Data
Books
Websites
Podcasts
Videos
Articles
On ethics
On color
On interactivity
A variety of useful toolkits have been designed to help support information visualization applications. Some include support for the full visualization pipeline from data to interactive graphics, while others focus only on a subset, typically graphics and interaction.
Visualization Cheatsheets
- D3 – JavaScript library for data-driven DOM manipulation, interaction and animation. Includes utilities for visualization techniques and SVG generation.
- Vega – Declarative language for representing visualizations. Vega will parse a visualization specification to produce a JavaScript-based visualization, using either HTML Canvas or SVG rendering. Vega is particularly useful for creating programs that produce visualizations as output.
- Vega-Lite – High-level visualization grammar that compiles concise specifications to full Vega specifications.
- Processing or p5.js – Java-like graphics and interaction language and IDE. Processing has a strong user community with many examples. p5.js is a sister project for JavaScript.
- Leaflet – Open-Source mapping library
- Tableau for Students – Free version of Tableau for students
- Tableau Public – Free version of Tableau for publishing on the web
- Voyager and Polestar – Web-based data exploration tools from UW's Interactive Data Lab
- Lyra – Interactive visualization design environment
- GGplot2 – Graphics language for R
- GGobi – Classic system for visualizations of multivariate data
Visualization Programming Environments
- Gephi – Graph analysis application for Windows, Mac, and Linux
- SNAP – Graph analysis library for C++ and Python
- Chroma.js – Javascript library for dealing with colors
- D3.js – Javascript library with modules for dealing with colors
- HCL Wizard – Tool for viewing, manipulating, and choosing HCL color palettes
- I Want Hue – Tool for generating and refining palettes of optimally distinct colors
- Colorbrewer – Tool for finding sequential, diverging, and qualitative color palettes
- Color Picker for Data – Tool for picking color palettes
- Accessible Color Matrix – Tool for building accessible color palettes
- Contrast Finder – Tool for finding good contrasts between two colors
- Chromaticity – Guidance for accessible visualization color design
- Color Oracle – Free color blindness simulator for Window, Mac and Linux
Data Literacy Resources (Fall semester 2018)
Slides
Day 1: https://docs.google.com/presentation/d/e/2PACX-1vSq7ytkZLPS-Nbs5JYbeKG8EuYotCxRgUewmbgWWWCqGIV-KUX4AXCa-_5bbdYNilRd46n6p3F9IbmT/pub?start=false&loop=false&delayms=3000
Day 2: https://docs.google.com/presentation/d/e/2PACX-1vQlmDzzilVBUXY9wKUZxfWvaQTAqa2nmmydKBiBsYszybYCSC6t_cUAA-RG_n4032oEj1KudNCIHuZx/pub?start=false&loop=false&delayms=3000
General / good reads
Data quality
- The Quartz Guide To Bad Data
- Tidy Data – although the principle of tidy data stems from an R developer and the examples in this document are made in R, "tidy data" is a very valuable standard that you should achieve when working with data. Once your data is "tidy", visualization in R (or in any other language / framework) becomes easier. You can also look at the more formal and less R-heavy scientific paper.
Regex
Geospatial data
Working with the CLI
The command line interface (CLI, often referred to as "shell" or "terminal") is a very powerful tool, and each operating system has one. Working with the CLI is easiest on Linux and Mac, and they are both similar since they are both based on Unix. I really recommend getting to know the 101 of working with the terminal, e.g. through this tutorial. Here are some more tips for working with data on the CLI. This Twitter account gives useful and sometimes funny tips on how to make the most of the terminal.
Data processing in R
