points
Properties
x
Required Positional
Iterable Column String Any
The x
aesthetic is foundational in plotting, representing the x-coordinate of elements within a plot. This aesthetic is crucial for positioning elements along the x-axis, thereby defining their placement within the plot's coordinate system.
Setting
x.constant(Any)
: Assigns a fixed x-coordinate to all elements within a layer, effectively positioning them at a specific point along the x-axis. This is useful for aligning elements across different layers or for creating reference lines. Example usage:x.constant(0.9)
sets the x-coordinate of all applicable elements to 0.9.
Mapping
x(Iterable)
: Each element's x-coordinate is linked to a value from an iterable collection, enabling the representation of data variations along the x-axis.x(ColumnReference | KProperty | DataColumn)
: element positions are associated with a DataFrame column, allowing for the visualization of data-driven positioning along the x-axis.x(String)
: ties element positions to data based on the column name in the DataFrame or by a key in a Map, offering flexibility in data representation through x-coordinates.
Characteristics of the x
aesthetic
Data Positioning — The
x
aesthetic is essential for determining where elements are placed along the x-axis, impacting how data is visualized and interpreted within the plot's spatial context.Scaling — Proper use of the
x
aesthetic can significantly enhance the dynamism and readability of a plot, facilitating the effective communication of complex data patterns and relationships.
y
Required Positional
Iterable Column String Any
The y
aesthetic is foundational in plotting, representing the y-coordinate of elements within a plot. This aesthetic is crucial for positioning elements along the y-axis, thereby defining their placement within the plot's coordinate system.
Setting
y.constant(Any)
: Assigns a fixed y-coordinate to all elements within a layer, effectively positioning them at a specific point along the y-axis. This is useful for aligning elements across different layers or for creating reference lines. Example usage:y.constant(13)
sets the y-coordinate of all applicable elements to 13.
Mapping
y(Iterable)
: Each element's y-coordinate is linked to a value from an iterable collection, enabling the representation of data variations along the y-axis.y(ColumnReference | KProperty | DataColumn)
: element positions are associated with a DataFrame column, allowing for the visualization of data-driven positioning along the y-axis.y(String)
: ties element positions to data based on the column name in the DataFrame or by a key in a Map, offering flexibility in data representation through y-coordinates.
Characteristics of the y
aesthetic
Data Positioning — The
y
aesthetic is essential for determining where elements are placed along the y-axis, impacting how data is visualized and interpreted within the plot's spatial context.Scaling — Proper use of the
y
aesthetic can significantly enhance the dynamism and readability of a plot, facilitating the effective communication of complex data patterns and relationships.
alpha
Optional NonPositional
Iterable Column String Double
The alpha aesthetic controls the transparency of elements in a plot. It provides a means to adjust the visual prominence of elements, ranging from fully transparent (0.0) to fully opaque (1.0).
Setting
alpha = Double
: applies a uniform transparency level to all elements within a layer. The Double value should be within the range of 0.0 (completely transparent) to 1.0 (completely opaque).
Mapping
alpha(Iterable)
: associates the transparency of each element with a value from an iterable collection.alpha(ColumnReference | KProperty | DataColumn)
: links transparency with data from a specified DataFrame column.alpha(String)
: transparency is associated with data from a DataFrame column specified by its name or with data from a Map by key.
Characteristics of the alpha
aesthetic
Value range — It is crucial to ensure that all alpha values are within the 0.0 to 1.0 range. Values outside this range will trigger an
IllegalArgumentException
.Scaling and validation — When mapping
alpha
to data, additional scaling adjustments and value validation ensure proper representation of transparency.
symbol
Optional NonPositional
Iterable Column String Symbol
The symbol
aesthetic is integral to plotting, especially in visualizations that rely on markers, such as scatter plots and point ranges. This aesthetic enables the customization of marker shapes, allowing for a diverse representation of data points.
Setting
symbol = Symbol
: Assigns a specific marker shape to all points within a layer:Symbol.ASTERIX
Symbol.BULLET
Symbol.CIRCLE
Symbol.CIRCLE_CROSS
Symbol.CIRCLE_FILLED
Symbol.CIRCLE_OPEN
Symbol.CIRCLE_PLUS
Symbol.CIRCLE_SMALL
Symbol.CROSS
Symbol.DIAMOND
Symbol.DIAMOND_FILLED
Symbol.DIAMOND_OPEN
Symbol.DIAMOND_PLUS
Symbol.PLUS
Symbol.SQUARE
Symbol.SQUARE_CROSS
Symbol.SQUARE_FILLED
Symbol.SQUARE_OPEN
Symbol.SQUARE_PLUS
Symbol.SQUARE_TRIANGLE
Symbol.STAR
Symbol.TRIANGLE
Symbol.TRIANGLE_DOWN_FILLED
Symbol.TRIANGLE_DOWN_OPEN
Symbol.TRIANGLE_FILLED
Symbol.TRIANGLE_OPEN
Mapping
symbol(Iterable)
: each marker's shape is linked to a value from an iterable collection, enabling variable marker shapes within a single layer to represent different data categories or values.symbol(ColumnReference | KProperty | DataColumn)
: marker shapes are associated with a DataFrame column, allowing the visualization of data-driven shape variations.symbol(String)
: ties marker shapes to data based on the column name in the DataFrame or by key in a Map, offering flexible and dynamic data representation through marker shapes.
color
Optional NonPositional
Iterable Column String Color
The color aesthetic is a key feature in data visualization, allowing you to set the color of plot elements to enhance the interpretability and visual appeal of your graphs. This aesthetic facilitates both the differentiation of data points and the conveyance of additional data dimensions through color.
Setting
color = Color
: assigns a uniform color to all elements within a layer. The value can be a predefinedColor
constant likeColor.RED
or a custom color defined byColor.hex("#ff0000")
.
Mapping
color(Iterable)
: links the color of each element to a value from an iterable collection, allowing for varied color assignments within a layer.color(ColumnReference | KProperty | DataColumn)
: associates element colors with a column in the DataFrame, enabling data-driven color variations.color(String)
: connects colors to data based on the column name in the DataFrame or by key in a Map, offering flexibility in data representation.
Characteristics of the color
aesthetic
Versatility in data representation — The
color
aesthetic can be used to represent different categories, intensities, or other data dimensions, providing a rich layer of information in visualizations.Scaling and validation — When mapping
color
to data, you can customize the color scale and adjust the mapping to fit specific visualization needs.
fillColor
Optional NonPositional
Iterable Column String Color
The fillColor
aesthetic plays a crucial role in defining the visual representation of plot elements by setting their fill color. It allows for both uniform color application and data-driven color variations.
Setting
fillColor = Color
: provides a uniform fill color to all elements within a layer, applicable for creating visually cohesive plot elements. The value can be a predefinedColor
constant likeColor.RED
or a custom color defined byColor.hex("#ff0000")
.
Mapping
fillColor(Iterable)
: associates the fill color of each element with a value from an iterable collection, allowing for a variety of fill colors within a single layer.fillColor(ColumnReference | KProperty | DataColumn)
: links element fill colors with a column in the DataFrame, enabling the visualization of data-driven color variations.fillColor(String)
: connects fill colors to data based on the column name in the DataFrame or by key in a Map, offering flexibility in data representation through color.
Characteristics of the fillColor
aesthetic
Versatility in data representation — The
fillColor
aesthetic can be used to represent different categories, intensities, or other data dimensions, providing a rich layer of information in visualizations.Scaling and validation — When mapping
fillColor
to data, you can customize the color scale and adjust the mapping to fit specific visualization needs.
size
Optional NonPositional
Iterable Column String Double
The size
aesthetic is pivotal in controlling the visual size of plot elements, such as the diameter of points in a scatter plot or the thickness of lines in a line plot. This aesthetic is versatile, being utilized across various layers including pie
, pointRanges
, points
, and text
.
Setting
size = Double
: imposes a uniform size across all applicable elements within a layer. The double value is factor indicating the size, e.g.,size = 0.3
.
Mapping
size(Iterable)
: each element's size is linked to a value from an iterable collection, allowing for varied sizes within a layer to represent data variations.size(ColumnReference | KProperty | DataColumn)
: element sizes are associated with a DataFrame column, enabling the visualization of data-driven size differences.size(String)
: sizes are tied to data based on the column name in the DataFrame or by a key in a Map, facilitating flexible data representation through size.
stroke
Optional NonPositional
Iterable Column String Double
The stroke
aesthetic affects the thickness of a symbol borders (shapes). This aesthetic can be used on pointRanges
and points
layers.
Setting
stroke = Number
: imposes a uniform size across all applicable elements within a layer. The number value is factor indicating the thickness if the point boundaries, e.g.,size = 5
.
Mapping
stroke(Iterable)
: each element's thickness is linked to a value from an iterable collection, allowing for varied sizes within a layer to represent data variations.stroke(ColumnReference | KProperty | DataColumn)
: element thickness are associated with a DataFrame column, enabling the visualization of data-driven size differences.stroke(String)
: thicknesses are tied to data based on the column name in the DataFrame or by a key in a Map, facilitating flexible data representation through thickness of the point boundaries.