Start: 19 June 2017
End: 23 June 2017
Location: Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany
Jonas Schöley, M.Sc.
"Visualizing Data" is an intensive five-day workshop where participants practice the trade of data visualization. Visualization will be taught as a design process: In order to design effective visualization one needs to have a clear communication purpose in mind, know the audience, know a wide range of visualization idioms, be fluent in the tools required to transform imagination into a finished product, and be able to evaluate the effectiveness of the visualization. This broad range of skills requires for an integration of theory and practice. Participants will learn about visualization theory, including human perception, marks and channels, the visualization design process, and best practices. They will create their own visualizations given a question and a dataset, recreate, criticize and improve upon existing visualizations, and – supported by the group and the lecturer – work on their own visualization project.
Upon completion of the course the participants will:
understand visualization as a design process (as opposed to a set of techniques)
be able to use R in conjunction with ggplot2, dplyr and tidyr to create a wide range of static visualizations
be able to rapidly explore a dataset using iterative visualization
formulate a question and design an effective visualization to answer it
be able to produce interactive visualizations with R and Shiny (a web-application framework for R)
5 day workshop combining lectures and practicals
5 hours per day
09:00--09:45 (Monday only) Orientation meeting
10:00--12:00 lecture + practical
12:00--13:00 lunch break
13:00--15:00 lecture + practical
15:00--15:30 coffee break
15:30--16:30 work on individual projects
visualization as a design process
getting the basics right
marks, channels and perception
introducing dplyr and tidyr
preparing data for visualization
transforming data for visualization
rapid iterative visualization
In their application for the course participants need to propose a visualization project which can be finished over the course of the week. It is suggested to produce an explanatory graphic for a particular data set and topic. A source for inspiration is at https://lisacharlotterost.github.io/.
Participants need to bring a laptop with the latest available versions of R (https://cran.r-project.org), R-Studio (https://rstudio.com) and Inkscape (https://inkscape.org) installed on it. They need a Dropbox account and about 500MB of free space in their Dropbox in order to sync to the shared course folder.
Participants must have basic experience in using R (loading data, installing and loading packages, indexing vectors, data.frames and matrices).
Participants pass the course if they finish the visualization project outlined in their proposal.
The two seminal sources for the course are:
This textbook is both comprehensive and approachable. It introduces visualization as a task driven design process as opposed to a set of ready-made techniques and teaches the knowledge necessary to design effective visualizations.
There is no tuition fee for this course. Students are expected to pay their own transportation and living costs. However, a limited number of scholarships are available on a competitive basis for outstanding candidates and for those applicants who might otherwise not be able to come.
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.
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 – Visualizing Data. You also need to attach the following items integrated in *a single pdf file*: (1) A two-page curriculum vitae, including a list of your scholarly publications. (2) A one-page letter from your supervisor at your home institution supporting your application. (3) A two-page statement of your research and how it relates to the course. Please include a visualization project proposal and a short description of your fluency in R. At the very end of your research statement, in a separate paragraph, please indicate (a) whether you would like to be considered for financial support and (b) if you would be able to come without financial aid from our side.
Send your email to Heiner Maier (firstname.lastname@example.org).
Application deadline is 31 March 2017.
Applicants will be informed of their acceptance by 30 April 2017.
Applications submitted after the deadline will be considered only if space is available.