heatmap

X

Type of x categories

Y

Type of y categories

x

^Required

^{Iterable Column String}

The x argument is used to provide the sample (or its x-part) on which the statistic is computed.

y

^Required

^{Iterable Column String}

The y argument is used to provide the y-part of the sample on which the statistic is computed.

weights

^Optional

^{Iterable Column String}

The weights argument is used to provide the weights of sample elements.

Stat.x

^Column<X>

The Stat.x contains x-part of categories.

Stat.y

^Column<Y>

The Stat.x contains y-part categories.

Stat.count

^Column<Int>

The Stat.count contains number of observations in this category (each one is specified by its x- and y- part)

Stat.countWeighted

^{Column<Double>}

The Stat.countWeighted contains weighted count (sum of weights in this category). Without weights its values matches Stat.count values.

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.

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

height

^{Optional NonPositional}

^{Iterable Column String Double}

The height aesthetic is fundamental in plotting tile-like elements, offering control over the vertical dimension of these elements within the plot. This aesthetic allows for precise adjustment of the height of tiles, crucial for visualizing data in formats like heatmaps or tile-based visualizations where the height of each tile may represent a value or category.

Setting

• height = Double: directly assigns a fixed height to all tile-like plot elements, providing a uniform appearance across the plot. This is particularly useful for standardizing the presentation of data when the differentiation is conveyed through other aesthetics like color. Example: height = 3.5 sets a uniform height of 3.5 units for all tiles in the plot.

Mapping

• height(Iterable): dynamically sets the height of tiles based on values from an iterable collection, allowing the height of each tile to vary according to the iterable's values.

• height(ColumnReference | KProperty | DataColumn): links the height of tiles to a DataFrame column, enabling the visualization of data-driven heights for each tile.

• height(String): associates the height of tiles with data based on the column name in the DataFrame or by key in a Map, offering flexibility in data representation through tile height.

width

^{Optional NonPositional}

^{Iterable Column String Double}

The width aesthetic is crucial for adjusting the width of visual elements across various plot types, including bars, boxes, crossBars, errorBars, and tiles.

Setting

• width = Double: Provides a uniform width to all applicable elements within a layer. The Double value is representation of width, for instance, width = 0.9 would set the width of elements to 0.9 units.

Mapping

• width(Iterable): each element's width is associated with a value from an iterable collection, allowing for the representation of data variations through width differences.

• width(ColumnReference | KProperty | DataColumn): element widths are linked to a DataFrame column, enabling visualization of data-driven width variations.

• width(String): connects widths to data based on the column name in the DataFrame or by a key in a Map, providing flexibility in representing data through width.

borderLine

The borderLine group of aesthetics provides a comprehensive way to customize the appearance of borders around plot elements such as boxes, bars, and other geometries that support border customization. This set of aesthetics allows you to adjust the color, line type, and width of the borderlines to enhance the visual appeal and clarity of your plots.

Available aesthetics within borderLine:

• borderLine.color — Specifies the color of the borderline.

• borderLine.type — Determines the style of the borderline.

• borderLine.width — Controls the thickness of the borderline.