Attractive, Interactive, Ready for the Web: Visualizing Your Data Using R

Digital humanities has brought big data and quantitative analytics into the humanities. Great effort has gone into compiling and curating large corpora and databases in numerous disciplines. However, along with big, complex data comes the question of how to make the information accessible for exploration – to the researcher looking for data, to the reader of an academic paper that has made use of the data, but also to the media or the interested lay person browsing the website of a project.
Staring at large tables, XML trees or text files of millions of words is rarely useful. Even if a database is made freely available, working with and exploring big data usually requires at least some skill in programming or even in some specialized toolset. This bottleneck effectively bars most lay users and researchers with different skillsets from interacting with interesting and potentially useful data, thus wasting the full potential of a database, the creation of which has more often than not incurred considerable human effort. Visualization of some aspects of the database is the obvious immediate remedy, but static figures often too fall short of providing an informative overview, and only provide selective views of the data.
At the same time, the web offers a dynamic medium for publishing graphs that could be interacted with by anyone from researchers to journalists to funding bodies.
This workshop teaches participants the basics of R, an immensely powerful and flexible language for data analytics and visualization. We will quickly go through just enough of the programming and explorative data analysis basics before diving into a variety of graphs - for numeric, categorical, textual, GIS, and network data, starting with simple static plots and moving on to creating interactive, animatable, multifunctional figures. We will also look into how to embed such app-like plots into a website, teaching materials, or your next conference slides (notably, some journals have already started accepting interactive figures in online publications). We will be making heavy use of modern packages such as plotly, ggplot2, quanteda, visNetwork, and rmarkdown. By the end of the workshop, you will know how to choose a suitable visualization for a given data type, and how to execute it either via traditional static plotting methods or the interactive alternatives. Prior programming experience is not required to participate.