Merge pull request #14 from TimonWeitkamp/review_perField_code

Growth trend

r_app/80_utils_agronomic_support.R

@@ -793,10 +793,14 @@ calculate_all_field_analysis_agronomic_support <- function(
   # Safely identify immediate child directories (not including root)
   # Use list.files + dir.exists filter instead of list.dirs for robustness
   all_entries <- list.files(current_mosaic_dir, full.names = FALSE)
-  field_dirs <- all_entries[sapply(
+  # Validate input and coerce-safe checks
-    file.path(current_mosaic_dir, all_entries),
+  if (is.null(current_mosaic_dir) || !is.character(current_mosaic_dir) || length(current_mosaic_dir) != 1) {
-    dir.exists
+    stop("current_mosaic_dir must be a single path string")
-  )]
+  }
+  paths <- file.path(current_mosaic_dir, all_entries)
+  # Use vapply to guarantee a logical vector (avoid sapply returning a list)
+  is_dir <- vapply(paths, dir.exists, logical(1))
+  field_dirs <- all_entries[is_dir]
   
   is_per_field <- length(field_dirs) > 0 && 
                   file.exists(file.path(current_mosaic_dir, field_dirs[1], week_file))
@@ -906,12 +910,10 @@ calculate_all_field_analysis_agronomic_support <- function(
   
   # Load historical field statistics to build weekly mean CI time series per field
   # (growth_decline_kpi expects temporal series, not spatial pixel arrays)
-  weekly_mean_ci_by_field <- list()
+  # Initialize list with one element per field (empty numeric vectors)
-  
+  n_fields <- nrow(field_boundaries_sf)
-  # Build list of weekly mean CI values for each field (4-week lookback)
+  weekly_mean_ci_by_field <- vector("list", n_fields)
-  for (field_idx in seq_len(nrow(field_boundaries_sf))) {
+  for (fi in seq_len(n_fields)) weekly_mean_ci_by_field[[fi]] <- numeric(0)
-    weekly_ci_values <- c()
-  }
   
   # Try to load historical data for trend calculation
   if (!is.null(output_dir) && !is.null(project_dir)) {
@@ -920,7 +922,7 @@ calculate_all_field_analysis_agronomic_support <- function(
         project_dir = project_dir,
         current_week = current_week,
         current_year = current_year,
-        reports_dir = output_dir,
+        kpi_reports_dir = output_dir,
         num_weeks = 4,
         auto_generate = FALSE,
         field_boundaries_sf = field_boundaries_sf
@@ -929,35 +931,61 @@ calculate_all_field_analysis_agronomic_support <- function(
       if (!is.null(historical_data) && length(historical_data) > 0) {
         message("  Building weekly mean CI time series from historical data...")
         
-        # Initialize list with empty vectors for each field
-        for (field_idx in seq_len(nrow(field_boundaries_sf))) {
-          weekly_mean_ci_by_field[[field_idx]] <- c()
-        }
-        
         # Extract Mean_CI from each historical week (reverse order to go chronologically)
+        possible_mean_cols <- c("Mean_CI", "mean_ci", "MeanCI", "meanCI", "mean.ci")
+        possible_id_cols <- c("Field_id", "field_id", "Field", "field", "Field_name", "field_name")
         for (hist_idx in rev(seq_along(historical_data))) {
           hist_week <- historical_data[[hist_idx]]
           hist_data <- hist_week$data
-          
+
-          # Extract Mean_CI column if available
+          # Skip empty week data
-          if ("Mean_CI" %in% names(hist_data)) {
+          if (is.null(hist_data) || length(hist_data) == 0) next
-            # Match fields between historical data and field_boundaries
+
-            for (field_idx in seq_len(nrow(field_boundaries_sf))) {
+          # Coerce to data.frame if needed
-              field_name <- field_boundaries_sf$field[field_idx]
+          if (!is.data.frame(hist_data)) {
-              
+            hist_data <- tryCatch(as.data.frame(hist_data, stringsAsFactors = FALSE), error = function(e) NULL)
-              # Find matching row in historical data by field name/ID
+          }
-              field_row <- which(
+          if (is.null(hist_data) || !is.data.frame(hist_data)) next
-                (hist_data$Field_id == field_name | hist_data$Field_name == field_name) &
+
-                !is.na(hist_data$Mean_CI)
+          mean_col <- intersect(possible_mean_cols, names(hist_data))
-              )
+          if (length(mean_col) == 0) {
-              
+            message(paste0("    Warning: historical week ", hist_week$week, "_", hist_week$year, " missing Mean_CI column - skipping"))
-              if (length(field_row) > 0) {
+            next
-                mean_ci_val <- as.numeric(hist_data$Mean_CI[field_row[1]])
+          }
-                if (!is.na(mean_ci_val)) {
+          mean_col <- mean_col[1]
-                  weekly_mean_ci_by_field[[field_idx]] <- c(weekly_mean_ci_by_field[[field_idx]], mean_ci_val)
+
-                }
+          id_col <- intersect(possible_id_cols, names(hist_data))
+          use_row_order <- FALSE
+          if (length(id_col) == 0) {
+            if (nrow(hist_data) == n_fields) {
+              use_row_order <- TRUE
+            } else {
+              message(paste0("    Warning: historical week ", hist_week$week, "_", hist_week$year, " has no id/name column and rowcount != n_fields - skipping"))
+              next
+            }
+          } else {
+            id_col <- id_col[1]
+          }
+
+          # Normalize to character columns for matching
+          hist_df <- as.data.frame(hist_data, stringsAsFactors = FALSE)
+          hist_df[[mean_col]] <- as.character(hist_df[[mean_col]])
+          if (!use_row_order) hist_df[[id_col]] <- as.character(hist_df[[id_col]])
+
+          for (field_idx in seq_len(n_fields)) {
+            mean_ci_val <- NA_real_
+            if (use_row_order) {
+              if (field_idx <= nrow(hist_df)) {
+                mean_ci_val <- suppressWarnings(as.numeric(hist_df[[mean_col]][field_idx]))
+              }
+            } else {
+              fid <- as.character(field_boundaries_sf$field[field_idx])
+              matches <- which(!is.na(hist_df[[id_col]]) & hist_df[[id_col]] == fid)
+              if (length(matches) > 0) {
+                mean_ci_val <- suppressWarnings(as.numeric(hist_df[[mean_col]][matches[1]]))
               }
             }
+            if (!is.na(mean_ci_val)) weekly_mean_ci_by_field[[field_idx]] <- c(weekly_mean_ci_by_field[[field_idx]], mean_ci_val)
           }
         }
         
@@ -1168,7 +1196,7 @@ calculate_all_field_analysis_agronomic_support <- function(
     project_dir = project_dir
   )
   
-  message(paste("\n✓ AURA KPI calculation complete. Week", current_week, current_year))
+  message(paste("\n✓ KPI calculation complete. Week", current_week, current_year))
   
   return(list(
     field_analysis_df = field_detail_df,  

r_app/80_utils_common.R

@@ -1063,7 +1063,7 @@ load_or_calculate_weekly_stats <- function(week_num, year, project_dir, field_bo
 }
 
 #' Load historical field data from CSV (4-week lookback)
-load_historical_field_data <- function(project_dir, current_week, current_year, reports_dir, num_weeks = 4, auto_generate = TRUE, field_boundaries_sf = NULL, daily_vals_dir = NULL) {
+load_historical_field_data <- function(project_dir, current_week, current_year, kpi_reports_dir, num_weeks = 4, auto_generate = TRUE, field_boundaries_sf = NULL, daily_vals_dir = NULL) {
   
   historical_data <- list()
   loaded_weeks <- c()
@@ -1075,7 +1075,7 @@ load_historical_field_data <- function(project_dir, current_week, current_year,
     target_year <- target$year
     
     csv_filename <- paste0(project_dir, "_field_analysis_week", sprintf("%02d_%d", target_week, target_year), ".csv")
-    csv_path <- file.path(reports_dir, "field_analysis", csv_filename)
+    csv_path <- file.path(kpi_reports_dir,  csv_filename)
     
     if (file.exists(csv_path)) {
       tryCatch({

r_app/parameters_project.R

@@ -52,7 +52,8 @@ CLIENT_TYPE_MAP <- list(
   "simba" = "agronomic_support",
   "john" = "agronomic_support",
   "huss" = "agronomic_support",
-  "aura" = "agronomic_support"
+  "aura" = "agronomic_support",
+  "tpc" = "agronomic_support"
 )
 
 #' Get client type for a project