This entry is part of the Chart Index, the reference library for the Chart Design Field Guide.

A time range chart shows daily values across a bounded run of dates. It uses the familiar weekly calendar structure of a calendar heatmap without requiring a full year, making short operational periods easier to scan.

What it is

Each day is a coloured cell, arranged into weeks and weekdays between a start and end date. Colour carries the measure; position carries the date and weekly rhythm.

When to use it

  • The analysis covers several weeks or months rather than a full year.
  • Weekday patterns, gaps, and exceptional days matter.
  • Readers know the relevant start and end dates, such as a campaign, incident, or delivery window.

When not to use it

  • Precise trends and rates of change matter. Use a line chart.
  • The interval is only a few days or spans many years.
  • Missing days cannot be distinguished clearly from zero values.

Design principles

Distinguish zero from missing

Use a defined colour for zero and a neutral empty state for missing data. Operational readers need to know whether nothing happened or nothing was recorded.

Preserve weekday alignment

Consistent rows make recurring Monday-to-Friday patterns visible. Label weekdays when the chart is large enough.

Use a sequential scale

Daily magnitude normally moves from low to high. A single-hue sequential palette supports that ordered reading.

Annotate exceptional dates

Label outages, launches, deadlines, and other known events that explain unusual cells.

Anatomy

Columns represent weeks, rows represent weekdays, cells represent dates, colour encodes value, and blank cells mark dates outside the range or missing observations.

  • Calendar heatmap - a longer calendar view, often covering a year.
  • Table heatmap - two-dimensional categorical comparison without calendar structure.
  • Line chart - continuous trend and slope over time.

Reading list

  • Nivo TimeRange documentation - bounded calendar ranges and weekday configuration.
  • Few, S. (2009). Now You See It. Visual patterns in time-oriented data.