Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Andrew Miles, Ph.D.

R is a free and open-source package for statistical analysis that is widely used in the social, health, physical, and computational sciences. Researchers gravitate to R because it is powerful, flexible, and has excellent graphics capabilities. It also has a large and rapidly growing community of users.

This course is designed as an introduction to R for those who are looking to use R for applied statistical tasks. Topics include data coding and management as well how to perform basic descriptive, bivariate, and multivariate analyses. We will also address the fundamentals of programming in R, using plots to explore data, and how R can simplify the process of exporting the results from statistical analyses. To be clear, this course does not teach the principles of data management or statistical analysis. Instead, it assumes prior knowledge of these topics and focuses on explaining how they can be implemented in R.

There is no way to cover all the possible uses of R in a single course, so an important theme will be helping participants understand the fundamentals of how R “thinks” so that they can begin to use R independently. For this reason, the course focuses on basic R functions and practical issues like interpreting output and getting help. After this course, participants will be well-equipped to tailor R to the sort of work they do.

This course is thoroughly hands-on. Participants are encouraged to write code along with the instructor, and to participate in the carefully-designed exercises that will be interspersed throughout the seminar. By the end of the course, participants can expect to log more than a dozen hours of guided practice coding in R.