split the data by truncating the time to a second, then process each group.
this will save the subsetting you are doing. also merge the data with direction
and size in the same frame. it looks like you can subset by "buy" to begin
with.
Sent from my iPad
On Jan 9, 2011, at 19:10, rivercode <aqua...@gmail.com> wrote:
>
>
> Have 40,000 rows of buy/sell trade data and am trying to add up the buys for
> each second, the code works but it is very slow. Any suggestions how to
> improve the sapply function ?
>
> secEP = endpoints(xSym$Direction, "secs") # vector of last second on an XTS
> timeseries object with multiple entries for each second.
> d = xSym$Direction
> s = xSym$Size
> buySize = sapply(1:(length(secEP)-1), function(y) {
> i = (secEP[y]+ 1):secEP[y+1]; # index of vectors between each secEP
> return(sum(as.numeric(s[i][d[i] == "buy"])));
> } )
>
> Object details:
>
> secEP = numeric Vector of one second Endpoints in xSym$Direction.
>
>> head(xSym$Direction)
> Direction
> 2011-01-05 09:30:00 "unkn"
> 2011-01-05 09:30:02 "sell"
> 2011-01-05 09:30:02 "buy"
> 2011-01-05 09:30:04 "buy"
> 2011-01-05 09:30:04 "buy"
> 2011-01-05 09:30:04 "buy"
>
>> head(xSym$Size)
> Size
> 2011-01-05 09:30:00 " 865"
> 2011-01-05 09:30:02 " 100"
> 2011-01-05 09:30:02 " 100"
> 2011-01-05 09:30:04 " 100"
> 2011-01-05 09:30:04 " 100"
> 2011-01-05 09:30:04 " 41"
>
> Thanks,
> Chris
>
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/Aggragating-subsets-of-data-in-larger-vector-with-sapply-tp3206445p3206445.html
> Sent from the R help mailing list archive at Nabble.com.
>
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