R

• R with RStudio Basics
  1. Writing and Running Commands
  2. Finding the Results
  3. Saving Commands and Results
  4. Saving Output Automatically
  5. R Language Elements
  6. R as a Calculator
  7. Using Help
  8. Using Functions
  9. Using Packages
  10. Next Steps

“Data Wrangling” is the process of preparing data for analysis, which includes importing, cleaning, recoding, restructuring, combining, and anything else that needs to be done before it can be analyzed. Data wrangling is a critical skill for quantitative research.

• Data Wrangling with R
  1. Defining Data
  2. Wrangling Vectors
  3. Wrangling Data Frames
  4. Restructuring with Base R
  5. Restructuring with the tidyverse
  6. Formulas
• Introduction to Web Scraping with R
  1. Introduction
  2. HTML
  3. CSS
  4. Scraping the Web
  5. Advanced Techniques
• Loops: Repetition and Iteration
• R Functions – Writing Functions
• Functions and Iteration in R

• Data Visualization in R with ggplot2
• Using R Plots in Documents
• Visualizing Regression Results in R
• Creating Shiny Apps

• Regression Diagnostics with R
• Panel Models in Stata and R
• Generalized Linear Models in R
  1. Preliminaries
  2. GLM Families
  3. GLM Model Evaluation
  4. Variable Selection
  5. Binary Response Variable (Logistic)
  6. Count Response Variable
• Mixed Models
  1. Introduction
  2. Models
  3. Testing Significance of Effects
  4. Diagnostics and Inference
  5. Multiple Random Parameters

• Running R/RStudio on SSCC Servers
• Using R Packages on SSCC Computers
• Installing R on Your Personal Computer
• Installing R Packages from Github on Silo
• Converting R Loops to Parallel Loops Using Slurm (The Easy Way)