Type
Edit this pageIf 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 |
Required. The encoded field’s type of measurement ( Note:
See also: |
Quantitative
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
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
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
.
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
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
GeoJSON data represents geographic shapes specified as GeoJSON.