POSIXct(X[,"DATE"]))
X <- do.call(merge, split(X$VALUE,X$ID))
My understanding is that data.table was created with time series in mind (also supported by Your presentation at LondonR), since most advanced time series objects in R (xts, timeSeries or zoo) need to be created as described in previous post, to have a really fast approach to reshape data and get a suitable format for specialized time series objects would be a very nice feature.
Thanks in advance,
Daniele
-----Original Message-----
From: Matthew Dowle [mailto:mdowle at mdowle.plus.com]
Sent: 06 April 2011 00:48
To: Daniele Amberti
Cc: datatable-help at r-forge.wu-wien.ac.at; Matthew Dowle
Subject: RE: [datatable-help] How to speed up grouping time series,help please
pls explain the big picture. I don't recognise why you're attempting to
do.call merge.
> Thanks for Your reply Matthew,
> On 10 ts, 10000 values each, it takes 5.7 seconds to reshape, I'm willing
> to reduce time at least by the half reducing my total batch time by 10
> minutes approximately (over 70 minutes total).
>
> I'm trying to do something like:
> do.call(mergfe, x[, .SD, by=ID]) but data.table is not designed to work
> this way (return a data.table), there is no problem in data.table itself.
> I'm trying to extract K (10) data.table from a data.table with keys ID,
> DATE and then CJ.
>
>
> Thanks in advance for any help.
> Best regards,
> Daniele
>
> -----Original Message-----
> From: datatable-help-bounces at r-forge.wu-wien.ac.at
> [mailto:datatable-help-bounces at r-forge.wu-wien.ac.at] On Behalf Of Matthew
> Dowle
> Sent: 05 April 2011 14:20
> To: datatable-help at r-forge.wu-wien.ac.at
> Subject: Re: [datatable-help] How to speed up grouping time series,help
> please
>
> It's easier to help if you provide timings along with your example
> reproducible code, please.
> How long is it taking, and how long do you think it should take?
> Please also try to avoid phrases such as "without success". Does that mean
> you got an error message (if so, what was it) or wrong result (if so, what
> was wrong)?
> Matthew
>
> "Daniele Amberti" <daniele.amberti at ors.it> wrote in message
> news:5C57984CA179A247803E12AAB0F7ABA6DB20979608 at adorsmail01.ors.local...
>>I retrieve for a few hundred times a group of time series (10-15 ts
>>with 10000 values each), on every group I do some calculation, graphs
>>etc. I wonder if there is a faster method than what presented below to
>>get an appropriate timeseries object.
>>
>> Making a query with RODBC for every group I get a data frame like this:
>>
>>> X
>> ID DATE VALUE
>> 14 3 2000-01-01 00:00:03 0.5726334
>> 4 1 2000-01-01 00:00:03 0.8830174
>> 1 1 2000-01-01 00:00:00 0.2875775
>> 15 3 2000-01-01 00:00:04 0.1029247
>> 11 3 2000-01-01 00:00:00 0.9568333
>> 9 2 2000-01-01 00:00:03 0.5514350
>> 7 2 2000-01-01 00:00:01 0.5281055
>> 6 2 2000-01-01 00:00:00 0.0455565
>> 12 3 2000-01-01 00:00:01 0.4533342
>> 8 2 2000-01-01 00:00:02 0.8924190
>> 3 1 2000-01-01 00:00:02 0.4089769
>> 13 3 2000-01-01 00:00:02 0.6775706
>>
>> And I want to get a timeSeries object or xts object like this:
>>
>> 1 2 3
>> 2000-01-01 00:00:00 0.2875775 0.0455565 0.9568333
>> 2000-01-01 00:00:01 NA 0.5281055 0.4533342
>> 2000-01-01 00:00:02 0.4089769 0.8924190 0.6775706
>> 2000-01-01 00:00:03 0.8830174 0.5514350 0.5726334
>> 2000-01-01 00:00:04 NA NA 0.1029247
>>
>> Both classes accept a matrix so if I can create a matrix like the one
>> represented above and an array of characters representing dates faster
>> than what possible with xts:::merge, for example, I will have a faster
>> implementation, this is the reason why I'm writing to datatable-help;
>> I red vignettes, tests and did tests trying to generate a set of
>> data.table (using .SD and by = ID) an then CJ but without success up
>> to now, any input to test this approach will be really appreciate.
>>
>> Input data can be sorted or unsorted (the most complicated case is in
>> the example, unsorted and missing data) in the sense that I can sort
>> in query if I can take an advantage from this.
>>
>> Below some code to generate the test case above.
>>
>> Thanks in advance for any input, best regards, Daniele
>>
>>
>> set.seed(123)
>> N <- 100 # number of observations, use 5 to replicate test case above
>> K <- 3 # number of timeseries ID
>>
>> X <- data.frame(
>> ID = rep(1:K, each = N),
>> DATE = as.character(rep(as.POSIXct("2000-01-01", tz = "GMT")+ 0:(N-1),
>> K)),
>> VALUE = runif(N*K), stringsAsFactors = FALSE)
>>
>> X <- X[sample(1:(N*K), N*K),] # sample observations to get random order
>> (optional)
>> X <- X[-(sample(1:nrow(X), floor(nrow(X)*0.2))),] # 20% missing
>>
>> head(X, 15)
>>
>>
>> # an implementation in xts:
>> xtsSplit <- function(x)
>> {
>> library(xts)
>> x <- xts(x[,c("ID","VALUE")], as.POSIXct(x[,"DATE"]))
>> x <- do.call(merge, split(x$VALUE,x$ID))
>> return(x)
>> }
>>
>> xtsSplitTime <- replicate(50,
>> system.time(xtsSplit(X))[[1]])
>> median(xtsTime)
>>
>>
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>
>
>
>
> ORS Srl
>
> Via Agostino Morando 1/3 12060 Roddi (Cn) - Italy
> Tel. +39 0173 620211
> Fax. +39 0173 620299 / +39 0173 433111
> Web Site www.ors.it
>
> ------------------------------------------------------------------------------------------------------------------------
> Qualsiasi utilizzo non autorizzato del presente messaggio e dei suoi
> allegati è vietato e potrebbe costituire reato.
> Se lei avesse ricevuto erroneamente questo messaggio, Le saremmo grati se
> provvedesse alla distruzione dello stesso
> e degli eventuali allegati.
> Opinioni, conclusioni o altre informazioni riportate nella e-mail, che non
> siano relative alle attività e/o
> alla missione aziendale di O.R.S. Srl si intendono non attribuibili alla
> società stessa, né la impegnano in alcun modo.
>
ORS Srl
Via Agostino Morando 1/3 12060 Roddi (Cn) - Italy
Tel. +39 0173 620211
Fax. +39 0173 620299 / +39 0173 433111
Web Site www.ors.it
------------------------------------------------------------------------------------------------------------------------
Qualsiasi utilizzo non autorizzato del presente messaggio e dei suoi allegati è vietato e potrebbe costituire reato.
Se lei avesse ricevuto erroneamente questo messaggio, Le saremmo grati se provvedesse alla distruzione dello stesso
e degli eventuali allegati.
Opinioni, conclusioni o altre informazioni riportate nella e-mail, che non siano relative alle attività e/o
alla missione aziendale di O.R.S. Srl si intendono non attribuibili alla società stessa, né la impegnano in alcun modo.
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