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R Programming

Data Science,

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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Rating 4.1 based on 3,408 ratings
Length 5 weeks
Starts Dec 7 (6 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

data science specialization

This is the second course in the Data Science specialization, and it is the foundation course for the rest of the classes.

Best course for learning R but everyone must complete the data science specialization courses for better use of it :) Very theoretical; massive gap between the course (theory) and the tests (application).

Perhaps the material here will be reinforced in later courses in the data science specialization.

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.

If you are doing the Data Science specialization, you are just getting warmed up... :) It's very good, but activities a lot and few time.

EASY Some though job to do Training is good, but assignments are even more interesting to work on.. in course purchase make th Very useful and challenging introduction into R the course is absolutely not for beginner who has little knowledge of R even they are interested in data science specialization... this should be made clear to avoid waste of time, efforts and budget.. not worth subscription Great design!

I am doing Data Science Specialization.

No one should take this course unless they are powering through it for the Data Science specialization.

This was a decent introduction to R, but my main issue is that the Data Science specialization is so dependent upon the R language.

I guess this course is part of one of the best Data Science specializations.

This is the base for the Data Science Specialization.

Excellent entry-level course on R. "R Programming" forces you to dive in deep.These skills serve as a strong basis for the rest of the data science specialization.Material is in depth, but presented clearly.

I was really hoping to complete the data science specialization, but I have found the first couple of courses to be not very cohesive.

The course and the data science specialization as a whole feels poorly designed.

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lexical scoping

Secondly, the difficulty of the topics is changing a lot along the way: some topics are quite demanding (lexical scoping) and then next something basic (debugging) is explained for a long time.

Lexical scoping was particularly difficult to understand at first, and I also had to rewatch it a few times, but it did help me a lot in actually learning the language.

I still don't know what I would ever use the Lexical scoping example for.

Swirl is a good option but learning it first time will make a great difference Learnt a lot in R especially Lexical Scoping, Datatypes, vector operations, loops and apply commands.

Still in the dark mostly about the true purpose of lexical scoping in practical situations.

Good base Generally good, but I didn't like the exercise on lexical scoping.

very rewarding course Very good course, however I find some of the chosen covered topic maybe too specialized (ie lexical scoping for example)...however maybe it will make more sense with time.

From the assignments I liked the lexical scoping assignment the least.

Very good introduction to R, and its use of data structures, indexing, logical arguments, lexical scoping etc.

But only 4 stars, because I wasn't really prepared for the 2nd Assignment (Lexical Scoping), based on the lectures, and the cache assignment operator wasn't well explained.

These are more often used in contect of data science usage of R. Some advanced ones like 'Lexical scoping could have been introduced a bit later in the course, or can put fundamental knowledge of R as a pre-requisite to the course.

I'd suggest to move the activity on lexical scoping to the second week, since that's when the notions of scoping are explained.

For instance, topics like lexical scoping covered in this course are usually tackled in a more Advanced R course.

A thoroughly enjoyable course but some of the assignments (in particular lexical scoping week 3 programming assignment) features concepts that were far outside of the week's lecture material.

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highly recommend this course

I highly recommend this course.

I would highly recommend this course to anyone that wants to become more familiar with R. The course is too basic.

I highly recommend this course!

I will highly recommend this course and swirl package to my friends.

I highly recommend this course for any one who wants to get hands-on experience with R programming for the first time.

I highly recommend this course for any level of programmer.

Thanks a lot :) I highly recommend this course if you want to learn how to program in R. I'm new to programming and I found this course really helpful.

I highly recommend this course to anyone interested in learning R. Also, the "Swirl" library that was used for basic R exercises was fantastic.

Would highly recommend this course.

But i learned a lot from this course and swirl practices were really helpful for me to understand all the functions on R. Highly recommend this course Well structured and crafted for a beginner to learn R programming and get a go ahead expertise in the language.

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step by step

I will say that the one good thing about it was introducing me to Swirl, an R tutorial that you can run in R and learn step by step.

Well the material is really good but i don't like the fact that since its supposed to be for beginners that we have to use stuff we really didn't see or explained in detail at the lectures so as a result i have to search long hours in the web.I understand seeking information is part of the course but spending so many hours trying to figure out things is not really the ideal,since people who choose distance learning are most times occupied with other duties and try to squeeze the courses into their schedule.I also think the amount of information especially on the first week that i just finished is too much for just one week and for someone new to the subject.I am currently taking another coursera class 'Python for beginners' which i can say is very comprehensive , step by step and is intended to beginners.

Step by step.

This R course is quite overwhelming for those who start learning R. R course from Duke university will walk through from step by step.

The material is hardly explained step by step.

Useful in many respects, but the uninitiated to the field need more time, slow increments in project difficulty and more thorough, step by step explanations.

The two assignments that I was the most able to benefit from have videos on YouTube with a step by step description of how they should be solved.

The lectures were a bit hard to follow and the assignments were way harder than the topics in lectures A very good foundation to understand R Excellent lessons with swirl Very helpful and step by step course nice A very good starter course to learn R Very useful and practical!

It will be much easier if you have a lot of exercises step by step mooving us to the final assignment.

The lectures introduce you to R step by step while the swirl exercises give you a hands on experience on the console.

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dr. peng

Reading poor slide content (poorly) is not teaching, Dr. Peng.

Although Dr. Peng's book provided a great deal of clarity that did not appear in the lectures and related material.

Thank you, Dr. Peng.

Hopefully, Dr. Peng or someone from JHU will see this feedback and be interested in making improvements to the curriculum.

Thank you Dr. Peng!

Dr. Peng is great at teaching, and the lectures are not hard to follow.

Also, Dr. Peng makes gross burping/drooling noises in the video from time to time.

I think Dr. Peng needs to improve his teaching skills.1.

Otherwise, Dr. Peng does a good job at providing material.

Thank you Dr. Peng and Team so so awesome Poorly organized and managed.

Dr. Peng has an easy manner about him, is clear and explains the basic concepts quite well.

Thanks Coursera :) Gave to little instruction and expected the assignments to be completed based on very little instruction First off, I would like to thank you Dr. Peng for making this course.

Dr. Peng is awesome!

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stack overflow

You will learn a lot, but as others have said, most of what you learn is self taught from google and stack overflow and browsing the forums.

I spent countless hours searching through stack overflow and the course forum to no avail.

But as a starting R programmer I found myself stuck on some of the materials without knowing where to turn except for stack overflow.

The Assignments do not require just what is being taught, and demands a lot of google and stack overflow research in order to solve the problems.

It is up to the student to figure out through stack overflow, youtube, and other search result to figure out how to complete these assignments.

google, stack overflow... etc...

I find myself doing other MOOCs, online tutorials and reading stack overflow for hours to find what I need to do the assignments.

You stomp into many things you didn't learn, but can easily look up for in Stack Overflow or another forum.

You just need to think, probably watch again some small lecture, and probably go to the Discussion Forums (or Stack Overflow in my case to know about how to do something specific in R), to solve the Programming Assignments.

I enjoyed the programming assignments which exercised my ability to troubleshoot problems and search for answers on google and stack overflow.

You will be required to seek help elsewhere (i.e., Stack Overflow) and answers to your questions will take days, if not longer.

Read the documentation of R functions, peruse through stack overflow and really step up your game.

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excelente curso

Excelente curso.

Excelente curso, aunque muy breve Good course to gain a good skills in R A good introduction to R. The practical assignments with Swirl will help a lot for those new into programming.

Very, and the best R programming course I've seen online as of yet.A must for anyone who wish to learn R Programming, and advance in Data Science very useful for beginners Excelente curso, me auxiliou e muito no aprimoramento das minhas habilidades com R. Estou maravilhado com as novas possibilidades.

Excelente curso para iniciar en la programación en R, explicado a la perfección y con ejercicios retadores y didácticos.

Thank you Excelente curso em R, está me ajudando muito.

it has helped me to learn r language from basic to intermediate Excelente curso I like most parts of this course.

Excelente Curso.

Un excelente curso, al menos para mí como principiante me pareció muy bueno It's great to learn it from here.

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johns hopkins university

Thanks Johns Hopkins University!

Thank you so much for sharing an awesome knowledge that everyone would like to come across.It feels a pleasure that i have been R certified from Johns Hopkins University, through Coursera.

This course was as equally bad as the rest of the courses in the "Data Science" track through Johns Hopkins University.

Thanks coursera and Johns Hopkins University for offering this course!

But trust me,take the leap and start R Programming from Johns Hopkins University.

R Programming is a really fascinating course where you can see award-winning lecturers from Johns Hopkins university teaching useful analytical tools and sophisticated models.

I would thank Coursera and Johns Hopkins university for putting together such courses in order for us to learn from such great experienced professors.

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steep learning curve

Otherwise, it would be a steep learning curve in R. Good Learning, chance to practice with real data NICE what is taught through the instructions doesn't have as much depth.

Steep learning curve but the resources to succeed are there.

Very steep learning curve, especially in the beginning.

But once that steep learning curve is overcome, you will enjoy all the benefits and, above all, will be well-armed to continue in the specialization.

Otherwise, it was quite a steep learning curve into the material.

THere is some disconnect sometimes between the lessons and the assignments, and often there's a steep learning curve for them.

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highly recommended

Highly recommended.

Excellent course to start with R. Highly recommended.

Highly recommended, but not for the unitiated without conviction or the faint of heart.

The videos do have links where you can find more reading material, when needed, although highly recommended.

..highly recommended...

Highly recommended to start programming in R. The new platform Sucks!!

Highly recommended!

This course is very good for beginners in R language and it is highly recommended for people looking to dive into the world of Data Analytics or Machine Learning.

Overall a highly recommended course for beginners.

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computing for data analysis

R Programming is a remake of Computing for Data Analysis, another course offered on Coursera by the same instructor, Roger Peng.

The slides and lectures are a bit smoother than Computing for Data Analysis but the content is mostly the same.

I passed computing for data analysis 6 months ago.

In comparison to "computing for data analysis" it is an improved version.

This course is an improvement in relation to Computing for Data Analysis (which I took twice, one of them successfully).

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discussion board

The first coding assignment has students utilize concepts only found in stickied threads in the discussion board.

An article posted on the course discussion board claims that this is due to the creators wanting to instill a "hacker mentality" in students, so they will work to find the answers themselves.

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 feel the lectures were clear, the supplemental swirl assignments were beneficial, and that the discussion boards were fruitful.

The discussion board and mentors are really good resources and can provide pointers to move along.

As well, the accompanying book published by the lecturer, learning materials, mentor Mr Warren and discussion board are invaluable for beginners like myself.

The discussion boards are chock full of thoroughly confused students trying to help each other out, and this is where the real learning occurred.

In fact, one of the most valuable aspect of the discussion boards was information about where else one could take courses in R. With the exception of a few TAs that single-handedly saved the course, there was no professor interaction on the boards, nor was there any attempt to clarify, correct mistakes or otherwise show at least a minimal interest.

Much of the methodology needed for the assessments is not taught within the course teaching and requires that students learn this independently from the discussion boards or external internet sources.

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Rating 4.1 based on 3,408 ratings
Length 5 weeks
Starts Dec 7 (6 weeks ago)
Cost $49
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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