A Brief Introduction to Programming with R

This course provides an in-depth introduction to R, a highly popular programming language used extensively in statistics and data science. Aimed at PhD candidates across various disciplines, such as chemistry and biology, the course is designed to teach essential R skills that are crucial for handling and analyzing research data.

Course Objectives:
* Foundational Skills: Learn to utilize R for fundamental tasks including data loading, cleaning, transformation, and aggregation.
* Statistical Analysis: Conduct simple statistical analyses to draw meaningful insights from your data.
* Data Visualization: Create compelling plots and visualizations to effectively communicate your findings.
* Package Management: Understand how to install and manage R packages, leveraging repositories like CRAN and Bioconductor.
* Reporting and Documentation: Generate comprehensive reports and documentation using R Markdown.
* Extending R: Gain a preliminary understanding of implementing custom functions to extend R’s base functionality.

Why R?
R is renowned for its simplicity compared to other programming languages and its extensive libraries that greatly enrich its capabilities. It is a preferred tool among scientists for its flexibility and comprehensive package options that streamline a wide range of statistical and data science tasks.

Learning Outcomes:
By the end of this course, the students will:
* Gain proficiency in writing R scripts to independently evaluate and analyze datasets.
* Understand the vast R ecosystem and know how to search for and apply relevant packages to their work.
* Possess a foundational understanding that will enable you to further explore R and deepen their skills through self-study and literature.

Course Format:
The course will be a blend of lectures and hands-on exercises to ensure a practical grasp of R. While students may not leave the course ready to develop their own R packages, they will have a clear comprehension of what is possible with R and be well-prepared to continue their R learning journey autonomously.
Embrace the power of R to enhance research with robust data analysis and visualization capabilities.

Target group: Students who have no or little experience in programming and who want to utilize methods other than Microsoft Excel for their data analysis.

Prerequisites: None

Evaluation: Participation

Teaching format: Presentations and hands-on exercises.

ECTS: 2 Year: 2024

Track segment(s):
Service

Teacher(s):
Christoph Büschl Christian Jansen Ricarda Aigner

Teaching assistant(s):