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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
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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Transcript
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Your Turn
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?
a) Joscel[iy]n
b) Jo[sc^z]el[yi]n
c) Jo[sc]el[yi]n
Which of these regular expressions matches food at the beginning of a string?
a) ^food
b) food
c) $food
d) food^
Learn More
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.
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