Scales are a convenient abstraction for a fundamental task in visualization: mapping a dimension of abstract data to a visual representation. Although most often used for position-encoding quantitative data, such as mapping a measurement in meters to a position in pixels for dots in a scatterplot, scales can represent virtually any visual encoding, such as diverging colors, stroke widths, or symbol size. Scales can also be used with virtually any type of data, such as named categorical data or discrete data that requires sensible breaks. For continuous quantitative data, you typically want a linear scale. (For time series data, a time scale.) If the distribution calls for it, consider transforming data using a power or log scale. A quantize scale may aid differentiation by rounding continuous data to a fixed set of discrete values; similarly, a quantile scale computes quantiles from a sample population, and a threshold scale allows you to specify arbitrary breaks in continuous data. For discrete ordinal (ordered) or categorical (unordered) data, an ordinal scale specifies an explicit mapping from a set of data values to a corresponding set of visual attributes (such as colors). The related band and point scales are useful for position-encoding ordinal data, such as bars in a bar chart or dots in an categorical scatterplot.
d3/d3-scale: Encodings that map abstract data to visual representation.