At the end of this course unit, students will be able to use R and RStudio to manage and explore life-science data. They will be able to identify and manipulate the main data types and structures, import and check a dataset, select, filter and transform data, identify and handle missing or aberrant values, perform simple descriptive calculations, and produce graphical representations appropriate for the data.
The course aims to provide students with a first level of autonomy in using R to manage, explore and visualise biological data. Emphasis is placed on understanding data and their structure, writing simple and reproducible commands, and solving practical problems using datasets.
The course introduces the R/RStudio environment and the basics of the R language: objects, variable classes, vectors, matrices, lists and data frames, as well as basic mathematical and statistical functions. It then covers data manipulation and sorting, logical criteria, missing and aberrant values, working directories and data import. A substantial part of the course is devoted to data visualisation, including histograms, bar plots, boxplots, scatterplots and graphical customisation.
Introduction to R and RStudio
Variable classes and object types
Basic mathematical and statistical functions
Dataset manipulation
Data selection, filtering, sorting and transformation
Aberrant and missing values
Data import and working-directory management
Creating graphs
Graph formatting and customisation
Integrative exercises in data management and visualisation
The sessions include numerous exercises carried out directly in R. These cover the creation and manipulation of objects, importing and checking datasets, selecting and transforming observations, simple descriptive statistics and creating graphs. Integrative exercises allow students to combine several of these skills using biological or environmental datasets.
Teaching combines short theoretical introductions with computer-based practical sessions, during which students directly apply the concepts covered using R. Exercises are progressive and use different datasets, with particular emphasis on developing autonomy in writing, understanding and debugging code.
Attendance at practical sessions is mandatory. Only duly justified absences are accepted. Any timetable conflict must be reported at the beginning of the academic year and before October. Otherwise, absence from a practical session prevents the student from taking the exam.
Assessment consists of two components:
Practical work (80%): assessed through an examination during the examination period.
Theory (20%): continuous assessment based on class attendance. The grade corresponds to the proportion of classes attended, multiplied by 20. Printed attendance sheets are distributed at the beginning of each class and must be signed. In the case of a duly justified absence, the corresponding class is excluded from the calculation.
In Q1, the examination consists exclusively of questions relating to the practical work. The final grade is the weighted average of the practical component (80%) and the continuously assessed theory component (20%).
If the overall Q1 grade is below 10/20, students may retake whichever component(s) they choose in Q2 or Q3. The examination will then contain practical questions as well as one or two theory questions. Only students retaking the theory component are required to answer the theory question(s).
An exemption for a successfully completed component is valid only between examination periods within the same academic year. A grade obtained for one component of the course therefore cannot be transferred to the following academic year.
Choosing to “sign” the course, i.e. requesting a grade of 0/20 for the examination, can only be done for the course unit as a whole. This request must be submitted through SIGALE. Requests sent by e-mail will not be considered.
The course syllabus, datasets required for the exercises and other supporting materials are made available to students through the institutional platforms. The syllabus contains the explanations, code examples and exercises used during the sessions. R and RStudio are the reference software used throughout the course.
| Training | Study programme | Block | Credits | Mandatory |
|---|---|---|---|---|
| Bachelor in Geography : General | Standard | 2 | 2 | Yes |
| Bachelor in Geology | Standard | 2 | 2 | Yes |
| Bachelor in Veterinary Medicine | Nouveau | 2 | 2 | No |