Technically, the code below works and results in a column that I'm
interested in working with for further processing. However, it is both
inefficient on lengthy (>100 yr) daily time series and is, frankly, not the
R way of doing things. Using the 'Daily' data.frame provided below, I'm
interested to know the propeR way of accomplishing this same task in an
efficient manner. I tried combinations of rollapply and difftime, but was
unsuccessful. Eric
Daily <- read.table(textConnection(" Date Q
1911-04-01 4.530695
1911-04-02 4.700596
1911-04-03 4.898814
1911-04-04 5.097032
1911-04-05 5.295250
1911-04-06 6.569508
1911-04-07 5.861587
1911-04-08 5.153666
1911-04-09 4.445745
1911-04-10 3.737824
1911-04-11 3.001586
1911-04-12 3.001586
1911-04-13 2.350298
1911-04-14 2.661784
1911-04-16 3.001586
1911-04-17 2.661784
1911-04-19 2.661784
1911-04-28 3.369705
1911-04-29 3.001586
1911-05-20 2.661784"),header=TRUE)
Daily$Date <- as.Date(Daily$Date)
Daily$tmdiff <- NA
for(i in seq(2,length(Daily$Date),by=1)){
Daily$tmdiff[i] <- as.numeric(difftime(Daily$Date[i],Daily$Date[i-1]))
}
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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.
