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Welcome to Data Cleaning with R
- What is Data Cleaning?
- Course Logistics and Materials
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Data Organization
- Data Organization Best Practices
- Tidy Data
- Grouping and Indicator Variables
- NA and Empty Values
- Data Sharing Best Practices
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Restructuring Data
- Tidyverse Refresher
- Working with Columns with across()
- Pivoting Data
- coalesce() and fill()
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Regular Expressions
- What are Regular Expressions?
- Understanding and Testing Regular Expressions
- Literal Characters and Metacharacters
- Metacharacters: Quantifiers
- Metacharacters: Alternation, Special Sequences, and Escapes
- Combining Metacharacters
- Regex in R
- Regular Expressions and Data Cleaning, Part 1
- Regular Expressions and Data Cleaning, Part 2
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Common Issues
- Common Issues in Data Cleaning
- Unusable Variable Names
- Whitespace
- Letter Case
- Missing, Implicit, or Misplaced Grouping Variables
- Compound Values
- Duplicated Values
- Broken Values
- Empty Rows and Columns
- Parsing Numbers
- Putting Everything Together
Data Cleaning with R
Missing, Implicit, or Misplaced Grouping Variables
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This lesson is called Missing, Implicit, or Misplaced Grouping Variables, part of the Data Cleaning with R course. This lesson is called Missing, Implicit, or Misplaced Grouping Variables, part of the Data Cleaning with R course.
Transcript
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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?
Learn More
To learn more about the unheadr package, check out its documentation website.
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