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62692-01 - Übung: Introduction to R 3 KP

Semester Herbstsemester 2022
Angebotsmuster unregelmässig
Dozierende Sepideh Alassi (sepideh.alassi@unibas.ch, BeurteilerIn)
Inhalt R is a simple but powerful programming language and an optimal choice as a tool for text and data analysis.

This course will offer an introduction into programming with R from the scratch; from installation and basics of R data objects, developing and implementing algorithms, control flow structures, writing functions and using package, working with data files, to analyzing real data with statistical tools and visualizing the results.
Lernziele By the end of this course, the students will have a comprehensive knowledge of programming with R.
Literatur - Learning R Programming, by Kun Ren, O’Reilly 2016.
- Hands-On Programming with R: Write Your Own Functions and Simulations, by Garrett Grolemund, O’Reilly 2014.
- Efficient R Programming, by Colin Gillespie, Robin Lovelace, O'Reilly 2016.
- Text Mining with R, by Julia Silge, David Robinson, O’Reilly 2017.
Bemerkungen The course will be taught in English.


Before every session, the teaching material will be uploaded to the ADAM workspace of the course. Please make sure to download the files of every session and have them ready when attending the course.

 

Teilnahmebedingungen No prior programming knowledge is required. The course will be consisted of hands on exercises therefore participants must bring their own laptops.

To directly dive into learning R without losing time, please download R and RStudio in advance. Here are a few instructions:
1. Install R (latest version 4.2.1)
Use the link below to download R from the CRAN mirror ETH Zürich
https://stat.ethz.ch/CRAN/
Depending on the operating system you use, choose the package and continue with the installation.

2. Install RStudio:
After installing R, you should install the RStudio that is an IDE for programming with R. Install the free version of the RStudio from the link below:
https://www.rstudio.com/products/rstudio/download/
After choosing the free package, you will be taken to the download page. Depending on your operating system, choose the package to download and install the RStudio.

The video in the link below shows the installation of both R and RStudio in step by step manner for Windows users.

https://www.youtube.com/watch?v=X_Mxya2Fis0&ab_channel=StatistikamPC

If you are a Mac OS or a Linux user, the installation is similar and far easier. Just download R and RStudio packages, and continue with the installation as any other package.

The number of participants is limited. In case of over-subscription, students of Digital Humanities will be admitted preferentially.
Unterrichtssprache Englisch
Einsatz digitaler Medien kein spezifischer Einsatz

 

Intervall wöchentlich
Datum 21.09.2022 – 14.12.2022
Zeit Mittwoch, 16.15-18.00 Kollegienhaus, Hörsaal 117
Datum Zeit Raum
Mittwoch 21.09.2022 16.15-18.00 Uhr Rosshofgasse (Schnitz), Seminarraum S 01
Mittwoch 28.09.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 120
Mittwoch 05.10.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 12.10.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 19.10.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 26.10.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 02.11.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 09.11.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 16.11.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 23.11.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 30.11.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 07.12.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Mittwoch 14.12.2022 16.15-18.00 Uhr Kollegienhaus, Hörsaal 117
Module Modul: Forschungspraxis und Vertiefung (Master Studiengang Sprache und Kommunikation)
Modul: Humanities and Social Science Coding (Master Studienfach Digital Humanities)
Modul: Methoden der Gesellschaftswissenschaften (Masterstudium: European Global Studies)
Modul: Transfer: Digital History (Master Studiengang Europäische Geschichte in globaler Perspektive )
Leistungsüberprüfung Lehrveranst.-begleitend
Hinweise zur Leistungsüberprüfung The evaluation of the course will be based on solutions submitted to the weekly exercises. There will be no examination.
An-/Abmeldung zur Leistungsüberprüfung Anmelden: Belegen; Abmelden: nicht erforderlich
Wiederholungsprüfung keine Wiederholungsprüfung
Skala Pass / Fail
Wiederholtes Belegen beliebig wiederholbar
Zuständige Fakultät Philosophisch-Historische Fakultät, studadmin-philhist@unibas.ch
Anbietende Organisationseinheit Digital Humanities Lab

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