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 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
Dr. Eric Nord is the primary author of the materials for this course.
- 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.
Text: 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.
Familiarity with basic statistics is assumed.