# Add new 2017 data
# see what data is new....
# current 2017 data
swns_stations_df_200 <- add_date_columns(swns_stations_df_200)
current_2017 <- swns_stations_df_200 %>%
filter(year == 2017)
# new 2017 data from david
new_2017_in <- read.csv(file.path("data","temperature2017ss.csv"))
names(new_2017)
# format data frame
new_2017 <- new_2017_in %>%
mutate(date_time = as.Date(paste(yr,month,days, sep = "-"),
format = "%Y-%m-%d")) %>%
select(stationid, date_time, temp_mean = avgtempc,
temp_min = mintempc, temp_max = maxtempc)
# only keep new_2017 rows that arent in current_2017
new_2017_aj <- new_2017 %>%
anti_join(current_2017, by = c("stationid","date_time")) %>%
# only keep stations from other years (geographic data present)
filter(stationid %in% current_2017$stationid) %>%
# join easting and northing data
left_join(as.data.frame(swns_stations_sp), by = "stationid")
# extract constant raster data to table
new_2017_constant_rasters <- swnsmodelr::extract_constant_raster_values(new_2017_aj,
rasters_list)
# extract solar rad data to table
#a)
solar_irradiance_rasters_df_2017 <- make_temporal_raster_df(
in_folder = "F:\\GOES_200",
start_date = ymd('2017-01-01'),
end_date = ymd('2017-12-31'),
date_chars = c(16,-5),
date_format = "%Y_%j",
extension = ".tif")
#b)
new_2017_solar_radiation <- extract_temporal_raster_values(temporal_rasters_df = solar_irradiance_rasters_df_2017,
temperatures_df = new_2017_constant_rasters,
col_name = "sum_irradiance",
verbose = TRUE)
# add dates
new_2017_200 <- add_date_columns(new_2017_solar_radiation)
# combine current and new 2017 data frames
all_2017 <- bind_rows(new_2017_200, swns_stations_df_200)
# select stations for modelling
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