Hey there,
After the Step 2 Part 2 post 2. Dealing with Missing Data in R: Omit, Approx, or Spline Part 2
I was thinking it would be helpful to put together several pieces of code across several posts, here is what we have so far.
clean_data=read.csv("ncdc_galesburg_daily_clean.csv",stringsAsFactors=FALSE)
clean_data$Date=as.Date(clean_data$Date)
clean_data$TMAX=round(clean_data$TMAX)
clean_data$TMIN=round(clean_data$TMIN)
clean_data$avgTemp=((clean_data$TMAX+clean_data$TMIN)/2.0)
clean_data$PRCP[is.na(clean_data$PRCP)]=0
clean_data$TMAX=na.spline(clean_data$TMAX)
clean_data$TMIN=na.spline(clean_data$TMIN)
clean_data$avgTemp=na.spline(clean_data$avgTemp)
require(xts)
avgTempXTS=xts(x=clean_data$avgTemp,order.by=clean_data$Date)
If you want to you could export this data as R binary format:
see saveRDS() and loadRDS()
or as csv
write.csv(clean_data,file='ncdc_galesburg_daily_splined.csv',row.names = F)
For future importing.