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
coalesce() and fill()
This lesson is locked
This lesson is called coalesce() and fill(), part of the Data Cleaning with R course. This lesson is called coalesce() and fill(), part of the Data Cleaning with R course.
If the video is not playing correctly, you can watch it in a new window
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
Load the fish landings data 'fish-landings.csv'
Fill the 'Fish' and 'Lake' columns
Reorder the numeric variables ('Commission reported total' first) and create a new column, coalescing the three numeric variables]
Learn More
John Mackintosh has a nice overview of the coalesce() function while Fisher Ankey has a nice overview of the fill() function.
You need to be signed-in to comment on this post. Login.