firm_data <- data.frame(name=c("ABC Manufacturing",
"Martin\'s Muffins",
"Down Home Appliances",
"Classic City Widgets",
"Watkinsville Diner"),
industry=c("Manufacturing",
"Food Services",
"Manufacturing",
"Manufacturing",
"Food Services"),
county=c("Clarke",
"Oconee",
"Clarke",
"Clarke",
"Oconee"),
employees=c(531, 6, 15, 211, 25))Topic 0: Introduction to R Programming
Material Covered in Class
There are a number of useful resources for R programming. I pointed out quite a few in the course syllabus. The material for this section mainly comes from Introduction to Data Science: Data Wrangling and Visualization with R by Rafael Irizarry. I’ll cover some introductory topics that I think are most useful.
The discussion in class will follow chapters 2 and 3 of my course notes for my undergraduate course, which are available here
Additional Material
Most Important Readings: Chapters 1 (Introduction), 2 (R Basics), 3 (Programming Basics), 6 (Importing Data), 20 (Reproducible Research)
Secondary Readings: (please read as you have time) Chapters 4-5 (The tidyverse and data.table), 7-10 (Data Visualization)
The remaining chapters of this book are all useful, but you can read them over the course of the semester as you have time.
List of useful R packages
AER— package containing data from Applied Econometrics with Rwooldridge— package containing data from Wooldridge’s text bookggplot2— package to produce sophisticated looking plotsdplyr— package containing tools to manipulate datahaven— package for loading different types of data filesfixest— package for working with panel datafixest— another package for working with panel dataivreg— package for IV regressions, diagnostics, etc.estimatr— package that runs regressions but with standard errors that economists often like more than the default options inRmodelsummary— package for producing nice output of more than one regression and summary statistics
Practice loading data
If, for some reason this doesn’t work, you can use the following code to reproduce this data