coderabbit fixes

r_app/80_utils_agronomic_support.R

@@ -373,7 +373,15 @@ calculate_gap_filling_kpi <- function(ci_raster, field_boundaries) {
     stringsAsFactors = FALSE
   )
 
-  for (i in seq_len(nrow(field_boundaries))) {
+  # Ensure field_boundaries_vect is valid and matches field_boundaries dimensions
+  n_fields_vect <- length(field_boundaries_vect)
+  n_fields_sf <- nrow(field_boundaries)
+  
+  if (n_fields_sf != n_fields_vect) {
+    warning(paste("Field boundary mismatch: nrow(field_boundaries)=", n_fields_sf, "vs length(field_boundaries_vect)=", n_fields_vect, ". Using actual SpatVector length."))
+  }
+
+  for (i in seq_len(n_fields_vect)) {
     field_vect <- field_boundaries_vect[i]
 
     # Extract CI values using helper function
@@ -521,7 +529,7 @@ create_summary_tables <- function(all_kpis) {
         `Fields Analyzed` = fields_with_data
       )
   } else {
-    data.frame(`Avg Gap Filling Success %` = NA_real_, `Avg NA % Pre-Interpolation` = NA_real_, `Fields Analyzed` = 0)
+    data.frame(`Avg Gap Filling Success %` = NA_real_, `Avg NA % Pre-Interpolation` = NA_real_, `Fields Analyzed` = 0, check.names = FALSE)
   }
   
   # Return as list (each element is a farm-level summary table)
@@ -573,7 +581,7 @@ create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
       `Yield Forecast (t/ha)` = tch_forecasted,
       `Decline Risk` = decline_severity,
       `Weed Risk` = weed_pressure_risk,
-      `Mean CI` = mean_ci_pct_change,
+      `CI Change %` = mean_ci_pct_change,
       `CV Value` = cv_value
     ) %>%
     # Add placeholder columns expected by reporting script (will be populated from other sources)
@@ -582,7 +590,7 @@ create_field_detail_table <- function(field_df, all_kpis, field_boundaries_sf) {
       `Gap Score` = NA_real_
     ) %>%
     select(field_idx, Field, `Field Size (ha)`, `Growth Uniformity`, `Yield Forecast (t/ha)`, 
-           `Gap Score`, `Decline Risk`, `Weed Risk`, `Mean CI`, `CV Value`)
+           `Gap Score`, `Decline Risk`, `Weed Risk`, `CI Change %`, `CV Value`)
   
   return(result)
 }
@@ -628,15 +636,31 @@ export_kpi_data <- function(all_kpis, kpi_summary, project_dir, output_dir, week
   if (!is.null(field_boundaries_sf)) {
     tryCatch({
       # Create a basic field_df from the boundaries
+      # Robust field name extraction with multiple fallbacks
+      field_name <- NA_character_
+      
+      # Check for 'name' column in the data.frame
+      if ("name" %in% names(field_boundaries_sf)) {
+        field_name <- field_boundaries_sf$name
+      } else if ("properties" %in% names(field_boundaries_sf)) {
+        # Extract from properties column (may be a list-column)
+        props <- field_boundaries_sf$properties
+        if (is.list(props) && length(props) > 0 && "name" %in% names(props[[1]])) {
+          field_name <- sapply(props, function(x) ifelse(is.null(x$name), NA_character_, x$name))
+        } else if (!is.list(props)) {
+          # Try direct access if properties is a simple column
+          field_name <- props
+        }
+      }
+      
+      # Fallback if name extraction didn't work
+      if (any(is.na(field_name)) || length(field_name) != nrow(field_boundaries_sf)) {
+        field_name <- paste0("Field_", 1:nrow(field_boundaries_sf))
+      }
+      
       field_df <- data.frame(
         field_idx = 1:nrow(field_boundaries_sf),
-        field_name = if (!is.null(field_boundaries_sf$properties$name)) {
+        field_name = field_name,
-          field_boundaries_sf$properties$name
-        } else if (!is.null(field_boundaries_sf$name)) {
-          field_boundaries_sf$name
-        } else {
-          paste0("Field_", 1:nrow(field_boundaries_sf))
-        },
         stringsAsFactors = FALSE
       )
       
@@ -678,7 +702,12 @@ export_kpi_data <- function(all_kpis, kpi_summary, project_dir, output_dir, week
   message(paste("✓ KPI RDS exported to:", rds_file))
   message("  Structure: list($summary_tables, $all_kpis, $field_details)")
   
-  return(list(excel = excel_file, rds = rds_file))
+  # Return including field_details for orchestrator to capture
+  return(list(
+    excel = excel_file,
+    rds = rds_file,
+    field_details = field_details_table
+  ))
 }
 
 # ============================================================================
@@ -797,14 +826,15 @@ calculate_all_kpis <- function(
   if (is.null(project_dir)) {
     project_dir <- "AURA"  # Fallback if not provided
   }
-  export_paths <- export_kpi_data(all_kpis, kpi_summary, project_dir, output_dir, current_week, current_year, field_boundaries_sf)
+  export_result <- export_kpi_data(all_kpis, kpi_summary, project_dir, output_dir, current_week, current_year, field_boundaries_sf)
   
   message(paste("\n✓", project_dir, "KPI calculation complete. Week", current_week, current_year, "\n"))
   
   # Return combined structure (for integration with 80_calculate_kpis.R)
+  # Capture field_details from export_result to propagate it out
   return(list(
     all_kpis = all_kpis,
     summary_tables = kpi_summary,
-    field_details = NULL  # Will be populated if export_kpi_data succeeds
+    field_details = export_result$field_details  # Propagate field_details from export_kpi_data
   ))
 }

r_app/90_CI_report_with_kpis_agronomic_support.Rmd

@@ -540,44 +540,6 @@ if (exists("field_details_table") && !is.null(field_details_table) && nrow(field
 }
 ```
 
-```{r create_field_details_table, message=FALSE, warning=FALSE, include=FALSE}
-# Create field_details_table from available data
-# For projects without KPI data, create minimal table from field names/geometries
-
-if (!exists("field_details_table") || is.null(field_details_table)) {
-  tryCatch({
-    if (!is.null(AllPivots0) && nrow(AllPivots0) > 0) {
-      # Get field names from geometries
-      field_names <- AllPivots0$field
-      
-      # Try to calculate field sizes (area) from geometry if available
-      field_sizes <- if ("geometry" %in% names(AllPivots0)) {
-        sf::st_area(AllPivots0) / 10000  # Convert m² to hectares
-      } else {
-        rep(NA_real_, length(field_names))
-      }
-      
-      # Create minimal field details table with actual data we have + NAs for missing KPI columns
-      field_details_table <- tibble::tibble(
-        Field = field_names,
-        `Field Size (ha)` = as.numeric(field_sizes),
-        `Growth Uniformity` = NA_character_,
-        `Yield Forecast (t/ha)` = NA_real_,
-        `Gap Score` = NA_real_,
-        `Decline Risk` = NA_character_,
-        `Weed Risk` = NA_character_,
-        `Mean CI` = NA_real_,
-        `CV Value` = NA_real_,
-        `Moran's I` = NA_real_
-      )
-      safe_log(paste("Created field_details_table from geometries for", nrow(field_details_table), "fields"))
-    }
-  }, error = function(e) {
-    safe_log(paste("Error creating field_details_table from geometries:", e$message), "WARNING")
-  })
-}
-```
-
 ```{r data, message=TRUE, warning=TRUE, include=FALSE}
 # Verify CI quadrant data is loaded from load_ci_data chunk
 if (!exists("CI_quadrant")) {
@@ -620,6 +582,44 @@ tryCatch({
 })
 ```
 
+```{r create_field_details_table, message=FALSE, warning=FALSE, include=FALSE}
+# Create field_details_table from available data (now that AllPivots0 is defined)
+# For projects without KPI data, create minimal table from field names/geometries
+
+if (!exists("field_details_table") || is.null(field_details_table)) {
+  tryCatch({
+    if (!is.null(AllPivots0) && nrow(AllPivots0) > 0) {
+      # Get field names from geometries
+      field_names <- AllPivots0$field
+      
+      # Try to calculate field sizes (area) from geometry if available
+      field_sizes <- if ("geometry" %in% names(AllPivots0)) {
+        sf::st_area(AllPivots0) / 10000  # Convert m² to hectares
+      } else {
+        rep(NA_real_, length(field_names))
+      }
+      
+      # Create minimal field details table with actual data we have + NAs for missing KPI columns
+      field_details_table <- tibble::tibble(
+        Field = field_names,
+        `Field Size (ha)` = as.numeric(field_sizes),
+        `Growth Uniformity` = NA_character_,
+        `Yield Forecast (t/ha)` = NA_real_,
+        `Gap Score` = NA_real_,
+        `Decline Risk` = NA_character_,
+        `Weed Risk` = NA_character_,
+        `Mean CI` = NA_real_,
+        `CV Value` = NA_real_,
+        `Moran's I` = NA_real_
+      )
+      safe_log(paste("Created field_details_table from geometries for", nrow(field_details_table), "fields"))
+    }
+  }, error = function(e) {
+    safe_log(paste("Error creating field_details_table from geometries:", e$message), "WARNING")
+  })
+}
+```
+
 # Section 2: Field-by-Field Analysis
 
 ## Overview of Field-Level Insights

r_app/parameters_project.R

@@ -39,7 +39,12 @@ suppressPackageStartupMessages({
 # This determines which scripts run and what outputs they produce
 
 CLIENT_TYPE_MAP <- list(
-  "angata" = "cane_supply"
+  "angata" = "cane_supply",
+  "chemba" = "agronomic_support",
+  "xinavane" = "agronomic_support",
+  "esa" = "agronomic_support",
+  "simba" = "agronomic_support",
+  "john" = "agronomic_support"
 )
 
 #' Get client type for a project
@@ -48,6 +53,7 @@ CLIENT_TYPE_MAP <- list(
 get_client_type <- function(project_name) {
   client_type <- CLIENT_TYPE_MAP[[project_name]]
   if (is.null(client_type)) {
+    warning(paste("Project '", project_name, "' not found in CLIENT_TYPE_MAP. Defaulting to 'agronomic_support'.", sep=""))
     return("agronomic_support")  # Default for all unlisted projects
   }
   return(client_type)

r_app/test_kpi_structure.R

@@ -1,32 +0,0 @@
-#!/usr/bin/env Rscript
-# Temporary script to inspect KPI structure
-
-x <- readRDS('laravel_app/storage/app/john/reports/kpis/field_level/AURA_KPI_week_06_2026.rds')
-
-cat("===== KPI RDS Structure =====\n")
-cat("Names:", paste(names(x), collapse=", "), "\n\n")
-
-cat("---- UNIFORMITY TABLE ----\n")
-cat("Class:", class(x$uniformity), "\n")
-if (is.data.frame(x$uniformity)) {
-  cat("Columns:", paste(names(x$uniformity), collapse=", "), "\n")
-  print(head(x$uniformity, 3))
-} else if (is.list(x$uniformity)) {
-  cat("List contents:", paste(names(x$uniformity), collapse=", "), "\n")
-}
-
-cat("\n---- AREA CHANGE TABLE ----\n")
-cat("Class:", class(x$area_change), "\n")
-if (is.data.frame(x$area_change)) {
-  cat("Columns:", paste(names(x$area_change), collapse=", "), "\n")
-  print(head(x$area_change, 3))
-} else if (is.list(x$area_change)) {
-  cat("List contents:", paste(names(x$area_change), collapse=", "), "\n")
-}
-
-cat("\n---- TCH FORECASTED TABLE ----\n")
-cat("Class:", class(x$tch_forecasted), "\n")
-if (is.data.frame(x$tch_forecasted)) {
-  cat("Columns:", paste(names(x$tch_forecasted), collapse=", "), "\n")
-  print(head(x$tch_forecasted, 3))
-}

test_rds_structure.R

@@ -1,35 +0,0 @@
-# Load the created RDS file
-rds_file <- "laravel_app/storage/app/john/reports/kpis/field_level/john_kpi_summary_tables_week06_2026.rds"
-data <- readRDS(rds_file)
-
-# Check structure
-cat("Top-level structure:\n")
-cat("Names:", paste(names(data), collapse=", "), "\n")
-cat("Has summary_tables:", "summary_tables" %in% names(data), "\n")
-cat("Has all_kpis:", "all_kpis" %in% names(data), "\n")
-cat("Has field_details:", "field_details" %in% names(data), "\n\n")
-
-# Check summary_tables structure
-if (!is.null(data$summary_tables)) {
-  cat("Summary tables (KPI names):\n")
-  cat("  -", paste(names(data$summary_tables), collapse=", "), "\n\n")
-  
-  # Check one example
-  cat("Uniformity KPI (first 3 rows):\n")
-  print(head(data$summary_tables$uniformity, 3))
-}
-
-cat("\n---\n")
-cat("all_kpis structure:\n")
-if (!is.null(data$all_kpis)) {
-  cat("  -", paste(names(data$all_kpis), collapse=", "), "\n")
-}
-
-cat("\n---\n")
-cat("field_details structure:\n")
-if (!is.null(data$field_details)) {
-  cat("Columns:", paste(colnames(data$field_details), collapse=", "), "\n")
-  print(data$field_details)
-} else {
-  cat("  NULL (expected if field_boundaries_sf had issues)\n")
-}

test_render_90.R

@@ -1,22 +0,0 @@
-# Test rendering of 90 report
-library(rmarkdown)
-
-# Use "john" project since it has the KPI data we just created
-render(
-  input = "r_app/90_CI_report_with_kpis_agronomic_support.Rmd",
-  params = list(
-    data_dir = "john",
-    report_date = "2026-02-04",
-    mail_day = "Monday",
-    borders = FALSE,
-    ci_plot_type = "mosaic",
-    colorblind_friendly = FALSE,
-    facet_by_season = FALSE,
-    x_axis_unit = "days"
-  ),
-  output_file = "test_90_john_2026_02_04.docx",
-  output_dir = "output",
-  quiet = FALSE
-)
-
-cat("\n✓ Report rendered!\n")