28 July 2023
Data Visualisation in Ronline course
Explore key theoretical principles and applications of data visualisation, learn about visual storytelling, and get practical tricks on how to get the most out of ggplot in R.
Need to know
You will need some familiarity with R statistical language (and with tidyverse), and basic knowledge of quantitative methods, such as descriptive statistics and data modeling (e.g. linear/logistic regression).
Day 1: Introduction & Principles of Data Visualisation
Covering the organisation and logistics of the whole course, before moving on to discussing some key principles for good data visualisation. You will be introduced to the basics of using ggplot for data visualisation.
Day 2: Data Visualisation for Exploratory and Descriptive Analysis
You will learn about ways in which data visualization can be used to explore one’s data in the early stages of a research project. There will be discussion on the different visualisation methods to use to better communicate different kinds of descriptive statistics.
Day 3: Data Visualisation for Model Inference
You will explore different alternatives on how to better communicate findings from statistical models using visualisations.
Day 4: Advanced ggplot tricks
Learn all the top tricks to get the most out of ggplot: e.g. dual x/y-axis, advanced faceting, label placing, combining multiple geoms in the same plot, using cool external fonts, and several others.
Day 5: Personalized Feedback
During the week, you will work on a visualisation communicating some key findings from your own research – gradually incorporating the things learned in the course. If you do not currently have a dataset to work on, one will be provided for the purpose of this final exercise. On this final day, you will present the figure, and receive feedback from course peers and the instructors.
How the course will work online
There will be daily 3 hour live sessions taking place. During the first 4 days, there will be a combination of lectures with discussion moments, as well as live coding sessions where you will go over code previously prepared by the instructor. There will be time reserved each day for asking questions regarding your own projects and needs.
You’ll also work on creating one figure of your own, incorporating the things learned throughout the course.
Andreu Casas is an Assistant Professor in the Department of Communication Science, Vrije Universiteit Amsterdam. He is computational political scientist working on political communication, public policy, legislative politics, and computational methods.
Researchers, professional analysts, and advanced students
Effective science communication relies heavily on the use of visuals. In this course, you will acquire skills on creating compelling data visualisations using R. By leveraging the power of visualisation, you will be able to communicate your research results with greater impact.
You will start by learning basic techniques for creating descriptive statistics and then progress to more advanced topics like effectively communicating the results of complex statistical models. The course will cover theoretical principles behind data visualisation, identifying the best visualisation options for different types of data, crafting a visual story, and practical tips for maximizing the potential of ggplot in R.
You can earn up to four credits for attending this course.
3 ECTS credits – Attend 100% of live sessions and engage fully with class activities.
4 ECTS credits – Attend 100% of live sessions, engage fully with class activities and complete a post-class assignment.
GBP 478: ECPR Member
GBP 956: ECPR Non-Member
Funding applications for the 2023 ECPR Summer School in Research Methods and Techniques are now closed.
For more details on funding opportunities for ECPR's other events, please visit our website.