Axis Types

Choosing the correct axis type is crucial for accurate data representation. This guide covers available axis types and their applications.

The axis type is set like this:

// The types are 'linear', 'logarithmic', 'datetime'  or 'category'
xAxis: {
    type: 'linear',
}

Linear AxisLinear Axis

A linear axis distributes values evenly across the axis with constant intervals between tick marks. The distance between 0 and 10 is the same as between 90 and 100.

Best Use CasesBest Use Cases

• General numeric data with uniform distribution
• Comparing absolute differences between values
• Data where proportional visual distance matters
• Most common default choice for numeric data

Logarithmic AxisLogarithmic Axis

A logarithmic axis uses a logarithmic scale where each interval represents a multiplication factor (typically powers of 10). The distance between 1 and 10 equals the distance between 10 and 100.

Best Use CasesBest Use Cases

• Data spanning multiple orders of magnitude
• Exponential growth patterns (population, viral spread)
• Financial data with percentage-based changes
• Scientific measurements (pH, decibels, earthquake magnitude)
• Comparing relative/proportional changes

Data RestrictionsData Restrictions

• All data points must be strictly positive

DateTime AxisDateTime Axis

A datetime axis is specifically designed for temporal data, handling dates and times with appropriate formatting, intervals, and timezone awareness.

Best Use CasesBest Use Cases

• Time series data
• Historical trends and forecasting
• Event timelines
• Any data with temporal relationships
• Scheduling and calendar visualizations

Data RestrictionsData Restrictions

• Only Unix timestamps are supported
• Timestamps must be provided in milliseconds (not seconds)
• Values must be positive integers representing time since Unix epoch (January 1, 1970)
• Other date formats (ISO strings, Date objects) must be converted to timestamps before use

Category AxisCategory Axis

A category axis displays discrete, non-numeric labels. Each category occupies equal space regardless of any inherent ordering or value.

Best Use CasesBest Use Cases

• Nominal data (names, labels, types)
• Ordinal data (ratings, size categories)
• Bar charts comparing distinct groups
• Data without meaningful numeric intervals

Data RestrictionsData Restrictions

• Values are treated as discrete labels, not numbers
• No mathematical interpolation between points
• Order is determined by data order
• Categories must be unique — duplicate values in the categories array will cause unexpected behavior

Important NotesImportant Notes

• Plotting continuous data on category axis loses interpolation
• Large number of categories may cause readability issues
• Consider grouping or filtering if categories exceed ~20-30 items