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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