Introduction to R Workshop – Basics and Hands-On Examples

👋 Welcome to the Introductory Workshop on R and RStudio! This workshop is designed to provide you with the essential knowledge and practical skills needed to begin working effectively with R for statistical analysis and data science applications.

🔭 Workshop Objective:
The main goals of this workshop are:

1. Introduce the core concepts and fundamentals of statistical programming in R.
2. Provide practical, hands-on examples to help you confidently navigate data analysis tasks.

⚙️ Technologies Used:
Throughout the workshop, we will use R and RStudio (https://posit.co/download/rstudio-desktop/) as the primary tools. We will focus on base R functionality, with some exposure to useful packages like the tidyverse and psych or Bibliometrix for extended capabilities. Most work will be done through R scripts and R Markdown documents (using the new framework Quarto).

📝 Preparation Checklist:
Before participating in the workshop, please complete the following steps:

Install:

• Install R and RStudio: Download Here
  • after installing R and RStudio, install all the necessary R packages for the workshops by running the script in an .R file: install R packages
• Install the multi-language, next generation version of R Markdown Quarto

Prepare yourself:

• Review basic statistical concepts such as mean, variance, standard deviation, hypothesis testing, and multiple linear regression.
• At best familiarize yourself with basic R programming concepts (objects, classes, data structures) by completing an introductory course such as DataCamp's Free Introduction to R.
  • Tip: Students can access DataCamp for free via GitHub Education Pack.
• (Optional) Browse through the recommended learning materials (online document) for additional preparation.
• (Optional) Browse through the recommended learning materials (folder) for additional preparation.

📢 Important Note:
This hands-on workshop assumes a basic familiarity with statistical thinking and programming logic. If you're new to R, don't worry — we'll start from the fundamentals! To get the most out of the workshop, it is highly recommended to review the preparation materials in advance.

💬 Questions?
Feel free to reach out to me with any questions, ideas, or feedback: julius.fenn@psychologie.uni-freiburg.de

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📍 Location and Registration

📍 Wo?
Hörsaal 1098, Kollegiengebäude I im Erdgeschoß, Platz der Universität 3, Freiburg

📅 Wann?

• Freitag, 09. Mai, 16 – 20 Uhr
• Samstag, 10. Mai, 10 – 16 Uhr
• Freitag, 16. Mai, 16 – 20 Uhr
• Samstag, 17. Mai, 10 – 16 Uhr

📚 Was?
Der Workshop richtet sich an Studierende und Doktoranden, die R für statistische Analysen erlernen möchten. Inhaltliche Schwerpunkte:

• Grundlagen zu R-Objekten, Datenstrukturen, Flow-Control und Funktionsprogrammierung
• Analyseverfahren: deskriptive Statistik, Hypothesentests und multiple lineare Regression
• Einführung in die Nutzung von R-Paketen und RMarkdown

🚀 Anmeldung:
Hier anmelden

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📚 Table of Contents – Workshop Overview

(Subject to changes!)

Part 1:

• What is statistics?
• Statistician vs. Data Scientist
• Why programming skills matter

Part 2:

• Essential software for students and researchers
• Basics of Knowledge Management

Part 3: Core Programming in R

1. Setting up an R project
2. Understanding R Objects
3. Working with Data Structures
4. Subsetting techniques
5. Flow Control (If statements, loops)
6. Writing and using Functions
7. File and Data Management
8. Installing and using R Packages (Recommended packages overview)

Part 4: Typical Analysis Workflows ("Cooking recipes")

• Descriptive Statistics (short overview)
• Hypothesis Testing (detailed)
• Multiple Linear Regression (in-depth)
• ...
• Bibliometrix: Quantitative Analysis of Publications

Part 5: Templates and Practical Tools

• Introduction to using RMarkdown for reproducible reports
• Pre-built templates for statistical analysis and reporting workflows