Hi Dan, On Mon, Apr 4, 2011 at 7:49 AM, Den Alpin <den.al...@gmail.com> wrote: > 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 > > > 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. > > Some considerations: > - Xts is generally faster than timeSeries > - both 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:::cbind, for examole,I will have a faster > implementation (package data.table ?). > - create timeseries objects in multithread and then merge (package plyr ?) > - faster merge algorithms? > > Below some code to generate the test case above: > > > set.seed(123) > N <- 5 # number of observations > 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) > > # use explicitly environments to avoid '<<-' > buildTimeSeriesFromDataFrame <- function(x, env) > { > { > if(exists("xx", envir = env)) # if exist variable xx in env cbind > assign("xx", > cbind(get("xx", env), timeSeries(x$VALUE, x$DATE, > format = '%Y-%m-%d %H:%M:%S', > zone = 'GMT', units = as.character(x$ID[1]))), > envir = env) > else # create xx in env > assign("xx", > timeSeries(x$VALUE, x$DATE, format = '%Y-%m-%d %H:%M:%S', > zone = 'GMT', units = as.character(x$ID[1])), > envir = env) > > return(TRUE) > } > } > > # use package plyr, faster than 'by' function > tsDaply <- function(...) > { > library(plyr) > e1 <- new.env(parent = baseenv()) #create a new env > res <- daply(X, "ID", buildTimeSeriesFromDataFrame, > env = e1) > return(get("xx", e1)) # return xx from env > } > > ##replicate 100 times > #Time03 <- replicate(100, > # system.time(tsDaply(X, X$ID))[[1]]) > #median(Time03) > > # result > tsDaply(X, X$ID) > > > Thanks in advance for any input, best regards, > Den > >
Here's how I would do it with xts: x <- xts(X[,c("ID","VALUE")], as.POSIXct(X[,"DATE"])) do.call(merge, split(x$VALUE,x$ID)) My xts solution compares favorably to your solution: > Time03 <- replicate(100, + system.time(tsDaply(X, X$ID))[[1]]) > median(Time03) [1] 0.02 > xtsTime <- replicate(100, + system.time(do.call(merge, split(x$VALUE,x$ID)))[[1]]) > median(xtsTime) [1] 0 Best, -- Joshua Ulrich | FOSS Trading: www.fosstrading.com ______________________________________________ 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.