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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
-
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
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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
Regex in R
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This lesson is called Regex in R, part of the Data Cleaning with R course. This lesson is called Regex in R, part of the Data Cleaning with R course.
Transcript
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Your Turn
Match the following regular expressions against the test vector below using str_detect
. Can you explain the matches?
Regular expressions
^dog
^[a-z]+$
\\d
test_vector <- c("Those dogs are small.","dogs and cats",
"34","(34)","rat","watchdog","placemat",
"BABY","2011_April","mice")
Learn More
To learn more about the stringr
package, check out the documentation website. There is also a stringr
cheatsheet. You also might check out Chapter 14 of R for Data Science as well as this blog post by Hugo Toscano on working with strings in R.
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