Refactor area calculation functions and enhance error handling; add area conversion utility

r_app/80_utils_agronomic_support.R

@@ -356,8 +356,8 @@ calculate_tch_forecasted_kpi <- function(field_statistics, harvesting_data = NUL
 #' Uses FOUR_WEEK_TREND_* thresholds defined in 80_utils_common.R:
 #'   - FOUR_WEEK_TREND_STRONG_GROWTH_MIN (0.3)
 #'   - FOUR_WEEK_TREND_GROWTH_MIN (0.1)
-#'   - FOUR_WEEK_TREND_STABLE_THRESHOLD (0.1 and -0.1)
-#'   - FOUR_WEEK_TREND_WEAK_DECLINE_THRESHOLD (-0.3)
+#'   - FOUR_WEEK_TREND_STABLE_THRESHOLD (0.1; used as lower bound via -FOUR_WEEK_TREND_STABLE_THRESHOLD)
+#'   - FOUR_WEEK_TREND_WEAK_DECLINE_THRESHOLD (-0.1)
 #'   - FOUR_WEEK_TREND_STRONG_DECLINE_MAX (-0.3)
 #'
 calculate_growth_decline_kpi <- function(ci_values_list) {
@@ -632,14 +632,25 @@ create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_wee
   # ============================================
   # GROUP 0b: FIELD AREA (from geometry)
   # ============================================
+  # Precompute unit label before tryCatch to avoid re-raising errors in error handler
+  unit_label <- get_area_unit_label(AREA_UNIT_PREFERENCE)
+  area_col_name <- paste0("Area_", unit_label)
+  
   # Calculate field area using unified function and preferred unit
   tryCatch({
     field_areas <- calculate_area_from_geometry(field_boundaries_sf, unit = AREA_UNIT_PREFERENCE)
-    unit_label <- get_area_unit_label(AREA_UNIT_PREFERENCE)
-    area_col_name <- paste0("Area_", unit_label)
+    
+    # Validate that calculated areas match the number of fields
+    n_fields <- nrow(field_boundaries_sf)
+    if (length(field_areas) != n_fields) {
+      stop(
+        "Mismatch: calculate_area_from_geometry returned ", length(field_areas),
+        " areas but field_boundaries_sf has ", n_fields, " fields"
+      )
+    }
     
     area_df <- data.frame(
-      field_idx = seq_along(field_areas),
+      field_idx = seq_len(n_fields),
       area_value = field_areas,
       stringsAsFactors = FALSE
     )
@@ -649,7 +660,7 @@ create_field_detail_table <- function(field_boundaries_sf, all_kpis, current_wee
       left_join(area_df, by = "field_idx")
   }, error = function(e) {
     message(paste("Warning: Could not calculate field areas:", e$message))
-    result[[paste0("Area_", get_area_unit_label(AREA_UNIT_PREFERENCE))]] <<- NA_real_
+    result[[area_col_name]] <<- NA_real_
   })
   
   # ============================================

r_app/80_utils_cane_supply.R

@@ -53,12 +53,22 @@ CI_CHANGE_INCREASE_THRESHOLD <- CI_CHANGE_RAPID_GROWTH_THRESHOLD    # Weekly CI
 calculate_field_acreages <- function(field_boundaries_sf, unit = AREA_UNIT_PREFERENCE) {
   tryCatch({
     # Get field identifier (handles pivot.geojson structure)
-    field_names <- field_boundaries_sf %>%
-      sf::st_drop_geometry() %>%
-      pull(any_of(c("field", "field_id", "Field_id", "name", "Name")))
+    # Use intersect() to find the first matching column, avoiding pull() ambiguity
+    field_names <- {
+      available_cols <- intersect(
+        colnames(field_boundaries_sf),
+        c("field", "field_id", "Field_id", "name", "Name")
+      )
       
-    if (length(field_names) == 0 || all(is.na(field_names))) {
-      field_names <- seq_len(nrow(field_boundaries_sf))
+      if (length(available_cols) > 0) {
+        # Extract the first matching column
+        field_boundaries_sf %>%
+          sf::st_drop_geometry() %>%
+          pull(available_cols[1])
+      } else {
+        # Fall back to sequential indices if no field name column exists
+        seq_len(nrow(field_boundaries_sf))
+      }
     }
     
     # Use unified area calculation function
@@ -70,15 +80,15 @@ calculate_field_acreages <- function(field_boundaries_sf, unit = AREA_UNIT_PREFE
     
     result_df <- data.frame(
       field = field_names,
-      area = areas,
-      stringsAsFactors = FALSE
+    warning(paste("Could not calculate areas from geometries -", e$message))      stringsAsFactors = FALSE
     )
     colnames(result_df) <- c("field", col_name)
     
     # Aggregate by field to handle multi-row fields (e.g., sub_fields)
+    # Use bare lambda to preserve original column name (not list() which creates suffixes)
     result_df %>%
       group_by(field) %>%
-      summarise(across(all_of(col_name), list(~ sum(., na.rm = TRUE))), .groups = "drop")
+      summarise(across(all_of(col_name), ~ sum(., na.rm = TRUE)), .groups = "drop")
   }, error = function(e) {
     message(paste("Warning: Could not calculate areas from geometries -", e$message))
     unit_label <- get_area_unit_label(unit)
@@ -600,21 +610,6 @@ calculate_field_analysis_cane_supply <- function(setup,
     reports_dir
   )
   
-  # cat("\n--- Per-field Results (first 10) ---\n")
-  # available_cols <- c("Field_id", "Acreage", "Age_week", "Mean_CI", "Four_week_trend", "Status_Alert", "Cloud_category")
-  # available_cols <- available_cols[available_cols %in% names(field_analysis_df)]
-  # if (length(available_cols) > 0) {
-  #   print(head(field_analysis_df[, available_cols], 10))
-  # }
-  
-  # ========== PHASE 10: CALCULATE FARM-LEVEL KPIS ==========
-  # farm_kpi_results <- calculate_farm_level_kpis(
-  #   field_analysis_df,
-  #   current_week,
-  #   current_year,
-  #   end_date
-  # )
-  
   # For now, farm-level KPIs are not implemented in CANE_SUPPLY workflow
   farm_kpi_results <- NULL
   

r_app/80_utils_common.R

@@ -86,12 +86,12 @@ PHASE_DEFINITIONS <- data.frame(
 
 #' Calculate field area from geometry in specified unit
 #'
-#' Unified function for calculating polygon area from sf or SpatVect geometries.
+#' Unified function for calculating polygon area from sf or SpatVector geometries.
 #' Uses equal-area projection (EPSG:6933) for accurate calculations across all zones.
 #'
-#' @param geometry sf or SpatVect object containing field polygons
+#' @param geometry sf or SpatVector object containing field polygons
 #'   If sf: must have geometry column (auto-detected)
-#'   If SpatVect: terra object with geometry
+#'   If SpatVector: terra object with geometry
 #' @param unit Character. Output unit: "hectare" (default) or "acre"
 #'
 #' @return Numeric vector of areas in specified unit
@@ -118,7 +118,7 @@ PHASE_DEFINITIONS <- data.frame(
 #' areas_ha <- calculate_area_from_geometry(fields_sf, unit = "hectare")
 #' areas_ac <- calculate_area_from_geometry(fields_sf, unit = "acre")
 #'
-#' # With SpatVect
+#' # With SpatVector
 #' library(terra)
 #' fields_vect <- vect("pivot.geojson")
 #' areas_ha <- calculate_area_from_geometry(fields_vect, unit = "hectare")
@@ -137,12 +137,12 @@ calculate_area_from_geometry <- function(geometry, unit = "hectare") {
       # Handle sf object
       geometry_proj <- sf::st_transform(geometry, 6933)
       areas_m2 <- as.numeric(sf::st_area(geometry_proj))
-    } else if (inherits(geometry, "SpatVect")) {
-      # Handle terra SpatVect object
+    } else if (inherits(geometry, "SpatVector")) {
+      # Handle terra SpatVector object
       geometry_proj <- terra::project(geometry, "EPSG:6933")
       areas_m2 <- as.numeric(terra::expanse(geometry_proj))
     } else {
-      stop("geometry must be an sf or terra SpatVect object. Got: ", 
+      stop("geometry must be an sf or terra SpatVector object. Got: ", 
            paste(class(geometry), collapse = ", "))
     }
     
@@ -1068,12 +1068,14 @@ calculate_field_statistics <- function(field_boundaries_sf, week_num, year,
       }
       
       # Guard: detect cloud-masked dates (CI == 0 indicates no-data)
-      # When any extracted value is 0, treat the entire date as cloud-masked
-      has_zeros <- any(extracted$CI == 0, na.rm = TRUE)
+      # Calculate proportion of zero values; only treat as cloud-masked if proportion exceeds threshold
+      prop_zero <- mean(extracted$CI == 0, na.rm = TRUE)
+      cloud_threshold <- 0.25  # 25% tolerance for zero pixels
       
-      if (has_zeros) {
+      if (prop_zero > cloud_threshold) {
         # Cloud-masked date: skip temporal analysis, set stats to NA
-        message(paste("    [CLOUD] Field", field_name, "- entire date is cloud-masked (CI==0)"))
+        message(paste("    [CLOUD] Field", field_name, "- date is cloud-masked (", 
+                      round(prop_zero * 100, 1), "% CI==0)"))
         
         results_list[[length(results_list) + 1]] <- data.frame(
           Field_id = field_name,
@@ -1089,7 +1091,7 @@ calculate_field_statistics <- function(field_boundaries_sf, week_num, year,
         next
       }
       
-      ci_vals <- extracted$CI[!is.na(extracted$CI)]
+      ci_vals <- extracted$CI[!is.na(extracted$CI) & extracted$CI > 0]
       
       if (length(ci_vals) == 0) {
         next
@@ -1847,21 +1849,8 @@ calculate_yield_prediction_kpi <- function(field_boundaries, harvesting_data, cu
                            pred_rf_current_season$predicted_Tcha < yield_quartiles[3], na.rm = TRUE)
       lowest_25_count <- sum(pred_rf_current_season$predicted_Tcha < yield_quartiles[1], na.rm = TRUE)
 
-      # Calculate total area
-      if (!inherits(field_boundaries, "SpatVector")) {
-        field_boundaries_vect <- terra::vect(field_boundaries)
-      } else {
-        field_boundaries_vect <- field_boundaries
-      }
-
-      # Handle both sf and SpatVector inputs for area calculation
-      if (inherits(field_boundaries, "sf")) {
-        field_boundaries_projected <- sf::st_transform(field_boundaries, "EPSG:6933")
-        field_areas <- sf::st_area(field_boundaries_projected) / 10000  # m² to hectares
-      } else {
-        field_boundaries_projected <- terra::project(field_boundaries_vect, "EPSG:6933")
-        field_areas <- terra::expanse(field_boundaries_projected) / 10000
-      }
+      # Calculate total area using centralized function
+      field_areas <- calculate_area_from_geometry(field_boundaries, unit = "hectare")
       total_area <- sum(as.numeric(field_areas))
 
       safe_log(paste("Total area:", round(total_area, 1), "hectares"))

r_app/90_CI_report_with_kpis_agronomic_support.Rmd

@@ -531,19 +531,20 @@ map_trend_to_arrow <- function(text_vec, include_text = FALSE) {
     }
     
     # Determine category and build output with translated labels
-    if (grepl("strong growth", text)) {
+    # Using word-boundary anchored patterns (perl=TRUE) to avoid substring mis-matches
+    if (grepl("\\bstrong growth\\b", text, perl = TRUE)) {
       arrow <- "↑↑"
       trans_key <- "Strong growth"
-    } else if (grepl("slight growth|weak growth|growth|increasing", text)) {
+    } else if (grepl("\\b(?:slight|weak) growth\\b|(?<!no\\s)\\bgrowth\\b|\\bincreasing\\b", text, perl = TRUE)) {
       arrow <- "↑"
       trans_key <- "Slight growth"
-    } else if (grepl("stable|no growth", text)) {
+    } else if (grepl("\\bstable\\b|\\bno growth\\b", text, perl = TRUE)) {
       arrow <- "→"
       trans_key <- "Stable"
-    } else if (grepl("weak decline|slight decline|moderate decline", text)) {
+    } else if (grepl("\\b(?:weak|slight|moderate) decline\\b", text, perl = TRUE)) {
       arrow <- "↓"
       trans_key <- "Slight decline"
-    } else if (grepl("strong decline|severe", text)) {
+    } else if (grepl("\\bstrong decline\\b|\\bsevere\\b", text, perl = TRUE)) {
       arrow <- "↓↓"
       trans_key <- "Strong decline"
     } else {
@@ -748,8 +749,7 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
 
       # Translate the table for visualization
       names(display_df) <- c(tr_key("KPI"), tr_key("Level"), tr_key("Count"), tr_key("Percent"))
-      display_df[, 1:2] <- lapply(display_df[, 1:2], function(col) sapply(col, t))
-      
+      display_df[, 1:2] <- lapply(display_df[, 1:2], function(col) sapply(col, tr_key))      
       ft <- flextable(display_df) %>%
         merge_v(j = tr_key("KPI")) %>%
         autofit()
@@ -1864,7 +1864,7 @@ metadata_info <- data.frame(
     format(Sys.time(), "%Y-%m-%d %H:%M:%S"),
     paste(tr_key("project"), toupper(project_dir)),
     paste(tr_key("week"), current_week, tr_key("of"), year),
-    ifelse(exists("AllPivots0"), nrow(AllPivots0 %>% filter(!is.na(field)) %>% group_by(field) %>% summarise()), tr_key("unknown")),
+    if (exists("AllPivots0")) { nrow(AllPivots0 %>% filter(!is.na(field)) %>% group_by(field) %>% summarise()) } else { tr_key("unknown") },
     tr_key("next_wed")
   )
 )

r_app/parameters_project.R

@@ -64,8 +64,35 @@ get_area_unit_label <- function(unit = AREA_UNIT_PREFERENCE) {
   } else if (unit_lower == "acre") {
     return("ac")
   } else {
-    warning("Unknown unit: ", unit, ". Defaulting to 'ha'")
-    return("ha")
+    warning("Unknown unit '", unit, "'. Falling back to AREA_UNIT_PREFERENCE ('", AREA_UNIT_PREFERENCE, "')")
+    return(get_area_unit_label(AREA_UNIT_PREFERENCE))
+  }
+}
+
+#' Get area conversion factor from hectares to requested unit
+#'
+#' Returns the numeric multiplier to convert 1 hectare into the requested unit.
+#' Case-insensitive unit matching. On unknown unit, warns and defaults to 1 (hectare).
+#'
+#' @param unit Character. Unit preference ("hectare" or "acre")
+#' @return Numeric. Conversion factor (1 for hectare, 2.47105 for acre)
+#'
+#' @details
+#' Conversion factors:
+#'   - "hectare" → 1 (identity)
+#'   - "acre" → 2.47105 (1 hectare ≈ 2.47105 acres)
+#'
+#'
+#' @export
+get_area_conversion_factor <- function(unit = AREA_UNIT_PREFERENCE) {
+  unit_lower <- tolower(unit)
+  if (unit_lower == "hectare") {
+    return(1)
+  } else if (unit_lower == "acre") {
+    return(2.47105)  # 1 hectare = 2.47105 acres (inverse of 0.404686)
+  } else {
+    warning("Unknown unit '", unit, "'. Falling back to AREA_UNIT_PREFERENCE ('", AREA_UNIT_PREFERENCE, "')")
+    return(get_area_conversion_factor(AREA_UNIT_PREFERENCE))
   }
 }