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.
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Johns Hopkins UniversityAbout this Course
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Intermediate Level
Familiarity with regression is recommended.
Approx. 57 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, English, Spanish, Japanese
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
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Familiarity with regression is recommended.
Approx. 57 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Portuguese (Brazilian), Vietnamese, Korean, German, Russian, English, Spanish, Japanese
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
25 hours to complete
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.
25 hours to complete
28 videos (Total 129 min), 9 readings, 8 quizzes
12 hours to complete
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.
12 hours to complete
13 videos (Total 91 min), 3 readings, 5 quizzes
9 hours to complete
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.
9 hours to complete
8 videos (Total 61 min), 2 readings, 4 quizzes
11 hours to complete
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.
11 hours to complete
6 videos (Total 42 min), 4 readings, 5 quizzes
Reviews
- 5 stars68.09%
- 4 stars22.16%
- 3 stars5.85%
- 2 stars2.07%
- 1 star1.81%
TOP REVIEWS FROM R PROGRAMMING
by YZJul 16, 2020
This is course is probably my favorite out of the Data Science: Foundations using R specialization. There was plenty of opportunity to practice and further develop by burgeoning R programming skill.
by JMAug 11, 2019
Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.
by AKMay 26, 2017
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.
by SBApr 27, 2016
This course fully meets my expectations. It provides a concise starting point and even manages to introduce advanced concepts such as the 'apply' family. The final assignment is fully appropriate.
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