Data visualization – the art/skill cocktail
Due to the ongoing COVID-19 pandemic, this course will be offered as an online course. The application deadline has been extended to May 15, 2020.
Start: 13 July 2020
End: 17 July 2020
- Ilya Kashnitsky, University of Southern Denmark
How to apply
Applications should be sent by email to the MPIDR (address below). Please begin your email message with a statement saying that you apply for course IDEM 181 – Data visualization. You also need to attach the following items integrated in *a single pdf file*:
- A two-page curriculum vitae, including a list of your scholarly publications.
- A one-page statement of your research and how it relates to the course. Please include a short description of your knowledge of data visualization and your fluency in R.
- Send your email to Christina Westphal (firstname.lastname@example.org).
- New application deadline is 15 May 2020.
- Applicants will be informed of their acceptance by 15 June 2020.
Applications submitted after the deadline will be considered only if space is available.
Preparing academic papers, researchers too often consider producing high quality plots as a secondary and less important task. To some extent this is driven by the widespread software legacy issues and mostly outdated limitations imposed by traditional scientific publishers. Yet, the modern tools place data visualization in the focus of research workflows when it comes to conveying the results. Hence, the ability to turn a large dataset into an insightful visualization is an increasingly valuable skill in academia.
The course aims to empower the participants with the flexibility that the R+tidyverse framework gives to visualize data (the practical examples use mostly demographic data). The course covers some aspects of data visualization theory and best/worst practice examples, but it's also practice oriented including live coding sessions and short lecture/showcase parts.
Practical coding sessions start from the basic introduction to tidy data manipulation and ggplot2 basics. Next, practical examples cover the creation of certain most useful types of plots. Important data visualization choices and caveats are discussed along the way. Special attention is devoted to producing geographical maps, which are no longer the luxury of professional cartographers but have turned, with the help of R, into yet another data visualization type. Going beyond ggplot2, the course presents an introduction to interactive data visualization.
Participants should have basic experience in using R. For those starting from scratch, it's a good idea to take some of the online introductory courses (swirl R package https://swirlstats.com/ is one nice option). Participants need a laptop or desktop computer with the latest versions of R and RStudio installed. More information regarding the R packages to install will be sent before the course starts.
Small exercises during the labs and a final data visualization challenge.
I suggest two recent books that are both freely available online
– by Claus Wilke – https://serialmentor.com/dataviz
– by Kieran Healy – https://socviz.co
There is no tuition fee for this course.
Recruitment of students
- Applicants should either be enrolled in a PhD program or have received their PhD.
- A maximum of 20 students will be admitted.
- The selection will be made by the MPIDR based on the applicants’ scientific qualifications.