merging a large list of xts objects
list, merge, r, xts, zoo
Solution
I use the strategy provided by Dominik in his answer to this question
I have turned it into a function in my qmao package. This code is also at the core of getSymbols.FI in the FinancialInstrument package.
do.call.rbind <- function(lst) {
while(length(lst) > 1) {
idxlst <- seq(from=1, to=length(lst), by=2)
lst <- lapply(idxlst, function(i) {
if(i==length(lst)) { return(lst[[i]]) }
return(rbind(lst[[i]], lst[[i+1]]))
})
}
lst[[1]]
}
If you want to `rbind` `data.frames`, @JoshuaUlrich has provided an elegant solution here
As far as I can tell (without looking very closely) memory is not an issue with any of the three solutions offered (@JoshuaUlrich's, @Alex's, and qmao::do.call.rbind). So, it comes down to speed...
library(xts)
l <- lapply(Sys.Date()-6000:1, function(x) {
N=60*8;xts(rnorm(N),as.POSIXct(x)-seq(N*60,1,-60))})
GS <- do.call.rbind
JU <- function(x) Reduce(rbind, x)
Alex <- function(x) do.call(rbind, lapply(x, as.data.frame)) #returns data.frame, not xts
identical(GS(l), JU(l)) #TRUE
library(rbenchmark)
benchmark(GS(l), JU(l), Alex(l), replications=1)
test replications elapsed relative user.self sys.self user.child sys.child
3 Alex(l) 1 89.575 109.9080 56.584 33.044 0 0
1 GS(l) 1 0.815 1.0000 0.599 0.216 0 0
2 JU(l) 1 209.783 257.4025 143.353 66.555 0 0
`do.call.rbind` clearly wins on speed.
Problem
I have a list of `xts` objects that are mutually exclusive days. I would like to `merge` the list into one large `xts` object. My attempt at doing this was to" ``` merged_reg_1_min_prices <- do.call(cbind, reg_1_min_prices) ``` However this seems to run out of memory. `reg_1_min_prices` is 6,000 days of 1 minute returns on mutually exclusive days so it's not very large. Does anyone know how to get around this? To be clear: `reg_1_min_prices` contains mutually exclusive days with 1 minute prices on each day and each entry in the list is an `xts` object.