EPSY 887: Intro to R for Academic Researchers
Instructor: Jason Bryer, Ph.D. (jason@bryer.org)
Class Time: Thursdays 4:15 p.m. - 7:05 p.m.
Class Location: AS 013
Grading: 3 credits, S/U grading
Course Website: https://epsy887.bryer.org
R is a statistical program language that has grown in popularity over the last two decades arguably becoming the de facto standard in statistics, data science, and learning analytics (see e.g. Muenchen, 2018; Suda, 2017). R was designed to be scriptable and extensible which makes it well suited for conducting reproducible research and creating publication quality figures. Moreover, the over 10,000 packages available on the Comprehensive R Archive Network (CRAN) provide researchers with access to a rich community and library of tools for conducting research.
This course will explore the skills and tools necessary for conducting data preparation and analysis with R. The first third of the course will focus on learning R. The middle third will explore some of the more common statistical procedures in R including: classification and regression trees; logistic regression; propensity score analysis; missing data imputation; and other topics as time permits. The final third of the class will be left for topics of special interest to students and their research agendas. Class examples will utilize the Programme of International Student Assessment (PISA), a large scale international study conducted every three years. Other open and freely available datasets will also be discussed as appropriate.
Learning Objectives
At the completion of this course, students will be able to…
- Conduct a complete analysis in R including data entry, tidying, analysis, and reporting.
- Structure analyses to facilitate reproducible research.
- Create advanced visualizations using the grammar of graphics.
- Conduct advanced statistical analyses in R.
Prerequisites
It is recommended that students have at least two graduate level statistics courses (EPSY 530 and EPSY 630 or equivalent). Those with fewer than two graduate statistics courses but with other relevant experience should get instructor permission. No prior experiences with R is expected, but some experience with using statistical software would be helpful.
Grading
This course is graded as pass/fail. Successful students will attend and participate in the weekly classes as well as contribute to the course wiki. Weekly assignments will be assigned as appropriate.
- Participation (10%) - Participate in class and the Slack discussions.
- Labs (20%) - R Labs will be assigned as appropriate.
- Website (30%) - Students are to create a website using either the
blogdownorbookdownR package. - Presentation (40%) - The culmination of the course will be a short (20 to 30 minutes) presentation and document outlining the analysis you conducted with your dataset. Students are encouraged to bring their own dataset (e.g. data to be used for a dissertation), but that is not necessary. Many free and public datasets are available for use and will be discussed in the first couple classes.
References
Muenchen, R. A. (2018). The popularity of data science software. Retrieved from http://r4stats.com/articles/popularity/
Suda, B. (2017). 2017 data science salary survey. Sebastopol, CA: O’Reilly Median, Inc.
Thieme, N. (2018). R generation: The story of a statistical programming language that became a subcultural phenomenon. Significance, 4(14), 14-19.
Academic Integrity
Whatever you produce for this course should be your own work and created specifically for this course. You cannot present work produced by others, nor offer any work that you presented or will present to another course. If you borrow text or media from another source or paraphrase substantial ideas from someone else, you must provide a reference to your source.
The University policy on academic dishonesty is clearly outlined in the Student Bulletin, and includes, but is not limited to plagiarism, cheating on examinations, multiple submissions, forgery, unauthorized collaboration, and falsification. These are serious infractions of University regulations and could result in a failing grade for the work in question, a failing grade in the course, or dismissal from the University.
http://www.albany.edu/undergraduate_bulletin/regulations.html
Reasonable Accommodation
Reasonable accommodations will be provided for students with documented physical, sensory, systemic, cognitive, learning and psychiatric disabilities. If you believe you have a disability requiring accommodation in this class, please notify the Director of Disabled Student Services (Campus Center 137, 442-5490). That office will provide the course instructor with verification of your dis- ability, and will recommend appropriate accommodations. For more information, visit the website of the UAlbany Office for Disabled Student Services.
http://www.albany.edu/studentlife/DSS/guidelines/accomodation.html