R is a free software environment for statistical computing and graphics. R is widely used among statisticians and data miners for developing statistical software and data analysis. R has substantially increased its popularity in recent years. It is used in the fields of biomedical sciences, computational finance and financial econometrics, social sciences, business analysis or data mining.

See the RStats Institute Statistics Software page to download R for free.

Although R contains very powerful packages that pretty much can do anything you want in statistics, there is a learning curve. For this reason, we’ve gathered several resources to learn this language.

**For Beginners in R**

A fun (yes, I know, fun and R seem for some to not go together) and interactive way to start learning the basic concepts of R is this free **Try-R** course by Code School.

You will learn about vectors, matrices, factors, data frames and calculating and plotting some basic statistics: mean, median, and standard deviation. You can check out another good **Introduction to R** beginners course by DataCamp here. After finishing this introductory R course, you'll master some very valuable R skills and are ready to undertake your first very own data analysis.

**For Beginners in Statistics and Data Analysis with R**

Once you get your feet under you with the introductory CodeSchool and DataCamp courses, apply your beginning knowledge to a statistics course using R offered by the University of Toronto through Coursera. This course by Alison Gibbs and Jeffrey Rosenthal applies R to the analysis of simple social and medical science data. **Making Sense of Data** will elegantly teach you the basics of statistics and how to use R for computing and plotting simple realistic data.

**More Advanced R Course**

When you know basic R and statistical analysis, tackle a more advanced course through Coursera: R Programming.

In this class, Dr. Roger D. Peng, from Johns Hopkins University, will teach you how to program in R and how to use R for effective data analysis. This class is part of the Johns Hopkins University Data Science Specialization, in which you can earn a Specialization Certificate by learning programming in R, getting and cleaning data, exploratory data analysis, regression, machine learning, and reproducible research.

Note: Although this course will teach you many practices and features of R in a very short period of time, this class can be overwhelming for beginners.

**Other Resources to Learn R**

Whether you need some other tutorial or are stuck with some feature of are, you can find some help with these resources:

*Thanks to Emmanuel Segui de Carreras for researching these resources. *