This class will teach you the skills you need in order to do research using R and give you a strong foundation you can build on to become an R expert. The course will be taught using RStudio and make use of the additional tools which are built into RStudio. We?ll cover the tools and commands you you need to start a project, import and format data, create graphs, carry out regression, and run diagnostics. In the time of this class, it would be impossible to show all what R can do or what you might want to do. We will cover the most common commands and options for those commands. With the skills from the class you will be able to learn additional commands and options as you need them.

No prior knowledge of R is required for this class. But if you have done some R programming and are looking to expand your knowledge, this just might be what you need. Also, if you are an experienced R user and have not stated using RStudio yet, this would also be an opportunity to learn what RStudio has to offer and get some exposure to using it. If you?re just trying to get through a statistics course this class will likely teach you more than you need, but the time spent will make your course easier while also preparing you to do research later.

The material covered in this class will be available online in the SSCC's Statistical Computing Knowledge Base. The materials will be posted to the web site as they are completed. The list of topics for the course and a tentative schedule are provided at the bottom of this page. If you cannot attend all the class sessions you can still benefit from the class if you work through the missed material on your own ? email the instructor to find out what was covered on a particular day.

A tentative schedule for the course follows

This lesson is an introduction to the integrated development environment (IDE) of RStudio. IDE is a fancy and maybe scary name for a set of tools that are designed to work together. The IDE is useful enhancement to R and you will be more effective in R by using it. We will cover project folder organization, files types, setting up a project, using Git for source control, and getting R results into documents with markdown.

This lesson is an introduction to setting up the R programming environment, getting data into R, and formatting the data so it ready to be used. We will cover setting working directories, installing libraries, reading data files, data types and changing between types.

This lesson is an introduction to examining your data. We will cover descriptive statistics such as summary statistics and frequencies as well as data visualization through graphs, such as scatter plots and histograms. The ggplot functionality will be used for much of the plotting.

This lesson is an introduction to regression. We will cover formula specification, what is contained in summary results, and tools for variable selection tools and parameter inference. Much of what is done for GLMs are either the same or extension of what is done for OLS. While all these lesson build on each other, understanding the material in this lesson is particularly important for the GLM lesson.

This lesson is an introduction to the diagnostic tools available. We will cover residual plots, normal probability plots, leverage and Cook?s distance plots, etc.

This lesson is an introduction to GLM and LMER (mixed models.) We will cover logistic regression, count data, and random effects.

Instructor: Banghart

Room: 3218 Sewell Social Sciences Building

Dates: 9/16, 9/18, 9/23, 9/25, 9/30, 10/2, 10/7, 10/9, 10/14

*This class is a series and you should plan on attending all of the sessions.*

Time: 12:00 - 1:00