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Data Cleaning with R

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  1. Welcome to Data Cleaning with R
    What is Data Cleaning?
  2. Course Logistics and Materials
  3. Data Organization
    Data Organization Best Practices
  4. Tidy Data
  5. Grouping and Indicator Variables
  6. NA and Empty Values
  7. Data Sharing Best Practices
  8. Restructuring Data
    Tidyverse Refresher
  9. Working with Columns with across()
  10. Pivoting Data
  11. coalesce() and fill()
  12. Regular Expressions
    What are Regular Expressions?
  13. Understanding and Testing Regular Expressions
  14. Literal Characters and Metacharacters
  15. Metacharacters: Quantifiers
  16. Metacharacters: Alternation, Special Sequences, and Escapes
  17. Combining Metacharacters
  18. Regex in R
  19. Regular Expressions and Data Cleaning, Part 1
  20. Regular Expressions and Data Cleaning, Part 2
  21. Common Issues
    Common Issues in Data Cleaning
  22. Unusable Variable Names
  23. Whitespace
  24. Letter Case
  25. Missing, Implicit, or Misplaced Grouping Variables
  26. Compound Values
  27. Duplicated Values
  28. Broken Values
  29. Empty Rows and Columns
  30. Parsing Numbers
  31. Putting Everything Together
Data Cleaning with R Missing, Implicit, or Misplaced Grouping Variables
Lesson 25 of 31
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Missing, Implicit, or Misplaced Grouping Variables

Your Turn

Load the primates2017 dataset bundled with 📦 unheadr and create a new column that groups the different species by taxonomic family.

In biology, taxonomic families all end in the suffix “DAE“

How many different ways can you identify the embedded subheaders in these data?

Solutions
Learn More

To learn more about the unheadr package, check out its documentation website.