R Programming for Research Workshop

Nick Michalak and Iris Wang

University of Michigan LSA Department of Psychology

Required Texts

• Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Sebastopol, CA: O'Reilly Media, Inc.
• The tidyverse style guide by Hadley Wickham

Philosophy

• ReadCollegePDX (2015, October 19). Hadley Wickham "Data Science with R". Retrieved from https://youtu.be/K-ss_ag2k9E?list=PLNtpLD4WiWbw9Cgcg6IU75u-44TrrN3A4
• Robinson, D. (2017, July 05). Teach the tidyverse to beginners. Variance Explained. Retreived from http://varianceexplained.org/r/teach-tidyverse/
• Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1-23.

Day 1. Installation and Introduction

Before Workshop

• Skim introduction (Wickham & Grolemund)
• Browse tidyverse.org
• Skim Hadley Wickham "Data Science with R" (ReedCollegePDX, 2016)
• Find one or two datasets you know well and are OK with others seeing.
  1. Preferably, find the raw (hasn't been "cleaned") data.
  2. Make a new folder. give it a good name. repeat with subfolders. (Hint: Skim some data management best practices from the Stanford Library or the Michigan Library guide)
  3. Put your raw data in there, somewhere.

During Workshop

• Introduction / philosophy
• Installing (and uninstalling) R and RStudio
  • Installing R (Macintosh / Windows)
  • Uninstalling R (Macintosh / Windows)
  • Installing RStudio
  • Uninstalling RStudio
• R environment
• Running R code
• Demonstrations
• tidyverse
• Exercises
• Resources
• Cheat Sheets
  • Base R Cheat Sheet

Day 2. Visualization

Before Workshop

• Skim Data visualization and Data import (Wickham & Grolemund)
• Skim magrittr and ggplot2
• Skim Anscombe's quartet
• Skim Matejka, J., & Fitzmaurice, G. (2017, May). Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1290-1294). ACM.
• Skim Weissgerber, T. L., Milic, N. M., Winham, S. J., & Garovic, V. D. (2015). Beyond bar and line graphs: time for a new data presentation paradigm. PLoS biology, 13(4), e1002128.
• Skim McCabe, C. J., Kim, D. S., & King, K. M. (2018). Improving Present Practices in the Visual Display of Interactions. Advances in Methods and Practices in Psychological Science, 2515245917746792.
  • Play with their R Shiny web application that accompanies the paper: interActive: A tool for the visual display of interactions

During Workshop

• Introduction and Demonstration
  • Anscombe's quartet
  • Matejka, J., & Fitzmaurice, G. (2017, May). Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1290-1294). ACM.
  • Weissgerber, T. L., Milic, N. M., Winham, S. J., & Garovic, V. D. (2015). Beyond bar and line graphs: time for a new data presentation paradigm. PLoS biology, 13(4), e1002128.
• ggplot2 and the grammar of graphics
• Demonstrations
• Exercises
• Cheat Sheets
  • Data Visualization Cheat Sheet
  • Data Import Cheat Sheet

Day 3. Workflow and Data Transformation

Before Workshop

• Skim Workflow: basics, Data transformation, and Tidy data (Wickham & Grolemund)
• Skim Files and Syntax from the tidyverse style guide (Wickham)

During Workshop

• Coding Basics
• Naming
• Calling Functions
• rep()
• filter()
• arrange()
• select()
• mutate()
• summarise()
• gather()
• spread()
• full_join(), left_join(), right_join(), inner_join()
• ifelse()
• Exercises in wrangling your own data
• Cheat Sheets
  • Data Transformation Cheat Sheet

Day 4. Summarizing and Modeling

Before Workshop

• your favorite regression or ANOVA text, or any tutorials at https://designingexperiments.com/supplements/
• Skim help("lm"), help("car"), and help("afex")
• Skim An introduction to the psych package: Part I: data entry and data description
• Skim An introduction to the psych package: Part II Scale construction and psychometrics
• Skim lavaan: tutorial
• Judd, C. M., Westfall, J., & Kenny, D. A. (2017). Experiments with more than one random factor: Designs, analytic models, and statistical power. Annual Review of Psychology, 68, 601-625.

During Workshop

• describe() and describeBy()
• t.test()
• lm() and Anova()
  • contrasts
• corr.test()
• pairs.panels() and cor.plot()
• lmer()
• sem()
• fa.parallel() and fa()

Day 5. Your Data

Before the Workshop

Browse RMarkdown from RStudio Skim Workflow: projects

• find one or two datasets you know well and are OK with others seeing.
  1. Preferably, find the raw (hasn't been "cleaned") data
  2. Make a new folder. give it a good name. repeat with subfolders. (Hint: Skim some data management best practices from the Stanford Library or the Michigan Library guide)
  3. Put your raw data in there, somewhere
• Skim The tidyverse style guide by Hadley Wickham

During the Workshop

• R Projects
• R Markdown
• Importing data
  • read_csv(), read_spss(), and read_stata()
• Writing code you and others can read

R Resources

Websites

• Quick-R a roadmap to the language and the code necessary to get started quickly (i.e. tutorials)
• RStudio Cheat Sheets just like it reads, these are cheat sheets for "favorite" R packages and more (e.g. dplyr, ggplot2, base, R Markdown, regular expressions)
• UCLA Institute for Digital Research and Education: R statistics and programming tutorials for R, among other helpful related resources
• The Personality Project: Using R for psychological research seemingly endless tutorials and explainers about R programming for (personality-themed) psychology research; also, some tutorials cover the psych package, which is written by Michigan Psychology alumni, William Revelle (1973)
• Richard Gonzalez's Advanced Statistical Methods Course Notes Nick's regression bible, complete with SPSS and R code for common procedures + detailed notes
• Doug Bonett's Quantitative Data Analysis Course R Functions includes functions for testing linear contrasts (standardized and unstandardized) that don't assume equal variances
• tidyverse: ggplot2 ggplot2 bible (also check out the rest of the tidyverse website)
• lavaan: latent variable analysis overview and tutorials for the best sem package (IMO) in R (disclaimer: no support for discrete latent variables, aka mixture modeling, latent class analysis)
• RExRepos: R code examples for a number of common data analysis tasks just like it reads, how-to guide for common procedures
• R Base Graphics: An Idiot's Guide if you want to plot with Base graphics like an R hipster?a hipstR, if you will?here's a jumping off point
• { swirl }: Learn R, in R "swirl teaches you R programming and data science interactively, at your own pace, and right in the R console!"
• A language, not a letter: Learning statistics in R "This online collection of tutorials was created by graduate students in psychology as a resource for other experimental psychologists interested in using R for statistical analyses and graphics. Each chapter was created to provide an overview of how to code a particular topic in the R language."
• STAT 545 @ UBC: Data wrangling, exploration, and analysis with R "Learn how to explore, groom, visualize, and analyze data and make all of that reproducible, reusable, and shareable using R"
• designingexperiments.com site accompanies Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition; Maxwell, Delaney, & Kelley, 2018). It's full of modeling examples for R, but it also includes some extremely useful website applications for power analyses for all sorts of common designs

Texts

• Beaujean, A. A. (2014). Latent variable modeling using R: A step-by-step guide. New York, NY: Routledge.
• Field, A., Miles., J., & Field, Z. (2012). Discovering statistics using R. London: SAGE Publications.
• Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. New York, NY: Cambridge University Press.
• Ismay, C. & Kim, A.Y. (2017). ModernDive: An Introduction to Statistical and Data Sciences via R.
• Navarro, D. (2015). Learning Statistics with R. Raleigh, North Carolina: Lulu Press, Inc.
• Maxwell, Delaney, & Kelley, (2018). Designing experiments and analyzing data: A model comparison perspective. (3rd ed.). Routledge.
• Wickham, H. (2015). Advanced R. Boca Raton, FL: CRC Press.
• Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. New York, NY: Springer.
• Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R. New York, NY: Springer.

Acknowledgements

• Iris and I couldn't have done this alone. We thank all of these thoughtful and helpful people:
  Josh Wondra (he started this workshop in the Psychology Department last summer and helped us take it over this summer); Brian Wallace and everyone at Psychology Student Academic Affairs (they approved us!); Rich Gonzalez (especially his Psychology 613/614 course); Adrienne Beltz and Pam Davis-Kean and everyone who's a part of the Psychology Methods Hour; Instructional Support Services and Blue Corps at the University of Michigan; and, of course, the R community, especially Hadley Wickham and Garrett Grolemund (they wrote the book!).