Ecological Data Analysis with R (REcoStat)

After completion of the course, the students are expected to be able to:

Competences:

• Work independently to perform statistical analyses in ecology, including identifying scientific hypotheses and testing them statistically. This includes understanding the biological background and significance of different statistical tests and outcomes
• Critically debate and replicate published analyses
• Know how to learn new types of analysis in R and feel confident doing it

Skills:

• Use R to load data sets and do basic data analysis tasks
• Write their own simple functions
• Use the R documentation to find solutions for coding problems
• Produce informative publication-quality figures, such as scatter plots, histograms and bar plots
• Test and summarize statistical models of ecological data
• Identify the assumptions of statistical tests and test if they are met
• Use standard linear regression, and derived techniques, such as generalized linear models
• Use the Rmarkdown syntax to produce a lab log of the analytical processes in a statistical analysis

Knowledge:

• Describe the basic elements of the R programming language
• Know the basic structure of academic programming languages
• Give an overview of the statistical methods available for analysis of observational data
• Explain the concept of pseudoreplication and detail the possible methods to deal with it
• Know functions implemented in R packages such as vegan and lme4 for ecological data analysis, tidyverseggplot2 for data handling and visualization