### About

Since its release in 1997, R has emerged as a popular tool for statistical analysis and research. The flexibility and extensibility of R are keys attributes that have driven its adoption. Some of the advantages of R are related to the command line interface (CLI) form in which it is used. However, this does add to the challenge of learning to use R. The goal of this course is to build upon the knowledge and experience gained in STAT 484. Specifically:

- Become familiar with using R for common statistical analyses
- Learn how to use R graphics to develop sophisticated figures
- Explore simple programming in R
- Develop good analytical practices including documenting analysis and data manipulation, and collaborating with others in the R user/learner community

### Course Topics

Course topics include:

- Linear models – regression
- Linear models – ANOVA I
- Linear models – ANOVA II - multiple way ANOVA
- Managing Projects and Producing Reports
- Visualizing Data I - enhancing scatter plots
- Visualizing Data II - errorbars and polygonsVisualizing Data II - enhancing barplots and and boxplots
- Mixed effects models - introduce lme(), lmer()
- Fitting other models. Non-linear least squared models, logistic regession
- Writing functions

### Course Author(s)

Dr. Eric Nord is the primary author of the materials for this course.

### Software

- Access to your own copy of R. Please make sure that you visit Statistical Software page
**for the latest information**about R. - RStudio is a very nice platform for using R that will run on Windows, Mac, and Linux. R studio adds many useful features to simplify using R. All the functions used in this class can be performed without RStudio, but I will be demonstrating their use within RStudio.

### Textbook

We will make extensive use of Essential R – the course notes for this class. You should download it and will probably find it useful to print it. You may also want to download additional resources in the compressed folder Essential R.zip.

**Other Books and Resources on R:**

*Statistics: An introduction using R.*2005. Michael J. Crawley. Wiley and Sons. (This was useful enough to me when I began learning R that I bought a copy.).*Using R for Introductory Statistics.*2004. John Verzani. Chapman & Hall/CRC. (An extension of SimpleR) https://www.crcpress.com. If I was going to require a text, this would be it.

### Assessment Plan

Grades will be based on three components: **weekly exercises** (36%), **participation **(20%) and a **project** of your choosing (44%).

Exercises will be graded with an emphasis on completeness rather than correctness - the point is to *do* them so you actually *use* R. Participation includes both in class and on-line participation, which will be through forums and a wiki on Canvas; I will count posting questions, tips, and helping others out as participation. The project should consist of an analysis or publication quality graphic based on data of your choosing. Ideally, you have data you want to analyze or present, so this should be useful to you.

### Prerequisites

Familiarity with basic statistics is assumed.