Subtleties of Color presents the dilemmas and their corresponding solutions of color choices in data visualization. Since, unlike computers, the human eyes perceive colors subjectively, color design precautions are necessary for a data visualization to serve its purpose of illuminating data.
Based on the three different types of data––sequential, divergent, and qualitative, the three basic aspects of colors, hue, saturation, and lightness, are to be manipulated separately or collectively to fit the dataset. Sequential data is best displayed with a palette that varies in both lightness and saturation, with the supplementary addition of shift in hue; divergent data is best displayed with bifurcated palettes with a neutral central color; qualitative data should be represented by a set of easily distinguishable colors, or colors that vary sharply in hue, saturation, or lightness. In general, palettes appropriate for its content and distinguishable to human perceptions are the perfect palettes.
In addition to the fundamentals, there are some subtle aspects of color design for data visualization. Considering the perceptual norms of colors, the color palettes should be tailored for each dataset based on intuitions, layering, complementary datasets, breakpoints, separations, and hierarchies.