Working with Columns with across()
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.
Transcript
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Your Turn
Load the midwest data bundled with
ggplot2
Keep 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.
Have any questions? Put them below and we will help you out!
Course Content
32 Lessons
1
What are Regular Expressions?
03:48
2
Understanding and Testing Regular Expressions
03:51
3
Literal Characters and Metacharacters
06:16
4
Metacharacters: Quantifiers
01:33
5
Metacharacters: Alternation, Special Sequences, and Escapes
02:53
6
Combining Metacharacters
05:18
7
Regex in R
02:58
8
Regular Expressions and Data Cleaning, Part 1
04:15
9
Regular Expressions and Data Cleaning, Part 2
12:00
1
Common Issues in Data Cleaning
03:17
2
Unusable Variable Names
10:11
3
Whitespace
11:10
4
Letter Case
06:52
5
Missing, Implicit, or Misplaced Grouping Variables
11:19
6
Compound Values
10:09
7
Duplicated Values
08:49
8
Broken Values
09:52
9
Empty Rows and Columns
11:30
10
Parsing Numbers
12:02
11
Putting Everything Together
25:50
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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.