R Programming

Intermediate Level
2 weeks at 10 hours a week
Flexible Schedule

Roger D. Peng, PhD , Jeff Leek, PhD , Brian Caffo, PhD

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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Develop Your Specialized Knowledge

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

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.