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Charts IconCharts

Visual representations of data.

Overview

Circle charts, also known as radial or pie charts, are used to visually represent data as a percentage of a whole. They are particularly effective for displaying progress or performance metrics on dashboards. Each circle chart includes a percentage value at its center, indicating the proportion of the chart that is filled.

Our dashboard also features line charts, but they are still a work in progress. I will update this page at a later date when they are more fleshed out.

Anatomy

The circle chart component consists of the following elements:

Circle Chart Anatomy
  1. Outer Circle: The boundary of the chart.
  2. Sweep Area: The colored portion of the chart that represents the percentage value.
  3. Percentage Label: The numeric value displayed at the center of the chart, indicating the percentage.

States

Circle charts can vary based on their state and the percentage they represent. The state of the chart correlates with the percentage value to convey different levels of urgency or completion:

Chart Image

Danger (0-25%)

Info: Indicates a critical issue or failure, typically shown in red.

View Code Bitbucket Repo
Chart Image

Warning (26-50%)

Info: Indicates a potential issue or a need for caution, typically shown in yellow.

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Chart Image

Success (51-100%)

Info: Indicates successful completion or a positive outcome, typically shown in green.

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Usage

Audit Card example

Audit Card

Info: Circle charts are typically placed inside of an 'audit' card to reflect a score, along with a heading, date and button group.

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Audit Card example

Audit Card Mobile

Info: A more compact version of the audit card with a small chart.

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Audit Card example

Overall Score Card

Info: A card with a large circle chart, which highlights the overall score of several audits.

View Code Bitbucket Repo

Accessibility

  • Color Contrast: Ensure sufficient contrast between the sweep area and the background for readability.

Crimes

X

DO NOT

Don't use circle charts for complex data sets that require detailed analysis; they are best for simple, high-level metrics.

Support

If you have any questions about our design system, please contact Stephen McGhee at: stephen.mcghee@ipacconsulting.com