smoothLine

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.

method

^Optional

^SmoothMethod

The method argument is used to specify the smoothing method.

• SmoothMethod.Linear(confidenceLevel: Double) - linear model;

• SmoothMethod.Polynomial(degree: Int, confidenceLevel: Double) - polynomial model;

• SmoothMethod.LOESS(span: Double, loessCriticalSize: Int, samplingSeed: Long, confidenceLevel: Double) - Local Polynomial Regression model.

smootherPointCount

^Optional

^Int

The n argument is used to specify the number of sampled points.

Stat.x

^{Column<Double>}

The Stat.x contains points x-coordinate.

Stat.y

^{Column<Double>}

The Stat.y contains points y-coordinate.

Stat.yMin

^{Column<Double>}

The Stat.yMin lower point-wise confidence interval around the mean in this point.

Stat.yMax

^{Column<Double>}

The Stat.yMax upper point-wise confidence interval around the mean in this point.

Stat.se

^{Column<Double>}

The Stat.se contains standard error in this point.

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.

width

^{Optional NonPositional}

^{Iterable Column String Double}

The width aesthetic is a critical component in data visualization, particularly useful in plots where the size of elements, such as lines, is a significant visual factor. The width aesthetic allows for the encoding of numerical values or categories through the visual dimension of size.

Setting

• width = Double: assigns a uniform width to all applicable elements within a layer. The value is a numerical representation of the width, for example, width = 0.3.

Mapping

• width(Iterable): links the width of each element to a value from an iterable collection, allowing for the representation of data variations through size differences.

• width(ColumnReference | KProperty | DataColumn): associates element widths with a column in the DataFrame, enabling the visualization of data-driven size variations.

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

type

^{Optional NonPositional}

^{Iterable Column String LineType}

The type aesthetic is used to specify the style of lines in a plot, allowing for a diverse range of visual representations. This aesthetic enables the customization of line styles, providing clarity and distinction in data visualizations.

Setting

• type = LineType: applies a specific line style to lines within a layer. The value can be one of the predefined LineType constants:

  • LineType.BLANK

  • LineType.SOLID

  • LineType.DASHED

  • LineType.DOTTED

  • LineType.DOTDASH

  • LineType.LONGDASH

  • LineType.TWODASH

Mapping

• type(Iterable): links the line style of each element to a value from an iterable collection, allowing for various line styles within a single layer.

• type(ColumnReference | KProperty | DataColumn): associates line styles with a column in the DataFrame, facilitating data-driven line style variations.

• type(String): connects line styles to data based on the column name in the DataFrame or by key in a Map, offering flexibility in line style representation.