This repository contains code, data, and other materials for learners enrolled in the Reproducible Rehabilitaton (ReproRehab) research education program. Specifically, this repository contains information for learners looking to get started with R. If you copy the repository to your local machine, you should have all of the scripts and data necessary to follow along with the video tutorials below and get started learning R. Drs. Liew and Finley also have repositories set up for learning Matlab (https://github.com/reprorehab/reprorehab2022/tree/main/Week%204/MATLAB) and Python (https://github.com/reprorehab/reprorehab2022/tree/main/IntroPython).
Please note this is not meant to be an exhaustive course on the R programming language. The content below is meant to be a general introduction, giving you a foothold in R that you can then apply to your own research topics and questions. For those enrolled in the ReproRehab program, you will have access to a teaching assistant who has expertise in R and further guide your learning. If you are accessing these modules outside of ReproRehab, then I would recommend several other online resources to complement the trianing I am trying to provide here (e.g., Hadley Wickham's R for Data Science: https://r4ds.had.co.nz/ and Danielle Navarro's Learning Statistics with R: https://learningstatisticswithr.com/).
Finally, I am using a project-based learning approach where I will spend as little time on abstract R coding problems as possible, and more time learning the specific skills required to wrangle, tidy, visualize, and analyze rehab-relevant data. This is not everyone's preferred way to learn and it has the limitation of creating very specific knowledge (i.e., you won't learn everything that is possible with R). If you like learning programming more abstractly, I encourage you to supplement (or replace!) these videos with other online courses that will address R more completely. However, I have a hard time staying motivated to learn unless there is a problem motivating why I am learning something. To that end, I have built these modules around learning to process and analyze EEG data in a cross-sectional study of younger and older adults (https://pubmed.ncbi.nlm.nih.gov/34999166/).
- Downloading and Installing R and R Studio (https://www.youtube.com/watch?v=enLJZQJT51o)
- Basics of R Programming: Functions, Arguments, and Object Classes (https://youtu.be/H5e5-TVJPvY)
- Importing, Merging and Exporting Data (https://youtu.be/1d53GFnbWaA)
- Wrangling Data (https://youtu.be/3hj1wsxoGS4)
- Tidy Data and Data Visualization (https://youtu.be/Ot4yBDO_Yqs)
- Live Coding example Data Analysis (https://youtu.be/wz1dU6MBAz8)
- How to do a one-sample t-test in R (https://www.youtube.com/watch?v=ZdgNOWY3cs4)
- How to do a two-sample t-test in R (https://www.youtube.com/watch?v=5oVKpyO84OI)
- How to do a one-way ANOVA in R (https://www.youtube.com/watch?v=KpRJzxvEdGs)
- How to do a multi-way ANOVA in R (https://www.youtube.com/watch?v=KpRJzxvEdGs)
- See also the repository for my graduate statistics course, which includes video lectures on the theory and details of data analysis (https://github.com/keithlohse/grad_stats)