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Data Cleaning with R
Welcome to Data Cleaning with R
- What is Data Cleaning?
- Course Logistics and Materials
- Data Organization Best Practices
- Tidy Data
- Grouping and Indicator Variables
- NA and Empty Values
- Data Sharing Best Practices
- Tidyverse Refresher
- Working with Columns with across()
- Pivoting Data
- coalesce() and fill()
- 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
- Common Issues in Data Cleaning
- Unusable Variable Names
- Letter Case
- Missing, Implicit, or Misplaced Grouping Variables
- Compound Values
- Duplicated Values
- Broken Values
- Empty Rows and Columns
- Parsing Numbers
- Putting Everything Together
Literal Characters and Metacharacters
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This lesson is called Literal Characters and Metacharacters, part of the Data Cleaning with R course. This lesson is called Literal Characters and Metacharacters, part of the Data Cleaning with R course.
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Write a regexp that can match tail, tool, tall, and toil
How can we match Jocelyn, Jocelin, and Joselyn but not Jozelyn using character sets?
Which of these regular expressions matches
food at the beginning of a string?
Gastón Sánchez has a nice book Handling Strings with R. Chapter 10 (Literal Characters) and Chapter 11 (Metacharacters) give an overview of these two concepts.