Visualising your data on the web using D3

Instructor: Xavier Ho

Contact: xavier.ho@data61.csiro.au

Etherpad: http://pad.software-carpentry.org/ResBaz2018-D3

Open science should be visible science. What better way is there to make your research visible and accessible than to put it on the internet. However, no one wants to read endless tables of data. We’d rather look at graphs, or, even better, have the possibility of interacting with the data. And we have probably all created some graphs. But in order to make them accessible to many people, we will have to move away from our specialized software to a more universal platform: the internet.

We want to:

  • Display our data on a website to increase visibility and accessibility of our research.

Our goal is to create a dynamic bubble plot (a prettier version of a scatter plot) and publish it on the internet.

Along the way, we will learn:

  • how to create our first own web page
  • how to change the appearance of certain elements on the page
  • how to integrate graphical elements into our page
  • how to publish our page
  • how to allow interaction with elements
  • how to store data for the use in webpages
  • how to create a graph in D3

Prerequisites

  • Github account and Github client for your operating system (Windows, Linux, iOS).
  • Participation in at least one SWC workshop.
  • Familiarity with at least one programming language: concept of loops, functions, and conditionals.
  • Familiarity with your favorite text editor (Sublime Text is a good option).
  • You will need to be comfortable with search engines!
  • Preferably Chrome or Firefox(because of its excellent developer tools).

Setup

  • It’s probably a good idea to install a GitHub client, if you are not already
  • comfortable with commandline git.

Topics

  1. HTML
  2. CSS
  3. Images and SVG
  4. Publishing with Github
  5. JavaScript
  6. JSON data format
  7. D3 Setup
  8. D3 Into the data
  9. D3 Add and remove
  10. D3 Transitions
  1. What now?

We are using gapminder data and the later lessons are based on an example by Mike Bostock. In order to make this example slightly easier, we interpolated the data. The data files can be found here.

Lessons created by Isabell Kiral-Kornek and Robert Kerr. Course upgraded to v4 and v5 by Xavier Ho.

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