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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. For example
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.
Casting a Temporal Field as an Ordinal Field
To treat a date-time field as a discrete field, you can cast it be an
This casting strategy can be useful for time units with low cardinality such as
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
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.
Required. The encoded field’s type of measurement (
type here describes semantic of the data rather than primitive data types in programming language sense (
string, etc.). The same primitive data type can have different types of measurement. For example, numeric data can represent quantitative, ordinal, or nominal data.