General information on the course

This online tutorial will accompany the seminar “Data Journalism”, an M.A. Seminar at the IfKW (Ludwig-Maximilians-Universität München, SS2022).

The course takes place in Oettingenstraße 67, Room 151 (Wednesday 14:15-15.45 pm). You can access all necessary information on the seminar’s syllabus, important dates and assignments via Moodle, “2023 WiSe Kobilke - Datenjournalismus” (PW: Datenjournalismus22). There, additional resources (e.g. Powerpoint slides, book resources, etc.) are uploaded to the material folders of the respective week.

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 your own data journalistic projects (analysis + visualization).
  3. If time allows, know how to use R and RStudio to host your project on a shiny app or an external website (e.g., your own blog).

In the first part of this seminar, we will work through this tutorial and solve exercises together. The tutorial is designed to provide the R skills that you need to put data journalistic project into real-world practice. In the second part of the seminar, you will work on your own project (“learning-by-doing”).

Each 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 peers)

What can I do if I have a question?

Questions are welcome!

It’s natural to have a lot of questions because you’ll be learning a lot of new stuff. Please do ask if you don’t understand something in this tutorial, have questions concerning exercises, or simply need to talk through some of the new stuff. It’s quite natural to get lost at times when learning a new programming language. Don’t worry; it’s extremely probable that everyone else is experiencing the same thing.

Therefore, it’s key that you ask questions:

  1. In our live sessions: Wednesday, 14:15-15:45.
  2. In the Moodle forum: Please, post all your questions to the Moodle forum. Every participant will be able to see your questions, submit replies, and view my responses. As a result, everyone will have access to the same information. To stay updated about current debates, I recommend that you turn on alerts about new entries in the forum.
  3. Email: Send me an email at if you have any private questions that you don’t want to address in front of the rest of the class.

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

Besides asking a question during the seminar (see What can I do if I have a question?), 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, programmers and data scientists google 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 a website called “CRAN”: https://cran.r-project.org/web/packages/dplyr/dplyr.pdf.

That is all there is to it. Let’s begin with our first tutorial: Tutorial: Installing & Understanding R/R Studio