Universität Wien
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270191 VU Introduction to R (2025S)

3.00 ECTS (2.00 SWS), SPL 27 - Chemie
Continuous assessment of course work
MIXED

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 15 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 07.05. 16:00 - 19:00 Seminarraum Physik Sensengasse 8 EG
  • Thursday 08.05. 16:00 - 19:00 Digital
  • Wednesday 14.05. 15:00 - 20:00 Seminarraum 2, Währinger Straße 29 1.UG
  • Thursday 15.05. 16:00 - 19:00 Digital
  • Wednesday 21.05. 15:00 - 20:00 Seminarraum 11, Währinger Straße 29 2.OG
  • Thursday 22.05. 16:00 - 19:00 Digital
  • Wednesday 28.05. 15:00 - 20:00 Seminarraum 11, Währinger Straße 29 2.OG
  • Wednesday 04.06. 16:00 - 19:00 Digital
  • Thursday 05.06. 15:00 - 20:00 Seminarraum 2, Währinger Straße 29 1.UG
  • Wednesday 11.06. 16:00 - 19:00 Digital
  • Thursday 12.06. 15:00 - 20:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Wednesday 18.06. 16:00 - 19:00 Digital
  • Wednesday 25.06. 15:00 - 20:00 Seminarraum 10, Währinger Straße 29 2.OG
  • Thursday 26.06. 16:00 - 19:00 Digital

Information

Aims, contents and method of the course

In this course you will learn to code by using the statistical programming language R. We will cover typical (simple) cheminformatic analyses and model selected aspects with R.

• Perform simple calculations
• Make simple plots
• Perform multiple operations in sequence, or at once
• Troubleshoot errors
• Exploratory data analysis
• Data wrangling
• Find help for functions
• Basic data modeling and interpretation of results
• Identify problems with your code/analysis (critical self-analysis)
• Format “clean” data and clean up “dirty” data

Assessment and permitted materials

Small scale project + oral evaluation + homework (bonus points).
All materials are allowed during the exam. Group work is not allowed.

Minimum requirements and assessment criteria

There are no prerequisites for this course. Participants are expected to bring their own laptops to class.

Final assessment is based on working R implementation of the small scale project + positive oral evaluation. Homework is not mandatory but earns bonus points if correct; homework is submitted within one week after first announcement.

Examination topics

Content of the lectures

Reading list

Literature references to the scientific literature will be available on Moodle.

Association in the course directory

BC-CHE II-8, CH-CBS-05, Synthese

Last modified: Mo 27.07.2026 11:12