R Programming
Instructor: Roger D. Peng, PhD , Jeff Leek, PhD , Brian Caffo, PhD
Intermediate Level • Approx. 57 hours • Flexible Schedule
What You'll Learn
- Understand critical programming language concepts
- Configure statistical programming software
- Make use of R loop functions and debugging tools
- Collect detailed information using R profiler
Skills You'll Gain
Computer Programming Tools
Data analysis
Debugging
Data Structures
Data Import/Export
Statistical Analysis
Performance Tuning
Program Development
Simulations
R Programming
Statistical Programming
Shareable Certificate
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Outcomes
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Learn new concepts from industry experts
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Gain a foundational understanding of a subject or tool
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Develop job-relevant skills with hands-on projects
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Earn a shareable career certificate
There are 4 modules in this course
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.
This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.