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Data Cleaning with R

Literal Characters and Metacharacters

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.

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.

Course Content

32 Lessons

What is Data Cleaning?

Course Logistics and Materials

Data Organization Best Practices

Grouping and Indicator Variables

NA and Empty Values

Data Sharing Best Practices

Tidyverse Refresher

Working with Columns with across()

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

Regular Expressions and Data Cleaning, Part 1

Regular Expressions and Data Cleaning, Part 2

Common Issues in Data Cleaning

Unusable Variable Names

Missing, Implicit, or Misplaced Grouping Variables

Compound Values

Duplicated Values

Empty Rows and Columns

Parsing Numbers

Putting Everything Together

Wrapping Up Data Cleaning with R