})
# Verify farm_health_data exists and has content
if (!exists("farm_health_data") || nrow(farm_health_data) == 0) {
safe_log("farm_health_data not found or empty, generating default data", "WARNING")
# Create minimal fallback data
tryCatch({
# Get fields from boundaries
fields <- unique(AllPivots0$field)
# Create basic data frame with just field names
farm_health_data <- data.frame(
field = fields,
mean_ci = rep(NA, length(fields)),
ci_change = rep(NA, length(fields)),
ci_uniformity = rep(NA, length(fields)),
status = rep("Unknown", length(fields)),
anomaly_type = rep("Unknown", length(fields)),
priority_level = rep(5, length(fields)), # Low priority
age_weeks = rep(NA, length(fields)),
harvest_readiness = rep("Unknown", length(fields)),
stringsAsFactors = FALSE
)
safe_log("Created fallback farm_health_data with basic field information")
}, error = function(e) {
safe_log(paste("Error creating fallback farm_health_data:", e$message), "ERROR")
farm_health_data <<- data.frame(
field = character(),
mean_ci = numeric(),
ci_change = numeric(),
ci_uniformity = numeric(),
status = character(),
anomaly_type = character(),
priority_level = numeric(),
age_weeks = numeric(),
harvest_readiness = character(),
stringsAsFactors = FALSE
)
})
}
# Calculate farm health summary metrics
tryCatch({
# Generate farm health summary data
farm_health_data <- generate_farm_health_summary(
field_boundaries = AllPivots0,
ci_current = CI,
ci_previous = CI_m1,
harvesting_data = harvesting_data
)
# Log the summary data
safe_log(paste("Generated farm health summary with", nrow(farm_health_data), "fields"))
}, error = function(e) {
safe_log(paste("Error in farm health calculation:", e$message), "ERROR")
# Create empty dataframe if calculation failed
farm_health_data <- data.frame(
field = character(),
mean_ci = numeric(),
ci_change = numeric(),
ci_uniformity = numeric(),
status = character(),
anomaly_type = character(),
priority_level = numeric(),
age_weeks = numeric(),
harvest_readiness = character(),
stringsAsFactors = FALSE
)
})
# Render the velocity and acceleration indicators
tryCatch({
# Create and display the indicators using the imported utility function
velocity_plot <- create_velocity_acceleration_indicator(
health_data = farm_health_data,
ci_current = CI,
ci_prev1 = CI_m1,
ci_prev2 = CI_m2,
ci_prev3 = CI_m3,
field_boundaries = AllPivots0
)
# Print the visualization
print(velocity_plot)
# Create a table of fields with significant velocity changes
field_ci_metrics <- list()
# Process each field to get metrics
fields <- unique(AllPivots0$field)
for (field_name in fields) {
tryCatch({
# Get field boundary
field_shape <- AllPivots0 %>% dplyr::filter(field == field_name)
if (nrow(field_shape) == 0) next
# Extract CI values
ci_curr_values <- terra::extract(CI, field_shape)
ci_prev1_values <- terra::extract(CI_m1, field_shape)
# Calculate metrics
mean_ci_curr <- mean(ci_curr_values$CI, na.rm = TRUE)
mean_ci_prev1 <- mean(ci_prev1_values$CI, na.rm = TRUE)
velocity <- mean_ci_curr - mean_ci_prev1
# Store in list
field_ci_metrics[[field_name]] <- list(
field = field_name,
ci_current = mean_ci_curr,
ci_prev1 = mean_ci_prev1,
velocity = velocity
)
}, error = function(e) {
safe_log(paste("Error processing field", field_name, "for velocity table:", e$message), "WARNING")
})
}
# Convert list to data frame
velocity_df <- do.call(rbind, lapply(field_ci_metrics, function(x) {
data.frame(
field = x$field,
ci_current = round(x$ci_current, 2),
ci_prev1 = round(x$ci_prev1, 2),
velocity = round(x$velocity, 2),
direction = ifelse(x$velocity >= 0, "Improving", "Declining")
)
}))
# Select top 5 positive and top 5 negative velocity fields
top_positive <- velocity_df %>%
dplyr::filter(velocity > 0) %>%
dplyr::arrange(desc(velocity)) %>%
dplyr::slice_head(n = 5)
top_negative <- velocity_df %>%
dplyr::filter(velocity < 0) %>%
dplyr::arrange(velocity) %>%
dplyr::slice_head(n = 5)
# Display the tables if we have data
if (nrow(top_positive) > 0) {
cat("<h4>Fields with Fastest Improvement</h4>")
knitr::kable(top_positive %>%
dplyr::select(Field = field,
`Current CI` = ci_current,
`Previous CI` = ci_prev1,
`Weekly Change` = velocity))
}
if (nrow(top_negative) > 0) {
cat("<h4>Fields with Fastest Decline</h4>")
knitr::kable(top_negative %>%
dplyr::select(Field = field,
`Current CI` = ci_current,
`Previous CI` = ci_prev1,
`Weekly Change` = velocity))
}
}, error = function(e) {
safe_log(paste("Error rendering velocity visualization:", e$message), "ERROR")
cat("<div class='alert alert-danger'>Error generating velocity visualization.</div>")
})
# Generate anomaly timeline visualization
tryCatch({
# Use the imported function to create the anomaly timeline
anomaly_timeline <- create_anomaly_timeline(
field_boundaries = AllPivots0,
ci_data = CI_quadrant,
days_to_include = 90  # Show last 90 days of data
)
# Display the timeline
print(anomaly_timeline)
}, error = function(e) {
safe_log(paste("Error generating anomaly timeline:", e$message), "ERROR")
cat("<div class='alert alert-danger'>Error generating anomaly timeline visualization.</div>")
})
# Verify farm_health_data exists and has content
if (!exists("farm_health_data") || nrow(farm_health_data) == 0) {
safe_log("farm_health_data not found or empty, generating default data", "WARNING")
# Create minimal fallback data
tryCatch({
# Get fields from boundaries
fields <- unique(AllPivots0$field)
# Create basic data frame with just field names
farm_health_data <- data.frame(
field = fields,
mean_ci = rep(NA, length(fields)),
ci_change = rep(NA, length(fields)),
ci_uniformity = rep(NA, length(fields)),
status = rep("Unknown", length(fields)),
anomaly_type = rep("Unknown", length(fields)),
priority_level = rep(5, length(fields)), # Low priority
age_weeks = rep(NA, length(fields)),
harvest_readiness = rep("Unknown", length(fields)),
stringsAsFactors = FALSE
)
safe_log("Created fallback farm_health_data with basic field information")
}, error = function(e) {
safe_log(paste("Error creating fallback farm_health_data:", e$message), "ERROR")
farm_health_data <<- data.frame(
field = character(),
mean_ci = numeric(),
ci_change = numeric(),
ci_uniformity = numeric(),
status = character(),
anomaly_type = character(),
priority_level = numeric(),
age_weeks = numeric(),
harvest_readiness = character(),
stringsAsFactors = FALSE
)
})
}
# ADVANCED ANALYTICS FUNCTIONS
# Note: These functions are now imported from executive_report_utils.R
# The utility file contains functions for velocity/acceleration indicators,
# anomaly timeline creation, age cohort mapping, and cohort performance charts
safe_log("Using analytics functions from executive_report_utils.R")
# Chunk 1: setup_parameters
# Set up basic report parameters from input values
report_date <- params$report_date
mail_day <- params$mail_day
borders <- params$borders
use_breaks <- params$use_breaks  # Whether to use breaks or continuous spectrum in visualizations
# Environment setup notes (commented out)
# # Activeer de renv omgeving
# renv::activate()
# renv::deactivate()
# # Optioneel: Herstel de omgeving als dat nodig is
# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren
# renv::restore()
# Chunk 2: load_libraries
# Configure knitr options
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# Path management
library(here)
# Spatial data libraries
library(sf)
library(terra)
library(exactextractr)
# library(raster) - Removed as it's no longer maintained
# Data manipulation and visualization
library(tidyverse)  # Includes dplyr, ggplot2, etc.
library(tmap)
library(lubridate)
library(zoo)
# Machine learning
library(rsample)
library(caret)
library(randomForest)
library(CAST)
# Load custom utility functions
tryCatch({
source("report_utils.R")
}, error = function(e) {
message(paste("Error loading report_utils.R:", e$message))
# Try alternative path if the first one fails
tryCatch({
source(here::here("r_app", "report_utils.R"))
}, error = function(e) {
stop("Could not load report_utils.R from either location: ", e$message)
})
})
# Load executive report utilities
tryCatch({
source("executive_report_utils.R")
}, error = function(e) {
message(paste("Error loading executive_report_utils.R:", e$message))
# Try alternative path if the first one fails
tryCatch({
source(here::here("r_app", "executive_report_utils.R"))
}, error = function(e) {
stop("Could not load executive_report_utils.R from either location: ", e$message)
})
})
# Chunk 1: setup_parameters
# Set up basic report parameters from input values
report_date <- params$report_date
mail_day <- params$mail_day
borders <- params$borders
use_breaks <- params$use_breaks  # Whether to use breaks or continuous spectrum in visualizations
# Environment setup notes (commented out)
# # Activeer de renv omgeving
# renv::activate()
# renv::deactivate()
# # Optioneel: Herstel de omgeving als dat nodig is
# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren
# renv::restore()
# Chunk 2: load_libraries
# Configure knitr options
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# Path management
library(here)
# Spatial data libraries
library(sf)
library(terra)
library(exactextractr)
# library(raster) - Removed as it's no longer maintained
# Data manipulation and visualization
library(tidyverse)  # Includes dplyr, ggplot2, etc.
library(tmap)
library(lubridate)
library(zoo)
# Machine learning
library(rsample)
library(caret)
library(randomForest)
library(CAST)
# Load custom utility functions
tryCatch({
source("report_utils.R")
}, error = function(e) {
message(paste("Error loading report_utils.R:", e$message))
# Try alternative path if the first one fails
tryCatch({
source(here::here("r_app", "report_utils.R"))
}, error = function(e) {
stop("Could not load report_utils.R from either location: ", e$message)
})
})
# Load executive report utilities
tryCatch({
source("executive_report_utils.R")
}, error = function(e) {
message(paste("Error loading executive_report_utils.R:", e$message))
# Try alternative path if the first one fails
tryCatch({
source(here::here("r_app", "executive_report_utils.R"))
}, error = function(e) {
stop("Could not load executive_report_utils.R from either location: ", e$message)
})
})
# Chunk 1: setup_parameters
# Set up basic report parameters from input values
report_date <- params$report_date
mail_day <- params$mail_day
borders <- params$borders
use_breaks <- params$use_breaks  # Whether to use breaks or continuous spectrum in visualizations
# Environment setup notes (commented out)
# # Activeer de renv omgeving
# renv::activate()
# renv::deactivate()
# # Optioneel: Herstel de omgeving als dat nodig is
# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren
# renv::restore()
# Chunk 2: load_libraries
# Configure knitr options
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# Path management
library(here)
# Spatial data libraries
library(sf)
library(terra)
library(exactextractr)
# library(raster) - Removed as it's no longer maintained
# Data manipulation and visualization
library(tidyverse)  # Includes dplyr, ggplot2, etc.
library(tmap)
library(lubridate)
library(zoo)
# Machine learning
library(rsample)
library(caret)
library(randomForest)
library(CAST)
# Load custom utility functions
# tryCatch({
#   source("report_utils.R")
# }, error = function(e) {
#   message(paste("Error loading report_utils.R:", e$message))
#   # Try alternative path if the first one fails
#   tryCatch({
source(here::here("r_app", "report_utils.R"))
#   }, error = function(e) {
#     stop("Could not load report_utils.R from either location: ", e$message)
#   })
# })
# Load executive report utilities
# tryCatch({
#   source("executive_report_utils.R")
# }, error = function(e) {
#   message(paste("Error loading executive_report_utils.R:", e$message))
#   # Try alternative path if the first one fails
#   tryCatch({
source(here::here("r_app", "executive_report_utils.R"))
# Chunk 1: setup_parameters
# Set up basic report parameters from input values
report_date <- params$report_date
mail_day <- params$mail_day
borders <- params$borders
use_breaks <- params$use_breaks  # Whether to use breaks or continuous spectrum in visualizations
# Environment setup notes (commented out)
# # Activeer de renv omgeving
# renv::activate()
# renv::deactivate()
# # Optioneel: Herstel de omgeving als dat nodig is
# # Je kunt dit commentaar geven als je het normaal niet wilt uitvoeren
# renv::restore()
# Chunk 2: load_libraries
# Configure knitr options
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
# Path management
library(here)
# Spatial data libraries
library(sf)
library(terra)
library(exactextractr)
# library(raster) - Removed as it's no longer maintained
# Data manipulation and visualization
library(tidyverse)  # Includes dplyr, ggplot2, etc.
library(tmap)
library(lubridate)
library(zoo)
# Machine learning
library(rsample)
library(caret)
library(randomForest)
library(CAST)
# Load custom utility functions
# tryCatch({
#   source("report_utils.R")
# }, error = function(e) {
#   message(paste("Error loading report_utils.R:", e$message))
#   # Try alternative path if the first one fails
#   tryCatch({
source(here::here("r_app", "report_utils.R"))
#   }, error = function(e) {
#     stop("Could not load report_utils.R from either location: ", e$message)
#   })
# })
# Load executive report utilities
# tryCatch({
#   source("executive_report_utils.R")
# }, error = function(e) {
#   message(paste("Error loading executive_report_utils.R:", e$message))
#   # Try alternative path if the first one fails
#   tryCatch({
source(here::here("r_app", "exec_dashboard", "executive_report_utils.R"))
#   }, error = function(e) {
#     stop("Could not load executive_report_utils.R from either location: ", e$message)
#   })
# })
# Chunk 3: initialize_project_config
# Set the project directory from parameters
project_dir <- params$data_dir
# Source project parameters with error handling
# tryCatch({
source(here::here("r_app", "parameters_project.R"))
# }, error = function(e) {
#   stop("Error loading parameters_project.R: ", e$message)
# })
# Log initial configuration
safe_log("Starting the R Markdown script")
safe_log(paste("mail_day params:", params$mail_day))
safe_log(paste("report_date params:", params$report_date))
safe_log(paste("mail_day variable:", mail_day))
# Set locale for consistent date formatting
Sys.setlocale("LC_TIME", "C")
# Initialize date variables from parameters
today <- as.character(report_date)
mail_day_as_character <- as.character(mail_day)
# Calculate week days
report_date_as_week_day <- weekdays(lubridate::ymd(today))
days_of_week <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
# Calculate initial week number
week <- lubridate::week(today)
safe_log(paste("Initial week calculation:", week, "today:", today))
# Calculate previous dates for comparisons
today_minus_1 <- as.character(lubridate::ymd(today) - 7)
today_minus_2 <- as.character(lubridate::ymd(today) - 14)
today_minus_3 <- as.character(lubridate::ymd(today) - 21)
# Log the weekday calculations for debugging
safe_log(paste("Report date weekday:", report_date_as_week_day))
safe_log(paste("Weekday index:", which(days_of_week == report_date_as_week_day)))
safe_log(paste("Mail day:", mail_day_as_character))
safe_log(paste("Mail day index:", which(days_of_week == mail_day_as_character)))
# Adjust week calculation based on mail day
if (which(days_of_week == report_date_as_week_day) > which(days_of_week == mail_day_as_character)) {
safe_log("Adjusting weeks because of mail day")
week <- lubridate::week(today) + 1
today_minus_1 <- as.character(lubridate::ymd(today))
today_minus_2 <- as.character(lubridate::ymd(today) - 7)
today_minus_3 <- as.character(lubridate::ymd(today) - 14)
}
# Generate subtitle for report
subtitle_var <- paste("Report generated on", Sys.Date())
# Calculate week numbers for previous weeks
week_minus_1 <- week - 1
week_minus_2 <- week - 2
week_minus_3 <- week - 3
# Format current week with leading zeros
week <- sprintf("%02d", week)
# Get years for each date
year <- lubridate::year(today)
year_1 <- lubridate::year(today_minus_1)
year_2 <- lubridate::year(today_minus_2)
year_3 <- lubridate::year(today_minus_3)
# Load CI index data with error handling
tryCatch({
CI_quadrant <- readRDS(here::here(cumulative_CI_vals_dir, "All_pivots_Cumulative_CI_quadrant_year_v2.rds"))
safe_log("Successfully loaded CI quadrant data")
}, error = function(e) {
stop("Error loading CI quadrant data: ", e$message)
})
# Get file paths for different weeks using the utility function
tryCatch({
path_to_week_current = get_week_path(weekly_CI_mosaic, today, 0)
path_to_week_minus_1 = get_week_path(weekly_CI_mosaic, today, -1)
path_to_week_minus_2 = get_week_path(weekly_CI_mosaic, today, -2)
path_to_week_minus_3 = get_week_path(weekly_CI_mosaic, today, -3)
# Log the calculated paths
safe_log("Required mosaic paths:")
safe_log(paste("Path to current week:", path_to_week_current))
safe_log(paste("Path to week minus 1:", path_to_week_minus_1))
safe_log(paste("Path to week minus 2:", path_to_week_minus_2))
safe_log(paste("Path to week minus 3:", path_to_week_minus_3))
# Validate that files exist
if (!file.exists(path_to_week_current)) warning("Current week mosaic file does not exist: ", path_to_week_current)
if (!file.exists(path_to_week_minus_1)) warning("Week minus 1 mosaic file does not exist: ", path_to_week_minus_1)
if (!file.exists(path_to_week_minus_2)) warning("Week minus 2 mosaic file does not exist: ", path_to_week_minus_2)
if (!file.exists(path_to_week_minus_3)) warning("Week minus 3 mosaic file does not exist: ", path_to_week_minus_3)
# Load raster data with terra functions
CI    <- terra::rast(path_to_week_current)$CI
CI_m1 <- terra::rast(path_to_week_minus_1)$CI
CI_m2 <- terra::rast(path_to_week_minus_2)$CI
CI_m3 <- terra::rast(path_to_week_minus_3)$CI
}, error = function(e) {
stop("Error loading raster data: ", e$message)
})