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62692-01 - Übung: Einführung in R 3 KP

Semester Herbstsemester 2021
Angebotsmuster einmalig
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 analysis and statistics. It helps with understanding the vocabulary used in texts, its structure, and semantics. It also facilitates the comparison of large sets of texts. This course will offer an introduction into learning R from the scratch; starting from installation and basics to writing functions and use of libraries to explore and visualize textual data and perform basic statistical analysis.
Lernziele The course is an introduction into the use of R for doing statistics and text analysis.
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 4.1.1)
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.
Unterrichtssprache Englisch
Einsatz digitaler Medien kein spezifischer Einsatz

 

Intervall wöchentlich
Datum 28.09.2021 – 21.12.2021
Zeit Dienstag, 14.15-16.00 Bernoullistrasse 30/32, kleiner Hörsaal 120
Datum Zeit Raum
Dienstag 28.09.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 05.10.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 12.10.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 19.10.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 26.10.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 02.11.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 09.11.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 16.11.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 23.11.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 30.11.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 07.12.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 14.12.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Dienstag 21.12.2021 14.15-16.00 Uhr Bernoullistrasse 30/32, kleiner Hörsaal 120
Module Modul: Humanities and Social Science Coding (Master Studienfach Digital Humanities)
Leistungsüberprüfung Lehrveranst.-begleitend
An-/Abmeldung zur Leistungsüberprüfung Anmelden: Belegen; Abmelden: nicht erforderlich
Wiederholungsprüfung keine Wiederholungsprüfung
Skala Pass / Fail
Wiederholtes Belegen nicht wiederholbar
Zuständige Fakultät Philosophisch-Historische Fakultät, studadmin-philhist@unibas.ch
Anbietende Organisationseinheit Digital Humanities Lab

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