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
>
> ------------------------------------------------------------------------------------------------------------------------
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> è vietato e potrebbe costituire reato.
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> provvedesse alla distruzione dello stesso
> e degli eventuali allegati.
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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>
--
Assistant Professor / Dobelman Family Junior Chair
Department of Statistics / Rice University
http://had.co.nz/
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