Just expand the weekly data set to have an entry for each day and cbind
SurveyData with the new Modified.sat.data.
I was having a similar problem but with dates and here was a solution suggested
by Gustaf Rydevik using rep() with the each option.
========================================================================
time<-rep(1:24, 365)
dates<-seq(as.Date("01012005",format="%d%m%Y"),as.Date("31122005",format="%d%m%Y"),by=1)
TimeFrame<-data.frame(time)
TimeFrame$dates<-rep(dates,each=24)
=========================================================================
--- On Fri, 11/7/08, t c <[EMAIL PROTECTED]> wrote:
> From: t c <[EMAIL PROTECTED]>
> Subject: [R] Faster way to combine data sets with different date ranges
> To: r-help@r-project.org
> Received: Friday, November 7, 2008, 6:19 PM
> I am trying to combine two data sets, one with daily values
> and one with weekly values. SurveyData conatins
> environmental data collected on a daily basis. sat.data
> contains satellite sea surface temperature that is an
> average of satellite measurements over a six day period. I
> would like to combine the two files so that my output file
> has the daily dates from SurveyData and the weekly average
> from sat.data that corresponds to that day. I have written
> a loop that does exactly what I want, but it takes a very
> long time. Is there a faster way to do this? What I have
> so far is below.
> Thanks,
> Tim
>
> #Compare dates
> for(i in 1:length(SurveyData$Date))
> {
> print(i)
> for(j in 1:length(sat.data$Date.Start))
> {
> if(
> sat.data$Date.Start[j]<=SurveyData$Date[i] &
> sat.data$Date.End[j]>=SurveyData$Date[i])
> survey.data[i]<- sat.data$data[j]
> }
> }
>
> #Create data frame with final data
> final.data<-data.frame(SurveyData$Record,SurveyData$Date,survey.data)
>
> #Write data to file
> write.csv(final.data,file="Combined.csv")
>
>
>
>
> [[alternative HTML version deleted]]
>
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