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Learner Reviews & Feedback for R Programming by Johns Hopkins University

4.5
stars
19,888 ratings
4,256 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

EJ
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!

WH
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!

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101 - 125 of 4,147 Reviews for R Programming

By Oka M S

Nov 25, 2020

I underestimate the lecture and it hit me back right in the face! The lesson is really good, and the assignment is really challenging. Not only we need to learn about R programming, but also some familiarity with git and github as version control method. The mentor respond is swift, additional lecture note from github is also really helpful. But it seems that sometime it will be really helpful if Coursera can facilitate to handle limited live session during lesson period so students can ask and get direct respond from lecturer, and might save hours of searching and experimenting if we can get a good directions at the time in need. Overall, excited to continue learning, thanks Coursera and ITB :D

By Edmund J L O

May 11, 2016

This is course was pretty hard for someone like me without any background in computer programming. I had to take it twice to pass it. Luckily, there are many wonderful people in Coursera and in R who are always willing to lend a hand. Even if you pass all the basic requirements of the course i encourage you to do an exploration on your own. There are so many things to learn to make your job easy and to give inspiration to improve your performance in whatever field your in. It might feel like you're not learning at times or it's too difficult to continue, but once you get there, you'll realize how this wonderful new tool can help you with data analysis and presentation.

By Zoey

Apr 30, 2018

If there's one thing about this course that beats all the other regular ways to learn basic R (e.g. datacamp, swirl, reading a textbook, udemy, etc.) it is the MCQ exercises and peer-graded assignments. I can't begin to describe how satisfying it is to have to figure out on your own just 5 cleverly written MCQs for hours and then have the answer in the console finally match one of the choices.

Yes, there are other ways of learning R, but I find this one just sticks in my mind and gamifies the whole learning process. This could just be the strength of Coursera's system, I don't know, I haven't done enough courses to tell. But tell you what, I love this course.

By Wei D

Aug 11, 2019

Great class. Lecture was very to the point. I was a bit hesitant on taking this class given my limited programming experience and other reviewer's comments that the homework was significantly harder than the homework. Now that i have completed the class, I mind that as long as I listened to the lectures and did the practice questions, I had no issues completing the homework assignments (granted, occasional google & stackoverflow consult was needed just like any other programming class). I find the course material easy to understand and perfect for a data newb or someone who wants an introduction to data science and processing. Highly recommend this class.

By Tomohiko J M

Nov 29, 2016

This was a challenging course. I have some experience in stats, but no experience with programming so I spent an extraordinary amount of time fumbling through the assignments. However, the effort was worth it. I am far from fluent in R, but I do feel like I know how to talk in R, pose questions about code, and begin to build functions with my knowledge. Have plenty more to learn, but fumbling through this course has definitely given me a good foundation. Tips for anyone thinking of taking the course: read the discussion forums, learn to look for answers online, and be patient if you're unfamiliar with programming languages.

By Garrett F

Apr 23, 2020

I am a programming beginner and this class took me many many hours to work through seemingly simple assignments. When I did arrive at the right answer, I was happy and proud and recognized my growth. I guess that's the nature of programming. I found that the swirl practice assignments were helpful, if not simply enjoyable. In the forums there were a select few mentors that were quite helpful. I did almost prefer the robotic voice of the Data Science Toolbox over the videos that were presented here. I would have not been able to do the final assignment without dplyr knowledge from Getting and Cleaning Data. Continuing on!

By Jonathan B

Dec 17, 2015

I rate this course as the beta-testing (not that I had completed this course prior the beta started).

1) the course is still very good with a lot of explanations and examples

2) I liked the part about debugging because we don't see often this topic when learning a new language.

3) I liked (but it's only a cosmetic thing) that all the slides have the same template/organization ; it's easier later when we looked back at the lessons to find what we search.

4) one (very) minor comment : I watched the videos with subtitles (english) and sometimes it also writes when the instructor thinks "loud", or repeat a word several times

By Paul L

Jul 4, 2018

5+ years ago as a graduate student I took a bio-statistics class focused on analysis of NGS data where we used R to do the statistics required in the homework assignments. In that class we mainly used the built-in functions at the console to calculate things like correlation coefficients, but didn't do much real programming or function writing. I took this course because I wanted a refresher in R and because I was interested in learning more about its programming capabilities. From that standpoint I'm really satisfied with the things I learned, especially given the fact that the course is quite short.

By George G

Jun 9, 2018

I loved the well-thought-out, tricky programming assignments. At the end, I wish there was an 'answer key' or 'hall of fame' for good examples of solutions to the programming assignments. I understand why they can't do this (oversharing/cheating/watering down the challenge for the next class), but it would be awesome to find out if there was a simpler, more elegant or readable solution. R is full of different ways to solve a problem, so it would help us to 'think in r' if we could see worked examples after we're done. That said, the challenge of the blank page is really where I learned the most.

By Alvin C Y H

Jun 30, 2020

Although there are significant disconnection between the level of difficulty of assignments and what is taught in the lecture videos, the assignment proves to be very challenging and would make your R programming skills improve leaps and bounds. Whenever stuck at assignments, I often search Stackoverflow for specific functions and would be able to find answers from there.

Overall, I think this course is suitable for learners who have some background in programming, and I would be continuing to take the specialization courses to find out more about the statistical packages of R like ggplot2.

By huasah23

Nov 26, 2018

This course provides me an overview understanding of R Programming. The professor not only teaches the important programming concepts but also teaches how to learn R programming well (e.g. how to ask good questions in the forum, how to solve problem via different functions). I think the grading of homework is creative and helpful. When I have to evaluate other people's programming work, I had to understand what's going on in the assignment. The swirl packages and each of the homework are time-consuming but really helps a lot for me to better understand and use the R programming.

By VADALI S G

Nov 21, 2016

It was very informative and understandable. This course seems difficult in the beginning as we need to remember various syntactic notations. When you are in such a situation, don't forget to start using swirl. Even if you are a quick learner of syntax, swirl takes your journey like a cake walk as it just plants all the course content into your brain. It is such an interactive,student friendly environment being provided in the course that it makes you fall in love with swirl, course and instructor's methodologies.I am really thankful to John Hopkins university for such a course.

By Anand K

Feb 10, 2016

The video lectures were engaging with interesting tidbits thrown in to make the potentially dull topic not dull. I personally liked the rhythm and pace with which Dr Peng delivered the content. Also, the swirl exercises are a critical element of this course and I often found it effective to sandwich the swirl exercises between the video and the quiz. Doing this provided an incentive to complete the swirl practice and also made the quiz/assignment less of an exercise in 'dart-throwing' and more of validating what you've learned. Overall, great course to get started with R!

By Marcelo S

Sep 8, 2017

Excellent Course in R Programming for beginners and advanced programmers alike. The programming assignments are a bit of a leap from the course material, so be prepared to be a hacker and search for solutions in the discussion forums, and save time for those assignments if you are new to programming.

Most of the theoretical background is not provided and not the focus of the course (such as mathematical statistics, linear modeling, etc), however, the R-programming aspect of them is presented in an understandable way so that the basics come through. Thank you, Dr. Peng.

By Martijn T

Jun 10, 2017

As a new programmer, I had to chew on this one for weeks and after looping through the video's over and over again eventually mastering it. The swirl exercises are absolutely essential, and so is the discussion forum (and uncle Google / hacking skills). Although frustrated occasionally - and watching others being frustrated or even giving up, I believe the entire course was set up brilliantly. From the assignments I liked the lexical scoping assignment the least. Would have been nice to be replaced by something little bit more encouraging.

By Christopher R

Feb 2, 2017

I finished this last year with a good background already in R. However, this course took a lot of time, a lot of stackoverflow searching and generally a lot of work! Now that I'm a year removed from the course, I can say that it's been one of the better, more rigorous, more practical and more applicable courses i've taken. I still spend A GREAT DEAL of my time looking for answers online but now the questions and tasks are much more complex now that I've taken my programming skills to higher levels, with lots of thanks to this course!

By Deleted A

Mar 15, 2016

The Data Science specialisation with John Hopkins University is definitely worthwhile and, as a relative novice in the area, I'd highly rate and recommend R Programming. The lecturer Mr Peng presents the information in a clear and concise way, conducting walk-throughs of different steps, and providing opportunity for practice exercises. As well, the accompanying book published by the lecturer, learning materials, mentor Mr Warren and discussion board are invaluable for beginners like myself. Thank you for a terrific course so far.

By Chuanhui L

Sep 28, 2016

Well-organized R programming course for academic amateur with some OOP knowledge before! By simply using Rgui, th is class focuses on techniques in ABC's of R programming including not limited to basci objects, data strcuture, controlling sentences, I/O and debugging suggestions.

Personally, I take this course for job purpose, but this course falls short for industrial techniques, such as reading/outputting excel, sophiticated operation on data frame, or combining R with Python and SQL.

Hope that this comment will be useful to you.

By Volodymyr C

Nov 23, 2018

Lectures are well-presented and programming assignments are engaging (but they took me about eight hours to do each week). Swirl was a great tool for getting to grips with how to use the functions discussed. Definitely recommend having a pen and pad with you to take notes throughout - have no idea how the course could be done without doing this. Overall, I found it an engaging and suitably-challenging course for someone new to R , and with a little tweaking of programming assignment 3, I think it could be an industry standard.

By Carolina S

Nov 18, 2020

This course was, without any doubt, a great investment of my time and money! As long as you put enough effort, you will for sure learn a lot. The content of the videos is very clear and the practical exercises in swirl are an excellent way of putting in practice what you learn. Programming assignments 1 and 3 were a bit though, when compared with the material covered in the classes. Nonetheless, if your work hard you can complete them and you will learn more than you might have expected!!

Congratulations to the instructors :)

By Stephen E

Dec 6, 2015

I think making this course self-paced is a great idea. This is a well put together course. My only concern is for those who have not done programming before. Maybe a prerequisite course could be recommended? I recall doing an excellent Python for beginners course recently. Personally I'm not a big fan of peer correcting, I know this is a big part of making Coursera work, but for classes like this one, I think something like SWIRL would work best. Anyway, that was my two cents. KEEP OF THE GREAT WORK, YOU GUYS ROCK! :)

By Wissam A

Jan 11, 2017

Wonderful course, it forced me to depend on myself. I had to search, I had to read and go through lots of articles, questions, solutions and tutorials online and also, in the discussion board to solve the assignments. I believe it is the best way for someone to get knee deep and learn the language. This is exactly the methodology that should be followed in the real world. One would have to search and find solutions for any issues that are encountered. I enjoyed it and moving on to completing the specialization.

By Farhan C

Aug 23, 2017

Amazing course! You get to learn the in-depth nuances of R-Programming. Not just that, this course makes you work really hard. You need to troubleshoot your own problems, and you find even better code, a smarter and an even more lean way of coding which is simply brilliant! If you're looking for a course that will help you grasp R Programming, I would strongly recommend going through the entire Data Science Specialization. Kudos to the Coursera and The Johns Hopkins Team for putting up this course together!

By Krish H

May 23, 2020

It is tougher than it seems. The idioms are R are difficult to master and more time is needed. It is almost as if knowing R is a pre-requisite for this course. The forums were quite helpful. If you spend more time during the week on R and take the same problem and try to solve it in many different ways then you would get more out of it. the lexical scoping, the libraries, the syntax for efficient processing using apply, pipelines are crucial to understand R and not treat it like another procedural language

By Jeff F

Feb 8, 2016

Excellent introductory discussions in the videos. The in-R Swirl tutorial is superb - a great way to force you to type out the basic concepts. Assignments are challenging as they leave a little ambiguity, making you get familiar with the help system and online searches. This course isn't the fastest way to get an overview of R, but it's a great way to get a little more than an overview, including some practical experience, if you're willing to put in a little time and work through some bumps.