Universität Wien

300197 VU Introduction to R for Anthropologists (2019S)

3.00 ECTS (2.00 SWS), SPL 30 - Biologie
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Mittwoch 06.03. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 13.03. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 20.03. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 27.03. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 03.04. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 10.04. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 08.05. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 15.05. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 22.05. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 29.05. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 05.06. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 12.06. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 19.06. 14:00 - 16:00 EDV-Raum 2 Ökologie
  • Mittwoch 26.06. 14:00 - 16:00 EDV-Raum 2 Ökologie

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Aims: The aim of this course is to introduce students to the programming language R used in various areas of biological anthropology, such as DNA analysis or geometric morphometrics, without the requirement of previous knowledge of programming languages. At the end of the course the student is expected to be able to use R to handle biological data and transform it into creative outputs.

Contents: The course will start with a general introduction to the R programming language and its relevance, followed by the topics of data manipulation, statistical analysis, data plotting, functions, optimization, and others. A non-binding list of topics to be presented is shown below:
- Variables and operators
- Data types
- Loops, statements and logic
- Functions and packages
- Input, output, and file management
- Data plotting and graphical output
- Statistic tests
- Speed and optimization

Method of the course: The course will be composed of 2 hour-long hybrid lectures per week, where the first hour should be focused on a theoretical presentation of the topic, and the second hour dedicated to practically applying that knowledge.

Art der Leistungskontrolle und erlaubte Hilfsmittel

After each lecture, part of a monthly assignment is given where the student must apply the taught knowledge in more thorough and creative pieces of code. A final exam will take place during the last lecture of the semester, where the student will need to apply all knowledge acquired in the course to handle and interpret data, and produce graphical outputs. Since the students may bring or access any materials during the practical lectures and the final exam to help them complete their tasks, such as web search or discussion with colleagues (the latter except during the final exam), special attention will be paid to plagiarism of other people’s code pieces.

Mindestanforderungen und Beurteilungsmaßstab

The assignments and the final exam are weighted as 60% and 40%, respectively. A minimum final normalized score of 50% (rounded down to the closest integer) is required to complete the course.

Prüfungsstoff

Continuous assessment of the taught topics.

Literatur

- Nathaniel D. Phillips. YaRrr! The Pirate’s Guide to R, 2017 ( https://bookdown.org/ndphillips/YaRrr/YaRrr.pdf )
- W. N. Venables, D. M. Smith, R Core Team. An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics Version 3.5.2, 2018 ( https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf )
- Trevor Martin. The Undergraduate Guide to R - A beginner‘s introduction to the R programming language ( http://www.biostat.jhsph.edu/~ajaffe/docs/undergradguidetoR.pdf )

Zuordnung im Vorlesungsverzeichnis

BAN 5

Letzte Änderung: Sa 22.10.2022 00:29