A Quick Introduction to R

R is a free and powerful programming language for data analysis and visualization. R is extensible, and its community maintains a large collection of high-quality packages for a variety of applications. Many R packages make complex data manipulations and analyses beginner friendly. This workshop is designed to kick-start your R journey. We will start with an overview of what R can do, and how you may use R in your everyday work. Next, we will learn how to setup an R programming environment (either on your own computer or in the cloud). We will then discuss R programming basics and work through a typical R data analysis workflow. You will also see a few R demo projects and try some hands-on exercises. We will end the workshop with an R learning roadmap, so you could continue to learn R on your own using various free resources online.

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What To Prepare

You don’t need to prepare anything for this workshop. However, if you plan to follow along some of the exercises we will do in the workshop, you can get a head start by installing R and RStudio Desktop before the workshop.

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. Currently, the UofT JupyterHub/RStudio service is a proof-of-concept installation so it is possible that you experience some minor issues.

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Workshop Materials

• Slides
• 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)
  • Basic Data and Programming Structures
    • R script: r2_data_and_programming_structures.R
    • R notebook:
  • R Data Science
    • R script: r3_data_manipulation_modelling_report_housing.R
    • Regression Report: sample regression report

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Free Learning 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
  • 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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