Don MacQueen
Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062
On 5/26/16, 4:59 PM, "R-help on behalf of Morway, Eric"
<r-help-bounces at r-project.org on behalf of emorway at usgs.gov> wrote:
>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]]
>
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