CODEWITHSUNDEEP
rdataframetemperatureweatherdata
J Elliott
J Elliott

Reputation: 11

Calculate daily temperature and precipitation given a csv of lat/lon and dates

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".

site lat lon date
d22002 35.1527 -84.0396 11/8/2017
d22002 35.1527 -84.0396 11/1/2017
d22066 36.4246 -84.4666 11/15/2017
d22065 35.2741 -81.5184 1/22/2017

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

library(daymetr)

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.

Upvotes: 0

Views: 127

Answers (1)

M--
M--

Reputation: 29143

library(readr)
library(dplyr)
library(daymetr)

## read the sites data
sites <- read_csv("templateforweatherdata.csv", 
                  col_types = cols(date = col_date(format = "%m/%d/%Y")))

## we only need to download each site once, hence getting unique values
sites_unique <- sites %>% distinct(site, .keep_all = TRUE)
write.csv(sites_unique, "unique_templateforweatherdata.csv", row.names = F)

## using the unique site list for download
## we still will be processing the dates based on the original file
site_weather2017 <- download_daymet_batch(
  file_location = "unique_templateforweatherdata.csv",
  start = 2017,
  end = 2017,
  internal = TRUE,
  force = FALSE,
  silent = FALSE,
  path = tempdir(),
  simplify = FALSE
)
#> Downloading DAYMET data for: d22002 at 35.1527/-84.0396 latitude/longitude !
#> Done !
#> Downloading DAYMET data for: d22066 at 36.4246/-84.4666 latitude/longitude !
#> Done !
#> Downloading DAYMET data for: d22065 at 35.2741/-81.5184 latitude/longitude !
#> Done !

## creating an empty list to store the filtered data
site_weather2017_data <- list()

## looping over the downloaded data
for(i in seq_along(site_weather2017)) {
  
  ## getting desired dates for each site
  site_dates <- subset(sites, site == site_weather2017[[i]]$site,
                       select = date)$date
  
  ## data for each site
  site_weather2017[[i]]$data %>% 
    ## adding columns for the header of each daymetr object
    ## i.e. site name, tile, lat/long/alt, and
    ## ... adding a column for date
    mutate(site = site_weather2017[[i]]$site,
           tile = site_weather2017[[i]]$tile,
           latitude = site_weather2017[[i]]$latitude,
           longitude = site_weather2017[[i]]$longitude,
           altitude = site_weather2017[[i]]$altitude,
           date = as.Date(as.numeric(yday), origin = paste0(year, "-01-01")), 
           .before = 1) %>% 
    ## filtering for desired dates
    filter(date %in% site_dates) -> site_weather2017_data[[i]]
}

## binding the rows together for a final dataframe
bind_rows(site_weather2017_data)

#>     site  tile latitude longitude altitude       date year yday dayl..s.
#> 1 d22002 11208  35.1527  -84.0396      607 2017-11-01 2017  304 37960.51
#> 2 d22002 11208  35.1527  -84.0396      607 2017-11-08 2017  311 37193.35
#> 3 d22066 11388  36.4246  -84.4666      455 2017-11-15 2017  318 36175.83
#> 4 d22065 11210  35.2741  -81.5184      266 2017-01-22 2017   21 35987.47
#>   prcp..mm.day. srad..W.m.2. swe..kg.m.2. tmax..deg.c. tmin..deg.c. vp..Pa.
#> 1          0.00       361.07            0        19.01         0.26  622.54
#> 2         10.84        97.65            0        18.38        13.12 1508.59
#> 3          0.00       291.03            0        13.50         0.62  638.79
#> 4         12.31        76.66            0        13.61         9.88 1217.81

Created on 2024-07-14 with reprex v2.0.2

Upvotes: 0

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