pointRanges

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.

yMin

^{Required Positional}

^{Iterable Column String Any}

The yMin aesthetic specifies the minimum y-coordinate for elements within plots, crucial for defining the vertical lower bounds of elements such as boxes, crossbars, error bars, line ranges, point ranges, rectangles, and ribbons. This aesthetic is fundamental in accurately portraying the lower limits of data within these types of visualizations.

Setting

• yMax.constant(Any): sets a uniform minimum y-coordinate across all relevant elements within a layer, ideal for establishing a fixed lower limit. For example, yMin.constant(0.1) uniformly sets the lower boundary of elements to a y-coordinate of 0.1.

Mapping

• yMax(Iterable): each element's minimum y-coordinate is linked to a value from an iterable collection, allowing for the depiction of varying lower boundaries within a layer to represent different data points or ranges.

• yMax(ColumnReference | KProperty | DataColumn): associates the minimum y-coordinate of elements with a DataFrame column, facilitating the visualization of data-driven lower boundaries.

• yMax(String): ties the minimum y-coordinate of elements to data based on the column name in the DataFrame or by key in a Map, offering adaptability in representing lower limits through y-coordinates.

Characteristics of the y aesthetic

• Scaling — The flexibility to map yMin to data or define it as a constant value affords extensive customization opportunities, allowing for precise tailoring of the visual representation of lower data boundaries.

yMax

^{Required Positional}

^{Iterable Column String Any}

The yMax aesthetic specifies the maximum y-coordinate for elements within plots, playing a critical role in defining the vertical extent of elements such as boxes, crossBars, errorBars, lineRanges, pointRanges, rectangles, and ribbons. This aesthetic is essential for accurately representing the upper boundaries of data within these plot types.

Setting

• yMax.constant(Any): establishes a uniform maximum y-coordinate across all applicable elements within a layer, useful for setting a fixed upper limit. Example usage: yMax.constant(0.9) uniformly sets the upper boundary of elements to a y-coordinate of 0.9.

Mapping

• yMax(Iterable): associates each element's maximum y-coordinate with a value from an iterable collection, allowing for varied upper boundaries within a single layer to represent different data points or ranges.

• yMax(ColumnReference | KProperty | DataColumn): links the maximum y-coordinate of elements to a DataFrame column, enabling the visualization of data-driven upper boundaries.

• yMax(String): connects the maximum y-coordinate of elements to data based on the column name in the DataFrame or by a key in a Map, offering flexibility in representing upper limits through y-coordinates.

Characteristics of the y aesthetic

• Scaling — Mapping yMax to data or setting it as a constant value provides extensive customization options, enabling precise control over the representation of upper data boundaries.

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.

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.

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 predefined Color constant like Color.RED or a custom color defined by Color.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.

innerLine

innerPoint