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 XML Configuration Files
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 with type converters, hash aggregation operations, and business metric calculations.

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. Identify top 3 products by total revenue
  6. Generate summary CSV with:
    • Month, Category, Revenue, Transaction_Count, Avg_Transaction
  7. 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

CSV.read(file, headers: true) gives hash-like access to columns

Hint 2

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

Hint 3

Use nested hashes: Hash.new { |h, k| h[k] = Hash.new(0) }

Hint 4

Calculate revenue per transaction: quantity * unit_price

Hint 5

Use group_by to aggregate by month or category

Hint 6

transform_values applies operation to all hash values

Hint 7

max_by finds the hash entry with maximum value

Provided Files (Read-only)

1. Basic sales analysis - 2 months

Input: 
analyze_sales('sales.csv')
Expected Output: 
Total revenue: $3,300
Best month: 2024-01 ($2,900)
Top category: Electronics (72.7%)

2. Single transaction

Input: 
analyze_sales('sales.csv')
Expected Output: 
Total revenue: $250
Best month: 2024-03 ($250)
Top category: Electronics (100.0%)

3. Multiple categories same month

Input: 
analyze_sales('sales.csv')
Expected Output: 
Total revenue: $1,800
Best month: 2024-01 ($1,800)
Top category: Electronics (55.6%)
+ 2 hidden test cases