R bootcamp topics

Slow R: basic introduction to R

This module provides a deliberately slow introduction to R that assumes that you have little or no experience with R and have not worked extensively with other programming languages.

• Slow R (Rick Gilmore) | Download

Other resources

• Kelly Black R tutorial: https://www.cyclismo.org/tutorial/R/index.html
• James LeBreton R introduction (from R Bootcamp 2017) | Rmd source

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Multilevel Models and Interactions

• Intro to MLM and interactions (Nilam Ram and Mimi Brinberg) | Rmd

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Structural equation modeling

• Structural equation modeling in lavaan (Michael Hallquist) | Rmd | Exercise

Other resources

• Alison Schreiber We R lavaan walkthrough
• Michael Hallquist PSY 597 SEM course in R
• lavaan tutorial

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Dynamical systems analysis using dynr

• Lecture: Dynamical modeling using dynr (Sy-Miin Chow) | Zip of all files
• Growth curve modeling demo | Rmd
• Process factor analysis demo | Rmd
• Vector autoregressive model demo | Rmd

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Data wrangling

• Tidy data and tidyr overview (Michael Hallquist) | Rmd
• dplyr and wrangling walkthrough (Michael Hallquist) | Rmd

Exercises

1. Review joins tutorial in Rstudio | Rmd
2. Complete tidyr exercises | Rmd | Key
3. Complete dplyr exercises | Rmd

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Data visualization

• Grammar of graphics | Rmd (Nate Hall)
• Basic visualization using ggplot2 | Rmd (Holly Zaharchuk, Rebecca Janis & Alicia Vallorani)
• Supplemental materials for lecture | Rmd
• ggplot2 practice exercise | Rmd

Other resources

• Jenny Bryan ggplot2 tutorial | Download
• ggplot2 documentation
• ggplot2 tutorial that provides useful guidance on detailed formatting: https://tutorials.iq.harvard.edu/R/Rgraphics/Rgraphics.html

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R-eproducible Science

• R-eproducible Psychological Science (Rick Gilmore)| Download Rmd.
• R Markdown example output using our own survey data | Download Rmd
• Complete APA format manuscript based on papaja using the survey data, both in the original R Markdown format Download Rmd version and the final PDF version.

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iPRACTISE data wrangling and visualization

Links to complete assessments:

• http://tinyurl.com/ipractiseA1
• http://tinyurl.com/ipractiseA2
• http://tinyurl.com/ipractiseA3
• http://tinyurl.com/ipractiseA4
• http://tinyurl.com/ipractiseB1
• http://tinyurl.com/ipractiseB2
• http://tinyurl.com/ipractiseB3
• http://tinyurl.com/ipractiseB4

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Basic data analyses

• Basic data analyses in R: correlation, regression, ANOVA, categorical data (Dan Albohn, Kayla Brown, Yiming Qian) | Rmd
• Data analysis supplemental materials | Rmd
• Data analysis practice exercises | Rmd

Resources from previous years

Best practices in R programming

Other resources

• Wickham R style guide

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Correlation and regression in R

• Basic correlation and regression walkthrough (Michael Hallquist) | Rmd

ANOVA and categorical data

• Basic ANOVA and categorical data walkthrough (Michael Hallquist) | Rmd

Other resources

• More about the aov_ez function
• Post hoc contrasts in ANOVA using the afex package
• Probing interactions using the jtools package

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Exploratory factor analysis

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Parallel computing and big data

Other parallel computing resources

• R parallel package documentation
• R doParallel package documentation
• Matt Jones Parallel R tutorial
• Jonathan Dursi ‘Beyond Single Core’ tutorial

Other big data resources

• Rstudio webinar about big data
• Useful big data presentation: http://www.columbia.edu/~sjm2186/EPIC_R/EPIC_R_BigData.pdf
• Programming with big data in R (pbdr): https://pbdr.org