Universität Wien

200100 SE Seminar in Applied Psychology: Mind and Brain (2024S)

Programming, Data Workflow and Data Visualization with R

4.00 ECTS (2.00 SWS), SPL 20 - Psychologie
Continuous assessment of course work

Anwendungsseminare können nur für das Pflichtmodul B verwendet werden! Eine Verwendung für das Modul A4 Freie Fächer ist nicht möglich.
Moodle

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. 20 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

This seminar aims to develop a basic understanding of programming with R, and also addresses the organization of research data and the creation of professional plots.
In order to take part in the seminar and to do the homework, you need to install R (https://cran.r-project.org ) and RStudio (RStudio Desktop; https://www.rstudio.com ) on your own computer or laptop. The R package Tidyverse (https://www.tidyverse.org ) is also required.
In the first session on March 6th, the topics will be presented and (if necessary) support will be provided for the installation of R/RStudio. Admission to the seminar will be also decided on March 6th.

  • Wednesday 06.03. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 13.03. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 20.03. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 10.04. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 17.04. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 24.04. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 08.05. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 15.05. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 22.05. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 29.05. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 05.06. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 12.06. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 19.06. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5
  • Wednesday 26.06. 09:45 - 11:15 Hörsaal H Psychologie KG Liebiggasse 5

Information

Aims, contents and method of the course

The course consists of classes with compulsory attendance as well as independent preparation and follow-up assignments at home (= small homework exercises).

R is a statistical programming language that is often used in psychology to process and analyze data.
In this course students will
- acquire basic programming knowledge in R (the goal is not to become a professional programmer, but rather to be able to write small scripts and functions),
- learn to prepare, examine and communicate data in a clean and reproducible way (avoid data chaos using "tidy" data and R notebooks),
- learn how to create illustrations and plots that can be used in publications and theses,
- carry out basic data analyzes in R (e.g., t-tests, ANOVAs, correlations),
- get a good understanding of R, which will be helpful for more advanced topics and courses (e.g. Bayesian statistics, hierarchical models, etc.).

Assessment and permitted materials

- regular attendance
- active participation in the classes (and in the online forum: asking and answering questions)
- regular homeworks (small programming tasks as preparation and follow-ups to the classes)
- Final assignment: Preparation and analysis of a real data set

Minimum requirements and assessment criteria

- Regular attendance is a requirement; a maximum of 2 classes may be missed without excuse
- Active participation: 20%
- Homework: 40%
- Final assignment: 40%

1 (sehr gut) 100% - 88%
2 (gut) 87% - 75%
3 (befriedigend) 74% - 62%
4 (genügend) 61% - 50%
5 (nicht genügend) 49% - 0%

Examination topics

There will be no final exam

Reading list

Venables, W. N., Smith, D. M., & R Core Team. (2023). An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics. https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10), 1–23. https://doi.org/10.18637/jss.v059.i10
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Wickham, H. & Grolemund, G. (2017). R for Data Science. O’Reilly. https://r4ds.had.co.nz/
Ross, Z., Wickham, H., & Robinson, D. (2017). Declutter your R workflow with tidy tools. PeerJ Preprints, 5(e3180v1), 1–20. https://doi.org/10.7287/peerj.preprints.3180v1

Association in the course directory

Last modified: Mo 04.03.2024 11:26