Refactor KPI calculation scripts for improved error handling and clarity

r_app/80_calculate_kpis.R

@@ -506,11 +506,10 @@ main <- function() {
       message("WARNING: No planting dates available. Using NA for all fields.")
       planting_dates <- data.frame(
         field_id = field_boundaries_sf$field,
-        date = rep(as.Date(NA), nrow(field_boundaries_sf)),
+        planting_date = rep(as.Date(NA), nrow(field_boundaries_sf)),
         stringsAsFactors = FALSE
       )
-    }
-  
+    }  
     # Build per-field configuration
     message("\nPreparing mosaic configuration for statistics calculation...")
     message("  ✓ Using per-field mosaic architecture (1 TIFF per field)")

r_app/80_utils_agronomic_support.R

@@ -368,9 +368,8 @@ calculate_gap_filling_kpi <- function(ci_raster, field_boundaries) {
   field_results <- data.frame()
 
   for (i in seq_len(nrow(field_boundaries))) {
-    field_name <- field_boundaries$field[i]
-    sub_field_name <- field_boundaries$sub_field[i]
-    field_vect <- field_boundaries_vect[i]
+    field_name <- if ("field" %in% names(field_boundaries)) field_boundaries$field[i] else NA_character_
+    sub_field_name <- if ("sub_field" %in% names(field_boundaries)) field_boundaries$sub_field[i] else NA_character_    field_vect <- field_boundaries_vect[i]
 
     # Extract CI values using helper function
     ci_values <- extract_ci_values(ci_raster, field_vect)

r_app/90_CI_report_with_kpis_agronomic_support.Rmd

@@ -168,7 +168,7 @@ if (!is.null(summary_file) && file.exists(summary_file)) {
 if (!is.null(field_details_file) && file.exists(field_details_file)) {
   safe_log(paste("Loading field details from:", field_details_file))
   field_details_table <- tryCatch(readRDS(field_details_file), error = function(e) { safe_log(paste("Failed to read field details RDS:", e$message), "ERROR"); NULL })
-  if (!is.null(field_details_table)) kpi_files_exist <- kpi_files_exist && TRUE
+  if (!is.null(field_details_table)) kpi_files_exist <- TRUE
 } else {
   safe_log(paste("Field details file not found. Searched:", paste(expected_field_details_names, collapse=", ")), "WARNING")
 }
@@ -235,6 +235,20 @@ tryCatch({
 
 # NOTE: Overview maps skipped for this report
 # Individual field sections load their own per-field mosaics directly
+
+# Load harvesting data (required for ci_plot() function)
+tryCatch({
+  harvesting_data <- load_harvesting_data(paths$data_dir)
+  if (!is.null(harvesting_data)) {
+    safe_log(paste("Successfully loaded harvesting data:", nrow(harvesting_data), "records"))
+  } else {
+    safe_log("No harvesting data available - ci_plot() will work with limited functionality", "WARNING")
+    harvesting_data <- NULL
+  }
+}, error = function(e) {
+  safe_log(paste("Error loading harvesting data:", e$message), "ERROR")
+  harvesting_data <<- NULL
+})
 ```
 
 
@@ -731,17 +745,33 @@ field_ages <- CI_quadrant %>%
   rename(Field = field, Age_days = DOY)
 
 # Clean up the field details table - remove sub field column and round numeric values
-field_details_clean <- field_details_table %>%
-  left_join(field_ages, by = "Field") %>%
-  mutate(
-    `Yield Forecast (t/ha)` = ifelse(is.na(Age_days) | Age_days < 240, NA_real_, `Yield Forecast (t/ha)`)
-  ) %>%
-  select(Field, `Field Size (ha)`, `Growth Uniformity`, `Yield Forecast (t/ha)`, `Gap Score`, `Decline Risk`, `Weed Risk`, `Mean CI`, `CV Value`) %>%  # Reorder columns as requested
-  mutate(
-    `Mean CI` = round(`Mean CI`, 2),    # Round to 2 decimal places
-    `CV Value` = round(`CV Value`, 2),   # Round to 2 decimal places
-    `Gap Score` = round(`Gap Score`, 0)  # Round to nearest integer
+# Check if field_details_table was loaded successfully
+if (!exists("field_details_table") || is.null(field_details_table)) {
+  # Initialize empty tibble with expected columns
+  field_details_clean <- tibble(
+    Field = character(),
+    `Field Size (ha)` = numeric(),
+    `Growth Uniformity` = character(),
+    `Yield Forecast (t/ha)` = numeric(),
+    `Gap Score` = numeric(),
+    `Decline Risk` = character(),
+    `Weed Risk` = character(),
+    `Mean CI` = numeric(),
+    `CV Value` = numeric()
   )
+} else {
+  field_details_clean <- field_details_table %>%
+    left_join(field_ages, by = "Field") %>%
+    mutate(
+      `Yield Forecast (t/ha)` = ifelse(is.na(Age_days) | Age_days < 240, NA_real_, `Yield Forecast (t/ha)`)
+    ) %>%
+    select(Field, `Field Size (ha)`, `Growth Uniformity`, `Yield Forecast (t/ha)`, `Gap Score`, `Decline Risk`, `Weed Risk`, `Mean CI`, `CV Value`) %>%  # Reorder columns as requested
+    mutate(
+      `Mean CI` = round(`Mean CI`, 2),    # Round to 2 decimal places
+      `CV Value` = round(`CV Value`, 2),   # Round to 2 decimal places
+      `Gap Score` = round(`Gap Score`, 0)  # Round to nearest integer
+    )
+}
 
 
 # Display the cleaned field table with flextable

r_app/91_CI_report_with_kpis_cane_supply.Rmd

@@ -242,10 +242,8 @@ if (!is.null(field_details_file) && file.exists(field_details_file)) {
   # Try to extract field_details from summary_data if available
   if (exists("summary_data") && !is.null(summary_data) && "field_analysis" %in% names(summary_data)) {
     field_details_table <- summary_data$field_analysis %>%
-      rename(`Mean CI` = Acreage, `CV Value` = CV, Field = Field_id)
-    safe_log("Extracted field details from field_analysis data")
-  }
-}
+      rename(`Mean CI` = Mean_CI, `CV Value` = CV, Field = Field_id)
+    safe_log("Extracted field details from field_analysis data")}
 
 if (kpi_files_exist) {
   safe_log("✓ KPI summary tables loaded successfully")
@@ -302,8 +300,6 @@ if (!is.null(cloud_file) && file.exists(cloud_file)) {
 }
 ```
 
-```
-
 ```{r calculate_dates_and_weeks, message=FALSE, warning=FALSE, include=FALSE, eval=TRUE}
 # Set locale for consistent date formatting
 Sys.setlocale("LC_TIME", "C")
@@ -475,6 +471,11 @@ tryCatch({
     warning("Error loading field analysis data:", e$message)
   })
   
+  # Fallback: if analysis_data failed to load, create an empty tibble with required columns
+  if (!exists("analysis_data") || is.null(analysis_data)) {
+    analysis_data <- tibble(Field_id = character(), Status_trigger = character())
+  }
+  
   # Define constants
   ACRE_CONV <- 4046.856
   TARGET_CRS <- 32736
@@ -785,12 +786,12 @@ if (cloud_coverage_available && !is.null(per_field_cloud_coverage)) {
   # Handle both old and new column naming conventions
   cloud_display <- per_field_cloud_coverage %>%
     mutate(
-      Field = if_else(exists("field", list(per_field_cloud_coverage)), field_id, 
-                      if_else(exists("Field", list(per_field_cloud_coverage)), Field, field_id)),
+      Field = if_else("field" %in% names(per_field_cloud_coverage), field_id, 
+                      if_else("Field" %in% names(per_field_cloud_coverage), Field, field_id)),
       Clear_Percent = pct_clear,
-      Cloud_Acreage = if_else(exists("Cloud_Acreage", list(per_field_cloud_coverage)), Cloud_Acreage, 
+      Cloud_Acreage = if_else("Cloud_Acreage" %in% names(per_field_cloud_coverage), Cloud_Acreage, 
                               as.numeric(NA)),
-      Total_Acreage = if_else(exists("Total_Acreage", list(per_field_cloud_coverage)), Total_Acreage,
+      Total_Acreage = if_else("Total_Acreage" %in% names(per_field_cloud_coverage), Total_Acreage,
                               as.numeric(NA))
     ) %>%
     select(Field, Cloud_category, Clear_Percent, missing_pixels, clear_pixels, total_pixels) %>%

r_app/DEBUG_remove_date_tiffs.R

@@ -235,12 +235,19 @@ main <- function() {
     file_path <- files_to_delete[[i]]
     
     if (!DRY_RUN) {
-      tryCatch({
+      # file.remove() returns logical; check the return value
+      success <- tryCatch({
         file.remove(file_path)
-        deleted_count <- deleted_count + 1
       }, error = function(e) {
-        error_count <<- error_count + 1
+        # If unexpected exception, treat as failure
+        FALSE
       })
+      
+      if (isTRUE(success)) {
+        deleted_count <- deleted_count + 1
+      } else {
+        error_count <- error_count + 1
+      }
     }
   }