Refactor KPI export function to excel file that matches cane supply strycture

r_app/80_calculate_kpis.R

@@ -382,6 +382,10 @@ main <- function() {
       project_dir = project_dir
     )
 
+    # Extract results 
+    field_analysis_df <- kpi_results$field_analysis_df
+    export_paths <- kpi_results$export_paths
+    
     cat("\n===  KPI CALCULATION COMPLETE ===\n")
     cat("Summary tables saved for Script 90 integration\n")
     cat("Output directory:", reports_dir_kpi, "\n\n")

r_app/80_utils_agronomic_support.R

@@ -464,35 +464,81 @@ create_summary_tables <- function(all_kpis) {
   return(kpi_summary)
 }
 
-#' Create detailed field-by-field KPI report
+#' Create detailed field-by-field KPI report (ALL KPIs in one row)
 #'
-#' @param field_df Data frame with field identifiers and acreage
-#' @param all_kpis List with all KPI results
 #' @param field_boundaries_sf SF object with field boundaries
+#' @param all_kpis List with all KPI results
+#' @param current_week Current week number
+#' @param current_year Current year
 #'
 #' @return Data frame with one row per field, all KPI columns
-create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
-  result <- field_df %>%
+create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_week, current_year) {
+  
+  # Start with field identifiers AND field_idx for joining
+  result <- field_boundaries_sf %>%
+    sf::st_drop_geometry() %>%
+    mutate(
+      field_idx = row_number(),  # ADD THIS: match the integer index used in KPI functions
+      Field_id = field,
+      Field_name = field,
+      Week = current_week,
+      Year = current_year
+    ) %>%
+    select(field_idx, Field_id, Field_name, Week, Year)  # Include field_idx first
+  
+  # Join all KPI results (now field_idx matches on both sides)
+  result <- result %>%
     left_join(
-      all_kpis$uniformity %>% select(field_idx, cv_value, uniformity_interpretation = interpretation),
-      by = c("field_idx")
+      all_kpis$uniformity %>% 
+        select(field_idx, CV = cv_value, Uniformity_Score = uniformity_score, 
+               Morans_I = morans_i, Uniformity_Interpretation = interpretation),
+      by = "field_idx"
     ) %>%
     left_join(
-      all_kpis$area_change %>% select(field_idx, mean_ci_pct_change),
-      by = c("field_idx")
+      all_kpis$area_change %>% 
+        select(field_idx, Weekly_CI_Change = mean_ci_pct_change, 
+               Area_Change_Interpretation = interpretation),
+      by = "field_idx"
     ) %>%
     left_join(
-      all_kpis$tch_forecasted %>% select(field_idx, tch_forecasted),
-      by = c("field_idx")
+      all_kpis$tch_forecasted %>% 
+        select(field_idx, Mean_CI = mean_ci, TCH_Forecasted = tch_forecasted, 
+               TCH_Lower = tch_lower_bound, TCH_Upper = tch_upper_bound, 
+               TCH_Confidence = confidence),
+      by = "field_idx"
     ) %>%
     left_join(
-      all_kpis$growth_decline %>% select(field_idx, decline_severity),
-      by = c("field_idx")
+      all_kpis$growth_decline %>% 
+        select(field_idx, Four_Week_Trend = four_week_trend, 
+               Trend_Interpretation = trend_interpretation, 
+               Decline_Severity = decline_severity),
+      by = "field_idx"
     ) %>%
     left_join(
-      all_kpis$weed_presence %>% select(field_idx, weed_pressure_risk),
-      by = c("field_idx")
+      all_kpis$weed_presence %>% 
+        select(field_idx, Fragmentation_Index = fragmentation_index, 
+               Weed_Pressure_Risk = weed_pressure_risk),
+      by = "field_idx"
     )
+  
+  # Add gap filling if available
+  if (!is.null(all_kpis$gap_filling) && nrow(all_kpis$gap_filling) > 0) {
+    result <- result %>%
+      left_join(
+        all_kpis$gap_filling %>% 
+          select(field_idx, Gap_Score = gap_score, Gap_Level = gap_level),
+        by = "field_idx"
+      )
+  }
+  
+  # Remove field_idx from final output (it was only needed for joining)
+  result <- result %>%
+    select(-field_idx)
+  
+  # Round numeric columns
+  result <- result %>%
+    mutate(across(where(is.numeric), ~ round(., 2)))
+  
   return(result)
 }
 
@@ -517,54 +563,26 @@ create_field_kpi_text <- function(all_kpis) {
 
 #' Export detailed KPI data to Excel/RDS 
 #'
-#' @param all_kpis List with all KPI results
-#' @param kpi_summary List with summary tables
+#' @param field_detail_df Data frame with all KPI columns (one row per field)
+#' @param kpi_summary List with summary tables (optional, for metadata)
 #' @param output_dir Directory for output files
 #' @param week Week number
 #' @param year Year
+#' @param project_dir Project name
 #' @return List of output file paths
-export_kpi_data <- function(all_kpis, kpi_summary, output_dir, week, year, project_dir) {
-  # Ensure output directory exists
-  if (!dir.exists(output_dir)) {
-    dir.create(output_dir, recursive = TRUE)
-  }
+export_kpi_data <- function(field_detail_df, kpi_summary, output_dir, week, year, project_dir) {
   
-  # Export all KPI tables to a single Excel file
-  excel_file <- paste0(project_dir, "_kpi_summary_tables_week", sprintf("%02d_%d", week, year), ".xlsx")
-  excel_path <- file.path(output_dir, excel_file)
-  
-  sheets <- list(
-    "Uniformity" = as.data.frame(kpi_summary$uniformity),
-    "Area_Change" = as.data.frame(kpi_summary$area_change),
-    "TCH_Forecast" = as.data.frame(kpi_summary$tch_forecast),
-    "Growth_Decline" = as.data.frame(kpi_summary$growth_decline),
-    "Weed_Pressure" = as.data.frame(kpi_summary$weed_pressure),
-    "Gap_Filling" = as.data.frame(kpi_summary$gap_filling)
+  # Use the common export function from 80_utils_common.R
+  export_paths <- export_field_analysis_excel(
+    field_df = field_detail_df,
+    summary_df = NULL,  # No separate summary sheet for agronomic support
+    project_dir = project_dir,
+    current_week = week,
+    year = year,
+    reports_dir = output_dir
   )
   
-  write_xlsx(sheets, excel_path)
-  message(paste("✓ AURA KPI data exported to:", excel_path))
-  
-  # Also export to RDS for programmatic access
-  rds_file <- paste0(project_dir, "_kpi_summary_tables_week", sprintf("%02d_%d", week, year), ".rds")
-  rds_path <- file.path(output_dir, rds_file)
-
-    # Save complete structure including metadata
-  kpi_export_data <- list(
-    kpis = all_kpis,
-    summary_tables = kpi_summary,
-    metadata = list(
-      week = week,
-      year = year,
-      project = project_dir,
-      created_at = Sys.time()
-    )
-  )
-  
-  saveRDS(kpi_export_data, rds_path)
-  message(paste("✓ AURA KPI RDS exported to:", rds_path))
-  
-  return(list(excel = excel_path, rds = rds_path))
+  return(export_paths)
 }
 
 # ============================================================================
@@ -685,22 +703,41 @@ calculate_all_field_analysis_agronomic_support <- function(
     gap_filling = gap_filling_kpi
   )
   
+  # Built single-sheet field detail table with all KPIs
+  message("\nBuilding comprehensive field detail table...")
+  field_detail_df <- create_field_detail_table(
+   field_boundaries_sf = field_boundaries_sf,
+   all_kpis = all_kpis,
+   current_week = current_week,
+   current_year = current_year
+  )
+
   # Create summary tables
   message("\nCreating summary tables...")
   kpi_summary <- create_summary_tables(all_kpis)
   
   # Export
-  message("\nExporting KPI data...")
-  export_paths <- export_kpi_data(all_kpis, kpi_summary, output_dir, current_week, current_year, project_dir)
+   message("\nExporting KPI data (single-sheet format)...")
+  export_paths <- export_kpi_data(
+    field_detail_df = field_detail_df,
+    kpi_summary = kpi_summary,
+    output_dir = output_dir,
+    week = current_week,
+    year = current_year,
+    project_dir = project_dir
+  )
   
   message(paste("\n✓ AURA KPI calculation complete. Week", current_week, current_year))
   
   return(list(
+    field_analysis_df = field_detail_df,  
     kpis = all_kpis,                      
     summary_tables = kpi_summary,         
+    export_paths = export_paths,
     metadata = list(
       week = current_week,
       year = current_year,
-      project = project_dir,
-      export_paths = export_paths) ))
+      project = project_dir
+    )
+  ))
 }