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 provides easy tools for common data analysis tasks. 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.

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What To Prepare

• Please install R and RStudio Desktop before the first session.

  RStudio is the most popular IDE (Integrated Development Environment) for R. Most people use RStudio if they want to write some R code.

  If you encounter technical difficulties installing the software, you can instead create a free RStudio Cloud account so you can run R and RStudio in the cloud via your browser.

  An alternative to RStudio Cloud is the UofT JupyterHub/RStudio system. Go to its home page, choose the RStudio option, and click Log in to start. You will need your UTORid.

• We will also use Google Colab, and I assume you all have a Google account.

  Google Colab lets you combine code and notes in a “notebook”. It is a hosted Jupyter Notebook service that requires no setup to use. It is a convenient coding environment to get started with R programming.

  UofT JupyterHub offers a similar notebook setting. Go to its home page, choose the Jupyter Notebook or JupyterLab option, and click Log in to start. You will need your UTORid.

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Part 1 (Overview & Basics)

Coming Soon

Part 2 (Data Manipulation)

Coming Soon

Part 3 (Visualization)

Coming Soon

Part 4 (Tidymodels, Time Series and Some R Finance Packages)

Coming Soon

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Resources

• From Zero to Hero
  • Step 1: Hands-on Programming with R (start here if you never programmed before.)
  • Step 2: R for Data Science (data science with R’s Tidyverse eco-system; 1st ed.)
  • Step 3: Advanced R (master R)
• R Graphics
  • R Graphics Cookbook
  • ggplot2: Elegant Graphics for Data Analysis
  • The R Graph Gallery (R graph samples with code)
• R Econometrics & Finance
  • Tidy Finance with R
  • Introduction to Econometrics with R
  • Forecasting: Principles and Practice (2nd ed.; 3rd ed.)
  • Portfolio Optimization (R & Python code available)
  • Financial Engineering Analytics: A Practice Manual Using R
  • Financial Risk Modelling and Portfolio Optimization with R (free access via UofT library)
  • Statistics and Data Analysis for Financial Engineering with R examples (book download; book site)
• R Machine Learning
  • An Introduction to Statistical Learning / with Applications in R (R & Python code available)
  • R Interface to Keras (deep learning with R)
  • Tensorflow for R (deep learning with R)
  • Torch for R (deep learning with R)
• Others
  • 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
  • Programming with R (from software carpentry)
  • R Cheat Sheets (cheat sheets for many popular R packages)
  • Many more R books here

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