If you convert the dates to R date objects, I think things
will be easier:
rawdata2$period_end_date = as.Date(rawdata2$period_end_date,format='%m/%d/%Y')
rawdata2$mon = as.numeric(format(rawdata2$period_end_date,'%m'))
rawdata2$year = as.numeric(format(rawdata2$period_end_date,'%Y'))
(I'm assuming you're using month/date/year.)
I can pretty much guarantee it will run in less than 18 hours :-)
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Thu, 28 Oct 2010, LCOG1 wrote:
Hi everyone
I have some records that include a date attribute for the date and time but
i need to separate the data and analyze it separately in GIS by Month and
Year, so i need to pull these attributes out and create their own attribute
field.
So the input:
RawData2.. returns
ID period_end_date
1 22 9/10/2007 0:00:00
2 44 2/2/2006 0:00:00
and i need to get
ID period_end_date Month Year
22 9/10/2007 0:00:00 9 2007
44 2/2/2006 0:00:00 2 2006
The below gets me this in list form which i can then add back into the
initial data frame BUT
i have over 4.5 million records and when i run the below it ran for more
than 18 hours and only go through about 2.7 millions records when i gave up
and ended the process.
So how can i make this more efficient and possibly add the new attributes
(month/year) to the data frame on the fly.
Thanks guys....
#Create sample data
RawData2..<-data.frame(ID=c(22,44),period_end_date=c("9/10/2007
0:00:00","2/2/2006 0:00:00"))
#Create lists to store month and year results
Data.Month_<-list()
Data.Year_<-list()
#pull out year/month attribute at put in own column
for(i in 1:length(RawData2..$ID)){
#Select Record
Data.X<-RawData..[i,]
#Separate date into month, day, and year
DateSplit<-strsplit(Data.X$period_end_date,"/")
#Select month
Month<-unlist(DateSplit)[1]
#Separate year from time attribute
Year.X<-strsplit(unlist(DateSplit)[3]," ")
Year.Y<-unlist(Year.X)[1]
Data.Month_[[i]]<-Month
Data.Year_[[i]]<-Year.Y
}
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