Visualizing and Communicating Data with R

Prerequisites (knowledge of topic)
Basic knowledge of descriptive statistics, data analysis and R is useful, but not necessary. Participants need to bring their own laptop and complete our detailed installation instructions for R and RStudio (both open source software) shared prior to the course.

Learning objectives
The creation and communication of data visualizations is a critical step in any data analytic project. Modern open-source software packages offer ever more powerful data visualizations tools. When applied with psychological and design principles in mind, users competent in these tools can produce data visualizations that easily tell more than a thousand words. In this course, participants learn how to employ state-of-the-art data visualization tools within the programming language R to create stunning, publication-ready data visualizations that communicate critical insights about data. Prior to, during, and after the course participants work their own data visualization project.

Course content
Each day will contain a series of short lectures and demonstrations that introduce and discuss new topics. The bulk of each day will be dedicated to hands-on, step-by-step exercises to help participants ‘learn by doing’. In these exercises, participants will learn how to read-in and prepare data, how to create various types of static and interactive data visualizations, how to tweak them to exactly fit one’s needs, and how to embed them in digital reports. Accompanying the course, each participant will work on his or her own data visualization project turning an initial visualization sketch into a one-page academic paper featuring a polished, well-designed figure. To advance these projects, participants will be able to draw on support from the instructors in the afternoons of course days two to four.

Day 1
Morning: Cognitive and design principles of good data visualizations
Afternoon: Introduction to R

Day 2
Morning: Reading-in, organizing and transforming data
Afternoon: Project sketch pitches

Day 3
Morning: Creating plots using the grammar of graphics
Afternoon: Visualizing statistical uncertainty, facets, networks, and maps

Day 4
Morning: Styling and exporting plots
Afternoon: Making visualizations interactive

Day 5
Morning: Reporting visualizations using Markdown
Afternoon: Final presentation and competition

Voluntary readings:
Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.
Healy, K. (2018). Data visualization: a practical introduction. Princeton University Press.

Examination part
The course grade is determined based on the quality of the initial project sketch (20%), the data visualization produced during the course (40%), and the one-page paper submitted after the course (40%).