This is a list of external resources that you may find helpful throughout (and after!) this course. Start with the course materials (slides and scripts) posted on D2L, but check these out for alternative explanations or if you want to take a deeper dive into a particular topic. If one isn't speaking to you, try another.
- GitHub Guides: Hello World. Branches and merges in GitHub.
- GitHub Guides: Forking Projects. Forking and pull requests in GitHub.
- Happy Git and GitHub for the useR (Jenny Bryan). How to take advantage of RStudio's convenient built-in features that integrate Git and GitHub.
- Big Data in Economics Lecture 2: Version Control with Git(Hub) (Grant McDermott). Getting started with Git at the command line.
- Data Science for Economists Ch. 3: Using Git and GitHub.com (Tyler Ransom). More on Git at the command line.
- Modern Data Science with R Appendix B: Introduction to R and RStudio (Baumer, Kaplan, and Horton). Installation, help, objects, vectors, indexing, operators, lists, matrices, data frames, attributes and classes, functions, packages.
- R Fundamentals: Parts 1 & 2 (UC Berkeley D-Lab). Scripts. Part 1: RStudio, variable assignment, logic, data classes, data frames. Part 2: Loading data, subsetting and combining data.
- Big Data in Economics Lecture 4: R Language Basics (Grant McDermott). Slides. Logic, evaluation, assignment, help, objects, names, indexing, lists.
- Cheat Sheet: Base R (RStudio).
- DataCamp tutorials:
- Introduction to R (free to everyone)
- Intermediate R (free for 6 months for enrolled students)
- R Fundamentals: Part 4 (UC Berkeley D-Lab). Script. Functions, for-loops, if/else, Monte Carlo simulations.
- Big Data in Economics Lecture 9: Functions in R (Grant McDermott). Function syntax, control flow, iteration, vectorization. The section on functional programming uses the tidyverse, which we're covering later in the course.
- EC 425/525 Lab 6: Adventures in Simulation in R (Ed Rubin). General simulation strategies, simulating IV in finite samples.
- DataCamp tutorial (free for 6 months for enrolled students): Introduction to Writing Functions in R.
- Intro to R Markdown (Danielle Navarro). Nice overview of why we're using R Markdown, and examples of how to use it.
- Using R Markdown for Class Assignments (Nate Taback). A pretty quick overview.
- Cheat Sheet: R Markdown (RStudio). Most of what you want to know on 2 pages.
- R Markdown Reference Guide (RStudio). A bit more comprehensive than the cheat sheet.
- R for Data Science Ch. 27: R Markdown (Hadley Wickham). Comprehensive introduction.
- Wrangling, Analyzing and Exporting Data with the Tidyverse (Montana State R Workshops Team). Interactive tutorial.
- Data Wrangling and Manipulation in R (UC Berkeley D-Lab). Slides with coding examples. Functions, for-loops, if/else, Monte Carlo simulations.
- Modern Data Science with R Chapters 4-6 (Baumer, Kaplan, and Horton). Chapter 4: Data wrangling with dplyr. Chapter 5: Joins. Chapter 6: Tidy data and tidyr.
- ModernDive Chapter 3: Data Wrangling (Ismay & Kim). Data wrangling with dplyr.
- RStudio Cheat Sheets:
- DataCamp tutorials (free for 6 months for enrolled students):
- R for Data Science (Hadley Wickham):
- RStudio Cheat Sheets:
- Quartz Guide to Bad Data.
- Best Practices for Scientific Computing (Wilson et al. 2014).
- Code and Data for the Social Sciences: A Practitioner's Guide (Gentzkow & Shapiro 2014).
- Coding for Economists: A Language-Agnostic Guide to Programming for Economists (Ljubica "LJ" Ristovska 2019).
- The tidyverse style guide (Hadley Wickham).
- Introduction to Data Science Chapter 8: Visualizing data distributions (Rafael A. Irizarry). Histograms, density plots, stratification.
- Introduction to Data Science Chapter 28: Smoothing (Rafael A. Irizarry). Bin smoothing, kernels, and local regression.
- DataCamp tutorial (free for 6 months for enrolled students): Exploratory Data Analysis in R.
- Introduction to Data Science Chapters 6-10: Data Visualization (Rafael A. Irizarry).
- Modern Data Science with R Chapters 2-3 (Baumer, Kaplan, and Horton). Chapter 2: Principles of data visualization. Chapter 3: Plotting with ggplot2.
- Data Visualization: A practical introduction (Kieran Healy). Online book for both principles and methods/examples.
- EC 425/525 Lab 5: Plotting in R (Ed Rubin).
- From Data to Viz (Yan Holtz & Conor Healy). "Leads you to the most appropriate graph for your data. It links to the code to build it and lists common caveats you should avoid."
- Cheat Sheet: Data visualization with ggplot2 (RStudio).
- An Economist's Guide to Visualizing Data (Jonathan Schwabish, Journal of Economic Perspectives 2014.)
- DataCamp tutorials (free for 6 months for enrolled students):
- Big Data in Economics Lecture 8: Regression Analysis in R (Grant McDermott).
- Lab 4: Regression with R (Ed Rubin).
- ISLR Ch. 7: Moving Beyond Linearity (James, Witten, Hastie, Tibshirani). Polynomial regressions, step functions, splines.
- DataCamp tutorials (free for 6 months for enrolled students):
- ISLR Ch. 2: Statistical Learning (James, Witten, Hastie, Tibshirani). Statistical learning, assessing model accuracy.
- ISLR Ch. 5: Resampling Methods (James, Witten, Hastie, Tibshirani). Cross-validation, the bootstrap.
- Prediction Policy Problems (Kleinberg, Ludwig, Mullainathan, and Obermeyer, AEA Papers & Proceedings 2015).