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

• Data Visualization in R with ggplot2
• Using R Plots in Documents
• Creating Shiny Apps
  1. Introduction to Shiny
  2. The Structure of a Shiny App
  3. Inputs
  4. Outputs
  5. Reactivity
  6. Error Messages
  7. Layouts
  8. Setting Up for the SSCC’s Shiny Server
  9. Embedding Shiny Apps
  10. Troubleshooting Errors

• Regression Diagnostics
• 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

• Using R Packages on SSCC Computers
• Running RStudio Server at the SSCC
• Installing R on Your Personal Computer
• Installing R Packages from Github on Silo