This is the R tutorial for the practical Umweltbiologie, implemented as a Jupyter notebook. To start the interactive session, klick on the link open in collab and install the required packages (takes some time). If your browser warns you about execution of this code, you can verify here that it doesn't do anything dangerous.
It briefly introduces the following topics based on applied examples with real data:
- reproducibility in science
- reading data
- data exploration
- scatter plot
- histogram
- boxplot
- barplot
- mosaic plot
- regular expressions
- statistical distributions
- analysis of variance and linear models
- the relationship between t test, AN(C)OVA and (general) linear models
- model fitting
- interactions
- first-aid transformations
- model diagnostics (residual analysis)
- model comparison and model selection
- contrasts
- multivariate analysis methods
- data impuation
- data aggregation
- Co-Plot
- Principal Component Analysis (PCA)
It is my no means complete, but aims at giving an overview and introduction for the course, where students should learn to design research questions and scientific experiments, carry out as well as analyze and communicate the results of these experiments. This tutorial focuses on the analysis and communication (plots) parts.
If you have any questions, suggestions or comments, do not hesitate to ask me.