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Monday 24 – Friday 28 July 2023
Minimum 2 hours of live teaching per day
10:00 – 13:00 CEST
This seminar-style course offers an engaging and interactive online teaching and learning experience, utilizing cutting-edge pedagogical tools. It is tailored for a discerning audience consisting of researchers, professional analysts, and advanced students, and enrollment is limited to a maximum of 16 participants to ensure personalized attention from the instructor.
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.
4 credits - Engage fully in class activities and complete a post-class assignment
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.
His research in political communication and public policy looks at how social media has shaped collective action dynamics; how social movements, interest groups, political parties, as well as the public, use public communications to influence the political agenda; the role of (social) media in increasing/ameliorating polarization; and the regulation of political speech by social media companies. His research on legislative politics looks at the conditions under which individual legislators and legislative groups influence policy through less prominent (e.g. amendments) and more informal (e.g. bundling legislation) mechanisms.
In addition, in all his research he develops and/or applies novel computation methods (text-as-data and images-as-data) that allow him to unlock important (classic and new) research questions that would be hard to address otherwise. His work has been published in the American Political Science Review, American Journal of Political Science, Science Advances, and the Annual Review of Political Science, among others.
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.
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.
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).
Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least two hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
Please check your course format before registering.
Live classes will be held daily for two hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.
In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.