Fix initial view

1_fetch.yml

@@ -15,7 +15,7 @@ targets:
 
   1_fetch:
     depends:
-      - 1_fetch/out/gw_sites.rds
+      - 1_fetch/out/historic_gw_site_info_filtered.rds
       - 1_fetch/out/gw_data.csv
 
 ##-- Historic GW sites and data --##

2_process.yml

@@ -66,6 +66,8 @@ targets:
       sites_sf = gw_sites_sf,
       sites_info = gw_quantile_site_info,
       proj_str = proj_str)
+    depends:
+     - '2_process/out/nws_states.shp'
 
   # Prepare data for peaks SVG
   gw_anomaly_data:

2_process/src/categorize_quantiles.R

@@ -1,7 +1,6 @@
 
 categorize_quantiles <- function(target_name, quantile_data_fn, anomaly_bins, anomaly_categories) {
-
-  quantile_data_fn <- "2_process/out/gw_daily_quantiles.csv"
+
   anomaly_bins <- scmake("anomaly_bins")
   anomaly_categories <- scmake("anomaly_categories")
 

2_process/src/compare_to_historic.R

@@ -16,30 +16,31 @@ compare_to_historic <- function(target_name, historic_quantile_fn, current_data_
   # See this comment on GitHub for more detail/background: 
   #   https://github.com/USGS-VIZLAB/gw-conditions/issues/9#issuecomment-854115170
   daily_quantiles <- purrr::map(gw_sites, function(site, current_data, historic_quantiles, inverse_sites) {
-
+  
     site_quantiles <- historic_quantiles %>% filter(site_no == site) 
 
     if(nrow(site_quantiles) == 0) {
       message(sprintf("Quantiles not available for %s, returning NA.", site))
-    } 
+    }
 
+    # Get percentiles for current values based on historical record
     site_current <- current_data %>% 
       filter(site_no == site) %>% 
       # Add flag for whether an inverse of the GWL value should be used
       mutate(is_inverse = site_no %in% inverse_sites) %>% 
       # Figure out the corresponding quantile for each daily value
       # If there are no non-NA quantiles available, return NA
+      # Account for values outside of historic range by using `rule = 2`.
+      # Repeats top or bottom known quantile for any values outside of historic range
       rowwise() %>% 
       mutate(daily_quant = ifelse(
         nrow(site_quantiles) > 0, 
         yes = approx(x = site_quantiles$quantile_va, y = site_quantiles$quantile_nm, 
-                     xout = GWL*ifelse(is_inverse, -1, 1))$y,
-        no = NA)) %>% 
+                     xout = GWL*ifelse(is_inverse, -1, 1), rule=2)$y,
+        no = NA)) %>%
       ungroup() %>% 
       select(-is_inverse)
 
-    # TODO: Handle any new max or new min values when using dates outside of dates used to calculate historic vals
-
     return(site_current)
   }, current_data, historic_quantiles, inverse_sites) %>% 
     bind_rows() %>% 

2_process/src/prep_data_for_visualizing.R

@@ -61,9 +61,12 @@ generate_months <- function(file_out, data_in){
 
 # Make sure the data being displayed and used to create labels 
 # fits within the time range asked for
+# Filter out sites w/ all NA GWL levels
 subset_to_date_range <- function(file_out, daily_data_fn, start_date, end_date) {
   read_csv(daily_data_fn) %>% 
     filter(Date >= start_date,
            Date <= end_date) %>% 
+    group_by(site_no) %>%
+    filter(!all(is.na(GWL))) %>%
     write_csv(file_out)
 }

3_visualize/out/gw-conditions-peaks-timeseries-s3copy.ind

@@ -1,2 +1,2 @@
-hash: 5231ce48f14301823508dafc1c6654fb
+hash: d61ba692aad93452088af885f79f9b36
 

lib/cfg/s3_config_viz.yml

@@ -1,3 +1,3 @@
-profile: 'prod'
+profile: 'default'
 
 bucket: 'water-visualizations-prod-website'