11. Analyze Sales Transactions and Generate Revenue Reports
1. Hello, World!
2. Rename Photos by Capture Date
3. Organize Downloads Folder by File Type
4. Delete Old Log Files Automatically
5. Find Duplicate Files Using MD5 Hashing
6. Generate Comprehensive File Inventory
7. Parse Web Server Logs for Performance Metrics
8. Extract Emails, URLs, and Phone Numbers from Text
9. Track Daily and Monthly Active Users from Apache Logs
10. Generate Formatted Text Reports with Column Alignment
11. Analyze Sales Transactions and Generate Revenue Reports
12. Clean and Validate CSV Data
13. Convert JSON API Responses to CSV
14. Parse and Validate Configuration Files
15. Build a CLI Calculator with Argument Parsing
16. Parse and Display Weather Data from JSON
17. Build a Simple Inventory System with Classes
18. Generate Markdown Table from CSV Data
19. Aggregate Sales Data by Time Period
20. Recursive Directory Size Calculator with Filtering
21. Parse and Transform Server Configuration
22. Find and Replace Across Multiple Files
23. Build a Simple Template Engine
24. CSV Data Validator with Business Rules
25. Build a Multi-Format Log Analyzer

Challenge

Problem

E-commerce companies receive daily exports from Shopify/Square and need automated monthly reports for management. This drill teaches you to parse sales CSV data, calculate monthly revenue by category, identify top-selling products, and generate summary reports with totals, averages, and percentage breakdowns. You'll master CSV processing, hash aggregation operations, and business metric calculations. Note: This drill parses CSV manually using string methods rather than Ruby's CSV library.

Difficulty: Intermediate

Instructions

  1. Read sales CSV with headers (Date, Product, Category, Quantity, Unit_Price, Customer_ID)
  2. Calculate total revenue per transaction (Quantity * Unit_Price)
  3. Group transactions by month and category
  4. Calculate monthly revenue by category
  5. Output summary statistics to console:
    • Total revenue: $X,XXX
    • Best month: Month ($X,XXX)
    • Top category: Category (X% of revenue)

Files

Editable
Read-only

Hints

Hint 1

Parse CSV manually: line.split(',')

Hint 2

Date.parse(string).strftime('%Y-%m') extracts year-month

Hint 3

Calculate revenue per transaction: quantity.to_i * unit_price.to_f

Hint 4

Use a hash to accumulate totals: hash[key] ||= 0; hash[key] += value

Hint 5

max_by { |k, v| v } finds the entry with maximum value

Hint 6

Format currency: num.to_s.reverse.gsub(/(\d{3})(?=\d)/, '\1,').reverse

Ruby 3.4

Provided Files (Read-only)

1. Correct total revenue

Input: 
analyze_sales('sales.csv')
Expected Output: 
Total revenue: $4,375
Best month: 2024-01 ($2,875)
Top category: Electronics (92.0%)

2. Best month identified

Input: 
analyze_sales('sales.csv')
puts 'Month verified'
Expected Output: 
Total revenue: $4,375
Best month: 2024-01 ($2,875)
Top category: Electronics (92.0%)
Month verified

3. Top category percentage

Input: 
analyze_sales('sales.csv')
puts 'Category verified'
Expected Output: 
Total revenue: $4,375
Best month: 2024-01 ($2,875)
Top category: Electronics (92.0%)
Category verified
+ 2 hidden test cases