Building my own visualizations, I’ve needed to bone-up on color theory. I thought I’d share what I’ve learned.
Schemes & Tools
There are numerous software tools out on the web, and available for download, for creating colors schemes. Most are general free-for-alls– paste in some hex codes please– and offer no assistance, beyond live preview, tagging, named schemes or Google ads. The best of the bunch is probably the flashy Kuler.
The ones I like better (like Color Scheme Designer) bring to the forefront categories stemming from color theory and a color wheel, selecting a set of colors from a single tangent (monochrome), opposite sides (complementary), analogic (or analogous, for nearby). There are also triadic (3) and a host of variations and combinations of these. It’s this type of color theory that designers study and provides the necessary grounding to understand a design and a “scheme” that supports it.
As I poke around building more visualizations, I quickly found that I needed “color schemes”– not so much color manipulation as coherent sets of colors. I started building my own tools, and then stopped, and stepped back to understand the theory behind color schemes. One of the easiest from Cynthia Brewer here. Ms. Brewer explains a few different types of color schemes specific to data visualization: binary, qualitative, linear, and divergent.
- Sequential schemes are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values.
- Diverging schemes put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues.
- Qualitative schemes do not imply magnitude differences between legend classes, and hues are used to create the primary visual differences between classes. Qualitative schemes are best suited to representing nominal or categorical data. Using these schemes could bring sanity to lots of charting libraries out there– and it’s quite sad that they don’t support such technology.
I’m building a “color factory” to provide functions for these. Provide the functions one (or two) colors from your palette and get a cohesive “color scheme” that should work with it– and even better– be appropriate to the data. These are all found in what I call the color factory. It’s nascent technology, and I’m interested in feedback.