Gentle Introduction to R Programming Language

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Georgia State University

by Ozlem Tuncel, PhD

Slides

• Introduction to R Programming Language by Ozlem Tuncel
  • This slide is based on materials prepared by Adam Kuczynski, Charles Lanfear and Rebecca Ferrell. I am indebted to their code, examples, and inspiration.
• I recommend checking the other website I created for visualization in R.

Hands-on exercise

• Exercise materials
• We are going to use replication materials from:
  • Appell, Benjamin J. and Cyanne E. Loyle. 2012. “The Economic Benefits of Justice: Post-conflict Justice and Foreign Direct Investment”. Journal of Peace Research. 49(5): 685–99.
• You can use this link to download CSV format.
• Or, you can use stevedata package and load your data with stevedata::EBJ

My go-to sources for learning R

I started learning R around 2015 and over time these four resources was my go-to to learn R.

• R Programming Course on Coursera
• R for Data Science by Hadley Wickham, Mine Cetinkaya-Rundel, and Garrett Grolemund
• Hands-On Programming with R by Garrett Grolemund
• Advanced R by Hadley Wickham

Very Incomprehensive Sources for Learning R

If you need more resources on specific things, I have created this incomprehensive list of sources to learn R.

📦 : R package

⭐: Very Comprehensive Guide (and Ozlem’s Favorite)

🌐: Useful webpage or instruction

📚: Book

General Resources

• ⭐ 🌐 Introduction to R Programming Course in University of Washington
• 🌐 Helpful R Cheat Sheet by Allie Choate
• ⭐ 📦 Swirl — Teaches R programming in the R Console
• 📚 Advanced R — Hadley Wickham’s book to improve your R programming skills
• ⭐ 🌐 R Workshop by Duke University
• 🌐 Beginner, Intermediate, and Advanced R Resources by R Studio
• 🌐 Learning Statistics with R by the psyTeachR team at the University of Glasgow
• 🌐 How to write nice R code?

Tables

• 🌐 How to Make Beautiful Tables in R — Covers gt, kable + kableExtra + formattable + DT packages
• 📦 gt — good table package
• 📦 🌐 broom and huxtable
• 📦 🌐 stargazer — stargazer package

Maps

• 📦 sf — Simple Features vignette

R Markdown

• 🌐 General Introduction by R Studio
• 🌐 Pimp my rmd — Improving your markdown products
• 🌐 Publishing websites through R Markdown
• 🌐 Markdown Cheat Sheet
• 🌐 Markdown and YAML cheatsheet
• 📚 🌐 blogdown

Regression Models

• 📦 tidymodels — Consistent framework for your statistical models
• 🌐 Robust and clustered standard errors with R

Quarto

• ⭐ 🌐 Getting Started with Quarto Presentations
• 🌐 Creating presentations with quarto
• ⭐ 🌐 A curated list of Quarto resources
• 🌐 Quarto guide by Allison Hill
• 🌐 A Quarto tip a day
• 🌐 Reveal.js to make slides with Quarto

Web Scraping

• 🌐 Web Scraping course by DataCamp
• 📦 rvest — scrape information from a webpage
  • 📦 rvest vignette
• 🌐 Scraping with R
• ⭐ 🌐 Scraping with RSelenium

Text Analysis

• 🌐 Topic Modelling in R
• 🌐 Basic text analysis in R
• 🌐 Regular expressions
• 🌐 Scaling with Word Fish approach
• 📦 quenteda — Package information and tutorials
• 📦 textreuse — Text reuse approach package
• 📦 tm — Text mining package
• 🌐 Quantitative Text Analysis Using R and quanteda by Kenneth Benoit
• ⭐ 🌐 regex practice website
• 🌐 Introduction to regex by O’Reilly
• 🌐 Text analysis using R

GitHub and Git in R

• 🌐 R Packages chapter directions on Git use by O’Reilly

Shiny R

• 🌐 Creating web applications with Shiny R

Miscellaneous but very useful

• 🌐 Package downloads over time — Useful when it is hard to decide which package to use
• 🌐 Creating websites in R — A tutorial by Emily Zabor using R Markdown and GitHub Pages
• ⭐ 🌐 Happy Git and GitHub for UseR — Comprehensive guide on how to use GitHub with R
• 📦 stm — Structural Topic Modelling package
• 📚 Text Mining with R
• 🌐 Outlines in R Studio
• 📦 margins — margins package copying functionality of Stata margins
• 📦 predtools — Calibration Plot
• 📦 ggsankey — Sankey diagram using ggplot2 features
• 🌐 Detailed ggsankey vignette
• 🌐 Translating Stata to R
• ⭐ 🌐 An introduction to Python for R Users
• 📦 sampleSelection package
• ⭐ 🌐 InteractionPoweR Shiny App for analytic power
• 📦 did package – Difference-in-Differences design package
• 🌐 Top R packages for political science and economics dataset

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