Get access to all lessons in this course.
-
Welcome to Data Cleaning with R
- What is Data Cleaning?
- Course Logistics and Materials
-
Data Organization
- Data Organization Best Practices
- Tidy Data
- Grouping and Indicator Variables
- NA and Empty Values
- Data Sharing Best Practices
-
Restructuring Data
- Tidyverse Refresher
- Working with Columns with across()
- Pivoting Data
- coalesce() and fill()
-
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
-
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
This lesson is locked
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
Click on the transcript to go to that point in the video. Please note that transcripts are auto generated and may contain minor inaccuracies.
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.
You need to be signed-in to comment on this post. Login.