okay

r_app/interpolate_growth_model.R

@@ -17,11 +17,15 @@ if (!is.character(project_dir)) {
 
 source("parameters_project.R")
 source("ci_extraction_utils.R")
-
+# Check if the file exists
-pivot_stats2 <- readRDS(here(cumulative_CI_vals_dir,"combined_CI_data.rds")) %>%
+file_path <- here(cumulative_CI_vals_dir, "combined_CI_data.rds")
-    ungroup() %>%
+pivot_stats2 <- data.frame()
-    group_by(field, sub_field) %>%
+if (file.exists(file_path)) {
-    summarise(across(everything(), ~ first(na.omit(.))), .groups = "drop")
+    pivot_stats2 <- readRDS(file_path) %>%
+        ungroup() %>%
+        group_by(field, sub_field) %>%
+        summarise(across(everything(), ~ first(na.omit(.))), .groups = "drop")
+}
 
    #%>% drop_na(pivot_quadrant)
 
@@ -61,7 +65,6 @@ extract_CI_data <- function(field_names, harvesting_data, field_CI_data, season)
   ApproxFun <- approxfun(x = filtered_field_CI_data$Date, y = filtered_field_CI_data$value)
   Dates <- seq.Date(ymd(min(filtered_field_CI_data$Date)), ymd(max(filtered_field_CI_data$Date)), by = 1)
   LinearFit <- ApproxFun(Dates)
-  
   # Combine interpolated data with the original CI data
   CI <- data.frame(Date = Dates, FitData = LinearFit) %>%
     left_join(., filtered_field_CI_data, by = "Date") %>%
@@ -71,7 +74,7 @@ extract_CI_data <- function(field_names, harvesting_data, field_CI_data, season)
   if (nrow(CI) == 0) {
     return(data.frame())
   }
-  
+
   # Add additional columns if data exists
   CI <- CI %>%
     mutate(DOY = seq(1, n(), 1),
@@ -93,23 +96,24 @@ CI_all <- map_df(years, function(yr) {
     filter(year == yr) %>%
     filter(!is.na(season_start)) %>%
     pull(sub_field)
-  
+
   # Filter sub_fields to only include those with value data in pivot_stats_long
   valid_sub_fields <- sub_fields %>%
     keep(~ any(pivot_stats_long$sub_field == .x))
-  
   # Extract data for each valid field
   map(valid_sub_fields, ~ extract_CI_data(.x, harvesting_data = harvesting_data, field_CI_data = pivot_stats_long, season = yr)) %>%
     list_rbind()
 })
 
-CI_all <- CI_all %>% group_by(model) %>% mutate(CI_per_day = FitData - lag(FitData),
+# it will crash here if CI_all is empty and will not overwrite the rds rendering growth_model.R useless
-                                                cumulative_CI = cumsum(FitData))
+# if(nrow(CI_all) > 0){
-
+    CI_all <- CI_all %>%
+            group_by(model) %>%
+            mutate(CI_per_day = FitData - lag(FitData), cumulative_CI = cumsum(FitData))
+# }
 
 message('CI_all cumulative')
 
-head(CI_all)
 message('show head')
 
 saveRDS(CI_all, here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds"))

r_app/parameters_project.R

@@ -18,6 +18,7 @@ daily_vrt <- here(data_dir, "vrt")
 harvest_dir <-  here(data_dir, "HarvestData")
 
 dir.create(here(laravel_storage_dir), showWarnings = FALSE)
+dir.create(here(reports_dir), showWarnings = FALSE)
 dir.create(here(data_dir), showWarnings = FALSE)
 dir.create(here(log_dir), showWarnings = FALSE)
 dir.create(here(extracted_CI_dir), showWarnings = FALSE)
@@ -67,4 +68,8 @@ log_file <- here(log_dir, paste0(format(Sys.Date(), "%Y%m%d"), ".log"))
 # Create a logging function
 log_message <- function(message) {
   cat(message, "\n", file = log_file, append = TRUE)
+}
+
+log_head <- function(list) {
+    log_message(paste(capture.output(str(head(AllPivots0))), collapse = "\n"))
 }