1. A practical overview of handling data in R, including merging
datasets directly from the original data files within R. During the
course this knowledge will be used to automatically update illustrations
and maps. Learning a proper Data handling strategy is important to
minimize the usual multiple versions of the dataset(s) that are created
by many students. At the same time, it is important to preserve the
original data to prevent irreversible errors due to manual handling.
This is of particular interest in many projects where data is added and
updated continuously.
2. A practical and theoretical background to choose suitable figures to
convey graphically the nature of a specific dataset and what to avoid.
3. An introduction for students to plot their data-points on maps in
vector- and raster data formats using GIS software. Visualizing data on
maps is an important part of many projects in the one health field.
4. An introduction to open science, with an emphasis on reproducible
data and scripts, and sharing these through DOIs.
The course will use free software within the R environment, including
packages such as tidyverse, dplyR, tidyR, and ggplot2. For GIS, QGIS
will be used. For DOI and data sharing, GitHub will be used. The
#tidytuesday project on GitHub will be the primary source of example
datasets.
Theoretical lectures will be mixed with presentations and hands-on
workshops. Students will work in groups to solve given problems that tie
back to the lectures using #tidytuesday data.
A final individual project will be given where the students will use
their own data (when available) or use the #tidytuesday datasets to
implement the learning objectives and present their project.
Teaching will be conducted as a one week on-campus class followed by an
independent project that will be presented via zoom.
Prerequisites
Admitted to a postgraduate program in biology, animal sciences, or
related subjects, or to a residency program in veterinary medicine.
Documented previous course in R is required.