SGBB22001U Ecological Data Analysis with R (REcoStat)

Volume 2022/2023

BSc Programme in Biology


A good working knowledge of statistical data analysis and visualization is fundamental for most job functions within ecology, including conservation planning, environmental assessments or scientific research. It is also a necessary basis for being able to do analytical or field-based MSc thesis projects. This course aims at giving biology students the tools to perform independent data analysis for projects in ecology, and to understand and critically debate statistical data analysis from published reports and scientific papers. The main tool used in the course is the scientific programming language R, which is the de facto standard for ecological data analysis. The format mixes lectures and discussions with group exercises, and the students will work independently on data analysis projects. The course is also intended to be taken together with the first block of a BSc project, to help the students who wish to incorporate an analytical component in their BSc.

Learning Outcome

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



  • 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



  • 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



  • 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
The students are assumed to have a basic knowledge of statistics corresponding to the introductory statistics course on the 1st year of the Biology BSc education at the University of Copenhagen. No previous experience with R or statistical software is assumed.
The teaching will consist of class-room teaching that blends lectures with practical exercises. Every week there will be a written assignment done in groups.
  • Category
  • Hours
  • Lectures
  • 21
  • Class Instruction
  • 7
  • Preparation
  • 115
  • Practical exercises
  • 28
  • Project work
  • 35
  • Total
  • 206
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)

The course emphasizes the use of peer-feedback, e.g., on the weekly reports. In addition the teachers give some formative feedback, both individually and collectively.

7,5 ECTS
Type of assessment
Written examination, 2 hours under invigilation
Exam registration requirements

Hand-in of all weekly reports. Attendance of 80% of exercises.

All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Several internal examiners

Same as ordinary exam.

If ten or fewer students have registered for re-exam, the exam form will changed to oral exam.

If the requirements for participating in the exam is not fulfilled before the exam the student should hand in a report detailing an independent data analysis of an ecological dataset in R, presented in RMarkdown with figures and a discussion of key assumptions. The volume of the report should correspond to ~5 A4 pages. The report should be handed in no later than three weeks before the exam. This report may not be part of the exam.

Criteria for exam assesment

See learning outcomes.