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If a field is specified, the channel definition must describe the encoded data’s type of measurement (level of measurement). The supported data types are:
Quantitative data expresses some kind of quantity. Typically this is numerical data. For example
Temporal data supports date-times and times such as
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
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)
medium and (2)
large. Similarly, we cannot say that
large is two times as large as
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
Casting a Binned Field as an Ordinal Field
Setting a binned field’s
"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.