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Free R Programming Courses

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Introduction to R
star   4.56 170.4K+ learners
1 hr
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R Studio Basics
star   4.54 5.6K+ learners
1 hr
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R for Data Science
star   4.54 13.9K+ learners
2 hrs
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Introduction to R
star   4.56 170.4K+ learners 1 hr
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R Studio Basics
star   4.54 5.6K+ learners 1 hr
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R for Data Science
star   4.54 13.9K+ learners 2 hrs

Learner reviews of the Free R Programming Courses

Our learners share their experiences of our courses

4.56
71%
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5.0

“Learning R Efficiently and Effectively”
The course provided clear explanations of R programming concepts, coupled with practical exercises that reinforced the learning material. The hands-on approach made it easy to grasp even complex topics, and the examples were highly relevant, making the entire learning experience enjoyable and productive.

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5.0

“Great Foundational Skills and Practical Exercises in R”
I appreciated the structured approach of the course, which made complex topics accessible and easy to grasp.

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5.0

“Enjoyable Aspects of the Experience”
The course video made the concepts easy to understand. The explanations were clear, and the visuals helped simplify complex topics, making learning much more efficient.

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5.0

“This Course Exceeded My Expectations in Every Way”
The course was exceptionally well-structured, offering a perfect balance between theoretical concepts and practical applications. It not only deepened my understanding of the subject but also inspired a genuine passion for the field. The insightful discussions and hands-on experience truly made the learning process engaging and rewarding.

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5.0

“Deep Dive into Data Analysis with R”
I enjoyed exploring R's powerful capabilities for data manipulation and visualization. Learning how to use functions like ggplot2 for creating insightful graphics and dplyr for efficient data processing significantly enhanced my analytical skills. The hands-on experience with real datasets allowed me to understand statistical concepts practically, making the learning process both engaging and rewarding.

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5.0

“The Course Provided an Excellent Hands-On Learning Experience with R”
It is a comprehensive and well-structured course for beginners. It offers a clear introduction to the basics of R programming, making it easy to follow along even for those with no prior experience. The practical examples and exercises are particularly helpful in reinforcing the concepts. A great starting point for anyone looking to dive into data analysis using R!

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5.0

“The Introduction to R Course by Great Learning”
Clear Structure: The course is well-organized, covering essential topics in a logical sequence. Participants can easily follow along and build on their knowledge progressively. Hands-On Learning: The use of built-in datasets allows learners to practice in real-time, reinforcing theoretical concepts through practical application. Engaging Delivery: The instructor's approach is lively and encouraging, making complex topics more accessible. This helps maintain learner engagement throughout the session.

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5.0

“Excellent and Informative Experience”
I really enjoyed how interactive and insightful the session was. The clarity of the concepts and real-world applications made the learning experience both engaging and valuable. The instructor was knowledgeable and answered all questions with patience, making it easy to follow along. Looking forward to more sessions like this!

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5.0

“It Was an Amazing and Excellent Experience to Gain Knowledge”
It was an amazing and excellent experience to gain knowledge. The way the tutor explained was excellent.

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5.0

“Understand the Basics of R”
This course was easy to follow, and I was able to grasp the basic ideas of R pretty fast.

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Get Free R Programming Courses

R is a popular programming language used to develop software applications. R in the subject is taken after its developers, Ross Ihaka and Robert Gentleman. It provides a free platform for statistical computation and graphics which is supported by the R Foundation for Statistical Computing. It is popularly used amongst statisticians and data scientists to extract the useful data for developing statistical software and also for data analysis. 

The R programming environment is used to clean, analyze and graph the data. It is popularly used amongst researchers from various disciplines to predict and display the results by technologies such as statistics and research methods. It is a free programming platform hence making it an attractive option. However, it does not completely depend on the programming code. It more conveniently uses a drop down menu and buttons to work with application development. 

R is used over other programming languages for various reason such as:

  • It is an open-source programming tool. Anybody can get hold of the code used to run an application and add the code into it. It performs trending analysis quickly and fixes the errors faster in a transparent manner. It brings together a group of programmers. 
  • Programmers code their own program in R and add up to the huge list of R tools. These codes are submitted in the form of packages. A few packages specialize in a particular kind of analysis and a few other work in a broader way. For example, the “pwr” package is specialized in conducting power analysis. On the other hand, “psych” package does actions ranging from descriptive statistics to item-response theory and to mediation analysis. In the beginning of the year 2017, there were about 10,000 packages, but later after the statistical approach was designed, the numbers increased drastically. 
  • Anybody who is interested can go through the code in the package. This can help in correcting the errors when the users are going through them. Authors of a particular piece of code will be notified about the errors when the other person corrects it or validates. This way, errors are found very quickly and also can be dealt with in an efficient way. One does not have to wait for a very long time for the newer version of the tool, it will be sonner available since the authors keep working on the packages. These package updates are released, making the process entirely transparent. 
  • The dynamics between the authors that codes and creates the new technologies and the users examining the data and the package is collaborative. It is more research oriented, always towards development. It can be as simple as Googling your doubts to being partners to the introduction of newer and fancier updates. It is a big community of coders and analysts. 
  • R is used literally for everything. It can do the job of tools like SPSS, SAS and STATA on a single platform. It can perform the descriptive analysis, regression equations, ANOVA or MANCOVA, and also hierarchical linear modelling of user’s desire. It also covers structural equation modeling that is normally accomplished by MPlus. Merging datasets, cleaning data, identifying rows and columns, updating sheets can also be accomplished by R instead of using Excel for these tasks. R can create plots and graphic images in both #d and interactive. The platform can be used with the text processors like Latex to integrate the results into the manuscripts. It can create APA formatted tables, complete it with good efficiency, horizontal lines and export them as .doc files. It is capable of performing both frequentist and Bayesian statistics. It makes use of a multicore processor and can run analysis in parallel. R bootstraps, simulates, randomizes, resample, multiples and imputes an object. 
  • R addresses many global challenges to perform reproducible research. It can average, sum, reverse-score and also produce item-sum theory. These operations are applied on the data. R uses scripting remedies to solve any problems, big or small. It manipulates data through codes and performs analysis on the data that the user requires. The data of an author is shared with another augmented by online databases. 
  • R is extensively used and the growth is rapid. It is an industry standard in the realm of data analytics which is also known as data science. POpular companies like Facebook, Merck, Pfizer hire psychology PhD students with solid hold on both statistics and programming. R is most apt for such career options. 

The free R Programming certification course offered by Great Learning shall take you through what the subject is, how it works, its features, and various applications of it. At the end of the course, you will be able to use the platform efficiently and perform combined tasks since it provides support to various operations performed on different tools at different times. You can also learn a free R Programming course online. You will gain a certificate after the successful completion of the course. Happy learning!

 

Meet your faculty

Meet industry experts who will teach you relevant skills in artificial intelligence

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Mr. Bharani Akella

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Frequently Asked Questions

What is R programming used for?

R programming language is used for statistical computing and graphics. It is used to clean, analyze and graph the developed or existing data. It is used by researchers of diverse disciplines to estimate and display the results. Statistics and research teachers also use it to display research methods. 

 

Is R programming hard to learn?

Yes! The R programming language is hard to learn. The language is much different from other programming languages. The syntax used to program in R language is hard to learn and understand, unlike languages like Python.

How do I start learning R programming?

R programming language can be learnt online for free. Register on to Great Learning Academy to learn a free R programming course online. You will be able to thoroughly understand and work with the language from the offered course.

How long will it take to learn R?

R programming language is one of the tough programming languages to learn. It will take roughly about 4-6 weeks to understand the basics and all the components, syntax of R. If you are one with the basic understanding of the programming language, then it might take 2-4 weeks to learn R programming language. You can learn R programming by registering on Great Learning Academy.