Instructors: Lauren Talluto (Group 0), Technikerstraße 25, Room 506
Martin Dalvai Ragnoli (Group 1)

Meeting locations:
Group 0: RR 18
Group 1: TBD

Course Description

This is a practical course covering the basics of programming in the R language. The course is intended for beginners with no prior programming experience.

Learning Objectives

Students will learn fundamental concepts of R programming, including the use of the RStudio IDE, key programming concepts (variables, conditions, loops, functions) as well as how to plan, structure, and debug code. Students will also get an overview of introductory concepts in data science, focused on essentials needed for biology research. Students will learn to use and interpret key functions for data analysis for some of the most commonly used statistical analyses in biology. Students will also learn the basics of data visualisation in R.

Schedule

Date & Time (Group 0)	Location (Group 0)	Date & Time (Group 1)	Location (Group 1)	Topics
Fri 17.03 14:15-17:00	RR 18	Thu 27.03 14:00-17:00	RR 18	Introduction, Basic data manipulation Basic plots and summary statistics Slides • HTML • PDF Exercises
Wed 19.03 12:30-14:15	RR 18	31.03 14:00-16:00	RR 14	R base graphics Simple statistics in R Slides • HTML • PDF Exercises
Fri 21.03 14:15-17:00	RR 18	01.04 12:00-14:00	RR 14	Catch-up session
Fri 28.03 16:30-18:30	RR 18	03.04 14:00-17:00	RR 19	Programming Slides • HTML • PDF Exercises
Fri 11.04 16:30-18:30	RR 18	04.04 17:00-19:00	RR 20	Intermediate data: reshaping, aggregating Exercises

Assessment

Students will be marked based on participation (coming to the exercises, working on them, asking questions) (40%) and on completion of the exercise sheets (links above, submission in OLAT).

Getting Help

If you are stuck, the first place you should start is by searching R’s help files. If this doesn’t get you there, I highly recommend StackOverflow as an excellent resource for help with all kinds of code.

It will be tempting to ask ChatGPT for help. I strongly advise you resist this temptation. ChatGPT might solve a particular problem for you, but this is only helpful if your only goal is to solve that particular problem. If your goal is to learn to produce your own code in R, you will be much more successful if you come up with the code on your own. ChatGPT also often produces code that is too complex for beginners; it may be confusing and do more than you really need.

Cheatsheets

These are very useful references that should be helpful during the course (created by the RStudio team and community).

• Base-R
• A denser, more comprehensive Base R cheatsheet by Tom Short.
• Using Rstudio like a power user.
• ggplot2 for more effective graphics (auch auf Deutsch)
• Rmarkdown, for creating reports and webpages with R code interleaved. (auch auf Deutsch)

Additional Resources

There are many great places to learn R, and this course can’t cover everything. If you want to go farther, or get a different perspective, here are a few things I recommend:

• fasteR by statistics professor Norm Matloff.
• The e-book intro2r by Alex Douglas et al.
• When you get further along, Hadley Wickham’s Advanced R book is excellent.

This course is original work, but is freely available without restrictions or guarantees.
Original software is MIT-licensed, all other material is CC-BY.