Universität Wien
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200185 SE Seminar in Applied Psychology: Mind and Brain (2022S)

Programming, Data Workflow and Data Visualization with R

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

Dieses Anwendungsseminar kann für alle Schwerpunkte absolviert werden.

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: German

Lecturers

Classes (iCal) - next class is marked with N

The seminar will be held online via Moodle/Zoom. For following the seminar and doing the homework, you will need to install R (https://www.r-project.org/) and RStudio (https://www.rstudio.com/) on your computers.
In the first session on March 7th, we will help everyone to run R and RStudio on their computers. We will send you a link for participating in the first session where we also decide about admission to the seminar.

  • Monday 07.03. 16:45 - 18:15 Digital
  • Monday 14.03. 16:45 - 18:15 Digital
  • Monday 21.03. 16:45 - 18:15 Digital
  • Monday 28.03. 16:45 - 18:15 Digital
  • Monday 04.04. 16:45 - 18:15 Digital
  • Monday 25.04. 16:45 - 18:15 Digital
  • Monday 02.05. 16:45 - 18:15 Digital
  • Monday 09.05. 16:45 - 18:15 Digital
  • Monday 16.05. 16:45 - 18:15 Digital
  • Monday 23.05. 16:45 - 18:15 Digital
  • Monday 30.05. 16:45 - 18:15 Digital
  • Monday 13.06. 16:45 - 18:15 Digital
  • Monday 20.06. 16:45 - 18:15 Digital
  • Monday 27.06. 16:45 - 18:15 Digital

Information

Aims, contents and method of the course

This course will be taught digital in Moodle/Zoom, and will include attendance sessions as well as independent preparation and follow-up homeworks (but don't worry, these homeworks are small ;-).

R is a programming language widely used in the field of Psychology to process and analyze data.
During this course, students will:
- acquire basic programming skills in R (the goal is not to become a professional programmer, but to be able to write small scripts and functions, which can be very helpful for your master thesis)
- do basic data analysis in R (e.g., t-test, ANOVA, correlations)
- learn to prepare, explore, and communicate data in a clean and reproducible way (avoid "data hell" by using tidy data and R notebooks)
- learn to produce publication ready figures and graphs with ggplot2 (drawing pictures is fun ;-)
- build a foundation for more advanced topics and courses (like e.g., Bayesian statistics and hierarchical models)

Assessment and permitted materials

- regular attendance
- active participation in the online class (and the online forum: asking and answering questions)
- regular homeworks: These homeworks will include small programming tasks as a follow-up of the previous attendance session and as a preparation for the upcoming attendance session.
- final project: In the final project, students will have to clean and analyze some real data

Minimum requirements and assessment criteria

- The basic requirement for a passing grade: attendance in the course with missing a maximum of two classes
. active participation: 20%
- homeworks: 40%
- final project: 40%

Examination topics

There won't be a written exam.

Reading list

Venables, W. N., Smithand, D. M., & R Core Team. (2020). 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

Note: All texts are available online.

Association in the course directory

Last modified: Tu 20.06.2023 11:47