Scale type	Explanation	Data type(s)	Example	Common Uses
scaleLinear	As inputs get larger, so does the output	Quantitative, ordinal (if numeric)	d3.scaleLinear() .domain([0, maxAge]) .range([0, width])	Axes: as age gets larger, your points will move further to the right. Bar graphs: as age gets larger, your bars get longer
scaleSqrt	As inputs get larger, so does the output. BUT based on square root growth.	Quantitative + wanting to make the radius of circles grow	d3.scaleSqrt() .domain([0, maxAge]) .range([0, maxRadius])	Every. Single. Time.: Making circles get bigger (instead of scaleLinear). It has to do with the fact that you perceive area when watching a circle grow, not radius size.
scaleOrdinal	Each input has a specific output	Nominal, sometimes ordinal	d3.scaleOrdinal() .domain(["America","Canada","Mexico"]) .range(["blue", "red", "green"])	Coloring: Different categories of elements get different specific colors
scaleThreshold	Break a quantitative value into different categories	Quantitative, ordinal (if numeric)	d3.scaleThreshold() .domain([50,100]) .range(["red", "white", "green"])	Two common uses: breaking a quantitative variable into color categories or turning a quantitative variable into an ordinal variable In the example: under 50 is red, 50-99 is white, and over 100 is green
scalePoint	Evenly spaced out circles/points	Nominal, sometimes ordinal	d3.scaleOrdinal() .domain(["America", "Canada", "Mexico"]) .range([0, width])	Spacing out categories evenly on an axis - think of the when-authors-published example I’ve shown a hundred times in class
scaleBand	Evenly spaced out bars/areas	Nominal, sometimes ordinal	d3.scaleBand() .domain(["America", "Canada", "Mexico"]) .range([0, width]) .padding(0.1)	Setting up the categorical axis of bar graphs