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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
Working with Columns with across()
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This lesson is called Working with Columns with across(), part of the Data Cleaning with R course. This lesson is called Working with Columns with across(), part of the Data Cleaning with R course.
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Transcript
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Your Turn
Load the midwest data bundled with
ggplot2Keep only rows for Ohio (OH)
Subset the ‘county’ column and all columns that match the string ‘pop‘ (hint: use a selection helper)
Square-root transform all numeric variables
Learn More
The tidyverse blog announcing dplyr 1.0 had a nice overview of the across() function.
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FELIPE SANCHEZ NAJERA
October 11, 2023
Hi! I just wanted to ask why the professor mutates the numeric variables into a log scale; what is the purpose or usefulness of this transformation instead of using the non-mutated data?
David Keyes Founder
October 11, 2023
I don't think the transformation really matters here. It's just a toy example in order to demonstrate how to work across multiple variables.