What is this site?

This site contains the material for the introductory R workshop, offered for Tilburg Master of Accountancy students. The workshop is a short introduction, consisting of two sessions (1h:45min each). Its purpose is to introduce Master students with no previous programming experience to the R programming language—especially with an eye towards the Master file and courses, such as Data-Driven Decision-Making. But we hope everyone with an interest in learning R will find something useful here.

Learning goals

  1. Be able to perform basic data operations in R using the tidyverse dialect
  2. Be able to construct standard data visualizations using the {ggplot2} R package
  3. Be able to compute standard descriptive statistics

How to use this site

You only learn coding by doing it. The site complements but is not a substitute for the workshop’s in-class learning. It is written with users in mind that have been at the workshop. It provides a summary of what we code there plus the rationale discussed. We will use the website margins for further commentary on coding rationale. We hope the site will help you to refresh your memory after the workshop when you start using R in your studies.

Margin comments, such as this one, contain comments on coding practices

Other useful ressources

This is a short, condensed workshop. We only have three and a half hours to introduce you to the basics, so we need to be quite selective. Here are a number of great resources to help you build on what you learned.

  • R for Data Science. This is a complete, open book introducing the tidyverse dialect of R for data science. It goes into more detail and extends on the basic data wrangling techniques that we covered. A great place if you look for more coverage of the core data manipulation methods.
  • Empirical Research in Accounting: Tools and Methods. A great ressource maintained by Ian Gow and Tony Ding. It contains R code and is essentially the companion to a course on financial accounting research that begins at an upper-undergraduate (“honours”) or introductory PhD level.
  • Data-Driven Decision-Making Lecture Notes. The lecture notes of the controlling track course “Data-Driven Decision-Making” contains further examples, cases and material on using R for data-driven decision making. For example, one chapter of interest might be the chapter on predictive analytics, introducing R frameworks to coding prediction models.
  • Tilburg Science Hub. You’ll find many useful tutorials here. The TSH is a great knowledge repository dedicated to help young researchers developing their empirical skill set. It offers many tutorials on how to process data, design workflows, and structure projects.

Best regards

Harm Schütt
Office: Koopmans Buidling, K 250