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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