f94a6317bd
- Changed gap calculation from Q25 to 2σ below median method (kpi_utils.R) - Integrated gap filling into script 80 with tile-based processing - Added Gap_score column to field analysis output (Excel/CSV/RDS) - Fixed memory issues by processing 25 tiles individually instead of merging - Fixed Field_id matching to ensure gap scores populate correctly Gap scores now calculate for all 1185 fields with range 0-11.25% Works with tile-based mosaics (Angata 5x5 grid) without memory errors
863 lines
33 KiB
R
863 lines
33 KiB
R
# 80_CALCULATE_KPIS.R (CONSOLIDATED KPI CALCULATION)
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# ============================================================================
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# UNIFIED KPI CALCULATION SCRIPT
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#
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# This script combines:
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# 1. Per-field weekly analysis (from 09c: field-level trends, phases, statuses)
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# 2. Farm-level KPI metrics (from old 09: 6 high-level indicators)
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#
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# FEATURES:
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# - Per-field analysis with SC-64 enhancements (4-week trends, CI percentiles, etc.)
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# - Farm-level KPI calculation (6 metrics for executive overview)
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# - Parallel processing (tile-aware, 1000+ fields supported)
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# - Comprehensive Excel + RDS + CSV exports (21 columns per spec)
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# - Test mode for development
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# CRITICAL INTEGRATIONS:
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#
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# 1. IMMINENT_PROB FROM HARVEST MODEL (MODEL_307)
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# [✓] Load script 31 output: {project}_week_{WW}_{YYYY}.csv
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# Columns: field, imminent_prob, detected_prob, week, year
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# [✓] LEFT JOIN to field_analysis_df by field
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# [✓] Use actual harvest probability data instead of placeholder
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#
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# 2. AGE FROM HARVEST.XLSX (SCRIPTS 22 & 23)
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# [✓] Load harvest.xlsx with planting_date (season_start)
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# [✓] Extract planting dates per field
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# [✓] Calculate Age_week = difftime(report_date, planting_date, units="weeks")
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#
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# COMMAND-LINE USAGE:
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# Option 1: Rscript 80_calculate_kpis.R 2026-01-14 angata
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# Arguments: [end_date] [project_dir]
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#
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# Option 2: Rscript 80_calculate_kpis.R 2026-01-14 angata 7
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# Arguments: [end_date] [project_dir] [offset_days]
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#
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# & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/80_calculate_kpis.R 2026-01-12 angata 7
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#
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# Usage in run_full_pipeline.R:
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# source("r_app/80_calculate_kpis.R")
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# main()
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# ============================================================================
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# NEXT INTEGRATIONS (See Linear issues for detailed requirements)
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# ============================================================================
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# 1. [✓] Load imminent_prob from script 31 (week_WW_YYYY.csv)
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# 2. [✓] Load planting_date from harvest.xlsx for field-specific age calculation
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# 3. [ ] Improve Status_trigger logic to use actual imminent_prob values
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# ============================================================================
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# ============================================================================
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# CONFIGURATION
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# ============================================================================
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FOUR_WEEK_TREND_STRONG_GROWTH_MIN <- 0.5
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FOUR_WEEK_TREND_GROWTH_MIN <- 0.1
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FOUR_WEEK_TREND_GROWTH_MAX <- 0.5
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FOUR_WEEK_TREND_NO_GROWTH_RANGE <- 0.1
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FOUR_WEEK_TREND_DECLINE_MAX <- -0.1
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FOUR_WEEK_TREND_DECLINE_MIN <- -0.5
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FOUR_WEEK_TREND_STRONG_DECLINE_MAX <- -0.5
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# CV TREND THRESHOLDS
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CV_TREND_THRESHOLD_SIGNIFICANT <- 0.05
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# CV_TREND_LONG_TERM (8-WEEK SLOPE) INTERPRETATION THRESHOLDS
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# Interpretation: Slope of CV over 8 weeks indicates field uniformity trend
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# Negative slope = CV decreasing = field becoming MORE uniform = GOOD
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# Positive slope = CV increasing = field becoming MORE patchy = BAD
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# Near zero = Homogenous growth (all crops progressing equally)
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CV_SLOPE_STRONG_IMPROVEMENT_MIN <- -0.03 # CV decreasing rapidly (>3% drop over 8 weeks)
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CV_SLOPE_IMPROVEMENT_MIN <- -0.02 # CV decreasing (2-3% drop over 8 weeks)
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CV_SLOPE_IMPROVEMENT_MAX <- -0.01 # Gradual improvement (1-2% drop over 8 weeks)
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CV_SLOPE_HOMOGENOUS_MIN <- -0.01 # Essentially stable (small natural variation)
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CV_SLOPE_HOMOGENOUS_MAX <- 0.01 # No change in uniformity (within ±1% over 8 weeks)
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CV_SLOPE_PATCHINESS_MIN <- 0.01 # Minor divergence (1-2% increase over 8 weeks)
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CV_SLOPE_PATCHINESS_MAX <- 0.02 # Growing patchiness (2-3% increase over 8 weeks)
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CV_SLOPE_SEVERE_MIN <- 0.02 # Severe fragmentation (>3% increase over 8 weeks)
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# PERCENTILE CALCULATIONS
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CI_PERCENTILE_LOW <- 0.10
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CI_PERCENTILE_HIGH <- 0.90
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# GERMINATION THRESHOLD (for germination_progress calculation)
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GERMINATION_CI_THRESHOLD <- 2.0
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# PLANTING DATE & AGE CONFIGURATION
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# Load from harvest.xlsx (scripts 22 & 23) - no fallback to uniform dates
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# HISTORICAL DATA LOOKBACK
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WEEKS_FOR_FOUR_WEEK_TREND <- 4
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WEEKS_FOR_CV_TREND_SHORT <- 2
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WEEKS_FOR_CV_TREND_LONG <- 8
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# ============================================================================
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# 1. Load required libraries
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# ============================================================================
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suppressPackageStartupMessages({
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library(here)
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library(sf)
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library(terra)
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library(dplyr)
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library(tidyr)
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library(lubridate)
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library(readr)
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library(readxl)
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library(writexl)
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library(purrr)
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library(furrr)
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library(future)
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library(caret)
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library(CAST)
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library(randomForest)
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tryCatch({
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library(torch)
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}, error = function(e) {
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message("Note: torch package not available - harvest model inference will be skipped")
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})
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})
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# ============================================================================
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# LOAD UTILITY FUNCTIONS FROM SEPARATED MODULES
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# ============================================================================
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tryCatch({
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source(here("r_app", "80_weekly_stats_utils.R"))
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}, error = function(e) {
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stop("Error loading 80_weekly_stats_utils.R: ", e$message)
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})
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tryCatch({
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source(here("r_app", "80_report_building_utils.R"))
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}, error = function(e) {
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stop("Error loading 80_report_building_utils.R: ", e$message)
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})
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tryCatch({
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source(here("r_app", "kpi_utils.R"))
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}, error = function(e) {
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stop("Error loading kpi_utils.R: ", e$message)
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})
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# ============================================================================
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# PHASE AND STATUS TRIGGER DEFINITIONS
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# ============================================================================
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PHASE_DEFINITIONS <- data.frame(
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phase = c("Germination", "Tillering", "Grand Growth", "Maturation"),
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age_start = c(0, 4, 17, 39),
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age_end = c(6, 16, 39, 200),
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stringsAsFactors = FALSE
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)
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STATUS_TRIGGERS <- data.frame(
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trigger = c(
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"germination_started",
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"germination_complete",
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"stress_detected_whole_field",
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"strong_recovery",
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"growth_on_track",
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"maturation_progressing",
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"harvest_ready"
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),
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age_min = c(0, 0, NA, NA, 4, 39, 45),
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age_max = c(6, 6, NA, NA, 39, 200, 200),
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description = c(
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"10% of field CI > 2",
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"70% of field CI >= 2",
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"CI decline > -1.5 + low CV",
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"CI increase > +1.5",
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"CI increasing consistently",
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"High CI, stable/declining",
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"Age 45+ weeks (ready to harvest)"
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),
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stringsAsFactors = FALSE
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)
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# ============================================================================
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# MAIN
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# ============================================================================
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# ============================================================================
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# MAIN
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# ============================================================================
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main <- function() {
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# Parse command-line arguments
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args <- commandArgs(trailingOnly = TRUE)
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# end_date (arg 1)
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# Priority: 1) Command-line arg, 2) Global end_date variable (for recursive calls), 3) Global end_date_str, 4) Sys.Date()
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end_date <- if (length(args) >= 1 && !is.na(args[1])) {
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as.Date(args[1])
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} else if (exists("end_date", envir = .GlobalEnv)) {
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global_date <- get("end_date", envir = .GlobalEnv)
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# Check if it's a valid Date with length > 0
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if (is.Date(global_date) && length(global_date) > 0 && !is.na(global_date)) {
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global_date
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} else if (exists("end_date_str", envir = .GlobalEnv)) {
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as.Date(get("end_date_str", envir = .GlobalEnv))
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} else {
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Sys.Date()
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}
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} else if (exists("end_date_str", envir = .GlobalEnv)) {
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as.Date(get("end_date_str", envir = .GlobalEnv))
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} else {
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Sys.Date()
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}
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# project_dir (arg 2)
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project_dir <- if (length(args) >= 2 && !is.na(args[2])) {
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as.character(args[2])
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} else if (exists("project_dir", envir = .GlobalEnv)) {
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get("project_dir", envir = .GlobalEnv)
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} else {
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"angata"
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}
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# offset (arg 3) - for backward compatibility with old 09
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offset <- if (length(args) >= 3 && !is.na(args[3])) {
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as.numeric(args[3])
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} else {
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7
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}
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# Validate end_date is a proper Date object
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if (is.null(end_date) || length(end_date) == 0 || !inherits(end_date, "Date")) {
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stop("ERROR: end_date is not valid. Got: ", class(end_date), " with length ", length(end_date))
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}
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assign("project_dir", project_dir, envir = .GlobalEnv)
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assign("end_date_str", format(end_date, "%Y-%m-%d"), envir = .GlobalEnv)
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message("\n", strrep("=", 70))
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message("80_CALCULATE_KPIs.R - CONSOLIDATED KPI CALCULATION")
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message(strrep("=", 70))
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message("Date:", format(end_date, "%Y-%m-%d"))
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message("Project:", project_dir)
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message("Mode: Per-field analysis (SC-64) + Farm-level KPIs")
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message("")
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# Load configuration and utilities
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# source(here("r_app", "crop_messaging_utils.R"))
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tryCatch({
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source(here("r_app", "parameters_project.R"))
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}, error = function(e) {
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stop("Error loading parameters_project.R: ", e$message)
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})
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tryCatch({
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source(here("r_app", "30_growth_model_utils.R"))
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}, error = function(e) {
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warning("30_growth_model_utils.R not found - yield prediction KPI will use placeholder data")
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})
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# ========== PER-FIELD ANALYSIS (SC-64) ==========
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message("\n", strrep("-", 70))
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message("PHASE 1: PER-FIELD WEEKLY ANALYSIS (SC-64 ENHANCEMENTS)")
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message(strrep("-", 70))
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current_week <- as.numeric(format(end_date, "%V"))
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year <- as.numeric(format(end_date, "%Y"))
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previous_week <- current_week - 1
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if (previous_week < 1) previous_week <- 52
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message(paste("Week:", current_week, "/ Year:", year))
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# Find tile files - approach from Script 20
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message("Finding tile files...")
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tile_pattern <- sprintf("week_%02d_%d_([0-9]{2})\\.tif", current_week, year)
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# Detect grid size subdirectory
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detected_grid_size <- NA
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if (dir.exists(weekly_tile_max)) {
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subfolders <- list.dirs(weekly_tile_max, full.names = FALSE, recursive = FALSE)
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grid_patterns <- grep("^\\d+x\\d+$", subfolders, value = TRUE)
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if (length(grid_patterns) > 0) {
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detected_grid_size <- grid_patterns[1]
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mosaic_dir <- file.path(weekly_tile_max, detected_grid_size)
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message(paste(" Using grid-size subdirectory:", detected_grid_size))
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}
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}
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tile_files <- list.files(mosaic_dir, pattern = tile_pattern, full.names = TRUE)
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if (length(tile_files) == 0) {
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stop(paste("No tile files found for week", current_week, year, "in", mosaic_dir))
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}
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message(paste(" Found", length(tile_files), "tiles"))
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# Load field boundaries
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tryCatch({
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boundaries_result <- load_field_boundaries(data_dir)
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if (is.list(boundaries_result) && "field_boundaries_sf" %in% names(boundaries_result)) {
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field_boundaries_sf <- boundaries_result$field_boundaries_sf
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} else {
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field_boundaries_sf <- boundaries_result
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}
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if (nrow(field_boundaries_sf) == 0) {
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stop("No fields loaded from boundaries")
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}
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message(paste(" Loaded", nrow(field_boundaries_sf), "fields"))
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}, error = function(e) {
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stop("ERROR loading field boundaries: ", e$message)
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})
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message("Loading historical field data for trend calculations...")
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# Load up to 8 weeks (max of 4-week and 8-week trend requirements)
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# Function gracefully handles missing weeks and loads whatever exists
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num_weeks_to_load <- max(WEEKS_FOR_FOUR_WEEK_TREND, WEEKS_FOR_CV_TREND_LONG) # Always 8
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message(paste(" Attempting to load up to", num_weeks_to_load, "weeks of historical data..."))
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# Only auto-generate on first call (not in recursive calls from within load_historical_field_data)
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allow_auto_gen <- !exists("_INSIDE_AUTO_GENERATE", envir = .GlobalEnv)
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historical_data <- load_historical_field_data(project_dir, current_week, reports_dir,
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num_weeks = num_weeks_to_load,
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auto_generate = allow_auto_gen,
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field_boundaries_sf = field_boundaries_sf)
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# Load harvest.xlsx for planting dates (season_start)
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message("\nLoading harvest data from harvest.xlsx for planting dates...")
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harvest_file_path <- file.path(data_dir, "harvest.xlsx")
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harvesting_data <- tryCatch({
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if (file.exists(harvest_file_path)) {
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harvest_raw <- readxl::read_excel(harvest_file_path)
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harvest_raw$season_start <- as.Date(harvest_raw$season_start)
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harvest_raw$season_end <- as.Date(harvest_raw$season_end)
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message(paste(" ✓ Loaded harvest data:", nrow(harvest_raw), "rows"))
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harvest_raw
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} else {
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message(paste(" WARNING: harvest.xlsx not found at", harvest_file_path))
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NULL
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}
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}, error = function(e) {
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message(paste(" ERROR loading harvest.xlsx:", e$message))
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NULL
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})
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planting_dates <- extract_planting_dates(harvesting_data, field_boundaries_sf)
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# Validate planting_dates
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if (is.null(planting_dates) || nrow(planting_dates) == 0) {
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message("WARNING: No planting dates available. Using NA for all fields.")
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planting_dates <- data.frame(
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field_id = field_boundaries_sf$field,
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date = rep(as.Date(NA), nrow(field_boundaries_sf)),
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stringsAsFactors = FALSE
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)
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}
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# SCRIPT 20 APPROACH: Loop through tiles, extract all fields from each tile
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# ============================================================================
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# NEW MODULAR APPROACH: Load/Calculate weekly stats, apply trends
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# ============================================================================
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# Build tile grid (needed by calculate_field_statistics)
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message("\nBuilding tile grid for current week...")
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tile_grid <- build_tile_grid(mosaic_dir, current_week, year)
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message("\nUsing modular RDS-based approach for weekly statistics...")
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# Load/calculate CURRENT week stats (from tiles or RDS cache)
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message("\n1. Loading/calculating CURRENT week statistics (week", current_week, ")...")
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current_stats <- load_or_calculate_weekly_stats(
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week_num = current_week,
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year = year,
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project_dir = project_dir,
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field_boundaries_sf = field_boundaries_sf,
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mosaic_dir = tile_grid$mosaic_dir,
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reports_dir = reports_dir,
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report_date = end_date
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)
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message(paste(" ✓ Loaded/calculated stats for", nrow(current_stats), "fields in current week"))
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# Load/calculate PREVIOUS week stats (from RDS cache or tiles)
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message("\n2. Loading/calculating PREVIOUS week statistics (week", previous_week, ")...")
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# Calculate report date for previous week (7 days before current)
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prev_report_date <- end_date - 7
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prev_stats <- load_or_calculate_weekly_stats(
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week_num = previous_week,
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year = year,
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project_dir = project_dir,
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field_boundaries_sf = field_boundaries_sf,
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mosaic_dir = tile_grid$mosaic_dir,
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reports_dir = reports_dir,
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report_date = prev_report_date
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)
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message(paste(" ✓ Loaded/calculated stats for", nrow(prev_stats), "fields in previous week"))
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message(paste(" Columns in prev_stats:", paste(names(prev_stats), collapse = ", ")))
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message(paste(" CV column non-NA values in prev_stats:", sum(!is.na(prev_stats$CV))))
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# Apply trend calculations (requires both weeks)
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message("\n3. Calculating trend columns...")
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current_stats <- calculate_kpi_trends(current_stats, prev_stats,
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project_dir = project_dir,
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reports_dir = reports_dir,
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current_week = current_week,
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year = year)
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message(paste(" ✓ Added Weekly_ci_change, CV_Trend_Short_Term, Four_week_trend, CV_Trend_Long_Term, nmr_of_weeks_analysed"))
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# Load weekly harvest probabilities from script 31 (if available)
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message("\n4. Loading harvest probabilities from script 31...")
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harvest_prob_file <- file.path(reports_dir, "kpis", "field_stats",
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sprintf("%s_harvest_imminent_week_%02d_%d.csv", project_dir, current_week, year))
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message(paste(" Looking for:", harvest_prob_file))
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imminent_prob_data <- tryCatch({
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if (file.exists(harvest_prob_file)) {
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prob_df <- readr::read_csv(harvest_prob_file, show_col_types = FALSE)
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message(paste(" ✓ Loaded harvest probabilities for", nrow(prob_df), "fields"))
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prob_df %>%
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select(field, imminent_prob, detected_prob) %>%
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rename(Field_id = field, Imminent_prob_actual = imminent_prob, Detected_prob = detected_prob)
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} else {
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message(paste(" INFO: Harvest probabilities not available (script 31 not run)"))
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NULL
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}
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}, error = function(e) {
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message(paste(" WARNING: Could not load harvest probabilities:", e$message))
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NULL
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})
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# ============================================================================
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# CALCULATE GAP FILLING KPI (2σ method from kpi_utils.R)
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# ============================================================================
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message("\nCalculating gap filling scores (2σ method)...")
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# Try single merged mosaic first, then fall back to merging tiles
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week_mosaic_file <- file.path(mosaic_dir, sprintf("week_%02d_%d.tif", current_week, year))
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gap_scores_df <- NULL
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|
||
if (file.exists(week_mosaic_file)) {
|
||
# Single merged mosaic exists - use it directly
|
||
tryCatch({
|
||
current_week_raster <- terra::rast(week_mosaic_file)
|
||
current_ci_band <- current_week_raster[[5]] # CI is the 5th band
|
||
names(current_ci_band) <- "CI"
|
||
|
||
message(paste(" Loaded single mosaic:", week_mosaic_file))
|
||
|
||
# Calculate gap scores for all fields
|
||
gap_result <- calculate_gap_filling_kpi(current_ci_band, field_boundaries_sf)
|
||
|
||
# Extract field-level results (use field column directly to match current_stats Field_id)
|
||
gap_scores_df <- gap_result$field_results %>%
|
||
mutate(Field_id = field) %>%
|
||
select(Field_id, gap_score)
|
||
|
||
message(paste(" ✓ Calculated gap scores for", nrow(gap_scores_df), "fields"))
|
||
message(paste(" Gap score range:", round(min(gap_scores_df$gap_score, na.rm=TRUE), 2), "-", round(max(gap_scores_df$gap_score, na.rm=TRUE), 2), "%"))
|
||
|
||
}, error = function(e) {
|
||
message(paste(" WARNING: Could not calculate gap scores from single mosaic:", e$message))
|
||
message(" Gap scores will be set to NA")
|
||
gap_scores_df <- NULL
|
||
})
|
||
|
||
} else {
|
||
# Single mosaic doesn't exist - check for tiles and process per-tile
|
||
message(" Single mosaic not found. Checking for tiles...")
|
||
|
||
# List all tiles for this week (e.g., week_04_2026_01.tif through week_04_2026_25.tif)
|
||
tile_pattern <- sprintf("week_%02d_%d_\\d{2}\\.tif$", current_week, year)
|
||
tile_files <- list.files(mosaic_dir, pattern = tile_pattern, full.names = TRUE)
|
||
|
||
if (length(tile_files) == 0) {
|
||
message(sprintf(" WARNING: No tiles found matching pattern: %s in %s", tile_pattern, mosaic_dir))
|
||
message(" Gap scores will be set to NA")
|
||
|
||
} else {
|
||
tryCatch({
|
||
message(sprintf(" Found %d tiles. Processing per-tile (memory efficient)...", length(tile_files)))
|
||
|
||
# Process each tile separately and accumulate results
|
||
all_tile_results <- list()
|
||
|
||
for (i in seq_along(tile_files)) {
|
||
tile_file <- tile_files[i]
|
||
|
||
# Load tile raster
|
||
tile_raster <- terra::rast(tile_file)
|
||
tile_ci_band <- tile_raster[[5]]
|
||
names(tile_ci_band) <- "CI"
|
||
|
||
# Calculate gap scores for fields in this tile
|
||
tile_gap_result <- calculate_gap_filling_kpi(tile_ci_band, field_boundaries_sf)
|
||
|
||
# Store results (only keep fields with non-NA scores, use field directly to match current_stats)
|
||
if (!is.null(tile_gap_result$field_results) && nrow(tile_gap_result$field_results) > 0) {
|
||
tile_results_clean <- tile_gap_result$field_results %>%
|
||
mutate(Field_id = field) %>%
|
||
select(Field_id, gap_score) %>%
|
||
filter(!is.na(gap_score))
|
||
|
||
if (nrow(tile_results_clean) > 0) {
|
||
all_tile_results[[i]] <- tile_results_clean
|
||
}
|
||
}
|
||
|
||
# Clear memory
|
||
rm(tile_raster, tile_ci_band, tile_gap_result)
|
||
gc(verbose = FALSE)
|
||
}
|
||
|
||
# Combine all tile results
|
||
if (length(all_tile_results) > 0) {
|
||
gap_scores_df <- bind_rows(all_tile_results)
|
||
|
||
# If a field appears in multiple tiles, take the maximum gap score
|
||
gap_scores_df <- gap_scores_df %>%
|
||
group_by(Field_id) %>%
|
||
summarise(gap_score = max(gap_score, na.rm = TRUE), .groups = "drop")
|
||
|
||
message(paste(" ✓ Calculated gap scores for", nrow(gap_scores_df), "fields across", length(all_tile_results), "tiles"))
|
||
message(paste(" Gap score range:", round(min(gap_scores_df$gap_score, na.rm=TRUE), 2), "-", round(max(gap_scores_df$gap_score, na.rm=TRUE), 2), "%"))
|
||
} else {
|
||
message(" WARNING: No gap scores calculated from any tiles")
|
||
gap_scores_df <- NULL
|
||
}
|
||
|
||
}, error = function(e) {
|
||
message(paste(" WARNING: Could not process tiles or calculate gap scores:", e$message))
|
||
message(" Gap scores will be set to NA")
|
||
gap_scores_df <- NULL
|
||
})
|
||
}
|
||
}
|
||
|
||
# ============================================================================
|
||
# Build final output dataframe with all 22 columns (including Gap_score)
|
||
# ============================================================================
|
||
|
||
message("\nBuilding final field analysis output...")
|
||
|
||
# Pre-calculate acreages with geometry validation
|
||
# This avoids geometry errors during field_analysis construction
|
||
acreage_lookup <- tryCatch({
|
||
lookup_df <- field_boundaries_sf %>%
|
||
sf::st_drop_geometry() %>%
|
||
as.data.frame() %>%
|
||
mutate(
|
||
geometry_valid = sapply(seq_len(nrow(field_boundaries_sf)), function(idx) {
|
||
tryCatch({
|
||
sf::st_is_valid(field_boundaries_sf[idx, ])
|
||
}, error = function(e) FALSE)
|
||
}),
|
||
area_ha = 0
|
||
)
|
||
|
||
# Calculate area for valid geometries
|
||
for (idx in which(lookup_df$geometry_valid)) {
|
||
tryCatch({
|
||
area_m2 <- as.numeric(sf::st_area(field_boundaries_sf[idx, ]))
|
||
lookup_df$area_ha[idx] <- area_m2 / 10000
|
||
}, error = function(e) {
|
||
lookup_df$area_ha[idx] <<- NA_real_
|
||
})
|
||
}
|
||
|
||
# Convert hectares to acres
|
||
lookup_df %>%
|
||
mutate(acreage = area_ha / 0.404686) %>%
|
||
select(field, acreage)
|
||
}, error = function(e) {
|
||
message(paste("Warning: Could not calculate acreages from geometries -", e$message))
|
||
data.frame(field = character(0), acreage = numeric(0))
|
||
})
|
||
|
||
field_analysis_df <- current_stats %>%
|
||
mutate(
|
||
# Column 2: Farm_Section (user fills)
|
||
Farm_Section = NA_character_,
|
||
# Column 3: Field_name (from GeoJSON - already have Field_id, can look up)
|
||
Field_name = Field_id,
|
||
# Column 4: Acreage (calculate from geometry)
|
||
Acreage = {
|
||
acreages_vec <- acreage_lookup$acreage[match(Field_id, acreage_lookup$field)]
|
||
if_else(is.na(acreages_vec), 0, acreages_vec)
|
||
},
|
||
# Columns 5-6: Already in current_stats (Mean_CI, Weekly_ci_change)
|
||
# Column 7: Four_week_trend (from current_stats)
|
||
# Column 8: Last_harvest_or_planting_date (from harvest.xlsx - season_start)
|
||
Last_harvest_or_planting_date = {
|
||
planting_dates$planting_date[match(Field_id, planting_dates$field_id)]
|
||
},
|
||
# Column 9: Age_week (calculated from report date and planting date)
|
||
Age_week = {
|
||
sapply(seq_len(nrow(current_stats)), function(idx) {
|
||
planting_dt <- Last_harvest_or_planting_date[idx]
|
||
if (is.na(planting_dt)) {
|
||
return(NA_real_)
|
||
}
|
||
round(as.numeric(difftime(end_date, planting_dt, units = "weeks")), 0)
|
||
})
|
||
},
|
||
# Column 10: Phase (recalculate based on updated Age_week)
|
||
Phase = {
|
||
sapply(Age_week, function(age) {
|
||
if (is.na(age)) return(NA_character_)
|
||
if (age >= 0 & age < 4) return("Germination")
|
||
if (age >= 4 & age < 17) return("Tillering")
|
||
if (age >= 17 & age < 39) return("Grand Growth")
|
||
if (age >= 39) return("Maturation")
|
||
NA_character_
|
||
})
|
||
},
|
||
# Column 11: nmr_of_weeks_analysed (already in current_stats from calculate_kpi_trends)
|
||
# Column 12: Germination_progress (calculated here from CI values)
|
||
# Bin Pct_pixels_CI_gte_2 into 10% intervals: 0-10%, 10-20%, ..., 80-90%, 90-95%, 95-100%
|
||
Germination_progress = sapply(Pct_pixels_CI_gte_2, function(pct) {
|
||
if (is.na(pct)) return(NA_character_)
|
||
if (pct >= 95) return("95-100%")
|
||
else if (pct >= 90) return("90-95%")
|
||
else if (pct >= 80) return("80-90%")
|
||
else if (pct >= 70) return("70-80%")
|
||
else if (pct >= 60) return("60-70%")
|
||
else if (pct >= 50) return("50-60%")
|
||
else if (pct >= 40) return("40-50%")
|
||
else if (pct >= 30) return("30-40%")
|
||
else if (pct >= 20) return("20-30%")
|
||
else if (pct >= 10) return("10-20%")
|
||
else return("0-10%")
|
||
}),
|
||
# Column 13: Imminent_prob (from script 31 or NA if not available)
|
||
Imminent_prob = {
|
||
if (!is.null(imminent_prob_data)) {
|
||
imminent_prob_data$Imminent_prob_actual[match(Field_id, imminent_prob_data$Field_id)]
|
||
} else {
|
||
rep(NA_real_, nrow(current_stats))
|
||
}
|
||
},
|
||
# Column 14: Status_Alert (based on harvest probability + crop health status)
|
||
# Priority order: Ready for harvest-check → Strong decline → Harvested/bare → NA
|
||
Status_Alert = {
|
||
sapply(seq_len(nrow(current_stats)), function(idx) {
|
||
imminent_prob <- Imminent_prob[idx]
|
||
age_w <- Age_week[idx]
|
||
weekly_ci_chg <- Weekly_ci_change[idx]
|
||
mean_ci_val <- Mean_CI[idx]
|
||
|
||
# Priority 1: Ready for harvest-check (imminent + mature cane ≥12 months)
|
||
if (!is.na(imminent_prob) && imminent_prob > 0.5 && !is.na(age_w) && age_w >= 52) {
|
||
return("Ready for harvest-check")
|
||
}
|
||
|
||
# Priority 2: Strong decline in crop health (drop ≥2 points but still >1.5)
|
||
if (!is.na(weekly_ci_chg) && weekly_ci_chg <= -2.0 && !is.na(mean_ci_val) && mean_ci_val > 1.5) {
|
||
return("Strong decline in crop health")
|
||
}
|
||
|
||
# Priority 3: Harvested/bare (Mean CI < 1.5)
|
||
if (!is.na(mean_ci_val) && mean_ci_val < 1.5) {
|
||
return("Harvested/bare")
|
||
}
|
||
|
||
# Fallback: no alert
|
||
NA_character_
|
||
})
|
||
},
|
||
# Columns 15-16: CI-based columns already in current_stats (CI_range, CI_Percentiles)
|
||
# Column 17: Already in current_stats (CV)
|
||
# Column 18: Already in current_stats (CV_Trend_Short_Term)
|
||
# Column 19: CV_Trend_Long_Term (from current_stats - raw slope value)
|
||
# Column 19b: CV_Trend_Long_Term_Category (categorical interpretation of slope)
|
||
# 3 classes: More uniform (slope < -0.01), Stable uniformity (-0.01 to 0.01), Less uniform (slope > 0.01)
|
||
CV_Trend_Long_Term_Category = {
|
||
sapply(current_stats$CV_Trend_Long_Term, function(slope) {
|
||
if (is.na(slope)) {
|
||
return(NA_character_)
|
||
} else if (slope < -0.01) {
|
||
return("More uniform")
|
||
} else if (slope > 0.01) {
|
||
return("Less uniform")
|
||
} else {
|
||
return("Stable uniformity")
|
||
}
|
||
})
|
||
},
|
||
# Columns 20-21: Already in current_stats (Cloud_pct_clear, Cloud_category)
|
||
# Bin Cloud_pct_clear into 10% intervals: 0-10%, 10-20%, ..., 80-90%, 90-95%, 95-100%
|
||
Cloud_pct_clear = sapply(Cloud_pct_clear, function(pct) {
|
||
if (is.na(pct)) return(NA_character_)
|
||
if (pct >= 95) return("95-100%")
|
||
else if (pct >= 90) return("90-95%")
|
||
else if (pct >= 80) return("80-90%")
|
||
else if (pct >= 70) return("70-80%")
|
||
else if (pct >= 60) return("60-70%")
|
||
else if (pct >= 50) return("50-60%")
|
||
else if (pct >= 40) return("40-50%")
|
||
else if (pct >= 30) return("30-40%")
|
||
else if (pct >= 20) return("20-30%")
|
||
else if (pct >= 10) return("10-20%")
|
||
else return("0-10%")
|
||
}),
|
||
# Column 22: Gap_score (2σ below median - from kpi_utils.R)
|
||
Gap_score = {
|
||
if (!is.null(gap_scores_df) && nrow(gap_scores_df) > 0) {
|
||
# Debug: Print first few gap scores
|
||
message(sprintf(" Joining %d gap scores to field_analysis (first 3: %s)",
|
||
nrow(gap_scores_df),
|
||
paste(head(gap_scores_df$gap_score, 3), collapse=", ")))
|
||
message(sprintf(" First 3 Field_ids in gap_scores_df: %s",
|
||
paste(head(gap_scores_df$Field_id, 3), collapse=", ")))
|
||
message(sprintf(" First 3 Field_ids in current_stats: %s",
|
||
paste(head(current_stats$Field_id, 3), collapse=", ")))
|
||
|
||
gap_scores_df$gap_score[match(current_stats$Field_id, gap_scores_df$Field_id)]
|
||
} else {
|
||
rep(NA_real_, nrow(current_stats))
|
||
}
|
||
}
|
||
) %>%
|
||
select(
|
||
all_of(c("Field_id", "Farm_Section", "Field_name", "Acreage", "Status_Alert",
|
||
"Last_harvest_or_planting_date", "Age_week", "Phase",
|
||
"Germination_progress",
|
||
"Mean_CI", "Weekly_ci_change", "Four_week_trend", "CI_range", "CI_Percentiles",
|
||
"CV", "CV_Trend_Short_Term", "CV_Trend_Long_Term", "CV_Trend_Long_Term_Category",
|
||
"Imminent_prob", "Cloud_pct_clear", "Cloud_category", "Gap_score"))
|
||
)
|
||
|
||
message(paste("✓ Built final output with", nrow(field_analysis_df), "fields and 22 columns (including Gap_score)"))
|
||
|
||
export_paths <- export_field_analysis_excel(
|
||
field_analysis_df,
|
||
NULL,
|
||
project_dir,
|
||
current_week,
|
||
year,
|
||
reports_dir
|
||
)
|
||
|
||
cat("\n--- Per-field Results (first 10) ---\n")
|
||
available_cols <- c("Field_id", "Acreage", "Age_week", "Mean_CI", "Four_week_trend", "Status_Alert", "Cloud_category")
|
||
available_cols <- available_cols[available_cols %in% names(field_analysis_df)]
|
||
if (length(available_cols) > 0) {
|
||
print(head(field_analysis_df[, available_cols], 10))
|
||
}
|
||
|
||
# ========== FARM-LEVEL KPI AGGREGATION ==========
|
||
# Aggregate the per-field analysis into farm-level summary statistics
|
||
|
||
cat("\n=== CALCULATING FARM-LEVEL KPI SUMMARY ===\n")
|
||
|
||
# Filter to only fields that have actual data (non-NA CI and valid acreage)
|
||
field_data <- field_analysis_df %>%
|
||
filter(!is.na(Mean_CI) & !is.na(Acreage)) %>%
|
||
filter(Acreage > 0)
|
||
|
||
if (nrow(field_data) > 0) {
|
||
|
||
if (nrow(field_data) > 0) {
|
||
# Create summary statistics
|
||
farm_summary <- list()
|
||
|
||
# 1. PHASE DISTRIBUTION
|
||
phase_dist <- field_data %>%
|
||
group_by(Phase) %>%
|
||
summarise(
|
||
num_fields = n(),
|
||
acreage = sum(Acreage, na.rm = TRUE),
|
||
.groups = 'drop'
|
||
) %>%
|
||
rename(Category = Phase)
|
||
|
||
farm_summary$phase_distribution <- phase_dist
|
||
|
||
# 2. STATUS ALERT DISTRIBUTION
|
||
status_dist <- field_data %>%
|
||
group_by(Status_Alert) %>%
|
||
summarise(
|
||
num_fields = n(),
|
||
acreage = sum(Acreage, na.rm = TRUE),
|
||
.groups = 'drop'
|
||
) %>%
|
||
rename(Category = Status_Alert)
|
||
|
||
farm_summary$status_distribution <- status_dist
|
||
|
||
# 3. CLOUD COVERAGE DISTRIBUTION
|
||
cloud_dist <- field_data %>%
|
||
group_by(Cloud_category) %>%
|
||
summarise(
|
||
num_fields = n(),
|
||
acreage = sum(Acreage, na.rm = TRUE),
|
||
.groups = 'drop'
|
||
) %>%
|
||
rename(Category = Cloud_category)
|
||
|
||
farm_summary$cloud_distribution <- cloud_dist
|
||
|
||
# 4. OVERALL STATISTICS
|
||
farm_summary$overall_stats <- data.frame(
|
||
total_fields = nrow(field_data),
|
||
total_acreage = sum(field_data$Acreage, na.rm = TRUE),
|
||
mean_ci = round(mean(field_data$Mean_CI, na.rm = TRUE), 2),
|
||
median_ci = round(median(field_data$Mean_CI, na.rm = TRUE), 2),
|
||
mean_cv = round(mean(field_data$CI_CV, na.rm = TRUE), 4),
|
||
week = current_week,
|
||
year = year,
|
||
date = as.character(end_date)
|
||
)
|
||
|
||
# Print summaries
|
||
cat("\n--- PHASE DISTRIBUTION ---\n")
|
||
print(phase_dist)
|
||
|
||
cat("\n--- STATUS TRIGGER DISTRIBUTION ---\n")
|
||
print(status_dist)
|
||
|
||
cat("\n--- CLOUD COVERAGE DISTRIBUTION ---\n")
|
||
print(cloud_dist)
|
||
|
||
cat("\n--- OVERALL FARM STATISTICS ---\n")
|
||
print(farm_summary$overall_stats)
|
||
|
||
farm_kpi_results <- farm_summary
|
||
} else {
|
||
farm_kpi_results <- NULL
|
||
}
|
||
} else {
|
||
farm_kpi_results <- NULL
|
||
}
|
||
|
||
# ========== FINAL SUMMARY ==========
|
||
|
||
cat("\n", strrep("=", 70), "\n")
|
||
cat("80_CALCULATE_KPIs.R - COMPLETION SUMMARY\n")
|
||
cat(strrep("=", 70), "\n")
|
||
cat("Per-field analysis fields analyzed:", nrow(field_analysis_df), "\n")
|
||
cat("Excel export:", export_paths$excel, "\n")
|
||
cat("RDS export:", export_paths$rds, "\n")
|
||
cat("CSV export:", export_paths$csv, "\n")
|
||
|
||
if (!is.null(farm_kpi_results)) {
|
||
cat("\nFarm-level KPIs: CALCULATED\n")
|
||
} else {
|
||
cat("\nFarm-level KPIs: SKIPPED (no valid tile data extracted)\n")
|
||
}
|
||
|
||
cat("\n✓ Consolidated KPI calculation complete!\n")
|
||
cat(" - Per-field data exported\n")
|
||
cat(" - Farm-level KPIs calculated\n")
|
||
cat(" - All outputs in:", reports_dir, "\n\n")
|
||
}
|
||
|
||
if (sys.nframe() == 0) {
|
||
main()
|
||
}
|