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The course offers an introduction to data analysis in the transdisciplinary field of empirical (medical) research in the programming language R. The R system for statistical computing is openly available at https://www.r-project.org and provides a simple and flexible software environment for statistical analyses and graphics. Tailored to the application in empirical research, the course covers the basics of programming and data formats in R, as well as the essential steps of a data analysis including data manipulation, descriptive statistics, statistical tests and graphical representations. Reflections on research methodology and transdisciplinarity are addressed and critical thinking is encouraged.
The Get R_eady module enhances digital skills in the context of data analysis and graphics. It stimulates critical thinking and provides tools for reproducible research. In applications corresponding to the course participants' backgrounds, the concepts are explained. Hands-on sessions facilitate the understanding of the participants and enhance the discussion.
The course is building on problem based learning and held in an interactive and diverse format, with short lectures, demonstrations and hands on practical exercises to be solved in small groups.
MA, PhD
Larger collections of data are becoming increasingly available. To exploit their potential, statistical analysis skills are needed.The direct link between data and visualization/reporting of results in all empirical research disciplines is highly relevant as several scientific fields habe been criticized for lacking reproducibility. Dynamic reporting tools can be used to directly link data and analysis output, enabling fast adaptation of changes in the dataset following e.g. after data preparation, validation or in the context of manuscript revisions. Tailored to the application of empirical research, the course covers the basics of dynamic report compilation in RMarkdown, including examples of dynamic reports for presentations, manuscripts, and html websites. Reflections on research methodology, especially reproducibility, open science and transdisciplinarity will take place. Exemplary reports from different disciplines will be compiled and presented by the students.
The Get R_eady module enhances digital skills in the context of data analysis and graphics. It stimulates critical thinking and provides tools for reproducible research. In applications corresponding to the course participants' backgrounds, the concepts are explained. Hands-on sessions facilitate the understanding of the participants and enhance the discussion. The course is building on problem based learning and held in an interactive and diverse format, with short lectures, demonstrations and hands on practical exercises to be solved in small groups.
Target group
MA, PhD
1 ECTS
You can find more information about the module here.
