Garbage in, garbage out.
—George Fuechsel [1]
Okay, so you’ve read this book, learned how to create good data for your projects, and now all your new datasets are squeaky clean and ready for analysis! However, your research colleague borrowed the book but didn’t read it and now has a collection of messy datasets. Now what? Well, next we’ll learn about some methods for detecting bad data and for cleaning it up, often referred to as a component of “data munging” or “data wrangling.”
The goal of data cleaning is to get the data ready for analysis. But how will you know when it is ready? You need ...
