From Capitals to Clean Code: Turning Global Country Data into Analytical Gold with SAS & R
C apitals, Chaos & Clean Code: Transforming a Global Countries Dataset into Analytical Gold with SAS & R 1. Introduction Imagine you are working on a global analytics project for a multinational organization. The dataset contains country names, capital cities, population, GDP, and region classifications. It looks clean at first glance but once you start analyzing, things fall apart. You notice countries like “india” , “INDIA” , and “India ” treated as separate entities. Some capitals are missing. Population values include negative numbers. GDP fields contain text like “NULL.” Dates are incorrectly formatted. Duplicate rows silently inflate your statistics. This is not just inconvenient it’s dangerous. In industries like clinical trials or financial analytics, poor data quality leads to flawed decisions. Imagine a clinical dataset where patient age is -5 or treatment dates are reversed. The consequences can be regulatory rejection or incorrect conclusions. This is w...