# --- Phase 1: Load Data ---
# This script assumes you have already run the simulation and added the
# results to your CSV file.

# Check if the file exists before attempting to load it
if (!file.exists('analyze.csv')) {
  stop("Error: 'analyze.csv' not found. Please create it and run the simulation first.")
}

# Load the CSV file with the simulation results
df <- read.csv('analyze.csv', stringsAsFactors = FALSE)

# Define the price points
initial_price <- 4.50
new_price <- 5.50

# --- Phase 2: Analysis & Synthesis ---
# Calculate the revenue impact based on the simulation data

# Calculate initial weekly revenue per customer
df$initial_revenue <- df$initial_visits_per_week * initial_price

# Calculate projected new weekly revenue per customer
df$new_revenue <- df$new_visits_per_week * new_price

# Calculate total revenue before and after
total_initial_revenue <- sum(df$initial_revenue)
total_new_revenue <- sum(df$new_revenue)
revenue_change <- total_new_revenue - total_initial_revenue
percent_change <- (revenue_change / total_initial_revenue) * 100

# --- Phase 3: Reporting the Results ---
# Display a clear summary of the simulation outcome.

cat("==================================================\n")
cat("      'The Daily Grind' Price Simulation Report      \n")
cat("==================================================\n\n")

cat("--- Customer Behavior Analysis (Sample) ---\n")
# To make the output cleaner, we'll show a sample of the results
print(head(df[, c('customer_id', 'persona_type', 'initial_visits_per_week', 'new_visits_per_week', 'rationale')]))

cat("\n--- Revenue Impact Analysis ---\n")
cat(sprintf("Initial Price per Latte: $%.2f\n", initial_price))
cat(sprintf("New Price per Latte:     $%.2f\n\n", new_price))

cat(sprintf("Projected Initial Weekly Revenue: $%.2f\n", total_initial_revenue))
cat(sprintf("Projected New Weekly Revenue:     $%.2f\n", total_new_revenue))
cat(sprintf("Net Change in Weekly Revenue:     $%.2f\n", revenue_change))
cat(sprintf("Percentage Change:                %.1f%%\n\n", percent_change))

cat("--- Conclusion ---\n")
if (revenue_change > 0) {
  cat("The simulation predicts a net INCREASE in revenue despite losing some customers.\n")
  cat("The price increase on loyal customers outweighs the loss from price-sensitive segments.\n")
} else {
  cat("The simulation predicts a net DECREASE in revenue.\n")
  cat("The loss of price-sensitive customers is not offset by the increased price for loyal ones.\n")
}

cat("Recommendation: Analyze the trade-off between short-term revenue gain/loss and long-term brand loyalty.\n")
cat("==================================================\n")