better handling of day of year 366

ecohealthalliance · Nov 3, 2023 · debc55d · debc55d
1 parent f0a8804
commit debc55d
Show file tree

Hide file tree

Showing 3 changed files with 790 additions and 423 deletions.
diff --git a/R/calculate_weather_anomalies.R b/R/calculate_weather_anomalies.R
@@ -37,12 +37,14 @@ calculate_weather_anomalies <- function(nasa_weather_transformed,
 
   anomalies <- map2(lag_intervals_start, lag_intervals_end, function(start, end){
 
-    # get lag dates
+    # get lag dates, removing doy 366
     lag_dates <- seq(date_selected - end, date_selected - start, by = "day")
+    lag_doys <- yday(lag_dates)
+    if(366 %in% lag_doys) lag_doys <- c(head(lag_doys, 1) - 1, lag_doys[lag_doys!=366])
 
     # Get historical means for lag period
-    doy_start <- yday(lag_dates[1])
-    doy_end <- yday(lag_dates[length(lag_dates)])
+    doy_start <- head(lag_doys, 1)
+    doy_end <- tail(lag_doys, 1)
     doy_start_frmt <- str_pad(doy_start, width = 3, side = "left", pad = "0")
     doy_end_frmt <- str_pad(doy_end, width = 3, side = "left", pad = "0")
     doy_range <- glue::glue("{doy_start_frmt}_to_{doy_end_frmt}")

diff --git a/_targets.R b/_targets.R
@@ -294,7 +294,7 @@ data_targets <- tar_plan(
 
   tar_target(lag_intervals, c(30, 60, 90)), 
   tar_target(lead_intervals, c(30, 60, 90, 120, 150)), 
-  tar_target(days_of_year, 1:366),
+  tar_target(days_of_year, 1:365),
   tar_target(model_dates_selected, set_model_dates(start_year = 2005, 
                                                    end_year = 2022, 
                                                    n_per_month = 2, 
@@ -318,6 +318,16 @@ data_targets <- tar_plan(
              format = "file", 
              repository = "local"),  
 
+  # save historical means to AWS bucket
+  tar_target(weather_historical_means_upload_aws_s3, 
+             aws_s3_upload(path = weather_historical_means,
+                           bucket =  aws_bucket,
+                           key = weather_historical_means, 
+                           check = TRUE), 
+             pattern = weather_historical_means,
+             cue = tar_cue("thorough")), # only run this if you need to upload new data  
+
+
   tar_target(weather_anomalies_directory, 
              create_data_directory(directory_path = "data/weather_anomalies")),
 
@@ -377,6 +387,16 @@ data_targets <- tar_plan(
              format = "file", 
              repository = "local"),  
 
+  # save historical means to AWS bucket
+  tar_target(ndvi_historical_means_upload_aws_s3, 
+             aws_s3_upload(path = ndvi_historical_means,
+                           bucket =  aws_bucket,
+                           key = ndvi_historical_means, 
+                           check = TRUE), 
+             pattern = ndvi_historical_means,
+             cue = tar_cue("thorough")), # only run this if you need to upload new data  
+
+
   tar_target(ndvi_anomalies_directory, 
              create_data_directory(directory_path = "data/ndvi_anomalies")),