Update CI report parameters and improve map legend configurations

- Changed report date in CI report for cane supply to "2026-02-04".
- Updated output file naming convention for agronomic support report to reflect new report date.
- Enhanced map creation functions to allow customizable legend positions and improved layout settings.
- Adjusted widths for map arrangements to ensure better visual representation.
- Fixed minor issues in ggplot aesthetics for clearer legend positioning and improved readability.
- Corrected field size unit from hectares to acres in KPI summary generation.

python_app/.gitignore

@@ -39,7 +39,6 @@ dist/
 *.bak
 *.swp
 *.swo
-*.swp
 
 *.png
 

r_app/80_utils_cane_supply.R

@@ -49,14 +49,14 @@ calculate_field_acreages <- function(field_boundaries_sf) {
       )
     
     # Calculate area for valid geometries
-    for (idx in which(lookup_df$geometry_valid)) {
+    valid_indices <- which(lookup_df$geometry_valid)
+    areas_ha <- vapply(valid_indices, function(idx) {
       tryCatch({
         area_m2 <- as.numeric(sf::st_area(field_boundaries_sf[idx, ]))
-        lookup_df$area_ha[idx] <- area_m2 / 10000
+        area_m2 / 10000
-      }, error = function(e) {
+      }, error = function(e) NA_real_)
-        lookup_df$area_ha[idx] <<- NA_real_
+    }, numeric(1))
-      })
+    lookup_df$area_ha[valid_indices] <- areas_ha
-    }
     
     # Convert hectares to acres
     lookup_df %>%

r_app/90_CI_report_with_kpis_agronomic_support.Rmd

@@ -3,7 +3,7 @@ params:
   ref: "word-styles-reference-var1.docx"
   output_file: "CI_report.docx"
   report_date: !r Sys.Date()
-  data_dir: "angata"
+  data_dir: "aura"
   mail_day: "Wednesday"
   borders: FALSE
   ci_plot_type: "both"
@@ -61,6 +61,10 @@ suppressPackageStartupMessages({
   library(officer)    # For Word document manipulation (custom formatting, headers, footers)
 })
 
+# Configure tmap for static plotting (required for legend.outside to work)
+tmap_mode("plot")  # CRITICAL: Must be "plot" mode for legends outside to render properly
+tmap_options(component.autoscale = FALSE)
+
 # Load custom utility functions
 tryCatch({
   source("report_utils.R")
@@ -271,6 +275,8 @@ if (exists("summary_tables") && !is.null(summary_tables)) {
 } else {
   safe_log("WARNING: summary_tables is NULL or does not exist", "WARNING")
 }
+
+# summary_tables  # Uncomment for debugging
 ```
 
 ```{r calculate_dates_and_weeks, message=FALSE, warning=FALSE, include=FALSE}
@@ -388,6 +394,15 @@ if (!is.null(CI_quadrant) && nrow(CI_quadrant) > 0) {
 }
 ```
 
+<!-- Dynamic cover page  -->
+::: {custom-style="Cover_title" style="text-align:center; margin-top:120px;"}
+<span style="font-size:100pt; line-height:1.0; font-weight:700;">Satellite Based Field Reporting</span>
+:::
+
+::: {custom-style="Cover_subtitle" style="text-align:center; margin-top:18px;"}
+<span style="font-size:20pt; font-weight:600;">Chlorophyll Index (CI) Monitoring Report — `r toupper(params$data_dir)` Farm (Week `r { rd <- params$report_date; rd <- if (inherits(rd, "Date")) rd else suppressWarnings(as.Date(rd)); if (is.na(rd)) rd <- Sys.Date(); if (!is.null(params$week)) params$week else format(rd, '%V') }`, `r { rd <- params$report_date; rd <- if (inherits(rd, "Date")) rd else suppressWarnings(as.Date(rd)); if (is.na(rd)) rd <- Sys.Date(); format(rd, '%Y') }`)</span>
+:::
+
 ## Report Summary
 
 **Farm Location:** `r toupper(project_dir)` Estate  
@@ -404,7 +419,7 @@ if (!is.null(CI_quadrant) && nrow(CI_quadrant) > 0) {
 
 ## Key Insights
 
-```{r key_insights, echo=TRUE, message=TRUE, warning=TRUE, results='asis'}
+```{r key_insights, echo=FALSE, results='asis'}
 # Calculate key insights from KPI data
 if (exists("summary_tables") && !is.null(summary_tables) && length(summary_tables) > 0) {
   
@@ -414,8 +429,7 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
   if (!is.null(summary_tables$uniformity) && nrow(summary_tables$uniformity) > 0) {
     cat("**Field Uniformity:**\n")
     uniformity_counts <- summary_tables$uniformity %>%
-      group_by(interpretation) %>%
+      dplyr::select(interpretation, count = field_count)
-      summarise(count = n(), .groups = 'drop')
     
     for (i in seq_len(nrow(uniformity_counts))) {
       status <- uniformity_counts$interpretation[i]
@@ -430,8 +444,7 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
   if (!is.null(summary_tables$area_change) && nrow(summary_tables$area_change) > 0) {
     cat("\n**Area Change Status:**\n")
     area_counts <- summary_tables$area_change %>%
-      group_by(interpretation) %>%
+      dplyr::select(interpretation, count = field_count)
-      summarise(count = n(), .groups = 'drop')
     
     for (i in seq_len(nrow(area_counts))) {
       status <- area_counts$interpretation[i]
@@ -446,10 +459,9 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
   if (!is.null(summary_tables$growth_decline) && nrow(summary_tables$growth_decline) > 0) {
     cat("\n**Growth Trends (4-Week):**\n")
     growth_counts <- summary_tables$growth_decline %>%
-      group_by(trend_interpretation) %>%
+      dplyr::select(trend_interpretation, count = field_count)
-      summarise(count = n(), .groups = 'drop')
     
-    for (i in 1:nrow(growth_counts)) {
+    for (i in seq_len(nrow(growth_counts))) {
       trend <- growth_counts$trend_interpretation[i]
       count <- growth_counts$count[i]
       if (!is.na(trend) && !is.na(count) && count > 0) {
@@ -462,8 +474,7 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
   if (!is.null(summary_tables$weed_pressure) && nrow(summary_tables$weed_pressure) > 0) {
     cat("\n**Weed/Pest Pressure Risk:**\n")
     weed_counts <- summary_tables$weed_pressure %>%
-      group_by(weed_pressure_risk) %>%
+      dplyr::select(weed_pressure_risk, count = field_count)
-      summarise(count = n(), .groups = 'drop')
     
     for (i in seq_len(nrow(weed_counts))) {
       risk <- weed_counts$weed_pressure_risk[i]
@@ -475,7 +486,7 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
   }
   
   # 5. Total fields analyzed
-  total_fields <- nrow(summary_tables$uniformity)
+  total_fields <- sum(summary_tables$uniformity$field_count, na.rm = TRUE)
   cat("\n**Total Fields Analyzed:** ", total_fields, "\n", sep="")
  
 } else {
@@ -515,11 +526,11 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
         dplyr::transmute(
           Level = as.character(.data[[level_col]]),
           Count = as.integer(round(as.numeric(.data[[count_col]]))),
-          Percent = dplyr::if_else(
+          Percent = if (is.na(total)) {
-            is.na(total),
+            NA_real_
-            NA_real_,
+          } else {
             round(Count / total * 100, 1)
-          )
+          }
         )
     }
 
@@ -554,21 +565,22 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
 
     if (nrow(combined_df) > 0) {
       combined_df <- combined_df %>%
+        dplyr::mutate(KPI_group = KPI) %>%
         dplyr::group_by(KPI) %>%
         dplyr::mutate(
           KPI_display = if_else(dplyr::row_number() == 1, KPI, "")
         ) %>%
-        dplyr::ungroup() %>%
+        dplyr::ungroup()
+
+      kpi_group_sizes <- rle(combined_df$KPI_group)$lengths
+
+      display_df <- combined_df %>%
         dplyr::select(KPI = KPI_display, Level, Count, Percent)
 
-      ft <- flextable(combined_df) %>%
+      ft <- flextable(display_df) %>%
         merge_v(j = "KPI") %>%
         autofit()
 
-      kpi_group_sizes <- combined_df %>%
-        dplyr::group_by(KPI) %>%
-        dplyr::tally() %>%
-        dplyr::pull(n)
       cum_rows <- cumsum(kpi_group_sizes)
       for (i in seq_along(cum_rows)) {
         if (i < length(cum_rows)) {
@@ -603,7 +615,7 @@ generate_field_alerts <- function(field_details_table) {
   }
   
   # Check for required columns
-  required_cols <- c("Field", "Field Size (ha)", "Growth Uniformity", "Yield Forecast (t/ha)", 
+  required_cols <- c("Field", "Field Size (acres)", "Growth Uniformity", "Yield Forecast (t/ha)", 
                      "Gap Score", "Decline Risk", "Weed Risk", "Mean CI", "CV Value", "Moran's I")
   missing_cols <- setdiff(required_cols, colnames(field_details_table))
   
@@ -623,7 +635,7 @@ generate_field_alerts <- function(field_details_table) {
     # Aggregate data for the field
     field_summary <- field_data %>%
       summarise(
-        field_size = sum(`Field Size (ha)`, na.rm = TRUE),
+        field_size = sum(`Field Size (acres)`, na.rm = TRUE),
         uniformity_levels = paste(unique(`Growth Uniformity`), collapse = "/"),
         avg_yield_forecast = mean(`Yield Forecast (t/ha)`, na.rm = TRUE),
         max_gap_score = max(`Gap Score`, na.rm = TRUE),
@@ -765,7 +777,7 @@ if (!exists("field_details_table") || is.null(field_details_table)) {
       
       # Try to calculate field sizes (area) from geometry if available
       field_sizes <- if (!is.null(sf::st_geometry(AllPivots0)) && !all(sf::st_is_empty(sf::st_geometry(AllPivots0)))) {
-        sf::st_area(sf::st_geometry(AllPivots0)) / 10000  # Convert m² to hectares
+        sf::st_area(sf::st_geometry(AllPivots0)) / 4046.86  # Convert m² to acres (1 acre = 4046.86 m²)
       } else {
         rep(NA_real_, length(field_names))
       }
@@ -773,7 +785,7 @@ if (!exists("field_details_table") || is.null(field_details_table)) {
       # 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),
+        `Field Size (acres)` = as.numeric(field_sizes),
         `Growth Uniformity` = NA_character_,
         `Yield Forecast (t/ha)` = NA_real_,
         `Gap Score` = NA_real_,
@@ -791,8 +803,6 @@ if (!exists("field_details_table") || is.null(field_details_table)) {
 }
 ```
 
-## Farm-Level Overview Maps
-
 ```{r aggregate_farm_level_rasters, message=FALSE, warning=FALSE, include=FALSE}
 # Aggregate per-field weekly mosaics into single farm-level rasters for visualization
 # This creates on-the-fly mosaics for current week and historical weeks without saving intermediate files
@@ -939,9 +949,8 @@ tryCatch({
 ```
 
 \newpage
-### Chlorophyll Index (CI) Overview Map - Current Week
 
-```{r render_farm_ci_map, echo=FALSE, message=FALSE, warning=FALSE, fig.height=5.5, fig.width=6.5, dpi=150, dev='png', message=FALSE, warning=FALSE}
+```{r render_farm_ci_map, echo=FALSE, message=FALSE, warning=FALSE, fig.height=6.8, fig.width=8.5, dpi=150, dev='png'}
 # Create farm-level chlorophyll index map with OpenStreetMap basemap
 tryCatch({
   if (!is.null(farm_ci_current_ll)) {
@@ -1015,13 +1024,13 @@ tryCatch({
     map <- map +
       # Add scale bar and theme
       ggspatial::annotation_scale(
-        location = "br",
+        location = "tr",
         width_hint = 0.25
       ) +
       ggplot2::theme_void() +
       ggplot2::theme(
-        legend.position = "bottom",
+        legend.position = "right",
-        legend.direction = "horizontal",
+        legend.direction = "vertical",
         legend.title = ggplot2::element_text(size = 10),
         legend.text = ggplot2::element_text(size = 9),
         plot.title = ggplot2::element_text(hjust = 0.5, size = 12, face = "bold"),
@@ -1047,10 +1056,7 @@ tryCatch({
 })
 ```
 
-\newpage
+```{r render_farm_ci_diff_map, echo=FALSE, message=FALSE, warning=FALSE, fig.height=6.8, fig.width=8.5, dpi=150, dev='png'}
-### Weekly Chlorophyll Index Difference Map
-
-```{r render_farm_ci_diff_map, echo=FALSE, fig.height=5.5, fig.width=6.5, dpi=150, dev='png', message=FALSE, warning=FALSE}
 # Create farm-level CI difference map (week-over-week change)
 tryCatch({
   if (!is.null(farm_ci_diff_week_ll)) {
@@ -1125,13 +1131,13 @@ tryCatch({
     map <- map +
       # Add scale bar and theme
       ggspatial::annotation_scale(
-        location = "br",
+        location = "tr",
         width_hint = 0.25
       ) +
       ggplot2::theme_void() +
       ggplot2::theme(
-        legend.position = "bottom",
+        legend.position = "right",
-        legend.direction = "horizontal",
+        legend.direction = "vertical",
         legend.title = ggplot2::element_text(size = 10),
         legend.text = ggplot2::element_text(size = 9),
         plot.title = ggplot2::element_text(hjust = 0.5, size = 12, face = "bold"),
@@ -1157,8 +1163,6 @@ tryCatch({
 })
 ```
 
-\newpage
-
 # Section 2: Field-by-Field Analysis
 
 ## Overview of Field-Level Insights
@@ -1174,33 +1178,10 @@ This section provides detailed, field-specific analyses including chlorophyll in
 
 \newpage
 
-```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=6.5, dpi=150, dev='png', message=TRUE, echo=FALSE, warning=TRUE, include=TRUE, results='asis'}
+```{r generate_field_visualizations, echo=FALSE, fig.height=3.8, fig.width=10, dev='png', dpi=150, results='asis'}
 # Generate detailed visualizations for each field using purrr::walk
-# DIAGNOSTIC MODE - Remove this after debugging
-cat("\n## DIAGNOSTIC: Starting field visualization processing\n\n")
 
 tryCatch({
-   # Check prerequisites
-   cat("- Fields to process:", nrow(AllPivots_merged), "\n")
-   cat("- Field names:", paste(AllPivots_merged$field, collapse = ", "), "\n")
-   cat("- Weekly mosaic directory:", weekly_CI_mosaic, "\n")
-   cat("- CI quadrant data available:", !is.null(CI_quadrant), "\n")
-   cat("- Harvesting data available:", !is.null(harvesting_data), "\n\n")
-  
-   # Check if ci_plot function exists
-   if (!exists("ci_plot")) {
-    cat("**ERROR: ci_plot() function not found!**\n\n")
-    stop("ci_plot function missing")
-   }
-  
-   if (!exists("cum_ci_plot")) {
-    cat("**ERROR: cum_ci_plot() function not found!**\n\n")
-    stop("cum_ci_plot function missing")
-   }
-  
-   cat("- ci_plot() function:", "FOUND", "\n")
-   cat("- cum_ci_plot() function:", "FOUND", "\n\n")
-
    # Prepare merged field list and week/year info
    AllPivots_merged <- AllPivots0 %>% 
     dplyr::filter(!is.na(field), !is.na(sub_field)) %>%
@@ -1227,9 +1208,7 @@ tryCatch({
   # Helper function to safely load per-field mosaic if it exists
   load_per_field_mosaic <- function(base_dir, field_name, week, year) {
     path <- file.path(base_dir, field_name, paste0("week_", sprintf("%02d", week), "_", year, ".tif"))
-    cat(paste("  [DEBUG] Field:", field_name, "trying path:", path, "\n"))
     if (file.exists(path)) {
-      cat(paste("  ✓ File found\n"))
       tryCatch({
         rast_obj <- terra::rast(path)
         # Extract CI band if present, otherwise first band
@@ -1242,8 +1221,6 @@ tryCatch({
         message(paste("Warning: Could not load", path, ":", e$message))
         return(NULL)
       })
-    } else {
-      cat(paste("  ✗ File NOT found\n"))
     }
     return(NULL)
   }
@@ -1254,7 +1231,7 @@ tryCatch({
     tryCatch({
       # Add page break before each field (except first)
       if (!is_first_field) {
-        cat("\\newpage\n\n")
+        cat("\\newpage\n")
       }
       is_first_field <<- FALSE
       
@@ -1301,7 +1278,7 @@ tryCatch({
           borders = borders,
           colorblind_friendly = colorblind_friendly
         )
-        cat("\n\n")
+        #cat("\n\n")
       } else {
         message(paste("Warning: No raster data found for field", field_name))
       }
@@ -1332,20 +1309,51 @@ tryCatch({
           benchmark_percentiles = c(10, 50, 90),
           benchmark_data = benchmarks
         )
-        cat("\n\n")
+        #cat("\n")
       }
       
       # Add field-specific KPI summary if available
-      # NOTE: generate_field_kpi_summary function not yet implemented
+      if (exists("field_details_table") && !is.null(field_details_table) && nrow(field_details_table) > 0) {
-      # Skipping field-level KPI text for now; KPI tables are available in Section 1
+        field_kpi <- field_details_table %>%
-      if (FALSE) {  # Disabled pending function implementation
+          dplyr::filter(Field_id == field_name)
-        # if (exists("field_details_table") && !is.null(field_details_table) && nrow(field_details_table) > 0) {
+        
-        #   kpi_summary <- generate_field_kpi_summary(field_name, field_details_table, CI_quadrant)
+        if (nrow(field_kpi) > 0) {
-        #   if (!is.null(kpi_summary)) {
+          # Format KPIs as compact single line (no interpretations, just values)
-        #     cat(kpi_summary)
+          kpi_parts <- c(
-        #     cat("\n\n")
+            sprintf("**CV:** %.2f", field_kpi$CV),
-        #   }
+            sprintf("**Mean CI:** %.2f", field_kpi$Mean_CI)
-        # }
+          )
+          
+          # Add Weekly_CI_Change if available (note: capital C and I)
+          if (!is.null(field_kpi$Weekly_CI_Change) && !is.na(field_kpi$Weekly_CI_Change)) {
+            change_sign <- ifelse(field_kpi$Weekly_CI_Change >= 0, "+", "")
+            kpi_parts <- c(kpi_parts, sprintf("**Δ CI:** %s%.2f", change_sign, field_kpi$Weekly_CI_Change))
+          }
+          
+          # Compact trend display with symbols
+          trend_compact <- case_when(
+            grepl("Strong growth", field_kpi$Trend_Interpretation, ignore.case = TRUE) ~ "↑↑",
+            grepl("Growth|Increasing", field_kpi$Trend_Interpretation, ignore.case = TRUE) ~ "↑",
+            grepl("Stable|No growth", field_kpi$Trend_Interpretation, ignore.case = TRUE) ~ "→",
+            grepl("Slight decline", field_kpi$Trend_Interpretation, ignore.case = TRUE) ~ "↓",
+            grepl("Strong decline|Severe", field_kpi$Trend_Interpretation, ignore.case = TRUE) ~ "↓↓",
+            TRUE ~ field_kpi$Trend_Interpretation
+          )
+          kpi_parts <- c(kpi_parts, sprintf("**Trend:** %s", trend_compact))
+          
+          if (!is.na(field_kpi$TCH_Forecasted) && field_kpi$TCH_Forecasted > 0) {
+            kpi_parts <- c(kpi_parts, sprintf("**Yield:** %.1f t/ha", field_kpi$TCH_Forecasted))
+          }
+          
+          kpi_parts <- c(
+            kpi_parts,
+            sprintf("**Gap:** %.0f", field_kpi$Gap_Score),
+            sprintf("**Weed:** %s", field_kpi$Weed_Pressure_Risk),
+            sprintf("**Decline:** %s", field_kpi$Decline_Severity)
+          )
+          
+          cat(paste(kpi_parts, collapse = " | "), "\n")
+        }
       }
       
     }, error = function(e) {
@@ -1396,90 +1404,96 @@ tryCatch({
 ```
 
 \newpage
-## KPI Summary by Field
+##  Detailed Field Performance Summary by Field
-
-## Detailed Field Performance Summary
 
 The following table provides a comprehensive overview of all monitored fields with their key performance metrics from the KPI analysis.
 
 ```{r detailed_field_table, echo=FALSE, results='asis'}
 # Detailed field performance table
-report_date_obj <- as.Date(report_date)
 
-# Initialize empty dataframe for field_ages if CI_quadrant is unavailable
-field_ages <- data.frame(Field = character(), Age_days = numeric())
-
-# Try to get field ages from CI quadrant if available
-if (!is.null(CI_quadrant) && nrow(CI_quadrant) > 0) {
-  tryCatch({
-    # Identify the current season for each field based on report_date
-    current_seasons <- CI_quadrant %>%
-      filter(Date <= report_date_obj) %>%
-      group_by(field, season) %>%
-      summarise(
-        season_start = min(Date),
-        season_end = max(Date),
-        .groups = 'drop'
-      ) %>%
-      group_by(field) %>%
-      filter(season == max(season)) %>%
-      select(field, season)
-    
-    # Get current field ages (most recent DOY for each field in their CURRENT SEASON only)
-    field_ages <- CI_quadrant %>%
-      inner_join(current_seasons, by = c("field", "season")) %>%
-      group_by(field) %>%
-      filter(DOY == max(DOY)) %>%
-      select(field, DOY) %>%
-      rename(Field = field, Age_days = DOY)
-  }, error = function(e) {
-    safe_log(paste("Error extracting field ages:", e$message), "WARNING")
-  })
-} else {
-  safe_log("CI quadrant data unavailable - field ages will not be included in detailed table", "WARNING")
-}
-
-# Transform field_details_table to display format with proper column names
 if (!exists("field_details_table") || is.null(field_details_table) || nrow(field_details_table) == 0) {
   safe_log("No field details available for table", "WARNING")
   cat("No field-level KPI data available for this report period.\n")
   
 } else {
-  # Map raw KPI columns to display names
+  # Calculate field sizes from boundaries (convert to acres)
-  field_details_clean <- field_details_table %>%
+  field_sizes_source <- if (exists("AllPivots_merged") && inherits(AllPivots_merged, "sf")) AllPivots_merged else AllPivots0
+  field_sizes_df <- field_sizes_source %>%
     mutate(
-      Field = Field_id,
+      field_size_acres = as.numeric(sf::st_area(geometry) / 4046.86)  # m² to acres
-      `Field Size (ha)` = NA_real_,  # Not available in KPI output, would need to come from boundaries
-      `Growth Uniformity` = Uniformity_Interpretation,
-      `Yield Forecast (t/ha)` = TCH_Forecasted,
-      `Gap Score` = Gap_Score,
-      `Decline Risk` = Decline_Severity,
-      `Weed Risk` = Weed_Pressure_Risk,
-      `Mean CI` = Mean_CI,
-      `CV Value` = CV
     ) %>%
-    left_join(field_ages, by = "Field") %>%
+    sf::st_drop_geometry() %>%
+    select(field, field_size_acres)
+  
+  # Get field ages from CI quadrant if available
+  field_ages_df <- if (!is.null(CI_quadrant) && nrow(CI_quadrant) > 0) {
+    tryCatch({
+      # Get current season and age for each field
+      CI_quadrant %>%
+        filter(Date <= as.Date(report_date)) %>%
+        group_by(field, season) %>%
+        summarise(last_date = max(Date), last_doy = max(DOY), .groups = 'drop') %>%
+        group_by(field) %>%
+        filter(season == max(season)) %>%
+        select(field, Age_days = last_doy)
+    }, error = function(e) {
+      data.frame(field = character(), Age_days = numeric())
+    })
+  } else {
+    data.frame(field = character(), Age_days = numeric())
+  }
+  
+  # Join field sizes and ages to KPI data, simplified column selection
+  field_details_clean <- field_details_table %>%
+    left_join(field_sizes_df, by = c("Field_id" = "field")) %>%
+    left_join(field_ages_df, by = c("Field_id" = "field")) %>%
     mutate(
       # Only show yield forecast for fields >= 240 days old
-      `Yield Forecast (t/ha)` = if_else(is.na(Age_days) | Age_days < 240, 
+      TCH_Forecasted = if_else(is.na(Age_days) | Age_days < 240, NA_real_, TCH_Forecasted),
-                                         NA_real_, 
-                                         `Yield Forecast (t/ha)`),
       # Round numeric columns
-      `Mean CI` = round(`Mean CI`, 2),
+      field_size_acres = round(field_size_acres, 1),
-      `CV Value` = round(`CV Value`, 2),
+      Mean_CI = round(Mean_CI, 2),
-      `Gap Score` = round(`Gap Score`, 0),
+      CV = round(CV, 2),
-      `Yield Forecast (t/ha)` = round(`Yield Forecast (t/ha)`, 1)
+      Gap_Score = round(Gap_Score, 0),
-    ) %>%
+      TCH_Forecasted = round(TCH_Forecasted, 1)
-    select(Field, `Field Size (ha)`, `Growth Uniformity`, `Yield Forecast (t/ha)`, 
+    )
-           `Gap Score`, `Decline Risk`, `Weed Risk`, `Mean CI`, `CV Value`)
   
-  # Display the cleaned field table with flextable
+  # Add Weekly_CI_Change if it exists in the data (note: capital C and I)
-  col_widths <- c(0.97, 0.73, 0.80, 0.80, 0.65, 0.73, 0.65, 0.56, 0.48)
+  if ("Weekly_CI_Change" %in% names(field_details_clean)) {
+    field_details_clean <- field_details_clean %>%
+      mutate(Weekly_CI_Change = round(Weekly_CI_Change, 2)) %>%
+      select(
+        Field = Field_id,
+        `Field Size (acres)` = field_size_acres,
+        `Growth Uniformity` = Uniformity_Interpretation,
+        `Mean CI` = Mean_CI,
+        `Weekly CI Change` = Weekly_CI_Change,
+        `Yield Forecast (t/ha)` = TCH_Forecasted,
+        `Gap Score` = Gap_Score,
+        `Decline Risk` = Decline_Severity,
+        `Weed Risk` = Weed_Pressure_Risk,
+        `CV Value` = CV
+      )
+  } else {
+    field_details_clean <- field_details_clean %>%
+      select(
+        Field = Field_id,
+        `Field Size (acres)` = field_size_acres,
+        `Growth Uniformity` = Uniformity_Interpretation,
+        `Mean CI` = Mean_CI,
+        `Yield Forecast (t/ha)` = TCH_Forecasted,
+        `Gap Score` = Gap_Score,
+        `Decline Risk` = Decline_Severity,
+        `Weed Risk` = Weed_Pressure_Risk,
+        `CV Value` = CV
+      )
+  }
   
+  # Display the cleaned field table with flextable (fit to page width)
   ft <- flextable(field_details_clean) %>%
     set_caption("Detailed Field Performance Summary") %>%
-    width(width = col_widths) %>%
+    theme_booktabs() %>%
-    theme_booktabs()
+    set_table_properties(width = 1, layout = "autofit")  # Fit to 100% page width with auto-adjust
   
   knit_print(ft)
 }
@@ -1595,4 +1609,4 @@ ft <- flextable(metadata_info) %>%
 ft
 ```
 
-*This report was automatically generated by the SmartCane monitoring system. For questions or additional analysis, please contact the technical team.*
+*This report was automatically generated by the SmartCane monitoring system. For questions or additional analysis, please contact the technical team at info@smartcane.ag.*

r_app/91_CI_report_with_kpis_cane_supply.Rmd

@@ -2,7 +2,7 @@
 params:
   ref: "word-styles-reference-var1.docx"
   output_file: CI_report.docx
-  report_date: "2025-09-30"
+  report_date: "2026-02-04"
   data_dir: "angata"
   mail_day: "Wednesday"
   borders: FALSE
@@ -61,6 +61,10 @@ suppressPackageStartupMessages({
   library(flextable)  # For formatted tables in Word output (professional table styling)
 })
 
+# Configure tmap for static plotting (required for legend.outside to work)
+tmap_mode("plot")  # CRITICAL: Must be "plot" mode for legends outside to render properly
+tmap_options(component.autoscale = FALSE)
+
 # Load custom utility functions
 tryCatch({
   source("r_app/report_utils.R")
@@ -1043,4 +1047,4 @@ ft <- flextable(metadata_info) %>%
 ft
 ```
 
-*This report was automatically generated by the SmartCane monitoring system. For questions or additional analysis, please contact the technical team.*
+*This report was automatically generated by the SmartCane monitoring system. For questions or additional analysis, please contact the technical team at info@smartcane.ag.*

r_app/MANUAL_PIPELINE_RUNNER.R

@@ -438,8 +438,8 @@
 #   rmarkdown::render(
 rmarkdown::render(
   "r_app/90_CI_report_with_kpis_agronomic_support.Rmd",
-  params = list(data_dir = "aura", report_date = as.Date("2022-12-08")),
+  params = list(data_dir = "aura", report_date = as.Date("2026-02-04")),
-  output_file = "SmartCane_Report_agronomic_support_aura_2022-12-08.docx",
+  output_file = "SmartCane_Report_agronomic_support_aura_2026-02-04.docx",
   output_dir = "laravel_app/storage/app/aura/reports"
 )
 #
@@ -450,7 +450,7 @@ rmarkdown::render(
 rmarkdown::render(
   "r_app/91_CI_report_with_kpis_cane_supply.Rmd",
   params = list(data_dir = "angata", report_date = as.Date("2026-02-04")),
-  output_file = "SmartCane_Report_basemap_test.docx",
+  output_file = "SmartCane_Report_cane_supply_angata_2026-02-04.docx",
   output_dir = "laravel_app/storage/app/angata/reports"
 )
 #

r_app/report_utils.R

@@ -24,7 +24,7 @@ subchunkify <- function(g, fig_height=7, fig_width=5) {
                       "\n`","``
   ")
   
-  cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
+  cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE)) 
 } 
 
 #' Creates a Chlorophyll Index map for a pivot
@@ -34,12 +34,13 @@ subchunkify <- function(g, fig_height=7, fig_width=5) {
 #' @param pivot_spans Additional boundary data for the field
 #' @param show_legend Whether to show the legend (default: FALSE)
 #' @param legend_is_portrait Whether to show the legend in portrait orientation (default: FALSE)
+#' @param legend_position Position for the legend when shown: "left", "right", "top", "bottom" (default: "bottom")
 #' @param week Week number to display in the title
 #' @param age Age of the crop in weeks
 #' @param borders Whether to display field borders (default: FALSE)
 #' @return A tmap object with the CI map
 #' 
-create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week, age, borders = FALSE, colorblind = FALSE){
+create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, legend_position = "bottom", week, age, borders = FALSE, colorblind = FALSE){
   # Input validation
   if (missing(pivot_raster) || is.null(pivot_raster)) {
     stop("pivot_raster is required")
@@ -64,26 +65,29 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend =
   map <- tm_shape(pivot_raster, unit = "m")
   
   # Add raster with continuous spectrum (fixed scale 8-1 for consistent comparison, reversed)
-  map <- map + tm_raster("CI",
+  map <- map + tm_raster(
-                          col_scale = tm_scale_continuous(values = palette,
+    "CI",
-                                                       limits = c(1,8)),
+    col.scale = tm_scale_continuous(
-                          col_legend = tm_legend(title = "CI",
+      values = palette,
-                                               orientation = if(legend_is_portrait) "portrait" else "landscape", 
+      limits = c(1, 8),
-                                               show = show_legend,
+      ticks = seq(1, 8, by = 1),
-                                               position = if(show_legend) tm_pos_out("left", "center") else c("left", "bottom"),
+      outliers.trunc = c(TRUE, TRUE)
-                                               reverse = TRUE
+    ),
-                                               ))
+    col.legend = tm_legend(
+      title = "CI",
+      orientation = if (legend_is_portrait) "portrait" else "landscape",
+      show = show_legend,
+      position = if (show_legend) tm_pos_out(legend_position, "center") else c("left", "bottom"),
+      reverse = TRUE
+    )
+  )
     # Add layout elements
-  map <- map + tm_title(text = paste0("Max CI week ", week,"\n",  age, " weeks (", age * 7, " days) old"),
+  map <- map + tm_layout(
-                        size = 0.7)
+    main.title = paste0("Max CI week ", week,"\n",  age, " weeks (", age * 7, " days) old"),
-  # Add layout configuration to prevent legend rescaling
+    main.title.size = 0.7,
-  map <- map + tm_layout(legend.position = c("left", "bottom"),
+    #legend.height = 0.85,  # Constrain vertical legend height to not exceed map
-                         legend.outside = FALSE,
+    asp = 1  # Fixed aspect ratio
-                         inner.margins = 0.05,
+  )
-                         asp = 1)  # Force 1:1 aspect ratio for consistent sizing
-  
-  # Add bounds/view settings for fixed aspect ratio
-  map <- map + tm_view(asp = 1)
   
   # Add borders if requested
   if (borders) {
@@ -105,13 +109,14 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend =
 #' @param pivot_spans Additional boundary data for the field
 #' @param show_legend Whether to show the legend (default: FALSE)
 #' @param legend_is_portrait Whether to show the legend in portrait orientation (default: FALSE)
+#' @param legend_position Position for the legend when shown: "left", "right", "top", "bottom" (default: "bottom")
 #' @param week_1 First week number for comparison
 #' @param week_2 Second week number for comparison
 #' @param age Age of the crop in weeks
 #' @param borders Whether to display field borders (default: TRUE)
 #' @return A tmap object with the CI difference map
 #'
-create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, week_1, week_2, age, borders = TRUE, colorblind = FALSE){
+create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend = F, legend_is_portrait = F, legend_position = "bottom", week_1, week_2, age, borders = TRUE, colorblind = FALSE){
   # Input validation
   if (missing(pivot_raster) || is.null(pivot_raster)) {
     stop("pivot_raster is required")
@@ -136,27 +141,30 @@ create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_lege
   map <- tm_shape(pivot_raster, unit = "m")
   
   # Add raster with continuous spectrum (centered at 0 for difference maps, fixed scale, reversed)
-  map <- map + tm_raster("CI",
+  map <- map + tm_raster(
-                          col_scale = tm_scale_continuous(values = palette,
+    "CI",
-                                                       midpoint = 0,
+    col.scale = tm_scale_continuous(
-                                                       limits = c(-3, 3)),
+      values = palette,
-                          col_legend = tm_legend(title = "CI diff.",
+      limits = c(-3, 3),
-                                               orientation = if(legend_is_portrait) "portrait" else "landscape",
+      ticks = seq(-3, 3, by = 1),
-                                               show = show_legend,
+      midpoint = 0,
-                                               position = if(show_legend) tm_pos_out("right", "center") else c("left", "bottom"),
+      outliers.trunc = c(TRUE, TRUE)
-                                               reverse = TRUE
+    ),
-                                               ))
+    col.legend = tm_legend(
+      title = "CI diff.",
+      orientation = if (legend_is_portrait) "portrait" else "landscape",
+      show = show_legend,
+      position = if (show_legend) tm_pos_out(legend_position, "center") else c("left", "bottom"),
+      reverse = TRUE
+    )
+  )
     # Add layout elements
-  map <- map + tm_title(text = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks (", age * 7, " days) old"),
+  map <- map + tm_layout(
-                        size = 0.7)
+    main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks (", age * 7, " days) old"),
-  # Add layout configuration to prevent legend rescaling
+    main.title.size = 0.7,
-  map <- map + tm_layout(legend.position = c("right", "bottom"),
+    #legend.height = 0.85,  # Constrain vertical legend height to not exceed map
-                         legend.outside = FALSE,
+    asp = 1  # Fixed aspect ratio
-                         inner.margins = 0.05,
+  )
-                         asp = 1)  # Force 1:1 aspect ratio for consistent sizing
-  
-  # Add bounds/view settings for fixed aspect ratio
-  map <- map + tm_view(asp = 1)
   
   # Add borders if requested
   if (borders) {
@@ -271,18 +279,16 @@ ci_plot <- function(pivotName,
     
     # Create historical maps only if data is available
     # Build list with all available maps - order matches original: [m2, m1, current, diff_1w, diff_3w]
-    # Widths match original hardcoded: c(0.23, 0.18, 0.18, 0.18, 0.23)
     maps_to_arrange <- list()
-    widths_to_use <- c()
     field_heading_note <- ""
     
     # Try to create 2-week ago map (legend on left)
     if (!is.null(singlePivot_m2)) {
       CImap_m2 <- create_CI_map(singlePivot_m2, AllPivots2, joined_spans2, 
                                 show_legend = TRUE, legend_is_portrait = TRUE, 
+                                legend_position = "left",
                                 week = week_minus_2, age = age - 2, borders = borders, colorblind = colorblind_friendly)
       maps_to_arrange <- c(maps_to_arrange, list(CImap_m2))
-      widths_to_use <- c(widths_to_use, 0.24)
     }
     
     # Try to create 1-week ago map
@@ -291,12 +297,10 @@ ci_plot <- function(pivotName,
                                 show_legend = FALSE, legend_is_portrait = FALSE, 
                                 week = week_minus_1, age = age - 1, borders = borders, colorblind = colorblind_friendly)
       maps_to_arrange <- c(maps_to_arrange, list(CImap_m1))
-      widths_to_use <- c(widths_to_use, 0.17)
     }
     
     # Always add current week map (center position)
     maps_to_arrange <- c(maps_to_arrange, list(CImap))
-    widths_to_use <- c(widths_to_use, 0.17)
     
     # Try to create 1-week difference map
     if (!is.null(abs_CI_last_week)) {
@@ -304,21 +308,17 @@ ci_plot <- function(pivotName,
                                                 show_legend = FALSE, legend_is_portrait = FALSE, 
                                                 week_1 = week, week_2 = week_minus_1, age = age, borders = borders, colorblind = colorblind_friendly)
       maps_to_arrange <- c(maps_to_arrange, list(CI_max_abs_last_week))
-      widths_to_use <- c(widths_to_use, 0.17)
     }
     
     # Try to create 3-week difference map (legend on right)
     if (!is.null(abs_CI_three_week)) {
       CI_max_abs_three_week <- create_CI_diff_map(abs_CI_three_week, AllPivots2, joined_spans2, 
                                                  show_legend = TRUE, legend_is_portrait = TRUE, 
+                                                 legend_position = "right",
                                                  week_1 = week, week_2 = week_minus_3, age = age, borders = borders, colorblind = colorblind_friendly)
       maps_to_arrange <- c(maps_to_arrange, list(CI_max_abs_three_week))
-      widths_to_use <- c(widths_to_use, 0.24)
     }
     
-    # Normalize widths to sum to 1
-    widths_to_use <- widths_to_use / sum(widths_to_use)
-    
     # Add note if historical data is limited
     if (length(maps_to_arrange) == 1) {
       field_heading_note <- " (Current week only - historical data not yet available)"
@@ -326,8 +326,21 @@ ci_plot <- function(pivotName,
       field_heading_note <- " (Limited historical data)"
     }
     
-    # Arrange the maps with normalized widths
+    # Arrange the maps in a row with more width for first and last (for legends)
-    tst <- do.call(tmap_arrange, c(maps_to_arrange, list(nrow = 1, widths = widths_to_use)))
+    # Give maps with legends (1st and 5th) more space: 23%, middle maps get 18% each
+    widths <- if (length(maps_to_arrange) == 5) {
+      c(0.23, 0.18, 0.18, 0.18, 0.23)
+    } else if (length(maps_to_arrange) == 4) {
+      c(0.25, 0.25, 0.25, 0.25)  # Equal if only 4 maps
+    } else if (length(maps_to_arrange) == 3) {
+      c(0.33, 0.33, 0.34)  # Equal if only 3 maps
+    } else if (length(maps_to_arrange) == 2) {
+      c(0.5, 0.5)  # Equal if only 2 maps
+    } else {
+      NULL  # Single map or other cases
+    }
+    
+    tst <- do.call(tmap_arrange, c(maps_to_arrange, list(nrow = 1, widths = widths)))
     
     # Output heading and map to R Markdown
     age_months <- round(age / 4.348, 1)
@@ -448,7 +461,14 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
       if (facet_on) {
         g <- ggplot2::ggplot(data = plot_data) +
           ggplot2::facet_wrap(~season, scales = "free_x") +
-          ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = "ci_value", col = "sub_field", group = "sub_field")) +
+          ggplot2::geom_line(
+            ggplot2::aes(
+              x = .data[[x_var]],
+              y = .data[["ci_value"]],
+              col = .data[["sub_field"]],
+              group = .data[["sub_field"]]
+            )
+          ) +
           ggplot2::labs(title = paste("Plot of", y_label),
                color = "Field Name",
                y = y_label,
@@ -458,10 +478,12 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
                                                    breaks = scales::breaks_pretty(),
                                                    labels = function(x) round(as.numeric(x - min(x)) / 30.44, 1))) +
           ggplot2::theme_minimal() +
-          ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
+              ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
                 axis.text.x.top = ggplot2::element_text(hjust = 0.5),
                 axis.title.x.top = ggplot2::element_text(size = 8),
-                legend.justification = c(1, 0), legend.position = c(1, 0),
+                legend.justification = c(1, 0),
+                legend.position = "inside",
+                legend.position.inside = c(1, 0),
                 legend.title = ggplot2::element_text(size = 8),
                 legend.text = ggplot2::element_text(size = 8)) +
           ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
@@ -490,22 +512,36 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
                 )
               ggplot2::geom_smooth(
                 data = benchmark_subset,
-                ggplot2::aes_string(x = "benchmark_x", y = "benchmark_value", group = "factor(percentile)"),
+                ggplot2::aes(
-                color = "gray70", size = 0.5, se = FALSE, inherit.aes = FALSE
+                  x = .data[["benchmark_x"]],
+                  y = .data[["benchmark_value"]],
+                  group = factor(.data[["percentile"]])
+                ),
+                color = "gray70", linewidth = 0.5, se = FALSE, inherit.aes = FALSE
               )
             }
           } +
           # Plot older seasons with lighter lines
           ggplot2::geom_line(
             data = plot_data %>% dplyr::filter(!is_latest),
-            ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season"),
+            ggplot2::aes(
-            size = 0.7, alpha = 0.4
+              x = .data[[x_var]],
+              y = .data[["ci_value"]],
+              col = .data[["season"]],
+              group = .data[["season"]]
+            ),
+            linewidth = 0.7, alpha = 0.4
           ) +
           # Plot latest season with thicker, more prominent line
           ggplot2::geom_line(
             data = plot_data %>% dplyr::filter(is_latest),
-            ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season"),
+            ggplot2::aes(
-            size = 1.5, alpha = 1
+              x = .data[[x_var]],
+              y = .data[["ci_value"]],
+              col = .data[["season"]],
+              group = .data[["season"]]
+            ),
+            linewidth = 1.5, alpha = 1
           ) +
           ggplot2::labs(title = paste("Plot of", y_label, "for Field", pivotName, title_suffix),
                color = "Season",
@@ -520,10 +556,12 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
             }
           } +
           ggplot2::theme_minimal() +
-          ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
+              ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
                 axis.text.x.top = ggplot2::element_text(hjust = 0.5),
                 axis.title.x.top = ggplot2::element_text(size = 8),
-                legend.justification = c(1, 0), legend.position = c(1, 0),
+                legend.justification = c(1, 0),
+                legend.position = "inside",
+                legend.position.inside = c(1, 0),
                 legend.title = ggplot2::element_text(size = 8),
                 legend.text = ggplot2::element_text(size = 8)) +
           ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
@@ -597,8 +635,12 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
               )
             ggplot2::geom_smooth(
               data = benchmark_subset,
-              ggplot2::aes_string(x = "benchmark_x", y = "benchmark_value", group = "factor(percentile)"),
+              ggplot2::aes(
-              color = "gray70", size = 0.5, se = FALSE, inherit.aes = FALSE
+                x = .data[["benchmark_x"]],
+                y = .data[["benchmark_value"]],
+                group = factor(.data[["percentile"]])
+              ),
+              color = "gray70", linewidth = 0.5, se = FALSE, inherit.aes = FALSE
             )
           }
         } +
@@ -606,14 +648,24 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
         # Plot older seasons with lighter lines
         ggplot2::geom_line(
           data = plot_data_both %>% dplyr::filter(!is_latest),
-          ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season"),
+          ggplot2::aes(
-          size = 0.7, alpha = 0.4
+            x = .data[[x_var]],
+            y = .data[["ci_value"]],
+            col = .data[["season"]],
+            group = .data[["season"]]
+          ),
+          linewidth = 0.7, alpha = 0.4
         ) +
         # Plot latest season with thicker, more prominent line
         ggplot2::geom_line(
           data = plot_data_both %>% dplyr::filter(is_latest),
-          ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season"),
+          ggplot2::aes(
-          size = 1.5, alpha = 1
+            x = .data[[x_var]],
+            y = .data[["ci_value"]],
+            col = .data[["season"]],
+            group = .data[["season"]]
+          ),
+          linewidth = 1.5, alpha = 1
         ) +
         ggplot2::labs(title = paste("CI Analysis for Field", pivotName),
              color = "Season",
@@ -630,12 +682,14 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
           }
         } +
         ggplot2::theme_minimal() +
-        ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
+          ggplot2::theme(axis.text.x = ggplot2::element_text(hjust = 0.5),
-              axis.text.x.top = ggplot2::element_text(hjust = 0.5),
+            axis.text.x.top = ggplot2::element_text(hjust = 0.5),
-              axis.title.x.top = ggplot2::element_text(size = 8),
+            axis.title.x.top = ggplot2::element_text(size = 8),
-              legend.justification = c(1, 0), legend.position = c(1, 0),
+            legend.justification = c(1, 0),
-              legend.title = ggplot2::element_text(size = 8),
+            legend.position = "inside",
-              legend.text = ggplot2::element_text(size = 8)) +
+            legend.position.inside = c(1, 0),
+            legend.title = ggplot2::element_text(size = 8),
+            legend.text = ggplot2::element_text(size = 8)) +
         ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
       
       # For the rolling mean data, we want to set reasonable y-axis limits
@@ -653,9 +707,11 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
       dummy_data[["season"]] <- factor("dummy", levels = levels(plot_data_both[["season"]]))
       
       g_both <- g_both + 
-        ggplot2::geom_point(data = dummy_data, 
+        ggplot2::geom_point(
-                   ggplot2::aes_string(x = x_var, y = "ci_value"), 
+          data = dummy_data,
-                   alpha = 0, size = 0)  # Invisible points to set scale
+          ggplot2::aes(x = .data[[x_var]], y = .data[["ci_value"]]),
+          alpha = 0, size = 0
+        )  # Invisible points to set scale
       
       # Display the combined faceted plot
       subchunkify(g_both, 2.8, 10)
@@ -692,9 +748,11 @@ cum_ci_plot2 <- function(pivotName){
            x = "Date", y = "CI Rate") +
       theme_minimal() +
       theme(axis.text.x = element_text(hjust = 0.5),
-            legend.justification = c(1, 0), legend.position = c(1, 0),
+        legend.justification = c(1, 0),
-            legend.title = element_text(size = 8),
+        legend.position = "inside",
-            legend.text = element_text(size = 8)) +
+        legend.position.inside = c(1, 0),
+        legend.title = element_text(size = 8),
+        legend.text = element_text(size = 8)) +
       annotate("text", x = midpoint_date, y = 2, label = "No data available", size = 6, hjust = 0.5)
     
     subchunkify(g, 3.2, 10)
@@ -1076,7 +1134,7 @@ generate_field_kpi_summary <- function(field_name, field_details_table, CI_quadr
     # For categorical data, take the most common value or highest risk level
     field_summary <- field_data %>%
       summarise(
-        field_size = sum(`Field Size (ha)`, na.rm = TRUE),
+        field_size = sum(`Field Size (acres)`, na.rm = TRUE),
         uniformity_levels = paste(unique(`Growth Uniformity`), collapse = "/"),
         avg_yield_forecast = ifelse(is.na(`Yield Forecast (t/ha)`[1]), NA, mean(`Yield Forecast (t/ha)`, na.rm = TRUE)),
         max_gap_score = max(`Gap Score`, na.rm = TRUE),