In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Offered By
About this Course
Familiarity with regression is recommended.
What you will 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 will gain
- Data Analysis
- Debugging
- R Programming
- Rstudio
Familiarity with regression is recommended.
Offered by

Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Syllabus - What you will learn from this course
Week 1: Background, Getting Started, and Nuts & Bolts
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.
Week 2: Programming with R
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.
Week 3: Loop Functions and Debugging
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.
Week 4: Simulation & Profiling
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.
Reviews
- 5 stars68.13%
- 4 stars22.20%
- 3 stars5.81%
- 2 stars2.06%
- 1 star1.77%
TOP REVIEWS FROM R PROGRAMMING
This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox. This is perhaps the best course on R Programming designed for a small duration.
The content is superbly designer for a beginner. The Swirl assignments need to make compulsory. Infact they contributed more to the learning process. More Swirl contents will make the course richer.
This course was almost excellent. The tutorials were amazing. I am just going to complain about Assignment 2; inverted matrices weren't a pre-requisite so it was hard to understand that assignment
This is a very thorough introduction to R. There are plenty of exercises to quickly get familiar with the language. Some good guided assignments really help getting familiar with coding functions.
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.