Cleaning Bad Data in R

Clean up your data in R. Learn how to identify and address data integrity issues such as missing and duplicate data, using R and the tidyverse.

Introduction

• Data is messy
• What you need to know

1. Missing Data

• Types of missing data
• Missing values
• Missing rows
• Aggregations and missing values

2. Duplicated Data

• Duplicated rows and values
• Aggregations in the data set

3. Formatting Data

• Converting dates
• Unit conversions
• Numbers stored as text
• Text improperly converted to numbers
• Inconsistent spellings

4. Outliers

• Screening for outliers
• Handling outliers
• Outliers use case
• Outliers in subgroups
• Detecting illogical values

5. Tidy Data

• What is tidy data?
• Variables, observations, and values
• Common data problems
• Wide vs. long data sets
• Making wide data sets long
• Making long data sets wide

6. Red Flags

• Suspicious values
• Suspicious multiples

Conclusion

• What's next?