R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical techniques such as linear and nonlinear modeling, classical statistical tests, time series analysis, classification, clustering and more. R is highly extensible. One of R's strengths is the ease with which well-designed publication-quality plots can be produced. R has been in use by the Data Scientist community for quite some time. In this session we will do an introduction to the language and show demos, examples, recommended tutorials, as we prepare to utilize and support R.

You will learn:

• How to review vectors, matrix, factors, lists, and data frames
• How to review how to select, aggregate, count, sum, or append to a data frame, and the packages that help us perform these actions
• How to review simple visualization types such as plots, histograms, and briefly touch on designing advanced charts with packages like ggplot2