General information on the course

This online tutorial will accompany the seminar “Automated procedures for analyzing business communication strategies: Focusing on the board game industry”, a B.A. Seminar at the IfKW (Ludwig-Maximilians-Universität München, SS2023).

The course takes place in Oettingenstraße 67, Room 033 (Thursday 16:15-17.45 pm). You can access all data via Sync & Share.

What can I learn from this tutorial?

After completing the entire tutorial, you will have acquired two important skills:

  1. Know how to use R and RStudio to complete your data management.
  2. Know how to use R and RStudio to successfully complete an automated analysis of social media or traditional media data.

During the first part of the seminar, we will work through the tutorial and complete exercises together. As you begin to feel more comfortable with R, we may switch to a flipped classroom model to allow for more hands-on practice under my direct supervision. This would involve you reading through the tutorial in between classes (approximately 30-45 minutes per week) and completing additional exercises together during class time.

Each chapter of this tutorial consists of:

  • an introduction to new functions/analysis methods in R, including corresponding R code
  • main take-aways that you should remember
  • information on additional tutorials / sources
  • exercises to practice your new skills (you’ll often work through these with your classmates)

What can I do if I have problems with my R code?

Besides asking me during the course ( :) ) there are some great places to have a look at when you encounter problems. To give you a head start, here’s a quick rundown of the three best places to look if you have a problem with your code:

  1. R’s integrated help function: Use the ?-function whenever possible. Let’s assume you struggle with creating a histogram for your data (hist function in R). You can open the R documentation of the hist function in R by writing:
?hist
Preview of ?hist in R:

  1. Search engines: Like Bing or Google. Yup, those who work with R keep googling all the time! Nobody knows all the code and errors by heart. Often you can find perfect answers to your questions on Stackoverflow, StatsExchange, or Rseek because other people had exactly the same problems. And more importantly, the communities on these websites are very friendly and helpful.

  2. Packages’ reference manuals: Finally, problems with R packages (we’ll get to packages later, see: Packages) can often be solved by looking at their reference manuals (an overview document containing all of a package’s functions). For example, you can learn more about dplyr (a data management package that we are going to use later in this tutorial) by visiting its reference manual on “CRAN” (Comprehensive R Archive Network), which is R’s main repository: https://cran.r-project.org/web/packages/dplyr/dplyr.pdf.

That’s everything there is. I hope you find my class and this tutorial to be a valuable resource on your journey with R and to automating your analyses.

Let’s now begin with our first tutorial: Tutorial: Installing & Understanding R/R Studio