Aggragating subsets of data in larger vector with sapply
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 <aquanyc at 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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