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.
R Programming
R Programming
This course is part of multiple programs.
Instructors: Roger D. Peng, PhD
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Gain insight into a topic and learn the fundamentals.
4.5
(22,303 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 57 hours
Learn at your own pace
94%
Most learners liked this course
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
Details to know
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Assessments
6 assignments
Taught in English
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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.
What's included
28 videos9 readings1 assignment7 programming assignments
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.
What's included
13 videos3 readings2 assignments3 programming assignments
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.
What's included
8 videos2 readings1 assignment2 programming assignments1 peer review
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.
What's included
6 videos4 readings2 assignments3 programming assignments
Instructors
Instructor ratings
4.4 (1,964 ratings)Offered by
Recommended if you're interested in Data Analysis
Johns Hopkins University
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4.5
22,303 reviews
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Showing 3 of 22303
AC
5
Reviewed on Jul 15, 2018
For anyone planning to set their foot in the vast field of data analytics and R, this course is what will set the momentum and actually make you fond of programming in R by the end of the course.
AB
5
Reviewed on Sep 6, 2017
Great course for people who work with data a lot. This course actually helps in looking at data in its basic forms, helps understand transformations better, and gives ideas about playing with it.
RV
5
Reviewed on Feb 11, 2022
Difficult to follow at times. it will take a lot of time to get through the assigments. but if you are serious about learning R or any programming, it is necessary to get thrown into the fire.
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