Universität Wien

300058 UE Introduction to R and Statistics for Anthropologists (2024S)

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

An/Abmeldung

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

Details

max. 20 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

module 1 - General introduction to R (Course Structure, vectors, matrices, factors, data frames, lists, conditional and control flow, loop, functions)
assignment 1
module 2 - Statistics and plotting in R using examples
assignment 2
Final exam on the 25.06.2024

Dienstag 05.03. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 19.03. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 09.04. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 23.04. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 30.04. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 07.05. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 14.05. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 21.05. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 28.05. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 04.06. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 11.06. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 18.06. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1
Dienstag 25.06. 09:45 - 11:15 Seminarraum 1.1 PC, Biologie Djerassiplatz 1, 1.003, Ebene 1

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

In this R course, we provide lectures, and practical examples to students to be able to undertake R as a widely used language to perform basic to intermediate statistical data analysis, and data visualisation. No prior knowledge in R is required.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Following each lecture, students will receive weekly or monthly assignments requiring them to apply the learned concepts in comprehensive and innovative code segments. A final assignment, scheduled for the semester's last week, will challenge students to employ their accumulated knowledge to manipulate and interpret data, generating graphical representations. The students are permitted to utilize external resources during practical sessions and assignments. Finally, we emphasis on individual's capability of problem solving, coding, and graphical output in the final assessment to assess each student's proficiency.

Mindestanforderungen und Beurteilungsmaßstab

The minimum requirement for enrolling in basic R programming language courses is a comfortable familiarity with computer software, including file handling, and a basic understanding of Microsoft Excel. In addition, some knowledge in statistics and mathematics would be beneficial. Computers with installed R and RStudio will be provided to student during the course, however access to a computer or laptop capable of running R and RStudio is necessary to complete the practical exercise outside the teaching time.
Our course is comprised of largely two modules: 1. General introduction to R and 2. statistics and plotting in R. There will be one assignment at the end of each module and one exam at the end of the course. Two assignments contribute 55% towards the final grade and the final exam 45%. A minimum final normalized score of 50% is necessary for course completion. Of note, this is a practical course and presence is required.

Prüfungsstoff

In this course, we aim to introduce the basics in R and play with examples so that the students get familiar with analysing using the tool R. Two assignments, each at the end of one module of teaching, are to assess how much students understood the taught topics, as well as final assessment.

Literatur

For achieving the learning objectives in a basic R programming course, students can rely on introductory textbooks, online tutorials, instructor-provided materials, and reputable online platforms like Coursera, DataCamp, etc. In addition, the literature below are recommended.

- 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: Di 06.02.2024 16:26