I have a csv with four columns: site ID, latitude, longitude, and a date. I need to calculate the average daily temperature and total daily precipitation for each site at each date. I am trying to use the package “daymetr”.

All my dates fall during 2017, so I used the following code to extract data:

site_weather2017 <- download_daymet_batch(
  file_location = "templateforweatherdata.csv",
  start = 2017,
  end = 2017,
  internal = TRUE,
  force = FALSE,
  silent = FALSE,
  path = tempdir(),
  simplify = FALSE
)

Which gives me a daymetr object with 365 days of temp/precip data for each lat/lon location. How do I narrow it down to match just the date specified in each column?

I have tried this code:

finaldata <- list()

for( i in 1:length(site_weather2017)){
  site <- site_weather2017[[i]]
  site.dat <- site$data
  
  #add a column of mean daily temperature
  site.dat["tmean"] <- rowMeans(site.dat[,c("tmax..deg.c.","tmin..deg.c.")])
  
  #choose timespan of interest
  beginDate <- yday(as.Date(format="%m-%d-%Y"))
 # s.Date(alldata$Date, format = "%Y-%m-%d")
  
  #subset to only timespan of interest
  subSite <- subset(site.dat, yday %in% beginDate:beginDate)
  
  subSite['precip'] <- subSite$prcp..mm.day.
  
  #calculate mean daily temperature, and sum of precipitation
  finaldata[[i]] <- data.frame(site=site$site, meanTemp=mean(subSite$tmean), 
                               sumPrecip=base::sum(subSite$prcp..mm.day.))
}

But temp and precip show up as NaN.