Charts, Data, and Composition Actions
These are direct immutable ChartProgram actions. Each accepts one option object and returns a new program.
createCanvas
createCanvas({ width?, height?, background?, margin? })
Create the program’s Canvas and plot bounds. Canvas options
editCanvas
editCanvas({ width?, height?, background?, margin? })
Edit Canvas properties and rematerialize connected consumers. Canvas options
editCompositionLayout
editCompositionLayout({ columns?, gap?, align?, padding? })
Edit spacing, cross-axis alignment, or outer padding on an existing composition.
columns changes wrapping only on a facet composition and is rejected for concat.
Omitted values are preserved, child identity is unchanged, and the parent snapshot
is rebuilt from retained child programs.
replaceCompositionChild
replaceCompositionChild({ target, program })
Replace one named child while preserving its slot ID and order. The replacement must already be a complete chart or composition program.
facet
facet({ id?, field, data?, columns?, gap?, align?, padding?, scales?, guides? })
Repeat one complete chart by a field on its common row-preserving dataset
ancestor. Values preserve source first appearance; scale policies can be
"shared" or "independent" by supported channel, and layered regression
data and other supported statistical descendants are recomputed per cell.
guides: { axes: "outer" } keeps axes only on occupied outer cells, while
guides: { legend: "shared" } promotes one compatible parent-owned legend at
the child legend’s configured left, right, top, or bottom edge. Top and
bottom promotion also preserves the child legend’s horizontal alignment;
author those options with createLegend before calling facet.
See Program composition.
editFacetHeaders
editFacetHeaders({ fontSize?, fontFamily?, fontWeight?, color?, offset? })
Edit the parent-owned repeated facet headers and rebuild the parent snapshot without changing child programs or facet value order.
editFacetScales
editFacetScales({ x?, y?, xOffset?, yOffset?, color?, size?, shape?, opacity?, strokeDash? })
Partially change used facet channels between "shared" and "independent".
Every cell is rederived from the retained pre-facet program while field, data,
value order, child IDs, layout, guides, headers, and title are preserved.
editFacetGuides
editFacetGuides({ axes?, legend? })
Partially change axes between "each" and "outer", or legend ownership
between false and "shared". Shared legend promotion requires concretely
compatible child scales and guide recipes.
createData
createData({ id?, values })
Create one immutable named dataset. Data
createScatterPlot
createScatterPlot({ id?, data?, coordinate?, x, y, color?, size?, shape?, point?, guides? })
Create a complete Cartesian point chart from required x/y fields and optional appearance encodings. Basic Charts
createLinePlot
createLinePlot({ id?, data?, coordinate?, x, y, color?, groupBy?, strokeDash?, line?, guides? })
Create a complete Cartesian line chart, including optional series grouping and appearance. Basic Charts
createBarPlot
createBarPlot({ id?, data?, coordinate?, x, y, color?, width?, bar?, guides? })
Create a complete vertical, horizontal, aggregate, ranged, grouped, or stacked bar chart through the existing bar policies. Basic Charts
createHistogram
createHistogram({ id?, data?, coordinate?, field, maxBins?, binStep?, binBoundaries?, stack?, xScale?, yScale?, color?, bar?, guides? })
Create a bar layer with atomic bin and count encodings. Exactly one bin mode may be specified. Basic Charts
createHeatmap
createHeatmap({ id?, data?, coordinate?, x, y, bin?, color?, rect?, guides? })
Create one rect cell per valid pre-gridded row, or bin raw quantitative x/y rows into ranged cells colored by count. Basic Charts
createParallelCoordinates
createParallelCoordinates({ id?, data?, coordinate?, dimensions, key?, missing?, color?, strokeDash?, line?, guides? })
Create one open line path per source row across an ordered list of dimension-
local scales and axes. Only dimensions is required.
Parallel Coordinates
filterData
filterData({ id, source?, field, oneOf | predicate | range })
Create an immutable named derived dataset using exactly one membership, comparison, or range filter. The source defaults to current data. Data
filterMarks
filterMarks({ target?, grain?, field | channel | property, op, ...operands })
Retain matching final mark items through the shared selector grammar, create one namespaced immutable member-row dataset, rebind the mark, and rematerialize its scales and connected guides without changing the source. Data
highlightMarks
highlightMarks({
id?, target?, select?, selection?, color?, opacity?, fill?, stroke?,
strokeWidth?, strokeDash?, shape?, size?, offset?, dimOthers?, bringToFront?
})
Select point, bar, line, area, arc, or rule items inline or reuse a stored selection, then apply mark-specific concrete emphasis, optional complement dimming, and selected-last order. Mark selection and highlighting
removeMarkHighlight
removeMarkHighlight({ selection? } = {})
Remove one highlight assignment, restore the target mark and categorical legend baseline, and retain the reusable selection. Selection lifecycle
createRegressionData
createRegressionData({
id, source?, x, y, groupBy?, method?, degree?, span?, confidence?, interval?
})
Create immutable linear, polynomial, or LOESS fitted rows at observed unique x values. Linear and polynomial fits support Student-t mean or prediction bounds; LOESS is line-only. Data
createDensityData
createDensityData({
id, source?, field, groupBy?, bandwidth?, extent?, steps?,
kernel?, normalization?, as?
})
Create immutable KDE rows on one shared inclusive sample grid. Source defaults
to current data, steps to 100, bandwidth to an automatic Scott-rule estimate,
kernel to "gaussian", and normalization to "unit".
Data
createTimeUnitData
createTimeUnitData({ id, source?, field, unit, as })
Create an immutable row-preserving dataset with one UTC year, quarter, month, day, hour, minute, or second bucket-start timestamp field. Time-unit data transforms
createWindowData
createWindowData({ id, source?, partitionBy?, sortBy?, operations })
Create an immutable derived dataset by applying ordered row-number, rank,
dense-rank, cumulative-sum, lag, lead, moving-mean, or moving-sum operations
within optional partitions. Moving frames include the current sorted row, require
a non-negative preceding, default following to 0, and truncate at partition
edges. The calculation follows a stable sort while the output preserves source
row order.
Window data transforms
createBin2DData
createBin2DData({
id, source?, x, y, bins?, extent?, includeEmpty?, members?, as?
})
Aggregate finite x/y pairs into deterministic rectangular cell bounds and counts. Reusing the logical ID creates an immutable revision and rematerializes direct visual consumers. Rectangular 2D bins
editBin2DData
editBin2DData({
target?, source?, x?, y?, bins?, extent?, includeEmpty?, members?, as?
})
Partially revise the current or unique logical 2D-bin owner. Omitted top-level transform options are preserved; successful edits create an immutable revision, rebind direct visual consumers, and safely release the prior revision. Rectangular 2D bins