# Question 1: Data Preparation and Inspection

product <- c("Laptop", "Smartphone", "Tablet", "Headphones",
             "Smartwatch", "Printer", "Keyboard", "Monitor")

unit_price <- c(185000, 95000, 68000, 12500, 28000, 42000, 8500, 56000)
units_sold <- c(35, 80, 45, 160, 90, 38, 210, 52)
unit_cost <- c(150000, 76000, 52000, 6500, 19000, 33000, 4200, 43000)
ad_cost <- c(180000, 220000, 90000, 60000, 85000, 45000, 50000, 75000)
orders <- c(35, 78, 44, 150, 88, 36, 200, 50)
returned_orders <- c(2, 5, 2, 12, 6, 3, 8, 2)

product_data <- data.frame(product, unit_price, units_sold, unit_cost,
                            ad_cost, orders, returned_orders)

head(product_data, 5)
nrow(product_data)
ncol(product_data)
names(product_data)
str(product_data)


# Question 2: Financial Performance Calculations

product_data$revenue <- product_data$unit_price * product_data$units_sold
product_data$cogs <- product_data$unit_cost * product_data$units_sold
product_data$gross_profit <- product_data$revenue - product_data$cogs
product_data$net_contrib <- product_data$gross_profit - product_data$ad_cost
product_data$profit_margin <- (product_data$net_contrib / product_data$revenue) * 100
product_data$return_rate <- (product_data$returned_orders / product_data$orders) * 100

print(product_data)


# Question 3: Descriptive Business Analysis

cat("Total revenue:", sum(product_data$revenue), "\n")
cat("Total COGS:", sum(product_data$cogs), "\n")
cat("Total gross profit:", sum(product_data$gross_profit), "\n")
cat("Total net contribution:", sum(product_data$net_contrib), "\n")

cat("Mean net contribution:", mean(product_data$net_contrib), "\n")
cat("Median net contribution:", median(product_data$net_contrib), "\n")

cat("Highest net contribution:", max(product_data$net_contrib), "\n")
cat("Lowest net contribution:", min(product_data$net_contrib), "\n")

cat("Maximum return rate:", max(product_data$return_rate), "\n")
cat("Minimum return rate:", min(product_data$return_rate), "\n")

sorted_nc <- sort(product_data$net_contrib)
print(sorted_nc)

nc_range <- max(product_data$net_contrib) - min(product_data$net_contrib)
cat("Range of net contribution:", nc_range, "\n")


# Question 4: Conditional Statements and Business Classification

n <- nrow(product_data)

profit_category <- character(n)
return_risk <- character(n)

for (i in 1:n) {
  if (product_data$net_contrib[i] >= 750000) {
    profit_category[i] <- "High Performer"
  } else if (product_data$net_contrib[i] >= 400000) {
    profit_category[i] <- "Moderate Performer"
  } else {
    profit_category[i] <- "Low Performer"
  }

  if (product_data$return_rate[i] > 5) {
    return_risk[i] <- "High Return Risk"
  } else {
    return_risk[i] <- "Acceptable Return Rate"
  }
}

product_data$profit_category <- profit_category
product_data$return_risk <- return_risk

report <- product_data[, c("product", "net_contrib", "profit_margin",
                           "return_rate", "profit_category", "return_risk")]

print(report)


# Question 5: Loops and Automated Business Reporting

# (i) Display product revenue and net contribution

for (i in 1:n) {
  cat(product_data$product[i],
      "Revenue:", product_data$revenue[i],
      "Net contribution:", product_data$net_contrib[i], "\n")
}

# (ii) Count profitability categories

high_count <- 0
moderate_count <- 0
low_count <- 0

for (i in 1:n) {
  if (product_data$profit_category[i] == "High Performer") {
    high_count <- high_count + 1
  } else if (product_data$profit_category[i] == "Moderate Performer") {
    moderate_count <- moderate_count + 1
  } else {
    low_count <- low_count + 1
  }
}

cat("High:", high_count,
    "| Moderate:", moderate_count,
    "| Low:", low_count, "\n")

# (iii) Display products requiring management attention

i <- 1
attention_count <- 0

while (i <= n) {
  if (product_data$net_contrib[i] < 400000 ||
      product_data$return_rate[i] > 5) {
    cat(product_data$product[i], "\n")
    attention_count <- attention_count + 1
  }

  i <- i + 1
}

# (iv) Total products requiring attention

cat("Products requiring attention:", attention_count, "\n")


# Question 6: User-Defined Functions

# (i) Calculate net contribution

calculate_contribution <- function(unit_price, units_sold, unit_cost, ad_cost) {
  revenue <- unit_price * units_sold
  cogs <- unit_cost * units_sold
  net <- revenue - cogs - ad_cost
  return(net)
}

# (ii) Classify product profitability

classify_product <- function(net_contribution) {
  if (net_contribution >= 750000) {
    return("High Performer")
  } else if (net_contribution >= 400000) {
    return("Moderate Performer")
  } else {
    return("Low Performer")
  }
}

# (iii) Test functions

laptop_nc <- calculate_contribution(185000, 35, 150000, 180000)
printer_nc <- calculate_contribution(42000, 38, 33000, 45000)

cat("Laptop:", laptop_nc, "-", classify_product(laptop_nc), "\n")
cat("Printer:", printer_nc, "-", classify_product(printer_nc), "\n")


# Question 7: Graphical Analysis, Exporting and Recommendations

# (i) Bar chart

barplot(product_data$net_contrib,
        names.arg = product_data$product,
        main = "Net Contribution by Product",
        xlab = "Product",
        ylab = "Net Contribution (LKR)",
        col = "blue",
        las = 2)

# (ii) Histogram

hist(product_data$net_contrib,
     main = "Distribution of Net Contribution",
     xlab = "Net Contribution (LKR)",
     col = "yellow")

# (iii) Scatter plot

plot(product_data$ad_cost, product_data$net_contrib,
     main = "Advertising Cost vs Net Contribution",
     xlab = "Advertising Cost (LKR)",
     ylab = "Net Contribution (LKR)",
     pch = 19,
     col = "green")

text(product_data$ad_cost, product_data$net_contrib,
     labels = product_data$product,
     pos = 3,
     cex = 0.7)

# (iv) Export to CSV

write.csv(product_data, "product_performance.csv", row.names = FALSE)