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If a field is specified, the channel definition must describe the encoded data’s type based on their level of measurement. The supported data types are: "quantitative", "temporal", "ordinal", "nominal", and "geojson".

Property Type Description
type String

The type of measurement ("quantitative", "temporal", "ordinal", or "nominal") for the encoded field or constant value (datum). It can also be a "geojson" type for encoding ‘geoshape’.

Vega-Lite automatically infers data types in many cases as discussed below. However, type is required for a field if: (1) the field is not nominal and the field encoding has no specified aggregate (except argmin and argmax), bin, scale type, custom sort order, nor timeUnit or (2) if you wish to use an ordinal scale for a field with bin or timeUnit.

Default value:

1) For a data field, "nominal" is the default data type unless the field encoding has aggregate, channel, bin, scale type, sort, or timeUnit that satisfies the following criteria:

  • "quantitative" is the default type if (1) the encoded field contains bin or aggregate except "argmin" and "argmax", (2) the encoding channel is latitude or longitude channel or (3) if the specified scale type is a quantitative scale.
  • "temporal" is the default type if (1) the encoded field contains timeUnit or (2) the specified scale type is a time or utc scale
  • "ordinal" is the default type if (1) the encoded field contains a custom sort order, (2) the specified scale type is an ordinal/point/band scale, or (3) the encoding channel is order.

2) For a constant value in data domain (datum):

  • "quantitative" if the datum is a number
  • "nominal" if the datum is a string
  • "temporal" if the datum is a date time object


  • Data type describes the semantics of the data rather than the primitive data types (number, string, etc.). The same primitive data type can have different types of measurement. For example, numeric data can represent quantitative, ordinal, or nominal data.
  • Data values for a temporal field can be either a date-time string (e.g., "2015-03-07 12:32:17", "17:01", "2015-03-16". "2015") or a timestamp number (e.g., 1552199579097).
  • When using with bin, the type property can be either "quantitative" (for using a linear bin scale) or "ordinal" (for using an ordinal bin scale).
  • When using with timeUnit, the type property can be either "temporal" (default, for using a temporal scale) or "ordinal" (for using an ordinal scale).
  • When using with aggregate, the type property refers to the post-aggregation data type. For example, we can calculate count distinct of a categorical field "cat" using {"aggregate": "distinct", "field": "cat"}. The "type" of the aggregate output is "quantitative".
  • Secondary channels (e.g., x2, y2, xError, yError) do not have type as they must have exactly the same type as their primary channels (e.g., x, y).

See also: type documentation.


Quantitative data expresses some kind of quantity. Typically this is numerical data. For example 7.3, 42.0, 12.1.

Quantitative data can represent either the ratio or interval level of measurement. By default, Vega-Lite includes zero values in the x, y, and size scales for quantitative fields, which is more appropriate for ratio data. However, you can manually set the scale’s zero property to false if you have interval data.


Temporal data supports date-times and times such as "2015-03-07 12:32:17", "17:01", "2015-03-16". "2015", 1552199579097 (timestamp).

Note that when a "temporal" type is used for a field, Vega-Lite will treat it as a continuous field and thus will use a time scale to map its data to visual values. For example, the following bar chart shows the mean precipitation for different months.


Ordinal data represents ranked order (1st, 2nd, …) by which the data can be sorted. However, as opposed to quantitative data, there is no notion of relative degree of difference between them. For illustration, a “size” variable might have the following values small, medium, large, extra-large. We know that medium is larger than small and same for extra-large larger than large. However, we cannot compare the magnitude of relative difference, for example, between (1) small and medium and (2) medium and large. Similarly, we cannot say that large is two times as large as small.

To use an ordinal field with a sort order other than the natural order, you may want to use the sort property to specify a customized order:

Note: If an encoding includes a custom sort order, Vega-Lite uses the ordinal type by default, so you actually can omit "type": "ordinal" from the specification above.

Casting a Temporal Field as an Ordinal Field

To treat a date-time field with timeUnit as a discrete field, you can cast it be an "ordinal" field. This type casting can be useful for time units with low cardinality such as "month".

Casting a Binned Field as an Ordinal Field

Setting a binned field’s type to "ordinal" produces a histogram with an ordinal scale.


Nominal data, also known as categorical data, differentiates between values based only on their names or categories. For example, gender, nationality, music genre, and name are nominal data. Numbers maybe used to represent the variables but the number do not determine magnitude or ordering. For example, if a nominal variable contains three values 1, 2, and 3. We cannot claim that 1 is less than 2 nor 3.


GeoJSON data represents geographic shapes specified as GeoJSON.