Event details

Academic Skills	Digital Literacy	Personal Development	Responsibility	Sustainability	Work Environment

After an introduction to the basic concepts and functionalities of R, we will go through a prototypical data analysis workflow in the course: Import, wrangling (transformation and cleaning), exploration, (basic) exploratory analysis and visualization, reporting. In addition, a module on the use and local execution of free language models (LLMs) in R will be integrated into the course. This includes the use of local and free LLMs in R (e.g. with llama.cpp, GPT4All or ollama) and their use cases.

Each topic of the course is first introduced and then worked through with a series of (hands-on) exercises.

Learning objectives:

You will be

• be able to import, process and analyze your data with R.
• beable to perform basic visualizations and analyses of your data with R.
• be able to create reproducible research reports with R Markdown.
• Able to use local and free language models in R to generate, analyze and process texts.
• be able to install and run local language models on your own computer.

Prerequisites:

• Students bring their own laptop to actively participate in the workshop
• Installation of RStudio (e.g. version 4.3.2, https://www.rstudio.com/)
• If necessary, installation of a LaTeX version (install.packages(‘tinytex’))

Workload for ECTS:

2 ECTS: Active participation in the workshop, written reflection report and data challenge with RMarkdown

 

Lecturer:

Veronika Batzdorfer is a post-doc researcher at the ITZ at KIT, where she works in the SoMe4Dem EU project. Her work focuses on linking digital trace data with other types of data and on natural language processing (NLP) techniques. Furthermore, she is interested in modeling temporal dynamics on social media platforms and topics such as the reproducibility of analysis pipelines and causal inference with text data (DAGs).