1 Introduction to PART I

In this first part, we present some fundamental material of statistics and data analysis, and R-specific issues that we regularly encounter during data analyses. This is also a commented collection of R-code that we documented for our own work. We hope this might be useful also for other readers.

1.1 Further reading

  • An Introduction to R is an introduction and manual for basic R usage.

  • R for Data Science: Introduces the tidyverse framework, explains how to get data into R, get it into the most useful structure, transform it, visualise it and model it.

There is a lot of online material to learn how to produce nice graphics with R, here some tips by a colleague of us (who does not think it is necessary to mention his name here, but thanks, Steffen!):

For plotting, here are some resources:

  • The ggplot2-book introduces the background of ggplots. It is the reference ot consult for getting an understanding on how ggplot works.

  • The R graph gallery is a user-friendly look-up sheet providing a lot of example code.

  • This is the book to the R graph gallery mentioned above.

  • And here is another graphics book