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 average completion time for this course is 10-hours.
This course is free. This course includes 4 missions and 1 guided project. This course is the 2nd course in the Data Analyst in R path.