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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
Course Logistics and Materials
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This lesson is called Course Logistics and Materials, part of the Data Cleaning with R course. This lesson is called Course Logistics and Materials, part of the Data Cleaning with R course.
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As demonstrated in the video, first create a new project.
Next, download the materials for the course using this code:
install.packages("usethis") # Install first if necessary usethis::use_course("rfortherestofus/data-cleaning-course") # Download course materials
Finally, copy the materials you downloaded into the new project you created.