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 workshop will introduce you the basics of using R for data science work. Through implementing a simple project, we will together work through a typical data analysis workflow: 1) import data; 2) manipulate data; 3) build models; 4) report results.

To get the most of this workshop, create a free RStudio Cloud account so that you could follow along some of the code we will work on during the workshop.

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Slides and Code

• 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.html)
    • A deep learning “hello world” example: recognizing handwritten digits from the MNIST dataset (R script: dl_hello_world.R).
  • 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)

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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
  • 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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