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feat: add mark_path, mark_polygon, mark_smooth, mark_hex, mark_density_2d, mark_corr
- mark_path: wraps geom_path (G2 Path, trajectories) - mark_polygon: wraps geom_polygon (G2 Polygon) - mark_smooth: wraps geom_smooth (VL layer+transform, G2 transform) - mark_hex: wraps geom_hex (G2 Heatmap, 2D binning) - mark_density_2d: wraps geom_density_2d with filled option (G2 Density contour) - mark_corr: correlation matrix heatmap with hclust reorder (G2 Cell) - All registered as unsupported on plotit_composite - Tests: 18 new tests covering basic, params, variants - 378 pass, 0 fail Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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‎NAMESPACE‎

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@@ -18,13 +18,19 @@ export(label_title)
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export(mark_area)
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export(mark_bar)
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export(mark_boxplot)
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export(mark_corr)
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export(mark_density)
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export(mark_density_2d)
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export(mark_hex)
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export(mark_histogram)
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export(mark_line)
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export(mark_map)
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export(mark_path)
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export(mark_point)
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export(mark_polygon)
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export(mark_rect)
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export(mark_rule)
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export(mark_smooth)
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export(mark_text)
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export(mark_violin)
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export(plotit)

‎R/mark.R‎

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@@ -498,6 +498,285 @@ S7::method(mark_rule, plotit_class) <- function(
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)
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}
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# ---- mark_path ----
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#' Path layer
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#'
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#' Adds a path layer connecting observations in their original order.
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#' Use for trajectories, time-ordered connected points, or custom
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#' drawing orders.
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#'
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#' @param plot A plotit object
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#' @param mapping Optional new aesthetics
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#' @param data Optional data for this layer
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#' @param position Position adjustment.
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#' @param rasterize If `TRUE`, rasterize via `ggrastr::rasterise()`.
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#' @param rasterize_dpi DPI for rasterization (default 300).
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#' @param rasterize_dev Graphics device for rasterization (default `"cairo"`).
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#' @param ... Other arguments passed to `geom_path`
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#' @return Modified plotit object
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#' @references
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#' AntV G2: \href{https://g2.antv.antgroup.com/en/api/mark/path}{Path}
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#' @examples
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#' df <- data.frame(x = 1:10, y = cumsum(runif(10, -1, 1)))
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#' plotit(df, encode(x = x, y = y)) |> mark_path()
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#' @export
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mark_path <- S7::new_generic(
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"mark_path", "plot",
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function(plot, mapping = NULL, data = NULL, position = NULL, ...,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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S7::S7_dispatch()
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}
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)
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._register_mark_method(mark_path, ggplot2::geom_path)
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# ---- mark_polygon ----
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#' Polygon layer
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#'
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#' Adds a filled polygon layer. Each group forms one polygon;
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#' subgroups are separated by `NA` rows or the `group` aesthetic.
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#'
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#' @param plot A plotit object
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#' @param mapping Optional new aesthetics
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#' @param data Optional data for this layer
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#' @param position Position adjustment.
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#' @param rasterize If `TRUE`, rasterize via `ggrastr::rasterise()`.
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#' @param rasterize_dpi DPI for rasterization (default 300).
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#' @param rasterize_dev Graphics device for rasterization (default `"cairo"`).
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#' @param ... Other arguments passed to `geom_polygon`
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#' @return Modified plotit object
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#' @references
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#' AntV G2: \href{https://g2.antv.antgroup.com/en/api/mark/polygon}{Polygon}
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#' @examples
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#' tri <- data.frame(x = c(0, 1, 0.5), y = c(0, 0, 1))
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#' plotit(tri, encode(x = x, y = y)) |> mark_polygon(fill = "skyblue")
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#' @export
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mark_polygon <- S7::new_generic(
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"mark_polygon", "plot",
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function(plot, mapping = NULL, data = NULL, position = NULL, ...,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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S7::S7_dispatch()
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}
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)
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._register_mark_method(mark_polygon, ggplot2::geom_polygon)
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# ---- mark_smooth ----
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#' Smoothed conditional mean layer
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#'
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#' Adds a smoothed conditional mean line with a confidence band.
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#' Aids the eye in seeing patterns in the presence of overplotting.
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#'
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#' @param plot A plotit object
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#' @param mapping Optional new aesthetics
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#' @param data Optional data for this layer
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#' @param position Position adjustment.
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#' @param method Smoothing method: `"auto"` (loess for n<1000, gam otherwise),
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#' `"lm"`, `"glm"`, `"gam"`, or `"loess"`.
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#' @param formula Formula to use in the smoothing function.
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#' @param se If `TRUE` (default), display confidence interval around smooth.
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#' @param rasterize If `TRUE`, rasterize via `ggrastr::rasterise()`.
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#' @param rasterize_dpi DPI for rasterization (default 300).
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#' @param rasterize_dev Graphics device for rasterization (default `"cairo"`).
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#' @param ... Other arguments passed to `geom_smooth`
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#' @return Modified plotit object
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#' @references
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#' Vega-Lite: achieved via \code{layer(point) + layer(line) + transform(regression)}
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#'
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#' AntV G2: achieved via transform pipeline
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#' @examples
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#' plotit(mtcars, encode(x = wt, y = mpg)) |> mark_smooth()
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#' @export
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mark_smooth <- S7::new_generic(
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"mark_smooth", "plot",
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function(plot, mapping = NULL, data = NULL, position = NULL, ...,
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method = NULL, formula = NULL, se = NULL,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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S7::S7_dispatch()
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}
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)
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#' @export
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S7::method(mark_smooth, plotit_class) <- function(
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plot, mapping = NULL, data = NULL, position = NULL, ...,
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method = NULL, formula = NULL, se = NULL,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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params <- rlang::list2(...)
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params$method <- method
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params$formula <- formula
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params$se <- se
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do.call(function(...) {
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._mark_impl(plot, mapping, data, position, ggplot2::geom_smooth,
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rasterize, rasterize_dpi, rasterize_dev, ...)
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}, params)
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}
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# ---- mark_hex ----
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#' Hexagonal heatmap layer
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#'
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#' Divides the x-y plane into hexagonal bins and fills each by the
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#' count (or other aggregation) of observations in that bin.
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#' Ideal for visualizing overplotting in large datasets.
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#'
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#' @param plot A plotit object
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#' @param mapping Optional new aesthetics
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#' @param data Optional data for this layer
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#' @param position Position adjustment.
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#' @param bins Number of bins along both axes (default 30).
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#' @param rasterize If `TRUE`, rasterize via `ggrastr::rasterise()`.
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#' @param rasterize_dpi DPI for rasterization (default 300).
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#' @param rasterize_dev Graphics device for rasterization (default `"cairo"`).
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#' @param ... Other arguments passed to `geom_hex`
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#' @return Modified plotit object
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#' @references
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#' AntV G2: \href{https://g2.antv.antgroup.com/en/api/mark/heatmap}{Heatmap} (corelib)
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#' @examples
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#' plotit(diamonds[sample(nrow(diamonds), 1000), ],
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#' encode(x = carat, y = price)) |> mark_hex(bins = 20)
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#' @export
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mark_hex <- S7::new_generic(
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"mark_hex", "plot",
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function(plot, mapping = NULL, data = NULL, position = NULL, ...,
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bins = NULL,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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S7::S7_dispatch()
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}
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)
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#' @export
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S7::method(mark_hex, plotit_class) <- function(
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plot, mapping = NULL, data = NULL, position = NULL, ...,
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bins = NULL,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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params <- rlang::list2(...)
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params$bins <- bins
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do.call(function(...) {
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._mark_impl(plot, mapping, data, position, ggplot2::geom_hex,
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rasterize, rasterize_dpi, rasterize_dev, ...)
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}, params)
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}
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# ---- mark_density_2d ----
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#' 2D density contour layer
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#'
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#' Adds 2D kernel density estimate contours. Use `filled = TRUE`
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#' for filled density bands via [ggplot2::geom_density_2d_filled].
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#'
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#' @param plot A plotit object
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#' @param mapping Optional new aesthetics
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#' @param data Optional data for this layer
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#' @param position Position adjustment.
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#' @param filled If `TRUE`, use filled density contours.
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#' @param bins Number of contour bins (for filled mode).
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#' @param rasterize If `TRUE`, rasterize via `ggrastr::rasterise()`.
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#' @param rasterize_dpi DPI for rasterization (default 300).
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#' @param rasterize_dev Graphics device for rasterization (default `"cairo"`).
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#' @param ... Other arguments passed to the underlying geom
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#' @return Modified plotit object
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#' @references
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#' AntV G2: \href{https://g2.antv.antgroup.com/en/api/mark/density}{Density} (corelib, contour mode)
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#' @examples
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#' plotit(iris, encode(x = Sepal.Width, y = Sepal.Length)) |>
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#' mark_density_2d()
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#' @export
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mark_density_2d <- S7::new_generic(
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"mark_density_2d", "plot",
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function(plot, mapping = NULL, data = NULL, position = NULL, ...,
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filled = FALSE, bins = NULL,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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S7::S7_dispatch()
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}
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)
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#' @export
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S7::method(mark_density_2d, plotit_class) <- function(
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plot, mapping = NULL, data = NULL, position = NULL, ...,
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filled = FALSE, bins = NULL,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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geom_fun <- if (filled) ggplot2::geom_density_2d_filled else ggplot2::geom_density_2d
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dots <- rlang::list2(...)
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if (!is.null(bins)) dots$bins <- bins
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# Only clear default_color when the layer provides colour/fill
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if (!is.null(mapping) && (!is.null(mapping$colour) || !is.null(mapping$fill))) {
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plot <- ._clear_default_color(plot, mapping)
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}
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pos <- position
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if (is.null(pos) && !is.null(plot@meta@dodge) && plot@meta@dodge > 0) {
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pos <- ggplot2::position_dodge(plot@meta@dodge)
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}
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geom <- if (is.null(pos)) {
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do.call(geom_fun, c(list(mapping = mapping, data = data), dots))
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} else {
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do.call(geom_fun, c(list(mapping = mapping, data = data, position = pos), dots))
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}
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.add_geom(plot, geom,
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rasterize = rasterize, rasterize_dpi = rasterize_dpi,
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rasterize_dev = rasterize_dev
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)
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}
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# ---- mark_corr ----
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#' Correlation matrix heatmap
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#'
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#' Computes a correlation matrix from numeric data columns, optionally
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#' reorders by hierarchical clustering, and renders it as a tile heatmap.
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#'
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#' @param plot A plotit object. Numeric columns are extracted from the
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#' plot data for correlation computation.
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#' @param method Correlation method: `"pearson"` (default), `"spearman"`,
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#' or `"kendall"`.
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#' @param reorder If `TRUE` (default), reorder rows and columns by
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#' hierarchical clustering.
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#' @param rasterize If `TRUE`, rasterize via `ggrastr::rasterise()`.
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#' @param rasterize_dpi DPI for rasterization (default 300).
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#' @param rasterize_dev Graphics device for rasterization (default `"cairo"`).
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#' @param ... Other arguments passed to `geom_tile`
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#' @return Modified plotit object
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#' @references
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#' AntV G2: \href{https://g2.antv.antgroup.com/en/api/mark/cell}{Cell} (correlation matrix expression)
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#' @examples
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#' plotit(mtcars, encode()) |> mark_corr()
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#' @export
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mark_corr <- S7::new_generic(
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"mark_corr", "plot",
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function(plot, method = c("pearson", "spearman", "kendall"),
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reorder = TRUE, ...,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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S7::S7_dispatch()
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}
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)
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#' @export
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S7::method(mark_corr, plotit_class) <- function(
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plot, method = c("pearson", "spearman", "kendall"),
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reorder = TRUE, ...,
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rasterize = FALSE, rasterize_dpi = 300, rasterize_dev = "cairo") {
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method <- match.arg(method)
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# Extract numeric columns from plot data
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raw_data <- plot@gg$data
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num_cols <- vapply(raw_data, is.numeric, logical(1))
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if (sum(num_cols) < 2) {
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cli::cli_abort("{.fn mark_corr} requires at least 2 numeric columns.")
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}
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mat <- stats::cor(raw_data[, num_cols, drop = FALSE], method = method)
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# Hierarchical clustering reorder
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if (reorder) {
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ord <- stats::hclust(stats::as.dist(1 - abs(mat)))$order
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mat <- mat[ord, ord]
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}
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# Melt to long form
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df <- expand.grid(
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Var1 = factor(rownames(mat), levels = rownames(mat)),
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Var2 = factor(colnames(mat), levels = colnames(mat))
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)
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df$value <- as.vector(mat)
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# Build tile
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mapping <- encode(x = Var1, y = Var2, fill = value)
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geom <- ggplot2::geom_tile(mapping = mapping, data = df, ...)
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.add_geom(plot, geom,
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rasterize = rasterize, rasterize_dpi = rasterize_dpi,
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rasterize_dev = rasterize_dev
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)
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}
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# ---- mark_bar (hand-written: geom_col vs geom_bar dispatch) ----
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#' Bar layer
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#'

‎R/zzz.R‎

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"mark_point", "mark_line", "mark_bar", "mark_boxplot",
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"mark_histogram", "mark_density", "mark_area", "mark_text",
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"mark_violin", "mark_map", "mark_rect", "mark_rule",
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"mark_path", "mark_polygon", "mark_smooth", "mark_hex",
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"mark_density_2d", "mark_corr",
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"scale_color", "scale_fill", "scale_size", "scale_alpha",
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"scale_shape", "scale_linetype", "scale_x", "scale_y",
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"project_cartesian", "project_polar", "project_parallel",

‎man/mark_corr.Rd‎

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