Timon Weitkamp
GitHub
commit
685d35e579
12
.claude/settings.local.json
Normal file
12
.claude/settings.local.json
Normal file
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@ -0,0 +1,12 @@
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{
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"permissions": {
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"allow": [
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"Bash(python -c \":*)",
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"Bash(where python)",
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"Bash(where py)",
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"Bash(where python3)",
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"Bash(where conda)",
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"Bash(/c/Users/timon/AppData/Local/r-miniconda/python.exe -c \":*)"
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]
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}
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}
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@ -71,54 +71,43 @@ load_combined_ci_data <- function(daily_vals_dir, harvesting_data = NULL) {
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safe_log(sprintf("Filtered to %d files within harvest season date range", length(all_daily_files)))
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}
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# Set up parallel future plan (Windows PSOCK multisession; Mac/Linux can use forking)
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# Automatically detect available cores and limit to reasonable number
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n_cores <- min(parallel::detectCores() - 1, 8) # Use max 8 cores (diminishing returns after)
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future::plan(strategy = future::multisession, workers = n_cores)
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safe_log(sprintf("Using %d parallel workers for file I/O", n_cores))
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# Parallel file reading: future_map_dfr processes each file in parallel
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# Returns combined dataframe directly (no need to rbind)
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combined_long <- furrr::future_map_dfr(
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all_daily_files,
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.progress = TRUE,
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.options = furrr::furrr_options(seed = TRUE),
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function(file) {
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# Extract date from filename: {YYYY-MM-DD}.rds
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filename <- basename(file)
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date_str <- tools::file_path_sans_ext(filename)
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# Parse date
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if (nchar(date_str) == 10 && grepl("^\\d{4}-\\d{2}-\\d{2}$", date_str)) {
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parsed_date <- as.Date(date_str, format = "%Y-%m-%d")
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} else {
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return(data.frame()) # Return empty dataframe if parse fails
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}
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if (is.na(parsed_date)) {
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return(data.frame())
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}
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# Read RDS file
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tryCatch({
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rds_data <- readRDS(file)
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if (is.null(rds_data) || nrow(rds_data) == 0) {
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return(data.frame())
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}
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# Add date column to the data
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rds_data %>%
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dplyr::mutate(Date = parsed_date)
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}, error = function(e) {
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return(data.frame()) # Return empty dataframe on error
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})
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}
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)
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# Return to sequential processing to avoid nested parallelism
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future::plan(future::sequential)
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# Adaptive core count: scale with file count to avoid parallel overhead on small projects
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n_files <- length(all_daily_files)
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n_cores_io <- if (n_files < 200) {
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1
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} else if (n_files < 600) {
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2
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} else if (n_files < 1500) {
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min(parallel::detectCores() - 1, 4)
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} else {
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min(parallel::detectCores() - 1, 8)
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}
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safe_log(sprintf("Using %d parallel workers for file I/O (%d files)", n_cores_io, n_files))
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read_one_file <- function(file) {
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filename <- basename(file)
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date_str <- tools::file_path_sans_ext(filename)
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if (nchar(date_str) != 10 || !grepl("^\\d{4}-\\d{2}-\\d{2}$", date_str)) return(data.frame())
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parsed_date <- as.Date(date_str, format = "%Y-%m-%d")
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if (is.na(parsed_date)) return(data.frame())
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tryCatch({
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rds_data <- readRDS(file)
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if (is.null(rds_data) || nrow(rds_data) == 0) return(data.frame())
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rds_data %>% dplyr::mutate(Date = parsed_date)
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}, error = function(e) data.frame())
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}
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if (n_cores_io > 1) {
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future::plan(strategy = future::multisession, workers = n_cores_io)
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combined_long <- furrr::future_map_dfr(
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all_daily_files, read_one_file,
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.progress = TRUE,
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.options = furrr::furrr_options(seed = TRUE)
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)
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future::plan(future::sequential)
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} else {
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combined_long <- purrr::map_dfr(all_daily_files, read_one_file)
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}
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if (nrow(combined_long) == 0) {
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safe_log("Warning: No valid CI data loaded from daily files", "WARNING")
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@ -244,57 +233,81 @@ generate_interpolated_ci_data <- function(years, harvesting_data, ci_data) {
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failed_fields <- list()
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total_fields <- 0
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successful_fields <- 0
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# Pre-compute total valid fields across all years to decide core count once
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total_valid_fields <- sum(sapply(years, function(yr) {
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sfs <- harvesting_data %>%
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dplyr::filter(year == yr, !is.na(season_start)) %>%
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dplyr::pull(sub_field)
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sum(sfs %in% unique(ci_data$sub_field))
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}))
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# Adaptive core count: scale with field count, avoid parallel overhead for small projects
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n_cores_interp <- if (total_valid_fields <= 1) {
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1
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} else if (total_valid_fields <= 10) {
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2
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} else if (total_valid_fields <= 50) {
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min(parallel::detectCores() - 1, 4)
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} else {
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min(parallel::detectCores() - 1, 8)
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}
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safe_log(sprintf("Interpolating %d fields across %d year(s) using %d worker(s)",
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total_valid_fields, length(years), n_cores_interp))
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# Set up parallel plan once before the year loop (avoid per-year startup cost)
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if (n_cores_interp > 1) {
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future::plan(strategy = future::multisession, workers = n_cores_interp)
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}
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# Process each year
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result <- purrr::map_df(years, function(yr) {
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# Get the fields harvested in this year with valid season start dates
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sub_fields <- harvesting_data %>%
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dplyr::filter(year == yr, !is.na(season_start)) %>%
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dplyr::pull(sub_field)
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if (length(sub_fields) == 0) {
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return(data.frame())
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}
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if (length(sub_fields) == 0) return(data.frame())
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# Filter sub_fields to only include those with value data in ci_data
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valid_sub_fields <- sub_fields %>%
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purrr::keep(~ any(ci_data$sub_field == .x))
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if (length(valid_sub_fields) == 0) {
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return(data.frame())
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}
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if (length(valid_sub_fields) == 0) return(data.frame())
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total_fields <<- total_fields + length(valid_sub_fields)
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safe_log(sprintf("Year %d: Processing %d fields in parallel", yr, length(valid_sub_fields)))
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# Set up parallel future plan for field interpolation
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# Allocate 1 core per ~100 fields (with minimum 2 cores)
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n_cores <- max(2, min(parallel::detectCores() - 1, ceiling(length(valid_sub_fields) / 100)))
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future::plan(strategy = future::multisession, workers = n_cores)
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# PARALLELIZE: Process all fields in parallel (each extracts & interpolates independently)
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result_list <- furrr::future_map(
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valid_sub_fields,
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.progress = TRUE,
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.options = furrr::furrr_options(seed = TRUE),
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function(field) {
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# Call with verbose=FALSE to suppress warnings during parallel iteration
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extract_CI_data(field,
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harvesting_data = harvesting_data,
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field_CI_data = ci_data,
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safe_log(sprintf("Year %d: Processing %d fields", yr, length(valid_sub_fields)))
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# Process fields — parallel if workers > 1, otherwise plain map (no overhead)
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if (n_cores_interp > 1) {
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result_list <- furrr::future_map(
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valid_sub_fields,
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.progress = TRUE,
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.options = furrr::furrr_options(seed = TRUE),
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function(field) {
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extract_CI_data(field,
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harvesting_data = harvesting_data,
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field_CI_data = ci_data,
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season = yr,
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verbose = FALSE)
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}
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)
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} else {
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result_list <- purrr::map(valid_sub_fields, function(field) {
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extract_CI_data(field,
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harvesting_data = harvesting_data,
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field_CI_data = ci_data,
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season = yr,
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verbose = FALSE)
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}
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)
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# Return to sequential processing
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future::plan(future::sequential)
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verbose = TRUE)
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})
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}
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# Process results and tracking
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for (i in seq_along(result_list)) {
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field_result <- result_list[[i]]
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field_name <- valid_sub_fields[i]
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if (nrow(field_result) > 0) {
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successful_fields <<- successful_fields + 1
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} else {
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@ -305,15 +318,16 @@ generate_interpolated_ci_data <- function(years, harvesting_data, ci_data) {
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)
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}
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}
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# Combine all results for this year
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result_list <- result_list[sapply(result_list, nrow) > 0] # Keep only non-empty
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if (length(result_list) > 0) {
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purrr::list_rbind(result_list)
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} else {
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data.frame()
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}
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result_list <- result_list[sapply(result_list, nrow) > 0]
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if (length(result_list) > 0) purrr::list_rbind(result_list) else data.frame()
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})
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# Tear down parallel plan once after all years are processed
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if (n_cores_interp > 1) {
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future::plan(future::sequential)
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}
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# Print summary
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safe_log(sprintf("\n=== Interpolation Summary ==="))
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@ -12,8 +12,8 @@ params:
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facet_by_season: FALSE
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x_axis_unit: "days"
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output:
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word_document:
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reference_docx: !expr file.path("word-styles-reference-var1.docx")
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word_document:
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reference_docx: !expr file.path("word-styles-reference-var1.docx")
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toc: no
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editor_options:
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chunk_output_type: console
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@ -472,9 +472,8 @@ tryCatch({
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translations <- do.call(rbind, translation_list)
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if (!is.null(translations)) {
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safe_log("Translations file succesfully loaded")
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} else {
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safe_log("Failed to load translations", "ERROR")
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safe_log("Translations file successfully loaded")
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} else { safe_log("Failed to load translations", "ERROR")
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translations <- NULL
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}
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}, error = function(e) {
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@ -498,72 +497,7 @@ tryCatch({
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localisation <<- NULL
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})
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# Helper function to handle missing translation keys
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tr_key <- function(key) {
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if (key %in% names(tr)) {
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txt <- glue(tr[key], .envir = parent.frame())
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txt <- gsub("\n", " \n", txt)
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return(enc2utf8(as.character(txt)))
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} else if (is.na(key)) {
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return(tr_key("NA"))
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} else if (key == "") {
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return("")
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} else {
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return(paste0(key))
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}
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}
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# ============================================================================
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# SHARED TREND MAPPING HELPER
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# ============================================================================
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# Canonical function for converting trend text to arrows/formatted text
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# Normalizes all legacy and current trend category names to standardized output
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# Used by: combined_kpi_table, field_details_table, and compact_field_display chunks
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map_trend_to_arrow <- function(text_vec, include_text = FALSE) {
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# Normalize: convert to character and lowercase
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text_lower <- tolower(as.character(text_vec))
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# Apply mapping to each element
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sapply(text_lower, function(text) {
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# Handle NA and empty values
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if (is.na(text) || text == "" || nchar(trimws(text)) == 0) {
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return(NA_character_)
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}
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# Determine category and build output with translated labels
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# Using word-boundary anchored patterns (perl=TRUE) to avoid substring mis-matches
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if (grepl("\\bstrong growth\\b", text, perl = TRUE)) {
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arrow <- "↑↑"
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trans_key <- "Strong growth"
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} else if (grepl("\\b(?:slight|weak) growth\\b|(?<!no\\s)\\bgrowth\\b|\\bincreasing\\b", text, perl = TRUE)) {
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arrow <- "↑"
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trans_key <- "Slight growth"
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} else if (grepl("\\bstable\\b|\\bno growth\\b", text, perl = TRUE)) {
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arrow <- "→"
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trans_key <- "Stable"
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} else if (grepl("\\b(?:weak|slight|moderate) decline\\b", text, perl = TRUE)) {
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arrow <- "↓"
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trans_key <- "Slight decline"
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} else if (grepl("\\bstrong decline\\b|\\bsevere\\b", text, perl = TRUE)) {
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arrow <- "↓↓"
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trans_key <- "Strong decline"
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} else {
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# Fallback: return "—" (em-dash) for arrow-only mode, raw text for text mode
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# This signals an unmatched trend value that should be logged
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return(if (include_text) as.character(text) else "—")
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}
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# Get translated label using tr_key()
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label <- tr_key(trans_key)
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# Return formatted output based on include_text flag
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if (include_text) {
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paste0(label, " (", arrow, ")")
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} else {
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arrow
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}
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}, USE.NAMES = FALSE)
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}
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# tr_key() and map_trend_to_arrow() are defined in 90_report_utils.R (sourced above)
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```
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<!-- Dynamic cover page -->
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@ -781,144 +715,12 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
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`r tr_key("field_alerts")`
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```{r field_alerts_table, echo=FALSE, results='asis'}
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# Generate alerts for all fields
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generate_field_alerts <- function(field_details_table) {
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if (is.null(field_details_table) || nrow(field_details_table) == 0) {
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return(NULL) # Return NULL to signal no data
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}
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# Check for required columns
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required_cols <- c("Field", "Growth Uniformity", "Yield Forecast (t/ha)",
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"Gap Score", "Decline Risk", "Patchiness Risk", "Mean CI", "CV Value", "Moran's I")
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missing_cols <- setdiff(required_cols, colnames(field_details_table))
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if (length(missing_cols) > 0) {
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message("Field details missing required columns: ", paste(missing_cols, collapse = ", "))
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return(NULL) # Return NULL if required columns are missing
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}
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alerts_list <- list()
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# Get unique fields
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unique_fields <- unique(field_details_table$Field)
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for (field_name in unique_fields) {
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field_data <- field_details_table %>% filter(Field == field_name)
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# Aggregate data for the field
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field_summary <- field_data %>%
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summarise(
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uniformity_levels = paste(unique(`Growth Uniformity`), collapse = "/"),
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avg_yield_forecast = mean(`Yield Forecast (t/ha)`, na.rm = TRUE),
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max_gap_score = max(`Gap Score`, na.rm = TRUE),
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highest_decline_risk = case_when(
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any(`Decline Risk` == "Very-high") ~ "Very-high",
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any(`Decline Risk` == "High") ~ "High",
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any(`Decline Risk` == "Moderate") ~ "Moderate",
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any(`Decline Risk` == "Low") ~ "Low",
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TRUE ~ "Unknown"
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),
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highest_patchiness_risk = case_when(
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any(`Patchiness Risk` == "High") ~ "High",
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any(`Patchiness Risk` == "Medium") ~ "Medium",
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any(`Patchiness Risk` == "Low") ~ "Low",
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any(`Patchiness Risk` == "Minimal") ~ "Minimal",
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TRUE ~ "Unknown"
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),
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avg_mean_ci = mean(`Mean CI`, na.rm = TRUE),
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avg_cv = mean(`CV Value`, na.rm = TRUE),
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.groups = 'drop'
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)
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# Generate alerts for this field based on simplified CV-Moran's I priority system (3 levels)
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field_alerts <- c()
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# Get CV and Moran's I values
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avg_cv <- field_summary$avg_cv
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morans_i <- mean(field_data[["Moran's I"]], na.rm = TRUE)
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# Determine priority level (1=Urgent, 2=Monitor, 3=No stress)
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priority_level <- get_field_priority_level(avg_cv, morans_i)
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# Generate alerts based on priority level
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if (priority_level == 1) {
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field_alerts <- c(field_alerts, tr_key("priority"))
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} else if (priority_level == 2) {
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field_alerts <- c(field_alerts, tr_key("monitor"))
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}
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# Priority 3: No alert (no stress)
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# Keep other alerts for decline risk, patchiness risk, gap score
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if (field_summary$highest_decline_risk %in% c("High", "Very-high")) {
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field_alerts <- c(field_alerts, tr_key("growth_decline"))
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}
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if (field_summary$highest_patchiness_risk == "High") {
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field_alerts <- c(field_alerts, tr_key("high_patchiness"))
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}
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if (field_summary$max_gap_score > 20) {
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field_alerts <- c(field_alerts, tr_key("gaps_present"))
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}
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# Only add alerts if there are any (skip fields with no alerts)
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if (length(field_alerts) > 0) {
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# Add to alerts list
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for (alert in field_alerts) {
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alerts_list[[length(alerts_list) + 1]] <- data.frame(
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Field = field_name,
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Alert = alert
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)
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}
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}
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}
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# Combine all alerts
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if (length(alerts_list) > 0) {
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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") %>%
|
||||
|
|
@ -1027,36 +829,23 @@ 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
|
||||
|
|
@ -1111,18 +900,7 @@ 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")
|
||||
|
|
@ -1396,14 +1174,8 @@ 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))
|
||||
|
|
@ -1413,26 +1185,8 @@ 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({
|
||||
|
|
@ -1571,38 +1325,7 @@ 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")
|
||||
})
|
||||
```
|
||||
\newpage
|
||||
|
||||
`r tr_key("detailed_field")`
|
||||
|
||||
|
|
@ -1700,7 +1423,7 @@ if (!exists("field_details_table") || is.null(field_details_table) || nrow(field
|
|||
field_details_clean <- field_details_clean %>%
|
||||
select(
|
||||
field = Field_id,
|
||||
field_size = field_size_acres,
|
||||
field_size = field_size_area,
|
||||
mean_ci = Mean_CI,
|
||||
yield_forecast = TCH_Forecasted,
|
||||
gap_score = Gap_Score,
|
||||
|
|
|
|||
|
|
@ -24,8 +24,35 @@ subchunkify <- function(g, fig_height=7, fig_width=5) {
|
|||
"\n`","``
|
||||
")
|
||||
|
||||
cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
|
||||
}
|
||||
cat(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE))
|
||||
}
|
||||
|
||||
#' Translate a key using the global `tr` vector, with an optional fallback.
|
||||
#' Unified replacement for the Rmd's tr_key() — covers both markdown text and
|
||||
#' plot/map labels. Supports {variable} placeholders resolved from the caller.
|
||||
#' Falls back to `fallback` (if provided) or the key string itself when missing.
|
||||
tr_key <- function(key, fallback = NULL) {
|
||||
tr_exists <- exists("tr", envir = globalenv(), inherits = FALSE)
|
||||
|
||||
if (tr_exists && !is.na(key) && key %in% names(get("tr", envir = globalenv()))) {
|
||||
raw <- get("tr", envir = globalenv())[[key]]
|
||||
} else if (!is.null(fallback)) {
|
||||
raw <- as.character(fallback)
|
||||
} else if (is.na(key)) {
|
||||
return(tr_key("NA"))
|
||||
} else if (identical(key, "")) {
|
||||
return("")
|
||||
} else {
|
||||
return(enc2utf8(as.character(key)))
|
||||
}
|
||||
|
||||
result <- tryCatch(
|
||||
as.character(glue::glue(raw, .envir = parent.frame())),
|
||||
error = function(e) as.character(raw)
|
||||
)
|
||||
# Convert literal \n (as stored in Excel cells) to real newlines
|
||||
enc2utf8(gsub("\\n", "\n", result, fixed = TRUE))
|
||||
}
|
||||
|
||||
#' Creates a Chlorophyll Index map for a pivot
|
||||
#'
|
||||
|
|
@ -74,7 +101,7 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend =
|
|||
outliers.trunc = c(TRUE, TRUE)
|
||||
),
|
||||
col.legend = tm_legend(
|
||||
title = "CI",
|
||||
title = tr_key("map_legend_ci_title", "CI"),
|
||||
orientation = if (legend_is_portrait) "portrait" else "landscape",
|
||||
show = show_legend,
|
||||
position = if (show_legend) tm_pos_out(legend_position, "center") else c("left", "bottom"),
|
||||
|
|
@ -82,8 +109,9 @@ create_CI_map <- function(pivot_raster, pivot_shape, pivot_spans, show_legend =
|
|||
)
|
||||
)
|
||||
# Add layout elements
|
||||
age_days <- age * 7
|
||||
map <- map + tm_layout(
|
||||
main.title = paste0("Max CI week ", week,"\n", age, " weeks (", age * 7, " days) old"),
|
||||
main.title = tr_key("map_title_max_ci", "Max CI week {week}\n{age} weeks ({age_days} days) old"),
|
||||
main.title.size = 0.7,
|
||||
#legend.height = 0.85, # Constrain vertical legend height to not exceed map
|
||||
asp = 1 # Fixed aspect ratio
|
||||
|
|
@ -151,7 +179,7 @@ create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_lege
|
|||
outliers.trunc = c(TRUE, TRUE)
|
||||
),
|
||||
col.legend = tm_legend(
|
||||
title = "CI diff.",
|
||||
title = tr_key("map_legend_ci_diff", "CI diff."),
|
||||
orientation = if (legend_is_portrait) "portrait" else "landscape",
|
||||
show = show_legend,
|
||||
position = if (show_legend) tm_pos_out(legend_position, "center") else c("left", "bottom"),
|
||||
|
|
@ -159,8 +187,9 @@ create_CI_diff_map <- function(pivot_raster, pivot_shape, pivot_spans, show_lege
|
|||
)
|
||||
)
|
||||
# Add layout elements
|
||||
age_days <- age * 7
|
||||
map <- map + tm_layout(
|
||||
main.title = paste0("CI change week ", week_1, " - week ", week_2, "\n", age, " weeks (", age * 7, " days) old"),
|
||||
main.title = tr_key("map_title_ci_change", "CI change week {week_1} - week {week_2}\n{age} weeks ({age_days} days) old"),
|
||||
main.title.size = 0.7,
|
||||
#legend.height = 0.85, # Constrain vertical legend height to not exceed map
|
||||
asp = 1 # Fixed aspect ratio
|
||||
|
|
@ -344,7 +373,7 @@ ci_plot <- function(pivotName,
|
|||
|
||||
# Output heading and map to R Markdown
|
||||
age_months <- round(age / 4.348, 1)
|
||||
cat(paste("## Field", pivotName, "-", age, "weeks/", age_months, "months after planting/harvest", field_heading_note, "\n\n"))
|
||||
cat(paste0("## ", tr_key("field_section_header", "Field {pivotName} - {age} weeks/ {age_months} months after planting/harvest"), field_heading_note, "\n\n"))
|
||||
print(tst)
|
||||
|
||||
}, error = function(e) {
|
||||
|
|
@ -400,7 +429,11 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
mean_rolling_10_days = zoo::rollapplyr(value, width = 10, FUN = mean, partial = TRUE))
|
||||
|
||||
data_ci2 <- data_ci2 %>% dplyr::mutate(season = as.factor(season))
|
||||
|
||||
|
||||
# Resolved translated labels (used for y-axis labels and facet strip labels)
|
||||
rolling_mean_label <- tr_key("lbl_rolling_mean_ci", "10-Day Rolling Mean CI")
|
||||
cumulative_label <- tr_key("lbl_cumulative_ci", "Cumulative CI")
|
||||
|
||||
# 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)
|
||||
|
|
@ -411,8 +444,8 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
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",
|
||||
ci_type == "value" ~ rolling_mean_label,
|
||||
ci_type == "cumulative_CI" ~ cumulative_label,
|
||||
TRUE ~ ci_type
|
||||
),
|
||||
benchmark_label = paste0(percentile, "th Percentile")
|
||||
|
|
@ -454,9 +487,9 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
}
|
||||
|
||||
x_label <- switch(x_unit,
|
||||
"days" = if (facet_on) "Date" else "Age of Crop (Days)",
|
||||
"weeks" = "Week Number")
|
||||
|
||||
"days" = if (facet_on) tr_key("lbl_date", "Date") else tr_key("lbl_age_of_crop_days", "Age of Crop (Days)"),
|
||||
"weeks" = tr_key("lbl_week_number", "Week Number"))
|
||||
|
||||
# Calculate dynamic max values for breaks
|
||||
max_dah <- max(plot_data$DAH, na.rm = TRUE) + 20
|
||||
max_week <- max(as.numeric(plot_data$week), na.rm = TRUE) + ceiling(20 / 7)
|
||||
|
|
@ -473,12 +506,12 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
group = .data[["sub_field"]]
|
||||
)
|
||||
) +
|
||||
ggplot2::labs(title = paste("Plot of", y_label),
|
||||
color = "Field Name",
|
||||
ggplot2::labs(title = paste(tr_key("lbl_plot_of", "Plot of"), y_label),
|
||||
color = tr_key("lbl_field_name", "Field Name"),
|
||||
y = y_label,
|
||||
x = x_label) +
|
||||
ggplot2::scale_x_date(date_breaks = "1 month", date_labels = "%m-%Y",
|
||||
sec.axis = ggplot2::sec_axis(~ ., name = "Age in Months",
|
||||
sec.axis = ggplot2::sec_axis(~ ., name = tr_key("lbl_age_in_months", "Age in Months"),
|
||||
breaks = scales::breaks_pretty(),
|
||||
labels = function(x) round(as.numeric(x - min(x)) / 30.44, 1))) +
|
||||
ggplot2::theme_minimal() +
|
||||
|
|
@ -547,16 +580,16 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
),
|
||||
linewidth = 1.5, alpha = 1
|
||||
) +
|
||||
ggplot2::labs(title = paste("Plot of", y_label, "for Field", pivotName, title_suffix),
|
||||
color = "Season",
|
||||
ggplot2::labs(title = paste(tr_key("lbl_plot_of", "Plot of"), y_label, tr_key("lbl_for_field", "for Field"), pivotName, title_suffix),
|
||||
color = tr_key("lbl_season", "Season"),
|
||||
y = y_label,
|
||||
x = x_label) +
|
||||
color_scale +
|
||||
{
|
||||
if (x_var == "DAH") {
|
||||
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)))
|
||||
ggplot2::scale_x_continuous(breaks = seq(0, 450, by = 50), sec.axis = ggplot2::sec_axis(~ . / 30.44, name = tr_key("lbl_age_in_months", "Age in Months"), breaks = seq(0, 14, by = 1)))
|
||||
} else if (x_var == "week") {
|
||||
ggplot2::scale_x_continuous(breaks = seq(0, max_week, by = 4), sec.axis = ggplot2::sec_axis(~ . / 4.348, name = "Age in Months", breaks = seq(0, 14, by = 1)))
|
||||
ggplot2::scale_x_continuous(breaks = seq(0, max_week, by = 4), sec.axis = ggplot2::sec_axis(~ . / 4.348, name = tr_key("lbl_age_in_months", "Age in Months"), breaks = seq(0, 14, by = 1)))
|
||||
}
|
||||
} +
|
||||
ggplot2::theme_minimal() +
|
||||
|
|
@ -581,19 +614,19 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
|
||||
# Generate plots based on plot_type
|
||||
if (plot_type == "absolute") {
|
||||
g <- create_plot("mean_rolling_10_days", "10-Day Rolling Mean CI", "")
|
||||
g <- create_plot("mean_rolling_10_days", rolling_mean_label, "")
|
||||
subchunkify(g, 2.8, 10)
|
||||
} else if (plot_type == "cumulative") {
|
||||
g <- create_plot("cumulative_CI", "Cumulative CI", "")
|
||||
g <- create_plot("cumulative_CI", cumulative_label, "")
|
||||
subchunkify(g, 2.8, 10)
|
||||
} else if (plot_type == "both") {
|
||||
# Create faceted plot with both CI types using pivot_longer approach
|
||||
plot_data_both <- data_ci3 %>%
|
||||
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",
|
||||
ci_type == "mean_rolling_10_days" ~ rolling_mean_label,
|
||||
ci_type == "cumulative_CI" ~ cumulative_label,
|
||||
TRUE ~ ci_type
|
||||
),
|
||||
is_latest = season == latest_season # Flag for latest season
|
||||
|
|
@ -607,9 +640,9 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
}
|
||||
|
||||
x_label <- switch(x_unit,
|
||||
"days" = if (facet_on) "Date" else "Age of Crop (Days)",
|
||||
"weeks" = "Week Number")
|
||||
|
||||
"days" = if (facet_on) tr_key("lbl_date", "Date") else tr_key("lbl_age_of_crop_days", "Age of Crop (Days)"),
|
||||
"weeks" = tr_key("lbl_week_number", "Week Number"))
|
||||
|
||||
# Choose color palette based on colorblind_friendly flag
|
||||
color_scale <- if (colorblind_friendly) {
|
||||
ggplot2::scale_color_brewer(type = "qual", palette = "Set2")
|
||||
|
|
@ -620,7 +653,10 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
# Calculate dynamic max values for breaks
|
||||
max_dah_both <- max(plot_data_both$DAH, na.rm = TRUE) + 20
|
||||
max_week_both <- max(as.numeric(plot_data_both$week), na.rm = TRUE) + ceiling(20 / 7)
|
||||
|
||||
|
||||
# Pre-evaluate translated title here (not inside labs()) so {pivotName} resolves correctly
|
||||
both_plot_title <- tr_key("lbl_ci_analysis_title", "CI Analysis for Field {pivotName}")
|
||||
|
||||
# Create the faceted plot
|
||||
g_both <- ggplot2::ggplot(data = plot_data_both) +
|
||||
# Add benchmark lines first (behind season lines)
|
||||
|
|
@ -636,8 +672,8 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
DAH
|
||||
},
|
||||
ci_type_label = case_when(
|
||||
ci_type == "value" ~ "10-Day Rolling Mean CI",
|
||||
ci_type == "cumulative_CI" ~ "Cumulative CI",
|
||||
ci_type == "value" ~ rolling_mean_label,
|
||||
ci_type == "cumulative_CI" ~ cumulative_label,
|
||||
TRUE ~ ci_type
|
||||
)
|
||||
)
|
||||
|
|
@ -675,18 +711,18 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
),
|
||||
linewidth = 1.5, alpha = 1
|
||||
) +
|
||||
ggplot2::labs(title = paste("CI Analysis for Field", pivotName),
|
||||
color = "Season",
|
||||
y = "CI Value",
|
||||
ggplot2::labs(title = both_plot_title,
|
||||
color = tr_key("lbl_season", "Season"),
|
||||
y = tr_key("lbl_ci_value", "CI Value"),
|
||||
x = x_label) +
|
||||
color_scale +
|
||||
{
|
||||
if (x_var == "DAH") {
|
||||
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)))
|
||||
ggplot2::scale_x_continuous(breaks = seq(0, 450, by = 50), sec.axis = ggplot2::sec_axis(~ . / 30.44, name = tr_key("lbl_age_in_months", "Age in Months"), breaks = seq(0, 14, by = 1)))
|
||||
} else if (x_var == "week") {
|
||||
ggplot2::scale_x_continuous(breaks = seq(0, max_week_both, by = 4), sec.axis = ggplot2::sec_axis(~ . / 4.348, name = "Age in Months", breaks = seq(0, 14, by = 1)))
|
||||
ggplot2::scale_x_continuous(breaks = seq(0, max_week_both, by = 4), sec.axis = ggplot2::sec_axis(~ . / 4.348, name = tr_key("lbl_age_in_months", "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::scale_x_date(breaks = "1 month", date_labels = "%b-%Y", sec.axis = ggplot2::sec_axis(~ ., name = tr_key("lbl_age_in_months", "Age in Months"), breaks = scales::breaks_pretty()))
|
||||
}
|
||||
} +
|
||||
ggplot2::theme_minimal() +
|
||||
|
|
@ -707,7 +743,7 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
|
|||
|
||||
# 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_type_label = rolling_mean_label,
|
||||
ci_value = c(0, 7),
|
||||
stringsAsFactors = FALSE
|
||||
)
|
||||
|
|
@ -749,11 +785,14 @@ cum_ci_plot2 <- function(pivotName){
|
|||
date_seq <- seq.Date(from = start_date, to = end_date, by = "month")
|
||||
midpoint_date <- start_date + (end_date - start_date) / 2
|
||||
|
||||
# Pre-evaluate translated title here (not inside labs()) so {pivotName} resolves correctly
|
||||
fallback_title <- tr_key("lbl_rolling_mean_fallback", "14 day rolling MEAN CI rate - Field {pivotName}")
|
||||
|
||||
g <- ggplot() +
|
||||
scale_x_date(limits = c(start_date, end_date), date_breaks = "1 month", date_labels = "%m-%Y") +
|
||||
scale_y_continuous(limits = c(0, 4)) +
|
||||
labs(title = paste("14 day rolling MEAN CI rate - Field ", pivotName),
|
||||
x = "Date", y = "CI Rate") +
|
||||
labs(title = fallback_title,
|
||||
x = tr_key("lbl_date", "Date"), y = tr_key("lbl_ci_rate", "CI Rate")) +
|
||||
theme_minimal() +
|
||||
theme(axis.text.x = element_text(hjust = 0.5),
|
||||
legend.justification = c(1, 0),
|
||||
|
|
@ -761,7 +800,7 @@ cum_ci_plot2 <- function(pivotName){
|
|||
legend.position.inside = c(1, 0),
|
||||
legend.title = element_text(size = 8),
|
||||
legend.text = element_text(size = 8)) +
|
||||
annotate("text", x = midpoint_date, y = 2, label = "No data available", size = 6, hjust = 0.5)
|
||||
annotate("text", x = midpoint_date, y = 2, label = tr_key("lbl_no_data", "No data available"), size = 6, hjust = 0.5)
|
||||
|
||||
subchunkify(g, 3.2, 10)
|
||||
|
||||
|
|
@ -1175,31 +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|(?<!no\\s)\\bgrowth\\b|\\bincreasing\\b", text, perl = TRUE)) {
|
||||
arrow <- "↑"; trans_key <- "Slight growth"
|
||||
} else if (grepl("\\bstable\\b|\\bno growth\\b", text, perl = TRUE)) {
|
||||
arrow <- "→"; trans_key <- "Stable"
|
||||
} else if (grepl("\\b(?:weak|slight|moderate) decline\\b", text, perl = TRUE)) {
|
||||
arrow <- "↓"; trans_key <- "Slight decline"
|
||||
} else if (grepl("\\bstrong decline\\b|\\bsevere\\b", text, perl = TRUE)) {
|
||||
arrow <- "↓↓"; trans_key <- "Strong decline"
|
||||
} else {
|
||||
return(if (include_text) as.character(text) else "—")
|
||||
}
|
||||
|
||||
label <- tr_key(trans_key)
|
||||
if (include_text) paste0(label, " (", arrow, ")") else arrow
|
||||
}, USE.NAMES = FALSE)
|
||||
}
|
||||
|
||||
# ==============================================================================
|
||||
# DATE / WEEK HELPERS
|
||||
# ==============================================================================
|
||||
|
||||
#' Extract ISO week and year from a date
|
||||
#'
|
||||
#' Returns the ISO 8601 week number and the corresponding ISO year for a given
|
||||
#' date. Note that the ISO year may differ from the calendar year near
|
||||
#' year-end boundaries (e.g. 2024-12-30 is ISO week 1 of 2025).
|
||||
#'
|
||||
#' @param date A \code{Date} object or a string coercible to \code{Date}.
|
||||
#' @return A named list with elements:
|
||||
#' \describe{
|
||||
#' \item{\code{week}}{Integer ISO week number (1–53).}
|
||||
#' \item{\code{year}}{Integer ISO year.}
|
||||
#' }
|
||||
#'
|
||||
get_week_year <- function(date) {
|
||||
date <- as.Date(date)
|
||||
list(
|
||||
week = as.integer(format(date, "%V")),
|
||||
year = as.integer(format(date, "%G"))
|
||||
)
|
||||
}
|
||||
|
||||
# ==============================================================================
|
||||
# RASTER HELPERS
|
||||
# ==============================================================================
|
||||
|
||||
#' Downsample a SpatRaster to a maximum cell count
|
||||
#'
|
||||
#' Reduces the resolution of a raster by integer aggregation when the number
|
||||
#' of cells exceeds \code{max_cells}. The aggregation factor is the smallest
|
||||
#' integer that brings the cell count at or below the limit.
|
||||
#'
|
||||
#' @param r A \code{SpatRaster} object, or \code{NULL}.
|
||||
#' @param max_cells Maximum number of cells to retain (default 2,000,000).
|
||||
#' @param fun Aggregation function passed to \code{terra::aggregate()}
|
||||
#' (default \code{"mean"}).
|
||||
#' @return The (possibly downsampled) \code{SpatRaster}, or \code{NULL} if
|
||||
#' \code{r} is \code{NULL}.
|
||||
#'
|
||||
downsample_raster <- function(r, max_cells = 2000000, fun = "mean") {
|
||||
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 = 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
|
||||
}
|
||||
|
|
|
|||
|
|
@ -439,14 +439,14 @@
|
|||
rmarkdown::render(
|
||||
"r_app/90_CI_report_with_kpis_agronomic_support.Rmd",
|
||||
params = list(data_dir = "aura", report_date = as.Date("2026-02-18"), language = "en" ),
|
||||
output_file = "SmartCane_Report_agronomic_support_aura_2026-02-18_en.docx",
|
||||
output_file = "SmartCane_Report_agronomic_support_aura_2026-02-18_en_test.docx",
|
||||
output_dir = "laravel_app/storage/app/aura/reports"
|
||||
)
|
||||
|
||||
rmarkdown::render(
|
||||
"r_app/90_CI_report_with_kpis_agronomic_support.Rmd",
|
||||
params = list(data_dir = "aura", report_date = as.Date("2026-02-18"), language = "es" ),
|
||||
output_file = "SmartCane_Report_agronomic_support_aura_2026-02-18_es.docx",
|
||||
output_file = "SmartCane_Report_agronomic_support_aura_2026-02-18_es_test.docx",
|
||||
output_dir = "laravel_app/storage/app/aura/reports"
|
||||
)
|
||||
#
|
||||
|
|
@ -461,179 +461,3 @@ rmarkdown::render(
|
|||
output_dir = "laravel_app/storage/app/angata/reports"
|
||||
)
|
||||
#
|
||||
# EXPECTED OUTPUT:
|
||||
# File: SmartCane_Report_*_{PROJECT}_{DATE}.docx
|
||||
# Location: laravel_app/storage/app/{PROJECT}/reports/
|
||||
# Script execution time: 5-10 minutes
|
||||
#
|
||||
# NOTE:
|
||||
# These are R Markdown files and cannot be run directly via Rscript
|
||||
# Use rmarkdown::render() from an R interactive session or wrapper script
|
||||
# See run_full_pipeline.R for an automated example
|
||||
#
|
||||
# ============================================================================
|
||||
|
||||
|
||||
# ==============================================================================
|
||||
# QUICK REFERENCE: Common Workflows
|
||||
# ==============================================================================
|
||||
#
|
||||
# WORKFLOW A: Weekly Update (Most Common)
|
||||
# ─────────────────────────────────────────────────────────────────────────
|
||||
# Goal: Process latest week of data through full pipeline
|
||||
#
|
||||
# Parameters:
|
||||
# $PROJECT = "angata"
|
||||
# $END_DATE = "2026-02-04" # Today or latest date available
|
||||
# $OFFSET = 7 # One week back
|
||||
#
|
||||
# Steps:
|
||||
# 1. SKIP Python download (if you already have data)
|
||||
# 2. Run R10: & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/10_create_per_field_tiffs.R angata 2026-02-04 7
|
||||
# (Argument order: [PROJECT] [END_DATE] [OFFSET])
|
||||
# 3. Run R20: & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/20_ci_extraction_per_field.R angata 2026-02-04 7
|
||||
# 4. Run R30: & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/30_interpolate_growth_model.R angata
|
||||
# 5. Run R21: & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/21_convert_ci_rds_to_csv.R angata
|
||||
# 6. Run R40: & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/40_mosaic_creation_per_field.R 2026-02-04 7 angata
|
||||
# (Argument order: [END_DATE] [OFFSET] [PROJECT])
|
||||
# 7. Run R80: & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/80_calculate_kpis.R 2026-02-04 angata 7
|
||||
# (Argument order: [END_DATE] [PROJECT] [OFFSET] - DIFFERENT from R40!)
|
||||
# 8. OPTIONAL R91 (Cane Supply) - Use automated runner:
|
||||
# & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/run_full_pipeline.R
|
||||
# OR from R console:
|
||||
# rmarkdown::render("r_app/91_CI_report_with_kpis_cane_supply.Rmd",
|
||||
# params=list(data_dir="angata", report_date=as.Date("2026-02-04")),
|
||||
# output_file="SmartCane_Report_cane_supply_angata_2026-02-04.docx",
|
||||
# output_dir="laravel_app/storage/app/angata/reports")
|
||||
#
|
||||
# Execution time: ~60-90 minutes total
|
||||
#
|
||||
#
|
||||
# WORKFLOW B: Initial Setup (Large Backfill)
|
||||
# ─────────────────────────────────────────────────────────────────────────
|
||||
# Goal: Process multiple weeks of historical data
|
||||
#
|
||||
# Steps:
|
||||
# 1. Python download (your entire date range)
|
||||
# 2. Run R10 with large offset to process all historical dates:
|
||||
# & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/10_create_per_field_tiffs.R angata 2026-02-04 365
|
||||
# (This processes from 2025-02-04 to 2026-02-04, covering entire year)
|
||||
# 3. Run R20 with large offset to process all historical dates:
|
||||
# & "C:\Program Files\R\R-4.4.3\bin\x64\Rscript.exe" r_app/20_ci_extraction_per_field.R angata 2026-02-04 365
|
||||
# (This processes from 2025-02-04 to 2026-02-04, covering entire year)
|
||||
# 4. Run R30 once (growth model full season)
|
||||
# 5. Run R21 once (CSV export)
|
||||
# 6. Run R40 with specific week windows as needed
|
||||
# 7. Run R80 for each week you want KPIs for
|
||||
|
||||
# 6. For each week, run:
|
||||
# - R40 with different END_DATE values (one per week)
|
||||
# - R80 with different WEEK/YEAR values (one per week)
|
||||
# - R91 optional (one per week report)
|
||||
#
|
||||
# Pro tip: Script R40 with offset=14 covers two weeks at once
|
||||
# Then R40 again with offset=7 for just one week
|
||||
#
|
||||
#
|
||||
# WORKFLOW C: Troubleshooting (Check Intermediate Outputs)
|
||||
# ─────────────────────────────────────────────────────────────────────────
|
||||
# Goal: Verify outputs before moving to next step
|
||||
#
|
||||
# After R10: Check field_tiles/{FIELD_ID}/ has #dates files
|
||||
# After R20: Check field_tiles_CI/{FIELD_ID}/ has same #dates files
|
||||
# After R30: Check Data/extracted_ci/cumulative_vals/ has All_pivots_*.rds
|
||||
# After R40: Check weekly_mosaic/{FIELD_ID}/ has week_WW_YYYY.tif per week
|
||||
# After R80: Check output/ has {PROJECT}_field_analysis_week*.xlsx
|
||||
#
|
||||
# ============================================================================
|
||||
|
||||
# ==============================================================================
|
||||
# TROUBLESHOOTING
|
||||
# ==============================================================================
|
||||
#
|
||||
# ISSUE: R20 not processing all field_tiles files
|
||||
# ────────────────────────────────────────────────
|
||||
# Symptom: field_tiles has 496 files, field_tiles_CI only has 5
|
||||
#
|
||||
# Possible causes:
|
||||
# 1. Source files incomplete or corrupted
|
||||
# 2. Script 20 skips because CI TIFF already exists (even if incomplete)
|
||||
# 3. Partial run from previous attempt
|
||||
#
|
||||
# Solutions:
|
||||
# 1. Delete the small number of files in field_tiles_CI/{FIELD}/ (don't delete all!)
|
||||
# rm laravel_app/storage/app/angata/field_tiles_CI/{fieldnum}/*
|
||||
# 2. Re-run Script 20
|
||||
# 3. If still fails, delete field_tiles_CI completely and re-run Script 20
|
||||
# rm -r laravel_app/storage/app/angata/field_tiles_CI/
|
||||
#
|
||||
# ISSUE: Script 80 says "No per-field mosaic files found"
|
||||
# ────────────────────────────────────────────────────────
|
||||
# Symptom: R80 fails to calculate KPIs
|
||||
#
|
||||
# Possible causes:
|
||||
# 1. Script 40 hasn't run yet (weekly_mosaic doesn't exist)
|
||||
# 2. Wrong END_DATE or WEEK/YEAR combination
|
||||
# 3. weekly_mosaic/{FIELD}/ directory missing (old format?)
|
||||
#
|
||||
# Solutions:
|
||||
# 1. Ensure Script 40 has completed: Check weekly_mosaic/{FIELD}/ exists with week_WW_YYYY.tif
|
||||
# 2. Verify END_DATE is within date range of available CI data
|
||||
# 3. For current week: End date must be THIS week (same ISO week as today)
|
||||
#
|
||||
# ISSUE: Python download fails ("Not authorized")
|
||||
# ────────────────────────────────────────────────
|
||||
# Symptom: python 00_download_8band_pu_optimized.py fails with authentication error
|
||||
#
|
||||
# Cause: PLANET_API_KEY environment variable not set
|
||||
#
|
||||
# Solution:
|
||||
# 1. Save your Planet API key: $env:PLANET_API_KEY = "your_key_here"
|
||||
# 2. Verify: $env:PLANET_API_KEY (should show your key)
|
||||
# 3. Try download again
|
||||
#
|
||||
# ISSUE: R30 takes too long
|
||||
# ──────────────────────────
|
||||
# Symptom: Script 30 running for >30 minutes
|
||||
#
|
||||
# Cause: LOESS interpolation is slow with many dates/fields
|
||||
#
|
||||
# Solution:
|
||||
# 1. This is normal - large date ranges slow down interpolation
|
||||
# 2. Subsequent runs are faster (cached results)
|
||||
# 3. If needed: reduce offset or run fewer weeks at a time
|
||||
#
|
||||
# ==============================================================================
|
||||
|
||||
# ==============================================================================
|
||||
# SUMMARY OF FILES CREATED BY EACH SCRIPT
|
||||
# ==============================================================================
|
||||
#
|
||||
# Script 10 creates:
|
||||
# laravel_app/storage/app/{PROJECT}/field_tiles/{FIELD}/{DATE}.tif
|
||||
#
|
||||
# Script 20 creates:
|
||||
# laravel_app/storage/app/{PROJECT}/field_tiles_CI/{FIELD}/{DATE}.tif
|
||||
# laravel_app/storage/app/{PROJECT}/Data/extracted_ci/daily_vals/{FIELD}/{DATE}.rds
|
||||
#
|
||||
# Script 30 creates:
|
||||
# laravel_app/storage/app/{PROJECT}/Data/extracted_ci/cumulative_vals/All_pivots_Cumulative_CI_quadrant_year_v2.rds
|
||||
#
|
||||
# Script 21 creates:
|
||||
# laravel_app/storage/app/{PROJECT}/ci_data_for_python.csv
|
||||
#
|
||||
# Python 31 creates:
|
||||
# laravel_app/storage/app/{PROJECT}/reports/kpis/field_stats/{PROJECT}_harvest_imminent_week_{WW}_{YYYY}.csv
|
||||
#
|
||||
# Script 40 creates:
|
||||
# laravel_app/storage/app/{PROJECT}/weekly_mosaic/{FIELD}/{DATE}/week_{WW}_{YYYY}.tif
|
||||
#
|
||||
# Script 80 creates:
|
||||
# laravel_app/storage/app/{PROJECT}/output/{PROJECT}_field_analysis_week{WW}_{YYYY}.xlsx
|
||||
# laravel_app/storage/app/{PROJECT}/output/{PROJECT}_field_analysis_week{WW}_{YYYY}.rds
|
||||
#
|
||||
# Script 90/91 creates:
|
||||
# laravel_app/storage/app/{PROJECT}/output/SmartCane_Report_week{WW}_{YYYY}.docx
|
||||
#
|
||||
# ==============================================================================
|
||||
|
||||
|
|
|
|||
Binary file not shown.
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Reference in a new issue