Dataviz Handbook

An opinionated field guide for designing honest, clear, and accessible charts — the editorial principles behind Blueprint Chart, distilled into a working reference.

This handbook is not API documentation. It is the editorial point of view that shapes every default in Blueprint Chart: what to draw, what to strip, what to highlight, and what to refuse.

What you'll find here

The handbook reflects how we think charts should be built for journalism, research, and any context where the reader's understanding matters more than the author's flourish.

Decision frameworks for picking the right chart type before you sketch a single axis.
Visual rules for axes, grid lines, color, typography, labels, and annotations.
A catalog of anti-patterns — what to avoid and why.
Accessibility guidance that treats CVD support, contrast, and keyboard reach as baseline, not bonus.

The shape of a good chart

Every page in this handbook orbits the same set of principles:

Purposefulness — every visual element earns its place.
Data-ink ratio — maximize pixels devoted to data; whisper everything else.
Start with grey — color becomes meaningful only when grey is the default.
Comparisons make the story — data in isolation has no meaning.
No 3D, ever — tilted surfaces lie.

If you read only one page, read Design Principles. Everything else is the application of those rules to a specific surface of the chart.

One chart that uses every principle

bpc

chart line {
  title = "2024 was the hottest year on record"
  description = "Deviation from the 1951–1980 average, in °C"
  source = "NASA GISS"
  colors = "#e15759"
  interpolation = "monotoneX"
  showVerticalAxis = false
  verticalGridStyle = "dashed"
  horizontalGridStyle = "none"
  lineSymbols = true
  lineSymbolShowOn = "firstLast"

  data {
    "1980" = 0.26
    "2000" = 0.42
    "2015" = 0.9
    "2024" = 1.29
  }

  annotation "2015" {
    text = "2015 Paris Agreement to limit global warming to 1.5°C"
    showLine = true
    lineStyle = curve-right
    showArrow = true
  }
}

From packages/lib/src/samples/temperature-anomaly.bpc

A takeaway title states the insight; a single red hue carries meaning; gridlines whisper; an annotation points the reader at the moment that matters. Each principle on every page traces back to a chart like this one.

Where to start

If you want to...	Read
Decide which chart fits your data	Choosing the Right Chart
Internalize the foundations	Design Principles
Avoid the most common mistakes	Anti-Patterns
Structure the chart frame	Frame Elements
Type for legibility	Typography
Pick safe, meaningful color	Color & Palettes
Tame axes and grids	Axes & Grid Lines
Label directly	Labels & Legends
Tell the story with annotations	Annotations
Design interaction honestly	Tooltips & Interaction
Make charts accessible to everyone	Accessibility

How this connects to Blueprint Chart

Blueprint Chart is the tool we built to make these principles the path of least resistance: grey defaults, baseline-zero where it matters, CVD-aware palettes, direct labeling, and a frame model that mirrors the hierarchy described here. The handbook is editorial; the Guide, DSL Spec, and API Reference are how you put it into practice.

Dataviz Handbook ​

What you'll find here ​

The shape of a good chart ​

One chart that uses every principle ​

Where to start ​

How this connects to Blueprint Chart ​

See also ​