"Data Literacy: Introduction to R"

Veronika Batzdorfer veronika.batzdorfer@kit.edu

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Course description

The open source software package R is free of charge and offers standard data analysis procedures as well as a comprehensive repertoire of highly specialized processes and procedures, even for complex applications. After providing an introduction to the basic concepts and functionalities of R, we will go through a prototypical data analysis workflow in the course: import, wrangling, exploration, (basic) analysis, reporting.

Learning objectives

By the end of the course participants should be:

• Comfortable with using R and RStudio
• Able to import, wrangle, and explore their data with R
• Able to conduct basic visualizations and analyses of their data with R
• Able to generate reproducible research reports using R Markdown
• Able to run LLMs locally in R Studio and evaluate model inference

Course Schedule

Day 1: [23-05]

Day	Time	Topic
Friday	12:00 - 13:00	Onboarding & Getting Started with R
Friday	13:00 - 13:15	Break
Friday	13:15 - 14:00	Data Types & Loading
Friday	14:00 - 15:00	Lunch Break
Friday	15:00 - 16:00	Data Wrangling
Friday	16:00 - 16:15	Break
Friday	16:15 - 17:00	Data Workflows & Github

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Day 2: [24-05]

Day	Time	Topic
Saturday	12:00 - 13:00	Data Wrangling 2.0
Saturday	13:00 - 13:15	Break
Saturday	13:15 - 14:00	Exploratory Analyses
Saturday	14:00 - 15:00	Lunch Break
Saturday	15:00 - 16:00	Data Visualization
Saturday	16:00 - 16:15	Break
Saturday	16:15 - 17:00	Relational Data

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Day 3: [23-06]

Day	Time	Topic
Monday	12:00 - 13:30	Recap & Confirmatory Analyses
Monday	13:30 - 14:30	Break
Monday	14:30 - 15:00	Reproducible Reporting with R Markdown
Monday	15:00 - 15:15	Break
Monday	15:15 - 16:00	Hands-on R Markdown

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Day 4: [24-06]

Day	Time	Topic
Tuesday	12:00 - 13:30	Excursion Shiny APP
Tuesday	13:30 - 14:30	Lunch Break
Tuesday	14:00 - 14:20	Excursion Applications in R (LLMs)
Tuesday	14:20 - 14:35	Break
Tuesday	14:35 - 16:00	Group data challenge

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Day 5: [25-06]

Day	Time	Topic
Wednesday	12:00 - 13:00	Group data challenge
Wednesday	13:00 - 13:15	Break
Wednesday	13:15 - 14:00	Group data challenge
Wednesday	14:00 - 15:00	Lunch Break
Wednesday	15:00 - 16:00	Group presentations and Wrap-up