Introduction

In Section 1, we provided a few resources to help you think about the sorts of data manipulations and statistics that we will teach you to use in our program in order to study human behavior. On this page, our goal is to provide resources for the R programming language that you will begin using this fall in Psych 610: Design & Analysis of Psychological Experiments I, as well as Psych 750: Programming for Human Behavioral Science Data. During your course work, we will provide examples, explanations, homework, and support to teach you all of the necessary statistical and programming skills you will need to engage in assigned projects. However, some students may choose to familiarize themselves with R before then. Below you’ll find resources to download the program, download the RStudio interface for R, and learn common concepts and tasks for using R.

Other programs, such as Tidymodels, will be taught later in the program. Resources for these tools will be included in the course materials and Community of Practice resource space.Topics in this Section:

1. Programming Languages and Systems
2. Resources Available Prior to Enrollment
3. Resources Available with a NetID
4. Books

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1. Programming Languages and Systems

Programming Language: R

• To download R for Windows, visit: https://cran.r-project.org/bin/windows/base/release.htm
• To download R for Mac, visit: https://cran.r-project.org/bin/macosx/
• R for Reproducible Scientific Analyses. This website focuses on teaching the fundamentals of the programming language R, including how to set up your project, understand and explore data, manipulate data, and other useful operations.
• Data Analysis and Visualization in R for Ecologists. This is another great resource that addresses similar topics but with greater depth. Across the top there are different tabs for different lessons about R programming.

Bash Terminal

• See instructions here for configuring the BASH shell for Windows 11: Configuration Instructions for Windows 11
• Instructions for Windows 10: Configuration Instructions for Windows 10

Python Install

• Please download and instal 64-bit Miniconda3 (minimal Anaconda install) 64-bit for Mac/PC: Miniconda3 install

Plain Language

• HTML: https://plain-text.co/
• PDF: https://kieranhealy.org/files/papers/plain-person-text.pdf

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2. Resources Available Prior to Enrollment

The Data Science Hub at UW-Madison provides multiple opportunities to learn computational skills and get help with your coding throughout the year.

• Data and Software Carpentries
  The University of Wisconsin-Madison has a long-standing relationship with The Carpentries, a global organization of researchers who volunteer their time and effort to create workshops that teach software engineering and data analysis skills to other researchers. Each semester, the Data Science Hub holds Carpentries workshops which teach skills such as R, Python, Git, Openrefine, Unix Shell, and more. These are open to anyone and you can see their upcoming schedule. These workshops have a discounted price for those already affiliated with the UW-Madison.
• Data Science Workshop
  A series of recorded workshops and online materials introducing R and other data science tools.
• Data Science Hub Consultations and Coding Meetup
  Data Science Hub facilitators can recommend learning pathways and project strategies, and liaise contacts with collaborators and data science experts. You can bring your questions and get help during their office hours which are currently held virtually on the Data Science Hub Slack channel. You can find more information at the Data Science Hub website.

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3. Resources Available with a NetID

The following include trainings and content that require a NetID and are available after enrollment. You will receive a NetID before your classes begin.

• Discounted Data and Software Carpentries workshops
  After enrollment, you’ll qualify for discounted Data Science Hub Workshops.
• Linkedin Learning
  LinkedIn Learning offers access to a full library of self-study software tutorials available anytime, anywhere. This service is provided by DoIT Academic Technology and can be accessed by UW-Madison faculty, researchers, staff, and students.
• Social Science Computing Cooperative (SSCC)
  The Social Science Computing Cooperative (SSCC) regularly holds training sessions each semester that teach tools like R, Python, Stata, NVivo, and others. You can find more information and see upcoming events here.
• UW-Madison Libraries
  The UW-Madison Libraries offer workshops on R and Python regularly. You can find the schedule of upcoming workshops on the library website.
• Center for High Throughput Computing (CHTC)
  If you find that your research no longer runs efficiently on your laptop and would benefit from more advanced computing resources, reach out to the Center for High Throughput Computing.

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4. Books

The UW-Madison Libraries provide access to a number of books that will be helpful to learning programming. Many of these texts are also available for free online.

• R For Data Science (Library)
  • Wickham, H. (2016). R For Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.
  • Find it at the UW-Madison Libraries: https://search.library.wisc.edu/catalog/9912314572302121
• R for Data Science (Open Source)
  • Wickham, H., & Grolemund, G. (2017). R For Data Science: Visualize, Model, Transform, Tidy, and Import Data. O’Reilly Media.
  • R for Data Science is a fantastic open source textbook that provides an introduction for R.
  • Find it online at: https://r4ds.had.co.nz
• Advanced R
  • Wickham, H.(2019). Advanced R. O’Reilly Media.
  • Written by the same author as R for Data Science, Wickman provides a more general resource for thinking and programming in R.
  • Find it online at: https://adv-r.hadley.nz
• Text Mining with R
  • Silge, J., & Robinson, D. (2017). Text Mining with R. O’Reilly Media
  • This book is an introduction of text mining using the tidytext package and other tidy tools in R.
  • Find it online at: https://www.tidytextmining.com/index.html