That's a bit better, but you're still creating an object in the global
environment, when you should be returning it from your function.
Hadley
On Mon, Mar 14, 2011 at 8:54 AM, Daniele Amberti <daniele.ambe...@ors.it> wrote:
> Thanks Hadley for Your interest, below some code without environments use
> (using timeSeries); I also made some experiments with .parallel = TRUE in
> daply to crate timeSeries objects and then bind them together but I have some
> problems.
>
> Thank You in advance,
> Daniele Amberti
>
> set.seed(123)
> N <- 10000
> X <- data.frame(
> ID = c(rep(1,N), rep(2,N,), rep(3,N), rep(4,N)),
> DATE = as.character(rep(as.POSIXct("2000-01-01", tz = "GMT")+ 0:(N-1), 4)),
> VALUE = runif(N*4), stringsAsFactors = FALSE)
> X <- X[sample(1:(N*4), N*4),]
> str(X)
> head(X)
>
> #define a variable in global env
> ATS <- NULL
>
> buildTimeSeriesFromDataFrame <- function(x)
> {
> library(timeSeries)
> if(!is.null(ATS)) # in global env
> {
> # assign in global env
> ATS <<- cbind(ATS,
> timeSeries(x$VALUE, x$DATE,
> format = '%Y-%m-%d %H:%M:%S',
> zone = 'GMT', units = as.character(x$ID[1])))
> } else
> {
> # assign in global env
> ATS <<- timeSeries(x$VALUE, x$DATE, format = '%Y-%m-%d %H:%M:%S',
> zone = 'GMT', units = as.character(x$ID[1]))
> }
> return(TRUE)
> }
>
> tsDaply <- function(...)
> {
> # assign in global env, to clean previous run
> ATS <<- NULL
> library(plyr)
> res <- daply(X, "ID", buildTimeSeriesFromDataFrame)
> return(res)
> }
>
> tsDaply(X, X$ID)
> head(ATS)
>
> #performance tests
> Time <- replicate(100,
> system.time(tsDaply(X, X$ID))[[1]])
> median(Time)
> hist(Time)
>
> ###
> #some multithread tests:
> ###
>
> library(doSMP)
> w <- startWorkers(workerCount = 2)
> registerDoSMP(w)
>
> # do not cbint ts, just create
> buildTimeSeriesFromDataFrame2 <- function(x)
> {
> library(timeSeries )
> xx <- timeSeries:::timeSeries(x$VALUE, x$DATE,
> format = '%Y-%m-%d %H:%M:%S',
> zone = 'GMT', units = as.character(x$ID[1]))
> return(xx)
> }
>
> #tsDaply2 <- function(...)
> #{
> # library(plyr)
> # res <- daply(X, "ID", buildTimeSeriesFromDataFrame2, .parallel = TRUE)
> # return(res)
> #}
>
> # tsDaply2 .parallel = TRUE return error:
> #Error in do.ply(i) : task 4 failed - "subscript out of bounds"
> #In addition: Warning messages:
> #1: <anonymous>: ... may be used in an incorrect context: '.fun(piece, ...)'
> #2: <anonymous>: ... may be used in an incorrect context: '.fun(piece, ...)'
>
>
> tsDaply2 <- function(...)
> {
> library(plyr)
> res <- daply(X, "ID", buildTimeSeriesFromDataFrame2, .parallel = FALSE)
> return(res)
> }
> # tsDaply2 .parallel = FALSE work but list discart timeSeries class
>
> # bind after ts creation
> res <- tsDaply2(X, X$ID)
> # list is not a timeSeries object
> str(cbind(t(res)))
> res <- as.timeSeries(cbind(t(res)))
>
> stopWorkers(w)
>
>
> -----Original Message-----
> From: h.wick...@gmail.com [mailto:h.wick...@gmail.com] On Behalf Of Hadley
> Wickham
> Sent: 14 March 2011 12:48
> To: Daniele Amberti
> Cc: r-help@r-project.org
> Subject: Re: [R] dataframe to a timeseries object - [ ] Message is from an
> unknown sender
>
> Well, I'd start by removing all explicit use of environments, which
> makes you code very hard to follow.
>
> Hadley
>
> On Monday, March 14, 2011, Daniele Amberti <daniele.ambe...@ors.it> wrote:
>> I found that plyr:::daply is more efficient than base:::by (am I doing
>> something wrong?), below updated code for comparison (I also fixed a couple
>> things).
>> Function daply from plyr package has also a .parallel argument and I wonder
>> if creating timeseries objects in parallel and then combining them would be
>> faster (Windows XP platform); does someone has experience with this topic? I
>> found only very simple examples about plyr and parallel computations and I
>> do not have a working example for such kind of implementation (daply that
>> return a list of timeseries objects).
>>
>> Thanks in advance,
>> Daniele Amberti
>>
>>
>> set.seed(123)
>>
>> N <- 10000
>> X <- data.frame(
>> ID = c(rep(1,N), rep(2,N,), rep(3,N), rep(4,N)),
>> DATE = as.character(rep(as.POSIXct("2000-01-01", tz = "GMT")+ 0:(N-1), 4)),
>> VALUE = runif(N*4), stringsAsFactors = FALSE)
>> X <- X[sample(1:(N*4), N*4),]
>> str(X)
>>
>> library(timeSeries)
>> buildTimeSeriesFromDataFrame <- function(x, env)
>> {
>> {
>> if(exists("xx", envir = env))
>> assign("xx",
>> cbind(get("xx", env), timeSeries(x$VALUE, x$DATE,
>> format = '%Y-%m-%d %H:%M:%S',
>> zone = 'GMT', units = as.character(x$ID[1]))),
>> envir = env)
>> else
>> assign("xx",
>> timeSeries(x$VALUE, x$DATE, format = '%Y-%m-%d %H:%M:%S',
>> zone = 'GMT', units = as.character(x$ID[1])),
>> envir = env)
>>
>> return(TRUE)
>> }
>> }
>>
>> tsBy <- function(...)
>> {
>> e1 <- new.env(parent = baseenv())
>> res <- by(X, X$ID, buildTimeSeriesFromDataFrame,
>> env = e1, simplify = TRUE)
>> return(get("xx", e1))
>> }
>>
>> Time01 <- replicate(100,
>> system.time(tsBy(X, X$ID, simplify = TRUE))[[1]])
>> median(Time01)
>> hist(Time01)
>> ATS <- tsBy(X, X$ID, simplify = TRUE)
>>
>>
>> library(xts)
>> buildXtsFromDataFrame <- function(x, env)
>> {
>> {
>> if(exists("xx", envir = env))
>> assign("xx",
>> cbind(get("xx", env), xts(x$VALUE,
>> as.POSIXct(x$DATE, tz = "GMT",
>> format = '%Y-%m-%d %H:%M:%S'),
>> tzone = 'GMT')),
>> envir = env)
>> else
>> assign("xx",
>> xts(x$VALUE, as.POSIXct(x$DATE, tz = "GMT",
>> format = '%Y-%m-%d %H:%M:%S'),
>> tzone = 'GMT'),
>> envir = env)
>>
>> return(TRUE)
>> }
>> }
>>
>> xtsBy <- function(...)
>> {
>> e1 <- new.env(parent = baseenv())
>> res <- by(X, X$ID, buildXtsFromDataFrame,
>> env = e1, simplify = TRUE)
>> return(get("xx", e1))
>> }
>>
>> Time02 <- replicate(100,
>> system.time(xtsBy(X, X$ID,simplify = TRUE))[[1]])
>> median(Time02)
>> hist(Time02)
>> AXTS <- xtsBy(X, X$ID, simplify = TRUE)
>>
>> plot(density(Time02), col = "red",
>> xlim = c(min(c(Time02, Time01)), max(c(Time02, Time01))))
>> lines(density(Time01), col = "blue")
>> #check equal, a still a problem with names
>> AXTS2 <- as.timeSeries(AXTS)
>> names(AXTS2) <- names(ATS)
>> identical(getDataPart(ATS), getDataPart(AXTS2))
>> identical(time(ATS), time(AXTS2))
>>
>> # with plyr library and daply instead of by:
>> library(plyr)
>>
>> tsDaply <- function(...)
>> {
>> e1 <- new.env(parent = baseenv())
>> res <- daply(X, "ID", buildTimeSeriesFromDataFrame,
>> env = e1)
>> return(get("xx", e1))
>> }
>>
>> Time03 <- replicate(100,
>> system.time(tsDaply(X, X$ID))[[1]])
>> median(Time03)
>> hist(Time03)
>>
>> xtsDaply <- function(...)
>> {
>> e1 <- new.env(parent = baseenv())
>> res <- daply(X, "ID", buildXtsFromDataFrame,
>> env = e1)
>> return(get("xx", e1))
>> }
>>
>> Time04 <- replicate(100,
>> system.time(xtsDaply(X, X$ID))[[1]])
>>
>> median(Time04)
>> hist(Time04)
>>
>> plot(density(Time04), col = "red",
>> xlim = c(
>> min(c(Time02, Time01, Time03, Time04)),
>> max(c(Time02, Time01, Time03, Time04))),
>> ylim = c(0,100))
>> lines(density(Time03), col = "blue")
>> lines(density(Time02))
>> lines(density(Time01))
>>
>>
>>
>>
>>
>> -----Original Message-----
>> From: Daniele Amberti
>> Sent: 11 March 2011 14:44
>> To: r-help@r-project.org
>> Subject: dataframe to a timeseries object
>>
>> I'm wondering which is the most efficient (time, than memory usage) way to
>> obtain a multivariate time series object from a data frame (the easiest data
>> structure to get data from a database trough RODBC).
>> I have a starting point using timeSeries or xts library (these libraries can
>> handle time zones), below you can find code to test.
>> Merging parallelization (cbind) is something I'm thinking at (suggestions
>> from users with experience on this topic is highly appreciated), any
>> suggestion is welcome.
>> My platform is Windows XP, R 2.12.1, latest available packages on CRAN for
>> timeSeries and xts.
>>
>>
>> set.seed(123)
>>
>> N <- 9000
>> X <- data.frame(
>> ID = c(rep(1,N), rep(2,N,), rep(3,N), rep(4,N)),
>> DATE = rep(as.POSIXct("2000-01-01", tz = "GMT")+ 0:(N-1), 4),
>> VALUE = runif(N*4))
>>
>> library(timeSeries)
>> buildTimeSeriesFromDataFrame <- function(x, env)
>> {
>> {
>> if(exists("xx", envir = env))
>> assign("xx",
>> cbind(get("xx", env), timeSeries(x$VALUE, x$DATE, format = '%Y-%m-%d
>> %H:%M:%S',
>> zone = 'GMT', units = as.character(x$ID[1]))),
>> envir = env)
>> else
>> assign("xx",
>> timeSeries(x$VALUE, x$DATE, format = '%Y-%m-%d %H:%M:%S',
>> zone = 'GMT', units = as.character(x$ID[1])),
>> envir = env)
>>
>> return(TRUE)
>> }
>> }
>>
>>
>> fooBy <- function(...)
>> {
>> e1 <- new.env(parent = baseenv())
>> res <- by(X, X$ID, buildTimeSeriesFromDataFrame,
>> env = e1, simplify = TRUE)
>> return(get("xx", e1))
>> }
>>
>> Time01 <- replicate(100,
>> system.time(fooBy(X,
>> X$ID, buildTimeSeriesFromDataFrame,
>> simplify = TRUE))[[1]])
>>
>> median(Time01)
>> hist(Time01)
>>
>> library(xts)
>>
>> buildXtsFromDataFrame <- function(x, env)
>> {
>> {
>> if(exists("xx", envir = env))
>> assign("xx",
>> cbind(get("xx", env), xts(x$VALUE,
>> as.POSIXct(x$DATE, format = '%Y-%m-%d %H:%M:%S'),
>> tzone = 'GMT')),
>> envir = env)
>> else
>> assign("xx",
>> xts(x$VALUE, as.POSIXct(x$DATE, format = '%Y-%m-%d %H:%M:%S'),
>> tzone = 'GMT'),
>> envir = env)
>>
>> return(TRUE)
>> }
>> }
>>
>> fooBy <- function(...)
>> {
>> e1 <- new.env(parent = baseenv())
>> res <- by(X, X$ID, buildXtsFromDataFrame,
>> env = e1, simplify = TRUE)
>> return(get("xx", e1))
>> }
>>
>> Time02 <- replicate(100,
>> system.time(fooBy(X,
>> X$ID, buildTimeSeriesFromDataFrame,
>> simplify = TRUE))[[1]])
>>
>> median(Time02)
>> hist(Time02)
>>
>> plot(density(Time02), xlim = c(min(c(Time02, Time01)), max(c(Time02,
>> Time01))))
>> lines(density(Time01))
>>
>>
>> Best regards,
>> Daniele Amberti
>>
>> 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.
>> ______________________________________________
>> 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.
>>
>
> --
> Assistant Professor / Dobelman Family Junior Chair
> Department of Statistics / Rice University
> http://had.co.nz/
> /
>
> 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.
>
> ______________________________________________
> 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.
>
--
Assistant Professor / Dobelman Family Junior Chair
Department of Statistics / Rice University
http://had.co.nz/
______________________________________________
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.
