Data Visualization Cheat Sheet

The core ideas of Data Visualization distilled into a single, scannable reference — perfect for review or quick lookup.

PiqCue — piqcue.com/data-visualization/cheatsheet

Quick Reference

Visual Encoding

The process of mapping data values to visual properties such as position, length, area, color, shape, and orientation. Effective encoding leverages the human visual system's strengths, using position and length for quantitative comparisons and color hue for categorical distinctions.

Data-Ink Ratio

A principle articulated by Edward Tufte stating that the proportion of ink in a graphic devoted to displaying actual data should be maximized. Non-data ink, such as unnecessary gridlines, borders, and decorations, should be minimized to reduce visual clutter.

Preattentive Processing

The rapid, unconscious detection of certain visual properties that occurs before focused attention is applied. Visual attributes like color, size, orientation, and motion are processed preattentively, allowing viewers to spot differences almost instantly in a visualization.

Gestalt Principles

A set of laws from perceptual psychology describing how humans naturally group visual elements. Key principles include proximity, similarity, enclosure, continuity, and connectedness, all of which influence how viewers interpret groupings and relationships in a visualization.

Chart Junk

A term coined by Edward Tufte referring to unnecessary or distracting decorative elements in a visualization that do not convey data. Chart junk includes excessive gridlines, gratuitous 3D effects, unnecessary textures, and decorative illustrations that compete with the data for the viewer's attention.

Color Scales and Palettes

Systematic mappings from data values to colors. Sequential scales use a gradient of lightness for ordered data, diverging scales use two contrasting hues meeting at a meaningful midpoint, and qualitative palettes use distinct hues for categorical data. Perceptually uniform palettes ensure equal data differences produce equal perceptual differences.

Exploratory vs. Explanatory Visualization

Two distinct purposes for visualization. Exploratory visualization is used during analysis to discover patterns, generate hypotheses, and understand data structure. Explanatory visualization is designed for communication, guiding an audience through specific findings with clear narrative and annotation.

Interactive Visualization

Visualizations that allow users to manipulate the display through actions like filtering, zooming, panning, brushing, and linking. Interaction enables exploration of large and complex datasets that cannot be fully represented in a single static view.

Small Multiples

A technique where the same type of chart is repeated for different subsets of the data, arranged in a grid. Each panel shares the same scales and axes, allowing direct comparison across categories, time periods, or conditions without the clutter of overlapping elements.

Marks and Channels

The fundamental building blocks of visualization design. Marks are geometric primitives representing data items (points, lines, areas), while channels are the visual properties of those marks that encode data attributes (position, size, color, shape, angle). The effectiveness of a channel depends on both the data type and the accuracy of human perception for that channel.

Key Terms at a Glance

Area Chart:A chart similar to a line chart but with the area below the line filled in, used to show cumulative totals or proportions over time.

Axis:A reference line in a chart that provides a scale for measuring and locating data values, typically horizontal (x-axis) and vertical (y-axis).

Brushing:An interactive technique allowing users to select a subset of data in one view, typically by clicking and dragging, to highlight corresponding elements.

Channel:A visual property of a mark (such as position, color, size, or shape) used to encode a data attribute.

Chart Junk:Unnecessary decorative elements in a graphic that do not convey data information.

Choropleth Map:A thematic map where geographic regions are shaded proportionally to a data variable.

Color Blindness Accessibility:Designing visualizations that remain readable for people with color vision deficiencies, using colorblind-safe palettes and redundant encoding.

Dashboard:A visual display of key metrics and data points consolidated on a single screen, designed for at-a-glance monitoring.

Data-Ink Ratio:The proportion of a graphic's total ink that is used to represent data, as opposed to non-data elements.

Diverging Scale:A color scale using two contrasting hues meeting at a meaningful midpoint, used for data with a natural center.

Encoding:The mapping of data attributes to visual properties such as position, color, size, and shape.

Faceting:Splitting data into subsets and displaying each subset in its own panel, also known as small multiples or trellis plots.

Gestalt Principles:Perceptual laws describing how humans group visual elements, including proximity, similarity, enclosure, and continuity.

Grammar of Graphics:A framework decomposing charts into layered components: data, aesthetics, geometry, statistics, coordinates, and facets.

Heatmap:A visualization using color intensity in a matrix layout to represent the magnitude of values across two categorical dimensions.

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