It is often that said that those who work with data spend at least half of their time cleaning it. Beautiful visualizations and intricately designed reports often hide the hours and hours of work spent getting data into the right format for analysis. Data does not speak for itself — at least not without some serious cleaning beforehand.
Data cleaning skills are essential to your success. Fortunately, R has some great packages to help with data cleaning. Unfortunately, knowing what these packages are and how to use them is not straightforward.
This course will help you learn how to take messy data and quickly clean it. You’ll learn to:
- Get your data into tidy format
- Use regular expressions to deal with complex character data
- Work with missing data
- Identify and deal with duplicate values
- And much, much more!
The 31 lessons in this course take you from the high-level thinking about best practices in data organization to the nitty gritty of cleaning messy data. You’ll learn both the why and the how of data cleaning. We can’t promise that you’ll have less data cleaning to do, but this course will make sure that the data cleaning you do is faster and more efficient than ever before.
Are there any prerequisites for this course?
You should be familiar with the fundamentals of the tidyverse (see the content in the Fundamentals of R course) before taking this course.
Can I get a refund if I’m not satisfied?
Absolutely! If you are unsatisfied with the course for any reason, contact me and I will give you a full refund, no questions asked.
Is this course live or self-paced?
It is entirely self-paced, giving you the freedom to learn whenever and wherever you’d like to.
Does my access to the material expire at any point?
Nope! Sign up now and you’ll have access to the course forever.
About the Instructor
Luis D. Verde Arregoitia
Luis D. Verde Arregoitia is a biologist by training with nearly a decade of R experience. Alongside his research on mammals, Luis has also spent years helping others learn to clean, restructure, and share analysis-ready data. An RStudio certified instructor, he enjoys teaching and blogging about R in both English and Spanish.