9 R Programming Courses (Online)

David Brown (Senior Developer) Updated January 2, 2024
9 R Programming Courses (Online)

Best R programming courses for beginners

1. R Programming For Absolute Beginners

4.7/5 (153721 students) Read the reviews
Bogdan Anastasiei Online course by Bogdan Anastasiei certification icon Certification available

This course will help you master the basics of R in a short time, as a first step to become a skilled R data scientist. The course is meant for absolute beginners, so you don’t have to know anything about R before starting. But after graduating this course you will have the most important R programming skills – and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course.

2. Statistics with R - Beginner Level

4.5/5 (122258 students) Read the reviews
Bogdan Anastasiei Online course by Bogdan Anastasiei certification icon Certification available

This course will teach you how to compute basic statistical analyses in the R program, how to build charts and tables, how to check for normality and outliers, and how to perform some basic statistical tests.

3. The R Programming For Data Science A-Z Complete Diploma

4.3/5 (54604 students) Read the reviews
SDE Arts | Octavo Online course by SDE Arts | Octavo certification icon Certification available

This course covers the R programming language from the basics to the advanced concepts. It will cover topics such as the R working tools and environment for professionals, the R syntax, variables, values, and assignments, data types, mathematical operations, character operations, logical values and Booleans, operators, data structures, decision making, repeat blocks of code, iterating over collections, functional programming, code reusing, statistics, data analysis, and graphics and data visualizations.

Best advanced R programming courses

1. Statistics with R - Intermediate Level

4.6/5 (31430 students) Read the reviews
Bogdan Anastasiei Online course by Bogdan Anastasiei certification icon Certification available

This course will teach you how to perform various statistical analyses in the R program. These include association tests like the Pearson correlation, the Spearman and Kendall correlation, the partial correlation, and the chi-square test for independence. You will also learn about the t tests, the analysis of variance (both univariate and multivariate), and a few non-parametric tests. In addition, you will be taught how to perform a multiple linear regression analysis, including how to check the regression assumptions and how to run a sequential (or hierarchical) regression in R. Finally, you will learn how to compute three important reliability indicators in R.

2. Statistics with R - Advanced Level

4.2/5 (27900 students) Read the reviews
Bogdan Anastasiei Online course by Bogdan Anastasiei certification icon Certification available

This course will teach you advanced statistical techniques in the R programming language. These techniques include the analysis of covariance, the within-subjects analysis of variance, the mixed analysis of variance, the binomial logistic regression, the ordinal logistic regression, the multinomial logistic regression, the multidimensional scaling, the principal component analysis, the factor analysis, the simple correspondence analysis, the multiple correspondence analysis, the cluster analysis, the k-means cluster analysis, the hierarchical cluster analysis, the simple discriminant analysis, and the multiple discriminant analysis.

3. Maps with R Leaflet

4.5/5 (4768 students) Read the reviews
Gregory Sward Online course by Gregory Sward certification icon Certification available

The Leaflet package for R is a popular library for creating beautiful maps. In this course, we'll be using it to analyze FBI homicide data in the United States.

Free R tutorials

1. R, ggplot, and Simple Linear Regression

4.4/5 (40877 students) Read the reviews
Charles Redmond Online course by Charles Redmond

This course is for anyone who wants to learn data science skills. The course is taught using R and ggplot2. It covers the basics of linear regression. The course should take two weeks to complete.

2. Polynomial Regression, R, and ggplot

4.7/5 (26404 students) Read the reviews
Charles Redmond Online course by Charles Redmond

This course is a sequel to the course "R, ggplot, and Simple Linear Regression". In this course, we take on polynomial regression and learn how to fit polynomials to data sets. Along the way, we will learn how to write our own functions in R and how to graph them with ggplot. At the conclusion of the course, we will learn how to fit a smoothing spline to data sets.
This course should take about a week to complete. You will need to have R and RStudio installed, and it would be best if you have a background in R and ggplot equivalent to what you would get if you viewed the first course.

3. Training Sets, Test Sets, R, and ggplot

4/5 (19049 students) Read the reviews
Charles Redmond Online course by Charles Redmond

The course will cover how to evaluate the performance of a regression model, how to row-slice data frames, how to use the predict function in R, and how to add titles and labels to plots. The course is designed to take about two weeks to complete.

If you want to learn the R programming language, there are many ways you can go about it.

You can find a variety of resources online that can help you get started, including tutorials, videos, and online courses. In this post, we’ll explore some of the best ways to learn R online.

Learning R – A Student’s Guide

Why learn R?

R is a programming language that is popular among statisticians and data scientists.

It is a powerful tool for data analysis and statistical computing. There are many reasons to learn R.

R is a free and open source software. This means that anyone can use and contribute to the development of R. R is also available on all major operating systems, so you can use it regardless of your platform of choice.

R is a highly versatile language. It can be used for data analysis, statistical computing, and machine learning.

R also has a large and active community of users, who have contributed a wealth of packages and tools to the language.

R is also great language for learning statistical programming and data science. It is concise and easy to read, and there is a wealth of online resources available to help you learn R.

What is R?

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing.

The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

Polls, surveys of data miners, and studies of scholarly literature databases show that R’s popularity has increased substantially in recent years.

How can I learn R?

There are plenty of ways to learn R, and it really depends on your level of experience and comfort with programming. If you’re just starting out, then we recommend checking out some of the courses we recomment above.

These will help you get started with the basics of the language, and will allow you to start writing your own programs in R. If you’re already familiar with another programming language, then learning R will be a breeze.

One of the best ways to learn R is by doing. There are plenty of online resources that will allow you to write and run your own R programs.

This is a great way to get a feel for the language, and to see how it works in action. Another great way to learn R is by working with real data.

  • This can be done in two ways. First, you can find datasets online that you can use to practice your skills.
  • Second, you can work with data that you already have, such as data from your own research.

This is a great way to learn about the language, and to see how it can be used to solve real-world problems.

Finally, we recommend taking some time to explore the community around R. There are plenty of forums, mailing lists, and IRC channels where you can ask questions and get help from other R users.

I recommend R ladies, R weekly, Stack overflow and the annual R conferences.

This is a great way to learn about the language, and to find out what other people are doing with it.

What are some example applications of R?

R is a programming language that is commonly used for statistical computing and data analysis. There are many different applications for R, some of which are listed below.

  • R can be used for exploratory data analysis, which involves manipulating and visualizing data to gain insights and understanding about the dataset.
  • R can be used for predictive modeling, which involves using statistical methods to build models that can predict future outcomes based on data.
  • R can be used for statistical testing, which involves using statistical methods to test hypotheses about data.
  • R can also be used for data visualization and maps, which involves creating visualizations to communicate data and insights.

Frequently Asked Questions

How can I learn R programming online?

There are a variety of ways that you can learn R programming online. You can find several free tutorials and lessons online, or you can choose to enroll in a more comprehensive course. Whichever route you choose, make sure that you find a reputable source of information so that you can learn the language correctly.

Can I learn R in 3 months?

You can learn the basics of R in three months, but to become proficient in R can take years.

How can I learn R language for free?

There are many ways to learn R language for free. You can find tutorials and videos on youtube, or join forums and discussion groups.

Can I teach myself R programming?

Yes, it is possible to teach oneself R programming. However, it may be difficult to find resources and support if one does not have any previous experience with coding.

Is R programming language easy to learn?

The answer to this question is both yes and no. R programming language can be easy to learn for some people and more difficult for others.

Is R or Python easier to learn?

Neither language is inherently easier or harder to learn; this varies depending on the person.

What is R used for in programming?

R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing.

Which is better R or Python?

It depends on what you mean by “better.” If you are looking for a language that is easier to learn, Python might be a better choice. If you are looking for a language that is more popular, R might be a better choice.

Is R same as Python?

No, R is not the same as Python.

What type of language is R programming?

R is a high-level, interpreted, and general-purpose programming language.

Conclusion

Overall, learning R online can be a rewarding experience. With the right courses and motivation, anyone can learn this powerful programming language.

While there are some challenges, such as finding good quality materials and staying motivated, these can be overcome with perseverance.

The benefits of learning R, such as the ability to perform sophisticated data analysis and the potential for career advancement, make it worth the effort.

Did you take one of the R courses recommended in this article, or do you have another one to recommend? please let us know in the comments below.

Related links

Sources

  1. Gentleman, Robert. R programming for bioinformatics. Chapman and Hall/CRC, 2008.
  2. Matloff, Norman. The art of R programming: A tour of statistical software design. No Starch Press, 2011.
  3. Chambers, John M. Software for data analysis: programming with R. Vol. 2. New York: Springer, 2008.
  4. Kaya, Efdal, et al. “Spatial data analysis with R programming for environment.” Human and ecological risk assessment: An International Journal 25.6 (2019): 1521-1530.
David Brown
About the author

David is a self-taught senior developer who lives in San Diego. He has over 10 years of experience in the software development industry and has worked on a variety of projects. He has a passion for coding, and a keen interest in the latest online education technology.

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