Next steps

This is the end of this R tutorial.

We have touched upon a few - what we believe are important - features of the R programming language and R-studio. We hope you want to learn more! For those of you that are interested in learning more, we have a couple of suggestions to help you find your way.

Learn about statistics

We believe that knowing about statistics is important if you want to be successful in data analyses. If you have no or limited undergraduate training in statistics, you might want to learn about statistics before you start working on your fluency in the R language.

Class room (Utrecht, The Netherlands):

1. 10-day course in biomedical statistics: Introductory biostatistics for researchers.

2. Masters program in epidemiology (including Medical Statistics specialisation track): MSc Epidemiology.

These programs combine learning about medical statistics with programming in R.

Virtual class room:

1. A nice visual introduction to the concepts of statistics (free!) by Daniel Kunin: Seeing theory.

2. Masters program in epidemiology: MSc Epidemiology online.

3. The R Programming Software and Statistics Tutorials on YouTube, by MarinStatsLectures.

Books:

1. Benjamin Yakir: ‘Introduction to Statistical Thinking (With R, Without Calculus)’. Available for free online here.

2. Field, Miles & Field: ‘Discovering Statistics Using R’. Available on Amazon.

Learn about the R programming language

Certainly, this R tutorial only covered some of the basics of programming in R. The following references will help you further:

Virtual class room:

1. The Coursera courses: R programming and Advanced R programming, free to audit (not free if you need a course certificate)

2. Try R, a website that introduces R interactively from within your browser.

3. Swirl, an R package that introduces R interactively from within the R console.

Book:

Grolemund & Wickham: ‘R for data science’, Available for free