trait VegaLite extends AnyRef
Vega-Lite specifications are JSON objects that describe a diverse range of interactive visualizations. Besides using a single view specification as a standalone visualization, Vega-Lite also provides operators for composing multiple view specifications into a layered or multi-view specification. These operators include layer, facet, concat, and repeat.
- Alphabetic
- By Inheritance
- VegaLite
- AnyRef
- Any
- by any2stringadd
- by StringFormat
- by Ensuring
- by ArrowAssoc
- Hide All
- Show All
- Public
- Protected
Abstract Value Members
- abstract val spec: JsObject
The specification
Concrete Value Members
- final def !=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def ##: Int
- Definition Classes
- AnyRef → Any
- def +(other: String): String
- def ->[B](y: B): (VegaLite, B)
- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- def autosize(type: String = "pad", resize: Boolean = false, contains: String = "content"): VegaLite
Sets the overall size of the visualization.
Sets the overall size of the visualization. The total size of a Vega-Lite visualization may be determined by multiple factors: specified width, height, and padding values, as well as content such as axes, legends, and titles.
- resize
A boolean flag indicating if autosize layout should be re-calculated on every view update.
- contains
Determines how size calculation should be performed, one of "content" or "padding". The default setting ("content") interprets the width and height settings as the data rectangle (plotting) dimensions, to which padding is then added. In contrast, the "padding" setting includes the padding within the view size calculations, such that the width and height settings indicate the total intended size of the view.
- def background(color: String): VegaLite
CSS color property to use as the background of the entire view.
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def config(properties: JsObject): VegaLite
Sets Vega-Lite configuration object that lists configuration properties of a visualization for creating a consistent theme.
Sets Vega-Lite configuration object that lists configuration properties of a visualization for creating a consistent theme. This property can only be defined at the top-level of a specification.
- def data(url: String, format: JsValue = JsUndefined): VegaLite
Sets the url of the data source.
Sets the url of the data source.
- url
An URL from which to load the data set.
- format
Type of input data: "json", "csv", "tsv", "dsv". Default value: The default format type is determined by the extension of the file URL. If no extension is detected, "json" will be used by default.
- def data(df: DataFrame): VegaLite
Sets a data frame describing the data source.
- def data(rows: JsObject*): VegaLite
Sets an array of objects describing the data source.
- def data(json: JsArray): VegaLite
Sets a JSON array describing the data source.
Sets a JSON array describing the data source. Set to null to ignore the parent’s data source. If no data is set, it is derived from the parent.
- def description(description: String): VegaLite
Sets the description of this mark for commenting purpose.
- def embed: String
Returns the HTML of plot specification with Vega Embed.
- def ensuring(cond: (VegaLite) => Boolean, msg: => Any): VegaLite
- def ensuring(cond: (VegaLite) => Boolean): VegaLite
- def ensuring(cond: Boolean, msg: => Any): VegaLite
- def ensuring(cond: Boolean): VegaLite
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable])
- def formatted(fmtstr: String): String
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- def iframe(id: String = java.util.UUID.randomUUID.toString): String
Returns the HTML wrapped in an iframe to render in notebooks.
Returns the HTML wrapped in an iframe to render in notebooks.
- id
the iframe HTML id.
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- def name(name: String): VegaLite
Sets the name of the visualization for later reference.
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- def padding(left: Int, top: Int, right: Int, bottom: Int): VegaLite
Specifies padding for each side.
Specifies padding for each side. The visualization padding, in pixels, is from the edge of the visualization canvas to the data rectangle.
- def padding(size: Int): VegaLite
Specifies padding for all sides.
Specifies padding for all sides. The visualization padding, in pixels, is from the edge of the visualization canvas to the data rectangle.
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- def title(title: String): VegaLite
Sets a descriptive title to a chart.
- def toString(): String
- Definition Classes
- VegaLite → AnyRef → Any
- def transform(transforms: JsObject*): VegaLite
An array of data transformations such as filter and new field calculation.
An array of data transformations such as filter and new field calculation. Data transformations in Vega-Lite are described via either view-level transforms (the transform property) or field transforms inside encoding (bin, timeUnit, aggregate, sort, and stack).
When both types of transforms are specified, the view-level transforms are executed first based on the order in the array. Then the inline transforms are executed in this order: bin, timeUnit, aggregate, sort, and stack.
- def transform(transforms: JsArray): VegaLite
An array of data transformations such as filter and new field calculation.
An array of data transformations such as filter and new field calculation. Data transformations in Vega-Lite are described via either view-level transforms (the transform property) or field transforms inside encoding (bin, timeUnit, aggregate, sort, and stack).
When both types of transforms are specified, the view-level transforms are executed first based on the order in the array. Then the inline transforms are executed in this order: bin, timeUnit, aggregate, sort, and stack.
- def usermeta(data: JsValue): VegaLite
Optional metadata that will be passed to Vega.
Optional metadata that will be passed to Vega. This object is completely ignored by Vega and Vega-Lite and can be used for custom metadata.
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
Deprecated Value Members
- def →[B](y: B): (VegaLite, B)
- Implicit
- This member is added by an implicit conversion from VegaLite toArrowAssoc[VegaLite] performed by method ArrowAssoc in scala.Predef.
- Definition Classes
- ArrowAssoc
- Annotations
- @deprecated
- Deprecated
(Since version 2.13.0) Use
->
instead. If you still wish to display it as one character, consider using a font with programming ligatures such as Fira Code.
Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.
Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.