https://github.com/cxli233/online_r_learning

Online R learning for applied statistics
https://github.com/cxli233/online_r_learning

r statistics teaching

Last synced: about 11 hours ago
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Online R learning for applied statistics

• Host: GitHub
• URL: https://github.com/cxli233/online_r_learning
• Owner: cxli233
• License: cc0-1.0
• Created: 2020-04-02T02:47:13.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-07-11T02:10:55.000Z (4 months ago)
• Last Synced: 2024-07-11T03:53:12.549Z (4 months ago)
• Topics: r, statistics, teaching
• Homepage:
• Size: 3.31 MB
• Stars: 456
• Watchers: 17
• Forks: 92
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# Online_R_learning

Online R learning for applied statistics

Release v5: 

[![DOI](https://zenodo.org/badge/252339170.svg)](https://zenodo.org/badge/latestdoi/252339170)

* Author: Chenxin Li, Ph.D., Assistant Research Scientist at Department of Crop & Soil Sciences and Center for Applied Genetic Technologies, UGA  

* Contact: [email protected] | [@ChenxinLi2](https://twitter.com/ChenxinLi2) | [@chenxinli2.bsky.social](https://bsky.app/profile/chenxinli2.bsky.social)

Required software:

* R: [R Download](https://cran.r-project.org/bin/)

* RStudio: [RStudio Download](https://www.rstudio.com/products/rstudio/download/)

* rmarkdown can be installed using the intall packages interface in RStudio

Interested in data visualization? The [data visualization module](https://github.com/cxli233/Quick_data_vis/) now has its own repository! 

# Content

This repository has 14 activities: 

0) Very basics of R coding  

1) Data wrangling with dplyr and tidyr 

2) Formatting ggplots – faceting, scales, guides, and themes 

3) Design custom palette in R 

4) One-way ANOVA and the compact letter display 

5) What to do when ANOVA assumption fails 

6) Randomized block design ANOVA 

7) Multifactorial deign ANOVA and interactions 

8) Repeated measures ANOVA

9) Split field and nested experimental design

10) Correlation and linear regression

11) Polynomial curve fitting 

12) Logistic regression  

13) Proportions, contingency tables and enrichment 

The first three units focus on how to tidy data and make pretty plots.   

Units 4 - 9 focus on ANOVA and Tukey tests.   

Units 10 - 12 focus on regression. 

Formal math is kept to minimum. The series focus on basic concepts, interpretation and execution in R.   

Each unit builds upon the previous. After each unit, there is also an exercise. 

# Getting started

1) Clone the repository to your machine by downloading the zip file

2) Unzip (and move the “Online_R_learning-master” folder to whichever folder you like on your machine).

3) Open RStudio  

4) Open .Rmd files under the R_codes folder 

5) Enjoy!