Dataviz.Garden
Powered by 🌱Roam GardenWhich color scale to use when visualizing data – Lisa Charlotte
Hues for categorical data
We go for hues to create categorical (aka unordered) color scales when we're representing data without intrinsic order.
When one segment of data is not more or less than the others.
Lisa's friendly reminder or what to keep in mind when using hues:
Give your hues different lightnesses so that they’d work in greyscale, too. It makes them look better and easier to distinguish, which is especially important for colorblind readers.
Meta example where colors denote colors, used by Lego over time (via r/dataisbeautiful):
Gradients for sequential data
Gradients in sequential color scales go from bright to dark (or vice-versa).
Gradients can be classed (=split into brackets, also called classified, stepped, quantized, graduated, binned or discrete) or unclassed (=one continuous gradient).
Lisa's reminders:
You can use only one hue in your sequential gradients (e.g., light blue to dark blue) – but almost all examples I show here use multiple hues (e.g., light yellow to dark blue). Using two or even more hues increases the color contrast between segments of your gradient, making it easier for readers to distinguish between them.
To decide which data values correspond to which color in your gradient is called “interpolation” and has a massive influence on how readers perceive your values.
Example:
