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.

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What To Prepare

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

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

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Session 1 (Overview)

• Slides (updated)
• Code
  • Motivation Examples
    • Analyze portfolio performance (R Script: performance_analysis.R)
    • Look for trends in R community through Twitter (R script: twitter_example.R; Hashtag #rstat and its Word Cloud)
    • Recognize handwritten digits, a deep learning “Hello World” example (R script: dl_hello_world.R)
  • Data and Programming Structures (R script: r2_data_and_programming_structures.R; R notebook: )
  • A regression example (Housing prices and clean air)
    • Simple regression using “pure” R (R script: r1_simple_regression_housing.R; R notebook: )
      
    • Multitple regression using “modern” R (R script: r3_data_manipulation_modelling_report_housing.R)
      • Data Import
      • Data Manipulation
      • Modelling
      • Report (sample regression report)
• Extra materials containing more details on data and programming structures (from past workshops; optional)
  • Data structures
    • Vectors
    • Derived data structures
  • Programming structures
• Reading list
  • R for Data Science (Chapter 1 Intro, 4 Workflow: basics, 5 Data transformation, 6 Workflow: scripts, 8 Workflow: projects, 10 Tibbles, 11 Data import, 18 Pipes, 19 Functions, 20 Vectors, and 21 Iteration.)

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Session 2 (Data Manipulation)

• Slides
• Code
  • Data Manipulation (R notebook: )
• Reading list
  • R for Data Science (Chapter 5 Data transformation, 12 Tidy data, and 13 Relational data.)

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Session 3 (Visualization)

• Slides
• Code
  • Visualization: ggplot
    • R notebook:
    • R markdown: part3_visualization.Rmd (Load this file in RStudio. Let’s take this opportunity to learn about R Markdown.)
  • Visualization: tidyquant charting, dygraph, and R markdown flexdashboard
    • R script: finance_charting.R
    • R markdown: time_series_flexboard.Rmd (Load this file in RStudio; HTML output: time_series_flexboard.html)
• Reading list
  • R for Data Science (Chapter 3 Data visualization, 7 Exploratory Data Analysis (EDS), and 28 Graphics for communication.)

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

• Slides (1. tidymodels; 2. time series and finance packages)
• Code
  • Tidymodels (R markdown: tidymodels_intro.Rmd; HTML: tidymodels_intro.html)
  • Time series and finance packages (R markdown: part4_timeseries_finance_pkg.Rmd; HTML: part4_timeseries_finance_pkg.html)

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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 Empirical Analysis
  • Introduction to Econometrics with R
  • Forecasting: Principles and Practice (2nd ed.; 3rd ed.)
  • Financial Engineering Analytics: A Practice Manual Using R
• R and Machine Learning
  • An Introduction to Statistical Learning / with Applications in R (you can download the book and its R code)
  • R Interface to Tensorflow (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

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