Data Cleaning & Wrangling with Tidyverse in R

What you'll build

• A messy real-world dataset cleaned into tidy, analysis-ready form
• A reproducible dplyr and tidyr wrangling pipeline
• A reusable cleaning workflow you can apply to your next dataset

What you'll learn

• What tidy data means and why it makes everything downstream easier
• The core dplyr verbs for filtering, transforming, and summarizing
• How to reshape and join data with tidyr
• How to write cleaning code you can rerun and trust

About Amrit

Princeton ORFE student and Lead Sports Trader at Premonition, with R&D experience across the Pittsburgh Penguins, Big League Advantage, and AthlyticZ K-12.