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 2-Day Seminar Taught by James P. Curley, Ph.D.

R is one of the fastest growing programming languages and has quickly become the lingua franca of data science. It is the major statistical software used for the organization, visualization and analysis of data throughout academia in fields such as psychology, economics, statistics, biostatistics, sociology, and education, as well as in the business and technology sectors.

R is a highly flexible and extensible language. It is supported by an enormous and ever-expanding suite of libraries and packages that provide state-of-the-art statistical techniques. R’s graphics and visualization tools are unmatched. The possibilities for data visualization are limited only by one’s imagination.

R is also a fantastic language for generating reproducible research. Users can perform statistical analyses and write reports or journal articles directly in R that can be reproduced by any other researcher. Undeniably, learning R is the most useful skill any researcher can add to their data science toolbox.

This two-day seminar will provide a comprehensive introduction to R. Course participants will learn R from the beginning–no previous experience is necessary. The course will be practical and hands-on consisting of working with real datasets and solving common problems that arise in research analysis. Throughout, the focus will be on learning tools in R and RStudio that facilitate the management, analysis, and visualization of data as a continuous and reproducible workflow. The course will demonstrate how to import and export data files, how to clean up and work with data, how to make exploratory and publication quality visualizations, and how to do standard parametric and non-parametric statistical analysis.