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Learner Reviews & Feedback for R Programming by Johns Hopkins University

21,629 ratings
4,688 reviews

About the Course

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....

Top reviews


Feb 2, 2016

"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!


Jul 11, 2016

Excellent course! I already knew a lot about R - but this class helped me solidify what I already knew, taught me lots of new tricks, and now I have a certificate that says I know `something' about R!

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4151 - 4175 of 4,596 Reviews for R Programming

By Vivekanand R

Oct 2, 2016

Needs clear instructions


Aug 6, 2020

No esta todo en español

By Nicholas E

Jun 9, 2020

course is really fast

By Vraj P

Jul 1, 2019

a little fast paced.

By Jiahui X

Apr 29, 2016

straight and narrow.

By Samuel Y

Aug 9, 2021

not for beginners

By Shreya S

Feb 17, 2017

nice to learn:))

By Goddess o P

May 9, 2022

Hard and boring

By Vikramaditya M

Apr 7, 2020

homeoworks are

By Sushmit R

Aug 20, 2017

Very helpful.

By Sumit K S

Jun 22, 2020

Need Update

By 柏一

Mar 22, 2016


By Brian L

Feb 27, 2019

too fast

By Andreas H

Jul 22, 2018

Too hard

By Benjamin S

Sep 25, 2017

Very cha

By Ajit K

Jun 22, 2020


By kishore

Feb 8, 2016


By Rahul

Jul 15, 2019


By Rahul M

Nov 11, 2017


By Jacob P H B

Aug 5, 2021

I want to begin this review by thanking Johns Hopkins and Coursera for putting this course together. In the age of "work/learn from home" and "upskilling" it is courses such as these that allow the layman who is unfamiliar with data science to learn basic programming. For that I am grateful. On the other hand, even as this course cost $50.00 (which is reasonable), I still cannot recommend it to other students. As a graduate student, I have had some exposure to R. My statistics class utilized R for HW assignments and basic regression models. Our TA taught us some intermediate coding methods via ggplot2 and dplyr. I primarily took this Coursera course as a refresher. It should be noted that the course description encourages - nay, states - that beginners should do just fine taking the class. Nothing could be further from the truth. The course instructor is clearly a brilliant man who is a leader in the field of data analytics. I'm sure he is also a very well-respected lecturer at JHU. However, I felt like this whole course he was speeding through the content. I had to use 0.75 speed during the lectures so I could hear everything that he was saying. On top of that, he used a lot of R jargon that made the content seem more exclusive to students who already have a background in data science. Finally, there were limited opportunities for students to apply the skills that we learned in each lecture in the actual R environment. He took screenshots of code, explained it in an opaque fashion, and then moved on to the next lesson. That may be fine for some students, but I personally like to 'drive' when I'm learning how to use a car. The one bright spot in this course was the interactive swirl sessions which did allow you to put some concepts to good use. It should be noted that these are optional. In my opinion, if you didn't do the swirl assignments then I can't see how you took anything away from the course at all. Still, the swirl module could be clunky at times and could use updating. In addition, some of the answers in swirl simply required you to copy and paste what you were previously shown, which isn't very challenging. My final qualm with this course is the "programming assignments." While the swirl assignments were probably too easy for the layman user, the programming assignments were insurmountable tasks. There is a HUGE gap between what we learned in the modules and what we were expected to perform in the assignments. I don't understand the logic of teaching concepts, implementing what we learned in simplistic interactive swirl sessions, and then taking on advanced assignments. How does that help the beginner student at all? The teacher encourages a "hacker mentality" but in my opinion, struggling and googling your way to get the right answers isn't learning, it's insufficient teaching. It would be one thing if this course was advertised to advanced users. If that was the case, fine, the student should probably be able to write some of this code. But that was not the case and from the comments, it seemed that many other students also struggled with the program assignments. The teacher also dove into statistical theories in week 4 without explaining them. No offense, but I don't understand how we can possibly grasp linear models and Poisson regressions in a sub ten minute video. In summary, don't take this course if you are beginner or looking for a refresher. The content is outdated, the pace is too fast, and the programming assignments are disconnected from what you learn in the videos. One would be better off watching YouTube videos or taking a Udemy course for free.

By Srinivas S

Nov 1, 2016

I am a very frustrated learner trying to write a constructive review here. I studied the course full time to get a certificate to put in my profile. R programming is considered nearly essential skill, if not fundamental, to data analysis. So I had high hopes going into this course.

Lecture videos: I cannot begin to tell you how many times I fell asleep watching the video lectures. This course is for you if you like listening to someone talking through pages and pages of copy/paste text from a command prompt. I, on the other hand, prefer learning by doing. Sadly, all the doing is clumped into the assignments (more on them later). Also, Dr. Peng makes gross burping/drooling noises in the video from time to time. I apologize if that sounded rude, but try listening to the lectures with headphones and you'll say the same. Video editing for pre-recorded videos is not rocket science.

Assignments: These were the biggest frustration throughout the course. Imagine you want to learn to play the piano (and you play a little guitar now) and you go to the piano class. Your piano teacher talks your ears off with music theory for several hours and then hangs you out to dry in front of an audience in a concert hall. You protest "But I have no clue of how to PLAY", but your teacher says "All the piano greats learned by fiddling around with a "hacker" mentality". You ask "Why did I pay $50 to hack on my own?"

Off-lecture help: If you take the course for the same reason I did, then thank your favorite god for the moderators in the discussion forums (mentors). 1 out of the 2 stars I give belongs solely to them and the fantastic work they do. The stickied posts in the forums offer a bit of help with the assignments (not enough, but still something). And they are also active in answering questions. The other star in my review goes to "Swirl". You will learn way more by doing the swirl exercises than watching the lectures by a long way.

In conclusion, I finished the course with nothing more than a rudimentary understanding of R despite the fine grades. Very little thought seems to have been put into the lectures. I would recommend this only if you want to show this certificate to someone. Otherwise, stay away!

By Kesha L

Jan 17, 2016

This course is VERY abstract and I find myself rushing through the videos to get to the practice/quiz so that I can trial and error my way through the project..... hoping for the best. Neither am I excited about starting the lesson each week because there is no real world problem/or data set I'm continuously practicing from. The lessons are essentially a reference guide and not a useful approach for teaching. If I wanted a reference guide, I'd just pick up one of the various handbooks/books on the markets that list R codes and their functions. A better way to teach/present this course is by infusing actual, real world examples or cases throughout the lessons instead of just listing a function and talking through its corresponding activity/response. Ideally, the real world example would be introduced in the first lesson in Week 1 and that data set would be used throughout the course(s) to apply and practice newly introduced functions. Teaching from this perspective would likely make the concepts much easier to grasp and importantly, RETAIN. The lessons, as they are currently presented, encourage rote memorization and rob the students of actually applying the concepts/code taught.

Also, I love the idea of this specialization. However, I think the professors need to work more closely with online instructional designers to make the entire series more palatable for online learning. It very much feels as if they have adapted a traditional course on their own without the help of professionals who are skilled at designing online courses. If the aforementioned is the case, the professor(s) should be commended for their efforts, but there is definitely more work to be done to make this an engaging course I'd recommend.

PS - I am taking notes and I have some experience with STATA so this type of coding/anaylsis is not unfamiliar to me. I can only imagine how novices may take to the lessons.

By John D M

Feb 26, 2018

I think it is disingenuous to rate the Data Science Specialization at Beginner level while the R Programming course being rated at Intermediate. It is really a course for experienced programmers. While I have *some* programming background (I won second place in an International Curl Programming contest in 2001), I think professional programmers would have a much easier time with it, although in looking at many online reviews I see professional programmers struggled with it. It did shake the rust off my coding skills, although if I were back in programming instructor mode I would keep the assignments, which were excellent, and work backwards to devise lectures that better supported them, and provide simple exercises that develop the skills needed.

I found that I spent about 35 hours on week 2 between lectures, the assignment, and test, with almost all of that spent on the test. As a busy mature adult the extra 30 or so hours was very unwelcome and I think quite unfair in terms of the stress load of making me wonder if I was going to make it through the material. In looking at many course review comments I see I am not the only one. You need to even out the workload.

When I was teaching JavaScript and Visual Basic at a university from 1999-2003 I found that stepping students through some examples then letting them solo on slightly altered examples while providing support if they were really stuck, then giving them more challenging examples to test their skills, was a very effective plan and helped them maintain their self confidence in the material. You could always give "Challenge" questions that differentiated between the B-plus and A-plus students that force them to develop their hacking skills. Unfortunately, I can't recommend your course until you improve the lessons in the manner I've described.

By Acacia P

Jan 9, 2018

The only reason I am giving this course 2 stars is because the Swirl modules were awesome. Swirl should be the main form of instruction in this course. I have taken one class using R a long time ago, and have a very very basic understanding of programming -- so not a TOTAL beginner, but basically a beginner -- and I found this course to be absolutely impossible to do using the materials provided. The quizzes and especially coding assignments seemed, for the most part, completely unrelated to the lecture contents. The lectures, in turn, contained all sorts of details that were not needed for the assignments and quizzes and I often felt lost. Lectures were generally barren of concrete examples that have any sort of meaning to the audience -- for example, when teaching about a function, instead of showing an example that actually illustrates how we, as data scientists, might ever want to use the function, its usage is demonstrated in a simple but ultimately meaningless (and therefore forgettable) way. It would have been much more useful to show the basic example and immediately follow by a real application. There were many, many, many functions that one seems to need to have memorized, but the course provided no tools to help facilitate that, or input on which functions to focus on. It was a big mess. I ended up teaching everything to myself by Googling... which is ok, I guess, but why am I taking a course, then? There has to be a better way to usher students through this process, even if the goal is ultimately to get us to be resourceful and find information on our own (like why did I spend all that time on lecture if I was just going to have to look everything up?). Swirl was seriously the only thing that saved it.

By Samantha C

Jun 27, 2017

I would have liked to see more repetition of functions discussed in the videos that slowly build up to the more advanced questions in the quizzes and assignments. The Swirl lessons are very prescriptive. While Swirl is good in theory, it aids you along too much for it to really be effective. I recommend adding practice assignments that have learners do a particular task independently multiple times with different variations so we can really learn what we're doing. Throughout the course, I would watch videos, understand everything being said, and then be completely lost when it was time to do the assignments. I'd use the forums and Google to complete them, but it felt like there was a disconnect between the videos and the assignments that made the course more difficult/time consuming. If there were an interim step of doing practice questions (not through an overly prescriptive package like Swirl, but one that makes you use solve the problem on your own and struggle just a little bit), I think that would've really help make this course more useful. Also, doing something once in an assignment doesn't mean it's committed to memory, so even though I was able to figure out the assignments, I don't think I've really retained as much info as possible. In terms of grading peer assignments, I don't feel that I know R well enough after taking this course to say whether someone else's code works or not (without running it and assuming that it doesn't match mine exactly). I appreciate the time and effort it must have taken to set this course up, but it fell short of my expectations and I don't really feel that I've gained much (if anything) from it.