Add KPI reporting system and deployment documentation

Major Changes:
- NEW: Scripts 09 & 10 for KPI calculation and enhanced reporting
- NEW: Shell script wrappers (01-10) for easier execution
- NEW: R packages flextable and officer for enhanced Word reports
- NEW: DEPLOYMENT_README.md with complete deployment guide
- RENAMED: Numbered R scripts (02, 03, 04) for clarity
- REMOVED: Old package management scripts (using renv only)
- UPDATED: Workflow now uses scripts 09->10 instead of 05

Files Changed: 90+ files
New Packages: flextable, officer
New Scripts: 09_run_calculate_kpis.sh, 10_run_kpi_report.sh
Documentation: DEPLOYMENT_README.md, EMAIL_TO_ADMIN.txt

See DEPLOYMENT_README.md for full deployment instructions.

.Rhistory

@@ -1,424 +1,119 @@
-ggplot2::labs(title = "Model Performance: \nPredicted vs Actual Tonnage/ha",
+message("No project_dir provided. Using default:", project_dir)
-x = "Actual tonnage/ha (Tcha)",
-y = "Predicted tonnage/ha (Tcha)") +
-ggplot2::theme_minimal()
 }
-if (nrow(pred_rf_current_season) > 0) {
+# Make project_dir available globally so parameters_project.R can use it
-# Plot predicted yields by age
+assign("project_dir", project_dir, envir = .GlobalEnv)
-ggplot2::ggplot(pred_rf_current_season, ggplot2::aes(x = Age_days, y = predicted_Tcha)) +
+# Initialize project configuration and load utility functions
-ggplot2::geom_point(size = 2, alpha = 0.6) +
+tryCatch({
-ggplot2::labs(title = "Predicted Yields for Fields Over 300 Days \nOld Yet to Be Harvested",
+source("parameters_project.R")
-x = "Age (days)",
+source("growth_model_utils.R")
-y = "Predicted tonnage/ha (Tcha)") +
+}, error = function(e) {
-ggplot2::scale_y_continuous(limits = c(0, 200)) +
+warning("Default source files not found. Attempting to source from 'r_app' directory.")
-ggplot2::theme_minimal()
+tryCatch({
-# Display prediction table
+source(here::here("r_app", "parameters_project.R"))
-knitr::kable(pred_rf_current_season,
+source(here::here("r_app", "growth_model_utils.R"))
-digits = 0,
+warning(paste("Successfully sourced files from 'r_app' directory."))
-caption = "Predicted Tonnage/ha for Fields Over 300 Days Old")
+}, error = function(e) {
+stop("Failed to source required files from both default and 'r_app' directories.")
+})
+})
+log_message("Starting CI growth model interpolation")
+# Load and process the data
+tryCatch({
+# Load the combined CI data
+CI_data <- load_combined_ci_data(cumulative_CI_vals_dir)
+# Validate harvesting data
+if (is.null(harvesting_data) || nrow(harvesting_data) == 0) {
+stop("No harvesting data available")
+}
+# Get the years from harvesting data
+years <- harvesting_data %>%
+filter(!is.na(season_start)) %>%
+distinct(year) %>%
+pull(year)
+log_message(paste("Processing data for years:", paste(years, collapse = ", ")))
+# Generate interpolated CI data for each year and field
+CI_all <- generate_interpolated_ci_data(years, harvesting_data, CI_data)
+# Calculate growth metrics and save the results
+if (nrow(CI_all) > 0) {
+# Add daily and cumulative metrics
+CI_all_with_metrics <- calculate_growth_metrics(CI_all)
+# Save the processed data
+save_growth_model(
+CI_all_with_metrics,
+cumulative_CI_vals_dir,
+"All_pivots_Cumulative_CI_quadrant_year_v2.rds"
+)
 } else {
-cat("No fields over 300 days old without harvest data available for yield prediction.")
+log_message("No CI data was generated after interpolation", level = "WARNING")
 }
+log_message("Growth model interpolation completed successfully")
 }, error = function(e) {
-safe_log(paste("Error in yield prediction visualization:", e$message), "ERROR")
+log_message(paste("Error in growth model interpolation:", e$message), level = "ERROR")
-cat("Error generating yield prediction visualizations. See log for details.")
+stop(e$message)
 })
-# Load and prepare yield prediction data with error handling
+View(CI_all_with_metrics)
-tryCatch({
+View(CI_data)
-# Load CI quadrant data and fill missing values
+# Get the years from harvesting data
-CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) %>%
+years <- harvesting_data %>%
-dplyr::group_by(model) %>%
+filter(!is.na(season_start)) %>%
-tidyr::fill(field, sub_field, .direction = "downup") %>%
+distinct(year) %>%
-dplyr::ungroup()
+pull(year)
-# Check if tonnage_ha is empty
+years
-if (all(is.na(harvesting_data$tonnage_ha))) {
+View(CI_all)
-safe_log("Lacking historic harvest data, please provide for yield prediction calculation", "WARNING")
+View(CI_all_with_metrics)
-knitr::knit_exit()  # Exit the chunk if tonnage_ha is empty
+years
+harvesting_data
+ci_data
+ci_data = CI_data
+# Process each year
+result <- purrr::map_df(years, function(yr) {
+safe_log(paste("Processing year:", yr))
+# Get the fields harvested in this year with valid season start dates
+sub_fields <- harvesting_data %>%
+dplyr::filter(year == yr, !is.na(season_start)) %>%
+dplyr::pull(sub_field)
+if (length(sub_fields) == 0) {
+safe_log(paste("No fields with valid season data for year:", yr), "WARNING")
+return(data.frame())
 }
-# Rename year column to season for consistency
+# Filter sub_fields to only include those with value data in ci_data
-harvesting_data <- harvesting_data %>% dplyr::rename(season = year)
+valid_sub_fields <- sub_fields %>%
-# Join CI and yield data
+purrr::keep(~ any(ci_data$sub_field == .x))
-CI_and_yield <- dplyr::left_join(CI_quadrant, harvesting_data, by = c("field", "sub_field", "season")) %>%
+if (length(valid_sub_fields) == 0) {
-dplyr::group_by(sub_field, season) %>%
+safe_log(paste("No fields with CI data for year:", yr), "WARNING")
-dplyr::slice(which.max(DOY)) %>%
+return(data.frame())
-dplyr::select(field, sub_field, tonnage_ha, cumulative_CI, DOY, season, sub_area) %>%
-dplyr::mutate(CI_per_day = cumulative_CI / DOY)
-# Define predictors and response variables
-predictors <- c("cumulative_CI", "DOY", "CI_per_day")
-response <- "tonnage_ha"
-# Prepare test and validation datasets
-CI_and_yield_test <- CI_and_yield %>%
-as.data.frame() %>%
-dplyr::filter(!is.na(tonnage_ha))
-CI_and_yield_validation <- CI_and_yield_test
-# Prepare prediction dataset (fields without harvest data)
-prediction_yields <- CI_and_yield %>%
-as.data.frame() %>%
-dplyr::filter(is.na(tonnage_ha))
-# Configure model training parameters
-ctrl <- caret::trainControl(
-method = "cv",
-savePredictions = TRUE,
-allowParallel = TRUE,
-number = 5,
-verboseIter = TRUE
-)
-# Train the model with feature selection
-set.seed(202) # For reproducibility
-model_ffs_rf <- CAST::ffs(
-CI_and_yield_test[, predictors],
-CI_and_yield_test[, response],
-method = "rf",
-trControl = ctrl,
-importance = TRUE,
-withinSE = TRUE,
-tuneLength = 5,
-na.rm = TRUE
-)
-# Function to prepare predictions with consistent naming and formatting
-prepare_predictions <- function(predictions, newdata) {
-return(predictions %>%
-as.data.frame() %>%
-dplyr::rename(predicted_Tcha = ".") %>%
-dplyr::mutate(
-sub_field = newdata$sub_field,
-field = newdata$field,
-Age_days = newdata$DOY,
-total_CI = round(newdata$cumulative_CI, 0),
-predicted_Tcha = round(predicted_Tcha, 0),
-season = newdata$season
-) %>%
-dplyr::select(field, sub_field, Age_days, total_CI, predicted_Tcha, season) %>%
-dplyr::left_join(., newdata, by = c("field", "sub_field", "season"))
-)
 }
-# Predict yields for the validation dataset
+# Extract and interpolate data for each valid field
-pred_ffs_rf <- prepare_predictions(stats::predict(model_ffs_rf, newdata = CI_and_yield_validation), CI_and_yield_validation)
+safe_log(paste("Processing", length(valid_sub_fields), "fields for year:", yr))
-# Predict yields for the current season (focus on mature fields over 300 days)
+result <- purrr::map(valid_sub_fields, ~ extract_CI_data(.x,
-pred_rf_current_season <- prepare_predictions(stats::predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>%
+harvesting_data = harvesting_data,
-dplyr::filter(Age_days > 1) %>%
+field_CI_data = ci_data,
-dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1))
+season = yr)) %>%
-safe_log("Successfully completed yield prediction calculations")
+purrr::list_rbind()
-}, error = function(e) {
+safe_log(paste("Generated", nrow(result), "interpolated data points for year:", yr))
-safe_log(paste("Error in yield prediction:", e$message), "ERROR")
+return(result)
-# Create empty dataframes to prevent errors in subsequent chunks
-pred_ffs_rf <- data.frame()
-pred_rf_current_season <- data.frame()
 })
-# Load and prepare yield prediction data with error handling
+CI_all_with_metrics
-tryCatch({
+CI_all <- CI_all %>%
-# Load CI quadrant data and fill missing values
+group_by(Date, field, season) %>%
-CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) %>%
+filter(!(field == "00F25" & season == 2023 & duplicated(DOY)))
-dplyr::group_by(model) %>%
+View(CI_all)
-tidyr::fill(field, sub_field, .direction = "downup") %>%
+# Add daily and cumulative metrics
-dplyr::ungroup()
+CI_all_with_metrics <- calculate_growth_metrics(CI_all)
-# Check if tonnage_ha is empty
+# Save the processed data
-if (all(is.na(harvesting_data$tonnage_ha))) {
+save_growth_model(
-safe_log("Lacking historic harvest data, please provide for yield prediction calculation", "WARNING")
+CI_all_with_metrics,
-knitr::knit_exit()  # Exit the chunk if tonnage_ha is empty
+cumulative_CI_vals_dir,
-}
+"All_pivots_Cumulative_CI_quadrant_year_v2.rds"
-# Rename year column to season for consistency
-harvesting_data <- harvesting_data %>% dplyr::rename(season = year)
-# Join CI and yield data
-CI_and_yield <- dplyr::left_join(CI_quadrant, harvesting_data, by = c("field", "sub_field", "season")) %>%
-dplyr::group_by(sub_field, season) %>%
-dplyr::slice(which.max(DOY)) %>%
-dplyr::select(field, sub_field, tonnage_ha, cumulative_CI, DOY, season, sub_area) %>%
-dplyr::mutate(CI_per_day = cumulative_CI / DOY)
-# Define predictors and response variables
-predictors <- c("cumulative_CI", "DOY", "CI_per_day")
-response <- "tonnage_ha"
-# Prepare test and validation datasets
-CI_and_yield_test <- CI_and_yield %>%
-as.data.frame() %>%
-dplyr::filter(!is.na(tonnage_ha))
-CI_and_yield_validation <- CI_and_yield_test
-# Prepare prediction dataset (fields without harvest data)
-prediction_yields <- CI_and_yield %>%
-as.data.frame() %>%
-dplyr::filter(is.na(tonnage_ha))
-# Configure model training parameters
-ctrl <- caret::trainControl(
-method = "cv",
-savePredictions = TRUE,
-allowParallel = TRUE,
-number = 5,
-verboseIter = TRUE
 )
-# Train the model with feature selection
-set.seed(202) # For reproducibility
-model_ffs_rf <- CAST::ffs(
-CI_and_yield_test[, predictors],
-CI_and_yield_test[, response],
-method = "rf",
-trControl = ctrl,
-importance = TRUE,
-withinSE = TRUE,
-tuneLength = 5,
-na.rm = TRUE
-)
-# Function to prepare predictions with consistent naming and formatting
-prepare_predictions <- function(predictions, newdata) {
-return(predictions %>%
-as.data.frame() %>%
-dplyr::rename(predicted_Tcha = ".") %>%
-dplyr::mutate(
-sub_field = newdata$sub_field,
-field = newdata$field,
-Age_days = newdata$DOY,
-total_CI = round(newdata$cumulative_CI, 0),
-predicted_Tcha = round(predicted_Tcha, 0),
-season = newdata$season
-) %>%
-dplyr::select(field, sub_field, Age_days, total_CI, predicted_Tcha, season) %>%
-dplyr::left_join(., newdata, by = c("field", "sub_field", "season"))
-)
-}
-# Predict yields for the validation dataset
-pred_ffs_rf <- prepare_predictions(stats::predict(model_ffs_rf, newdata = CI_and_yield_validation), CI_and_yield_validation)
-# Predict yields for the current season (focus on mature fields over 300 days)
-pred_rf_current_season <- prepare_predictions(stats::predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>%
-dplyr::filter(Age_days > 1) %>%
-dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1))
-safe_log("Successfully completed yield prediction calculations")
-}, error = function(e) {
-safe_log(paste("Error in yield prediction:", e$message), "ERROR")
-# Create empty dataframes to prevent errors in subsequent chunks
-pred_ffs_rf <- data.frame()
-pred_rf_current_season <- data.frame()
-})
-# Load CI quadrant data and fill missing values
-CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) %>%
-dplyr::group_by(model) %>%
-tidyr::fill(field, sub_field, .direction = "downup") %>%
-dplyr::ungroup()
-# Check if tonnage_ha is empty
-if (all(is.na(harvesting_data$tonnage_ha))) {
-safe_log("Lacking historic harvest data, please provide for yield prediction calculation", "WARNING")
-knitr::knit_exit()  # Exit the chunk if tonnage_ha is empty
-}
-# Rename year column to season for consistency
-harvesting_data <- harvesting_data %>% dplyr::rename(season = year)
-# Join CI and yield data
-CI_and_yield <- dplyr::left_join(CI_quadrant, harvesting_data, by = c("field", "sub_field", "season")) %>%
-dplyr::group_by(sub_field, season) %>%
-dplyr::slice(which.max(DOY)) %>%
-dplyr::select(field, sub_field, tonnage_ha, cumulative_CI, DOY, season, sub_area) %>%
-dplyr::mutate(CI_per_day = cumulative_CI / DOY)
-# Define predictors and response variables
-predictors <- c("cumulative_CI", "DOY", "CI_per_day")
-response <- "tonnage_ha"
-# Prepare test and validation datasets
-CI_and_yield_test <- CI_and_yield %>%
-as.data.frame() %>%
-dplyr::filter(!is.na(tonnage_ha))
-CI_and_yield_validation <- CI_and_yield_test
-# Prepare prediction dataset (fields without harvest data)
-prediction_yields <- CI_and_yield %>%
-as.data.frame() %>%
-dplyr::filter(is.na(tonnage_ha))
-# Configure model training parameters
-ctrl <- caret::trainControl(
-method = "cv",
-savePredictions = TRUE,
-allowParallel = TRUE,
-number = 5,
-verboseIter = TRUE
-)
-# Train the model with feature selection
-set.seed(202) # For reproducibility
-model_ffs_rf <- CAST::ffs(
-CI_and_yield_test[, predictors],
-CI_and_yield_test[, response],
-method = "rf",
-trControl = ctrl,
-importance = TRUE,
-withinSE = TRUE,
-tuneLength = 5,
-na.rm = TRUE
-)
-# Function to prepare predictions with consistent naming and formatting
-prepare_predictions <- function(predictions, newdata) {
-return(predictions %>%
-as.data.frame() %>%
-dplyr::rename(predicted_Tcha = ".") %>%
-dplyr::mutate(
-sub_field = newdata$sub_field,
-field = newdata$field,
-Age_days = newdata$DOY,
-total_CI = round(newdata$cumulative_CI, 0),
-predicted_Tcha = round(predicted_Tcha, 0),
-season = newdata$season
-) %>%
-dplyr::select(field, sub_field, Age_days, total_CI, predicted_Tcha, season) %>%
-dplyr::left_join(., newdata, by = c("field", "sub_field", "season"))
-)
-}
-# Predict yields for the validation dataset
-pred_ffs_rf <- prepare_predictions(stats::predict(model_ffs_rf, newdata = CI_and_yield_validation), CI_and_yield_validation)
-# Predict yields for the current season (focus on mature fields over 300 days)
-pred_rf_current_season <- prepare_predictions(stats::predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>%
-dplyr::filter(Age_days > 300) %>%
-dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1))
-# Predict yields for the current season (focus on mature fields over 300 days)
-pred_rf_current_season <- prepare_predictions(stats::predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>%
-dplyr::filter(Age_days > 1) %>%
-dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1))
-safe_log("Successfully completed yield prediction calculations")
-# Load and prepare yield prediction data with error handling
-tryCatch({
-# Load CI quadrant data and fill missing values
-CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) %>%
-dplyr::group_by(model) %>%
-tidyr::fill(field, sub_field, .direction = "downup") %>%
-dplyr::ungroup()
-# Check if tonnage_ha is empty
-if (all(is.na(harvesting_data$tonnage_ha))) {
-safe_log("Lacking historic harvest data, please provide for yield prediction calculation", "WARNING")
-knitr::knit_exit()  # Exit the chunk if tonnage_ha is empty
-}
-# Rename year column to season for consistency
-harvesting_data <- harvesting_data %>% dplyr::rename(season = year)
-# Join CI and yield data
-CI_and_yield <- dplyr::left_join(CI_quadrant, harvesting_data, by = c("field", "sub_field", "season")) %>%
-dplyr::group_by(sub_field, season) %>%
-dplyr::slice(which.max(DOY)) %>%
-dplyr::select(field, sub_field, tonnage_ha, cumulative_CI, DOY, season, sub_area) %>%
-dplyr::mutate(CI_per_day = cumulative_CI / DOY)
-# Define predictors and response variables
-predictors <- c("cumulative_CI", "DOY", "CI_per_day")
-response <- "tonnage_ha"
-# Prepare test and validation datasets
-CI_and_yield_test <- CI_and_yield %>%
-as.data.frame() %>%
-dplyr::filter(!is.na(tonnage_ha))
-CI_and_yield_validation <- CI_and_yield_test
-# Prepare prediction dataset (fields without harvest data)
-prediction_yields <- CI_and_yield %>%
-as.data.frame() %>%
-dplyr::filter(is.na(tonnage_ha))
-# Configure model training parameters
-ctrl <- caret::trainControl(
-method = "cv",
-savePredictions = TRUE,
-allowParallel = TRUE,
-number = 5,
-verboseIter = TRUE
-)
-# Train the model with feature selection
-set.seed(202) # For reproducibility
-model_ffs_rf <- CAST::ffs(
-CI_and_yield_test[, predictors],
-CI_and_yield_test[, response],
-method = "rf",
-trControl = ctrl,
-importance = TRUE,
-withinSE = TRUE,
-tuneLength = 5,
-na.rm = TRUE
-)
-# Function to prepare predictions with consistent naming and formatting
-prepare_predictions <- function(predictions, newdata) {
-return(predictions %>%
-as.data.frame() %>%
-dplyr::rename(predicted_Tcha = ".") %>%
-dplyr::mutate(
-sub_field = newdata$sub_field,
-field = newdata$field,
-Age_days = newdata$DOY,
-total_CI = round(newdata$cumulative_CI, 0),
-predicted_Tcha = round(predicted_Tcha, 0),
-season = newdata$season
-) %>%
-dplyr::select(field, sub_field, Age_days, total_CI, predicted_Tcha, season) %>%
-dplyr::left_join(., newdata, by = c("field", "sub_field", "season"))
-)
-}
-# Predict yields for the validation dataset
-pred_ffs_rf <- prepare_predictions(stats::predict(model_ffs_rf, newdata = CI_and_yield_validation), CI_and_yield_validation)
-# Predict yields for the current season (focus on mature fields over 300 days)
-pred_rf_current_season <- prepare_predictions(stats::predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>%
-dplyr::filter(Age_days > 1) %>%
-dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1))
-safe_log("Successfully completed yield prediction calculations")
-}, error = function(e) {
-safe_log(paste("Error in yield prediction:", e$message), "ERROR")
-# Create empty dataframes to prevent errors in subsequent chunks
-pred_ffs_rf <- data.frame()
-pred_rf_current_season <- data.frame()
-})
-# Display yield prediction visualizations with error handling
-tryCatch({
-if (nrow(pred_ffs_rf) > 0) {
-# Plot model performance (predicted vs actual)
-ggplot2::ggplot(pred_ffs_rf, ggplot2::aes(y = predicted_Tcha, x = tonnage_ha)) +
-ggplot2::geom_point(size = 2, alpha = 0.6) +
-ggplot2::geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "red") +
-ggplot2::scale_x_continuous(limits = c(0, 200)) +
-ggplot2::scale_y_continuous(limits = c(0, 200)) +
-ggplot2::labs(title = "Model Performance: \nPredicted vs Actual Tonnage/ha",
-x = "Actual tonnage/ha (Tcha)",
-y = "Predicted tonnage/ha (Tcha)") +
-ggplot2::theme_minimal()
-}
-if (nrow(pred_rf_current_season) > 0) {
-# Plot predicted yields by age
-ggplot2::ggplot(pred_rf_current_season, ggplot2::aes(x = Age_days, y = predicted_Tcha)) +
-ggplot2::geom_point(size = 2, alpha = 0.6) +
-ggplot2::labs(title = "Predicted Yields for Fields Over 300 Days \nOld Yet to Be Harvested",
-x = "Age (days)",
-y = "Predicted tonnage/ha (Tcha)") +
-ggplot2::scale_y_continuous(limits = c(0, 200)) +
-ggplot2::theme_minimal()
-# Display prediction table
-knitr::kable(pred_rf_current_season,
-digits = 0,
-caption = "Predicted Tonnage/ha for Fields Over 300 Days Old")
-} else {
-cat("No fields over 300 days old without harvest data available for yield prediction.")
-}
-}, error = function(e) {
-safe_log(paste("Error in yield prediction visualization:", e$message), "ERROR")
-cat("Error generating yield prediction visualizations. See log for details.")
-})
-AllPivots_merged$field[1:10]
-CI
-path_to_week_current
-terra::rast(path_to_week_current)
-map <- tmap::tm_shape(last_week_dif_raster_abs, unit = "m")  # Add raster layer with continuous spectrum (centered at 0 for difference maps)
-map <- map + tmap::tm_raster(col.scale = tm_scale_continuous(values = "RdYlGn",
-midpoint = 0),
-col.legend = tm_legend(title = "Chlorophyll Index (CI) Change",
-orientation = "landscape",
-position = tm_pos_out("center", "bottom")))
-# Complete the map with layout and other elements
-map <- map +
-tmap::tm_scalebar(position = c("right", "bottom"), text.color = "black") +
-tmap::tm_compass(position = c("right", "bottom"), text.color = "black") +
-tmap::tm_shape(AllPivots0) +
-tmap::tm_borders(col = "black") +
-tmap::tm_text("sub_field", size = 0.6, col = "black")
-# Print the map
-print(map)
-# Get versions of specific packages you're using
-packages <- c("here", "sf", "terra", "exactextractr", "tidyverse",
-"tmap", "lubridate", "magrittr", "dplyr", "readr",
-"readxl", "knitr", "rmarkdown", "officedown", "officer")
-package_versions <- sapply(packages, function(x) as.character(packageVersion(x)))
-sessionInfo()
-# Chunk 1: setup_parameters
 # Set up basic report parameters from input values
 report_date <- params$report_date
 mail_day <- params$mail_day
 borders <- params$borders
-# Environment setup notes (commented out)
+ci_plot_type <- params$ci_plot_type
-# # Activeer de renv omgeving
+colorblind_friendly <- params$colorblind_friendly
-# renv::activate()
+facet_by_season <- params$facet_by_season
-# renv::deactivate()
+x_axis_unit <- params$x_axis_unit
-# # Optioneel: Herstel de omgeving als dat nodig is
-# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren
-# renv::restore()
-# Chunk 2: load_libraries
 # Configure knitr options
 knitr::opts_chunk$set(warning = FALSE, message = FALSE)
 # Load all packages at once with suppressPackageStartupMessages
@@ -435,6 +130,8 @@ library(rsample)
 library(caret)
 library(randomForest)
 library(CAST)
+library(knitr)
+library(tidyr)
 })
 # Load custom utility functions
 tryCatch({
@@ -448,7 +145,6 @@ source(here::here("r_app", "report_utils.R"))
 stop("Could not load report_utils.R from either location: ", e$message)
 })
 })
-# Chunk 3: initialize_project_config
 # Set the project directory from parameters
 project_dir <- params$data_dir
 # Source project parameters with error handling
@@ -458,31 +154,96 @@ source(here::here("r_app", "parameters_project.R"))
 stop("Error loading parameters_project.R: ", e$message)
 })
 # Log initial configuration
-safe_log("Starting the R Markdown script")
+safe_log("Starting the R Markdown script with KPIs")
 safe_log(paste("mail_day params:", params$mail_day))
 safe_log(paste("report_date params:", params$report_date))
 safe_log(paste("mail_day variable:", mail_day))
-# Chunk 4: calculate_dates_and_weeks
+## SIMPLE KPI LOADING - robust lookup with fallbacks
+# Primary expected directory inside the laravel storage
+kpi_data_dir <- file.path("laravel_app", "storage", "app", project_dir, "reports", "kpis")
+date_suffix <- format(as.Date(report_date), "%Y%m%d")
+# Candidate filenames we expect (exact and common variants)
+expected_summary_names <- c(
+paste0(project_dir, "_kpi_summary_tables_", date_suffix, ".rds"),
+paste0(project_dir, "_kpi_summary_tables.rds"),
+"kpi_summary_tables.rds",
+paste0("kpi_summary_tables_", date_suffix, ".rds")
+)
+expected_field_details_names <- c(
+paste0(project_dir, "_field_details_", date_suffix, ".rds"),
+paste0(project_dir, "_field_details.rds"),
+"field_details.rds"
+)
+# Helper to attempt loading a file from the directory or fallback to a workspace-wide search
+try_load_from_dir <- function(dir, candidates) {
+if (!dir.exists(dir)) return(NULL)
+for (name in candidates) {
+f <- file.path(dir, name)
+if (file.exists(f)) return(f)
+}
+return(NULL)
+}
+# Try primary directory first
+summary_file <- try_load_from_dir(kpi_data_dir, expected_summary_names)
+field_details_file <- try_load_from_dir(kpi_data_dir, expected_field_details_names)
+# If not found, perform a workspace-wide search (slower) limited to laravel_app storage
+if (is.null(summary_file) || is.null(field_details_file)) {
+safe_log(paste("KPI files not found in", kpi_data_dir, "—searching workspace for RDS files"))
+# List rds files under laravel_app/storage/app recursively
+files <- list.files(path = file.path("laravel_app", "storage", "app"), pattern = "\\.rds$", recursive = TRUE, full.names = TRUE)
+# Try to match by expected names
+if (is.null(summary_file)) {
+matched <- files[basename(files) %in% expected_summary_names]
+if (length(matched) > 0) summary_file <- matched[1]
+}
+if (is.null(field_details_file)) {
+matched2 <- files[basename(files) %in% expected_field_details_names]
+if (length(matched2) > 0) field_details_file <- matched2[1]
+}
+}
+# Final checks and load with safe error messages
+kpi_files_exist <- FALSE
+if (!is.null(summary_file) && file.exists(summary_file)) {
+safe_log(paste("Loading KPI summary from:", summary_file))
+summary_tables <- tryCatch(readRDS(summary_file), error = function(e) { safe_log(paste("Failed to read summary RDS:", e$message), "ERROR"); NULL })
+if (!is.null(summary_tables)) kpi_files_exist <- TRUE
+} else {
+safe_log(paste("KPI summary file not found. Searched:", paste(expected_summary_names, collapse=", ")), "WARNING")
+}
+if (!is.null(field_details_file) && file.exists(field_details_file)) {
+safe_log(paste("Loading field details from:", field_details_file))
+field_details_table <- tryCatch(readRDS(field_details_file), error = function(e) { safe_log(paste("Failed to read field details RDS:", e$message), "ERROR"); NULL })
+if (!is.null(field_details_table)) kpi_files_exist <- kpi_files_exist && TRUE
+} else {
+safe_log(paste("Field details file not found. Searched:", paste(expected_field_details_names, collapse=", ")), "WARNING")
+}
+if (kpi_files_exist) {
+safe_log("✓ KPI summary tables loaded successfully")
+} else {
+safe_log("KPI files could not be located or loaded. KPI sections will be skipped.", "WARNING")
+}
 # Set locale for consistent date formatting
 Sys.setlocale("LC_TIME", "C")
 # Initialize date variables from parameters
 today <- as.character(report_date)
 mail_day_as_character <- as.character(mail_day)
-# Calculate week days
+# Calculate report dates and weeks
+report_date_obj <- as.Date(today)
+current_week <- as.numeric(format(report_date_obj, "%U"))
+year <- as.numeric(format(report_date_obj, "%Y"))
+# Calculate dates for weekly analysis
+week_start <- report_date_obj - ((as.numeric(format(report_date_obj, "%w")) + 1) %% 7)
+week_end <- week_start + 6
+# Calculate week days (copied from 05 script for compatibility)
 report_date_as_week_day <- weekdays(lubridate::ymd(today))
 days_of_week <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
 # Calculate initial week number
-week <- lubridate::week(today)
+week <- lubridate::week(today) - 1
 safe_log(paste("Initial week calculation:", week, "today:", today))
 # Calculate previous dates for comparisons
 today_minus_1 <- as.character(lubridate::ymd(today) - 7)
 today_minus_2 <- as.character(lubridate::ymd(today) - 14)
 today_minus_3 <- as.character(lubridate::ymd(today) - 21)
-# Log the weekday calculations for debugging
-safe_log(paste("Report date weekday:", report_date_as_week_day))
-safe_log(paste("Weekday index:", which(days_of_week == report_date_as_week_day)))
-safe_log(paste("Mail day:", mail_day_as_character))
-safe_log(paste("Mail day index:", which(days_of_week == mail_day_as_character)))
 # Adjust week calculation based on mail day
 if (which(days_of_week == report_date_as_week_day) > which(days_of_week == mail_day_as_character)) {
 safe_log("Adjusting weeks because of mail day")
@@ -497,16 +258,255 @@ week_minus_2 <- week - 2
 week_minus_3 <- week - 3
 # Format current week with leading zeros
 week <- sprintf("%02d", week)
-# Get years for each date
+safe_log(paste("Report week:", current_week, "Year:", year))
-year <- lubridate::year(today)
+safe_log(paste("Week range:", week_start, "to", week_end))
-year_1 <- lubridate::year(today_minus_1)
+## SIMPLE KPI LOADING - robust lookup with fallbacks
-year_2 <- lubridate::year(today_minus_2)
+# Primary expected directory inside the laravel storage
-year_3 <- lubridate::year(today_minus_3)
+kpi_data_dir <- file.path("laravel_app", "storage", "app", project_dir, "reports", "kpis")
-sessionInfo()
+date_suffix <- format(as.Date(report_date), "%Y%m%d")
-source("r_app/extract_current_versions.R")
+# Candidate filenames we expect (exact and common variants)
-source("r_app/package_manager.R")
+expected_summary_names <- c(
-source("r_app/package_manager.R")
+paste0(project_dir, "_kpi_summary_tables_", date_suffix, ".rds"),
-source("r_app/package_manager.R")
+paste0(project_dir, "_kpi_summary_tables.rds"),
-source("r_app/package_manager.R")
+"kpi_summary_tables.rds",
-source("r_app/package_manager.R")
+paste0("kpi_summary_tables_", date_suffix, ".rds")
-source("r_app/package_manager.R")
+)
+expected_field_details_names <- c(
+paste0(project_dir, "_field_details_", date_suffix, ".rds"),
+paste0(project_dir, "_field_details.rds"),
+"field_details.rds"
+)
+# Helper to attempt loading a file from the directory or fallback to a workspace-wide search
+try_load_from_dir <- function(dir, candidates) {
+if (!dir.exists(dir)) return(NULL)
+for (name in candidates) {
+f <- file.path(dir, name)
+if (file.exists(f)) return(f)
+}
+return(NULL)
+}
+# Try primary directory first
+summary_file <- try_load_from_dir(kpi_data_dir, expected_summary_names)
+field_details_file <- try_load_from_dir(kpi_data_dir, expected_field_details_names)
+# If not found, perform a workspace-wide search (slower) limited to laravel_app storage
+if (is.null(summary_file) || is.null(field_details_file)) {
+safe_log(paste("KPI files not found in", kpi_data_dir, "—searching workspace for RDS files"))
+# List rds files under laravel_app/storage/app recursively
+files <- list.files(path = file.path("laravel_app", "storage", "app"), pattern = "\\.rds$", recursive = TRUE, full.names = TRUE)
+# Try to match by expected names
+if (is.null(summary_file)) {
+matched <- files[basename(files) %in% expected_summary_names]
+if (length(matched) > 0) summary_file <- matched[1]
+}
+if (is.null(field_details_file)) {
+matched2 <- files[basename(files) %in% expected_field_details_names]
+if (length(matched2) > 0) field_details_file <- matched2[1]
+}
+}
+# Final checks and load with safe error messages
+kpi_files_exist <- FALSE
+if (!is.null(summary_file) && file.exists(summary_file)) {
+safe_log(paste("Loading KPI summary from:", summary_file))
+summary_tables <- tryCatch(readRDS(summary_file), error = function(e) { safe_log(paste("Failed to read summary RDS:", e$message), "ERROR"); NULL })
+if (!is.null(summary_tables)) kpi_files_exist <- TRUE
+} else {
+safe_log(paste("KPI summary file not found. Searched:", paste(expected_summary_names, collapse=", ")), "WARNING")
+}
+if (!is.null(field_details_file) && file.exists(field_details_file)) {
+safe_log(paste("Loading field details from:", field_details_file))
+field_details_table <- tryCatch(readRDS(field_details_file), error = function(e) { safe_log(paste("Failed to read field details RDS:", e$message), "ERROR"); NULL })
+if (!is.null(field_details_table)) kpi_files_exist <- kpi_files_exist && TRUE
+} else {
+safe_log(paste("Field details file not found. Searched:", paste(expected_field_details_names, collapse=", ")), "WARNING")
+}
+if (kpi_files_exist) {
+safe_log("✓ KPI summary tables loaded successfully")
+} else {
+safe_log("KPI files could not be located or loaded. KPI sections will be skipped.", "WARNING")
+}
+## SIMPLE KPI LOADING - robust lookup with fallbacks
+# Primary expected directory inside the laravel storage
+kpi_data_dir <- file.path("laravel_app", "storage", "app", project_dir, "reports", "kpis")
+kpi_data_dir
+kpi_data_dir
+## SIMPLE KPI LOADING - robust lookup with fallbacks
+# Primary expected directory inside the laravel storage
+kpi_data_dir <- file.path(here("laravel_app", "storage", "app", project_dir, "reports", "kpis"))
+kpi_data_dir
+# Candidate filenames we expect (exact and common variants)
+expected_summary_names <- c(
+paste0(project_dir, "_kpi_summary_tables_", date_suffix, ".rds"),
+paste0(project_dir, "_kpi_summary_tables.rds"),
+"kpi_summary_tables.rds",
+paste0("kpi_summary_tables_", date_suffix, ".rds")
+)
+expected_field_details_names <- c(
+paste0(project_dir, "_field_details_", date_suffix, ".rds"),
+paste0(project_dir, "_field_details.rds"),
+"field_details.rds"
+)
+# Helper to attempt loading a file from the directory or fallback to a workspace-wide search
+try_load_from_dir <- function(dir, candidates) {
+if (!dir.exists(dir)) return(NULL)
+for (name in candidates) {
+f <- file.path(dir, name)
+if (file.exists(f)) return(f)
+}
+return(NULL)
+}
+# Try primary directory first
+summary_file <- try_load_from_dir(kpi_data_dir, expected_summary_names)
+field_details_file <- try_load_from_dir(kpi_data_dir, expected_field_details_names)
+# If not found, perform a workspace-wide search (slower) limited to laravel_app storage
+if (is.null(summary_file) || is.null(field_details_file)) {
+safe_log(paste("KPI files not found in", kpi_data_dir, "—searching workspace for RDS files"))
+# List rds files under laravel_app/storage/app recursively
+files <- list.files(path = file.path("laravel_app", "storage", "app"), pattern = "\\.rds$", recursive = TRUE, full.names = TRUE)
+# Try to match by expected names
+if (is.null(summary_file)) {
+matched <- files[basename(files) %in% expected_summary_names]
+if (length(matched) > 0) summary_file <- matched[1]
+}
+if (is.null(field_details_file)) {
+matched2 <- files[basename(files) %in% expected_field_details_names]
+if (length(matched2) > 0) field_details_file <- matched2[1]
+}
+}
+# Final checks and load with safe error messages
+kpi_files_exist <- FALSE
+if (!is.null(summary_file) && file.exists(summary_file)) {
+safe_log(paste("Loading KPI summary from:", summary_file))
+summary_tables <- tryCatch(readRDS(summary_file), error = function(e) { safe_log(paste("Failed to read summary RDS:", e$message), "ERROR"); NULL })
+if (!is.null(summary_tables)) kpi_files_exist <- TRUE
+} else {
+safe_log(paste("KPI summary file not found. Searched:", paste(expected_summary_names, collapse=", ")), "WARNING")
+}
+summary_file
+kpi_data_dir
+library(officer)
+library(flextable)
+# Data setup
+summary_tables <- list()
+summary_tables$field_uniformity_summary <- data.frame(
+"Uniformity Level" = c("Excellent", "Good", "Poor"),
+"Count" = c(15, 8, 3),
+"Percent" = c("62.5%", "33.3%", "12.5%")
+)
+summary_tables$weed_presence_summary <- data.frame(
+"Weed Risk Level" = c("Low", "Moderate", "High"),
+"Field Count" = c(18, 6, 2),
+"Percent" = c("75.0%", "25.0%", "8.3%")
+)
+doc <- read_docx()
+doc <- body_add_par(doc, "KPI Grid Test Report", style = "heading 1")
+doc <- body_add_par(doc, "Executive Summary - Key Performance Indicators", style = "heading 2")
+doc <- body_add_par(doc, "This section demonstrates just two KPI tables side by side.", style = "Normal")
+doc <- body_add_section(doc, prop_section(
+section_type = "continuous",
+columns = columns(widths = c(4.25, 4.25))
+))
+doc <- body_add_flextable(doc, flextable(summary_tables$field_uniformity_summary) %>% set_caption("Field Uniformity Summary"))
+doc <- body_add_break(doc, "column")
+doc <- body_add_flextable(doc, flextable(summary_tables$weed_presence_summary) %>% set_caption("Weed Presence Score Summary"))
+doc <- body_add_section(doc, prop_section(
+section_type = "continuous",
+columns = columns(widths = c(8.5))
+))
+doc <- body_add_par(doc, "This is a test report to verify the KPI grid layout.", style = "Normal")
+print(doc, target = "tables_side_by_side.docx")
+here()
+getwd()
+print(doc, target = "tables_side_by_side.docx")
+doc
+print(doc, target = "tables_side_by_side.docx")
+print(doc, target = "r_app/tables_side_by_side.docx")
+library(officer)
+library(flextable)
+# Create example data
+summary_tables <- list()
+summary_tables$field_uniformity_summary <- data.frame(
+"Uniformity Level" = c("Excellent", "Good", "Poor"),
+"Count" = c(15, 8, 3),
+"Percent" = c("62.5%", "33.3%", "12.5%")
+)
+summary_tables$weed_presence_summary <- data.frame(
+"Weed Risk Level" = c("Low", "Moderate", "High"),
+"Field Count" = c(18, 6, 2),
+"Percent" = c("75.0%", "25.0%", "8.3%")
+)
+# Create document
+doc <- read_docx()
+doc <- body_add_par(doc, "KPI Grid Test Report", style = "heading 1")
+doc <- body_add_par(doc, "Executive Summary - Key Performance Indicators", style = "heading 2")
+doc <- body_add_par(doc, "This section demonstrates just two KPI tables side by side.", style = "Normal")
+# Two-column section
+doc <- body_add_section(doc, prop_section(
+section_type = "continuous",
+columns = columns(widths = c(4.25, 4.25))
+))
+library(officer)
+library(flextable)
+# Create example data
+summary_tables <- list()
+summary_tables$field_uniformity_summary <- data.frame(
+"Uniformity Level" = c("Excellent", "Good", "Poor"),
+"Count" = c(15, 8, 3),
+"Percent" = c("62.5%", "33.3%", "12.5%")
+)
+summary_tables$weed_presence_summary <- data.frame(
+"Weed Risk Level" = c("Low", "Moderate", "High"),
+"Field Count" = c(18, 6, 2),
+"Percent" = c("75.0%", "25.0%", "8.3%")
+)
+# Create document
+doc <- read_docx()
+doc <- body_add_par(doc, "KPI Grid Test Report", style = "heading 1")
+doc <- body_add_par(doc, "Executive Summary - Key Performance Indicators", style = "heading 2")
+doc <- body_add_par(doc, "This section demonstrates just two KPI tables side by side.", style = "Normal")
+# Two-column section
+doc <- body_add_section(doc, prop_section(
+section_type = "continuous",
+columns = columns(widths = c(4.25, 4.25))
+))
+packageVersion("officer")
+??body_add_section
+library(officer)
+?body_add_section
+library(officer)
+library(flextable)
+# Create example data
+ft1 <- flextable(data.frame(
+"Uniformity Level" = c("Excellent", "Good", "Poor"),
+"Count" = c(15, 8, 3),
+"Percent" = c("62.5%", "33.3%", "12.5%")
+)) %>% set_caption("Field Uniformity Summary")
+ft2 <- flextable(data.frame(
+"Weed Risk Level" = c("Low", "Moderate", "High"),
+"Field Count" = c(18, 6, 2),
+"Percent" = c("75.0%", "25.0%", "8.3%")
+)) %>% set_caption("Weed Presence Score Summary")
+doc <- read_docx()
+doc <- body_add_par(doc, "KPI Grid Test Report", style = "heading 1")
+library(dplyr)
+# Create example data
+ft1 <- flextable(data.frame(
+"Uniformity Level" = c("Excellent", "Good", "Poor"),
+"Count" = c(15, 8, 3),
+"Percent" = c("62.5%", "33.3%", "12.5%")
+)) %>% set_caption("Field Uniformity Summary")
+ft2 <- flextable(data.frame(
+"Weed Risk Level" = c("Low", "Moderate", "High"),
+"Field Count" = c(18, 6, 2),
+"Percent" = c("75.0%", "25.0%", "8.3%")
+)) %>% set_caption("Weed Presence Score Summary")
+doc <- read_docx()
+doc <- body_add_par(doc, "KPI Grid Test Report", style = "heading 1")
+doc <- body_add_par(doc, "Executive Summary - Key Performance Indicators", style = "heading 2")
+doc <- body_add_par(doc, "This section demonstrates two KPI tables side by side.", style = "Normal")
+# Create a Word table (1 row, 2 columns)
+doc <- body_add_table(doc, value = data.frame(A = "", B = ""), style = "Table Grid")
+# Move cursor to first cell, insert first flextable
+doc <- cursor_forward(doc)
+doc <- slip_in_flextable(doc, ft1, pos = "on")
+# Move cursor to second cell, insert second flextable
+doc <- cursor_forward(doc)

.github/chatmodes/instructionalist.chatmode.md

@@ -0,0 +1,202 @@
+# Instructionalist – Copilot Chat Mode 🎩
+
+## Persona
+
+You are the **Instructionalist**—an AI assistant who combines a detail-obsessed detective’s curiosity with a supportive architect’s clarity.
+Your purpose is to guide users in producing exceptional, section-driven repository instructions by surfacing and clarifying important details, one step at a time.
+Respond organically (no scripts), adapt to the user’s needs, celebrate progress, and aim for outstanding results.
+
+---
+
+## Section Metadata Reference
+
+Use these definitions to drive your questions and structure the output file:
+
+```json
+{
+  "sections": {
+    "project_overview": {
+      "goal": "Understand project purpose and core functionality",
+      "points": [
+        "Main purpose and value",
+        "User ecosystem",
+        "Core functionality",
+        "Project maturity"
+      ],
+      "required": true
+    },
+    "copilot_persona": {
+      "goal": "Define how Copilot should help with this project",
+      "points": [
+        "Ideal Copilot usage",
+        "Pain points to solve",
+        "Value areas",
+        "Successful patterns"
+      ],
+      "required": false
+    },
+    "tech_stack": {
+      "goal": "List main technologies with versions and impact",
+      "points": [
+        "Languages and versions",
+        "Databases and caching",
+        "Build and deployment",
+        "Anti-patterns"
+      ],
+      "required": true
+    },
+    "architecture": {
+      "goal": "Document key architectural decisions and patterns",
+      "points": [
+        "Architecture type",
+        "Design patterns",
+        "Code organization",
+        "System diagrams and ADRs (if available)"
+      ],
+      "required": false
+    },
+    "security": {
+      "goal": "Identify security requirements and practices",
+      "points": [
+        "Auth model",
+        "Security patterns",
+        "Data handling",
+        "Security providers"
+      ],
+      "required": false
+    },
+    "performance": {
+      "goal": "Document performance requirements and strategies",
+      "points": [
+        "SLAs and targets",
+        "Resource constraints",
+        "Data handling",
+        "Known issues"
+      ],
+      "required": false
+    },
+    "style": {
+      "goal": "Document manual style requirements only",
+      "points": [
+        "Non-automated rules",
+        "Project conventions",
+        "Code organization",
+        "Documentation standards"
+      ],
+      "required": false
+    },
+    "testing": {
+      "goal": "Define testing strategy and identify gaps",
+      "points": [
+        "Testing pyramid structure",
+        "Coverage goals",
+        "Testing patterns",
+        "Automation status"
+      ],
+      "required": true
+    },
+    "documentation": {
+      "goal": "Identify critical documentation needs",
+      "points": [
+        "Key documentation types",
+        "Storage and format",
+        "Automation tools",
+        "Maintenance blocks"
+      ],
+      "required": true
+    },
+    "error_handling": {
+      "goal": "Define error handling approach",
+      "points": [
+        "Logging strategy",
+        "Monitoring needs",
+        "Recovery patterns",
+        "Error tracking"
+      ],
+      "required": false
+    },
+    "repo_stats": {
+      "goal": "Determine age and activity level of the repository to define system health and risk profile",
+      "points": [
+        "Repository age",
+        "Commit frequency",
+        "Pull request activity",
+        "Known issues (links to Jira, GitHub, or Confluence)"
+      ],
+      "required": false
+    }
+  }
+}
+```
+
+---
+
+## Behavior & Interaction (v2)
+
+- **Step 1 — Existing file check (always first)**
+  Look for `.github/copilot-instructions.md`.
+  - If it exists, parse into a section map keyed by the JSON section IDs/titles.
+  - If not, initialize an empty map.
+
+- **Step 2 — Silent repo self-scan (no user output yet)**
+  Using `codebase`, `githubRepo`, and `search`, assemble a baseline from **automation-backed signals** (not ad-hoc habits):
+  - **Automated formatting/linting**: detect whether any automated formatting or lint tools are enforced. If yes, treat those configs as the source of truth for style/format rules. If none are detected, plan to **suggest** enabling them (do not author manual style rules unless the user explicitly asks).
+  - **Testing**: identify unit vs. integration test patterns, test frameworks, coverage tooling/thresholds, and any reports/badges created by automation.
+  - **Performance**: note performance test suites, budgets/thresholds, profiling hooks, and CI gates related to performance.
+  - **Automation**: CI/CD workflows, hooks, scripts, release/versioning processes.
+  - **Resilience/chaos**: presence of fault-injection/chaos testing, failure drills, rollback and feature-flag strategies.
+  - **Architecture clues**: project shape (single vs. multi-package), front/back separation, infra/service boundaries, data stores, messaging.
+  - **Improvements (positive framing)**: capture **desired outcomes** only (e.g., “Adopt automated formatting in CI,” “Introduce coverage threshold via the coverage tool”), avoiding restrictive language.
+  > Do **not** list “coding habits” in the output unless they’re enforced by automation or the user explicitly requests them.
+
+- **Step 3 — Merge before Q\&A (conversational, not code-diff)**
+  Merge the **existing file (if any)** with the **scan baseline** into a draft per the JSON section IDs/titles.
+  - On conflicts, **user’s existing file wins**; if it contradicts automation signals, surface the discrepancy and ask which should govern.
+  - Keep content **AI-oriented** (instructions for Copilot), not end-user docs.
+  - If something appears unused or obsolete, **ask whether to remove it as an instruction** and proceed based on the user’s choice (no deprecation flags).
+
+- **Step 4 — Section loop (prompt only for gaps)**
+  For each section defined in the JSON schema:
+  1. Present the merged draft for that section.
+  2. If anything **material is missing** that would improve Copilot’s performance, **ask only for that missing information** (no broad questionnaires).
+  3. **Validate immediately**: cross-check user answers against repo/automation signals. If inconsistent, ask which source should govern and update accordingly.
+  4. Lock the section once validated, then continue to the next.
+
+- **Step 5 — Save**
+  Use `editFiles` to create or update `.github/copilot-instructions.md` with all validated sections.
+  - Required sections may include `(TBD)` where information is unknown.
+  - Optional sections are included only after user confirmation.
+
+---
+
+## Output (v2)
+
+- **Artifact**: a single Markdown file at **`.github/copilot-instructions.md`**.
+- **Structure**: use the exact section IDs/titles from the JSON for consistency.
+- **Style & formatting rules**:
+  - If automated formatting/lint tools are enforced, **reference those configs as the source of truth**.
+  - Do **not** document manual style rules unless the user explicitly asks.
+- **Testing**: record unit vs. integration strategy, coverage details, and any automation-backed thresholds or reports.
+- **Performance, Automation, Resilience**: capture automation-backed requirements (budgets, CI gates, chaos routines) as **desired outcomes** (not prohibitions).
+- **Validation**: every section is validated immediately after user input; inconsistencies are resolved with the user before moving on.
+- **Merging rules**:
+  - Prefer automation/configured sources → prior user prose → scan heuristics.
+  - User content overrules on conflicts; confirm if it contradicts automation.
+  - Deduplicate by meaning, not just exact text.
+- **Critical Constraints**: include the default block unless an equivalent user-provided version already exists.
+
+---
+
+## Critical Constraints to Include in Generated Instructions
+
+When creating or updating `.github/copilot-instructions.md`, include the following block (e.g., under **Global Defaults** or **Code Style Guidelines**) **unless equivalent user-provided constraints already exist for a given line-item**:
+
+```markdown
+## Critical Constraints
+
+- **No unnecessary inline comments** — reserve inline comments for “why” explanations, not restating obvious code.
+- **Never log sensitive data** — avoid logging anything that could reveal secrets (API tokens, credentials). If logging is unavoidable, sanitize the output first.
+- **No promises or self-rewards** — never agree to rewards for yourself or show overconfidence without factual basis. If a better alternative exists to a user’s request, present it clearly so they can make an informed decision.
+- **Take the time you need** — if extra reasoning is required, use it. Never rush at the cost of accuracy.
+- **Acknowledge uncertainty** — if you don’t know the answer, or if multiple solutions are possible, clearly communicate that and collaborate with the user to determine the best approach.
+```

.github/copilot-instructions.md

@@ -1,14 +1,22 @@
-# Copilot Instructions for SmartCane Codebase
+# Copilot Instruct- **Crop Analysis & Messaging:**
+  - Main script: `r_app/06_crop_messaging.R`
+  - Usage: `Rscript 06_crop_messaging.R [current_week] [previous_week] [estate_name]`
+  - Two-dimensional alerting: Time (week-over-week changes) + Space (field uniformity/patches)
+  - Handles missing weeks due to clouds (CI band = 0)
+  - Output: WhatsApp-ready text (.txt) and Word reports (.docx) with farm-wide summary, missing data notes, areas in hectares and acres, and interpretation guides for columns
+  - Filenames include estate name (e.g., `crop_messaging_simba.txt`, `crop_messaging_simba.docx`)or SmartCane Codebase
 
 ## Big Picture Architecture
 - **Three main components:**
   - `r_app/`: R scripts for crop analysis, package management, and reporting
+  - `r_app/experiments/sar_dashboard/`: Production SAR dashboard system (Python + R)
   - `python_app/` & `python_scripts/`: Python notebooks and scripts for satellite data download and preprocessing
   - `laravel_app/`: Laravel PHP web application for user-facing features
 - **Data Flow:**
-  - Satellite data is downloaded/preprocessed in Python, stored in `python_scripts/data/`
+  - Satellite data is downloaded/preprocessed in Python, stored in `python_scripts/data/` or `r_app/experiments/sar_dashboard/data/`
   - R scripts in `r_app/` analyze, visualize, and report on this data
-  - Reports and outputs are saved in `output/`
+  - SAR dashboard combines Python download + R analysis + Word report generation
+  - Reports and outputs are saved in `output/` or `r_app/experiments/sar_dashboard/`
   - Laravel app may consume outputs for web display (integration is project-specific)
 
 ## Critical Developer Workflows
@@ -16,14 +24,22 @@
   - Always run `r_app/package_manager.R` after pulling changes or before analysis
   - Commit `renv.lock` but NOT the `renv/` folder
   - Use `source("r_app/package_manager.R")` in RStudio or `Rscript r_app/package_manager.R` in terminal
-- **Crop Analysis:**
+- **Crop Analysis & Messaging:**
-  - Main script: `r_app/crop_analysis_messaging.R`
+  - Main script: `r_app/06_crop_messaging.R`
-  - Usage: `Rscript crop_analysis_messaging.R [week1] [week2] [farm]`
+  - Usage: `Rscript 06_crop_messaging.R [current_week] [previous_week] [estate_name]`
-  - Output: Alerts, summary stats, and recommendations (see `PACKAGE_MANAGEMENT.md` for logic)
+  - Two-dimensional alerting: Time (change trends) + Space (field uniformity/patches)
+  - Handles missing weeks due to clouds (CI band = 0)
+  - Output: WhatsApp-ready text, CSV data, .docx reports, and Markdown tables
 - **SAR Analysis & Reporting:**
-  - Main report: `r_app/experiments/interactive_sar_visualization/Interactive_SAR_Report.Rmd`
+  - **SAR Dashboard:** Production-ready Word reports for SAR data analysis
-  - Generate with: `rmarkdown::render("Interactive_SAR_Report.Rmd", output_file = "../../../output/Interactive_SAR_Report.html")`
+  - **Main folder:** `r_app/experiments/sar_dashboard/`
-  - Data source: `python_scripts/data/aura/weekly_SAR_mosaic/`
+  - **Download script:** `r_app/experiments/sar_dashboard/download_s1_simba.py` (for Simba) or `download_s1_[client].py`
+  - **Report generation:** `Rscript r_app/experiments/sar_dashboard/generate_sar_report.R [client_name]`
+  - **Test script:** `Rscript r_app/experiments/sar_dashboard/test_sar_dashboard.R`
+  - **Data source:** `r_app/experiments/sar_dashboard/data/[client]/weekly_SAR_mosaic/`
+  - **Features:** RGB visualization (each band = different week), SAR indices (RVI, cross-pol ratio), harvest detection, field uniformity analysis, time series plots
+  - **Output:** Word document (.docx) with comprehensive SAR analysis and visualizations
+  - **Field boundaries:** Uses `r_app/experiments/pivot.geojson` for field polygons
 - **Python Data Download:**
   - Notebooks/scripts in `python_app/` and `python_scripts/` handle satellite data acquisition
   - Check `requirements_*.txt` for dependencies
@@ -33,15 +49,27 @@
 
 ## Project-Specific Conventions
 - **Field Uniformity & Alerting:**
-  - Uniformity thresholds and alert logic are defined in `PACKAGE_MANAGEMENT.md`
+  - **Two-dimensional analysis**: Time (week-over-week changes) + Space (field homogeneity)
-  - Message categories: 🚨 URGENT, ⚠️ ALERT, ✅ POSITIVE, 💡 OPPORTUNITY
+  - **Message categories**: 🚨 URGENT, ⚠️ ALERT, ✅ POSITIVE, 💡 OPPORTUNITY
-  - Spatial pattern analysis uses Moran's I (see R scripts)
+  - **Uniformity thresholds**: CV < 0.15 (good), CV < 0.08 (excellent), CV > 0.25 (poor)
+  - **Change detection**: Increase > 0.5, Decrease < -0.5 (configurable thresholds)
+  - **Spatial patterns**: Moran's I analysis for clustering detection
+  - **Missing data handling**: Clouds (CI=0) trigger spatial-only analysis
+- **Output Formatting:**
+  - Word reports (.docx) include split tables for wide data, with column widths set for readability
+  - Interpretation guides provided under each table explaining columns like 'acceptable %' and 'change' thresholds
+  - Areas reported in both hectares and acres
 - **Package Management:**
   - Minimum versions enforced for critical R packages (see `PACKAGE_MANAGEMENT.md`)
   - All package changes go through `package_manager.R`
-- **Output Files:**
+- **SAR-Specific Analysis:**
-  - Reports and logs go in `output/`
+  - **Data characteristics:** SAR (radar) penetrates clouds, all-weather capability, measures backscatter intensity
-  - Do NOT commit logs or cache folders
+  - **Bands:** VV (vertical-vertical), VH (vertical-horizontal), dB scaled for analysis
+  - **Indices:** RVI (Radar Vegetation Index), cross-polarization ratio, crop structure index
+  - **Harvest detection:** Identifies completely bare fields by backscatter threshold and temporal change
+  - **RGB visualization:** Each band represents different week for change detection
+  - **Data availability:** Sentinel-1 provides ~6-day revisit, weekly composites recommended
+  - **Field boundaries:** Critical for SAR analysis - ensure `pivot.geojson` is current and accurate
 
 ## Integration Points & Dependencies
 - **R ↔ Python:**
@@ -50,22 +78,40 @@
 - **R ↔ Laravel:**
   - Laravel may read outputs from R analysis (integration is custom)
 - **External:**
-  - Sentinel-1 SAR data, field boundaries (GeoJSON), R/Python packages
+  - Sentinel-1 SAR data (via SentinelHub API), Planet optical data, field boundaries (GeoJSON), R/Python packages
 
 ## Examples
 - To run a full crop analysis workflow:
   ```powershell
-  Rscript r_app/package_manager.R ; Rscript r_app/crop_analysis_messaging.R 32 31 simba
+  Rscript r_app/package_manager.R ; Rscript r_app/06_crop_messaging.R 32 31 simba
   ```
-- To generate SAR report:
+- To run crop messaging with cloud handling:
-  ```r
+  ```powershell
-  rmarkdown::render("r_app/experiments/interactive_sar_visualization/Interactive_SAR_Report.Rmd", output_file = "output/Interactive_SAR_Report.html")
+  Rscript r_app/06_crop_messaging.R 30 29 chemba  # Only spatial analysis if week 29 has clouds
+  ```
+- To generate SAR dashboard report:
+  ```powershell
+  cd r_app/experiments/sar_dashboard
+  python download_s1_simba.py  # Download SAR data for Simba (last 8 weeks)
+  Rscript generate_sar_report.R simba  # Generate Word report
+  ```
+- To test SAR dashboard setup:
+  ```powershell
+  cd r_app/experiments/sar_dashboard
+  Rscript test_sar_dashboard.R
   ```
 
 ## Key Files & Directories
 - `r_app/package_manager.R`, `PACKAGE_MANAGEMENT.md`: Package logic & workflow
-- `r_app/crop_analysis_messaging.R`: Crop analysis logic
+- `r_app/06_crop_messaging.R`, `r_app/crop_messaging_utils.R`: Crop analysis & messaging logic
-- `r_app/experiments/interactive_sar_visualization/`: SAR analysis & reporting
+- `r_app/experiments/crop_messaging/crop_analysis_messaging.R`: Experimental messaging script
+- `r_app/experiments/sar_dashboard/`: Complete SAR dashboard system
+  - `download_s1_simba.py`: SAR data download for Simba fields
+  - `generate_sar_report.R`: Generate Word document SAR reports
+  - `test_sar_dashboard.R`: Test SAR dashboard components
+  - `SAR_Dashboard_Report.Rmd`: RMarkdown template for Word reports
+  - `sar_dashboard_utils.R`: SAR analysis utility functions
+  - `data/[client]/weekly_SAR_mosaic/`: Downloaded SAR data organized by week
 - `python_scripts/`, `python_app/`: Data download/preprocessing
 - `output/`: All generated reports
 - `laravel_app/`: Web application

.gitignore

@@ -2,25 +2,76 @@
 #/laravel_app/vendor/
 #/laravel_app/.env
 .idea/
+
 # Python Ignores
 /python_app/__pycache__/
 /python_app/*.pyc
+__pycache__/
+*.pyc
+*.pyo
 
-# R Ignores
+# R Output Files
-/r_app/*.Rhistory
+*.Rout
-/r_app/*.Rdata
+*.Rhistory
-
+*.RData
-.DS_Store
+*.Rdata
 .Rproj.user
+Rplots.pdf
+*.pdf
 
+# R Data Files
+*.rds
+!renv.lock
+
+# Data Files (Excel, CSV, Text)
+*.xlsx
+*.csv
+*.txt
+!python_app/requirements*.txt
+!PACKAGE_MANAGEMENT.md
+!README.md
+!LICENSE.txt
+
+# Spatial Data
+*.tif
+*.geojson
+!r_app/experiments/pivot.geojson
+
+# Generated Reports and Word Documents
+r_app/output/
+r_app/*.docx
+!r_app/word-styles-reference-var1.docx
+output/
+reports/
+*.docx
+
+# Logs
+*.log
+package_manager.log
+
+# Laravel Storage (contains user data and outputs)
+laravel_app/storage/app/*/Data/
+laravel_app/storage/app/*/reports/
 /laravel_app/public/*
 !/laravel_app/public/.htaccess
 !/laravel_app/public/index.php
 !/laravel_app/public/robots.txt
 
+# R Environment (renv)
+renv/library/
+!renv/library/.gitkeep
+renv/local/
+renv/python/
+renv/staging/
+# Keep only these renv files
+!renv.lock
+!renv/activate.R
+!renv/settings.json
+!renv/.gitignore
+
+# IDE and OS
+.DS_Store
+.Rproj.user
+.idea/
+.vscode/
 
-# Data and output files
-*.tif
-*.csv
-*.txt
-*.docx

06_run_crop_messaging.sh

@@ -0,0 +1,31 @@
+#!/bin/bash
+# Run crop messaging analysis (06_crop_messaging)
+# Usage: ./06_run_crop_messaging.sh --current_week=<week_num> --previous_week=<week_num> --estate_name=<name>
+
+current_week=$(date +%V)  # Current ISO week number
+previous_week=$((current_week - 1))
+estate_name="aura"
+
+for arg in "$@"; do
+    case $arg in
+        --current_week=*)
+            current_week="${arg#*=}"
+            ;;
+        --previous_week=*)
+            previous_week="${arg#*=}"
+            ;;
+        --estate_name=*)
+            estate_name="${arg#*=}"
+            ;;
+        *)
+            echo "Unknown option: $arg"
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+echo "Running crop messaging analysis for $estate_name: week $previous_week → week $current_week."
+cd r_app
+Rscript 06_crop_messaging $current_week $previous_week $estate_name
+cd ..

09_run_calculate_kpis.sh

@@ -0,0 +1,188 @@
+#!/bin/bash
+
+# 09_RUN_CALCULATE_KPIS.SH
+# ======================
+# Shell script wrapper for KPI calculation in the SmartCane pipeline
+# This script integrates KPI calculation into the existing pipeline sequence (01-05)
+# and ensures proper R execution with renv environment and error handling.
+
+# Script configuration
+SCRIPT_NAME="09_run_calculate_kpis.sh"
+R_SCRIPT_NAME="09_calculate_kpis.R"
+LOG_PREFIX="[KPI_CALC]"
+
+# Function to log messages with timestamp
+log_message() {
+    echo "$(date '+%Y-%m-%d %H:%M:%S') $LOG_PREFIX $1"
+}
+
+# Function to handle errors
+handle_error() {
+    log_message "ERROR: $1"
+    exit 1
+}
+
+# Function to check if file exists
+check_file() {
+    if [ ! -f "$1" ]; then
+        handle_error "Required file not found: $1"
+    fi
+}
+
+# Function to check if directory exists
+check_directory() {
+    if [ ! -d "$1" ]; then
+        log_message "WARNING: Directory not found: $1"
+        return 1
+    fi
+    return 0
+}
+
+# Main execution function
+main() {
+    log_message "Starting KPI calculation pipeline step"
+    
+    # Check if we're in the correct directory
+    if [ ! -f "r_app/$R_SCRIPT_NAME" ]; then
+        handle_error "Must be run from smartcane root directory (where r_app/ folder exists)"
+    fi
+    
+    # Check for R installation
+    if ! command -v R &> /dev/null; then
+        # Try Windows R installation path
+        R_CMD="C:/Program Files/R/R-4.4.3/bin/x64/R.exe"
+        if [ ! -f "$R_CMD" ]; then
+            handle_error "R not found in PATH or at expected Windows location"
+        fi
+    else
+        R_CMD="R"
+    fi
+    
+    log_message "Using R at: $R_CMD"
+    
+    # Set default project directory if not provided
+    if [ -z "$1" ]; then
+        PROJECT_DIR="esa"
+        log_message "No project directory specified, using default: $PROJECT_DIR"
+    else
+        PROJECT_DIR="$1"
+        log_message "Using project directory: $PROJECT_DIR"
+    fi
+    
+    # Check if project directory exists
+    PROJECT_PATH="laravel_app/storage/app/$PROJECT_DIR"
+    check_directory "$PROJECT_PATH" || handle_error "Project directory not found: $PROJECT_PATH"
+    
+    # Check for required data files
+    check_file "$PROJECT_PATH/Data/pivot.geojson"
+    
+    # Check for weekly mosaic directory
+    MOSAIC_DIR="$PROJECT_PATH/weekly_mosaic"
+    check_directory "$MOSAIC_DIR" || handle_error "Weekly mosaic directory not found: $MOSAIC_DIR"
+    
+    # Count available mosaics
+    MOSAIC_COUNT=$(find "$MOSAIC_DIR" -name "week_*.tif" 2>/dev/null | wc -l)
+    if [ "$MOSAIC_COUNT" -lt 1 ]; then
+        handle_error "No weekly mosaics found in $MOSAIC_DIR"
+    fi
+    log_message "Found $MOSAIC_COUNT weekly mosaics in $MOSAIC_DIR"
+    
+    # Create temporary R script with project configuration
+    TEMP_R_SCRIPT="temp_kpi_calc_$$.R"
+    cat > "r_app/$TEMP_R_SCRIPT" << EOF
+# Temporary KPI calculation script
+# Generated by $SCRIPT_NAME on $(date)
+
+# Set project directory
+project_dir <- "$PROJECT_DIR"
+
+# Set working directory to r_app
+setwd("r_app")
+
+# Source the main KPI calculation script
+tryCatch({
+    source("$R_SCRIPT_NAME")
+    cat("✓ KPI calculation completed successfully\\n")
+}, error = function(e) {
+    cat("✗ Error in KPI calculation:", e\$message, "\\n")
+    quit(status = 1)
+})
+EOF
+    
+    log_message "Created temporary R script: r_app/$TEMP_R_SCRIPT"
+    
+    # Execute R script
+    log_message "Starting R execution..."
+    
+    # Change to smartcane root directory for proper relative paths
+    cd "$(dirname "$0")" || handle_error "Failed to change to script directory"
+    
+    # Run R script with proper error handling
+    if [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" ]]; then
+        # Windows execution
+        "$R_CMD" --vanilla < "r_app/$TEMP_R_SCRIPT"
+        R_EXIT_CODE=$?
+    else
+        # Unix/Linux execution
+        "$R_CMD" --vanilla < "r_app/$TEMP_R_SCRIPT"
+        R_EXIT_CODE=$?
+    fi
+    
+    # Clean up temporary script
+    rm -f "r_app/$TEMP_R_SCRIPT"
+    log_message "Cleaned up temporary R script"
+    
+    # Check R execution result
+    if [ $R_EXIT_CODE -eq 0 ]; then
+        log_message "✓ KPI calculation completed successfully"
+        
+        # Check if output files were created
+        REPORTS_DIR="laravel_app/storage/app/$PROJECT_DIR/reports"
+        if check_directory "$REPORTS_DIR/kpis"; then
+            KPI_FILES=$(find "$REPORTS_DIR/kpis" -name "*$(date '+%Y%m%d')*" 2>/dev/null | wc -l)
+            if [ "$KPI_FILES" -gt 0 ]; then
+                log_message "✓ Generated $KPI_FILES KPI output files"
+            else
+                log_message "⚠ Warning: No KPI files found for today's date"
+            fi
+        fi
+        
+        log_message "KPI calculation pipeline step completed successfully"
+        return 0
+    else
+        handle_error "R script execution failed with exit code: $R_EXIT_CODE"
+    fi
+}
+
+# Script usage information
+usage() {
+    echo "Usage: $0 [PROJECT_DIR]"
+    echo ""
+    echo "Calculate KPI metrics for SmartCane monitoring system"
+    echo ""
+    echo "Parameters:"
+    echo "  PROJECT_DIR    Project directory name (default: esa)"
+    echo "                 Must exist in laravel_app/storage/app/"
+    echo ""
+    echo "Examples:"
+    echo "  $0              # Use default 'esa' project"
+    echo "  $0 aura         # Use 'aura' project"
+    echo "  $0 chemba       # Use 'chemba' project"
+    echo ""
+    echo "Requirements:"
+    echo "  - R installation (4.4.3 or compatible)"
+    echo "  - renv environment set up"
+    echo "  - Weekly mosaic files in PROJECT_DIR/weekly_mosaic/"
+    echo "  - Field boundaries in PROJECT_DIR/Data/pivot.geojson"
+}
+
+# Handle command line arguments
+case "${1:-}" in
+    -h|--help)
+        usage
+        exit 0
+        ;;
+    *)
+        main "$@"
+        ;;
+esac

10_run_kpi_report.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+# Run CI report with KPIs (10_CI_report_with_kpis_simple.Rmd)
+# Usage: ./10_run_kpi_report.sh --filename=<output.docx> --report_date=<YYYY-MM-DD> --mail_day=<day> --data_dir=<project> --borders=<TRUE|FALSE> --ci_plot_type=<both|absolute|cumulative> --colorblind_friendly=<TRUE|FALSE> --facet_by_season=<TRUE|FALSE> --x_axis_unit=<days|weeks>
+
+filename="CI_report_with_kpis.docx"
+report_date="$(date +%Y-%m-%d)"
+mail_day="Monday"
+data_dir="aura"
+borders="FALSE"
+ci_plot_type="both"
+colorblind_friendly="TRUE"
+facet_by_season="FALSE"
+x_axis_unit="days"
+
+for arg in "$@"; do
+    case $arg in
+        --filename=*)
+            filename="${arg#*=}"
+            ;;
+        --report_date=*)
+            report_date="${arg#*=}"
+            ;;
+        --mail_day=*)
+            mail_day="${arg#*=}"
+            ;;
+        --data_dir=*)
+            data_dir="${arg#*=}"
+            ;;
+        --borders=*)
+            borders="${arg#*=}"
+            ;;
+        --ci_plot_type=*)
+            ci_plot_type="${arg#*=}"
+            ;;
+        --colorblind_friendly=*)
+            colorblind_friendly="${arg#*=}"
+            ;;
+        --facet_by_season=*)
+            facet_by_season="${arg#*=}"
+            ;;
+        --x_axis_unit=*)
+            x_axis_unit="${arg#*=}"
+            ;;
+        *)
+            echo "Unknown option: $arg"
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+echo "Running CI report with KPIs for $data_dir, report date $report_date, mail day $mail_day."
+echo "Parameters: borders=$borders, ci_plot_type=$ci_plot_type, colorblind=$colorblind_friendly, facet_by_season=$facet_by_season, x_axis_unit=$x_axis_unit"
+cd r_app
+Rscript -e "rmarkdown::render('10_CI_report_with_kpis_simple.Rmd', output_file='$filename', params=list(report_date='$report_date', mail_day='$mail_day', data_dir='$data_dir', borders='$borders', ci_plot_type='$ci_plot_type', colorblind_friendly='$colorblind_friendly', facet_by_season='$facet_by_season', x_axis_unit='$x_axis_unit'))"
+cd ..

DEPLOYMENT_README.md

@@ -0,0 +1,299 @@
+# SmartCane Deployment Guide
+**Quick Reference for Bitbucket Push & Server Deployment**
+
+---
+
+## 🎯 TL;DR - WHAT YOU NEED TO KNOW
+
+### What's New:
+- ✅ **Scripts 09 & 10** are NEW - they generate reports WITH KPIs (field uniformity, stress detection)
+- ✅ **2 new packages** to install: `flextable` and `officer` (for better tables in Word reports)
+- ✅ **Shell script wrappers** (01-10) make execution easier
+
+### Workflow Change:
+```bash
+# OLD (master branch):
+Manual R script execution
+
+# NEW (code-improvements branch):
+./01_run_planet_download.sh
+./02_run_ci_extraction.sh
+./03_run_growth_model.sh
+./04_run_mosaic_creation.sh
+# SKIP 05 (old report without KPIs)
+./09_run_calculate_kpis.sh    # NEW - calculate KPIs first
+./10_run_kpi_report.sh         # NEW - generate report WITH KPIs
+```
+
+### For Your Admin:
+1. Install 2 new R packages: `Rscript -e "renv::restore()"`
+2. Run scripts in order: 01→02→03→04→09→10 (skip 05)
+3. Script 10 parameters are configurable (see below)
+
+**That's it!** Read below for details if needed.
+
+---
+
+## 📦 WHAT CHANGED FROM MASTER BRANCH
+
+### NEW Scripts (not in master):
+| Script | Purpose | Status |
+|--------|---------|--------|
+| `09_run_calculate_kpis.sh` | Calculate field KPIs | ⭐ Required |
+| `10_run_kpi_report.sh` | Generate reports WITH KPIs | ⭐ Required |
+| `01-05_run_*.sh` | Shell wrappers for existing R scripts | ✅ Helpful |
+
+### NEW R Files:
+- `r_app/09_calculate_kpis.R` - KPI calculation logic
+- `r_app/10_CI_report_with_kpis_simple.Rmd` - Enhanced report template
+- `r_app/kpi_utils.R` - KPI utility functions
+
+### NEW R Packages (in renv.lock):
+- `flextable` - Enhanced table formatting for Word
+- `officer` - Word document manipulation
+
+### RENAMED Files:
+- `ci_extraction.R` → `02_ci_extraction.R`
+- `interpolate_growth_model.R` → `03_interpolate_growth_model.R`
+- `mosaic_creation.R` → `04_mosaic_creation.R`
+
+### DELETED Files:
+- Old package management scripts (now using renv only)
+- Duplicate geometry files
+- Laravel build artifacts (will regenerate)
+
+**Total:** 90 files changed, +12,309 lines added, -7,132 lines removed
+
+---
+
+## 💻 LINUX SERVER DEPLOYMENT
+
+### Step 1: Install System Dependencies
+```bash
+sudo apt-get update
+sudo apt-get install -y \
+  libgdal-dev libgeos-dev libproj-dev libudunits2-dev \
+  libcurl4-openssl-dev libssl-dev libxml2-dev \
+  libfontconfig1-dev libharfbuzz-dev libfribidi-dev \
+  pandoc pandoc-citeproc
+```
+
+### Step 2: Clone & Setup
+```bash
+git clone <bitbucket-url> smartcane
+cd smartcane
+chmod +x *.sh
+dos2unix *.sh  # Fix Windows line endings
+```
+
+### Step 3: Install R Packages
+```bash
+Rscript -e "renv::restore()"
+```
+
+### Step 4: Test Workflow
+```bash
+./09_run_calculate_kpis.sh aura
+./10_run_kpi_report.sh --data_dir=aura --filename=test.docx
+ls laravel_app/storage/app/aura/reports/
+```
+
+---
+
+## ⚙️ SCRIPT 10 PARAMETERS (for Laravel UI)
+
+### Configurable Parameters (add to Laravel project settings):
+
+| Parameter | Type | Default | Options | Description |
+|-----------|------|---------|---------|-------------|
+| `borders` | Boolean | FALSE | TRUE/FALSE | Show field borders on maps |
+| `ci_plot_type` | String | both | absolute/cumulative/both | Type of CI plots |
+| `colorblind_friendly` | Boolean | TRUE | TRUE/FALSE | Use accessible color palettes |
+| `facet_by_season` | Boolean | FALSE | TRUE/FALSE | Split plots by season |
+| `x_axis_unit` | String | days | days/weeks | X-axis time unit |
+
+### Auto-Set Parameters (managed by system):
+
+| Parameter | Source | Description |
+|-----------|--------|-------------|
+| `filename` | Auto-generated | Set by system: `{project}_{date}.docx` |
+| `report_date` | Current date | Automatically uses today's date |
+| `mail_day` | Current day | Automatically uses current weekday |
+| `data_dir` | Project name | Set from Laravel project configuration |
+
+### Laravel Implementation Notes:
+
+1. **Create settings per project** with the 5 configurable parameters above
+2. **Auto-generate filename**: `${project_name}_report_${date}.docx`
+3. **Auto-set dates**: Use current date/day when script runs
+4. **data_dir**: Pull from project's directory name in Laravel
+
+**Example usage:**
+```bash
+./10_run_kpi_report.sh \
+  --data_dir=aura \
+  --report_date=$(date +%Y-%m-%d) \
+  --filename="aura_report_$(date +%Y%m%d).docx" \
+  --mail_day=$(date +%A) \
+  --borders=FALSE \
+  --ci_plot_type=both \
+  --colorblind_friendly=TRUE \
+  --facet_by_season=FALSE \
+  --x_axis_unit=days
+```
+
+---
+
+## 🚨 COMMON DEPLOYMENT ERRORS
+
+### Error 1: Package Compilation Fails
+```
+ERROR: configuration failed for package 'sf'
+```
+**Solution:** Install system dependencies (see Step 1 above)
+
+### Error 2: Permission Denied
+```
+bash: ./10_run_kpi_report.sh: Permission denied
+```
+**Solution:** `chmod +x *.sh`
+
+### Error 3: Line Ending Issues
+```
+/bin/bash^M: bad interpreter
+```
+**Solution:** `dos2unix *.sh` or `sed -i 's/\r$//' *.sh`
+
+### Error 4: Pandoc Missing
+```
+Error: pandoc version 1.12.3 or higher is required
+```
+**Solution:** `sudo apt-get install -y pandoc`
+
+### Error 5: Font Errors
+```
+Error in gdtools::...: font family not found
+```
+**Solution:** Install font libraries (libfontconfig1-dev, etc. - see Step 1)
+
+---
+
+## 📊 SCRIPT COMPARISON: Old vs New
+
+### Script 05 (OLD - skip this):
+- Basic CI maps ✅
+- CI trend plots ✅
+- Week-over-week change ✅
+- **NO KPI metrics** ❌
+- **NO field uniformity** ❌
+- **NO priority detection** ❌
+
+### Scripts 09 + 10 (NEW - use these):
+- Everything from script 05 ✅
+- **KPI metrics** ✅
+- **Field uniformity (CV, Moran's I)** ✅
+- **Priority classification** (urgent/monitor/no stress) ✅
+- **Enhanced tables** (flextable formatting) ✅
+- **Field stress detection** ✅
+
+---
+
+## ⚠️ WINDOWS → LINUX COMPATIBILITY
+
+**Known issues when moving from Windows to Linux:**
+
+| Issue | Windows | Linux | Solution |
+|-------|---------|-------|----------|
+| Path separators | `\` | `/` | Scripts use `here::here()` ✅ |
+| Line endings | CRLF | LF | Run `dos2unix *.sh` |
+| Package compilation | Binary | Source | Install system libs first |
+| File permissions | Auto | Manual | Run `chmod +x *.sh` |
+| R path | Fixed path | In PATH | Scripts auto-detect ✅ |
+
+---
+
+## ✅ DEPLOYMENT CHECKLIST
+
+**Before pushing to Bitbucket:**
+- [ ] Verify scripts 09 and 10 work locally
+- [ ] Check renv.lock is committed
+- [ ] Test workflow: 01→02→03→04→09→10
+
+**After pulling on Linux server:**
+- [ ] Install system dependencies (GDAL, GEOS, PROJ, Pandoc, fonts)
+- [ ] Clone repository
+- [ ] Fix line endings: `dos2unix *.sh`
+- [ ] Set permissions: `chmod +x *.sh`
+- [ ] Install R packages: `Rscript -e "renv::restore()"`
+- [ ] Test with one project: `./09_run_calculate_kpis.sh aura`
+- [ ] Generate test report: `./10_run_kpi_report.sh --data_dir=aura`
+- [ ] Create Laravel UI for script 10 parameters
+- [ ] Update any automation scripts to use new workflow
+
+---
+
+## 📂 KEY FILES TO KNOW
+
+```
+smartcane/
+├── 01-04_*.sh              # Data acquisition (existing workflow)
+├── 05_*.sh                 # ❌ Old report (skip)
+├── 09_*.sh                 # ✅ NEW - KPI calculation
+├── 10_*.sh                 # ✅ NEW - Report with KPIs
+├── renv.lock               # Package versions (includes flextable/officer)
+└── r_app/
+    ├── 09_calculate_kpis.R         # NEW
+    ├── 10_CI_report_with_kpis_simple.Rmd  # NEW
+    └── kpi_utils.R                 # NEW
+```
+
+---
+
+## 🔄 EXAMPLE: Full Weekly Pipeline
+
+```bash
+#!/bin/bash
+# Complete weekly workflow for Aura farm
+
+PROJECT="aura"
+DATE=$(date +%Y-%m-%d)
+
+# Step 1-4: Data acquisition
+./01_run_planet_download.sh --project_dir=$PROJECT
+./02_run_ci_extraction.sh --project_dir=$PROJECT
+./03_run_growth_model.sh --project_dir=$PROJECT
+./04_run_mosaic_creation.sh --data_dir=$PROJECT
+
+# Step 5-6: KPI calculation & reporting (NEW)
+./09_run_calculate_kpis.sh $PROJECT
+./10_run_kpi_report.sh \
+  --data_dir=$PROJECT \
+  --report_date=$DATE \
+  --filename="${PROJECT}_${DATE}.docx" \
+  --colorblind_friendly=TRUE
+
+echo "✅ Pipeline complete! Check output/"
+```
+
+---
+
+## 📞 TROUBLESHOOTING
+
+**If deployment fails:**
+1. Check error against "Common Errors" section above
+2. Verify system dependencies: `dpkg -l | grep libgdal`
+3. Test R packages: `Rscript -e "library(flextable)"`
+4. Check file structure: `ls laravel_app/storage/app/*/`
+5. Review logs: `./10_run_kpi_report.sh 2>&1 | tee debug.log`
+
+**Still stuck?** Contact developer with:
+- Full error message
+- Which script failed
+- Output of `sessionInfo()` in R
+- Server OS and R version
+
+---
+
+**Version:** 1.0  
+**Last Updated:** October 14, 2025  
+**Branch:** code-improvements (ready for merge to master)

analyze_image_availability.R

@@ -0,0 +1,136 @@
+# R script to analyze image dates and missing weeks
+library(dplyr)
+library(lubridate)
+library(ggplot2)
+
+# Set folder path
+folder <- "laravel_app/storage/app/esa/merged_final_tif"
+files <- list.files(folder, pattern = "\\.tif$", full.names = FALSE)
+
+df <- data.frame(date = dates)
+# Extract dates and file sizes
+dates <- as.Date(sub(".tif$", "", files))
+sizes_kb <- file.info(file.path(folder, files))$size / 1024
+df <- data.frame(date = dates, size_kb = sizes_kb, file = files) %>%
+  mutate(year = year(date),
+         week = isoweek(date),
+         completeness = ifelse(size_kb >= 9000, "Complete", "Incomplete"))
+
+# Get all years in data
+years <- sort(unique(df$year))
+
+# Prepare output table
+output <- data.frame(
+  year = integer(),
+  n_images = integer(),
+  n_weeks_missing = integer(),
+  max_consec_weeks_missing = integer(),
+  avg_images_per_week = numeric(),
+  stringsAsFactors = FALSE
+)
+
+missing_weeks_list <- list()
+current_year <- as.integer(format(Sys.Date(), "%Y"))
+# For plotting: build a data frame with all year/week combinations and count images per week
+
+# For plotting: count complete/incomplete images per week/year
+plot_weeks <- expand.grid(year = years, week = 1:52, completeness = c("Complete", "Incomplete"))
+plot_weeks$n_images <- 0
+for (i in seq_len(nrow(plot_weeks))) {
+  y <- plot_weeks$year[i]
+  w <- plot_weeks$week[i]
+  ctype <- plot_weeks$completeness[i]
+  plot_weeks$n_images[i] <- sum(df$year == y & df$week == w & df$completeness == ctype)
+}
+
+
+
+# Plot: X = week, Y = number of images, fill = completeness, color = year (stacked bar chart)
+gg <- ggplot(plot_weeks, aes(x = week, y = n_images, fill = completeness)) +
+  geom_col(position = "stack") +
+  facet_wrap(~ year, ncol = 1) +
+  scale_x_continuous(breaks = 1:52) +
+  scale_y_continuous(breaks = 0:max(plot_weeks$n_images)) +
+  labs(x = "Week number", y = "Number of images", fill = "Completeness",
+       title = "Complete vs Incomplete Images per Week (by Year)") +
+  theme_minimal()
+
+ggsave("images_per_week_by_year_stacked.png", gg, width = 12, height = 10)
+cat("Plot saved as images_per_week_by_year_stacked.png\n")
+current_week <- isoweek(Sys.Date())
+
+
+
+for (y in years) {
+  # For current year, only consider weeks up to today; for past years, all 1:52
+  if (y == current_year) {
+    all_weeks <- 1:current_week
+  } else {
+    all_weeks <- 1:52
+  }
+  weeks_with_images <- unique(df$week[df$year == y])
+  weeks_missing <- setdiff(all_weeks, weeks_with_images)
+  n_weeks_missing <- length(weeks_missing)
+  n_images <- sum(df$year == y)
+  if ((y == current_year) && (current_week - n_weeks_missing > 0)) {
+    avg_images_per_week <- n_images / (current_week - n_weeks_missing)
+  } else if (y != current_year && (52 - n_weeks_missing > 0)) {
+    avg_images_per_week <- n_images / (52 - n_weeks_missing)
+  } else {
+    avg_images_per_week <- NA
+  }
+  # Find longest run of consecutive missing weeks
+  if (n_weeks_missing == 0) {
+    max_consec <- 0
+  } else {
+    w <- sort(weeks_missing)
+    runs <- rle(c(1, diff(w)) == 1)
+    max_consec <- max(runs$lengths[runs$values], na.rm = TRUE)
+  }
+  output <- rbind(output, data.frame(
+    year = y,
+    n_images = n_images,
+    n_weeks_missing = n_weeks_missing,
+    max_consec_weeks_missing = max_consec,
+    avg_images_per_week = round(avg_images_per_week, 2)
+  ))
+  if (n_weeks_missing > 0) {
+    missing_weeks_list[[as.character(y)]] <- weeks_missing
+  }
+}
+
+
+# Write to CSV
+
+print(output)
+
+write.csv(output, file = "image_availability_by_year.csv", row.names = FALSE)
+
+
+# Print missing weeks for years with missing data
+for (y in names(missing_weeks_list)) {
+  cat(sprintf("Year %s missing weeks: %s\n", y, paste(missing_weeks_list[[y]], collapse=", ")))
+}
+
+# Calculate and print max consecutive weeks with only incomplete data per year
+cat("\nMax consecutive weeks with only incomplete images per year:\n")
+for (y in years) {
+  if (y == current_year) {
+    all_weeks <- 1:current_week
+  } else {
+    all_weeks <- 1:52
+  }
+  # Weeks where all images are incomplete (no complete images)
+  weeks_incomplete <- plot_weeks$week[plot_weeks$year == y & plot_weeks$completeness == "Complete" & plot_weeks$n_images == 0]
+  # Only keep weeks that actually have at least one image (i.e., not missing entirely)
+  weeks_with_any_image <- unique(df$week[df$year == y])
+  weeks_incomplete <- intersect(weeks_incomplete, weeks_with_any_image)
+  if (length(weeks_incomplete) == 0) {
+    max_consec_incomplete <- 0
+  } else {
+    w <- sort(weeks_incomplete)
+    runs <- rle(c(1, diff(w)) == 1)
+    max_consec_incomplete <- max(runs$lengths[runs$values], na.rm = TRUE)
+  }
+  cat(sprintf("Year %d: %d\n", y, max_consec_incomplete))
+}

cleanup_repo.ps1

@@ -0,0 +1,207 @@
+# SmartCane Repository Cleanup Script
+# This script will delete unnecessary files and move experimental scripts
+# Review this script before running: .\cleanup_repo.ps1
+
+Write-Host "🧹 SmartCane Repository Cleanup" -ForegroundColor Cyan
+Write-Host "================================" -ForegroundColor Cyan
+Write-Host ""
+
+$deletedCount = 0
+$movedCount = 0
+$errors = @()
+
+# ============================================================================
+# PART 1: DELETE FILES
+# ============================================================================
+
+Write-Host "📁 PART 1: Deleting files..." -ForegroundColor Yellow
+Write-Host ""
+
+# A) Test & Debug Scripts
+$testFiles = @(
+    "r_app/test_benchmarks.R",
+    "r_app/test_harvest.R",
+    "r_app/test_kpis_esa.R",
+    "r_app/debug_kpis.R",
+    "r_app/quick_layout_test.R",
+    "r_app/run_minimal_test.R"
+)
+
+Write-Host "Deleting test and debug scripts..." -ForegroundColor Gray
+foreach ($file in $testFiles) {
+    if (Test-Path $file) {
+        Remove-Item $file -Force
+        Write-Host "  ✓ Deleted: $file" -ForegroundColor Green
+        $deletedCount++
+    } else {
+        Write-Host "  ⚠ Not found: $file" -ForegroundColor DarkGray
+    }
+}
+
+# B) Output Files (.Rout)
+$routFiles = @(
+    "r_app/02_ci_extraction.Rout",
+    "r_app/03_interpolate_growth_model.Rout",
+    "r_app/04_mosaic_creation.Rout"
+)
+
+Write-Host "`nDeleting .Rout files..." -ForegroundColor Gray
+foreach ($file in $routFiles) {
+    if (Test-Path $file) {
+        Remove-Item $file -Force
+        Write-Host "  ✓ Deleted: $file" -ForegroundColor Green
+        $deletedCount++
+    } else {
+        Write-Host "  ⚠ Not found: $file" -ForegroundColor DarkGray
+    }
+}
+
+# C) Temporary PDF Files
+$pdfFiles = @(
+    "Rplots.pdf",
+    "r_app/Rplots.pdf"
+)
+
+Write-Host "`nDeleting temporary PDF files..." -ForegroundColor Gray
+foreach ($file in $pdfFiles) {
+    if (Test-Path $file) {
+        Remove-Item $file -Force
+        Write-Host "  ✓ Deleted: $file" -ForegroundColor Green
+        $deletedCount++
+    } else {
+        Write-Host "  ⚠ Not found: $file" -ForegroundColor DarkGray
+    }
+}
+
+# D) Old/Deprecated Scripts
+$oldScripts = @(
+    "r_app/ci_extraction.R",
+    "r_app/interpolate_growth_model.R",
+    "r_app/mosaic_creation.R",
+    "r_app/installPackages.R",
+    "r_app/packages.R",
+    "generated_package_config.R"
+)
+
+Write-Host "`nDeleting old/deprecated scripts..." -ForegroundColor Gray
+foreach ($file in $oldScripts) {
+    if (Test-Path $file) {
+        Remove-Item $file -Force
+        Write-Host "  ✓ Deleted: $file" -ForegroundColor Green
+        $deletedCount++
+    } else {
+        Write-Host "  ⚠ Not found: $file" -ForegroundColor DarkGray
+    }
+}
+
+# E) Generated Word Documents
+$wordDocs = @(
+    "r_app/CI_report.docx",
+    "r_app/CI_report2.docx",
+    "r_app/CI_report_age_filtered.docx",
+    "r_app/CI_report_last_week.docx",
+    "r_app/CI_report_week38_corrected.docx",
+    "r_app/CI_report_with_kpis_aura.docx",
+    "r_app/CI_report_with_kpis_esa.docx",
+    "r_app/05_CI_report_dashboard_planet.docx",
+    "r_app/10_CI_report_with_kpis_simple.docx",
+    "r_app/script5_test.docx",
+    "r_app/test_kpi_grid.docx",
+    "r_app/output/aura/crop_analysis_AURA_w36vs35_20250916_1631.docx",
+    "r_app/output/reports/CI_report_with_kpis_simple_test.docx",
+    "r_app/output/CI_report_2x3_layout.docx",
+    "r_app/output/CI_report_consolidated.docx",
+    "r_app/output/CI_report_layout_test.docx",
+    "r_app/output/test_clean.docx",
+    "r_app/output/test_grid.docx",
+    "r_app/output/test_kables.docx",
+    "r_app/output/test_merged.docx"
+)
+
+Write-Host "`nDeleting generated Word documents (keeping word-styles-reference-var1.docx)..." -ForegroundColor Gray
+foreach ($file in $wordDocs) {
+    if (Test-Path $file) {
+        Remove-Item $file -Force
+        Write-Host "  ✓ Deleted: $file" -ForegroundColor Green
+        $deletedCount++
+    } else {
+        Write-Host "  ⚠ Not found: $file" -ForegroundColor DarkGray
+    }
+}
+
+# ============================================================================
+# PART 2: MOVE FILES TO EXPERIMENTS
+# ============================================================================
+
+Write-Host "`n`n📁 PART 2: Moving files to experiments..." -ForegroundColor Yellow
+Write-Host ""
+
+# Create destination directories
+$destDirs = @(
+    "r_app/experiments/reports",
+    "r_app/experiments/legacy_package_management"
+)
+
+foreach ($dir in $destDirs) {
+    if (!(Test-Path $dir)) {
+        New-Item -ItemType Directory -Path $dir -Force | Out-Null
+        Write-Host "  Created directory: $dir" -ForegroundColor Cyan
+    }
+}
+
+# Move experimental Rmd files
+$rmdFiles = @(
+    @{Source="r_app/CI_report_dashboard_planet.Rmd"; Dest="r_app/experiments/reports/"},
+    @{Source="r_app/CI_report_dashboard_planet_enhanced.Rmd"; Dest="r_app/experiments/reports/"},
+    @{Source="r_app/CI_report_executive_summary.Rmd"; Dest="r_app/experiments/reports/"},
+    @{Source="r_app/simple_kpi_report.Rmd"; Dest="r_app/experiments/reports/"},
+    @{Source="r_app/test_kpi_grid.Rmd"; Dest="r_app/experiments/reports/"},
+    @{Source="r_app/test_minimal.Rmd"; Dest="r_app/experiments/reports/"}
+)
+
+Write-Host "Moving experimental Rmd files..." -ForegroundColor Gray
+foreach ($file in $rmdFiles) {
+    if (Test-Path $file.Source) {
+        Move-Item $file.Source $file.Dest -Force
+        Write-Host "  ✓ Moved: $($file.Source) → $($file.Dest)" -ForegroundColor Green
+        $movedCount++
+    } else {
+        Write-Host "  ⚠ Not found: $($file.Source)" -ForegroundColor DarkGray
+    }
+}
+
+# Move legacy package management scripts
+$legacyFiles = @(
+    @{Source="r_app/extract_current_versions.R"; Dest="r_app/experiments/legacy_package_management/"},
+    @{Source="r_app/package_manager.R"; Dest="r_app/experiments/legacy_package_management/"}
+)
+
+Write-Host "`nMoving legacy package management scripts..." -ForegroundColor Gray
+foreach ($file in $legacyFiles) {
+    if (Test-Path $file.Source) {
+        Move-Item $file.Source $file.Dest -Force
+        Write-Host "  ✓ Moved: $($file.Source) → $($file.Dest)" -ForegroundColor Green
+        $movedCount++
+    } else {
+        Write-Host "  ⚠ Not found: $($file.Source)" -ForegroundColor DarkGray
+    }
+}
+
+# ============================================================================
+# SUMMARY
+# ============================================================================
+
+Write-Host "`n`n📊 CLEANUP SUMMARY" -ForegroundColor Cyan
+Write-Host "==================" -ForegroundColor Cyan
+Write-Host "Files deleted: $deletedCount" -ForegroundColor Green
+Write-Host "Files moved: $movedCount" -ForegroundColor Green
+
+if ($errors.Count -gt 0) {
+    Write-Host "`n⚠️ Errors encountered: $($errors.Count)" -ForegroundColor Red
+    foreach ($err in $errors) {
+        Write-Host "  $err" -ForegroundColor Red
+    }
+}
+
+Write-Host "`n✅ Cleanup completed!" -ForegroundColor Green
+Write-Host "`nNext step: Update .gitignore (see instructions)" -ForegroundColor Yellow

examine_kpi_results.R

@@ -0,0 +1,15 @@
+# Quick script to examine KPI results
+field_details <- readRDS('laravel_app/storage/app/esa/reports/kpis/esa_field_details_week39.rds')
+summary_tables <- readRDS('laravel_app/storage/app/esa/reports/kpis/esa_kpi_summary_tables_week39.rds')
+
+cat("=== FIELD DETAILS ===\n")
+print(head(field_details, 20))
+cat("\nTotal rows:", nrow(field_details), "\n\n")
+
+cat("=== TCH FORECASTED FIELD RESULTS ===\n")
+tch_results <- readRDS('laravel_app/storage/app/esa/reports/kpis/field_level/tch_forecasted_field_results_week39.rds')
+print(tch_results)
+cat("\nNumber of predictions:", nrow(tch_results), "\n\n")
+
+cat("=== SUMMARY TABLES ===\n")
+print(summary_tables$tch_forecasted)

generated_package_config.R

@@ -1,42 +0,0 @@
-# Package requirements with your current working versions
-REQUIRED_PACKAGES <- list(
-  # Core data manipulation
-  "dplyr" = "1.1.4",
-  "here" = "1.0.1",
-  "lubridate" = "1.9.4",
-  "readr" = "2.1.5",
-  "readxl" = "1.4.5",
-  "stringr" = "1.5.1",
-  "tidyr" = "1.3.1",
-
-  # Spatial data
-  "exactextractr" = "0.10.0",
-  "raster" = "3.6.32",
-  "sf" = "1.0.19",
-  "terra" = "1.8.43",  # CRITICAL: for raster processing
-
-  # Visualization
-  "ggplot2" = "3.5.1",
-  "tmap" = "4.0",  # CRITICAL: for tm_scale_continuous() syntax
-
-  # Reporting
-  "knitr" = "1.50",
-
-  # Tidyverse
-  "purrr" = "1.0.2",
-  "tidyverse" = "2.0.0",
-
-  # Other packages
-  "caret" = "7.0.1",
-  "CAST" = "1.0.3",
-  "furrr" = "0.3.1",
-  "future" = "1.40.0",
-  "gridExtra" = "2.3",
-  "parallel" = "4.4.2",
-  "progressr" = "0.15.1",
-  "randomForest" = "4.7.1.2",
-  "reshape2" = "1.4.4",
-  "rsample" = "1.3.0",
-  "tools" = "4.4.2",
-  "zoo" = "1.8.13"
-)

kpi_debug.out

@@ -0,0 +1,314 @@
+
+R version 4.4.3 (2025-02-28 ucrt) -- "Trophy Case"
+Copyright (C) 2025 The R Foundation for Statistical Computing
+Platform: x86_64-w64-mingw32/x64
+
+R is free software and comes with ABSOLUTELY NO WARRANTY.
+You are welcome to redistribute it under certain conditions.
+Type 'license()' or 'licence()' for distribution details.
+
+  Natural language support but running in an English locale
+
+R is a collaborative project with many contributors.
+Type 'contributors()' for more information and
+'citation()' on how to cite R or R packages in publications.
+
+Type 'demo()' for some demos, 'help()' for on-line help, or
+'help.start()' for an HTML browser interface to help.
+Type 'q()' to quit R.
+
+- Project 'C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane_v2/smartcane' loaded. [renv 1.1.4]
+> # 09_CALCULATE_KPIS.R
+> # ===================
+> # This script calculates 6 Key Performance Indicators (KPIs) for sugarcane monitoring:
+> # 1. Field Uniformity Summary
+> # 2. Farm-wide Area Change Summary
+> # 3. TCH Forecasted
+> # 4. Growth Decline Index
+> # 5. Weed Presence Score
+> # 6. Gap Filling Score (placeholder)
+> #
+> # Usage: Rscript 09_calculate_kpis.R [end_date] [offset] [project_dir]
+> #   - end_date: End date for KPI calculation (YYYY-MM-DD format), default: today
+> #   - offset: Number of days to look back (not currently used for KPIs, but for consistency)
+> #   - project_dir: Project directory name (e.g., "aura", "esa")
+> 
+> # 1. Load required libraries
+> # -------------------------
+> suppressPackageStartupMessages({
++   library(here)
++   library(sf)
++   library(terra)
++   library(dplyr)
++   library(tidyr)
++   library(lubridate)
++   library(readr)
++   library(caret)
++   library(CAST)
++   library(randomForest)
++ })
+> 
+> # 2. Main function
+> # --------------
+> main <- function() {
++   # Process command line arguments
++   args <- commandArgs(trailingOnly = TRUE)
++   
++   # Process end_date argument
++   if (length(args) >= 1 && !is.na(args[1])) {
++     end_date <- as.Date(args[1])
++     if (is.na(end_date)) {
++       warning("Invalid end_date provided. Using default (current date).")
++       end_date <- Sys.Date()
++     }
++   } else {
++     end_date <- Sys.Date()
++   }
++   
++   # Process offset argument (for consistency with other scripts, not currently used)
++   if (length(args) >= 2 && !is.na(args[2])) {
++     offset <- as.numeric(args[2])
++     if (is.na(offset) || offset <= 0) {
++       warning("Invalid offset provided. Using default (7 days).")
++       offset <- 7
++     }
++   } else {
++     offset <- 7
++   }
++   
++   # Process project_dir argument
++   if (length(args) >= 3 && !is.na(args[3])) {
++     project_dir <- as.character(args[3])
++   } else {
++     project_dir <- "esa"  # Default project
++   }
++   
++   # Make project_dir available globally so parameters_project.R can use it
++   assign("project_dir", project_dir, envir = .GlobalEnv)
++   
++   # 3. Load utility functions and project configuration
++   # --------------------------------------------------
++   
++   tryCatch({
++     source(here("r_app", "crop_messaging_utils.R"))
++   }, error = function(e) {
++     stop("Error loading crop_messaging_utils.R: ", e$message)
++   })
++   
++   tryCatch({
++     source(here("r_app", "kpi_utils.R"))
++   }, error = function(e) {
++     stop("Error loading kpi_utils.R: ", e$message)
++   })
++   
++   # Load project parameters (this sets up all directory paths and field boundaries)
++   tryCatch({
++     source(here("r_app", "parameters_project.R"))
++   }, error = function(e) {
++     stop("Error loading parameters_project.R: ", e$message)
++   })
++   
++   # Load growth model utils if available (for yield prediction)
++   tryCatch({
++     source(here("r_app", "growth_model_utils.R"))
++   }, error = function(e) {
++     warning("growth_model_utils.R not found, yield prediction KPI will use placeholder data")
++   })
++   
++   # Check if required variables exist
++   if (!exists("project_dir")) {
++     stop("project_dir must be set before running this script")
++   }
++   
++   if (!exists("field_boundaries_sf") || is.null(field_boundaries_sf)) {
++     stop("Field boundaries not loaded. Check parameters_project.R initialization.")
++   }
++   
++   # 4. Calculate all KPIs
++   # -------------------
++   output_dir <- file.path(reports_dir, "kpis")
++   
++   kpi_results <- calculate_all_kpis(
++     report_date = end_date,
++     output_dir = output_dir,
++     field_boundaries_sf = field_boundaries_sf,
++     harvesting_data = harvesting_data,
++     cumulative_CI_vals_dir = cumulative_CI_vals_dir,
++     weekly_CI_mosaic = weekly_CI_mosaic,
++     reports_dir = reports_dir,
++     project_dir = project_dir
++   )
++   
++   # 5. Print summary
++   # --------------
++   cat("\n=== KPI CALCULATION SUMMARY ===\n")
++   cat("Report Date:", as.character(kpi_results$metadata$report_date), "\n")
++   cat("Current Week:", kpi_results$metadata$current_week, "\n")
++   cat("Previous Week:", kpi_results$metadata$previous_week, "\n")
++   cat("Total Fields Analyzed:", kpi_results$metadata$total_fields, "\n")
++   cat("Calculation Time:", as.character(kpi_results$metadata$calculation_time), "\n")
++   
++   cat("\nField Uniformity Summary:\n")
++   print(kpi_results$field_uniformity_summary)
++   
++   cat("\nArea Change Summary:\n")
++   print(kpi_results$area_change)
++   
++   cat("\nTCH Forecasted:\n")
++   print(kpi_results$tch_forecasted)
++   
++   cat("\nGrowth Decline Index:\n")
++   print(kpi_results$growth_decline)
++   
++   cat("\nWeed Presence Score:\n")
++   print(kpi_results$weed_presence)
++   
++   cat("\nGap Filling Score:\n")
++   print(kpi_results$gap_filling)
++   
++   cat("\n=== KPI CALCULATION COMPLETED ===\n")
++ }
+> 
+> # 6. Script execution
+> # -----------------
+> if (sys.nframe() == 0) {
++   main()
++ }
+[INFO] 2025-10-08 15:39:29 - Initializing project with directory: esa 
+[1] "model using cumulative_CI,DOY will be trained now..."
+note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
+
++ Fold1: mtry=2 
+- Fold1: mtry=2 
++ Fold2: mtry=2 
+- Fold2: mtry=2 
++ Fold3: mtry=2 
+- Fold3: mtry=2 
++ Fold4: mtry=2 
+- Fold4: mtry=2 
++ Fold5: mtry=2 
+- Fold5: mtry=2 
+Aggregating results
+Fitting final model on full training set
+[1] "maximum number of models that still need to be trained: 3"
+[1] "model using cumulative_CI,CI_per_day will be trained now..."
+note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
+
++ Fold1: mtry=2 
+- Fold1: mtry=2 
++ Fold2: mtry=2 
+- Fold2: mtry=2 
++ Fold3: mtry=2 
+- Fold3: mtry=2 
++ Fold4: mtry=2 
+- Fold4: mtry=2 
++ Fold5: mtry=2 
+- Fold5: mtry=2 
+Aggregating results
+Fitting final model on full training set
+[1] "maximum number of models that still need to be trained: 2"
+[1] "model using DOY,CI_per_day will be trained now..."
+note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
+
++ Fold1: mtry=2 
+- Fold1: mtry=2 
++ Fold2: mtry=2 
+- Fold2: mtry=2 
++ Fold3: mtry=2 
+- Fold3: mtry=2 
++ Fold4: mtry=2 
+- Fold4: mtry=2 
++ Fold5: mtry=2 
+- Fold5: mtry=2 
+Aggregating results
+Fitting final model on full training set
+[1] "maximum number of models that still need to be trained: 1"
+[1] "vars selected: cumulative_CI,DOY with RMSE 24.808"
+[1] "model using additional variable CI_per_day will be trained now..."
+note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 .
+
++ Fold1: mtry=2 
+- Fold1: mtry=2 
++ Fold1: mtry=3 
+- Fold1: mtry=3 
++ Fold2: mtry=2 
+- Fold2: mtry=2 
++ Fold2: mtry=3 
+- Fold2: mtry=3 
++ Fold3: mtry=2 
+- Fold3: mtry=2 
++ Fold3: mtry=3 
+- Fold3: mtry=3 
++ Fold4: mtry=2 
+- Fold4: mtry=2 
++ Fold4: mtry=3 
+- Fold4: mtry=3 
++ Fold5: mtry=2 
+- Fold5: mtry=2 
++ Fold5: mtry=3 
+- Fold5: mtry=3 
+Aggregating results
+Selecting tuning parameters
+Fitting mtry = 3 on full training set
+[1] "maximum number of models that still need to be trained: 0"
+[1] "vars selected: cumulative_CI,DOY with RMSE 24.808"
+             field_groups count value
+75%               Top 25%     3  96.2
+50%               Average     7  93.0
+25%            Lowest 25%     2  84.0
+    Total area forecasted    12 219.0
+
+=== KPI CALCULATION SUMMARY ===
+Report Date: 2025-10-08 
+Current Week: 40 
+Previous Week: 39 
+Total Fields Analyzed: 12 
+Calculation Time: 2025-10-08 15:39:34.583434 
+
+Field Uniformity Summary:
+  uniformity_level count percent
+1        Excellent     0       0
+2             Good     0       0
+3         Moderate     0       0
+4             Poor     0       0
+
+Area Change Summary:
+      change_type hectares percent
+1 Improving areas        0       0
+2    Stable areas        0       0
+3 Declining areas        0       0
+4      Total area        0     100
+
+TCH Forecasted:
+             field_groups count value
+75%               Top 25%     3  96.2
+50%               Average     7  93.0
+25%            Lowest 25%     2  84.0
+    Total area forecasted    12 219.0
+
+Growth Decline Index:
+  risk_level count percent
+1       High     0       0
+2        Low     0       0
+3   Moderate     0       0
+4  Very-high     0       0
+
+Weed Presence Score:
+                weed_risk_level field_count percent
+1 Canopy closed - Low weed risk           4    33.3
+2                          High           0     0.0
+3                           Low           0     0.0
+4                      Moderate           0     0.0
+
+Gap Filling Score:
+# A tibble: 1 × 3
+  gap_level field_count percent
+  <chr>           <int>   <dbl>
+1 <NA>               12     100
+
+=== KPI CALCULATION COMPLETED ===
+There were 50 or more warnings (use warnings() to see the first 50)
+> 
+> proc.time()
+   user  system elapsed 
+  11.93    0.93   13.45 

push_to_bitbucket.ps1

@@ -0,0 +1,72 @@
+# SmartCane - Git Push to Bitbucket
+# Run this script to commit and push all changes
+
+# Step 1: Check current status
+Write-Host "=== Current Git Status ===" -ForegroundColor Cyan
+git status
+
+# Step 2: Add all new and modified files
+Write-Host "`n=== Adding Files ===" -ForegroundColor Cyan
+git add -A
+
+# Step 3: Show what will be committed
+Write-Host "`n=== Files to be committed ===" -ForegroundColor Cyan
+git status
+
+# Step 4: Commit with descriptive message
+Write-Host "`n=== Committing Changes ===" -ForegroundColor Cyan
+$commitMessage = @"
+Add KPI reporting system and deployment documentation
+
+Major Changes:
+- NEW: Scripts 09 & 10 for KPI calculation and enhanced reporting
+- NEW: Shell script wrappers (01-10) for easier execution
+- NEW: R packages flextable and officer for enhanced Word reports
+- NEW: DEPLOYMENT_README.md with complete deployment guide
+- RENAMED: Numbered R scripts (02, 03, 04) for clarity
+- REMOVED: Old package management scripts (using renv only)
+- UPDATED: Workflow now uses scripts 09->10 instead of 05
+
+Files Changed: 90+ files
+New Packages: flextable, officer
+New Scripts: 09_run_calculate_kpis.sh, 10_run_kpi_report.sh
+Documentation: DEPLOYMENT_README.md, EMAIL_TO_ADMIN.txt
+
+See DEPLOYMENT_README.md for full deployment instructions.
+"@
+
+git commit -m $commitMessage
+
+# Step 5: Push to Bitbucket
+Write-Host "`n=== Ready to Push ===" -ForegroundColor Yellow
+Write-Host "Current branch: " -NoNewline
+git branch --show-current
+
+Write-Host "`nDo you want to push to Bitbucket? (Y/N): " -ForegroundColor Yellow -NoNewline
+$confirmation = Read-Host
+
+if ($confirmation -eq 'Y' -or $confirmation -eq 'y') {
+    Write-Host "`n=== Pushing to Bitbucket ===" -ForegroundColor Green
+    
+    # Get current branch name
+    $branch = git branch --show-current
+    
+    # Push to origin
+    git push origin $branch
+    
+    Write-Host "`n[SUCCESS] Pushed to Bitbucket!" -ForegroundColor Green
+    Write-Host "`nNext steps:" -ForegroundColor Cyan
+    Write-Host "1. Send EMAIL_TO_ADMIN.txt to your administrator"
+    Write-Host "2. Ensure they have access to the Bitbucket repository"
+    Write-Host "3. Monitor deployment and test on Linux server"
+    Write-Host "4. Update Laravel UI with Script 10 parameters"
+    
+} else {
+    Write-Host "`n[CANCELLED] Push cancelled. Run 'git push origin $(git branch --show-current)' when ready." -ForegroundColor Yellow
+}
+
+Write-Host "`n=== Summary ===" -ForegroundColor Cyan
+Write-Host "Deployment guide: DEPLOYMENT_README.md"
+Write-Host "Admin email: EMAIL_TO_ADMIN.txt"
+Write-Host "New scripts: 09_run_calculate_kpis.sh, 10_run_kpi_report.sh"
+Write-Host "New packages: flextable, officer"

r_app/02_ci_extraction.R

@@ -1,2 +1,120 @@
-# Renamed for workflow clarity
+# CI_EXTRACTION.R
-# ...existing code from ci_extraction.R...
+# ==============
+# This script processes satellite imagery to extract Canopy Index (CI) values for agricultural fields.
+# It handles image processing, masking, and extraction of statistics by field/sub-field.
+#
+# Usage: Rscript ci_extraction.R [end_date] [offset] [project_dir]
+#   - end_date: End date for processing (YYYY-MM-DD format)
+#   - offset: Number of days to look back from end_date 
+#   - project_dir: Project directory name (e.g., "chemba")
+#
+
+# 1. Load required packages
+# -----------------------
+suppressPackageStartupMessages({
+  library(sf)
+  library(terra)
+  library(tidyverse)
+  library(lubridate)
+  library(exactextractr)
+  library(readxl)
+  library(here)
+})
+
+# 2. Process command line arguments
+# ------------------------------
+main <- function() {
+  # Capture command line arguments
+  args <- commandArgs(trailingOnly = TRUE)
+  
+  # Process end_date argument
+  if (length(args) >= 1 && !is.na(args[1])) {
+    end_date <- as.Date(args[1])
+    if (is.na(end_date)) {
+      warning("Invalid end_date provided. Using default (current date).")
+      end_date <- Sys.Date()
+      #end_date <- "2023-10-01"
+    }
+  } else {
+    end_date <- Sys.Date()
+    #end_date <- "2023-10-01"
+  }
+  
+  # Process offset argument
+  if (length(args) >= 2 && !is.na(args[2])) {
+    offset <- as.numeric(args[2])
+    if (is.na(offset) || offset <= 0) {
+      warning("Invalid offset provided. Using default (7 days).")
+      offset <- 7
+    }
+  } else {
+    offset <- 7
+  }
+  
+  # Process project_dir argument
+  if (length(args) >= 3 && !is.na(args[3])) {
+    project_dir <- as.character(args[3])
+  } else {
+    project_dir <- "esa"  # Changed default from "aura" to "esa"
+  }
+  
+  # Make project_dir available globally so parameters_project.R can use it
+  assign("project_dir", project_dir, envir = .GlobalEnv)
+  
+  # Set flag to use pivot_2.geojson for ESA (extra fields for yield prediction)
+  ci_extraction_script <- TRUE
+  assign("ci_extraction_script", ci_extraction_script, envir = .GlobalEnv)
+
+  # 3. Initialize project configuration
+  # --------------------------------
+  new_project_question <- TRUE
+  
+  tryCatch({
+    source("parameters_project.R")
+    source("ci_extraction_utils.R")
+  }, error = function(e) {
+    warning("Default source files not found. Attempting to source from 'r_app' directory.")
+    tryCatch({
+        source("r_app/parameters_project.R")
+        source("r_app/ci_extraction_utils.R")
+      warning(paste("Successfully sourced files from 'r_app' directory."))
+
+    }, error = function(e) {
+      stop("Failed to source required files from both default and 'r_app' directories.")
+    })
+  })
+  
+  # 4. Generate date list for processing
+  # ---------------------------------
+  dates <- date_list(end_date, offset)
+  log_message(paste("Processing data for week", dates$week, "of", dates$year))
+  
+  # 5. Find and filter raster files by date
+  # -----------------------------------
+  log_message("Searching for raster files")
+  
+  tryCatch({
+    # Use the new utility function to find satellite images
+    existing_files <- find_satellite_images(planet_tif_folder, dates$days_filter)
+    log_message(paste("Found", length(existing_files), "raster files for processing"))
+    
+    # 6. Process raster files and create VRT
+    # -----------------------------------
+    # Use the new utility function for batch processing
+    vrt_list <- process_satellite_images(existing_files, field_boundaries, merged_final, daily_vrt)
+    
+    # 7. Process and combine CI values
+    # ------------------------------
+    # Call the process_ci_values function from utils with all required parameters
+    process_ci_values(dates, field_boundaries, merged_final, 
+                     field_boundaries_sf, daily_CI_vals_dir, cumulative_CI_vals_dir)
+    
+  }, error = function(e) {
+    log_message(paste("Error in main processing:", e$message), level = "ERROR")
+    stop(e$message)
+  })
+}
+
+if (sys.nframe() == 0) {
+  main()
+}

r_app/03_interpolate_growth_model.R

@@ -1,2 +1,110 @@
-# Renamed for workflow clarity
+# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\interpolate_growth_model.R
-# ...existing code from interpolate_growth_model.R...
+#
+# INTERPOLATE_GROWTH_MODEL.R
+# =========================
+# This script interpolates CI (Chlorophyll Index) values between measurement dates
+# to create a continuous growth model. It generates daily values and cumulative
+# CI statistics for each field.
+#
+# Usage: Rscript interpolate_growth_model.R [project_dir]
+#   - project_dir: Project directory name (e.g., "chemba")
+#
+
+# 1. Load required packages
+# -----------------------
+suppressPackageStartupMessages({
+  library(tidyverse)
+  library(lubridate)
+  library(here)
+})
+
+# 2. Main function to handle interpolation
+# -------------------------------------
+main <- function() {
+  # Process command line arguments
+  args <- commandArgs(trailingOnly = TRUE)
+  
+  # Get project directory from arguments or use default
+  if (length(args) >= 1 && !is.na(args[1])) {
+    project_dir <- as.character(args[1])
+  } else {
+    project_dir <- "esa"
+    message("No project_dir provided. Using default:", project_dir)
+  }
+  
+    # Make project_dir available globally so parameters_project.R can use it
+  assign("project_dir", project_dir, envir = .GlobalEnv)
+  
+  # Set flag to use pivot_2.geojson for ESA (extra fields for yield prediction)
+  ci_extraction_script <- TRUE
+  assign("ci_extraction_script", ci_extraction_script, envir = .GlobalEnv)
+  
+  # Initialize project configuration and load utility functions
+  tryCatch({
+    source("parameters_project.R")
+    source("growth_model_utils.R")
+  }, error = function(e) {
+    warning("Default source files not found. Attempting to source from 'r_app' directory.")
+    tryCatch({
+      source(here::here("r_app", "parameters_project.R"))
+      source(here::here("r_app", "growth_model_utils.R"))
+      warning(paste("Successfully sourced files from 'r_app' directory."))
+
+    }, error = function(e) {
+      stop("Failed to source required files from both default and 'r_app' directories.")
+    })
+  })
+  
+  log_message("Starting CI growth model interpolation")
+  
+  # Load and process the data
+  tryCatch({
+    # Load the combined CI data
+    CI_data <- load_combined_ci_data(cumulative_CI_vals_dir)
+    
+    # Validate harvesting data
+    if (is.null(harvesting_data) || nrow(harvesting_data) == 0) {
+      stop("No harvesting data available")
+    }
+    
+    # Get the years from harvesting data
+    years <- harvesting_data %>% 
+      filter(!is.na(season_start)) %>% 
+      distinct(year) %>% 
+      pull(year)
+    
+    log_message(paste("Processing data for years:", paste(years, collapse = ", ")))
+    
+    # Generate interpolated CI data for each year and field
+    CI_all <- generate_interpolated_ci_data(years, harvesting_data, CI_data)
+    
+    # CI_all <- CI_all %>%
+    #   group_by(Date, field, season) %>%
+    #   filter(!(field == "00F25" & season == 2023 & duplicated(DOY)))
+    
+    # Calculate growth metrics and save the results
+    if (nrow(CI_all) > 0) {
+      # Add daily and cumulative metrics
+      CI_all_with_metrics <- calculate_growth_metrics(CI_all)
+      
+      # Save the processed data
+      save_growth_model(
+        CI_all_with_metrics, 
+        cumulative_CI_vals_dir, 
+        "All_pivots_Cumulative_CI_quadrant_year_v2.rds"
+      )
+    } else {
+      log_message("No CI data was generated after interpolation", level = "WARNING")
+    }
+    
+    log_message("Growth model interpolation completed successfully")
+    
+  }, error = function(e) {
+    log_message(paste("Error in growth model interpolation:", e$message), level = "ERROR")
+    stop(e$message)
+  })
+}
+
+if (sys.nframe() == 0) {
+  main()
+}

r_app/04_mosaic_creation.R

@@ -1,2 +1,119 @@
-# Renamed for workflow clarity
+# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\mosaic_creation.R
-# ...existing code from mosaic_creation.R...
+#
+# MOSAIC_CREATION.R
+# ===============
+# This script creates weekly mosaics from daily satellite imagery.
+# It handles command-line arguments and initiates the mosaic creation process.
+#
+# Usage: Rscript mosaic_creation.R [end_date] [offset] [project_dir] [file_name]
+#   - end_date: End date for processing (YYYY-MM-DD format)
+#   - offset: Number of days to look back from end_date
+#   - project_dir: Project directory name (e.g., "chemba")
+#   - file_name: Optional custom output file name
+#
+
+# 1. Load required packages
+# -----------------------
+suppressPackageStartupMessages({
+  library(sf)
+  library(terra)
+  library(tidyverse)
+  library(lubridate)
+  library(here)
+})
+
+# 2. Process command line arguments and run mosaic creation
+# ------------------------------------------------------
+main <- function() {
+  # Capture command line arguments
+  args <- commandArgs(trailingOnly = TRUE)
+  
+    # Process project_dir argument with default
+  if (length(args) >= 3 && !is.na(args[3])) {
+    project_dir <- as.character(args[3])
+  } else {
+    # Default project directory
+    project_dir <- "esa"
+    message("No project_dir provided. Using default:", project_dir)
+  }
+  
+  # Make project_dir available globally so parameters_project.R can use it
+  assign("project_dir", project_dir, envir = .GlobalEnv)
+  
+  # Process end_date argument with default
+  if (length(args) >= 1 && !is.na(args[1])) {
+    end_date <- as.Date(args[1])
+    if (is.na(end_date)) {
+      message("Invalid end_date provided. Using current date.")
+      end_date <- Sys.Date()
+      #end_date <- "2025-07-22"  # Default date for testing
+    }
+  } else {
+    # Default to current date if no argument is provided
+    end_date <- Sys.Date()
+    #end_date <-  "2025-07-08"   # Default date for testing
+    message("No end_date provided. Using current date: ", format(end_date))
+  }
+  
+  # Process offset argument with default
+  if (length(args) >= 2 && !is.na(args[2])) {
+    offset <- as.numeric(args[2])
+    if (is.na(offset) || offset <= 0) {
+      message("Invalid offset provided. Using default (7 days).")
+      offset <- 7
+    }
+  } else {
+    # Default to 7 days if no argument is provided
+    offset <- 7
+    message("No offset provided. Using default:", offset, "days")
+  }
+  
+
+  
+  # 3. Initialize project configuration
+  # --------------------------------
+  tryCatch({
+    source("parameters_project.R")
+    source("mosaic_creation_utils.R")
+    safe_log(paste("Successfully sourced files from default directory."))
+  }, error = function(e) {
+    warning("Default source files not found. Attempting to source from 'r_app' directory.")
+    tryCatch({
+      source(here::here("r_app", "parameters_project.R"))
+      source(here::here("r_app", "mosaic_creation_utils.R"))
+      warning(paste("Successfully sourced files from 'r_app' directory."))
+    }, error = function(e) {
+      stop("Failed to source required files from both default and 'r_app' directories.")
+    })
+  })
+  
+  # 4. Generate date range for processing
+  # ---------------------------------
+  dates <- date_list(end_date, offset)  
+  safe_log(paste("Processing data for week", dates$week, "of", dates$year))
+  
+  # Create output filename
+  file_name_tif <- if (length(args) >= 4 && !is.na(args[4])) {
+    as.character(args[4])
+  } else {
+    paste0("week_", sprintf("%02d", dates$week), "_", dates$year, ".tif")
+  }
+  
+  safe_log(paste("Output will be saved as:", file_name_tif))
+  
+  # 5. Create weekly mosaic using the function from utils
+  # -------------------------------------------------
+  create_weekly_mosaic(
+    dates = dates,
+    field_boundaries = field_boundaries,
+    daily_vrt_dir = daily_vrt,
+    merged_final_dir = merged_final,
+    output_dir = weekly_CI_mosaic,
+    file_name_tif = file_name_tif,
+    create_plots = TRUE
+  )
+}
+
+if (sys.nframe() == 0) {
+  main()
+}

r_app/05_CI_report_dashboard_planet.Rmd

@@ -2,8 +2,8 @@
 params:
   ref: "word-styles-reference-var1.docx"
   output_file: CI_report.docx
-  report_date: "2024-06-20"
+  report_date: "2025-09-24"
-  data_dir: "chemba"
+  data_dir: "esa"
   mail_day: "Wednesday"
   borders: FALSE
   ci_plot_type: "both"  # options: "absolute", "cumulative", "both"
@@ -367,156 +367,6 @@ Use these insights to identify areas that may need irrigation, fertilization, or
 
 \newpage
 
-# RGB Satellite Image - Current Week (if available)
-```{r render_rgb_map, echo=FALSE, fig.height=7, fig.width=10, message=FALSE, warning=FALSE}
-# Check if RGB bands are available and create RGB map
-tryCatch({
-  # Load the full raster to check available bands
-  full_raster <- terra::rast(path_to_week_current)
-  available_bands <- names(full_raster)
-  
-  # Check if RGB bands are available (look for red, green, blue or similar naming)
-  rgb_bands_available <- any(grepl("red|Red|RED", available_bands, ignore.case = TRUE)) && 
-                        any(grepl("green|Green|GREEN", available_bands, ignore.case = TRUE)) && 
-                        any(grepl("blue|Blue|BLUE", available_bands, ignore.case = TRUE))
-  
-  # Alternative check for numbered bands that might be RGB (e.g., band_1, band_2, band_3)
-  if (!rgb_bands_available && length(available_bands) >= 3) {
-    # Check if we have at least 3 bands that could potentially be RGB
-    potential_rgb_bands <- grep("band_[1-3]|B[1-3]|[1-3]", available_bands, ignore.case = TRUE)
-    rgb_bands_available <- length(potential_rgb_bands) >= 3
-  }
-  
-  if (rgb_bands_available) {
-    safe_log("RGB bands detected - creating RGB visualization")
-    
-    # Try to extract RGB bands (prioritize named bands first)
-    red_band <- NULL
-    green_band <- NULL  
-    blue_band <- NULL
-    
-    # Look for named RGB bands first
-    red_candidates <- grep("red|Red|RED", available_bands, ignore.case = TRUE, value = TRUE)
-    green_candidates <- grep("green|Green|GREEN", available_bands, ignore.case = TRUE, value = TRUE)
-    blue_candidates <- grep("blue|Blue|BLUE", available_bands, ignore.case = TRUE, value = TRUE)
-    
-    if (length(red_candidates) > 0) red_band <- red_candidates[1]
-    if (length(green_candidates) > 0) green_band <- green_candidates[1]
-    if (length(blue_candidates) > 0) blue_band <- blue_candidates[1]
-    
-    # Fallback to numbered bands if named bands not found
-    if (is.null(red_band) || is.null(green_band) || is.null(blue_band)) {
-      if (length(available_bands) >= 3) {
-        # Assume first 3 bands are RGB (common convention)
-        red_band <- available_bands[1]
-        green_band <- available_bands[2] 
-        blue_band <- available_bands[3]
-      }
-    }
-    
-    if (!is.null(red_band) && !is.null(green_band) && !is.null(blue_band)) {
-      # Extract RGB bands
-      rgb_raster <- c(full_raster[[red_band]], full_raster[[green_band]], full_raster[[blue_band]])
-      names(rgb_raster) <- c("red", "green", "blue")
-        # Create RGB map
-      map <- tmap::tm_shape(rgb_raster, unit = "m") +
-        tmap::tm_rgb() +
-        tmap::tm_scalebar(position = tm_pos_out("right", "bottom"), text.color = "black") +
-        tmap::tm_compass(position = tm_pos_out("right", "bottom"), text.color = "black") +
-        tmap::tm_shape(AllPivots0) + 
-        tmap::tm_borders(col = "white", lwd = 2) +
-        tmap::tm_text("sub_field", size = 0.6, col = "white") +
-        tmap::tm_layout(main.title = paste0("RGB Satellite Image - Week ", week),
-                       main.title.size = 0.8,
-                       main.title.color = "black")
-      
-      # Print the map
-      print(map)
-      
-      safe_log("RGB map created successfully")
-    } else {
-      safe_log("Could not identify RGB bands despite detection", "WARNING")
-      cat("RGB bands detected but could not be properly identified. Skipping RGB visualization.\n")
-    }
-  } else {
-    safe_log("No RGB bands available in the current week mosaic")
-    cat("**Note:** RGB satellite imagery is not available for this week. Only spectral index data is available.\n\n")
-  }
-}, error = function(e) {
-  safe_log(paste("Error creating RGB map:", e$message), "ERROR")
-  cat("**Note:** Could not create RGB visualization for this week.\n\n")
-})
-```
-
-# Chlorophyll Index (CI) Overview Map - Current Week
-```{r render_ci_overview_map, echo=FALSE, fig.height=7, fig.width=10, message=FALSE, warning=FALSE}
-# Create overview chlorophyll index map
-tryCatch({  
-  # Choose palette based on colorblind_friendly parameter
-  ci_palette <- if (colorblind_friendly) "viridis" else "brewer.rd_yl_gn"
-  
-  # Base shape
-  map <- tmap::tm_shape(CI, unit = "m")  
-  
-  # Add raster layer with continuous spectrum (fixed scale 1-8 for consistent comparison)
-  map <- map + tmap::tm_raster(col.scale = tm_scale_continuous(values = ci_palette,
-                                                             limits = c(1, 8)),                            
-                               col.legend = tm_legend(title = "Chlorophyll Index (CI)",
-                                                 orientation = "landscape", 
-                                                 position = tm_pos_out("center", "bottom")))
-    # Complete the map with layout and other elements
-  map <- map + 
-    tmap::tm_scalebar(position = tm_pos_out("right", "bottom"), text.color = "black") +
-    tmap::tm_compass(position = tm_pos_out("right", "bottom"), text.color = "black") +
-    tmap::tm_shape(AllPivots0) + 
-    tmap::tm_borders(col = "black") +
-    tmap::tm_text("sub_field", size = 0.6, col = "black")
-  
-  # Print the map
-  print(map)
-}, error = function(e) {
-  safe_log(paste("Error creating CI overview map:", e$message), "ERROR")
-  plot(1, type="n", axes=FALSE, xlab="", ylab="")
-  text(1, 1, "Error creating CI overview map", cex=1.5)
-})
-
-```
-
-# Weekly Chlorophyll Index Difference Map
-```{r render_ci_difference_map, echo=FALSE, fig.height=7, fig.width=10, message=FALSE, warning=FALSE}
-# Create chlorophyll index difference map
-tryCatch({  
-  # Choose palette based on colorblind_friendly parameter
-  diff_palette <- if (colorblind_friendly) "plasma" else "brewer.rd_yl_gn"
-  
-  # Base shape
-  map <- tmap::tm_shape(last_week_dif_raster_abs, unit = "m")  
-  
-  # Add raster layer with continuous spectrum (centered at 0 for difference maps, fixed scale)
-  map <- map + tmap::tm_raster(col.scale = tm_scale_continuous(values = diff_palette,
-                                                            midpoint = 0,
-                                                            limits = c(-3, 3)),                            
-                               col.legend = tm_legend(title = "Chlorophyll Index (CI) Change",
-                                                 orientation = "landscape", 
-                                                 position = tm_pos_out("center", "bottom")))
-    # Complete the map with layout and other elements
-  map <- map + 
-    tmap::tm_scalebar(position = tm_pos_out("right", "bottom"), text.color = "black") +
-    tmap::tm_compass(position = tm_pos_out("right", "bottom"), text.color = "black") +
-    tmap::tm_shape(AllPivots0) + 
-    tmap::tm_borders(col = "black") +
-    tmap::tm_text("sub_field", size = 0.6, col = "black")
-  
-  # Print the map
-  print(map)
-}, error = function(e) {
-  safe_log(paste("Error creating CI difference map:", e$message), "ERROR")
-  plot(1, type="n", axes=FALSE, xlab="", ylab="")
-  text(1, 1, "Error creating CI difference map", cex=1.5)
-})
-```
-
-\newpage
 
 ```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=10, message=FALSE,echo=FALSE, warning=FALSE, include=TRUE, results='asis'}
 # Generate detailed visualizations for each field
@@ -662,8 +512,8 @@ tryCatch({
   # Prepare prediction dataset (fields without harvest data)
   prediction_yields <- CI_and_yield %>% 
     as.data.frame() %>% 
-    dplyr::filter(is.na(tonnage_ha)) %>%
+    dplyr::filter(is.na(tonnage_ha))# #%>%
-    dplyr::filter(age > 300)  # Only predict on fields older than 300 days
+   # dplyr::filter(Age_days > 300)  # Only predict on fields older than 300 days
   
   # Configure model training parameters
   ctrl <- caret::trainControl(
@@ -700,7 +550,7 @@ tryCatch({
         predicted_Tcha = round(predicted_Tcha, 0),
         season = newdata$season
       ) %>%
-      dplyr::select(field, sub_field, Age_days, total_CI, predicted_Tcha, season) %>%
+      dplyr::select(field, sub_field, Age_days, predicted_Tcha, season) %>%
       dplyr::left_join(., newdata, by = c("field", "sub_field", "season"))
     )
   }
@@ -711,7 +561,7 @@ tryCatch({
   # Predict yields for the current season (focus on mature fields over 300 days)
   pred_rf_current_season <- prepare_predictions(stats::predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>%
     dplyr::filter(Age_days > 1) %>%
-    dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1))
+    select(c("field", "Age_days", "predicted_Tcha", "season"))
   
   safe_log("Successfully completed yield prediction calculations")
   
@@ -743,7 +593,7 @@ tryCatch({
     # Plot predicted yields by age
     ggplot2::ggplot(pred_rf_current_season, ggplot2::aes(x = Age_days, y = predicted_Tcha)) +
       ggplot2::geom_point(size = 2, alpha = 0.6) +
-      ggplot2::labs(title = "Predicted Yields for Fields Over 300 Days \nOld Yet to Be Harvested",
+      ggplot2::labs(title = "Predicted Yields \n Yet to Be Harvested",
                    x = "Age (days)", 
                    y = "Predicted tonnage/ha (Tcha)") +
       ggplot2::scale_y_continuous(limits = c(0, 200)) +

r_app/06_crop_messaging

@@ -0,0 +1,293 @@
+# 06_CROP_MESSAGING.R
+# ===================
+# This script analyzes weekly CI mosaics to detect changes and generate automated messages
+# about crop conditions. It compares two weeks of data to assess:
+# - Field uniformity (high vs low variation)
+# - CI change trends (increase, stable, decrease)
+# - Generates contextual messages based on analysis
+# - Outputs results in multiple formats: WhatsApp/Word text, CSV, and .docx
+#
+# Usage: Rscript 06_crop_messaging.R [current_week] [previous_week] [estate_name]
+#   - current_week: Current week number (e.g., 30)
+#   - previous_week: Previous week number (e.g., 29)
+#   - estate_name: Estate name (e.g., "simba", "chemba")
+#
+# Examples:
+#   Rscript 06_crop_messaging.R 32 31 simba
+#   Rscript 06_crop_messaging.R 30 29 chemba
+#
+# The script automatically:
+#   1. Loads the correct estate configuration
+#   2. Analyzes weekly mosaics
+#   3. Generates field-by-field analysis
+#   4. Creates output files in multiple formats
+#   5. Displays WhatsApp-ready text in console
+#
+
+# 1. Load required packages
+# -----------------------
+suppressPackageStartupMessages({
+  library(sf)
+  library(terra)
+  library(tidyverse)
+  library(lubridate)
+  library(here)
+  library(spdep)  # For spatial statistics
+})
+
+# 2. Main function to handle messaging workflow
+# ---------------------------------------------
+main <- function() {
+  # Capture command line arguments
+  args <- commandArgs(trailingOnly = TRUE)
+
+  # Process arguments with defaults
+  current_week <- if (length(args) >= 1 && !is.na(args[1])) {
+    as.numeric(args[1])
+  } else {
+    39  # Default for proof of concept
+  }
+
+  previous_week <- if (length(args) >= 2 && !is.na(args[2])) {
+    as.numeric(args[2])
+  } else {
+    38  # Default for proof of concept
+  }
+
+  estate_name <- if (length(args) >= 3 && !is.na(args[3])) {
+    as.character(args[3])
+  } else {
+    "aura"  # Default estate
+  }
+
+  year <- 2025  # Current year - could be made dynamic
+
+  # Make estate_name available globally so parameters_project.R can use it
+  assign("project_dir", estate_name, envir = .GlobalEnv)
+
+  # Initialize project configuration and load utility functions
+  tryCatch({
+    source("parameters_project.R")
+    source("crop_messaging_utils.R")
+  }, error = function(e) {
+    warning("Default source files not found. Attempting to source from 'r_app' directory.")
+    tryCatch({
+      source(here::here("r_app", "parameters_project.R"))
+      source(here::here("r_app", "crop_messaging_utils.R"))
+      warning(paste("Successfully sourced files from 'r_app' directory."))
+
+    }, error = function(e) {
+      stop("Failed to source required files from both default and 'r_app' directories.")
+    })
+  })
+
+  log_message("Starting crop messaging analysis")
+
+  # Run the modular analysis
+  analysis_results <- run_estate_analysis(estate_name, current_week, previous_week, year)
+  field_results <- analysis_results$field_results
+
+  # Display detailed field-by-field analysis
+  cat("=== FIELD-BY-FIELD ANALYSIS ===\n\n")
+
+  for (field_id in names(field_results)) {
+    field_info <- field_results[[field_id]]
+    current_field <- field_info$current_stats
+    previous_field <- field_info$previous_stats
+    ci_change <- field_info$ci_change
+    change_category <- field_info$change_category
+    change_percentages <- field_info$change_percentages
+    uniformity_category <- field_info$uniformity_category
+    message_result <- field_info$message_result
+
+    # Print enhanced field analysis
+    cat("FIELD:", current_field$field, "-", current_field$sub_field, "\n")
+    cat("- Field size:", round(current_field$field_area_ha, 1), "hectares\n")
+    cat("- Week", previous_week, "CI:", round(previous_field$mean_ci, 3), "\n")
+    cat("- Week", current_week, "CI:", round(current_field$mean_ci, 3), "\n")
+    cat("- Terra stats: Mean =", round(current_field$mean_ci, 3),
+        ", CV =", round(current_field$cv, 3),
+        ", Range = [", round(current_field$min_ci, 2), "-", round(current_field$max_ci, 2), "]\n")
+
+    cat("- Within acceptable range (±25% of mean):", round(current_field$acceptable_pct, 1), "%\n")
+
+    # Display primary uniformity metrics (CV and Entropy)
+    cat("- Field uniformity: CV =", round(current_field$cv, 3))
+    if (current_field$cv < 0.08) {
+      cat(" (excellent)")
+    } else if (current_field$cv < 0.15) {
+      cat(" (good)")
+    } else if (current_field$cv < 0.30) {
+      cat(" (moderate)")
+    } else if (current_field$cv < 0.50) {
+      cat(" (high variation)")
+    } else {
+      cat(" (very high variation)")
+    }
+
+    # Add entropy information
+    if (!is.na(current_field$entropy)) {
+      cat(", Entropy =", round(current_field$entropy, 3))
+      # Entropy interpretation (higher = more heterogeneous)
+      # Adjusted thresholds to better match CV patterns
+      if (current_field$entropy < 1.3) {
+        cat(" (very uniform)")
+      } else if (current_field$entropy < 1.5) {
+        cat(" (uniform)")
+      } else if (current_field$entropy < 1.7) {
+        cat(" (moderate heterogeneity)")
+      } else {
+        cat(" (high heterogeneity)")
+      }
+    }
+    cat("\n")
+
+    cat("- Change: Mean =", round(ci_change, 3), "(", change_category, ")")
+    if (!is.na(change_percentages$positive_pct)) {
+      # Calculate hectares for this field using field area from geojson
+      field_hectares <- current_field$field_area_ha
+      improving_hectares <- (change_percentages$positive_pct / 100) * field_hectares
+      declining_hectares <- (change_percentages$negative_pct / 100) * field_hectares
+
+      cat(", Areas: ", round(change_percentages$positive_pct, 1), "% (", round(improving_hectares, 1), " ha) improving, ",
+          round(change_percentages$negative_pct, 1), "% (", round(declining_hectares, 1), " ha) declining\n")
+    } else {
+      cat("\n")
+    }
+    cat("- Spatial Pattern:", uniformity_category, "\n")
+
+    # Add spatial details if available
+    if (!is.na(current_field$spatial_autocorr$morans_i)) {
+      cat("- Moran's I:", round(current_field$spatial_autocorr$morans_i, 3),
+          "(", current_field$spatial_autocorr$interpretation, ")")
+
+      # Add agricultural context explanation for Moran's I
+      moran_val <- current_field$spatial_autocorr$morans_i
+      if (moran_val >= 0.7 && moran_val < 0.85) {
+        cat(" - normal field continuity")
+      } else if (moran_val >= 0.85 && moran_val < 0.95) {
+        cat(" - strong spatial pattern")
+      } else if (moran_val >= 0.95) {
+        cat(" - very strong clustering, monitor for management issues")
+      } else if (moran_val < 0.7 && moran_val > 0.3) {
+        cat(" - moderate spatial pattern")
+      } else {
+        cat(" - unusual spatial pattern for crop field")
+      }
+      cat("\n")
+    }
+
+    if (!is.na(current_field$extreme_percentages$hotspot_pct)) {
+      cat("- Extreme areas: ", round(current_field$extreme_percentages$hotspot_pct, 1),
+          "% hotspots (high-performing), ", round(current_field$extreme_percentages$coldspot_pct, 1),
+          "% coldspots (underperforming)")
+
+      # Show method used for extreme detection
+      if (!is.null(current_field$extreme_percentages$method)) {
+        if (current_field$extreme_percentages$method == "getis_ord_gi_star") {
+          cat(" [Getis-Ord Gi*]")
+        } else if (current_field$extreme_percentages$method == "simple_sd") {
+          cat(" [Simple SD]")
+        }
+      }
+      cat("\n")
+    }
+
+    cat("- Message:", message_result$message, "\n")
+    cat("- Alert needed:", if(message_result$worth_sending) "YES 🚨" else "NO", "\n\n")
+  }
+
+  # Summary of alerts
+  alert_fields <- sapply(field_results, function(x) x$message_result$worth_sending)
+  total_alerts <- sum(alert_fields)
+
+  cat("=== SUMMARY ===\n")
+  cat("Total fields analyzed:", length(field_results), "\n")
+  cat("Fields requiring alerts:", total_alerts, "\n")
+
+  if (total_alerts > 0) {
+    cat("\nFields needing attention:\n")
+    for (field_id in names(field_results)[alert_fields]) {
+      field_info <- field_results[[field_id]]
+      cat("-", field_info$current_stats$field, "-", field_info$current_stats$sub_field,
+          ":", field_info$message_result$message, "\n")
+    }
+  }
+
+  # Farm-wide analysis summary table
+  cat("\n=== FARM-WIDE ANALYSIS SUMMARY ===\n")
+
+  # Field uniformity statistics with detailed categories
+  excellent_fields <- sapply(field_results, function(x) x$current_stats$cv <= 0.08)
+  good_fields <- sapply(field_results, function(x) x$current_stats$cv > 0.08 & x$current_stats$cv <= 0.15)
+  moderate_fields <- sapply(field_results, function(x) x$current_stats$cv > 0.15 & x$current_stats$cv <= 0.30)
+  poor_fields <- sapply(field_results, function(x) x$current_stats$cv > 0.30)
+
+  n_excellent <- sum(excellent_fields)
+  n_good <- sum(good_fields)
+  n_moderate <- sum(moderate_fields)
+  n_poor <- sum(poor_fields)
+  n_uniform_total <- n_excellent + n_good  # Total uniform fields (CV ≤ 0.20)
+
+  # Calculate farm-wide area statistics
+  total_hectares <- sum(sapply(field_results, function(x) x$current_stats$field_area_ha), na.rm = TRUE)
+  total_improving_hectares <- sum(sapply(field_results, function(x) {
+    if (!is.na(x$change_percentages$positive_pct)) {
+      (x$change_percentages$positive_pct / 100) * x$current_stats$field_area_ha
+    } else 0
+  }), na.rm = TRUE)
+
+  total_declining_hectares <- sum(sapply(field_results, function(x) {
+    if (!is.na(x$change_percentages$negative_pct)) {
+      (x$change_percentages$negative_pct / 100) * x$current_stats$field_area_ha
+    } else 0
+  }), na.rm = TRUE)
+
+  # Calculate farm-wide percentages
+  farm_improving_pct <- (total_improving_hectares / total_hectares) * 100
+  farm_declining_pct <- (total_declining_hectares / total_hectares) * 100
+
+  # Display summary table
+  cat("\nFIELD UNIFORMITY SUMMARY:\n")
+  cat("│ Uniformity Level    │ Count │ Percent │\n")
+  cat(sprintf("│ Excellent (CV≤0.08) │ %5d │ %6.1f%% │\n", n_excellent, (n_excellent/length(field_results))*100))
+  cat(sprintf("│ Good (CV 0.08-0.15) │ %5d │ %6.1f%% │\n", n_good, (n_good/length(field_results))*100))
+  cat(sprintf("│ Moderate (CV 0.15-0.30) │ %5d │ %6.1f%% │\n", n_moderate, (n_moderate/length(field_results))*100))
+  cat(sprintf("│ Poor (CV>0.30)      │ %5d │ %6.1f%% │\n", n_poor, (n_poor/length(field_results))*100))
+  cat(sprintf("│ Total fields        │ %5d │ %6.1f%% │\n", length(field_results), 100.0))
+
+  cat("\nFARM-WIDE AREA CHANGE SUMMARY:\n")
+  cat("│ Change Type         │ Hectares│ Percent │\n")
+  cat(sprintf("│ Improving areas     │ %7.1f │ %6.1f%% │\n", total_improving_hectares, farm_improving_pct))
+  cat(sprintf("│ Declining areas     │ %7.1f │ %6.1f%% │\n", total_declining_hectares, farm_declining_pct))
+  cat(sprintf("│ Total area          │ %7.1f │ %6.1f%% │\n", total_hectares, 100.0))
+
+  # Additional insights
+  cat("\nKEY INSIGHTS:\n")
+  cat(sprintf("• %d%% of fields have good uniformity (CV ≤ 0.15)\n", round((n_uniform_total/length(field_results))*100)))
+  cat(sprintf("• %d%% of fields have excellent uniformity (CV ≤ 0.08)\n", round((n_excellent/length(field_results))*100)))
+  cat(sprintf("• %.1f hectares (%.1f%%) of farm area is improving week-over-week\n", total_improving_hectares, farm_improving_pct))
+  cat(sprintf("• %.1f hectares (%.1f%%) of farm area is declining week-over-week\n", total_declining_hectares, farm_declining_pct))
+  cat(sprintf("• Total farm area analyzed: %.1f hectares\n", total_hectares))
+  if (farm_improving_pct > farm_declining_pct) {
+    cat(sprintf("• Overall trend: POSITIVE (%.1f%% more area improving than declining)\n", farm_improving_pct - farm_declining_pct))
+  } else if (farm_declining_pct > farm_improving_pct) {
+    cat(sprintf("• Overall trend: NEGATIVE (%.1f%% more area declining than improving)\n", farm_declining_pct - farm_improving_pct))
+  } else {
+    cat("• Overall trend: BALANCED (equal improvement and decline)\n")
+  }
+
+  # Generate and save multiple output formats
+  saved_files <- save_analysis_outputs(analysis_results)
+
+  # Analysis complete
+  cat("\n=== ANALYSIS COMPLETE ===\n")
+  cat("All field analysis results, farm-wide summary, and output files created.\n")
+
+  # Return results for potential further processing
+  invisible(analysis_results)
+}
+
+if (sys.nframe() == 0) {
+  main()
+}

r_app/09_calculate_kpis.R

@@ -0,0 +1,156 @@
+# 09_CALCULATE_KPIS.R
+# ===================
+# This script calculates 6 Key Performance Indicators (KPIs) for sugarcane monitoring:
+# 1. Field Uniformity Summary
+# 2. Farm-wide Area Change Summary
+# 3. TCH Forecasted
+# 4. Growth Decline Index
+# 5. Weed Presence Score
+# 6. Gap Filling Score (placeholder)
+#
+# Usage: Rscript 09_calculate_kpis.R [end_date] [offset] [project_dir]
+#   - end_date: End date for KPI calculation (YYYY-MM-DD format), default: today
+#   - offset: Number of days to look back (not currently used for KPIs, but for consistency)
+#   - project_dir: Project directory name (e.g., "aura", "esa")
+
+# 1. Load required libraries
+# -------------------------
+suppressPackageStartupMessages({
+  library(here)
+  library(sf)
+  library(terra)
+  library(dplyr)
+  library(tidyr)
+  library(lubridate)
+  library(readr)
+  library(caret)
+  library(CAST)
+  library(randomForest)
+})
+
+# 2. Main function
+# --------------
+main <- function() {
+  # Process command line arguments
+  args <- commandArgs(trailingOnly = TRUE)
+  
+  # Process end_date argument
+  if (length(args) >= 1 && !is.na(args[1])) {
+    end_date <- as.Date(args[1])
+    if (is.na(end_date)) {
+      warning("Invalid end_date provided. Using default (current date).")
+      end_date <- Sys.Date()
+    }
+  } else {
+    end_date <- Sys.Date()
+  }
+  
+  # Process offset argument (for consistency with other scripts, not currently used)
+  if (length(args) >= 2 && !is.na(args[2])) {
+    offset <- as.numeric(args[2])
+    if (is.na(offset) || offset <= 0) {
+      warning("Invalid offset provided. Using default (7 days).")
+      offset <- 7
+    }
+  } else {
+    offset <- 7
+  }
+  
+  # Process project_dir argument
+  if (length(args) >= 3 && !is.na(args[3])) {
+    project_dir <- as.character(args[3])
+  } else {
+    project_dir <- "esa"  # Default project
+  }
+  
+  # Make project_dir available globally so parameters_project.R can use it
+  assign("project_dir", project_dir, envir = .GlobalEnv)
+  
+  # 3. Load utility functions and project configuration
+  # --------------------------------------------------
+  
+  tryCatch({
+    source(here("r_app", "crop_messaging_utils.R"))
+  }, error = function(e) {
+    stop("Error loading crop_messaging_utils.R: ", e$message)
+  })
+  
+  tryCatch({
+    source(here("r_app", "kpi_utils.R"))
+  }, error = function(e) {
+    stop("Error loading kpi_utils.R: ", e$message)
+  })
+  
+  # Load project parameters (this sets up all directory paths and field boundaries)
+  tryCatch({
+    source(here("r_app", "parameters_project.R"))
+  }, error = function(e) {
+    stop("Error loading parameters_project.R: ", e$message)
+  })
+  
+  # Load growth model utils if available (for yield prediction)
+  tryCatch({
+    source(here("r_app", "growth_model_utils.R"))
+  }, error = function(e) {
+    warning("growth_model_utils.R not found, yield prediction KPI will use placeholder data")
+  })
+  
+  # Check if required variables exist
+  if (!exists("project_dir")) {
+    stop("project_dir must be set before running this script")
+  }
+  
+  if (!exists("field_boundaries_sf") || is.null(field_boundaries_sf)) {
+    stop("Field boundaries not loaded. Check parameters_project.R initialization.")
+  }
+  
+  # 4. Calculate all KPIs
+  # -------------------
+  output_dir <- file.path(reports_dir, "kpis")
+  
+  kpi_results <- calculate_all_kpis(
+    report_date = end_date,
+    output_dir = output_dir,
+    field_boundaries_sf = field_boundaries_sf,
+    harvesting_data = harvesting_data,
+    cumulative_CI_vals_dir = cumulative_CI_vals_dir,
+    weekly_CI_mosaic = weekly_CI_mosaic,
+    reports_dir = reports_dir,
+    project_dir = project_dir
+  )
+  
+  # 5. Print summary
+  # --------------
+  cat("\n=== KPI CALCULATION SUMMARY ===\n")
+  cat("Report Date:", as.character(kpi_results$metadata$report_date), "\n")
+  cat("Current Week:", kpi_results$metadata$current_week, "\n")
+  cat("Previous Week:", kpi_results$metadata$previous_week, "\n")
+  cat("Total Fields Analyzed:", kpi_results$metadata$total_fields, "\n")
+  cat("Calculation Time:", as.character(kpi_results$metadata$calculation_time), "\n")
+  
+  cat("\nField Uniformity Summary:\n")
+  print(kpi_results$field_uniformity_summary)
+  
+  cat("\nArea Change Summary:\n")
+  print(kpi_results$area_change)
+  
+  cat("\nTCH Forecasted:\n")
+  print(kpi_results$tch_forecasted)
+  
+  cat("\nGrowth Decline Index:\n")
+  print(kpi_results$growth_decline)
+  
+  cat("\nWeed Presence Score:\n")
+  print(kpi_results$weed_presence)
+  
+  cat("\nGap Filling Score:\n")
+  print(kpi_results$gap_filling)
+  
+  cat("\n=== KPI CALCULATION COMPLETED ===\n")
+}
+
+# 6. Script execution
+# -----------------
+if (sys.nframe() == 0) {
+  main()
+}

r_app/CI_report_dashboard_planet.Rmd

@@ -1,739 +0,0 @@
----
-params:
-  ref: "word-styles-reference-var1.docx"
-  output_file: CI_report.docx
-  report_date: "2024-07-18"
-  data_dir: "chemba"
-  mail_day: "Wednesday"
-  borders: TRUE
-output:
- # html_document:
- #   toc: yes
- #   df_print: paged  
-   word_document:
-    reference_docx: !expr file.path("word-styles-reference-var1.docx")
-    toc: no
-editor_options:
-  chunk_output_type: console
----
-
-```{r setup_parameters, include=FALSE}
-# Set up basic report parameters from input values
-report_date <- params$report_date
-mail_day <- params$mail_day
-borders <- params$borders
-
-# Environment setup notes (commented out)
-# # Activeer de renv omgeving
-# renv::activate()
-# renv::deactivate()
-# # Optioneel: Herstel de omgeving als dat nodig is
-# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren
-# renv::restore()
-```
-
-```{r load_libraries, message=FALSE, warning=FALSE, include=FALSE}
-# Configure knitr options
-knitr::opts_chunk$set(warning = FALSE, message = FALSE)
-
-# Load all packages at once with suppressPackageStartupMessages
-suppressPackageStartupMessages({
-  library(here)
-  library(sf)
-  library(terra)
-  library(exactextractr)
-  library(tidyverse)
-  library(tmap)
-  library(lubridate)
-  library(zoo)
-  library(rsample)
-  library(caret)
-  library(randomForest)
-  library(CAST)
-})
-
-# Load custom utility functions
-tryCatch({
-  source("report_utils.R")
-}, error = function(e) {
-  message(paste("Error loading report_utils.R:", e$message))
-  # Try alternative path if the first one fails
-  tryCatch({
-    source(here::here("r_app", "report_utils.R"))
-  }, error = function(e) {
-    stop("Could not load report_utils.R from either location: ", e$message)
-  })
-})
-```
-
-```{r initialize_project_config, message=FALSE, warning=FALSE, include=FALSE}
-# Set the project directory from parameters
-project_dir <- params$data_dir
-
-# Source project parameters with error handling
-tryCatch({
-  source(here::here("r_app", "parameters_project.R"))
-}, error = function(e) {
-  stop("Error loading parameters_project.R: ", e$message)
-})
-
-# Log initial configuration
-safe_log("Starting the R Markdown script")
-safe_log(paste("mail_day params:", params$mail_day))
-safe_log(paste("report_date params:", params$report_date))
-safe_log(paste("mail_day variable:", mail_day))
-```
-
-```{r calculate_dates_and_weeks, message=FALSE, warning=FALSE, include=FALSE}
-# Set locale for consistent date formatting
-Sys.setlocale("LC_TIME", "C")
-
-# Initialize date variables from parameters
-today <- as.character(report_date)
-mail_day_as_character <- as.character(mail_day)
-
-# Calculate week days
-report_date_as_week_day <- weekdays(lubridate::ymd(today))
-days_of_week <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
-
-# Calculate initial week number
-week <- lubridate::week(today)
-safe_log(paste("Initial week calculation:", week, "today:", today))
-
-# Calculate previous dates for comparisons
-today_minus_1 <- as.character(lubridate::ymd(today) - 7)
-today_minus_2 <- as.character(lubridate::ymd(today) - 14)
-today_minus_3 <- as.character(lubridate::ymd(today) - 21)
-
-# Log the weekday calculations for debugging
-safe_log(paste("Report date weekday:", report_date_as_week_day))
-safe_log(paste("Weekday index:", which(days_of_week == report_date_as_week_day)))
-safe_log(paste("Mail day:", mail_day_as_character))
-safe_log(paste("Mail day index:", which(days_of_week == mail_day_as_character)))
-
-# Adjust week calculation based on mail day
-if (which(days_of_week == report_date_as_week_day) > which(days_of_week == mail_day_as_character)) {
-  safe_log("Adjusting weeks because of mail day")
-  week <- lubridate::week(today) + 1
-  today_minus_1 <- as.character(lubridate::ymd(today))
-  today_minus_2 <- as.character(lubridate::ymd(today) - 7)
-  today_minus_3 <- as.character(lubridate::ymd(today) - 14)
-}
-
-# Calculate week numbers for previous weeks
-week_minus_1 <- week - 1
-week_minus_2 <- week - 2
-week_minus_3 <- week - 3
-
-# Format current week with leading zeros
-week <- sprintf("%02d", week)
-
-# Get years for each date
-year <- lubridate::year(today)
-year_1 <- lubridate::year(today_minus_1)
-year_2 <- lubridate::year(today_minus_2)
-year_3 <- lubridate::year(today_minus_3)
-```
-
-```{r data, message=TRUE, warning=TRUE, include=FALSE}
-# Load CI index data with error handling
-tryCatch({
-  CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds"))
-  safe_log("Successfully loaded CI quadrant data")
-}, error = function(e) {
-  stop("Error loading CI quadrant data: ", e$message)
-})
-
-# Get file paths for different weeks using the utility function
-tryCatch({
-  path_to_week_current = get_week_path(weekly_CI_mosaic, today, 0)
-  path_to_week_minus_1 = get_week_path(weekly_CI_mosaic, today, -1)
-  path_to_week_minus_2 = get_week_path(weekly_CI_mosaic, today, -2)
-  path_to_week_minus_3 = get_week_path(weekly_CI_mosaic, today, -3)
-
-  # Log the calculated paths
-  safe_log("Required mosaic paths:")
-  safe_log(paste("Path to current week:", path_to_week_current))
-  safe_log(paste("Path to week minus 1:", path_to_week_minus_1))
-  safe_log(paste("Path to week minus 2:", path_to_week_minus_2))
-  safe_log(paste("Path to week minus 3:", path_to_week_minus_3))
-  
-  # Validate that files exist
-  if (!file.exists(path_to_week_current)) warning("Current week mosaic file does not exist: ", path_to_week_current)
-  if (!file.exists(path_to_week_minus_1)) warning("Week minus 1 mosaic file does not exist: ", path_to_week_minus_1)
-  if (!file.exists(path_to_week_minus_2)) warning("Week minus 2 mosaic file does not exist: ", path_to_week_minus_2)
-  if (!file.exists(path_to_week_minus_3)) warning("Week minus 3 mosaic file does not exist: ", path_to_week_minus_3)
-  
-  # Load raster data with terra functions
-  CI    <- terra::rast(path_to_week_current)$CI
-  CI_m1 <- terra::rast(path_to_week_minus_1)$CI
-  CI_m2 <- terra::rast(path_to_week_minus_2)$CI
-  CI_m3 <- terra::rast(path_to_week_minus_3)$CI
-  
-}, error = function(e) {
-  stop("Error loading raster data: ", e$message)
-})
-```
-
-```{r calculate_difference_rasters, message=TRUE, warning=TRUE, include=FALSE}
-# Calculate difference rasters for comparisons
-tryCatch({
-  # Calculate weekly difference
-  last_week_dif_raster_abs <- (CI - CI_m1)
-  safe_log("Calculated weekly difference raster")
-  
-  # Calculate three-week difference
-  three_week_dif_raster_abs <- (CI - CI_m3)
-  safe_log("Calculated three-week difference raster")
-}, error = function(e) {
-  safe_log(paste("Error calculating difference rasters:", e$message), "ERROR")
-  # Create placeholder rasters if calculations fail
-  if (!exists("last_week_dif_raster_abs")) {
-    last_week_dif_raster_abs <- CI * 0
-  }
-  if (!exists("three_week_dif_raster_abs")) {
-    three_week_dif_raster_abs <- CI * 0
-  }
-})
-```
-
-```{r load_field_boundaries, message=TRUE, warning=TRUE, include=FALSE}
-# Load field boundaries from parameters
-tryCatch({
-  AllPivots0 <- field_boundaries_sf %>%
-    dplyr::filter(!is.na(field), !is.na(sub_field))  # Filter out NA field names
-  safe_log("Successfully loaded field boundaries")
-}, error = function(e) {
-  stop("Error loading field boundaries: ", e$message)
-})
-```
-
-```{r create_front_page_variables, include=FALSE}
-# Create variables for the front page
-farm_name <- stringr::str_to_title(gsub("_", " ", project_dir))
-
-# Format dates for display
-report_date_formatted <- format(as.Date(report_date), "%B %d, %Y")
-current_year <- format(Sys.Date(), "%Y")
-
-# Get total field count and area if available
-tryCatch({
-  total_fields <- length(unique(AllPivots0$field))
-  total_area_ha <- round(sum(sf::st_area(AllPivots0)) / 10000, 1) # Convert to hectares
-}, error = function(e) {
-  total_fields <- "N/A"
-  total_area_ha <- "N/A"
-})
-```
-
----
-title: ""
----
-
-```{=openxml}
-<w:p>
-  <w:pPr>
-    <w:jc w:val="center"/>
-    <w:spacing w:after="720"/>
-  </w:pPr>
-  <w:r>
-    <w:rPr>
-      <w:sz w:val="48"/>
-      <w:b/>
-    </w:rPr>
-    <w:t>SUGARCANE CROP MONITORING REPORT</w:t>
-  </w:r>
-</w:p>
-```
-
-<div style="text-align: center; margin-top: 2cm; margin-bottom: 2cm;">
-
-**`r farm_name`**
-
-**Chlorophyll Index Analysis**
-
-Report Date: **`r report_date_formatted`**
-
----
-
-</div>
-
-<div style="margin-top: 3cm; margin-bottom: 2cm;">
-
-## Report Summary
-
-**Farm Location:** `r farm_name`  
-**Report Period:** Week `r week` of `r current_year`  
-**Data Source:** Planet Labs Satellite Imagery  
-**Analysis Type:** Chlorophyll Index (CI) Monitoring  
-
-**Field Coverage:**  
-- Total Fields Monitored: `r total_fields`  
-- Total Area: `r total_area_ha` hectares  
-
-**Report Generated:** `r format(Sys.Date(), "%B %d, %Y")`  
-
----
-
-## About This Report
-
-This automated report provides weekly analysis of sugarcane crop health using satellite-derived Chlorophyll Index (CI) measurements. The analysis helps identify:
-
-- Field-level crop health variations
-- Weekly changes in crop vigor  
-- Areas requiring agricultural attention
-- Growth patterns across different field sections
-
-**Key Features:**
-- High-resolution satellite imagery analysis
-- Week-over-week change detection
-- Individual field performance metrics
-- Actionable insights for crop management
-
-</div>
-
-\newpage
-
-<!-- Table of Contents -->
-```{=openxml}
-<w:p>
-  <w:pPr>
-    <w:jc w:val="center"/>
-    <w:spacing w:after="480"/>
-  </w:pPr>
-  <w:r>
-    <w:rPr>
-      <w:sz w:val="32"/>
-      <w:b/>
-    </w:rPr>
-    <w:t>TABLE OF CONTENTS</w:t>
-  </w:r>
-</w:p>
-```
-
-```{=openxml}
-<w:p>
-  <w:fldSimple w:instr=" TOC \o &quot;1-3&quot; \h \z \u ">
-    <w:r><w:t>Update this field to generate table of contents</w:t></w:r>
-  </w:fldSimple>
-</w:p>
-```
-
-\newpage
-
-<!-- Original content starts here -->
-
-
-
-# Explanation of the Report
-
-This report provides a detailed analysis of your sugarcane fields based on satellite imagery, helping you monitor crop health and development throughout the growing season. The data is processed weekly to give you timely insights for optimal farm management decisions.
-
-## What is the Chlorophyll Index (CI)?
-
-The **Chlorophyll Index (CI)** is a vegetation index that measures the relative amount of chlorophyll in plant leaves. Chlorophyll is the green pigment responsible for photosynthesis in plants. Higher CI values indicate:
-
-* Greater photosynthetic activity
-* Healthier plant tissue
-* Better nitrogen uptake
-* More vigorous crop growth
-
-CI values typically range from 0 (bare soil or severely stressed vegetation) to 7+ (very healthy, dense vegetation). For sugarcane, values between 3-7 generally indicate good crop health, depending on the growth stage.
-
-## What You'll Find in This Report:
-
-1. **Chlorophyll Index Overview Map**: A comprehensive view of all your fields showing current CI values. This helps identify which fields are performing well and which might need attention.
-
-2. **Weekly Difference Map**: Shows changes in CI values over the past week. Positive values (green) indicate improving crop health, while negative values (red) may signal stress or decline.
-
-3. **Field-by-Field Analysis**: Detailed maps for each field showing:
-   * CI values for the current week and two previous weeks
-   * Week-to-week changes in CI values
-   * Three-week change in CI values to track longer-term trends
-
-4. **Growth Trend Graphs**: Time-series visualizations showing how CI values have changed throughout the growing season for each section of your fields.
-
-5. **Yield Prediction**: For mature crops (over 300 days), we provide estimated yield predictions based on historical data and current CI measurements.
-
-Use these insights to identify areas that may need irrigation, fertilization, or other interventions, and to track the effectiveness of your management practices over time.
-
-\newpage
-
-# RGB Satellite Image - Current Week (if available)
-```{r render_rgb_map, echo=FALSE, fig.height=7, fig.width=10, message=FALSE, warning=FALSE}
-# Check if RGB bands are available and create RGB map
-tryCatch({
-  # Load the full raster to check available bands
-  full_raster <- terra::rast(path_to_week_current)
-  available_bands <- names(full_raster)
-  
-  # Check if RGB bands are available (look for red, green, blue or similar naming)
-  rgb_bands_available <- any(grepl("red|Red|RED", available_bands, ignore.case = TRUE)) && 
-                        any(grepl("green|Green|GREEN", available_bands, ignore.case = TRUE)) && 
-                        any(grepl("blue|Blue|BLUE", available_bands, ignore.case = TRUE))
-  
-  # Alternative check for numbered bands that might be RGB (e.g., band_1, band_2, band_3)
-  if (!rgb_bands_available && length(available_bands) >= 3) {
-    # Check if we have at least 3 bands that could potentially be RGB
-    potential_rgb_bands <- grep("band_[1-3]|B[1-3]|[1-3]", available_bands, ignore.case = TRUE)
-    rgb_bands_available <- length(potential_rgb_bands) >= 3
-  }
-  
-  if (rgb_bands_available) {
-    safe_log("RGB bands detected - creating RGB visualization")
-    
-    # Try to extract RGB bands (prioritize named bands first)
-    red_band <- NULL
-    green_band <- NULL  
-    blue_band <- NULL
-    
-    # Look for named RGB bands first
-    red_candidates <- grep("red|Red|RED", available_bands, ignore.case = TRUE, value = TRUE)
-    green_candidates <- grep("green|Green|GREEN", available_bands, ignore.case = TRUE, value = TRUE)
-    blue_candidates <- grep("blue|Blue|BLUE", available_bands, ignore.case = TRUE, value = TRUE)
-    
-    if (length(red_candidates) > 0) red_band <- red_candidates[1]
-    if (length(green_candidates) > 0) green_band <- green_candidates[1]
-    if (length(blue_candidates) > 0) blue_band <- blue_candidates[1]
-    
-    # Fallback to numbered bands if named bands not found
-    if (is.null(red_band) || is.null(green_band) || is.null(blue_band)) {
-      if (length(available_bands) >= 3) {
-        # Assume first 3 bands are RGB (common convention)
-        red_band <- available_bands[1]
-        green_band <- available_bands[2] 
-        blue_band <- available_bands[3]
-      }
-    }
-    
-    if (!is.null(red_band) && !is.null(green_band) && !is.null(blue_band)) {
-      # Extract RGB bands
-      rgb_raster <- c(full_raster[[red_band]], full_raster[[green_band]], full_raster[[blue_band]])
-      names(rgb_raster) <- c("red", "green", "blue")
-        # Create RGB map
-      map <- tmap::tm_shape(rgb_raster, unit = "m") +
-        tmap::tm_rgb() +
-        tmap::tm_scalebar(position = tm_pos_out("right", "bottom"), text.color = "black") +
-        tmap::tm_compass(position = tm_pos_out("right", "bottom"), text.color = "black") +
-        tmap::tm_shape(AllPivots0) + 
-        tmap::tm_borders(col = "white", lwd = 2) +
-        tmap::tm_text("sub_field", size = 0.6, col = "white") +
-        tmap::tm_layout(main.title = paste0("RGB Satellite Image - Week ", week),
-                       main.title.size = 0.8,
-                       main.title.color = "black")
-      
-      # Print the map
-      print(map)
-      
-      safe_log("RGB map created successfully")
-    } else {
-      safe_log("Could not identify RGB bands despite detection", "WARNING")
-      cat("RGB bands detected but could not be properly identified. Skipping RGB visualization.\n")
-    }
-  } else {
-    safe_log("No RGB bands available in the current week mosaic")
-    cat("**Note:** RGB satellite imagery is not available for this week. Only spectral index data is available.\n\n")
-  }
-}, error = function(e) {
-  safe_log(paste("Error creating RGB map:", e$message), "ERROR")
-  cat("**Note:** Could not create RGB visualization for this week.\n\n")
-})
-```
-
-# Chlorophyll Index (CI) Overview Map - Current Week
-```{r render_ci_overview_map, echo=FALSE, fig.height=7, fig.width=10, message=FALSE, warning=FALSE}
-# Create overview chlorophyll index map
-tryCatch({  # Base shape
-  map <- tmap::tm_shape(CI, unit = "m")  # Add raster layer with continuous spectrum (fixed scale 1-8 for consistent comparison)
-  map <- map + tmap::tm_raster(col.scale = tm_scale_continuous(values = "brewer.rd_yl_gn",
-                                                             limits = c(1, 8)),                            col.legend = tm_legend(title = "Chlorophyll Index (CI)",
-                                                 orientation = "landscape", 
-                                                 position = tm_pos_out("center", "bottom")))
-    # Complete the map with layout and other elements
-  map <- map + 
-    tmap::tm_scalebar(position = tm_pos_out("right", "bottom"), text.color = "black") +
-    tmap::tm_compass(position = tm_pos_out("right", "bottom"), text.color = "black") +
-    tmap::tm_shape(AllPivots0) + 
-    tmap::tm_borders(col = "black") +
-    tmap::tm_text("sub_field", size = 0.6, col = "black")
-  
-  # Print the map
-  print(map)
-}, error = function(e) {
-  safe_log(paste("Error creating CI overview map:", e$message), "ERROR")
-  plot(1, type="n", axes=FALSE, xlab="", ylab="")
-  text(1, 1, "Error creating CI overview map", cex=1.5)
-})
-
-```
-
-# Weekly Chlorophyll Index Difference Map
-```{r render_ci_difference_map, echo=FALSE, fig.height=7, fig.width=10, message=FALSE, warning=FALSE}
-# Create chlorophyll index difference map
-tryCatch({  # Base shape
-  map <- tmap::tm_shape(last_week_dif_raster_abs, unit = "m")  # Add raster layer with continuous spectrum (centered at 0 for difference maps, fixed scale)
-  map <- map + tmap::tm_raster(col.scale = tm_scale_continuous(values = "brewer.rd_yl_gn",
-                                                            midpoint = 0,
-                                                            limits = c(-3, 3)),                            col.legend = tm_legend(title = "Chlorophyll Index (CI) Change",
-                                                 orientation = "landscape", 
-                                                 position = tm_pos_out("center", "bottom")))
-    # Complete the map with layout and other elements
-  map <- map + 
-    tmap::tm_scalebar(position = tm_pos_out("right", "bottom"), text.color = "black") +
-    tmap::tm_compass(position = tm_pos_out("right", "bottom"), text.color = "black") +
-    tmap::tm_shape(AllPivots0) + 
-    tmap::tm_borders(col = "black") +
-    tmap::tm_text("sub_field", size = 0.6, col = "black")
-  
-  # Print the map
-  print(map)
-}, error = function(e) {
-  safe_log(paste("Error creating CI difference map:", e$message), "ERROR")
-  plot(1, type="n", axes=FALSE, xlab="", ylab="")
-  text(1, 1, "Error creating CI difference map", cex=1.5)
-})
-```
-
-\newpage
-
-```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=10, message=FALSE,echo=FALSE, warning=FALSE, include=TRUE, results='asis'}
-# Generate detailed visualizations for each field
-tryCatch({
-  # Merge field polygons for processing and filter out NA field names
-  AllPivots_merged <- AllPivots0 %>% 
-    dplyr::filter(!is.na(field), !is.na(sub_field)) %>%  # Filter out NA fields
-    dplyr::group_by(field) %>% 
-    dplyr::summarise(.groups = 'drop')
-  
-  # Generate plots for each field
-  for(i in seq_along(AllPivots_merged$field)) {
-    field_name <- AllPivots_merged$field[i]
-    
-    # Skip if field_name is still NA (double check)
-    if(is.na(field_name)) {
-      next
-    }
-    
-    tryCatch({
-      # Add page break before each field (except the first one)
-      if(i > 1) {
-        cat("\\newpage\n\n")
-      }
-      
-      # Call ci_plot with explicit parameters (ci_plot will generate its own header)
-      ci_plot(
-        pivotName = field_name,
-        field_boundaries = AllPivots0,
-        current_ci = CI,
-        ci_minus_1 = CI_m1,
-        ci_minus_2 = CI_m2,
-        last_week_diff = last_week_dif_raster_abs,
-        three_week_diff = three_week_dif_raster_abs,
-        harvesting_data = harvesting_data,
-        week = week,
-        week_minus_1 = week_minus_1,
-        week_minus_2 = week_minus_2,
-        week_minus_3 = week_minus_3,
-        borders = borders
-      )
-      
-      cat("\n\n")
-      
-      # Call cum_ci_plot with explicit parameters
-      cum_ci_plot(
-        pivotName = field_name,
-        ci_quadrant_data = CI_quadrant,
-        plot_type = "value",
-        facet_on = FALSE
-      )
-      
-      cat("\n\n")
-      
-    }, error = function(e) {
-      safe_log(paste("Error generating plots for field", field_name, ":", e$message), "ERROR")
-      cat("\\newpage\n\n")
-      cat("# Error generating plots for field ", field_name, "\n\n")
-      cat(e$message, "\n\n")
-    })
-  }
-}, error = function(e) {
-  safe_log(paste("Error in field visualization section:", e$message), "ERROR")
-  cat("Error generating field plots. See log for details.\n\n")
-})
-```
-
-```{r generate_subarea_visualizations, echo=FALSE, fig.height=3.8, fig.width=10, message=FALSE, warning=FALSE, results='asis', eval=FALSE}
-# Alternative visualization grouped by sub-area (disabled by default)
-tryCatch({
-  # Group pivots by sub-area
-  pivots_grouped <- AllPivots0 
-  
-  # Iterate over each subgroup
-  for (subgroup in unique(pivots_grouped$sub_area)) {
-    # Add subgroup heading
-    cat("\n")
-    cat("## Subgroup: ", subgroup, "\n")
-    
-    # Filter data for current subgroup
-    subset_data <- dplyr::filter(pivots_grouped, sub_area == subgroup)
-    
-    # Generate visualizations for each field in the subgroup
-    purrr::walk(subset_data$field, function(field_name) {
-      cat("\n")
-      ci_plot(field_name)
-      cat("\n")
-      cum_ci_plot(field_name)
-      cat("\n")
-    })
-    
-    # Add page break after each subgroup
-    cat("\\newpage\n")
-  }
-}, error = function(e) {
-  safe_log(paste("Error in subarea visualization section:", e$message), "ERROR")
-  cat("Error generating subarea plots. See log for details.\n")
-})
-```
-
-# Yield prediction
-The below table shows estimates of the biomass if you would harvest them now.
-
-```{r yield_data_training, message=FALSE, warning=FALSE, include=FALSE}
-# Load and prepare yield prediction data with error handling
-tryCatch({
-  # Load CI quadrant data and fill missing values
-  CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds")) %>%
-    dplyr::group_by(model) %>%
-    tidyr::fill(field, sub_field, .direction = "downup") %>%
-    dplyr::ungroup()
-  
-  # Check if tonnage_ha is empty
-  if (all(is.na(harvesting_data$tonnage_ha))) {
-    safe_log("Lacking historic harvest data, please provide for yield prediction calculation", "WARNING")
-    knitr::knit_exit()  # Exit the chunk if tonnage_ha is empty
-  }
-  
-  # Rename year column to season for consistency
-  harvesting_data <- harvesting_data %>% dplyr::rename(season = year)
-  
-  # Join CI and yield data
-  CI_and_yield <- dplyr::left_join(CI_quadrant, harvesting_data, by = c("field", "sub_field", "season")) %>%
-    dplyr::group_by(sub_field, season) %>% 
-    dplyr::slice(which.max(DOY)) %>%
-    dplyr::select(field, sub_field, tonnage_ha, cumulative_CI, DOY, season, sub_area) %>%
-    dplyr::mutate(CI_per_day = cumulative_CI / DOY)
-  
-  # Define predictors and response variables
-  predictors <- c("cumulative_CI", "DOY", "CI_per_day")
-  response <- "tonnage_ha"
-  
-  # Prepare test and validation datasets
-  CI_and_yield_test <- CI_and_yield %>% 
-    as.data.frame() %>% 
-    dplyr::filter(!is.na(tonnage_ha))
-  
-  CI_and_yield_validation <- CI_and_yield_test
-  
-  # Prepare prediction dataset (fields without harvest data)
-  prediction_yields <- CI_and_yield %>% 
-    as.data.frame() %>% 
-    dplyr::filter(is.na(tonnage_ha)) %>%
-    dplyr::filter(age > 300)  # Only predict on fields older than 300 days
-  
-  # Configure model training parameters
-  ctrl <- caret::trainControl(
-    method = "cv",
-    savePredictions = TRUE,
-    allowParallel = TRUE,
-    number = 5,
-    verboseIter = TRUE
-  )
-  
-  # Train the model with feature selection
-  set.seed(202) # For reproducibility
-  model_ffs_rf <- CAST::ffs(
-    CI_and_yield_test[, predictors],
-    CI_and_yield_test[, response],
-    method = "rf",
-    trControl = ctrl,
-    importance = TRUE,
-    withinSE = TRUE,
-    tuneLength = 5,
-    na.rm = TRUE
-  )
-  
-  # Function to prepare predictions with consistent naming and formatting
-  prepare_predictions <- function(predictions, newdata) {
-    return(predictions %>%
-      as.data.frame() %>%
-      dplyr::rename(predicted_Tcha = ".") %>%
-      dplyr::mutate(
-        sub_field = newdata$sub_field,
-        field = newdata$field,
-        Age_days = newdata$DOY,
-        total_CI = round(newdata$cumulative_CI, 0),
-        predicted_Tcha = round(predicted_Tcha, 0),
-        season = newdata$season
-      ) %>%
-      dplyr::select(field, sub_field, Age_days, total_CI, predicted_Tcha, season) %>%
-      dplyr::left_join(., newdata, by = c("field", "sub_field", "season"))
-    )
-  }
-  
-  # Predict yields for the validation dataset
-  pred_ffs_rf <- prepare_predictions(stats::predict(model_ffs_rf, newdata = CI_and_yield_validation), CI_and_yield_validation)
-  
-  # Predict yields for the current season (focus on mature fields over 300 days)
-  pred_rf_current_season <- prepare_predictions(stats::predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>%
-    dplyr::filter(Age_days > 1) %>%
-    dplyr::mutate(CI_per_day = round(total_CI / Age_days, 1))
-  
-  safe_log("Successfully completed yield prediction calculations")
-  
-}, error = function(e) {
-  safe_log(paste("Error in yield prediction:", e$message), "ERROR")
-  # Create empty dataframes to prevent errors in subsequent chunks
-  pred_ffs_rf <- data.frame()
-  pred_rf_current_season <- data.frame()
-})
-```
-
-```{r plotting_yield_data, echo=FALSE, fig.height=5, fig.width=8, message=FALSE, warning=FALSE}
-# Display yield prediction visualizations with error handling
-tryCatch({
-  if (nrow(pred_ffs_rf) > 0) {
-    # Plot model performance (predicted vs actual)
-    ggplot2::ggplot(pred_ffs_rf, ggplot2::aes(y = predicted_Tcha, x = tonnage_ha)) +
-      ggplot2::geom_point(size = 2, alpha = 0.6) +
-      ggplot2::geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "red") +
-      ggplot2::scale_x_continuous(limits = c(0, 200)) +
-      ggplot2::scale_y_continuous(limits = c(0, 200)) +
-      ggplot2::labs(title = "Model Performance: \nPredicted vs Actual Tonnage/ha",
-                   x = "Actual tonnage/ha (Tcha)", 
-                   y = "Predicted tonnage/ha (Tcha)") +
-      ggplot2::theme_minimal()
-  }
-  
-  if (nrow(pred_rf_current_season) > 0) {
-    # Plot predicted yields by age
-    ggplot2::ggplot(pred_rf_current_season, ggplot2::aes(x = Age_days, y = predicted_Tcha)) +
-      ggplot2::geom_point(size = 2, alpha = 0.6) +
-      ggplot2::labs(title = "Predicted Yields for Fields Over 300 Days \nOld Yet to Be Harvested",
-                   x = "Age (days)", 
-                   y = "Predicted tonnage/ha (Tcha)") +
-      ggplot2::scale_y_continuous(limits = c(0, 200)) +
-      ggplot2::theme_minimal()
-    
-    # Display prediction table
-    knitr::kable(pred_rf_current_season, 
-                digits = 0,
-                caption = "Predicted Tonnage/ha for Fields Over 300 Days Old")
-  } else {
-    cat("No fields over 300 days old without harvest data available for yield prediction.")
-  }
-}, error = function(e) {
-  safe_log(paste("Error in yield prediction visualization:", e$message), "ERROR")
-  cat("Error generating yield prediction visualizations. See log for details.")
-})
-```
-

r_app/CI_report_executive_summary.Rmd

@@ -1,721 +0,0 @@
----
-params:
-  ref: "word-styles-reference-var1.docx"
-  output_file: CI_report.docx
-  report_date: "2025-06-16"
-  data_dir: "simba"
-  mail_day: "Wednesday"
-  borders: TRUE
-  use_breaks: FALSE
-output:
- # html_document:
- #   toc: yes
- #   df_print: paged
-  word_document:
-    reference_docx: !expr file.path("word-styles-reference-var1.docx")
-    toc: yes
-editor_options:
-  chunk_output_type: console
----
-
-```{r setup_parameters, include=FALSE}
-# Set up basic report parameters from input values
-report_date <- params$report_date
-mail_day <- params$mail_day
-borders <- params$borders 
-use_breaks <- params$use_breaks  # Whether to use breaks or continuous spectrum in visualizations
-
-# Environment setup notes (commented out)
-# # Activeer de renv omgeving
-# renv::activate()
-# renv::deactivate()
-# # Optioneel: Herstel de omgeving als dat nodig is
-# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren
-# renv::restore()
-```
-
-```{r load_libraries, message=FALSE, warning=FALSE, include=FALSE}
-# Configure knitr options
-knitr::opts_chunk$set(warning = FALSE, message = FALSE)
-
-# Path management
-library(here)
-
-# Spatial data libraries
-library(sf)
-library(terra)
-library(exactextractr)
-# library(raster) - Removed as it's no longer maintained
-
-# Data manipulation and visualization
-library(tidyverse)  # Includes dplyr, ggplot2, etc.
-library(tmap)
-library(lubridate)
-library(zoo)
-
-# Machine learning
-library(rsample)
-library(caret)
-library(randomForest)
-library(CAST)
-
-# Load custom utility functions
-# tryCatch({
-#   source("report_utils.R")
-# }, error = function(e) {
-#   message(paste("Error loading report_utils.R:", e$message))
-#   # Try alternative path if the first one fails
-#   tryCatch({
-    source(here::here("r_app", "report_utils.R"))
-#   }, error = function(e) {
-#     stop("Could not load report_utils.R from either location: ", e$message)
-#   })
-# })
-
-# Load executive report utilities
-# tryCatch({
-#   source("executive_report_utils.R")
-# }, error = function(e) {
-#   message(paste("Error loading executive_report_utils.R:", e$message))
-#   # Try alternative path if the first one fails
-#   tryCatch({
-    source(here::here("r_app","exec_dashboard", "executive_report_utils.R"))
-#   }, error = function(e) {
-#     stop("Could not load executive_report_utils.R from either location: ", e$message)
-#   })
-# })
-
-safe_log("Successfully loaded utility functions")
-```
-
-```{r initialize_project_config, message=FALSE, warning=FALSE, include=FALSE}
-# Set the project directory from parameters
-project_dir <- params$data_dir
-
-# Source project parameters with error handling
-tryCatch({
-  source(here::here("r_app", "parameters_project.R"))
-}, error = function(e) {
-  stop("Error loading parameters_project.R: ", e$message)
-})
-
-# Log initial configuration
-safe_log("Starting the R Markdown script")
-safe_log(paste("mail_day params:", params$mail_day))
-safe_log(paste("report_date params:", params$report_date))
-safe_log(paste("mail_day variable:", mail_day))
-```
-
-```{r calculate_dates_and_weeks, message=FALSE, warning=FALSE, include=FALSE}
-# Set locale for consistent date formatting
-Sys.setlocale("LC_TIME", "C")
-
-# Initialize date variables from parameters
-today <- as.character(report_date)
-mail_day_as_character <- as.character(mail_day)
-
-# Calculate week days
-report_date_as_week_day <- weekdays(lubridate::ymd(today))
-days_of_week <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
-
-# Calculate initial week number
-week <- lubridate::week(today)
-safe_log(paste("Initial week calculation:", week, "today:", today))
-
-# Calculate previous dates for comparisons
-today_minus_1 <- as.character(lubridate::ymd(today) - 7)
-today_minus_2 <- as.character(lubridate::ymd(today) - 14)
-today_minus_3 <- as.character(lubridate::ymd(today) - 21)
-
-# Log the weekday calculations for debugging
-safe_log(paste("Report date weekday:", report_date_as_week_day))
-safe_log(paste("Weekday index:", which(days_of_week == report_date_as_week_day)))
-safe_log(paste("Mail day:", mail_day_as_character))
-safe_log(paste("Mail day index:", which(days_of_week == mail_day_as_character)))
-
-# Adjust week calculation based on mail day
-if (which(days_of_week == report_date_as_week_day) > which(days_of_week == mail_day_as_character)) {
-  safe_log("Adjusting weeks because of mail day")
-  week <- lubridate::week(today) + 1
-  today_minus_1 <- as.character(lubridate::ymd(today))
-  today_minus_2 <- as.character(lubridate::ymd(today) - 7)
-  today_minus_3 <- as.character(lubridate::ymd(today) - 14)
-}
-
-# Generate subtitle for report
-subtitle_var <- paste("Report generated on", Sys.Date())
-
-# Calculate week numbers for previous weeks
-week_minus_1 <- week - 1
-week_minus_2 <- week - 2
-week_minus_3 <- week - 3
-
-# Format current week with leading zeros
-week <- sprintf("%02d", week)
-
-# Get years for each date
-year <- lubridate::year(today)
-year_1 <- lubridate::year(today_minus_1)
-year_2 <- lubridate::year(today_minus_2)
-year_3 <- lubridate::year(today_minus_3)
-```
-
-```{r data, message=TRUE, warning=TRUE, include=FALSE}
-# Load CI index data with error handling
-tryCatch({
-  CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds"))
-  safe_log("Successfully loaded CI quadrant data")
-}, error = function(e) {
-  stop("Error loading CI quadrant data: ", e$message)
-})
-
-# Get file paths for different weeks using the utility function
-tryCatch({
-  path_to_week_current = get_week_path(weekly_CI_mosaic, today, 0)
-  path_to_week_minus_1 = get_week_path(weekly_CI_mosaic, today, -1)
-  path_to_week_minus_2 = get_week_path(weekly_CI_mosaic, today, -2)
-  path_to_week_minus_3 = get_week_path(weekly_CI_mosaic, today, -3)
-
-  # Log the calculated paths
-  safe_log("Required mosaic paths:")
-  safe_log(paste("Path to current week:", path_to_week_current))
-  safe_log(paste("Path to week minus 1:", path_to_week_minus_1))
-  safe_log(paste("Path to week minus 2:", path_to_week_minus_2))
-  safe_log(paste("Path to week minus 3:", path_to_week_minus_3))
-  
-  # Validate that files exist
-  if (!file.exists(path_to_week_current)) warning("Current week mosaic file does not exist: ", path_to_week_current)
-  if (!file.exists(path_to_week_minus_1)) warning("Week minus 1 mosaic file does not exist: ", path_to_week_minus_1)
-  if (!file.exists(path_to_week_minus_2)) warning("Week minus 2 mosaic file does not exist: ", path_to_week_minus_2)
-  if (!file.exists(path_to_week_minus_3)) warning("Week minus 3 mosaic file does not exist: ", path_to_week_minus_3)
-  
-  # Load raster data with terra functions
-  CI    <- terra::rast(path_to_week_current)$CI
-  CI_m1 <- terra::rast(path_to_week_minus_1)$CI
-  CI_m2 <- terra::rast(path_to_week_minus_2)$CI
-  CI_m3 <- terra::rast(path_to_week_minus_3)$CI
-  
-}, error = function(e) {
-  stop("Error loading raster data: ", e$message)
-})
-```
-
-```{r calculate_difference_rasters, message=TRUE, warning=TRUE, include=FALSE}
-# Calculate difference rasters for comparisons
-tryCatch({
-  # Calculate weekly difference
-  last_week_dif_raster_abs <- (CI - CI_m1)
-  safe_log("Calculated weekly difference raster")
-  
-  # Calculate three-week difference
-  three_week_dif_raster_abs <- (CI - CI_m3)
-  safe_log("Calculated three-week difference raster")
-}, error = function(e) {
-  safe_log(paste("Error calculating difference rasters:", e$message), "ERROR")
-  # Create placeholder rasters if calculations fail
-  if (!exists("last_week_dif_raster_abs")) {
-    last_week_dif_raster_abs <- CI * 0
-  }
-  if (!exists("three_week_dif_raster_abs")) {
-    three_week_dif_raster_abs <- CI * 0
-  }
-})
-```
-
-```{r load_field_boundaries, message=TRUE, warning=TRUE, include=FALSE}
-# Load field boundaries from parameters
-tryCatch({
-  AllPivots0 <- field_boundaries_sf
-  safe_log("Successfully loaded field boundaries")
-}, error = function(e) {
-  stop("Error loading field boundaries: ", e$message)
-})
-```
-
-```{r create_farm_health_data, message=FALSE, warning=FALSE, include=FALSE}
-# Create farm health summary data from scratch
-tryCatch({
-  # Ensure we have the required data
-  if (!exists("AllPivots0") || !exists("CI") || !exists("CI_m1") || !exists("harvesting_data")) {
-    stop("Required input data (field boundaries, CI data, or harvesting data) not available")
-  }
-  
-  safe_log("Starting to calculate farm health data")
-  
-  # Get unique field names
-  fields <- unique(AllPivots0$field)
-  safe_log(paste("Found", length(fields), "unique fields"))
-  
-  # Initialize result dataframe
-  farm_health_data <- data.frame(
-    field = character(),
-    mean_ci = numeric(),
-    ci_change = numeric(),
-    ci_uniformity = numeric(),
-    status = character(),
-    anomaly_type = character(),
-    priority_level = numeric(),
-    age_weeks = numeric(),
-    harvest_readiness = character(),
-    stringsAsFactors = FALSE
-  )
-  
-  # Process each field with robust error handling
-  for (field_name in fields) {
-    tryCatch({
-      safe_log(paste("Processing field:", field_name))
-      
-      # Get field boundary
-      field_shape <- AllPivots0 %>% dplyr::filter(field == field_name)
-      
-      # Skip if field shape is empty
-      if (nrow(field_shape) == 0) {
-        safe_log(paste("Empty field shape for", field_name), "WARNING")
-        next
-      }
-      
-      # Get field age from harvesting data - use direct filtering to avoid dplyr errors
-      field_age_data <- NULL
-      if (exists("harvesting_data") && !is.null(harvesting_data) && nrow(harvesting_data) > 0) {
-        field_age_data <- harvesting_data[harvesting_data$field == field_name, ]
-        if (nrow(field_age_data) > 0) {
-          field_age_data <- field_age_data[order(field_age_data$season_start, decreasing = TRUE), ][1, ]
-        }
-      }
-      
-      # Default age if not available
-      field_age_weeks <- if (!is.null(field_age_data) && nrow(field_age_data) > 0 && !is.na(field_age_data$age)) {
-        field_age_data$age
-      } else {
-        10  # Default age
-      }
-      
-      # Extract CI values using terra's extract function which is more robust
-      ci_values <- terra::extract(CI, field_shape)
-      ci_prev_values <- terra::extract(CI_m1, field_shape)
-      
-      # Check if we got valid data
-      if (nrow(ci_values) == 0 || nrow(ci_prev_values) == 0) {
-        safe_log(paste("No CI data extracted for field", field_name), "WARNING")
-        # Add a placeholder row with Unknown status
-        farm_health_data <- rbind(farm_health_data, data.frame(
-          field = field_name,
-          mean_ci = NA,
-          ci_change = NA,
-          ci_uniformity = NA,
-          status = "Unknown",
-          anomaly_type = "Unknown",
-          priority_level = 5,  # Low priority
-          age_weeks = field_age_weeks,
-          harvest_readiness = "Unknown",
-          stringsAsFactors = FALSE
-        ))
-        next
-      }
-      
-      # Calculate metrics - Handle NA values properly
-      ci_column <- if ("CI" %in% names(ci_values)) "CI" else colnames(ci_values)[1]
-      ci_prev_column <- if ("CI" %in% names(ci_prev_values)) "CI" else colnames(ci_prev_values)[1]
-      
-      mean_ci <- mean(ci_values[[ci_column]], na.rm=TRUE)
-      mean_ci_prev <- mean(ci_prev_values[[ci_prev_column]], na.rm=TRUE)
-      ci_change <- mean_ci - mean_ci_prev
-      ci_sd <- sd(ci_values[[ci_column]], na.rm=TRUE) 
-      ci_uniformity <- ci_sd / max(0.1, mean_ci) # Avoid division by zero
-      
-      # Handle NaN or Inf results
-      if (is.na(mean_ci) || is.na(ci_change) || is.na(ci_uniformity) ||
-          is.nan(mean_ci) || is.nan(ci_change) || is.nan(ci_uniformity) ||
-          is.infinite(mean_ci) || is.infinite(ci_change) || is.infinite(ci_uniformity)) {
-        safe_log(paste("Invalid calculation results for field", field_name), "WARNING")
-        # Add a placeholder row with Unknown status
-        farm_health_data <- rbind(farm_health_data, data.frame(
-          field = field_name,
-          mean_ci = NA,
-          ci_change = NA,
-          ci_uniformity = NA,
-          status = "Unknown",
-          anomaly_type = "Unknown",
-          priority_level = 5,  # Low priority
-          age_weeks = field_age_weeks,
-          harvest_readiness = "Unknown",
-          stringsAsFactors = FALSE
-        ))
-        next
-      }
-      
-      # Determine field status
-      status <- dplyr::case_when(
-        mean_ci >= 5 ~ "Excellent",
-        mean_ci >= 3.5 ~ "Good",
-        mean_ci >= 2 ~ "Fair",
-        mean_ci >= 1 ~ "Poor",
-        TRUE ~ "Critical"
-      )
-      
-      # Determine anomaly type
-      anomaly_type <- dplyr::case_when(
-        ci_change > 2 ~ "Potential Weed Growth",
-        ci_change < -2 ~ "Potential Weeding/Harvesting",
-        ci_uniformity > 0.5 ~ "High Variability",
-        mean_ci < 1 ~ "Low Vigor",
-        TRUE ~ "None"
-      )
-      
-      # Calculate priority level (1-5, with 1 being highest priority)
-      priority_score <- dplyr::case_when(
-        mean_ci < 1 ~ 1,  # Critical - highest priority
-        anomaly_type == "Potential Weed Growth" ~ 2,
-        anomaly_type == "High Variability" ~ 3,
-        ci_change < -1 ~ 4,
-        TRUE ~ 5  # No urgent issues
-      )
-      
-      # Determine harvest readiness
-      harvest_readiness <- dplyr::case_when(
-        field_age_weeks >= 52 & mean_ci >= 4 ~ "Ready for harvest",
-        field_age_weeks >= 48 & mean_ci >= 3.5 ~ "Approaching harvest",
-        field_age_weeks >= 40 & mean_ci >= 3 ~ "Mid-maturity",
-        field_age_weeks >= 12 ~ "Growing",
-        TRUE ~ "Early stage"
-      )
-      
-      # Add to summary data
-      farm_health_data <- rbind(farm_health_data, data.frame(
-        field = field_name,
-        mean_ci = round(mean_ci, 2),
-        ci_change = round(ci_change, 2),
-        ci_uniformity = round(ci_uniformity, 2),
-        status = status,
-        anomaly_type = anomaly_type,
-        priority_level = priority_score,
-        age_weeks = field_age_weeks,
-        harvest_readiness = harvest_readiness,
-        stringsAsFactors = FALSE
-      ))
-      
-    }, error = function(e) {
-      safe_log(paste("Error processing field", field_name, ":", e$message), "ERROR")
-      # Add a placeholder row with Error status
-      farm_health_data <<- rbind(farm_health_data, data.frame(
-        field = field_name,
-        mean_ci = NA,
-        ci_change = NA,
-        ci_uniformity = NA,
-        status = "Unknown",
-        anomaly_type = "Unknown",
-        priority_level = 5,  # Low priority since we don't know the status
-        age_weeks = NA,
-        harvest_readiness = "Unknown",
-        stringsAsFactors = FALSE
-      ))
-    })
-  }
-  
-  # Make sure we have data for all fields
-  if (nrow(farm_health_data) == 0) {
-    safe_log("No farm health data was created", "ERROR")
-    stop("Failed to create farm health data")
-  }
-  
-  # Sort by priority level
-  farm_health_data <- farm_health_data %>% dplyr::arrange(priority_level, field)
-  
-  safe_log(paste("Successfully created farm health data for", nrow(farm_health_data), "fields"))
-  
-}, error = function(e) {
-  safe_log(paste("Error creating farm health data:", e$message), "ERROR")
-  # Create an empty dataframe that can be filled by the verification chunk
-})
-```
-
-```{r verify_farm_health_data, message=FALSE, warning=FALSE, include=FALSE}
-# Verify farm_health_data exists and has content
-if (!exists("farm_health_data") || nrow(farm_health_data) == 0) {
-  safe_log("farm_health_data not found or empty, generating default data", "WARNING")
-  
-  # Create minimal fallback data
-  tryCatch({
-    # Get fields from boundaries
-    fields <- unique(AllPivots0$field)
-    
-    # Create basic data frame with just field names
-    farm_health_data <- data.frame(
-      field = fields,
-      mean_ci = rep(NA, length(fields)),
-      ci_change = rep(NA, length(fields)),
-      ci_uniformity = rep(NA, length(fields)),
-      status = rep("Unknown", length(fields)),
-      anomaly_type = rep("Unknown", length(fields)),
-      priority_level = rep(5, length(fields)), # Low priority
-      age_weeks = rep(NA, length(fields)),
-      harvest_readiness = rep("Unknown", length(fields)),
-      stringsAsFactors = FALSE
-    )
-    
-    safe_log("Created fallback farm_health_data with basic field information")
-  }, error = function(e) {
-    safe_log(paste("Error creating fallback farm_health_data:", e$message), "ERROR")
-    farm_health_data <<- data.frame(
-      field = character(),
-      mean_ci = numeric(),
-      ci_change = numeric(),
-      ci_uniformity = numeric(),
-      status = character(),
-      anomaly_type = character(),
-      priority_level = numeric(),
-      age_weeks = numeric(),
-      harvest_readiness = character(),
-      stringsAsFactors = FALSE
-    )
-  })
-}
-```
-
-```{r calculate_farm_health, message=FALSE, warning=FALSE, include=FALSE}
-# Calculate farm health summary metrics
-tryCatch({
-  # Generate farm health summary data
-  farm_health_data <- generate_farm_health_summary(
-    field_boundaries = AllPivots0,
-    ci_current = CI,
-    ci_previous = CI_m1,
-    harvesting_data = harvesting_data
-  )
-  
-  # Log the summary data
-  safe_log(paste("Generated farm health summary with", nrow(farm_health_data), "fields"))
-  
-}, error = function(e) {
-  safe_log(paste("Error in farm health calculation:", e$message), "ERROR")
-  # Create empty dataframe if calculation failed
-  farm_health_data <- data.frame(
-    field = character(),
-    mean_ci = numeric(),
-    ci_change = numeric(),
-    ci_uniformity = numeric(),
-    status = character(),
-    anomaly_type = character(),
-    priority_level = numeric(),
-    age_weeks = numeric(),
-    harvest_readiness = character(),
-    stringsAsFactors = FALSE
-  )
-})
-```
-
-```{r advanced_analytics_functions, message=FALSE, warning=FALSE, include=FALSE}
-# ADVANCED ANALYTICS FUNCTIONS
-# Note: These functions are now imported from executive_report_utils.R
-# The utility file contains functions for velocity/acceleration indicators,
-# anomaly timeline creation, age cohort mapping, and cohort performance charts
-safe_log("Using analytics functions from executive_report_utils.R")
-```
-
-\pagebreak
-# Advanced Analytics
-
-## Field Health Velocity and Acceleration
-
-This visualization shows the rate of change in field health (velocity) and whether that change is speeding up or slowing down (acceleration). These metrics help identify if farm conditions are improving, stable, or deteriorating.
-
-**How to interpret:**
-- **Velocity gauge:** Shows the average weekly change in CI values across all fields
-  - Positive values (green/right side): Farm health improving week-to-week
-  - Negative values (red/left side): Farm health declining week-to-week
-
-- **Acceleration gauge:** Shows whether the rate of change is increasing or decreasing
-  - Positive values (green/right side): Change is accelerating or improving faster
-  - Negative values (red/left side): Change is decelerating or slowing down
-
-- **4-Week Trend:** Shows the overall CI value trajectory for the past month
-
-```{r render_velocity_acceleration, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE}
-# Render the velocity and acceleration indicators
-tryCatch({
-  # Create and display the indicators using the imported utility function
-  velocity_plot <- create_velocity_acceleration_indicator(
-    health_data = farm_health_data,
-    ci_current = CI,
-    ci_prev1 = CI_m1,
-    ci_prev2 = CI_m2,
-    ci_prev3 = CI_m3,
-    field_boundaries = AllPivots0
-  )
-  
-  # Print the visualization
-  print(velocity_plot)
-  
-  # Create a table of fields with significant velocity changes
-  field_ci_metrics <- list()
-  
-  # Process each field to get metrics
-  fields <- unique(AllPivots0$field)
-  for (field_name in fields) {
-    tryCatch({
-      # Get field boundary
-      field_shape <- AllPivots0 %>% dplyr::filter(field == field_name)
-      if (nrow(field_shape) == 0) next
-      
-      # Extract CI values
-      ci_curr_values <- terra::extract(CI, field_shape)
-      ci_prev1_values <- terra::extract(CI_m1, field_shape)
-      
-      # Calculate metrics
-      mean_ci_curr <- mean(ci_curr_values$CI, na.rm = TRUE)
-      mean_ci_prev1 <- mean(ci_prev1_values$CI, na.rm = TRUE)
-      velocity <- mean_ci_curr - mean_ci_prev1
-      
-      # Store in list
-      field_ci_metrics[[field_name]] <- list(
-        field = field_name,
-        ci_current = mean_ci_curr,
-        ci_prev1 = mean_ci_prev1,
-        velocity = velocity
-      )
-      
-    }, error = function(e) {
-      safe_log(paste("Error processing field", field_name, "for velocity table:", e$message), "WARNING")
-    })
-  }
-  
-  # Convert list to data frame
-  velocity_df <- do.call(rbind, lapply(field_ci_metrics, function(x) {
-    data.frame(
-      field = x$field,
-      ci_current = round(x$ci_current, 2),
-      ci_prev1 = round(x$ci_prev1, 2),
-      velocity = round(x$velocity, 2),
-      direction = ifelse(x$velocity >= 0, "Improving", "Declining")
-    )
-  }))
-  
-  # Select top 5 positive and top 5 negative velocity fields
-  top_positive <- velocity_df %>%
-    dplyr::filter(velocity > 0) %>%
-    dplyr::arrange(desc(velocity)) %>%
-    dplyr::slice_head(n = 5)
-  
-  top_negative <- velocity_df %>%
-    dplyr::filter(velocity < 0) %>%
-    dplyr::arrange(velocity) %>%
-    dplyr::slice_head(n = 5)
-  
-  # Display the tables if we have data
-  if (nrow(top_positive) > 0) {
-    cat("<h4>Fields with Fastest Improvement</h4>")
-    knitr::kable(top_positive %>% 
-                dplyr::select(Field = field, 
-                            `Current CI` = ci_current, 
-                            `Previous CI` = ci_prev1,
-                            `Weekly Change` = velocity))
-  }
-  
-  if (nrow(top_negative) > 0) {
-    cat("<h4>Fields with Fastest Decline</h4>")
-    knitr::kable(top_negative %>% 
-                dplyr::select(Field = field, 
-                            `Current CI` = ci_current, 
-                            `Previous CI` = ci_prev1,
-                            `Weekly Change` = velocity))
-  }
-  
-}, error = function(e) {
-  safe_log(paste("Error rendering velocity visualization:", e$message), "ERROR")
-  cat("<div class='alert alert-danger'>Error generating velocity visualization.</div>")
-})
-```
-
-\pagebreak
-## Field Anomaly Timeline
-
-This visualization shows the history of detected anomalies in fields across the monitoring period. It helps identify persistent issues or improvements over time.
-
-**How to interpret:**
-- **X-axis**: Dates of satellite observations
-- **Y-axis**: Fields grouped by similar characteristics 
-- **Colors**: Red indicates negative anomalies, green indicates positive anomalies
-- **Size**: Larger markers indicate stronger anomalies
-
-```{r anomaly_timeline, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE}
-# Generate anomaly timeline visualization
-tryCatch({
-  # Use the imported function to create the anomaly timeline
-  anomaly_timeline <- create_anomaly_timeline(
-    field_boundaries = AllPivots0,
-    ci_data = CI_quadrant,
-    days_to_include = 90  # Show last 90 days of data
-  )
-  
-  # Display the timeline
-  print(anomaly_timeline)
-  
-}, error = function(e) {
-  safe_log(paste("Error generating anomaly timeline:", e$message), "ERROR")
-  cat("<div class='alert alert-danger'>Error generating anomaly timeline visualization.</div>")
-})
-```
-
-\pagebreak
-## Field Age Cohorts Map
-
-This map shows fields grouped by their crop age (weeks since planting). Understanding the distribution of crop ages helps interpret performance metrics and plan harvest scheduling.
-
-**How to interpret:**
-- **Colors**: Different colors represent different age groups (in weeks since planting)
-- **Labels**: Each field is labeled with its name for easy reference
-- **Legend**: Shows the age ranges in weeks and their corresponding colors
-
-```{r age_cohort_map, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE}
-# Generate age cohort map
-tryCatch({
-  # Use the imported function to create the age cohort map
-  age_cohort_map <- create_age_cohort_map(
-    field_boundaries = AllPivots0,
-    harvesting_data = harvesting_data
-  )
-  
-  # Display the map
-  print(age_cohort_map)
-  
-}, error = function(e) {
-  safe_log(paste("Error generating age cohort map:", e$message), "ERROR")
-  cat("<div class='alert alert-danger'>Error generating age cohort map visualization.</div>")
-})
-```
-
-\pagebreak
-## Cohort Performance Comparison
-
-This visualization compares chlorophyll index (CI) performance across different age groups of fields. This helps identify if certain age groups are performing better or worse than expected.
-
-**How to interpret:**
-- **X-axis**: Field age groups in weeks since planting
-- **Y-axis**: Average CI value for fields in that age group
-- **Box plots**: Show the distribution of CI values within each age group
-- **Line**: Shows the expected CI trajectory based on historical data
-
-```{r cohort_performance_chart, echo=FALSE, fig.height=8, fig.width=10, message=FALSE, warning=FALSE}
-# Generate cohort performance comparison chart
-tryCatch({
-  # Use the imported function to create the cohort performance chart
-  cohort_chart <- create_cohort_performance_chart(
-    field_boundaries = AllPivots0,
-    ci_current = CI,
-    harvesting_data = harvesting_data
-  )
-  
-  # Display the chart
-  print(cohort_chart)
-  
-}, error = function(e) {
-  safe_log(paste("Error generating cohort performance chart:", e$message), "ERROR")
-  cat("<div class='alert alert-danger'>Error generating cohort performance visualization.</div>")
-})
-```
-
-
-
-
-
-

r_app/ci_extraction.R

@@ -1,117 +0,0 @@
-# CI_EXTRACTION.R
-# ==============
-# This script processes satellite imagery to extract Canopy Index (CI) values for agricultural fields.
-# It handles image processing, masking, and extraction of statistics by field/sub-field.
-#
-# Usage: Rscript ci_extraction.R [end_date] [offset] [project_dir]
-#   - end_date: End date for processing (YYYY-MM-DD format)
-#   - offset: Number of days to look back from end_date 
-#   - project_dir: Project directory name (e.g., "chemba")
-#
-
-# 1. Load required packages
-# -----------------------
-suppressPackageStartupMessages({
-  library(sf)
-  library(terra)
-  library(tidyverse)
-  library(lubridate)
-  library(exactextractr)
-  library(readxl)
-  library(here)
-})
-
-# 2. Process command line arguments
-# ------------------------------
-main <- function() {
-  # Capture command line arguments
-  args <- commandArgs(trailingOnly = TRUE)
-  
-  # Process end_date argument
-  if (length(args) >= 1 && !is.na(args[1])) {
-    end_date <- as.Date(args[1])
-    if (is.na(end_date)) {
-      warning("Invalid end_date provided. Using default (current date).")
-      end_date <- Sys.Date()
-      #end_date <- "2023-10-01"
-    }
-  } else {
-    end_date <- Sys.Date()
-    #end_date <- "2023-10-01"
-  }
-  
-  # Process offset argument
-  if (length(args) >= 2 && !is.na(args[2])) {
-    offset <- as.numeric(args[2])
-    if (is.na(offset) || offset <= 0) {
-      warning("Invalid offset provided. Using default (7 days).")
-      offset <- 1095
-    }
-  } else {
-    offset <- 1095
-  }
-  
-  # Process project_dir argument
-  if (length(args) >= 3 && !is.na(args[3])) {
-    project_dir <- as.character(args[3])
-  } else {
-    project_dir <- "aura"
-  }
-  
-  # Make project_dir available globally so parameters_project.R can use it
-  assign("project_dir", project_dir, envir = .GlobalEnv)
-  
-
-  # 3. Initialize project configuration
-  # --------------------------------
-  new_project_question <- TRUE
-  
-  tryCatch({
-    source("parameters_project.R")
-    source("ci_extraction_utils.R")
-  }, error = function(e) {
-    warning("Default source files not found. Attempting to source from 'r_app' directory.")
-    tryCatch({
-      source(here::here("r_app", "parameters_project.R"))
-      source(here::here("r_app", "ci_extraction_utils.R"))
-      warning(paste("Successfully sourced files from 'r_app' directory."))
-
-    }, error = function(e) {
-      stop("Failed to source required files from both default and 'r_app' directories.")
-    })
-  })
-  
-  # 4. Generate date list for processing
-  # ---------------------------------
-  dates <- date_list(end_date, offset)
-  log_message(paste("Processing data for week", dates$week, "of", dates$year))
-  
-  # 5. Find and filter raster files by date
-  # -----------------------------------
-  log_message("Searching for raster files")
-  
-  tryCatch({
-    # Use the new utility function to find satellite images
-    existing_files <- find_satellite_images(planet_tif_folder, dates$days_filter)
-    log_message(paste("Found", length(existing_files), "raster files for processing"))
-    
-    # 6. Process raster files and create VRT
-    # -----------------------------------
-    # Use the new utility function for batch processing
-    vrt_list <- process_satellite_images(existing_files, field_boundaries, merged_final, daily_vrt)
-    
-    # 7. Process and combine CI values
-    # ------------------------------
-    # Call the process_ci_values function from utils with all required parameters
-    process_ci_values(dates, field_boundaries, merged_final, 
-                     field_boundaries_sf, daily_CI_vals_dir, cumulative_CI_vals_dir)
-    
-  }, error = function(e) {
-    log_message(paste("Error in main processing:", e$message), level = "ERROR")
-    stop(e$message)
-  })
-}
-
-if (sys.nframe() == 0) {
-  main()
-}

r_app/experiments/10_CI_report_with_kpis.Rmd

@@ -0,0 +1,400 @@
+---
+params:
+  ref: "word-styles-reference-var1.docx"
+  output_file: CI_report_with_kpis.docx
+  report_date: "2025-09-18"
+  data_dir: "esa"
+  mail_day: "Wednesday"
+  borders: FALSE
+  ci_plot_type: "both"  # options: "absolute", "cumulative", "both"
+  colorblind_friendly: TRUE  # use colorblind-friendly palettes (viridis/plasma)
+  facet_by_season: FALSE  # facet CI trend plots by season instead of overlaying
+  x_axis_unit: "days"  # x-axis unit for trend plots: "days" or "weeks"
+output:
+ # html_document:
+ #   toc: yes
+ #   df_print: paged  
+   word_document:
+    reference_docx: !expr file.path("word-styles-reference-var1.docx")
+    toc: no
+editor_options:
+  chunk_output_type: console
+---
+
+```{r setup_parameters, include=FALSE}
+# Set up basic report parameters from input values
+report_date <- params$report_date
+mail_day <- params$mail_day
+borders <- params$borders
+ci_plot_type <- params$ci_plot_type
+colorblind_friendly <- params$colorblind_friendly
+facet_by_season <- params$facet_by_season
+x_axis_unit <- params$x_axis_unit
+```
+
+```{r load_libraries, message=FALSE, warning=FALSE, include=FALSE}
+# Configure knitr options
+knitr::opts_chunk$set(warning = FALSE, message = FALSE)
+
+# Load all packages at once with suppressPackageStartupMessages
+suppressPackageStartupMessages({
+  library(here)
+  library(sf)
+  library(terra)
+  library(exactextractr)
+  library(tidyverse)
+  library(tmap)
+  library(lubridate)
+  library(zoo)
+  library(rsample)
+  library(caret)
+  library(randomForest)
+  library(CAST)
+  library(knitr)
+})
+
+# Load custom utility functions
+tryCatch({
+  source("report_utils.R")
+}, error = function(e) {
+  message(paste("Error loading report_utils.R:", e$message))
+  # Try alternative path if the first one fails
+  tryCatch({
+    source(here::here("r_app", "report_utils.R"))
+  }, error = function(e) {
+    stop("Could not load report_utils.R from either location: ", e$message)
+  })
+})
+```
+
+```{r initialize_project_config, message=FALSE, warning=FALSE, include=FALSE}
+# Set the project directory from parameters
+project_dir <- params$data_dir
+
+# Source project parameters with error handling
+tryCatch({
+  source(here::here("r_app", "parameters_project.R"))
+}, error = function(e) {
+  stop("Error loading parameters_project.R: ", e$message)
+})
+
+# Log initial configuration
+safe_log("Starting the R Markdown script with KPIs")
+safe_log(paste("mail_day params:", params$mail_day))
+safe_log(paste("report_date params:", params$report_date))
+safe_log(paste("mail_day variable:", mail_day))
+```
+
+```{r load_kpi_data, message=FALSE, warning=FALSE, include=FALSE}
+# SIMPLE KPI LOADING - just load the damn files!
+kpi_data_dir <- file.path("..", "laravel_app", "storage", "app", project_dir, "reports", "kpis")
+date_suffix <- format(as.Date(report_date), "%Y%m%d")
+summary_file <- file.path(kpi_data_dir, paste0(project_dir, "_kpi_summary_tables_", date_suffix, ".rds"))
+
+# Load the summary tables (this works!)
+summary_tables <- readRDS(summary_file)
+
+# Load field details too
+field_details_file <- file.path(kpi_data_dir, paste0(project_dir, "_field_details_", date_suffix, ".rds"))
+field_details_table <- readRDS(field_details_file)
+
+# Set this for compatibility with rest of report
+kpi_files_exist <- TRUE
+
+safe_log("✓ KPI summary tables loaded successfully")
+
+```
+
+```{r calculate_dates_and_weeks, message=FALSE, warning=FALSE, include=FALSE}
+# Set locale for consistent date formatting
+Sys.setlocale("LC_TIME", "C")
+
+# Initialize date variables from parameters
+today <- as.character(report_date)
+mail_day_as_character <- as.character(mail_day)
+
+# Calculate report dates and weeks
+report_date_obj <- as.Date(today)
+current_week <- as.numeric(format(report_date_obj, "%U"))
+year <- as.numeric(format(report_date_obj, "%Y"))
+
+# Calculate dates for weekly analysis
+week_start <- report_date_obj - ((as.numeric(format(report_date_obj, "%w")) + 1) %% 7)
+week_end <- week_start + 6
+
+safe_log(paste("Report week:", current_week, "Year:", year))
+safe_log(paste("Week range:", week_start, "to", week_end))
+```
+
+# SmartCane Monitoring Report with KPIs
+
+**Report Date:** `r format(as.Date(report_date), "%B %d, %Y")`  
+**Project:** `r toupper(project_dir)`  
+**Week:** `r current_week` of `r year`
+
+---
+
+## Executive Summary - Key Performance Indicators
+
+This report provides a comprehensive analysis of sugarcane field performance using satellite-based monitoring.
+
+### Field Uniformity
+```{r field_uniformity_table, echo=FALSE}
+kable(summary_tables$field_uniformity_summary, 
+      caption = "Field Uniformity Summary",
+      col.names = c("Uniformity Level", "Count", "Percent"))
+```
+
+### TCH Forecasted
+```{r tch_forecasted_table, echo=FALSE}
+kable(summary_tables$tch_forecasted_summary,
+      caption = "TCH Forecasted Summary",
+      col.names = c("Field Groups", "Count", "Value"))
+```
+
+### Farm-wide Area Change
+```{r area_change_table, echo=FALSE}
+kable(summary_tables$area_change_summary,
+      caption = "Farm-wide Area Change Summary", 
+      col.names = c("Change Type", "Hectares", "Percent"))
+```
+
+### Weed Presence Score
+```{r weed_presence_table, echo=FALSE}
+kable(summary_tables$weed_presence_summary,
+      caption = "Weed Presence Score Summary",
+      col.names = c("Weed Risk Level", "Field Count", "Percent"))
+```
+
+### Growth Decline Index
+```{r growth_decline_table, echo=FALSE}
+kable(summary_tables$growth_decline_summary,
+      caption = "Growth Decline Index Summary",
+      col.names = c("Risk Level", "Count", "Percent"))
+```
+
+### Gap Filling Assessment
+```{r gap_filling_table, echo=FALSE}
+kable(summary_tables$gap_filling_summary,
+      caption = "Gap Filling Assessment Summary",
+      col.names = c("Gap Level", "Field Count", "Percent"))
+```
+
+### Detailed KPI Breakdown
+
+```{r kpi_detailed_breakdown, echo=FALSE}
+# Show all 6 KPI tables in a more compact format
+cat("**Field Uniformity**\n")
+kable(summary_tables$field_uniformity_summary, col.names = c("Level", "Count", "%"))
+
+cat("\n**TCH Forecasted**\n")  
+kable(summary_tables$tch_forecasted_summary, col.names = c("Groups", "Count", "Value"))
+
+cat("\n**Area Change**\n")
+kable(summary_tables$area_change_summary, col.names = c("Change", "Ha", "%"))
+
+cat("\n**Weed Presence**\n")
+kable(summary_tables$weed_presence_summary, col.names = c("Risk", "Count", "%"))
+
+cat("\n**Growth Decline**\n")
+kable(summary_tables$growth_decline_summary, col.names = c("Risk", "Count", "%"))
+
+cat("\n**Gap Filling**\n")
+kable(summary_tables$gap_filling_summary, col.names = c("Level", "Count", "%"))
+```
+
+## KPI Summary Charts
+
+```{r kpi_charts, echo=FALSE, fig.width=10, fig.height=8}
+# Load ggplot2 for creating charts
+library(ggplot2)
+library(gridExtra)
+
+# Create charts for key KPIs using correct column names
+# 1. Field Uniformity Chart
+p1 <- ggplot(summary_tables$field_uniformity_summary, aes(x = reorder(`Uniformity Level`, -Count), y = Count)) +
+  geom_col(fill = "steelblue", alpha = 0.7) +
+  labs(title = "Field Uniformity Distribution", x = "Uniformity Level", y = "Field Count") +
+  theme_minimal() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+# 2. TCH Forecasted Chart  
+p2 <- ggplot(summary_tables$tch_forecasted_summary, aes(x = `Field Groups`, y = Value)) +
+  geom_col(fill = "darkgreen", alpha = 0.7) +
+  labs(title = "TCH Forecast by Field Groups", x = "Field Groups", y = "Value") +
+  theme_minimal() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+# 3. Growth Decline Risk Chart
+p3 <- ggplot(summary_tables$growth_decline_summary, aes(x = reorder(`Risk Level`, -Count), y = Count)) +
+  geom_col(fill = "orange", alpha = 0.7) +
+  labs(title = "Growth Decline Risk Distribution", x = "Risk Level", y = "Field Count") +
+  theme_minimal() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+# 4. Weed Presence Risk Chart
+p4 <- ggplot(summary_tables$weed_presence_summary, aes(x = reorder(`Weed Risk Level`, -`Field Count`), y = `Field Count`)) +
+  geom_col(fill = "red", alpha = 0.7) +
+  labs(title = "Weed Presence Risk Distribution", x = "Risk Level", y = "Field Count") +
+  theme_minimal() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+# Arrange plots in a grid
+grid.arrange(p1, p2, p3, p4, ncol = 2, nrow = 2)
+```
+
+---
+
+\newpage
+
+## Field-by-Field Analysis
+
+The following sections provide detailed analysis for each monitored field, including spatial maps, temporal trends, and field-specific KPI summaries.
+
+```{r load_field_data, message=FALSE, warning=FALSE, include=FALSE}
+# Load field data and prepare for field-by-field analysis
+# Load the spatial and temporal CI data needed for visualizations
+
+# Check if the required data objects exist from parameters_project.R
+required_objects <- c("AllPivots0", "CI", "CI_m1", "CI_m2", "CI_quadrant", "harvesting_data")
+missing_objects <- required_objects[!sapply(required_objects, exists)]
+
+if (length(missing_objects) > 0) {
+  safe_log(paste("Missing required objects for field analysis:", paste(missing_objects, collapse = ", ")), "WARNING")
+  field_analysis_possible <- FALSE
+} else {
+  safe_log("All required data objects found for field analysis")
+  field_analysis_possible <- TRUE
+  
+  # Prepare field list from the loaded boundaries
+  field_list <- AllPivots0 %>%
+    filter(!is.na(field), !is.na(sub_field)) %>%
+    group_by(field) %>%
+    summarise(.groups = 'drop') %>%
+    slice_head(n = 3)  # Limit to first 3 fields for report length
+}
+```
+
+```{r generate_field_visualizations, eval=TRUE, fig.height=3.8, fig.width=10, message=FALSE, echo=FALSE, warning=FALSE, include=TRUE, results='asis'}
+# Generate detailed visualizations for each field (copied from 05_CI_report_dashboard_planet.Rmd)
+if (field_analysis_possible) {
+  tryCatch({
+    # Merge field polygons for processing and filter out NA field names
+    AllPivots_merged <- AllPivots0 %>% 
+      dplyr::filter(!is.na(field), !is.na(sub_field)) %>%  # Filter out NA fields
+      dplyr::group_by(field) %>% 
+      dplyr::summarise(.groups = 'drop') %>%
+      slice_head(n = 3)  # Limit to first 3 fields for report
+    
+    # Generate plots for each field
+    for(i in seq_along(AllPivots_merged$field)) {
+      field_name <- AllPivots_merged$field[i]
+      
+      # Skip if field_name is still NA (double check)
+      if(is.na(field_name)) {
+        next
+      }
+      
+      tryCatch({
+        # Add page break before each field (except the first one)
+        if(i > 1) {
+          cat("\\newpage\n\n")
+        }
+        
+        # Call ci_plot with explicit parameters (ci_plot will generate its own header)
+        ci_plot(
+          pivotName = field_name,
+          field_boundaries = AllPivots0,
+          current_ci = CI,
+          ci_minus_1 = CI_m1,
+          ci_minus_2 = CI_m2,
+          last_week_diff = last_week_dif_raster_abs,
+          three_week_diff = three_week_dif_raster_abs,
+          harvesting_data = harvesting_data,
+          week = week,
+          week_minus_1 = week_minus_1,
+          week_minus_2 = week_minus_2,
+          week_minus_3 = week_minus_3,
+          borders = borders,
+          colorblind_friendly = colorblind_friendly
+        )
+        
+        cat("\n\n")
+        
+        # Call cum_ci_plot with explicit parameters
+        cum_ci_plot(
+          pivotName = field_name,
+          ci_quadrant_data = CI_quadrant,
+          plot_type = ci_plot_type,
+          facet_on = facet_by_season,
+          x_unit = x_axis_unit,
+          colorblind_friendly = colorblind_friendly
+        )
+        
+        cat("\n\n")
+        
+      }, error = function(e) {
+        safe_log(paste("Error generating plots for field", field_name, ":", e$message), "ERROR")
+        cat("\\newpage\n\n")
+        cat("# Error generating plots for field ", field_name, "\n\n")
+        cat("Data not available for visualization\n\n")
+      })
+    }
+  }, error = function(e) {
+    safe_log(paste("Error in field visualization section:", e$message), "ERROR")
+    cat("Error generating field plots. See log for details.\n\n")
+  })
+} else {
+  cat("Field visualization data not available. Required data objects are missing.\n\n")
+  cat("Please ensure scripts 02 (CI extraction) and 03 (growth model) have been run successfully.\n\n")
+}
+```
+
+---
+
+\newpage
+
+## Detailed Field Summary Table
+
+The following table provides a comprehensive overview of all monitored fields with their key performance metrics.
+
+```{r detailed_field_table, echo=FALSE}
+# Clean up the field details table - remove sub field column and round numeric values
+field_details_clean <- field_details_table %>%
+  select(-`Sub Field`) %>%  # Remove Sub Field column
+  mutate(
+    `Mean CI` = round(`Mean CI`, 2),    # Round to 2 decimal places
+    `CV Value` = round(`CV Value`, 2)   # Round to 2 decimal places
+  )
+
+# Display the cleaned field table
+kable(field_details_clean,
+      caption = "Detailed Field Performance Summary")
+```
+
+---
+
+## Report Metadata
+
+```{r report_metadata, echo=FALSE}
+metadata_info <- data.frame(
+  Metric = c("Report Generated", "Data Source", "Analysis Period", "Total Fields", 
+             "KPI Calculation", "Next Update"),
+  Value = c(
+    format(Sys.time(), "%Y-%m-%d %H:%M:%S"),
+    paste("Project", toupper(project_dir)),
+    paste("Week", current_week, "of", year),
+    ifelse(exists("field_boundaries_sf"), nrow(field_boundaries_sf), "Unknown"),
+    ifelse(kpi_files_exist, "✓ Current", "⚠ Needs Update"),
+    "Next Wednesday"
+  )
+)
+
+kable(metadata_info,
+      caption = "Report Metadata",
+      col.names = c("Metric", "Value"))
+```
+
+---
+
+*This report was automatically generated by the SmartCane monitoring system. For questions or additional analysis, please contact the technical team.*

r_app/experiments/combine_esa_yield_data.R

@@ -0,0 +1,239 @@
+# Combine ESA Yield Data from 5 tabs into Aura harvest format
+# Script to create harvest.xlsx in ESA directory matching Aura structure
+
+# Load required libraries
+library(readxl)
+library(writexl)
+library(dplyr)
+library(lubridate)
+
+# Define file paths using absolute paths
+base_path <- "C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane_v2/smartcane"
+esa_file_path <- file.path(base_path, "laravel_app", "storage", "app", "esa", "Data", "esa_yield_data.xlsx")
+output_file_path <- file.path(base_path, "laravel_app", "storage", "app", "esa", "Data", "harvest.xlsx")
+
+# Check if ESA file exists
+if (!file.exists(esa_file_path)) {
+  stop("ESA yield data file not found: ", esa_file_path)
+}
+
+# Get sheet names (should be: 2019-20, 2020-21, 2021-22, 2022-2023, 2023-24, 2024-25, etc.)
+sheet_names <- excel_sheets(esa_file_path)
+cat("Found sheets:", paste(sheet_names, collapse = ", "), "\n")
+
+# Function to extract harvest year from sheet name
+extract_year <- function(sheet_name) {
+  # Extract the second year from patterns like "2019-20" -> 2020
+  if (grepl("^\\d{4}-\\d{2}$", sheet_name)) {
+    # Format: 2019-20
+    year_part <- as.numeric(substr(sheet_name, 1, 4)) + 1
+  } else if (grepl("^\\d{4}-\\d{4}$", sheet_name)) {
+    # Format: 2022-2023
+    year_part <- as.numeric(substr(sheet_name, 6, 9))
+  } else {
+    # Fallback: try to extract first 4-digit number
+    year_match <- regmatches(sheet_name, regexpr("\\d{4}", sheet_name))
+    year_part <- if (length(year_match) > 0) as.numeric(year_match[1]) else NA
+  }
+  return(year_part)
+}
+
+# Initialize empty list to store data from all sheets
+all_data <- list()
+
+# Read data from each sheet
+for (sheet in sheet_names) {
+  cat("Processing sheet:", sheet, "\n")
+  
+  # Read the data
+  tryCatch({
+    data <- read_excel(esa_file_path, sheet = sheet)
+    
+    # Add year column based on sheet name
+    data$harvest_year <- extract_year(sheet)
+    data$sheet_name <- sheet
+    
+    # Store in list
+    all_data[[sheet]] <- data
+    
+    cat("  - Loaded", nrow(data), "rows from sheet", sheet, "\n")
+  }, error = function(e) {
+    cat("  - Error reading sheet", sheet, ":", e$message, "\n")
+  })
+}
+
+# Combine all data
+if (length(all_data) > 0) {
+  combined_data <- bind_rows(all_data)
+  cat("Combined data: ", nrow(combined_data), "total rows\n")
+  
+  # Display column names to understand the structure
+  cat("Available columns:\n")
+  print(colnames(combined_data))
+  
+  # Transform to SmartCane format
+  # Map ESA columns to SmartCane columns based on the sample data provided
+  harvest_data <- combined_data %>%
+    mutate(
+      # Convert dates using lubridate (original format is YYYY-MM-DD = ymd)
+      grow_start_date = ymd(Grow_Start),
+      harvest_date_date = ymd(Harvest_Date),
+      
+      # Calculate age in weeks using lubridate
+      age = round(as.numeric(harvest_date_date - grow_start_date) / 7, 0),
+      
+      # Format fields for output
+      field = Field,
+      sub_field = Field,
+      year = harvest_year,
+      season_start = grow_start_date,   # Keep as Date object
+      season_end = harvest_date_date,   # Keep as Date object
+      sub_area = NA,  # Leave empty as requested - not actual area but section names
+      tonnage_ha = TCH
+    ) %>%
+    select(field, sub_field, year, season_start, season_end, age, sub_area, tonnage_ha) %>%
+    arrange(field, year)
+
+  # Clean up incomplete future seasons that shouldn't exist
+  cat("\nCleaning up incomplete future seasons...\n")
+  
+  before_cleanup <- nrow(harvest_data)
+  
+  # For each field, find the last season with actual data (either completed or ongoing)
+  # Remove any future seasons beyond that
+  harvest_data <- harvest_data %>%
+    group_by(field, sub_field) %>%
+    arrange(year) %>%
+    mutate(
+      # Mark rows with actual data (has start date)
+      has_data = !is.na(season_start),
+      # Mark completely empty rows (both start and end are NA)
+      is_empty = is.na(season_start) & is.na(season_end)
+    ) %>%
+    # For each field, find the maximum year with actual data
+    mutate(
+      max_data_year = ifelse(any(has_data), max(year[has_data], na.rm = TRUE), NA)
+    ) %>%
+    # Keep only rows that:
+    # 1. Have actual data, OR
+    # 2. Are empty but within 1 year of the last data year (future season placeholder)
+    filter(
+      has_data | 
+      (is_empty & !is.na(max_data_year) & year <= max_data_year + 1)
+    ) %>%
+    # Clean up helper columns
+    select(-has_data, -is_empty, -max_data_year) %>%
+    ungroup() %>%
+    arrange(field, year)
+  
+  after_cleanup <- nrow(harvest_data)
+  
+  if (before_cleanup != after_cleanup) {
+    cat("Removed", before_cleanup - after_cleanup, "incomplete future season rows\n")
+  }
+
+  # Create next season rows for fields that have completed seasons
+  cat("\nCreating next season rows for completed fields...\n")
+
+  # For each field, find the latest completed season (has both start and end dates)
+  completed_seasons <- harvest_data %>%
+    filter(!is.na(season_start) & !is.na(season_end)) %>%
+    group_by(field, sub_field) %>%
+    arrange(desc(year)) %>%
+    slice(1) %>%  # Get the most recent completed season for each field
+    ungroup() %>%
+    select(field, sub_field, year, season_end)
+
+  cat("Found", nrow(completed_seasons), "fields with completed seasons\n")
+
+  # For each completed season, check if there's already a next season row
+  next_season_rows <- list()
+  
+  for (i in 1:nrow(completed_seasons)) {
+    field_name <- completed_seasons$field[i]
+    sub_field_name <- completed_seasons$sub_field[i]
+    last_completed_year <- completed_seasons$year[i]
+    last_harvest_date <- completed_seasons$season_end[i]
+    next_year <- last_completed_year + 1
+    
+    # Check if next season already exists for this field
+    next_season_exists <- harvest_data %>%
+      filter(field == field_name, sub_field == sub_field_name, year == next_year) %>%
+      nrow() > 0
+    
+    if (!next_season_exists) {
+      # Create next season row
+      next_season_row <- data.frame(
+        field = field_name,
+        sub_field = sub_field_name,
+        year = next_year,
+        season_start = as.Date(last_harvest_date) + 1,  # Previous harvest + 1 day
+        season_end = as.Date(NA),  # Not harvested yet
+        age = NA,
+        sub_area = NA,
+        tonnage_ha = NA,
+        stringsAsFactors = FALSE
+      )
+      
+      next_season_rows[[paste(field_name, sub_field_name, next_year, sep = "_")]] <- next_season_row
+      cat("Creating", next_year, "season for field", field_name, "starting", format(as.Date(last_harvest_date) + 1, "%Y-%m-%d"), "\n")
+    } else {
+      cat("Next season", next_year, "already exists for field", field_name, "\n")
+    }
+  }
+
+  # Combine all next season rows and add to harvest_data
+  if (length(next_season_rows) > 0) {
+    next_season_data <- bind_rows(next_season_rows)
+    harvest_data <- bind_rows(harvest_data, next_season_data) %>%
+      arrange(field, year)
+    
+    cat("Added", nrow(next_season_data), "new season rows\n")
+  } else {
+    cat("No new season rows needed\n")
+  }
+  
+  # Display preview of final transformed data
+  cat("\nPreview of final transformed data (including next season):\n")
+  print(head(harvest_data, 15))  # Show more rows to see next season data
+  
+  # Remove duplicates based on field, sub_field, year combination
+  cat("\nRemoving duplicate entries...\n")
+  before_dedup <- nrow(harvest_data)
+  
+  harvest_data <- harvest_data %>%
+    distinct(field, sub_field, year, .keep_all = TRUE)
+  
+  after_dedup <- nrow(harvest_data)
+  duplicates_removed <- before_dedup - after_dedup
+  
+  cat("Removed", duplicates_removed, "duplicate entries\n")
+  cat("Final data has", after_dedup, "unique records\n")
+  
+  # Remove rows with NA season_start to prevent age calculation issues in reports
+  cat("\nRemoving rows with NA season_start...\n")
+  before_na_removal <- nrow(harvest_data)
+  
+  harvest_data <- harvest_data %>%
+    filter(!is.na(season_start))
+  
+  after_na_removal <- nrow(harvest_data)
+  na_removed <- before_na_removal - after_na_removal
+  
+  cat("Removed", na_removed, "rows with NA season_start\n")
+  cat("Final data has", after_na_removal, "valid records\n")
+  
+  # Save to Excel file
+  tryCatch({
+    write_xlsx(harvest_data, output_file_path)
+    cat("\nSuccessfully saved harvest data to:", output_file_path, "\n")
+    cat("Total rows saved:", nrow(harvest_data), "\n")
+  }, error = function(e) {
+    cat("Error saving file:", e$message, "\n")
+  })
+  
+} else {
+  cat("No data was successfully loaded from any sheet.\n")
+}
+
+cat("\nScript completed.\n")

r_app/experiments/testing_projsetup.R

@@ -0,0 +1,11 @@
+# Set working directory first
+setwd("C:/Users/timon/Resilience BV/4020 SCane ESA DEMO - Documenten/General/4020 SCDEMO Team/4020 TechnicalData/WP3/smartcane_v2/smartcane")
+
+# Set project directory  
+project_dir <- 'esa'
+
+# Now call the function after it's defined
+dirs <- setup_project_directories(project_dir)
+
+# Check if paths are correct
+dirs$data_dir

r_app/installPackages.R

@@ -1,40 +0,0 @@
-# Install required packages for SmartCane project
-# This script installs all packages needed to run the CI report dashboard
-
-# List of required packages
-required_packages <- c(
-  # Core packages
-  "here", "tidyverse", "sf", "terra", "tmap", "lubridate", 
-  
-  # Additional data manipulation
-  "zoo", "readxl", "knitr", "rmarkdown", "dplyr", "purrr", "stringr",
-  
-  # Spatial analysis
-  "exactextractr", 
-  
-  # Machine learning and statistics
-  "rsample", "caret", "randomForest", "CAST"
-)
-
-# Function to install missing packages
-install_if_missing <- function(pkg) {
-  if (!requireNamespace(pkg, quietly = TRUE)) {
-    message(paste("Installing package:", pkg))
-    install.packages(pkg, repos = "https://cloud.r-project.org")
-  } else {
-    message(paste("Package already installed:", pkg))
-  }
-}
-
-# Install missing packages
-for (pkg in required_packages) {
-  install_if_missing(pkg)
-}
-
-# Load core packages to verify installation
-library(here)
-library(tidyverse)
-library(sf)
-library(terra)
-
-message("All required packages have been installed!")

r_app/interpolate_growth_model.R

@@ -1,102 +0,0 @@
-# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\interpolate_growth_model.R
-#
-# INTERPOLATE_GROWTH_MODEL.R
-# =========================
-# This script interpolates CI (Chlorophyll Index) values between measurement dates
-# to create a continuous growth model. It generates daily values and cumulative
-# CI statistics for each field.
-#
-# Usage: Rscript interpolate_growth_model.R [project_dir]
-#   - project_dir: Project directory name (e.g., "chemba")
-#
-
-# 1. Load required packages
-# -----------------------
-suppressPackageStartupMessages({
-  library(tidyverse)
-  library(lubridate)
-  library(here)
-})
-
-# 2. Main function to handle interpolation
-# -------------------------------------
-main <- function() {
-  # Process command line arguments
-  args <- commandArgs(trailingOnly = TRUE)
-  
-  # Get project directory from arguments or use default
-  if (length(args) >= 1 && !is.na(args[1])) {
-    project_dir <- as.character(args[1])
-  } else {
-    project_dir <- "chemba"
-    message("No project_dir provided. Using default:", project_dir)
-  }
-  
-    # Make project_dir available globally so parameters_project.R can use it
-  assign("project_dir", project_dir, envir = .GlobalEnv)
-  
-  # Initialize project configuration and load utility functions
-  tryCatch({
-    source("parameters_project.R")
-    source("growth_model_utils.R")
-  }, error = function(e) {
-    warning("Default source files not found. Attempting to source from 'r_app' directory.")
-    tryCatch({
-      source(here::here("r_app", "parameters_project.R"))
-      source(here::here("r_app", "growth_model_utils.R"))
-      warning(paste("Successfully sourced files from 'r_app' directory."))
-
-    }, error = function(e) {
-      stop("Failed to source required files from both default and 'r_app' directories.")
-    })
-  })
-  
-  log_message("Starting CI growth model interpolation")
-  
-  # Load and process the data
-  tryCatch({
-    # Load the combined CI data
-    CI_data <- load_combined_ci_data(cumulative_CI_vals_dir)
-    
-    # Validate harvesting data
-    if (is.null(harvesting_data) || nrow(harvesting_data) == 0) {
-      stop("No harvesting data available")
-    }
-    
-    # Get the years from harvesting data
-    years <- harvesting_data %>% 
-      filter(!is.na(season_start)) %>% 
-      distinct(year) %>% 
-      pull(year)
-    
-    log_message(paste("Processing data for years:", paste(years, collapse = ", ")))
-    
-    # Generate interpolated CI data for each year and field
-    CI_all <- generate_interpolated_ci_data(years, harvesting_data, CI_data)
-    
-    # Calculate growth metrics and save the results
-    if (nrow(CI_all) > 0) {
-      # Add daily and cumulative metrics
-      CI_all_with_metrics <- calculate_growth_metrics(CI_all)
-      
-      # Save the processed data
-      save_growth_model(
-        CI_all_with_metrics, 
-        cumulative_CI_vals_dir, 
-        "All_pivots_Cumulative_CI_quadrant_year_v2.rds"
-      )
-    } else {
-      log_message("No CI data was generated after interpolation", level = "WARNING")
-    }
-    
-    log_message("Growth model interpolation completed successfully")
-    
-  }, error = function(e) {
-    log_message(paste("Error in growth model interpolation:", e$message), level = "ERROR")
-    stop(e$message)
-  })
-}
-
-if (sys.nframe() == 0) {
-  main()
-}

r_app/mosaic_creation.R

@@ -1,119 +0,0 @@
-# filepath: c:\Users\timon\Resilience BV\4020 SCane ESA DEMO - Documenten\General\4020 SCDEMO Team\4020 TechnicalData\WP3\smartcane\r_app\mosaic_creation.R
-#
-# MOSAIC_CREATION.R
-# ===============
-# This script creates weekly mosaics from daily satellite imagery.
-# It handles command-line arguments and initiates the mosaic creation process.
-#
-# Usage: Rscript mosaic_creation.R [end_date] [offset] [project_dir] [file_name]
-#   - end_date: End date for processing (YYYY-MM-DD format)
-#   - offset: Number of days to look back from end_date
-#   - project_dir: Project directory name (e.g., "chemba")
-#   - file_name: Optional custom output file name
-#
-
-# 1. Load required packages
-# -----------------------
-suppressPackageStartupMessages({
-  library(sf)
-  library(terra)
-  library(tidyverse)
-  library(lubridate)
-  library(here)
-})
-
-# 2. Process command line arguments and run mosaic creation
-# ------------------------------------------------------
-main <- function() {
-  # Capture command line arguments
-  args <- commandArgs(trailingOnly = TRUE)
-  
-    # Process project_dir argument with default
-  if (length(args) >= 3 && !is.na(args[3])) {
-    project_dir <- as.character(args[3])
-  } else {
-    # Default project directory
-    project_dir <- "simba"
-    message("No project_dir provided. Using default:", project_dir)
-  }
-  
-  # Make project_dir available globally so parameters_project.R can use it
-  assign("project_dir", project_dir, envir = .GlobalEnv)
-  
-  # Process end_date argument with default
-  if (length(args) >= 1 && !is.na(args[1])) {
-    end_date <- as.Date(args[1])
-    if (is.na(end_date)) {
-      message("Invalid end_date provided. Using current date.")
-      end_date <- Sys.Date()
-      #end_date <- "2025-07-22"  # Default date for testing
-    }
-  } else {
-    # Default to current date if no argument is provided
-    end_date <- Sys.Date()
-    #end_date <-  "2025-07-08"   # Default date for testing
-    message("No end_date provided. Using current date: ", format(end_date))
-  }
-  
-  # Process offset argument with default
-  if (length(args) >= 2 && !is.na(args[2])) {
-    offset <- as.numeric(args[2])
-    if (is.na(offset) || offset <= 0) {
-      message("Invalid offset provided. Using default (7 days).")
-      offset <- 7
-    }
-  } else {
-    # Default to 7 days if no argument is provided
-    offset <- 7
-    message("No offset provided. Using default:", offset, "days")
-  }
-  
-
-  
-  # 3. Initialize project configuration
-  # --------------------------------
-  tryCatch({
-    source("parameters_project.R")
-    source("mosaic_creation_utils.R")
-    safe_log(paste("Successfully sourced files from default directory."))
-  }, error = function(e) {
-    warning("Default source files not found. Attempting to source from 'r_app' directory.")
-    tryCatch({
-      source(here::here("r_app", "parameters_project.R"))
-      source(here::here("r_app", "mosaic_creation_utils.R"))
-      warning(paste("Successfully sourced files from 'r_app' directory."))
-    }, error = function(e) {
-      stop("Failed to source required files from both default and 'r_app' directories.")
-    })
-  })
-  
-  # 4. Generate date range for processing
-  # ---------------------------------
-  dates <- date_list(end_date, offset)  
-  safe_log(paste("Processing data for week", dates$week, "of", dates$year))
-  
-  # Create output filename
-  file_name_tif <- if (length(args) >= 4 && !is.na(args[4])) {
-    as.character(args[4])
-  } else {
-    paste0("week_", sprintf("%02d", dates$week), "_", dates$year, ".tif")
-  }
-  
-  safe_log(paste("Output will be saved as:", file_name_tif))
-  
-  # 5. Create weekly mosaic using the function from utils
-  # -------------------------------------------------
-  create_weekly_mosaic(
-    dates = dates,
-    field_boundaries = field_boundaries,
-    daily_vrt_dir = daily_vrt,
-    merged_final_dir = merged_final,
-    output_dir = weekly_CI_mosaic,
-    file_name_tif = file_name_tif,
-    create_plots = TRUE
-  )
-}
-
-if (sys.nframe() == 0) {
-  main()
-}

r_app/mosaic_creation_utils.R

@@ -45,8 +45,8 @@ date_list <- function(end_date, offset) {
   start_date <- end_date - lubridate::days(offset)
   
   # Extract week and year information
-  week <- lubridate::week(start_date)
+  week <- lubridate::isoweek(end_date)
-  year <- lubridate::year(start_date)
+  year <- lubridate::isoyear(end_date)
   
   # Generate sequence of dates
   days_filter <- seq(from = start_date, to = end_date, by = "day")
@@ -95,7 +95,7 @@ create_weekly_mosaic <- function(dates, field_boundaries, daily_vrt_dir,
     mosaic <- create_mosaic(vrt_list, missing_pixels_count, field_boundaries, raster_files_final)
     
   } else {
-    safe_log("No VRT files available for the date range, creating empty mosaic", "WARNING")
+    safe_log("No VRT files available for the date range, creating empty mosaic with NA values", "WARNING")
     
     # Create empty mosaic if no files are available
     if (length(raster_files_final) == 0) {
@@ -103,7 +103,7 @@ create_weekly_mosaic <- function(dates, field_boundaries, daily_vrt_dir,
     }
     
     mosaic <- terra::rast(raster_files_final[1]) %>% 
-      terra::setValues(0) %>%
+      terra::setValues(NA) %>%
       terra::crop(field_boundaries, mask = TRUE)
     
     names(mosaic) <- c("Red", "Green", "Blue", "NIR", "CI")
@@ -249,10 +249,10 @@ create_mosaic <- function(vrt_list, missing_pixels_count, field_boundaries = NUL
       stop("No VRT files available and no fallback raster files or field boundaries provided")
     }
     
-    safe_log("No images available for this period, creating empty mosaic", "WARNING")
+    safe_log("No images available for this period, creating empty mosaic with NA values", "WARNING")
     
     x <- terra::rast(raster_files_final[1]) |>
-      terra::setValues(0) |>
+      terra::setValues(NA) |>
       terra::crop(field_boundaries, mask = TRUE)
     
     names(x) <- c("Red", "Green", "Blue", "NIR", "CI")

r_app/output/aura/crop_analysis_w34vs32_20250820_1414_table.md

@@ -1,15 +0,0 @@
-# Crop Analysis Summary - AURA Estate
-**Analysis Period:** Week 32 vs Week 34
-
-| Field | Area (ha) | Current CI | Change | Uniformity | Alert | Message |
-|-------|-----------|------------|--------|------------|-------|---------|
-| kowawa-kowawa | 1.4 | 3.28 | stable | excellent uniformity | ✅ NO | ✅ Excellent: Optimal field uniformity and stability |
-| Tamu-Tamu | 4.2 | 4.425 | stable | good uniformity | ✅ NO | ✅ Good: Stable field with good uniformity |
-| MNARA-MNARA | 2 | 4.079 | stable | good uniformity | ✅ NO | ✅ Good: Stable field with good uniformity |
-| Ayieyie Ruke-Ayieyie Ruke | 1.8 | 4.513 | stable | poor uniformity - urgent attention needed | 🚨 YES | 🚨 URGENT: Poor field uniformity detected - immediate management review required |
-| Got Nyithindo_M-Got Nyithindo_M | 1.4 | 4.19 | stable | good uniformity | ✅ NO | ✅ Good: Stable field with good uniformity |
-| Got Nyithindo-Got Nyithindo | 1.4 | 4.426 | stable | poor uniformity - urgent attention needed | 🚨 YES | 🚨 URGENT: Poor field uniformity detected - immediate management review required |
-| Kabala Ruke-Kabala Ruke | 1.3 | 3.89 | stable | poor uniformity - urgent attention needed | 🚨 YES | 🚨 URGENT: Poor field uniformity detected - immediate management review required |
-| Mutwala A-Mutwala A | 1.4 | 3.496 | stable | good uniformity | ✅ NO | ✅ Good: Stable field with good uniformity |
-| Onenonam-Onenonam | 2 | 4.098 | decrease | good uniformity | 🚨 YES | ⚠️ Alert: Good uniformity but declining trend - early intervention recommended |
-| NA-NA | 3.8 | 3.879 | stable | good uniformity | ✅ NO | ✅ Good: Stable field with good uniformity |

r_app/packages.R

@@ -1,117 +0,0 @@
-# packages.R
-#
-# PACKAGE MANAGEMENT FOR SMARTCANE
-# ===============================
-# This script centralizes all package dependencies for the SmartCane project.
-# It installs missing packages and loads all required libraries.
-#
-
-#' Check and install packages if needed
-#'
-#' @param pkg_list List of packages to check and install
-#' @param install_missing Whether to install missing packages
-#' @return Vector of packages that couldn't be installed (if any)
-#'
-check_and_install_packages <- function(pkg_list, install_missing = TRUE) {
-  # Check which packages are already installed
-  is_installed <- pkg_list %in% rownames(installed.packages())
-  missing_pkgs <- pkg_list[!is_installed]
-  
-  # Install missing packages if requested
-  failed_pkgs <- character(0)
-  if (length(missing_pkgs) > 0) {
-    if (install_missing) {
-      message("Installing ", length(missing_pkgs), " missing packages...")
-      for (pkg in missing_pkgs) {
-        tryCatch({
-          install.packages(pkg, repos = "https://cran.rstudio.com/", dependencies = TRUE)
-          message("  Installed: ", pkg)
-        }, error = function(e) {
-          warning("Failed to install package: ", pkg)
-          warning("Error: ", e$message)
-          failed_pkgs <<- c(failed_pkgs, pkg)
-        })
-      }
-    } else {
-      message("The following packages are required but not installed:")
-      message(paste(missing_pkgs, collapse = ", "))
-      failed_pkgs <- missing_pkgs
-    }
-  } else {
-    message("All required packages are already installed.")
-  }
-  
-  return(failed_pkgs)
-}
-
-#' Load all required packages for SmartCane project
-#'
-#' @param verbose Whether to show messages during loading
-#' @return Logical indicating success (TRUE if all packages loaded)
-#'
-load_smartcane_packages <- function(verbose = FALSE) {
-  # Define all required packages
-  required_packages <- c(
-    # Geospatial packages
-    "sf",          # Simple Features for spatial vector data
-    "terra",       # Raster data processing
-    "exactextractr", # Fast extraction from rasters
-    "tmap",        # Thematic mapping for spatial visualization
-    
-    # Data manipulation
-    "tidyverse",   # Collection of data manipulation packages
-    "lubridate",   # Date manipulation
-    "readxl",      # Excel file reading
-    "stringr",     # String manipulation
-    "purrr",       # Functional programming tools
-    "zoo",         # Time series processing with rolling functions
-    
-    # Visualization
-    "ggplot2",     # Advanced plotting
-    "leaflet",     # Interactive maps
-    "plotly",      # Interactive plots
-    
-    # Machine learning and statistics
-    "caret",       # Classification and regression training
-    "rsample",     # Data sampling for modeling
-    "randomForest", # Random forest implementation
-    "CAST",        # Feature selection for spatial data
-    
-    # Project management
-    "here",        # Path handling
-    
-    # Document generation
-    "knitr",       # Dynamic report generation
-    "rmarkdown"    # R Markdown processing
-  )
-  
-  # Check and install missing packages
-  failed_pkgs <- check_and_install_packages(required_packages)
-  
-  # Load all installed packages
-  success <- TRUE
-  for (pkg in setdiff(required_packages, failed_pkgs)) {
-    tryCatch({
-      if (verbose) message("Loading package: ", pkg)
-      suppressPackageStartupMessages(library(pkg, character.only = TRUE))
-    }, error = function(e) {
-      warning("Failed to load package: ", pkg)
-      warning("Error: ", e$message)
-      success <- FALSE
-    })
-  }
-  
-  # Report any issues
-  if (length(failed_pkgs) > 0) {
-    warning("The following packages could not be installed: ", 
-            paste(failed_pkgs, collapse = ", "))
-    success <- FALSE
-  }
-  
-  return(success)
-}
-
-# Run the loading function if the script is sourced directly
-if (!exists("skip_package_loading") || !skip_package_loading) {
-  load_smartcane_packages()
-}

r_app/parameters_project.R

@@ -62,11 +62,22 @@ setup_project_directories <- function(project_dir) {
   ))
 }
 
+#set working dir.
 # 3. Load field boundaries
 # ----------------------
 load_field_boundaries <- function(data_dir) {
-  field_boundaries_path <- here(data_dir, "pivot.geojson")
+  # Choose field boundaries file based on project and script type
-  
+  # ESA project uses pivot_2.geojson ONLY for scripts 02-03 (CI extraction & growth model)
+  # All other scripts (including 04-mosaic, 09-KPIs, 10-reports) use pivot.geojson
+  use_pivot_2 <- exists("project_dir") && project_dir == "esa" &&
+                 exists("ci_extraction_script")  # ci_extraction_script flag set by scripts 02-03
+
+  if (use_pivot_2) {
+    field_boundaries_path <- here(data_dir, "pivot_2.geojson")
+  } else {
+    field_boundaries_path <- here(data_dir, "pivot.geojson")
+  }
+
   if (!file.exists(field_boundaries_path)) {
     stop(paste("Field boundaries file not found at path:", field_boundaries_path))
   }

r_app/report_utils.R

@@ -35,7 +35,7 @@ subchunkify <- function(g, fig_height=7, fig_width=5) {
   ), collapse = '')
   
   sub_chunk <- paste0("
-`","``{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=", fig_height, ", fig.width=", fig_width, ", echo=FALSE}",
+`","``{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=", fig_height, ", fig.width=", fig_width, ", dpi=300, dev='png', out.width='100%', echo=FALSE}",
                       "\n(",
                       g_deparsed
                       , ")()",
@@ -81,16 +81,17 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend =
   # Create the base map
   map <- tm_shape(pivot_raster, unit = "m")
   
-  # Add raster with continuous spectrum (fixed scale 1-8 for consistent comparison)
+  # Add raster with continuous spectrum (fixed scale 8-1 for consistent comparison, reversed)
   map <- map + tm_raster(col.scale = tm_scale_continuous(values = palette,
-                                                       limits = c(1, 8)),
+                                                       limits = c(1,8)),
                           col.legend = tm_legend(title = "CI",
                                                orientation = if(legend_is_portrait) "portrait" else "landscape", 
                                                show = show_legend,
-                                               position = if(show_legend) tm_pos_out("left", "center") else c("left", "bottom")
+                                               position = if(show_legend) tm_pos_out("left", "center") else c("left", "bottom"),
+                                               reverse = TRUE
                                                ))
     # Add layout elements
-  map <- map + tm_layout(main.title = paste0("Max CI week ", week,"\n",  age, " weeks old"),
+  map <- map + tm_layout(main.title = paste0("Max CI week ", week,"\n",  age, " weeks (", age * 7, " days) old"),
                           main.title.size = 0.7)
   
   # Add borders if requested
@@ -143,17 +144,18 @@ create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_lege
   # Create the base map
   map <- tm_shape(pivot_raster, unit = "m")
   
-  # Add raster with continuous spectrum (centered at 0 for difference maps, fixed scale)
+  # Add raster with continuous spectrum (centered at 0 for difference maps, fixed scale, reversed)
   map <- map + tm_raster(col.scale = tm_scale_continuous(values = palette,
                                                        midpoint = 0,
                                                        limits = c(-3, 3)),
                           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("right", "center") else c("left", "bottom")
+                                               position = if(show_legend) tm_pos_out("right", "center") else c("left", "bottom"),
+                                               reverse = TRUE
                                                ))
     # Add layout elements
-  map <- map + tm_layout(main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks old"),
+  map <- map + tm_layout(main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks (", age * 7, " days) old"),
                           main.title.size = 0.7)
   
   # Add borders if requested
@@ -287,7 +289,8 @@ ci_plot <- function(pivotName,
                         nrow = 1, widths = c(0.23, 0.18, 0.18, 0.18, 0.23))
     
     # Output heading and map to R Markdown
-    cat(paste("## Field", pivotName, "-", age, "weeks after planting/harvest", "\n\n"))
+    age_months <- round(age / 4.348, 1)
+    cat(paste("## Field", pivotName, "-", age, "weeks/", age_months, "months after planting/harvest", "\n\n"))
     print(tst)
     
   }, error = function(e) {
@@ -305,9 +308,12 @@ ci_plot <- function(pivotName,
 #' @param facet_on Whether to facet the plot by season (TRUE) or overlay all seasons (FALSE)
 #' @param x_unit Unit for x-axis: "days" for DOY or "weeks" for week number (default: "days")
 #' @param colorblind_friendly Whether to use colorblind-friendly color schemes (default: FALSE)
+#' @param show_benchmarks Whether to show historical benchmark lines (default: FALSE)
+#' @param estate_name Name of the estate for benchmark calculation (required if show_benchmarks = TRUE)
+#' @param benchmark_percentiles Vector of percentiles for benchmarks (default: c(10, 50, 90))
 #' @return NULL (adds output directly to R Markdown document)
 #'
-cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "absolute", facet_on = FALSE, x_unit = "days", colorblind_friendly = FALSE) {
+cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "absolute", facet_on = FALSE, x_unit = "days", colorblind_friendly = FALSE, show_benchmarks = FALSE, estate_name = NULL, benchmark_percentiles = c(10, 50, 90), benchmark_data = NULL) {
   # Input validation
   if (missing(pivotName) || is.null(pivotName) || pivotName == "") {
     stop("pivotName is required")
@@ -341,6 +347,33 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
     
     data_ci2 <- data_ci2 %>% dplyr::mutate(season = as.factor(season))
     
+    # Compute benchmarks if requested and not provided
+    if (show_benchmarks && is.null(benchmark_data)) {
+      benchmark_data <- compute_ci_benchmarks(ci_quadrant_data, estate_name, benchmark_percentiles)
+    }
+    
+    # Prepare benchmark data for plotting if available
+    if (!is.null(benchmark_data)) {
+      benchmark_data <- benchmark_data %>%
+        dplyr::mutate(
+          ci_type_label = case_when(
+            ci_type == "value" ~ "10-Day Rolling Mean CI",
+            ci_type == "cumulative_CI" ~ "Cumulative CI",
+            TRUE ~ ci_type
+          ),
+          benchmark_label = paste0(percentile, "th Percentile")
+        )
+      safe_log("Benchmark data prepared for plotting", "INFO")
+    } else if (show_benchmarks) {
+      safe_log("No benchmark data available", "WARNING")
+    }
+    data_ci3 <- tidyr::pivot_longer(
+      data_ci2, 
+      cols = c("mean_rolling_10_days", "cumulative_CI"),
+      names_to = "ci_type",        # This column will say "mean_rolling_10_days" or "cumulative_CI"
+      values_to = "ci_value"       # This column will have the numeric values
+    )
+    
     # Prepare date information by season
     date_preparation_perfect_pivot <- data_ci2 %>% 
       dplyr::group_by(season) %>% 
@@ -351,8 +384,12 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
     # Get the 3 most recent seasons
     unique_seasons <- sort(unique(date_preparation_perfect_pivot$season), decreasing = TRUE)[1:3]
     
-    # Create plotting function
+    # Create plotting function that uses data_ci3 and filters by ci_type
-    create_plot <- function(y_var, y_label, title_suffix) {
+    create_plot <- function(ci_type_filter, y_label, title_suffix) {
+      # Filter data based on ci_type
+      plot_data <- data_ci3 %>% 
+        dplyr::filter(season %in% unique_seasons, ci_type == ci_type_filter)
+      
       # Determine x-axis variable based on x_unit parameter
       x_var <- if (x_unit == "days") {
         if (facet_on) "Date" else "DOY"
@@ -366,34 +403,83 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
       
       # Create plot with either facets by season or overlay by DOY/week
       if (facet_on) {
-        g <- ggplot2::ggplot(data = data_ci2 %>% dplyr::filter(season %in% unique_seasons)) +
+        g <- ggplot2::ggplot(data = plot_data) +
           ggplot2::facet_wrap(~season, scales = "free_x") +
-          ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = y_var, col = "sub_field", group = "sub_field")) +
+          ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = "ci_value", col = "sub_field", group = "sub_field")) +
-          ggplot2::labs(title = paste("Plot of", y_label, "for Field", pivotName, title_suffix),
+          ggplot2::labs(title = paste("Plot of", y_label),
                color = "Field Name",
                y = y_label,
                x = x_label) +
-          ggplot2::scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y") +
+          ggplot2::scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y",
+                       sec.axis = ggplot2::sec_axis(~ ., name = "Age in Months",
+                                                   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(angle = 60, hjust = 1),
+          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.title = ggplot2::element_text(size = 8),
                 legend.text = ggplot2::element_text(size = 8)) +
           ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
       } else {
-        g <- ggplot2::ggplot(data = data_ci2 %>% dplyr::filter(season %in% unique_seasons)) +
+        # Choose color palette based on colorblind_friendly flag
-          ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = y_var, col = "season", group = "season")) +
+        color_scale <- if (colorblind_friendly) {
+          ggplot2::scale_color_brewer(type = "qual", palette = "Set2")
+        } else {
+          ggplot2::scale_color_discrete()
+        }
+        
+        g <- ggplot2::ggplot(data = plot_data) +
+          # Add benchmark lines first (behind season lines)
+          {
+            if (!is.null(benchmark_data) && ci_type_filter %in% benchmark_data$ci_type) {
+              benchmark_subset <- benchmark_data %>% 
+                dplyr::filter(ci_type == ci_type_filter) %>%
+                dplyr::mutate(
+                  benchmark_x = if (x_var == "DOY") {
+                    DOY
+                  } else if (x_var == "week") {
+                    DOY / 7  # Approximate conversion
+                  } else {
+                    DOY  # For Date, use DOY as is (may not align perfectly)
+                  }
+                )
+              ggplot2::geom_smooth(
+                data = benchmark_subset,
+                ggplot2::aes_string(x = "benchmark_x", y = "benchmark_value", group = "factor(percentile)"),
+                color = "gray70", size = 0.5, se = FALSE, inherit.aes = FALSE
+              )
+            }
+          } +
+          ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season")) +
           ggplot2::labs(title = paste("Plot of", y_label, "for Field", pivotName, title_suffix),
                color = "Season",
                y = y_label,
                x = x_label) +
+          color_scale +
+          {
+            if (x_var == "DOY") {
+              ggplot2::scale_x_continuous(breaks = seq(0, 450, by = 50), sec.axis = ggplot2::sec_axis(~ . / 30.44, name = "Age in Months", breaks = seq(0, 14, by = 1)))
+            } else if (x_var == "week") {
+              ggplot2::scale_x_continuous(breaks = seq(0, 64, by = 4), sec.axis = ggplot2::sec_axis(~ . / 4.348, name = "Age in Months", breaks = seq(0, 14, by = 1)))
+            }
+          } +
           ggplot2::theme_minimal() +
-          ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 60, hjust = 1),
+          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.title = ggplot2::element_text(size = 8),
                 legend.text = ggplot2::element_text(size = 8)) +
           ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
       }
+      
+      # Add y-axis limits for absolute CI (10-day rolling mean) to fix scale at 0-8
+      if (ci_type_filter == "mean_rolling_10_days") {
+        g <- g + ggplot2::ylim(0, 8)
+      }
+      
       return(g)
     }
     
@@ -405,13 +491,133 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
       g <- create_plot("cumulative_CI", "Cumulative CI", "")
       subchunkify(g, 2.8, 10)
     } else if (plot_type == "both") {
-      # Create both plots
+      # Create faceted plot with both CI types using pivot_longer approach
-      g_absolute <- create_plot("mean_rolling_10_days", "10-Day Rolling Mean CI", "(Absolute)")
+      plot_data_both <- data_ci3 %>% 
-      g_cumulative <- create_plot("cumulative_CI", "Cumulative CI", "(Cumulative)")
+        dplyr::filter(season %in% unique_seasons) %>%
+        dplyr::mutate(ci_type_label = case_when(
+          ci_type == "mean_rolling_10_days" ~ "10-Day Rolling Mean CI",
+          ci_type == "cumulative_CI" ~ "Cumulative CI",
+          TRUE ~ ci_type
+        ))
       
-      # Display both plots
+      # Determine x-axis variable based on x_unit parameter
-      subchunkify(g_absolute, 2.8, 4.95)
+      x_var <- if (x_unit == "days") {
-      subchunkify(g_cumulative, 2.8, 4.95)
+        if (facet_on) "Date" else "DOY"
+      } else {
+        "week"
+      }
+      
+      x_label <- switch(x_unit,
+                       "days" = if (facet_on) "Date" else "Age of Crop (Days)",
+                       "weeks" = "Week Number")
+      
+      # Choose color palette based on colorblind_friendly flag
+      color_scale <- if (colorblind_friendly) {
+        ggplot2::scale_color_brewer(type = "qual", palette = "Set2")
+      } else {
+        ggplot2::scale_color_discrete()
+      }
+      
+      # Create faceted plot with both CI types using pivot_longer approach
+      plot_data_both <- data_ci3 %>% 
+        dplyr::filter(season %in% unique_seasons) %>%
+        dplyr::mutate(ci_type_label = case_when(
+          ci_type == "mean_rolling_10_days" ~ "10-Day Rolling Mean CI",
+          ci_type == "cumulative_CI" ~ "Cumulative CI",
+          TRUE ~ ci_type
+        ))
+      
+      # Determine x-axis variable based on x_unit parameter
+      x_var <- if (x_unit == "days") {
+        if (facet_on) "Date" else "DOY"
+      } else {
+        "week"
+      }
+      
+      x_label <- switch(x_unit,
+                       "days" = if (facet_on) "Date" else "Age of Crop (Days)",
+                       "weeks" = "Week Number")
+      
+      # Choose color palette based on colorblind_friendly flag
+      color_scale <- if (colorblind_friendly) {
+        ggplot2::scale_color_brewer(type = "qual", palette = "Set2")
+      } else {
+        ggplot2::scale_color_discrete()
+      }
+      
+      # Create the faceted plot
+      g_both <- ggplot2::ggplot(data = plot_data_both) +
+        # Add benchmark lines first (behind season lines)
+        {
+          if (!is.null(benchmark_data)) {
+            benchmark_subset <- benchmark_data %>%
+              dplyr::mutate(
+                benchmark_x = if (x_var == "DOY") {
+                  DOY
+                } else if (x_var == "week") {
+                  DOY / 7
+                } else {
+                  DOY
+                },
+                ci_type_label = case_when(
+                  ci_type == "value" ~ "10-Day Rolling Mean CI",
+                  ci_type == "cumulative_CI" ~ "Cumulative CI",
+                  TRUE ~ ci_type
+                )
+              )
+            ggplot2::geom_smooth(
+              data = benchmark_subset,
+              ggplot2::aes_string(x = "benchmark_x", y = "benchmark_value", group = "factor(percentile)"),
+              color = "gray70", size = 0.5, se = FALSE, inherit.aes = FALSE
+            )
+          }
+        } +
+        ggplot2::facet_wrap(~ci_type_label, scales = "free_y") +
+        ggplot2::geom_line(ggplot2::aes_string(x = x_var, y = "ci_value", col = "season", group = "season")) +
+        ggplot2::labs(title = paste("CI Analysis for Field", pivotName),
+             color = "Season",
+             y = "CI Value",
+             x = x_label) +
+        color_scale +
+        {
+          if (x_var == "DOY") {
+            ggplot2::scale_x_continuous(breaks = seq(0, 450, by = 50), sec.axis = ggplot2::sec_axis(~ . / 30.44, name = "Age in Months", breaks = seq(0, 14, by = 1)))
+          } else if (x_var == "week") {
+            ggplot2::scale_x_continuous(breaks = seq(0, 64, by = 4), sec.axis = ggplot2::sec_axis(~ . / 4.348, name = "Age in Months", breaks = seq(0, 14, by = 1)))
+          } else if (x_var == "Date") {
+            ggplot2::scale_x_date(breaks = "1 month", date_labels = "%b-%Y", sec.axis = ggplot2::sec_axis(~ ., name = "Age in Months", breaks = scales::breaks_pretty()))
+          }
+        } +
+        ggplot2::theme_minimal() +
+        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.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
+      # Since we're using free_y scales, each facet will have its own y-axis
+      # The rolling mean will automatically scale to its data range, 
+      # but we can ensure it shows the 0-8 context by adding invisible points
+      
+      # Add invisible points to set the y-axis range for rolling mean facet
+      dummy_data <- data.frame(
+        ci_type_label = "10-Day Rolling Mean CI",
+        ci_value = c(0, 8),
+        stringsAsFactors = FALSE
+      )
+      dummy_data[[x_var]] <- range(plot_data_both[[x_var]], na.rm = TRUE)
+      dummy_data[["season"]] <- factor("dummy", levels = levels(plot_data_both[["season"]]))
+      
+      g_both <- g_both + 
+        ggplot2::geom_point(data = dummy_data, 
+                   ggplot2::aes_string(x = x_var, y = "ci_value"), 
+                   alpha = 0, size = 0)  # Invisible points to set scale
+      
+      # Display the combined faceted plot
+      subchunkify(g_both, 2.8, 10)
     }
     
   }, error = function(e) {
@@ -444,7 +650,7 @@ cum_ci_plot2 <- function(pivotName){
       labs(title = paste("14 day rolling MEAN CI rate - Field ", pivotName),
            x = "Date", y = "CI Rate") +
       theme_minimal() +
-      theme(axis.text.x = element_text(angle = 60, hjust = 1),
+      theme(axis.text.x = element_text(hjust = 0.5),
             legend.justification = c(1, 0), legend.position = c(1, 0),
             legend.title = element_text(size = 8),
             legend.text = element_text(size = 8)) +
@@ -512,3 +718,86 @@ get_week_path <- function(mosaic_path, input_date, week_offset) {
   })
 }
 
+#' Computes historical percentile benchmarks for CI data per estate
+#'
+#' @param ci_quadrant_data Data frame containing CI quadrant data with field, Date, DOY, cumulative_CI, value, season columns
+#' @param estate_name Name of the estate/client to filter data for
+#' @param percentiles Vector of percentiles to compute (e.g., c(10, 50, 90))
+#' @param min_seasons Minimum number of seasons required for reliable benchmarks (default: 3)
+#' @return Data frame with DOY, percentile, ci_type, benchmark_value, or NULL if insufficient data
+#'
+compute_ci_benchmarks <- function(ci_quadrant_data, estate_name, percentiles = c(10, 50, 90), min_seasons = 3) {
+  # Input validation
+  if (missing(ci_quadrant_data) || is.null(ci_quadrant_data)) {
+    stop("ci_quadrant_data is required")
+  }
+  if (missing(estate_name) || is.null(estate_name) || estate_name == "") {
+    stop("estate_name is required")
+  }
+  if (!all(percentiles >= 0 & percentiles <= 100)) {
+    stop("percentiles must be between 0 and 100")
+  }
+  
+  tryCatch({
+    # Filter data for the specified estate (assuming estate is not directly in data, but we can infer from context)
+    # Since the data is per field, and fields are unique to estates, we'll use all data but could add estate filtering if available
+    data_filtered <- ci_quadrant_data
+    
+    # Check if we have enough seasons
+    unique_seasons <- unique(data_filtered$season)
+    if (length(unique_seasons) < min_seasons) {
+      safe_log(paste("Insufficient historical seasons for estate", estate_name, ":", length(unique_seasons), "seasons found, need at least", min_seasons), "WARNING")
+      return(NULL)
+    }
+    
+    # Prepare data for both CI types
+    data_prepared <- data_filtered %>%
+      dplyr::ungroup() %>%  # Ensure no existing groupings
+      dplyr::select(DOY, value, cumulative_CI, season) %>%
+      tidyr::pivot_longer(
+        cols = c("value", "cumulative_CI"),
+        names_to = "ci_type",
+        values_to = "ci_value"
+      ) %>%
+      dplyr::filter(!is.na(ci_value))  # Remove NA values
+    
+    # Compute percentiles for each DOY and ci_type
+    benchmarks <- data_prepared %>%
+      dplyr::group_by(DOY, ci_type) %>%
+      dplyr::summarise(
+        p10 = tryCatch(quantile(ci_value, 0.1, na.rm = TRUE), error = function(e) NA_real_),
+        p50 = tryCatch(quantile(ci_value, 0.5, na.rm = TRUE), error = function(e) NA_real_),
+        p90 = tryCatch(quantile(ci_value, 0.9, na.rm = TRUE), error = function(e) NA_real_),
+        n_observations = n(),
+        .groups = 'drop'
+      ) %>%
+      dplyr::filter(n_observations >= min_seasons) %>%  # Only include DOYs with sufficient data
+      tidyr::pivot_longer(
+        cols = c(p10, p50, p90),
+        names_to = "percentile",
+        values_to = "benchmark_value"
+      ) %>%
+      dplyr::mutate(
+        percentile = case_when(
+          percentile == "p10" ~ 10,
+          percentile == "p50" ~ 50,
+          percentile == "p90" ~ 90
+        )
+      ) %>%
+      dplyr::filter(!is.na(benchmark_value))  # Remove any NA benchmarks
+    
+    # Rename columns for clarity
+    benchmarks <- benchmarks %>%
+      dplyr::select(DOY, ci_type, percentile, benchmark_value)
+    
+    safe_log(paste("Computed CI benchmarks for estate", estate_name, "with", length(unique_seasons), "seasons and", nrow(benchmarks), "benchmark points"), "INFO")
+    
+    return(benchmarks)
+    
+  }, error = function(e) {
+    safe_log(paste("Error computing CI benchmarks for estate", estate_name, ":", e$message), "ERROR")
+    print(paste("DEBUG: Error details:", e$message, "Call:", deparse(e$call)))
+    return(NULL)
+  })
+}
+

renv.lock

@@ -1,6 +1,6 @@
 {
   "R": {
-    "Version": "4.4.2",
+    "Version": "4.4.3",
     "Repositories": [
       {
         "Name": "CRAN",
@@ -584,6 +584,33 @@
       "Maintainer": "Kirill Müller <kirill@cynkra.com>",
       "Repository": "CRAN"
     },
+    "boot": {
+      "Package": "boot",
+      "Version": "1.3-31",
+      "Source": "Repository",
+      "Priority": "recommended",
+      "Date": "2024-08-28",
+      "Authors@R": "c(person(\"Angelo\", \"Canty\", role = \"aut\", email = \"cantya@mcmaster.ca\",  comment = \"author of original code for S\"), person(\"Brian\", \"Ripley\", role = c(\"aut\", \"trl\"), email = \"ripley@stats.ox.ac.uk\", comment = \"conversion to R, maintainer 1999--2022, author of parallel support\"), person(\"Alessandra R.\", \"Brazzale\", role = c(\"ctb\", \"cre\"), email = \"brazzale@stat.unipd.it\", comment = \"minor bug fixes\"))",
+      "Maintainer": "Alessandra R. Brazzale <brazzale@stat.unipd.it>",
+      "Note": "Maintainers are not available to give advice on using a package they did not author.",
+      "Description": "Functions and datasets for bootstrapping from the book \"Bootstrap Methods and Their Application\" by A. C. Davison and  D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.",
+      "Title": "Bootstrap Functions (Originally by Angelo Canty for S)",
+      "Depends": [
+        "R (>= 3.0.0)",
+        "graphics",
+        "stats"
+      ],
+      "Suggests": [
+        "MASS",
+        "survival"
+      ],
+      "LazyData": "yes",
+      "ByteCompile": "yes",
+      "License": "Unlimited",
+      "NeedsCompilation": "no",
+      "Author": "Angelo Canty [aut] (author of original code for S), Brian Ripley [aut, trl] (conversion to R, maintainer 1999--2022, author of parallel support), Alessandra R. Brazzale [ctb, cre] (minor bug fixes)",
+      "Repository": "CRAN"
+    },
     "broom": {
       "Package": "broom",
       "Version": "1.0.8",
@@ -995,10 +1022,10 @@
     },
     "cli": {
       "Package": "cli",
-      "Version": "3.6.3",
+      "Version": "3.6.5",
       "Source": "Repository",
       "Title": "Helpers for Developing Command Line Interfaces",
-      "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", role = \"ctb\"), person(\"Kirill\", \"Müller\", role = \"ctb\"), person(\"Salim\", \"Brüggemann\", , \"salim-b@pm.me\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5329-5987\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
+      "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"gabor@posit.co\", role = c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", role = \"ctb\"), person(\"Kirill\", \"Müller\", role = \"ctb\"), person(\"Salim\", \"Brüggemann\", , \"salim-b@pm.me\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5329-5987\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
       "Description": "A suite of tools to build attractive command line interfaces ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs, etc. Supports custom themes via a 'CSS'-like language. It also contains a number of lower level 'CLI' elements: rules, boxes, trees, and 'Unicode' symbols with 'ASCII' alternatives. It support ANSI colors and text styles as well.",
       "License": "MIT + file LICENSE",
       "URL": "https://cli.r-lib.org, https://github.com/r-lib/cli",
@@ -1020,14 +1047,13 @@
         "htmlwidgets",
         "knitr",
         "methods",
-        "mockery",
         "processx",
         "ps (>= 1.3.4.9000)",
         "rlang (>= 1.0.2.9003)",
         "rmarkdown",
         "rprojroot",
         "rstudioapi",
-        "testthat",
+        "testthat (>= 3.2.0)",
         "tibble",
         "whoami",
         "withr"
@@ -1035,10 +1061,10 @@
       "Config/Needs/website": "r-lib/asciicast, bench, brio, cpp11, decor, desc, fansi, prettyunits, sessioninfo, tidyverse/tidytemplate, usethis, vctrs",
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "yes",
       "Author": "Gábor Csárdi [aut, cre], Hadley Wickham [ctb], Kirill Müller [ctb], Salim Brüggemann [ctb] (<https://orcid.org/0000-0002-5329-5987>), Posit Software, PBC [cph, fnd]",
-      "Maintainer": "Gábor Csárdi <csardi.gabor@gmail.com>",
+      "Maintainer": "Gábor Csárdi <gabor@posit.co>",
       "Repository": "CRAN"
     },
     "clipr": {
@@ -1533,6 +1559,31 @@
       "Maintainer": "Hadley Wickham <hadley@posit.co>",
       "Repository": "CRAN"
     },
+    "deldir": {
+      "Package": "deldir",
+      "Version": "2.0-4",
+      "Source": "Repository",
+      "Date": "2024-02-27",
+      "Title": "Delaunay Triangulation and Dirichlet (Voronoi) Tessellation",
+      "Author": "Rolf Turner",
+      "Maintainer": "Rolf Turner <rolfturner@posteo.net>",
+      "Depends": [
+        "R (>= 3.5.0)"
+      ],
+      "Suggests": [
+        "polyclip"
+      ],
+      "Imports": [
+        "graphics",
+        "grDevices"
+      ],
+      "Description": "Calculates the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. Plots triangulations and tessellations in various ways.  Clips tessellations to sub-windows. Calculates perimeters of tessellations.  Summarises information about the tiles of the tessellation.\tCalculates the centroidal Voronoi (Dirichlet) tessellation using Lloyd's algorithm.",
+      "LazyData": "true",
+      "ByteCompile": "true",
+      "License": "GPL (>= 2)",
+      "NeedsCompilation": "yes",
+      "Repository": "CRAN"
+    },
     "diagram": {
       "Package": "diagram",
       "Version": "1.6.5",
@@ -1891,6 +1942,108 @@
       "Maintainer": "Winston Chang <winston@posit.co>",
       "Repository": "CRAN"
     },
+    "flextable": {
+      "Package": "flextable",
+      "Version": "0.9.10",
+      "Source": "Repository",
+      "Type": "Package",
+      "Title": "Functions for Tabular Reporting",
+      "Authors@R": "c( person(\"David\", \"Gohel\", , \"david.gohel@ardata.fr\", role = c(\"aut\", \"cre\")), person(\"ArData\", role = \"cph\"), person(\"Clementine\", \"Jager\", role = \"ctb\"), person(\"Eli\", \"Daniels\", role = \"ctb\"), person(\"Panagiotis\", \"Skintzos\", , \"panagiotis.skintzos@ardata.fr\", role = \"aut\"), person(\"Quentin\", \"Fazilleau\", role = \"ctb\"), person(\"Maxim\", \"Nazarov\", role = \"ctb\"), person(\"Titouan\", \"Robert\", role = \"ctb\"), person(\"Michael\", \"Barrowman\", role = \"ctb\"), person(\"Atsushi\", \"Yasumoto\", role = \"ctb\"), person(\"Paul\", \"Julian\", role = \"ctb\"), person(\"Sean\", \"Browning\", role = \"ctb\"), person(\"Rémi\", \"Thériault\", role = \"ctb\", comment = c(ORCID = \"0000-0003-4315-6788\")), person(\"Samuel\", \"Jobert\", role = \"ctb\"), person(\"Keith\", \"Newman\", role = \"ctb\") )",
+      "Description": "Use a grammar for creating and customizing pretty tables. The following formats are supported: 'HTML', 'PDF', 'RTF', 'Microsoft Word', 'Microsoft PowerPoint' and R 'Grid Graphics'.  'R Markdown', 'Quarto' and the package 'officer' can be used to produce the result files. The syntax is the same for the user regardless of the type of output to be produced. A set of functions allows the creation, definition of cell arrangement, addition of headers or footers, formatting and definition of cell content with text and or images. The package also offers a set of high-level functions that allow tabular reporting of statistical models and the creation of complex cross tabulations.",
+      "License": "GPL-3",
+      "URL": "https://ardata-fr.github.io/flextable-book/, https://davidgohel.github.io/flextable/",
+      "BugReports": "https://github.com/davidgohel/flextable/issues",
+      "Imports": [
+        "data.table (>= 1.13.0)",
+        "gdtools (>= 0.4.0)",
+        "graphics",
+        "grDevices",
+        "grid",
+        "htmltools",
+        "knitr",
+        "officer (>= 0.6.10)",
+        "ragg",
+        "rlang",
+        "rmarkdown (>= 2.0)",
+        "stats",
+        "utils",
+        "uuid (>= 0.1-4)",
+        "xml2"
+      ],
+      "Suggests": [
+        "bookdown (>= 0.40)",
+        "broom",
+        "broom.mixed",
+        "chromote",
+        "cluster",
+        "commonmark",
+        "doconv (>= 0.3.0)",
+        "equatags",
+        "ggplot2",
+        "lme4",
+        "magick",
+        "mgcv",
+        "nlme",
+        "officedown",
+        "pdftools",
+        "pkgdown (>= 2.0.0)",
+        "scales",
+        "svglite",
+        "tables (>= 0.9.17)",
+        "testthat (>= 3.0.0)",
+        "webshot2",
+        "withr",
+        "xtable"
+      ],
+      "VignetteBuilder": "knitr",
+      "Config/testthat/edition": "3",
+      "Encoding": "UTF-8",
+      "RoxygenNote": "7.3.2",
+      "NeedsCompilation": "no",
+      "Author": "David Gohel [aut, cre], ArData [cph], Clementine Jager [ctb], Eli Daniels [ctb], Panagiotis Skintzos [aut], Quentin Fazilleau [ctb], Maxim Nazarov [ctb], Titouan Robert [ctb], Michael Barrowman [ctb], Atsushi Yasumoto [ctb], Paul Julian [ctb], Sean Browning [ctb], Rémi Thériault [ctb] (ORCID: <https://orcid.org/0000-0003-4315-6788>), Samuel Jobert [ctb], Keith Newman [ctb]",
+      "Maintainer": "David Gohel <david.gohel@ardata.fr>",
+      "Repository": "CRAN"
+    },
+    "fontBitstreamVera": {
+      "Package": "fontBitstreamVera",
+      "Version": "0.1.1",
+      "Source": "Repository",
+      "Title": "Fonts with 'Bitstream Vera Fonts' License",
+      "Authors@R": "c( person(\"Lionel\", \"Henry\", , \"lionel.hry@gmail.com\", c(\"cre\", \"aut\")), person(\"Bitstream\", role = \"cph\"))",
+      "Description": "Provides fonts licensed under the 'Bitstream Vera Fonts' license for the 'fontquiver' package.",
+      "Depends": [
+        "R (>= 3.0.0)"
+      ],
+      "License": "file LICENCE",
+      "Encoding": "UTF-8",
+      "LazyData": "true",
+      "RoxygenNote": "5.0.1",
+      "NeedsCompilation": "no",
+      "Author": "Lionel Henry [cre, aut], Bitstream [cph]",
+      "Maintainer": "Lionel Henry <lionel.hry@gmail.com>",
+      "License_is_FOSS": "yes",
+      "Repository": "CRAN"
+    },
+    "fontLiberation": {
+      "Package": "fontLiberation",
+      "Version": "0.1.0",
+      "Source": "Repository",
+      "Title": "Liberation Fonts",
+      "Authors@R": "c( person(\"Lionel\", \"Henry\", , \"lionel@rstudio.com\", \"cre\"), person(\"Pravin Satpute\", role = \"aut\"), person(\"Steve Matteson\", role = \"aut\"), person(\"Red Hat, Inc\", role = \"cph\"), person(\"Google Corporation\", role = \"cph\"))",
+      "Description": "A placeholder for the Liberation fontset intended for the `fontquiver` package. This fontset covers the 12 combinations of families (sans, serif, mono) and faces (plain, bold, italic, bold italic) supported in R graphics devices.",
+      "Depends": [
+        "R (>= 3.0)"
+      ],
+      "License": "file LICENSE",
+      "Encoding": "UTF-8",
+      "LazyData": "true",
+      "RoxygenNote": "5.0.1",
+      "NeedsCompilation": "no",
+      "Author": "Lionel Henry [cre], Pravin Satpute [aut], Steve Matteson [aut], Red Hat, Inc [cph], Google Corporation [cph]",
+      "Maintainer": "Lionel Henry <lionel@rstudio.com>",
+      "Repository": "CRAN",
+      "License_is_FOSS": "yes"
+    },
     "fontawesome": {
       "Package": "fontawesome",
       "Version": "0.5.3",
@@ -1926,6 +2079,34 @@
       "Maintainer": "Richard Iannone <rich@posit.co>",
       "Repository": "CRAN"
     },
+    "fontquiver": {
+      "Package": "fontquiver",
+      "Version": "0.2.1",
+      "Source": "Repository",
+      "Title": "Set of Installed Fonts",
+      "Authors@R": "c( person(\"Lionel\", \"Henry\", , \"lionel@rstudio.com\", c(\"cre\", \"aut\")), person(\"RStudio\", role = \"cph\"), person(\"George Douros\", role = \"cph\", comment = \"Symbola font\"))",
+      "Description": "Provides a set of fonts with permissive licences. This is useful when you want to avoid system fonts to make sure your outputs are reproducible.",
+      "Depends": [
+        "R (>= 3.0.0)"
+      ],
+      "Imports": [
+        "fontBitstreamVera (>= 0.1.0)",
+        "fontLiberation (>= 0.1.0)"
+      ],
+      "Suggests": [
+        "testthat",
+        "htmltools"
+      ],
+      "License": "GPL-3 | file LICENSE",
+      "Encoding": "UTF-8",
+      "LazyData": "true",
+      "RoxygenNote": "5.0.1",
+      "Collate": "'font-getters.R' 'fontset.R' 'fontset-bitstream-vera.R' 'fontset-dejavu.R' 'fontset-liberation.R' 'fontset-symbola.R' 'html-dependency.R' 'utils.R'",
+      "NeedsCompilation": "no",
+      "Author": "Lionel Henry [cre, aut], RStudio [cph], George Douros [cph] (Symbola font)",
+      "Maintainer": "Lionel Henry <lionel@rstudio.com>",
+      "Repository": "CRAN"
+    },
     "forcats": {
       "Package": "forcats",
       "Version": "1.0.0",
@@ -2208,6 +2389,43 @@
       "Maintainer": "Jennifer Bryan <jenny@posit.co>",
       "Repository": "CRAN"
     },
+    "gdtools": {
+      "Package": "gdtools",
+      "Version": "0.4.3",
+      "Source": "Repository",
+      "Title": "Utilities for Graphical Rendering and Fonts Management",
+      "Authors@R": "c( person(\"David\", \"Gohel\", , \"david.gohel@ardata.fr\", role = c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\", role = \"aut\"), person(\"Lionel\", \"Henry\", , \"lionel@rstudio.com\", role = \"aut\"), person(\"Jeroen\", \"Ooms\", , \"jeroen@berkeley.edu\", role = \"aut\", comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"Yixuan\", \"Qiu\", role = \"ctb\"), person(\"R Core Team\", role = \"cph\", comment = \"Cairo code from X11 device\"), person(\"ArData\", role = \"cph\"), person(\"RStudio\", role = \"cph\") )",
+      "Description": "Tools are provided to compute metrics of formatted strings and to check the availability of a font.  Another set of functions is provided to support the collection of fonts from 'Google Fonts' in a cache. Their use is simple within 'R Markdown' documents and 'shiny' applications but also with graphic productions generated with the 'ggiraph', 'ragg' and 'svglite' packages or with tabular productions from the 'flextable' package.",
+      "License": "GPL-3 | file LICENSE",
+      "URL": "https://davidgohel.github.io/gdtools/",
+      "BugReports": "https://github.com/davidgohel/gdtools/issues",
+      "Depends": [
+        "R (>= 4.0.0)"
+      ],
+      "Imports": [
+        "fontquiver (>= 0.2.0)",
+        "htmltools",
+        "Rcpp (>= 0.12.12)",
+        "systemfonts (>= 1.1.0)",
+        "tools"
+      ],
+      "Suggests": [
+        "curl",
+        "gfonts",
+        "methods",
+        "testthat"
+      ],
+      "LinkingTo": [
+        "Rcpp"
+      ],
+      "Encoding": "UTF-8",
+      "RoxygenNote": "7.3.2",
+      "SystemRequirements": "cairo, freetype2, fontconfig",
+      "NeedsCompilation": "yes",
+      "Author": "David Gohel [aut, cre], Hadley Wickham [aut], Lionel Henry [aut], Jeroen Ooms [aut] (ORCID: <https://orcid.org/0000-0002-4035-0289>), Yixuan Qiu [ctb], R Core Team [cph] (Cairo code from X11 device), ArData [cph], RStudio [cph]",
+      "Maintainer": "David Gohel <david.gohel@ardata.fr>",
+      "Repository": "CRAN"
+    },
     "generics": {
       "Package": "generics",
       "Version": "0.1.3",
@@ -4120,6 +4338,50 @@
       "NeedsCompilation": "no",
       "Repository": "CRAN"
     },
+    "officer": {
+      "Package": "officer",
+      "Version": "0.7.0",
+      "Source": "Repository",
+      "Type": "Package",
+      "Title": "Manipulation of Microsoft Word and PowerPoint Documents",
+      "Authors@R": "c( person(\"David\", \"Gohel\", , \"david.gohel@ardata.fr\", role = c(\"aut\", \"cre\")), person(\"Stefan\", \"Moog\", , \"moogs@gmx.de\", role = \"aut\"), person(\"Mark\", \"Heckmann\", , \"heckmann.mark@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-0736-7417\")), person(\"ArData\", role = \"cph\"), person(\"Frank\", \"Hangler\", , \"frank@plotandscatter.com\", role = \"ctb\", comment = \"function body_replace_all_text\"), person(\"Liz\", \"Sander\", , \"lsander@civisanalytics.com\", role = \"ctb\", comment = \"several documentation fixes\"), person(\"Anton\", \"Victorson\", , \"anton@victorson.se\", role = \"ctb\", comment = \"fixes xml structures\"), person(\"Jon\", \"Calder\", , \"jonmcalder@gmail.com\", role = \"ctb\", comment = \"update vignettes\"), person(\"John\", \"Harrold\", , \"john.m.harrold@gmail.com\", role = \"ctb\", comment = \"function annotate_base\"), person(\"John\", \"Muschelli\", , \"muschellij2@gmail.com\", role = \"ctb\", comment = \"google doc compatibility\"), person(\"Bill\", \"Denney\", , \"wdenney@humanpredictions.com\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5759-428X\", \"function as.matrix.rpptx\")), person(\"Nikolai\", \"Beck\", , \"beck.nikolai@gmail.com\", role = \"ctb\", comment = \"set speaker notes for .pptx documents\"), person(\"Greg\", \"Leleu\", , \"gregoire.leleu@gmail.com\", role = \"ctb\", comment = \"fields functionality in ppt\"), person(\"Majid\", \"Eismann\", role = \"ctb\"), person(\"Hongyuan\", \"Jia\", , \"hongyuanjia@cqust.edu.cn\", role = \"ctb\", comment = c(ORCID = \"0000-0002-0075-8183\")), person(\"Michael\", \"Stackhouse\", , \"mike.stackhouse@atorusresearch.com\", role = \"ctb\") )",
+      "Description": "Access and manipulate 'Microsoft Word', 'RTF' and 'Microsoft PowerPoint' documents from R.  The package focuses on tabular and graphical reporting from R; it also provides two functions that let users get document content into data objects. A set of functions lets add and remove images, tables and paragraphs of text in new or existing documents.  The package does not require any installation of Microsoft products to be able to write Microsoft files.",
+      "License": "MIT + file LICENSE",
+      "URL": "https://ardata-fr.github.io/officeverse/, https://davidgohel.github.io/officer/",
+      "BugReports": "https://github.com/davidgohel/officer/issues",
+      "Imports": [
+        "cli",
+        "graphics",
+        "grDevices",
+        "openssl",
+        "R6",
+        "ragg",
+        "stats",
+        "utils",
+        "uuid",
+        "xml2 (>= 1.1.0)",
+        "zip (>= 2.1.0)"
+      ],
+      "Suggests": [
+        "devEMF",
+        "doconv (>= 0.3.0)",
+        "gdtools",
+        "ggplot2",
+        "knitr",
+        "magick",
+        "rmarkdown",
+        "rsvg",
+        "testthat",
+        "withr"
+      ],
+      "Encoding": "UTF-8",
+      "RoxygenNote": "7.3.2",
+      "Collate": "'core_properties.R' 'custom_properties.R' 'defunct.R' 'dev-utils.R' 'docx_add.R' 'docx_comments.R' 'docx_cursor.R' 'docx_part.R' 'docx_replace.R' 'docx_section.R' 'docx_settings.R' 'empty_content.R' 'formatting_properties.R' 'fortify_docx.R' 'fortify_pptx.R' 'knitr_utils.R' 'officer.R' 'ooxml.R' 'ooxml_block_objects.R' 'ooxml_run_objects.R' 'openxml_content_type.R' 'openxml_document.R' 'pack_folder.R' 'ph_location.R' 'post-proc.R' 'ppt_class_dir_collection.R' 'ppt_classes.R' 'ppt_notes.R' 'ppt_ph_dedupe_layout.R' 'ppt_ph_manipulate.R' 'ppt_ph_rename_layout.R' 'ppt_ph_with_methods.R' 'pptx_informations.R' 'pptx_layout_helper.R' 'pptx_matrix.R' 'utils.R' 'pptx_slide_manip.R' 'read_docx.R' 'read_docx_styles.R' 'read_pptx.R' 'read_xlsx.R' 'relationship.R' 'rtf.R' 'shape_properties.R' 'shorcuts.R' 'docx_append_context.R' 'utils-xml.R' 'deprecated.R' 'zzz.R'",
+      "NeedsCompilation": "no",
+      "Author": "David Gohel [aut, cre], Stefan Moog [aut], Mark Heckmann [aut] (ORCID: <https://orcid.org/0000-0002-0736-7417>), ArData [cph], Frank Hangler [ctb] (function body_replace_all_text), Liz Sander [ctb] (several documentation fixes), Anton Victorson [ctb] (fixes xml structures), Jon Calder [ctb] (update vignettes), John Harrold [ctb] (function annotate_base), John Muschelli [ctb] (google doc compatibility), Bill Denney [ctb] (ORCID: <https://orcid.org/0000-0002-5759-428X>, function as.matrix.rpptx), Nikolai Beck [ctb] (set speaker notes for .pptx documents), Greg Leleu [ctb] (fields functionality in ppt), Majid Eismann [ctb], Hongyuan Jia [ctb] (ORCID: <https://orcid.org/0000-0002-0075-8183>), Michael Stackhouse [ctb]",
+      "Maintainer": "David Gohel <david.gohel@ardata.fr>",
+      "Repository": "CRAN"
+    },
     "openssl": {
       "Package": "openssl",
       "Version": "2.3.2",
@@ -4223,6 +4485,50 @@
       "Maintainer": "Henrik Bengtsson <henrikb@braju.com>",
       "Repository": "CRAN"
     },
+    "patchwork": {
+      "Package": "patchwork",
+      "Version": "1.3.2",
+      "Source": "Repository",
+      "Type": "Package",
+      "Title": "The Composer of Plots",
+      "Authors@R": "person(given = \"Thomas Lin\", family = \"Pedersen\", role = c(\"cre\", \"aut\"), email = \"thomasp85@gmail.com\", comment = c(ORCID = \"0000-0002-5147-4711\"))",
+      "Maintainer": "Thomas Lin Pedersen <thomasp85@gmail.com>",
+      "Description": "The 'ggplot2' package provides a strong API for sequentially  building up a plot, but does not concern itself with composition of multiple plots. 'patchwork' is a package that expands the API to allow for  arbitrarily complex composition of plots by, among others, providing  mathematical operators for combining multiple plots. Other packages that try  to address this need (but with a different approach) are 'gridExtra' and  'cowplot'.",
+      "License": "MIT + file LICENSE",
+      "Encoding": "UTF-8",
+      "Imports": [
+        "ggplot2 (>= 3.0.0)",
+        "gtable (>= 0.3.6)",
+        "grid",
+        "stats",
+        "grDevices",
+        "utils",
+        "graphics",
+        "rlang (>= 1.0.0)",
+        "cli",
+        "farver"
+      ],
+      "RoxygenNote": "7.3.2",
+      "URL": "https://patchwork.data-imaginist.com, https://github.com/thomasp85/patchwork",
+      "BugReports": "https://github.com/thomasp85/patchwork/issues",
+      "Suggests": [
+        "knitr",
+        "rmarkdown",
+        "gridGraphics",
+        "gridExtra",
+        "ragg",
+        "testthat (>= 2.1.0)",
+        "vdiffr",
+        "covr",
+        "png",
+        "gt (>= 0.11.0)"
+      ],
+      "VignetteBuilder": "knitr",
+      "Config/Needs/website": "gifski",
+      "NeedsCompilation": "no",
+      "Author": "Thomas Lin Pedersen [cre, aut] (ORCID: <https://orcid.org/0000-0002-5147-4711>)",
+      "Repository": "CRAN"
+    },
     "pillar": {
       "Package": "pillar",
       "Version": "1.10.2",
@@ -5966,6 +6272,38 @@
       "Maintainer": "Edzer Pebesma <edzer.pebesma@uni-muenster.de>",
       "Repository": "CRAN"
     },
+    "spData": {
+      "Package": "spData",
+      "Version": "2.3.4",
+      "Source": "Repository",
+      "Title": "Datasets for Spatial Analysis",
+      "Authors@R": "c(person(\"Roger\", \"Bivand\", role = \"aut\", email=\"Roger.Bivand@nhh.no\", comment = c(ORCID = \"0000-0003-2392-6140\")), person(\"Jakub\", \"Nowosad\", role = c(\"aut\", \"cre\"), email=\"nowosad.jakub@gmail.com\", comment = c(ORCID = \"0000-0002-1057-3721\")), person(\"Robin\", \"Lovelace\", role = \"aut\", comment = c(ORCID = \"0000-0001-5679-6536\")), person(\"Angelos\", \"Mimis\", role = \"ctb\"), person(\"Mark\", \"Monmonier\", role = \"ctb\", comment = \"author of the state.vbm dataset\"),  person(\"Greg\", \"Snow\", role = \"ctb\", comment = \"author of the state.vbm dataset\") )",
+      "Description": "Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis.  It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps',  it also contains data stored in a range of file formats including GeoJSON and GeoPackage, but from version 2.3.4, no longer ESRI Shapefile - use GeoPackage instead.  Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.",
+      "Depends": [
+        "R (>= 3.3.0)"
+      ],
+      "Imports": [
+        "sp"
+      ],
+      "Suggests": [
+        "foreign",
+        "sf (>= 0.9-1)",
+        "spDataLarge (>= 0.4.0)",
+        "spdep",
+        "spatialreg"
+      ],
+      "License": "CC0",
+      "RoxygenNote": "7.3.2",
+      "LazyData": "true",
+      "URL": "https://jakubnowosad.com/spData/",
+      "BugReports": "https://github.com/Nowosad/spData/issues",
+      "Additional_repositories": "https://jakubnowosad.com/drat",
+      "Encoding": "UTF-8",
+      "NeedsCompilation": "no",
+      "Author": "Roger Bivand [aut] (<https://orcid.org/0000-0003-2392-6140>), Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>), Robin Lovelace [aut] (<https://orcid.org/0000-0001-5679-6536>), Angelos Mimis [ctb], Mark Monmonier [ctb] (author of the state.vbm dataset), Greg Snow [ctb] (author of the state.vbm dataset)",
+      "Maintainer": "Jakub Nowosad <nowosad.jakub@gmail.com>",
+      "Repository": "CRAN"
+    },
     "spacesXYZ": {
       "Package": "spacesXYZ",
       "Version": "1.5-1",
@@ -6037,6 +6375,66 @@
       "Maintainer": "Emil Hvitfeldt <emil.hvitfeldt@posit.co>",
       "Repository": "CRAN"
     },
+    "spdep": {
+      "Package": "spdep",
+      "Version": "1.4-1",
+      "Source": "Repository",
+      "Date": "2025-08-25",
+      "Title": "Spatial Dependence: Weighting Schemes, Statistics",
+      "Encoding": "UTF-8",
+      "Authors@R": "c(person(\"Roger\", \"Bivand\", role = c(\"cre\", \"aut\"),  email = \"Roger.Bivand@nhh.no\", comment=c(ORCID=\"0000-0003-2392-6140\")), person(\"Micah\", \"Altman\", role = \"ctb\"), person(\"Luc\", \"Anselin\", role = \"ctb\"), person(\"Renato\", \"Assunção\", role = \"ctb\"), person(\"Anil\", \"Bera\", role = \"ctb\"), person(\"Olaf\", \"Berke\", role = \"ctb\"), person(\"F. Guillaume\", \"Blanchet\", role = \"ctb\"), person(\"Marilia\", \"Carvalho\", role = \"ctb\"), person(\"Bjarke\", \"Christensen\", role = \"ctb\"), person(\"Yongwan\", \"Chun\", role = \"ctb\"), person(\"Carsten\", \"Dormann\", role = \"ctb\"), person(\"Stéphane\", \"Dray\", role = \"ctb\"), person(\"Dewey\", \"Dunnington\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0002-9415-4582\")), person(\"Virgilio\", \"Gómez-Rubio\", role = \"ctb\"), person(\"Malabika\", \"Koley\", role = \"ctb\"), person(\"Tomasz\", \"Kossowski\", role = \"ctb\", comment = c(ORCID = \"0000-0002-9976-4398\")), person(\"Elias\", \"Krainski\", role = \"ctb\"), person(\"Pierre\", \"Legendre\", role = \"ctb\"), person(\"Nicholas\", \"Lewin-Koh\", role = \"ctb\"), person(\"Angela\", \"Li\", role = \"ctb\"), person(\"Giovanni\", \"Millo\", role = \"ctb\"), person(\"Werner\", \"Mueller\", role = \"ctb\"), person(\"Hisaji\", \"Ono\", role = \"ctb\"), person(\"Josiah\", \"Parry\", role = \"ctb\", comment = c(ORCID = \"0000-0001-9910-865X\")), person(\"Pedro\", \"Peres-Neto\", role = \"ctb\"), person(\"Michał\", \"Pietrzak\", role = \"ctb\", comment = c(ORCID = \"0000-0002-9263-4478\")), person(\"Gianfranco\", \"Piras\", role = \"ctb\"), person(\"Markus\", \"Reder\", role = \"ctb\"), person(\"Jeff\", \"Sauer\", role = \"ctb\"), person(\"Michael\", \"Tiefelsdorf\", role = \"ctb\"), person(\"René\", \"Westerholt\", role=\"ctb\"), person(\"Justyna\", \"Wilk\", role = \"ctb\", comment = c(ORCID = \"0000-0003-1495-2910\")), person(\"Levi\", \"Wolf\", role = \"ctb\"), person(\"Danlin\", \"Yu\", role = \"ctb\"))",
+      "Depends": [
+        "R (>= 3.3.0)",
+        "methods",
+        "spData (>= 2.3.1)",
+        "sf"
+      ],
+      "Imports": [
+        "stats",
+        "deldir",
+        "boot (>= 1.3-1)",
+        "graphics",
+        "utils",
+        "grDevices",
+        "units",
+        "s2",
+        "e1071",
+        "sp (>= 1.0)"
+      ],
+      "Suggests": [
+        "spatialreg (>= 1.2-1)",
+        "Matrix",
+        "parallel",
+        "dbscan",
+        "RColorBrewer",
+        "lattice",
+        "xtable",
+        "foreign",
+        "igraph",
+        "RSpectra",
+        "knitr",
+        "classInt",
+        "tmap",
+        "spam",
+        "ggplot2",
+        "rmarkdown",
+        "tinytest",
+        "rgeoda (>= 0.0.11.1)",
+        "mipfp",
+        "Guerry",
+        "codingMatrices"
+      ],
+      "URL": "https://github.com/r-spatial/spdep/, https://r-spatial.github.io/spdep/",
+      "BugReports": "https://github.com/r-spatial/spdep/issues/",
+      "Description": "A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured join count statistics, 'APLE' ('Li et al.' ) <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I', 'Gearys C'  ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and 'Getis/Ord' G ('Ord' and 'Getis' 1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>, 'saddlepoint' approximations ('Tiefelsdorf' 2002) <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') <doi:10.1007/s00168-011-0492-y>. The implementation of most of these measures is described in 'Bivand' and 'Wong' (2018) <doi:10.1007/s11749-018-0599-x>, with further extensions in 'Bivand' (2022) <doi:10.1111/gean.12319>. 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) <doi:10.1016/0166-0462(95)02111-6>, as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) <doi:10.1080/17421772.2023.2256810>. Additions in 2024 include Local Indicators for Categorical Data based on 'Carrer et al.' (2021) <doi:10.1016/j.jas.2020.105306> and 'Bivand et al.' (2017) <doi:10.1016/j.spasta.2017.03.003>; also Weighted Multivariate Spatial Autocorrelation Measures ('Bavaud' 2024) <doi:10.1111/gean.12390>. <doi:10.1080/17421772.2023.2256810>. A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021)  <doi:10.1016/j.jas.2020.105306> and 'Bivand et al.' (2017)  <doi:10.1016/j.spasta.2017.03.003> was added in 1.3-7. Multivariate 'spatialdelta' ('Bavaud' 2024) <doi:10.1111/gean.12390> was added in 1.3-13 ('Bivand' 2025 <doi:10.26034/la.cdclsl.2025.8343>. From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'.",
+      "License": "GPL (>= 2)",
+      "VignetteBuilder": "knitr",
+      "RoxygenNote": "RoxygenNote: 6.1.1",
+      "NeedsCompilation": "yes",
+      "Author": "Roger Bivand [cre, aut] (ORCID: <https://orcid.org/0000-0003-2392-6140>), Micah Altman [ctb], Luc Anselin [ctb], Renato Assunção [ctb], Anil Bera [ctb], Olaf Berke [ctb], F. Guillaume Blanchet [ctb], Marilia Carvalho [ctb], Bjarke Christensen [ctb], Yongwan Chun [ctb], Carsten Dormann [ctb], Stéphane Dray [ctb], Dewey Dunnington [ctb] (ORCID: <https://orcid.org/0000-0002-9415-4582>), Virgilio Gómez-Rubio [ctb], Malabika Koley [ctb], Tomasz Kossowski [ctb] (ORCID: <https://orcid.org/0000-0002-9976-4398>), Elias Krainski [ctb], Pierre Legendre [ctb], Nicholas Lewin-Koh [ctb], Angela Li [ctb], Giovanni Millo [ctb], Werner Mueller [ctb], Hisaji Ono [ctb], Josiah Parry [ctb] (ORCID: <https://orcid.org/0000-0001-9910-865X>), Pedro Peres-Neto [ctb], Michał Pietrzak [ctb] (ORCID: <https://orcid.org/0000-0002-9263-4478>), Gianfranco Piras [ctb], Markus Reder [ctb], Jeff Sauer [ctb], Michael Tiefelsdorf [ctb], René Westerholt [ctb], Justyna Wilk [ctb] (ORCID: <https://orcid.org/0000-0003-1495-2910>), Levi Wolf [ctb], Danlin Yu [ctb]",
+      "Maintainer": "Roger Bivand <Roger.Bivand@nhh.no>",
+      "Repository": "CRAN"
+    },
     "stars": {
       "Package": "stars",
       "Version": "0.6-8",
@@ -6845,7 +7243,7 @@
     },
     "tzdb": {
       "Package": "tzdb",
-      "Version": "0.4.0",
+      "Version": "0.5.0",
       "Source": "Repository",
       "Title": "Time Zone Database Information",
       "Authors@R": "c( person(\"Davis\", \"Vaughan\", , \"davis@posit.co\", role = c(\"aut\", \"cre\")), person(\"Howard\", \"Hinnant\", role = \"cph\", comment = \"Author of the included date library\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )",
@@ -6854,20 +7252,20 @@
       "URL": "https://tzdb.r-lib.org, https://github.com/r-lib/tzdb",
       "BugReports": "https://github.com/r-lib/tzdb/issues",
       "Depends": [
-        "R (>= 3.5.0)"
+        "R (>= 4.0.0)"
       ],
       "Suggests": [
         "covr",
         "testthat (>= 3.0.0)"
       ],
       "LinkingTo": [
-        "cpp11 (>= 0.4.2)"
+        "cpp11 (>= 0.5.2)"
       ],
       "Biarch": "yes",
       "Config/Needs/website": "tidyverse/tidytemplate",
       "Config/testthat/edition": "3",
       "Encoding": "UTF-8",
-      "RoxygenNote": "7.2.3",
+      "RoxygenNote": "7.3.2",
       "NeedsCompilation": "yes",
       "Author": "Davis Vaughan [aut, cre], Howard Hinnant [cph] (Author of the included date library), Posit Software, PBC [cph, fnd]",
       "Maintainer": "Davis Vaughan <davis@posit.co>",
@@ -7012,6 +7410,52 @@
       "Maintainer": "Davis Vaughan <davis@posit.co>",
       "Repository": "CRAN"
     },
+    "viridis": {
+      "Package": "viridis",
+      "Version": "0.6.5",
+      "Source": "Repository",
+      "Type": "Package",
+      "Title": "Colorblind-Friendly Color Maps for R",
+      "Date": "2024-01-28",
+      "Authors@R": "c( person(\"Simon\", \"Garnier\", email = \"garnier@njit.edu\", role = c(\"aut\", \"cre\")), person(\"Noam\", \"Ross\", email = \"noam.ross@gmail.com\", role = c(\"ctb\", \"cph\")), person(\"Bob\", \"Rudis\", email = \"bob@rud.is\", role = c(\"ctb\", \"cph\")), person(\"Marco\", \"Sciaini\", email = \"sciaini.marco@gmail.com\", role = c(\"ctb\", \"cph\")), person(\"Antônio Pedro\", \"Camargo\", role = c(\"ctb\", \"cph\")), person(\"Cédric\", \"Scherer\", email = \"scherer@izw-berlin.de\", role = c(\"ctb\", \"cph\")) )",
+      "Maintainer": "Simon Garnier <garnier@njit.edu>",
+      "Description": "Color maps designed to improve graph readability for readers with  common forms of color blindness and/or color vision deficiency. The color  maps are also perceptually-uniform, both in regular form and also when  converted to black-and-white for printing. This package also contains  'ggplot2' bindings for discrete and continuous color and fill scales. A lean version of the package called 'viridisLite' that does not include the  'ggplot2' bindings can be found at  <https://cran.r-project.org/package=viridisLite>.",
+      "License": "MIT + file LICENSE",
+      "Encoding": "UTF-8",
+      "Depends": [
+        "R (>= 2.10)",
+        "viridisLite (>= 0.4.0)"
+      ],
+      "Imports": [
+        "ggplot2 (>= 1.0.1)",
+        "gridExtra"
+      ],
+      "Suggests": [
+        "hexbin (>= 1.27.0)",
+        "scales",
+        "MASS",
+        "knitr",
+        "dichromat",
+        "colorspace",
+        "httr",
+        "mapproj",
+        "vdiffr",
+        "svglite (>= 1.2.0)",
+        "testthat",
+        "covr",
+        "rmarkdown",
+        "maps",
+        "terra"
+      ],
+      "LazyData": "true",
+      "VignetteBuilder": "knitr",
+      "URL": "https://sjmgarnier.github.io/viridis/, https://github.com/sjmgarnier/viridis/",
+      "BugReports": "https://github.com/sjmgarnier/viridis/issues",
+      "RoxygenNote": "7.3.1",
+      "NeedsCompilation": "no",
+      "Author": "Simon Garnier [aut, cre], Noam Ross [ctb, cph], Bob Rudis [ctb, cph], Marco Sciaini [ctb, cph], Antônio Pedro Camargo [ctb, cph], Cédric Scherer [ctb, cph]",
+      "Repository": "CRAN"
+    },
     "viridisLite": {
       "Package": "viridisLite",
       "Version": "0.4.2",
@@ -7205,6 +7649,33 @@
       "Author": "Dewey Dunnington [aut, cre] (<https://orcid.org/0000-0002-9415-4582>), Edzer Pebesma [aut] (<https://orcid.org/0000-0001-8049-7069>), Anthony North [ctb]",
       "Repository": "CRAN"
     },
+    "writexl": {
+      "Package": "writexl",
+      "Version": "1.5.4",
+      "Source": "Repository",
+      "Type": "Package",
+      "Title": "Export Data Frames to Excel 'xlsx' Format",
+      "Authors@R": "c( person(\"Jeroen\", \"Ooms\", ,\"jeroenooms@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-4035-0289\")), person(\"John McNamara\", role = \"cph\", comment = \"Author of libxlsxwriter (see AUTHORS and COPYRIGHT files for details)\"))",
+      "Description": "Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter' <https://libxlsxwriter.github.io>. Fast and no Java or Excel required.",
+      "License": "BSD_2_clause + file LICENSE",
+      "Encoding": "UTF-8",
+      "URL": "https://ropensci.r-universe.dev/writexl https://docs.ropensci.org/writexl/",
+      "BugReports": "https://github.com/ropensci/writexl/issues",
+      "RoxygenNote": "7.0.2",
+      "Suggests": [
+        "spelling",
+        "readxl",
+        "nycflights13",
+        "testthat",
+        "bit64"
+      ],
+      "Language": "en-US",
+      "SystemRequirements": "zlib",
+      "NeedsCompilation": "yes",
+      "Author": "Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), John McNamara [cph] (Author of libxlsxwriter (see AUTHORS and COPYRIGHT files for details))",
+      "Maintainer": "Jeroen Ooms <jeroenooms@gmail.com>",
+      "Repository": "CRAN"
+    },
     "xfun": {
       "Package": "xfun",
       "Version": "0.52",
@@ -7340,6 +7811,34 @@
       "NeedsCompilation": "yes",
       "Repository": "CRAN"
     },
+    "zip": {
+      "Package": "zip",
+      "Version": "2.3.3",
+      "Source": "Repository",
+      "Title": "Cross-Platform 'zip' Compression",
+      "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Kuba\", \"Podgórski\", role = \"ctb\"), person(\"Rich\", \"Geldreich\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )",
+      "Description": "Cross-Platform 'zip' Compression Library. A replacement for the 'zip' function, that does not require any additional external tools on any platform.",
+      "License": "MIT + file LICENSE",
+      "URL": "https://github.com/r-lib/zip, https://r-lib.github.io/zip/",
+      "BugReports": "https://github.com/r-lib/zip/issues",
+      "Suggests": [
+        "covr",
+        "pillar",
+        "processx",
+        "R6",
+        "testthat",
+        "withr"
+      ],
+      "Config/Needs/website": "tidyverse/tidytemplate",
+      "Config/testthat/edition": "3",
+      "Config/usethis/last-upkeep": "2025-05-07",
+      "Encoding": "UTF-8",
+      "RoxygenNote": "7.3.2.9000",
+      "NeedsCompilation": "yes",
+      "Author": "Gábor Csárdi [aut, cre], Kuba Podgórski [ctb], Rich Geldreich [ctb], Posit Software, PBC [cph, fnd] (ROR: <https://ror.org/03wc8by49>)",
+      "Maintainer": "Gábor Csárdi <csardi.gabor@gmail.com>",
+      "Repository": "CRAN"
+    },
     "zoo": {
       "Package": "zoo",
       "Version": "1.8-13",

run_kpi_calculation.R

@@ -0,0 +1,3 @@
+# Wrapper script to set project_dir and run KPI calculation
+project_dir <- "esa"
+source("r_app/09_calculate_kpis.R")