https://github.com/pdwaggoner/intro-to-r

Files and Scripts from my (now-very-dated) Intro to R course at William & Mary
https://github.com/pdwaggoner/intro-to-r

Last synced: 24 days ago
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Files and Scripts from my (now-very-dated) Intro to R course at William & Mary

• Host: GitHub
• URL: https://github.com/pdwaggoner/intro-to-r
• Owner: pdwaggoner
• Created: 2019-03-07T16:56:06.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-21T15:49:42.000Z (about 5 years ago)
• Last Synced: 2023-09-11T23:10:28.392Z (about 1 year ago)
• Language: HTML
• Homepage:
• Size: 677 KB
• Stars: 7
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Intro-to-R

This is the repo for my "Introduction to Computation in R" short course for MPP students at the College of William & Mary (Spring 2019). Several chunks of code were shared, collected and adapted from some great open source code from generous people including: Ling Zhu, Scott Basinger, Thomas Leeper, Ryan Kennedy, Albert Kim, Jeff Lewis, Hadley Wickham, Kenneth Benoit, Jon Slapin, Julia Silge, and Andrew Gelman. I thank these folks for making some code widely available. The final product is my own, and thus I assume all mistakes and errors. 

Dependent on your preferences, I have included `HTML` (with `CSS`) as well as the `.Rmd` files for each day's content. 

And in the same open source spirit, feel free to fork, adapt, change, update, and share any or all of the code in this repo for any reason. Hope its useful! Don't hesitate to [reach out](https://pdwaggoner.github.io/) if you have any questions.

## Overview of Topics Covered:

* Day 1 - Introduction to R (High Level // RStudio)

* Day 2 - Data & Packages

* Day 3 - Data Management (Wrangling via Tidyverse _and_ Base R)

* Day 4 - Basic Programming

* Day 5 - Basic Numeric Relationships

* Day 6 - OLS (Fit and Diagnostics)

* Day 7 - Binary Response Models (Fit and Diagnostics)

* Day 8 - Event Count Models (Fit and Diagnostics)

* Day 9 - Data Visualization, pt. 1 (Base R)

* Day 10 - Data Visualization, pt. 2 (ggplot2 // advanced visualization)

* Day 11 - Basic Webscraping

* Day 12 - Natural Language Processing & Quantitative Text Analysis (Staging text docs and fitting text models)