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.
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. Currently, the UofT JupyterHub/RStudio service is a proof-of-concept installation so it is possible that you experience some minor issues.
Session 1 (Overview)
- Slides
- Code
- Motivation Examples
- Housing prices and clean air (R script: r1_simple_regression_housing.R; R notebook:
) - 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)
- Housing prices and clean air (R script: r1_simple_regression_housing.R; R notebook:
- 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 Import
- Data Manipulation
- Modelling
- Report (sample regression report)
- Motivation Examples
- Extra materials containing more details on data and programming structures (Optional)
- 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.)
Session 2 (Data Manipulation)
- Slides
- Code
- Data Manipulation (R notebook: )
- Data Manipulation (R notebook:
- Reading list
- R for Data Science (Chapter 5 Data transformation, 12 Tidy data, and 13 Relational data.)
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.)
- R notebook:
- 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)
- Visualization: ggplot
Session 4 (Tidymodels, Time Series and Some R Finance Packages)
I inserted a Intro to Tidymodels R markdown notebook (optional). Some of you are currently working on industry projects using R and machine learning. The Tidymodels framework could be useful for your projects.
- Slides
- Code
- Tidymodels (optional; 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)
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
- 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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