diff --git a/r_app/30_growth_model_utils.R b/r_app/30_growth_model_utils.R index cca107e..189d968 100644 --- a/r_app/30_growth_model_utils.R +++ b/r_app/30_growth_model_utils.R @@ -71,54 +71,43 @@ load_combined_ci_data <- function(daily_vals_dir, harvesting_data = NULL) { safe_log(sprintf("Filtered to %d files within harvest season date range", length(all_daily_files))) } - # Set up parallel future plan (Windows PSOCK multisession; Mac/Linux can use forking) - # Automatically detect available cores and limit to reasonable number - n_cores <- min(parallel::detectCores() - 1, 8) # Use max 8 cores (diminishing returns after) - future::plan(strategy = future::multisession, workers = n_cores) - safe_log(sprintf("Using %d parallel workers for file I/O", n_cores)) - - # Parallel file reading: future_map_dfr processes each file in parallel - # Returns combined dataframe directly (no need to rbind) - combined_long <- furrr::future_map_dfr( - all_daily_files, - .progress = TRUE, - .options = furrr::furrr_options(seed = TRUE), - function(file) { - # Extract date from filename: {YYYY-MM-DD}.rds - filename <- basename(file) - date_str <- tools::file_path_sans_ext(filename) - - # Parse date - if (nchar(date_str) == 10 && grepl("^\\d{4}-\\d{2}-\\d{2}$", date_str)) { - parsed_date <- as.Date(date_str, format = "%Y-%m-%d") - } else { - return(data.frame()) # Return empty dataframe if parse fails - } - - if (is.na(parsed_date)) { - return(data.frame()) - } - - # Read RDS file - tryCatch({ - rds_data <- readRDS(file) - - if (is.null(rds_data) || nrow(rds_data) == 0) { - return(data.frame()) - } - - # Add date column to the data - rds_data %>% - dplyr::mutate(Date = parsed_date) - - }, error = function(e) { - return(data.frame()) # Return empty dataframe on error - }) - } - ) - - # Return to sequential processing to avoid nested parallelism - future::plan(future::sequential) + # Adaptive core count: scale with file count to avoid parallel overhead on small projects + n_files <- length(all_daily_files) + n_cores_io <- if (n_files < 200) { + 1 + } else if (n_files < 600) { + 2 + } else if (n_files < 1500) { + min(parallel::detectCores() - 1, 4) + } else { + min(parallel::detectCores() - 1, 8) + } + safe_log(sprintf("Using %d parallel workers for file I/O (%d files)", n_cores_io, n_files)) + + read_one_file <- function(file) { + filename <- basename(file) + date_str <- tools::file_path_sans_ext(filename) + if (nchar(date_str) != 10 || !grepl("^\\d{4}-\\d{2}-\\d{2}$", date_str)) return(data.frame()) + parsed_date <- as.Date(date_str, format = "%Y-%m-%d") + if (is.na(parsed_date)) return(data.frame()) + tryCatch({ + rds_data <- readRDS(file) + if (is.null(rds_data) || nrow(rds_data) == 0) return(data.frame()) + rds_data %>% dplyr::mutate(Date = parsed_date) + }, error = function(e) data.frame()) + } + + if (n_cores_io > 1) { + future::plan(strategy = future::multisession, workers = n_cores_io) + combined_long <- furrr::future_map_dfr( + all_daily_files, read_one_file, + .progress = TRUE, + .options = furrr::furrr_options(seed = TRUE) + ) + future::plan(future::sequential) + } else { + combined_long <- purrr::map_dfr(all_daily_files, read_one_file) + } if (nrow(combined_long) == 0) { safe_log("Warning: No valid CI data loaded from daily files", "WARNING") @@ -244,57 +233,81 @@ generate_interpolated_ci_data <- function(years, harvesting_data, ci_data) { failed_fields <- list() total_fields <- 0 successful_fields <- 0 - + + # Pre-compute total valid fields across all years to decide core count once + total_valid_fields <- sum(sapply(years, function(yr) { + sfs <- harvesting_data %>% + dplyr::filter(year == yr, !is.na(season_start)) %>% + dplyr::pull(sub_field) + sum(sfs %in% unique(ci_data$sub_field)) + })) + + # Adaptive core count: scale with field count, avoid parallel overhead for small projects + n_cores_interp <- if (total_valid_fields <= 1) { + 1 + } else if (total_valid_fields <= 10) { + 2 + } else if (total_valid_fields <= 50) { + min(parallel::detectCores() - 1, 4) + } else { + min(parallel::detectCores() - 1, 8) + } + + safe_log(sprintf("Interpolating %d fields across %d year(s) using %d worker(s)", + total_valid_fields, length(years), n_cores_interp)) + + # Set up parallel plan once before the year loop (avoid per-year startup cost) + if (n_cores_interp > 1) { + future::plan(strategy = future::multisession, workers = n_cores_interp) + } + # Process each year result <- purrr::map_df(years, function(yr) { # Get the fields harvested in this year with valid season start dates sub_fields <- harvesting_data %>% dplyr::filter(year == yr, !is.na(season_start)) %>% dplyr::pull(sub_field) - - if (length(sub_fields) == 0) { - return(data.frame()) - } - + + if (length(sub_fields) == 0) return(data.frame()) + # Filter sub_fields to only include those with value data in ci_data valid_sub_fields <- sub_fields %>% purrr::keep(~ any(ci_data$sub_field == .x)) - - if (length(valid_sub_fields) == 0) { - return(data.frame()) - } - + + if (length(valid_sub_fields) == 0) return(data.frame()) + total_fields <<- total_fields + length(valid_sub_fields) - safe_log(sprintf("Year %d: Processing %d fields in parallel", yr, length(valid_sub_fields))) - - # Set up parallel future plan for field interpolation - # Allocate 1 core per ~100 fields (with minimum 2 cores) - n_cores <- max(2, min(parallel::detectCores() - 1, ceiling(length(valid_sub_fields) / 100))) - future::plan(strategy = future::multisession, workers = n_cores) - - # PARALLELIZE: Process all fields in parallel (each extracts & interpolates independently) - result_list <- furrr::future_map( - valid_sub_fields, - .progress = TRUE, - .options = furrr::furrr_options(seed = TRUE), - function(field) { - # Call with verbose=FALSE to suppress warnings during parallel iteration - extract_CI_data(field, - harvesting_data = harvesting_data, - field_CI_data = ci_data, + safe_log(sprintf("Year %d: Processing %d fields", yr, length(valid_sub_fields))) + + # Process fields — parallel if workers > 1, otherwise plain map (no overhead) + if (n_cores_interp > 1) { + result_list <- furrr::future_map( + valid_sub_fields, + .progress = TRUE, + .options = furrr::furrr_options(seed = TRUE), + function(field) { + extract_CI_data(field, + harvesting_data = harvesting_data, + field_CI_data = ci_data, + season = yr, + verbose = FALSE) + } + ) + } else { + result_list <- purrr::map(valid_sub_fields, function(field) { + extract_CI_data(field, + harvesting_data = harvesting_data, + field_CI_data = ci_data, season = yr, - verbose = FALSE) - } - ) - - # Return to sequential processing - future::plan(future::sequential) - + verbose = TRUE) + }) + } + # Process results and tracking for (i in seq_along(result_list)) { field_result <- result_list[[i]] field_name <- valid_sub_fields[i] - + if (nrow(field_result) > 0) { successful_fields <<- successful_fields + 1 } else { @@ -305,15 +318,16 @@ generate_interpolated_ci_data <- function(years, harvesting_data, ci_data) { ) } } - + # Combine all results for this year - result_list <- result_list[sapply(result_list, nrow) > 0] # Keep only non-empty - if (length(result_list) > 0) { - purrr::list_rbind(result_list) - } else { - data.frame() - } + result_list <- result_list[sapply(result_list, nrow) > 0] + if (length(result_list) > 0) purrr::list_rbind(result_list) else data.frame() }) + + # Tear down parallel plan once after all years are processed + if (n_cores_interp > 1) { + future::plan(future::sequential) + } # Print summary safe_log(sprintf("\n=== Interpolation Summary ===")) diff --git a/r_app/90_CI_report_with_kpis_agronomic_support.Rmd b/r_app/90_CI_report_with_kpis_agronomic_support.Rmd index f4f8179..098d00b 100644 --- a/r_app/90_CI_report_with_kpis_agronomic_support.Rmd +++ b/r_app/90_CI_report_with_kpis_agronomic_support.Rmd @@ -498,59 +498,7 @@ tryCatch({ localisation <<- NULL }) -# tr_key() is defined in 90_report_utils.R (sourced above) - -# ============================================================================ -# SHARED TREND MAPPING HELPER -# ============================================================================ -# Canonical function for converting trend text to arrows/formatted text -# Normalizes all legacy and current trend category names to standardized output -# Used by: combined_kpi_table, field_details_table, and compact_field_display chunks -map_trend_to_arrow <- function(text_vec, include_text = FALSE) { - # Normalize: convert to character and lowercase - text_lower <- tolower(as.character(text_vec)) - - # Apply mapping to each element - sapply(text_lower, function(text) { - # Handle NA and empty values - if (is.na(text) || text == "" || nchar(trimws(text)) == 0) { - return(NA_character_) - } - - # Determine category and build output with translated labels - # Using word-boundary anchored patterns (perl=TRUE) to avoid substring mis-matches - if (grepl("\\bstrong growth\\b", text, perl = TRUE)) { - arrow <- "↑↑" - trans_key <- "Strong growth" - } else if (grepl("\\b(?:slight|weak) growth\\b|(? @@ -768,144 +716,12 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table `r tr_key("field_alerts")` ```{r field_alerts_table, echo=FALSE, results='asis'} -# Generate alerts for all fields -generate_field_alerts <- function(field_details_table) { - if (is.null(field_details_table) || nrow(field_details_table) == 0) { - return(NULL) # Return NULL to signal no data - } - - # Check for required columns - required_cols <- c("Field", "Growth Uniformity", "Yield Forecast (t/ha)", - "Gap Score", "Decline Risk", "Patchiness Risk", "Mean CI", "CV Value", "Moran's I") - missing_cols <- setdiff(required_cols, colnames(field_details_table)) - - if (length(missing_cols) > 0) { - message("Field details missing required columns: ", paste(missing_cols, collapse = ", ")) - return(NULL) # Return NULL if required columns are missing - } - - alerts_list <- list() - - # Get unique fields - unique_fields <- unique(field_details_table$Field) - - for (field_name in unique_fields) { - field_data <- field_details_table %>% filter(Field == field_name) - - # Aggregate data for the field - field_summary <- field_data %>% - summarise( - uniformity_levels = paste(unique(`Growth Uniformity`), collapse = "/"), - avg_yield_forecast = mean(`Yield Forecast (t/ha)`, na.rm = TRUE), - max_gap_score = max(`Gap Score`, na.rm = TRUE), - highest_decline_risk = case_when( - any(`Decline Risk` == "Very-high") ~ "Very-high", - any(`Decline Risk` == "High") ~ "High", - any(`Decline Risk` == "Moderate") ~ "Moderate", - any(`Decline Risk` == "Low") ~ "Low", - TRUE ~ "Unknown" - ), - highest_patchiness_risk = case_when( - any(`Patchiness Risk` == "High") ~ "High", - any(`Patchiness Risk` == "Medium") ~ "Medium", - any(`Patchiness Risk` == "Low") ~ "Low", - any(`Patchiness Risk` == "Minimal") ~ "Minimal", - TRUE ~ "Unknown" - ), - avg_mean_ci = mean(`Mean CI`, na.rm = TRUE), - avg_cv = mean(`CV Value`, na.rm = TRUE), - .groups = 'drop' - ) - - # Generate alerts for this field based on simplified CV-Moran's I priority system (3 levels) - field_alerts <- c() - - # Get CV and Moran's I values - avg_cv <- field_summary$avg_cv - morans_i <- mean(field_data[["Moran's I"]], na.rm = TRUE) - - # Determine priority level (1=Urgent, 2=Monitor, 3=No stress) - priority_level <- get_field_priority_level(avg_cv, morans_i) - - # Generate alerts based on priority level - if (priority_level == 1) { - field_alerts <- c(field_alerts, tr_key("priority")) - } else if (priority_level == 2) { - field_alerts <- c(field_alerts, tr_key("monitor")) - } - # Priority 3: No alert (no stress) - - # Keep other alerts for decline risk, patchiness risk, gap score - if (field_summary$highest_decline_risk %in% c("High", "Very-high")) { - field_alerts <- c(field_alerts, tr_key("growth_decline")) - } - if (field_summary$highest_patchiness_risk == "High") { - field_alerts <- c(field_alerts, tr_key("high_patchiness")) - } - if (field_summary$max_gap_score > 20) { - field_alerts <- c(field_alerts, tr_key("gaps_present")) - } - - # Only add alerts if there are any (skip fields with no alerts) - if (length(field_alerts) > 0) { - # Add to alerts list - for (alert in field_alerts) { - alerts_list[[length(alerts_list) + 1]] <- data.frame( - Field = field_name, - Alert = alert - ) - } - } - } - - # Combine all alerts - if (length(alerts_list) > 0) { - alerts_df <- do.call(rbind, alerts_list) - return(alerts_df) - } else { - return(data.frame(Field = character(), Alert = character())) - } -} +# generate_field_alerts() is defined in 90_report_utils.R (sourced above). +# field_details_table has already been normalised by normalize_field_details_columns(). # Generate and display alerts table if (exists("field_details_table") && !is.null(field_details_table) && nrow(field_details_table) > 0) { - # Adapter: Map normalized column names back to legacy names for generate_field_alerts() - # (generates from the normalized schema created by normalize_field_details_columns + column_mappings) - field_details_for_alerts <- field_details_table - - # Rename normalized columns back to legacy names (only if they exist) - if ("Field_id" %in% names(field_details_for_alerts)) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(Field = Field_id) - } - if ("Mean_CI" %in% names(field_details_for_alerts)) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`Mean CI` = Mean_CI) - } - if ("CV" %in% names(field_details_for_alerts) && !("CV Value" %in% names(field_details_for_alerts))) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`CV Value` = CV) - } - if ("TCH_Forecasted" %in% names(field_details_for_alerts)) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`Yield Forecast (t/ha)` = TCH_Forecasted) - } - if ("Gap_Score" %in% names(field_details_for_alerts)) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`Gap Score` = Gap_Score) - } - if ("Uniformity_Category" %in% names(field_details_for_alerts)) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`Growth Uniformity` = Uniformity_Category) - } - if ("Decline_Risk" %in% names(field_details_for_alerts)) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`Decline Risk` = Decline_Risk) - } - if ("Decline_Severity" %in% names(field_details_for_alerts) && !("Decline Risk" %in% names(field_details_for_alerts))) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`Decline Risk` = Decline_Severity) - } - if ("Patchiness_Risk" %in% names(field_details_for_alerts)) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`Patchiness Risk` = Patchiness_Risk) - } - if ("Morans_I" %in% names(field_details_for_alerts)) { - field_details_for_alerts <- field_details_for_alerts %>% dplyr::rename(`Moran's I` = Morans_I) - } - - alerts_data <- generate_field_alerts(field_details_for_alerts) + alerts_data <- generate_field_alerts(field_details_table) if (!is.null(alerts_data) && nrow(alerts_data) > 0) { ft <- flextable(alerts_data) %>% # set_caption("Field Alerts Summary") %>% @@ -1014,36 +830,23 @@ if (!exists("field_details_table") || is.null(field_details_table)) { tryCatch({ safe_log("Starting farm-level raster aggregation for overview maps") - # Helper function to safely aggregate mosaics for a specific week - aggregate_mosaics_safe <- function(week_num, year_num, label) { - tryCatch({ - safe_log(paste("Aggregating mosaics for", label, "(week", week_num, ",", year_num, ")")) - - # Call the utility function from 90_report_utils.R - # This function reads all per-field mosaics and merges them into a single raster - farm_mosaic <- aggregate_per_field_mosaics_to_farm_level( - weekly_mosaic_dir = weekly_CI_mosaic, - target_week = week_num, - target_year = year_num - ) - - if (!is.null(farm_mosaic)) { - safe_log(paste("✓ Successfully aggregated farm mosaic for", label, "")) - return(farm_mosaic) - } else { - safe_log(paste("Warning: Farm mosaic is NULL for", label), "WARNING") - return(NULL) - } - }, error = function(e) { - safe_log(paste("Error aggregating mosaics for", label, ":", e$message), "WARNING") - return(NULL) - }) - } - - # Aggregate mosaics for three weeks: current, week-1, week-3 - farm_mosaic_current <- aggregate_mosaics_safe(current_week, current_iso_year, "current week") - farm_mosaic_minus_1 <- aggregate_mosaics_safe(as.numeric(week_minus_1), week_minus_1_year, "week-1") - farm_mosaic_minus_3 <- aggregate_mosaics_safe(as.numeric(week_minus_3), week_minus_3_year, "week-3") + # Aggregate per-field mosaics into farm-level rasters for current, week-1, week-3 + # aggregate_per_field_mosaics_to_farm_level() is defined in 90_report_utils.R (sourced above) + farm_mosaic_current <- aggregate_per_field_mosaics_to_farm_level( + weekly_mosaic_dir = weekly_CI_mosaic, + target_week = current_week, + target_year = current_iso_year + ) + farm_mosaic_minus_1 <- aggregate_per_field_mosaics_to_farm_level( + weekly_mosaic_dir = weekly_CI_mosaic, + target_week = as.numeric(week_minus_1), + target_year = week_minus_1_year + ) + farm_mosaic_minus_3 <- aggregate_per_field_mosaics_to_farm_level( + weekly_mosaic_dir = weekly_CI_mosaic, + target_week = as.numeric(week_minus_3), + target_year = week_minus_3_year + ) # Extract CI band (5th band, or named "CI") from each aggregated mosaic farm_ci_current <- NULL @@ -1098,18 +901,7 @@ tryCatch({ AllPivots0_ll <- AllPivots0 target_crs <- "EPSG:4326" - downsample_raster <- function(r, max_cells = 2000000) { - if (is.null(r)) { - return(NULL) - } - n_cells <- terra::ncell(r) - if (!is.na(n_cells) && n_cells > max_cells) { - fact <- ceiling(sqrt(n_cells / max_cells)) - safe_log(paste("Downsampling raster by factor", fact), "INFO") - return(terra::aggregate(r, fact = fact, fun = mean, na.rm = TRUE)) - } - r - } + # downsample_raster() is defined in 90_report_utils.R (sourced above) if (!is.null(farm_ci_current) && !terra::is.lonlat(farm_ci_current)) { farm_ci_current_ll <- terra::project(farm_ci_current, target_crs, method = "bilinear") @@ -1383,14 +1175,8 @@ tryCatch({ dplyr::group_by(field) %>% dplyr::summarise(.groups = 'drop') - # Helper to get week/year from a date - get_week_year <- function(date) { - list( - week = as.numeric(format(date, "%V")), - year = as.numeric(format(date, "%G")) - ) - } - + # get_week_year() is defined in 90_report_utils.R (sourced above) + # Calculate week/year for current and historical weeks current_ww <- get_week_year(as.Date(today)) minus_1_ww <- get_week_year(as.Date(today) - lubridate::weeks(1)) @@ -1400,26 +1186,8 @@ tryCatch({ message(paste("Processing", nrow(AllPivots_merged), "fields for weeks:", current_ww$week, minus_1_ww$week, minus_2_ww$week, minus_3_ww$week)) - # Helper function to safely load per-field mosaic if it exists - load_per_field_mosaic <- function(base_dir, field_name, week, year) { - path <- file.path(base_dir, field_name, paste0("week_", sprintf("%02d", week), "_", year, ".tif")) - if (file.exists(path)) { - tryCatch({ - rast_obj <- terra::rast(path) - # Extract CI band if present, otherwise first band - if ("CI" %in% names(rast_obj)) { - return(rast_obj[["CI"]]) - } else if (nlyr(rast_obj) > 0) { - return(rast_obj[[1]]) - } - }, error = function(e) { - message(paste("Warning: Could not load", path, ":", e$message)) - return(NULL) - }) - } - return(NULL) - } - + # load_per_field_mosaic() is defined in 90_report_utils.R (sourced above) + # Iterate through fields using purrr::walk purrr::walk(AllPivots_merged$field, function(field_name) { tryCatch({ @@ -1558,38 +1326,7 @@ tryCatch({ }) ``` -```{r generate_subarea_visualizations, eval=FALSE, echo=FALSE, fig.height=3.8, fig.width=6.5, message=FALSE, warning=FALSE, dpi=150, results='asis'} -# 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") -}) -``` +cat("\\newpage\n\n") `r tr_key("detailed_field")` diff --git a/r_app/90_report_utils.R b/r_app/90_report_utils.R index a748fb7..b713290 100644 --- a/r_app/90_report_utils.R +++ b/r_app/90_report_utils.R @@ -1214,31 +1214,295 @@ generate_field_kpi_summary <- function(field_name, field_details_table, CI_quadr #' Normalize field_details_table column structure #' -#' Standardizes column names and ensures all expected KPI columns exist. -#' Handles Field → Field_id rename and injects missing columns as NA. +#' Standardizes column names from various legacy and pipeline-generated schemas +#' into a single canonical set, then ensures all expected KPI columns exist +#' (adding \code{NA} columns for any that are absent). #' -#' @param field_details_table data.frame to normalize -#' @return data.frame with standardized column structure +#' Rename rules applied in order: +#' \itemize{ +#' \item \code{Field} → \code{Field_id} +#' \item \code{Mean CI} → \code{Mean_CI} +#' \item \code{CV Value} → \code{CV} +#' \item \code{TCH_Forecasted} / \code{Yield Forecast (t/ha)} → \code{TCH_Forecasted} +#' \item \code{Gap Score} → \code{Gap_Score} +#' \item \code{Growth Uniformity} / \code{Uniformity_Category} → \code{Uniformity_Interpretation} +#' \item \code{Decline_Risk} → \code{Decline_Severity} +#' \item \code{Moran's I} / \code{Morans_I} → \code{Morans_I} +#' } +#' +#' @param field_details_table A data.frame to normalize. +#' @return A data.frame with standardized column names and all expected KPI +#' columns present (missing ones filled with \code{NA}). normalize_field_details_columns <- function(field_details_table) { if (is.null(field_details_table) || nrow(field_details_table) == 0) { return(field_details_table) } - - # Rename Field → Field_id if needed - if ("Field" %in% names(field_details_table) && !("Field_id" %in% names(field_details_table))) { - field_details_table <- field_details_table %>% - dplyr::rename(Field_id = Field) + + rename_if_missing <- function(df, from, to) { + if (from %in% names(df) && !to %in% names(df)) + df <- dplyr::rename(df, !!to := !!rlang::sym(from)) + df } - + + field_details_table <- field_details_table %>% + rename_if_missing("Field", "Field_id") %>% + rename_if_missing("Mean CI", "Mean_CI") %>% + rename_if_missing("CV Value", "CV") %>% + rename_if_missing("Yield Forecast (t/ha)", "TCH_Forecasted") %>% + rename_if_missing("Gap Score", "Gap_Score") %>% + rename_if_missing("Growth Uniformity", "Uniformity_Interpretation") %>% + rename_if_missing("Uniformity_Category", "Uniformity_Interpretation") %>% + rename_if_missing("Decline_Risk", "Decline_Severity") %>% + rename_if_missing("Moran's I", "Morans_I") + # Ensure all expected KPI columns exist; add as NA if missing - expected_cols <- c("Field_id", "Mean_CI", "CV", "TCH_Forecasted", "Gap_Score", - "Trend_Interpretation", "Weekly_CI_Change", "Uniformity_Interpretation", - "Decline_Severity", "Patchiness_Risk") + expected_cols <- c( + "Field_id", "Mean_CI", "CV", "Morans_I", "TCH_Forecasted", "Gap_Score", + "Trend_Interpretation", "Weekly_CI_Change", "Uniformity_Interpretation", + "Decline_Severity", "Patchiness_Risk" + ) for (col in expected_cols) { if (!col %in% names(field_details_table)) { field_details_table[[col]] <- NA } } - + return(field_details_table) } + +# ============================================================================== +# TREND / ARROW HELPERS +# ============================================================================== + +#' Map trend text to arrow symbols or formatted labels +#' +#' Converts trend category strings (e.g. \code{"strong growth"}, +#' \code{"slight decline"}) to Unicode arrow symbols, optionally combined with +#' translated text labels. Normalises legacy and current trend category names +#' to a canonical output. Vectorised over \code{text_vec}. +#' +#' @param text_vec Character vector of trend category strings. +#' @param include_text Logical. If \code{TRUE}, returns +#' \code{"Label (arrow)"}; if \code{FALSE} (default), returns the arrow +#' symbol only. +#' @return Character vector the same length as \code{text_vec}. \code{NA} is +#' returned for missing / empty inputs; an em-dash (\code{"—"}) is returned +#' for unrecognised values when \code{include_text = FALSE}. +#' @seealso \code{\link{tr_key}} +#' +map_trend_to_arrow <- function(text_vec, include_text = FALSE) { + text_lower <- tolower(as.character(text_vec)) + + sapply(text_lower, function(text) { + if (is.na(text) || nchar(trimws(text)) == 0) return(NA_character_) + + if (grepl("\\bstrong growth\\b", text, perl = TRUE)) { + arrow <- "↑↑"; trans_key <- "Strong growth" + } else if (grepl("\\b(?:slight|weak) growth\\b|(? max_cells) { + fact <- ceiling(sqrt(n_cells / max_cells)) + safe_log(paste("Downsampling raster by factor", fact), "INFO") + return(terra::aggregate(r, fact = fact, fun = fun, na.rm = TRUE)) + } + r +} + +#' Load the CI band from a per-field weekly mosaic +#' +#' Locates the weekly mosaic TIF for the given field and week via +#' \code{\link{get_per_field_mosaic_path}}, loads it with +#' \code{terra::rast()}, and returns the CI band (the layer named \code{"CI"}, +#' or the first layer as a fallback). +#' +#' @param base_dir Path to the \code{weekly_mosaic} directory. +#' @param field_name Name of the field sub-directory. +#' @param week ISO week number. +#' @param year ISO year. +#' @return A single-layer \code{SpatRaster} (CI band), or \code{NULL} if the +#' file does not exist or cannot be loaded. +#' @seealso \code{\link{get_per_field_mosaic_path}} +#' +load_per_field_mosaic <- function(base_dir, field_name, week, year) { + path <- get_per_field_mosaic_path(base_dir, field_name, week, year) + if (is.null(path)) return(NULL) + + tryCatch({ + rast_obj <- terra::rast(path) + if ("CI" %in% names(rast_obj)) { + return(rast_obj[["CI"]]) + } else if (terra::nlyr(rast_obj) > 0) { + return(rast_obj[[1]]) + } + NULL + }, error = function(e) { + safe_log(paste("Could not load mosaic:", path, "-", e$message), "WARNING") + NULL + }) +} + +# ============================================================================== +# FIELD ALERT GENERATION +# ============================================================================== + +#' Generate field-level alert flags from normalised KPI data +#' +#' Evaluates each field's CV, Moran's I, decline severity, patchiness risk, +#' and gap score against threshold rules, returning a tidy data frame of +#' translated alert messages. Only fields that trigger at least one alert are +#' included in the output. +#' +#' Expects a table that has been passed through +#' \code{\link{normalize_field_details_columns}}, which guarantees the columns +#' \code{Field_id}, \code{CV}, \code{Morans_I}, \code{Decline_Severity}, +#' \code{Patchiness_Risk}, and \code{Gap_Score} are present. +#' +#' Alert rules: +#' \itemize{ +#' \item Priority 1 (Urgent) or 2 (Monitor) from +#' \code{\link{get_field_priority_level}} based on CV / Moran's I. +#' \item Decline risk High or Very-high. +#' \item Patchiness risk High. +#' \item Gap score \eqn{> 20}. +#' } +#' +#' @param field_details_table A data frame normalised by +#' \code{\link{normalize_field_details_columns}}. +#' @return A data frame with columns \code{Field} and \code{Alert}, one row +#' per alert per field. Returns an empty 0-row data frame when no alerts +#' are triggered, or \code{NULL} if the input is empty / missing required +#' columns. +#' @seealso \code{\link{get_field_priority_level}}, \code{\link{normalize_field_details_columns}} +#' +generate_field_alerts <- function(field_details_table) { + if (is.null(field_details_table) || nrow(field_details_table) == 0) { + return(NULL) + } + + required_cols <- c("Field_id", "CV", "Morans_I", "Decline_Severity", + "Patchiness_Risk", "Gap_Score") + missing_cols <- setdiff(required_cols, names(field_details_table)) + if (length(missing_cols) > 0) { + safe_log(paste("generate_field_alerts: missing columns:", + paste(missing_cols, collapse = ", ")), "WARNING") + return(NULL) + } + + summaries <- field_details_table %>% + dplyr::group_by(Field_id) %>% + dplyr::summarise( + avg_cv = mean(CV, na.rm = TRUE), + avg_morans_i = mean(Morans_I, na.rm = TRUE), + max_gap = suppressWarnings(max(Gap_Score, na.rm = TRUE)), + highest_decline = dplyr::case_when( + any(Decline_Severity == "Very-high", na.rm = TRUE) ~ "Very-high", + any(Decline_Severity == "High", na.rm = TRUE) ~ "High", + any(Decline_Severity == "Moderate", na.rm = TRUE) ~ "Moderate", + any(Decline_Severity == "Low", na.rm = TRUE) ~ "Low", + TRUE ~ "Unknown" + ), + highest_patchiness = dplyr::case_when( + any(Patchiness_Risk == "High", na.rm = TRUE) ~ "High", + any(Patchiness_Risk == "Medium", na.rm = TRUE) ~ "Medium", + any(Patchiness_Risk == "Low", na.rm = TRUE) ~ "Low", + any(Patchiness_Risk == "Minimal", na.rm = TRUE) ~ "Minimal", + TRUE ~ "Unknown" + ), + .groups = "drop" + ) %>% + dplyr::mutate( + priority = purrr::map2_int(avg_cv, avg_morans_i, get_field_priority_level), + max_gap = dplyr::if_else(is.infinite(max_gap), NA_real_, max_gap) + ) + + alerts <- summaries %>% + dplyr::mutate( + a_priority = dplyr::case_when( + priority == 1 ~ tr_key("priority"), + priority == 2 ~ tr_key("monitor"), + TRUE ~ NA_character_ + ), + a_decline = dplyr::if_else( + highest_decline %in% c("High", "Very-high"), tr_key("growth_decline"), NA_character_ + ), + a_patch = dplyr::if_else( + highest_patchiness == "High", tr_key("high_patchiness"), NA_character_ + ), + a_gap = dplyr::if_else( + !is.na(max_gap) & max_gap > 20, tr_key("gaps_present"), NA_character_ + ) + ) %>% + tidyr::pivot_longer( + cols = c(a_priority, a_decline, a_patch, a_gap), + names_to = NULL, + values_to = "Alert" + ) %>% + dplyr::filter(!is.na(Alert)) %>% + dplyr::select(Field = Field_id, Alert) + + if (nrow(alerts) == 0) { + return(data.frame(Field = character(), Alert = character())) + } + + alerts +} diff --git a/r_app/translations/translations.xlsx b/r_app/translations/translations.xlsx index ed311a5..bf4973d 100644 Binary files a/r_app/translations/translations.xlsx and b/r_app/translations/translations.xlsx differ