Working With Vectorized Functions
Build your understanding of the importance of writing vectorized code for making the most of R's functionality.
1. R Functions as Alternatives to Loops
2. How Does Vectorization Make Code Faster?
3. A Vectorized Function for If-Else Statements
4. Multiple Cases: Nesting Functions to Chain If-Else Statements
5. Functions for Solving "Split-Apply-Combine" Problems
6. Grouping and Summarizing Data Frames
7. Summarizing a Data Frame by Multiple Variables
8. Chaining Functions Together Using the Pipe Operator
9. Next Steps
Intermediate R Programming
The median completion time for this course is 6.4 hours.
This course is free. This course includes five missions. This course is the second course in the Data Analyst in R path.