R is a programming language. Show
R is often used for statistical computing and graphical presentation to analyze and visualize data. Start learning R now » Examples in Each ChapterWith our "Try it Yourself" editor, you can edit R code and view the result. ExampleHow to output some text, and how to do a simple calculation in R: "Hello
World!" Result:
Try it Yourself » ExampleHow you can use R to easily create a graph with numbers from 1 to 10 on both the x and y axis: plot(1:10) Result:  Try it Yourself » We recommend reading this tutorial, in the sequence listed in the left menu. R ExercisesMy LearningTrack your progress with the free "My Learning" program here at W3Schools. Log into your account, and start earning points! This is an optional feature. You can study W3Schools without using My Learning.
R ExamplesLearn by examples! This tutorial supplements all explanations with clarifying examples. See All R Examples R QuizLearn by taking a quiz! This quiz will give you a signal of how much you know about R. Take the R Quiz RStudio TeamRStudio’s recommended professional data science solution for every team. Learn MoreRStudio WorkbenchTake control of your R and Python code An integrated development environment for R and Python, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management. RStudio ConnectEasily share your insights Share data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. RStudio Package ManagerControl and distribute packages Control, organize, and govern your use of R packages to increase reproducibility and decrease the time you spend installing and troubleshooting. About this CourseIn 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. 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 What you will learn
Skills you will gain
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 InstructorsOffered byJohns Hopkins UniversityThe 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 courseWeek 1Week 1: Background, Getting Started, and Nuts & BoltsThis 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. 28 videos (Total 129 min), 9 readings, 8 quizzes Week 2Week 2: Programming with RWelcome 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. 13 videos (Total 91 min), 3 readings, 5 quizzes Week 3Week 3: Loop Functions and DebuggingWe 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. 8 videos (Total 61 min), 2 readings, 4 quizzes Week 4Week 4: Simulation & ProfilingThis 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. 6 videos (Total 42 min), 4 readings, 5 quizzes Reviews
TOP REVIEWS FROM R PROGRAMMINGby BPJul 4, 2019 This course was almost excellent. The tutorials were amazing. I am just going to complain about Assignment 2; inverted matrices weren't a pre-requisite so it was hard to understand that assignment by GPMay 7, 2017 Helpful in learning the basics and then some. However, the course assumes you know certain things about the R language and a lot of catching up had to be done (learning from outside sources etc.).
by RRFeb 21, 2017 I am pleasantly surprised with the quality of this course. For a beginner, the Swirl exercises are incredibly helpful and I was able to build confidence in working with R because of them. Thank you! by RDMar 3, 2016 A great introduction to slightly more complicated R programming. Basic concepts covered well and it builds nicely to the point where you feel like you can apply your knowledge to real world examples View all reviews
More questions? Visit the Learner Help Center. What is R used for?R is a programming language for statistical computing and graphics that you can use to clean, analyze, and graph your data. It is widely used by researchers from diverse disciplines to estimate and display results and by teachers of statistics and research methods.
Which is better R or Python?If you're passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you. If, on the other hand, you're interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit.
Why R is so popular?R is the most popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured. This has made R, the standard language for performing statistical operations. R allows various features that set it apart from other Data Science languages.
Why R is called R?The "R" name is derived from the first letter of the names of its two developers, Ross Ihaka and Robert Gentleman, who were associated with the University of Auckland at the time.
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