R – most minimal learning resources

With December last year, I started to learn data science using R. As a self-determined learner, I had not only to find resources, that are adequate to my learning goals and status of competencies but also to plan and organize my spare time for my self-organized continuing education.This article reports about my strange learning behavior and closes with 3 tips on R learning resources for the beginner, suggesting the most minimal learning set for the prospective data scientist.

R – most minimal learning resources
© 2016 The R Foundation (CC-BY-SA 4.0)

With December last year, I have started to learn data science using R. In the meanwhile I have some intermediate knowledge and have started to look into more advanced topics. For me, it is a very strange but important experience to change the perspective: As a professional teacher in educational technology, I am now using educational technology myself to learn something. And as a self-determined learner, I have not only to find resources, that are adequate to my learning goals and status of competencies but also to plan and organize my spare time for my self-organized continuing education.

It turned out that with every new learning activity I pursued two objectives: On the one hand to learn the subject and on the other hand to judge the educational value of the learning material. It turned out that I am dedicated to a learning style that is comparative. I like to learn in comparing different teaching approaches, to repeat a complicated set of facts and procedure with different explanations and hands-on exercises.

My strange learning behavior

This results in a pretty strange behavior:

  • I am reading several books on the same subject at the same time. 
  • I am reading books not always from start to end, but I select chapters covering a subject I am interested in.
  • I use several media at the same time: Reading books in hardcover and as eBooks, consulting web pages and fora, follow online exercises and at the same experimenting with own data and own challenges.

This behavior is not very cheap: I bought more than 20 books, visited and paid for two MOOCs courses and printed out much online material. (Yes, what a shame: I printed OER material and helically coiled it to a binder. I am not very good at reading on the screen and converting PDFs into eBook did not always result in material easy to read – especially if they included complicated tables and charts.

The advantage of this seldom learning behavior: I have developed some in-depth, multi-perspective knowledge and achieved an overview about adequate learning resources. For the prospective data scientist, the problem is not to find learning material. Quite the contrary! There is a huge amount of learning resources available for free and the real art is to select a minimum to start with.

3 tips to start with R – most minimal learning resources

I cannot (yet?) oversee all the material and I doubt if this is ever possible with this amount – and continually growing – of material. But still, I believe that my experiences could be valuable for people starting a learning career in data science. My own challenge is to limit the advice to just 5 free learning resources where each educational resource is of a different type and has one next step.

  1. Setup the Environment:
    Start with: Install R for your system: https://cloud.r-project.org/ 
    Follow up: Install RStudio Desktop: https://www.rstudio.com/products/rstudio/download/
    Shortest installation guide I found: http://web.cs.ucla.edu/~gulzar/rstudio/
  2. Book:
    Start with: Grolemund, G. (2014). Hands-On Programming with R: Write Your Own Functions and Simulations (1st ed.). Sebastopol, CA: O’Reilly and Associates. (See my review. See appendix A for a more detailed installation guidance of R & RStudio (pp. 198-192)
    Follow up:  Grolemund, G., & Wickham, H. (n.d.). R for Data Science. Retrieved from http://r4ds.had.co.nz/
  3. Tutorial / Help:
    Start with: Swirl: Learn R, in R.
    Follow upStack Overflow is the largest online community for programmers to learn. Google your questions, starting with "r <here comes your question>"

Von Peter Baumgartner

Seit mehr als 30 Jahren treiben mich die Themen eLearning/Blended Learning und (Hochschul)-Didaktik um. Als Universitätsprofessor hat sich dieses Interesse in 13 Bücher, knapp über 200 Artikel und 20 betreuten Dissertationen niedergeschlagen. Jetzt in der Pension beschäftige ich mich zunehmend auch mit Open Science und Data Science Education.

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