Main content



Loading wiki pages...

Wiki Version:
# **A gentle crash course in R using tidyverse** ## Loek Brinkman & Nick Michalak ![]( ![]( # **Introduction** > - What is R and why use it in your research? > - R Studio environment > - Programming basics > - Installing the tidyverse # **Data visualization** > - ggplot2 and the grammar of graphics > - demonstrations > - exercises # **Data reading and "wrangling"** > - filtering datasets > - selecting variables > - creating new variables > - custom data summaries > - grouping data transformations and summaries # **Data summarizing and modeling** > - descriptive statistics > - t-tests, ANOVAs, and linear regression > - correlations > - if there's time ... > + linear mixed effects models > + structural equal models > + exploratory factor analysis # **Wrap-up and resources** # **R resources** ## **websites** > - [**Quick-R**]( a roadmap to the language and the code necessary to get started quickly (i.e. tutorials) > - [**R Studio 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"_ > - [****]( 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** > - Ron Dotsch (adapted course slides) > - SIPS organizers > - R for Data Science (workshop modeled off book)
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.