Introduction to R

R is an open-source programming language for statistical computing and graphics. R is extensible, and has a large collection of high-quality user contributed packages that makes data analysis easy for beginners. This mini-course will introduce you the fundamentals of R programming with a focus on data management, data visualization and quantitative finance applications in R.

---

Session 1 (Overview)

• Slides

• Code
  • Motivation
    • A simple regression example (R script: r1_simple_regression_housing.R; R notebook: )
    • A twitter example (R script: twitter_example.R; Word Cloud for #rstat: twitter_word_cloud.png)
    • A “hello world” deep learning example: recognizing handwritten digits from the MNIST dataset.
  • Data and Programming Structures (R script: r2_data_and_programming_structures.R; R notebook: )
  • Extending the simple regression example (R script: r3_data_manipulation_modelling_report_housing.R)
    • Data Manipulation
    • Modelling
    • Report
• Extra material containing more details (Optional)
  • Data structures
    • Vectors
    • Derived data structures
  • Programming structures

---

Session 2 (Data Manipulation)

• Slides
• Code
  • Data Manipulation (R notebook: )

---

Session 3 (Visualization)

• Slides
• Code
  • Visualization (R notebook: )
  • Visualization (R markdown: part3_visualization.Rmd); Note: load this file in RStudio. Let’s take this opportunity to learn about R Markdown.

---

Session 4 (Time Series and Some R Finance Packages)

• Slides
• Code
  • Time series and finance packages (R markdown: part4_timeseries_finance_pkg.Rmd; HTML: part4_timeseries_finance_pkg.html)

---

Free Resources

• R Programming
  • Hands-on Programming with R (for absolute beginners, i.e., if you have never programmed before)
  • R for Data Science (learn how to manipulate, analyze and visualize data using R’s Tidyverse packages)
  • Advanced R (if you plan to be really good at R)
  • R Graphics Cookbook
  • ggplot2: Elegant Graphics for Data Analysis
  • The R Graph Gallery (R graph samples with code)
  • R Cheat Sheets (cheat sheets for many popular R packages)
  • R for Stata Users (if you are from the Stata world)
• R for Empirical Analysis
  • Introduction to Econometrics with R
  • Forecasting: Principles and Practice
  • Financial Engineering Analytics: A Practice Manual Using R
• R and Machine Learning
  • An Introduction to Statistical Learning / with Applications in R (book and code)
  • R interface to Keras (deep learning in R)
• Other Tutorials on Econometrics & Statistics using R
  • A Short R Tutorial by Germán Rodríguez
  • Introductory Econometrics Examples (data and examples from Wooldridge)
  • STAT545 by Jenny Bryan : Data wrangling, exploration, and analysis with R

---

Back to TDMDAL Computing Page