R → Python API map¶

Every R export has a Python equivalent with matching semantics. The JSON config both wrappers produce is identical (byte-for-byte where possible), so the same engine renders both.

Chart construction¶

R export	Python equivalent
`myIO()`	`MyIO()`
`addIoLayer()`	`MyIO.add_layer()`
`linkCharts()`	`pymyio.link_charts()` (module-level)
`setLinked()`	n/a — Crosstalk-specific; use `link_charts()`

Chart-level setters¶

R export	Python equivalent
`setMargin()`	`MyIO.set_margin()`
`setAxisFormat()`	`MyIO.set_axis_format()`
`setAxisLimits()`	`MyIO.set_axis_limits()`
`setColorScheme()`	`MyIO.set_color_scheme()`
`setReferenceLines()`	`MyIO.set_reference_lines()`
`setTheme()`	`MyIO.set_theme()`
`setTransitionSpeed()`	`MyIO.set_transition_speed()`
`setToolTipOptions()`	`MyIO.set_tooltip_options()`
`defineCategoricalAxis()`	`MyIO.define_categorical_axis()`
`flipAxis()`	`MyIO.flip_axis()`
`suppressLegend()`	`MyIO.suppress_legend()`
`suppressAxis()`	`MyIO.suppress_axis()`

Interactions¶

R export	Python equivalent
`setBrush()`	`MyIO.set_brush()`
`setAnnotation()`	`MyIO.set_annotation()`
`setExportOptions()`	`MyIO.set_export_options()`
`setFacet()`	`MyIO.set_facet()`
`setLayerOpacity()`	`MyIO.set_layer_opacity()`
`setSlider()`	`MyIO.set_slider()`
`setToggle()`	`MyIO.set_toggle()`
`setLinkedCursor()`	`MyIO.set_linked_cursor()`
`dragPoints()`	`MyIO.drag_points()`

Shiny bindings¶

R export	Python equivalent
`myIOOutput`	`pymyio.shiny.output_myio`
`renderMyIO`	`pymyio.shiny.render_myio`
—	`pymyio.shiny.reactive_brush` (thin wrapper over `shinywidgets.reactive_read`)
—	`pymyio.shiny.reactive_annotated`
—	`pymyio.shiny.reactive_rollover`
—	`pymyio.shiny.example_app()`

Diagnostics¶

R export	Python equivalent
`myIO_last_error()`	`MyIOWidget.last_error` traitlet

LLM tool calling¶

Schema-backed helpers for letting an LLM propose and validate myIO specs. Both the Pythonic names and the myio_* R-parity aliases are exported from pymyio.

R export	Python equivalent
`myio_list_chart_types()`	`pymyio.list_chart_types()` (alias `myio_list_chart_types`)
`myio_chart_schema()`	`pymyio.get_chart_schema()` (alias `myio_chart_schema`)
`myio_validate_spec()`	`pymyio.validate_spec()` (alias `myio_validate_spec`)
`myio_list_functions()`	`pymyio.list_functions()` (alias `myio_list_functions`)
`myio_function_signature()`	`pymyio.get_function_signature()` (alias `myio_function_signature`)
`myio_validate_call()`	`pymyio.validate_call()` (alias `myio_validate_call`)
`myio_load_schema()`	`pymyio.load_schema()`

Error objects use stable code values (pymyio.ERROR_CODES) shared with the R and JS implementations.

These helpers run in-process — call them directly, or wrap them as tools in your own agent loop. If you instead want a ready-to-run MCP server for any MCP-capable agent, the myIO sibling already ships one in JavaScript: @mortonanalytics/myio-mcp (Node, stdio transport, same six tools). It is driven by the same generated myio-schema.json, so R, JS, and Python validate identically. See the myIO LLM tool-calling guide.

Data containers accepted¶

Both MyIO(data=…) and add_layer(data=…) accept:

pandas DataFrame
polars DataFrame (uses .to_dicts())
list[dict]
any object exposing .to_dict(orient="records")

Rendering¶

Use case	Python
Display in Jupyter / VS Code / marimo	bare expression in a cell
Programmatic widget	`chart.render()` → `MyIOWidget`
JSON config only	`chart.to_config()` → `dict`
Static HTML	`pymyio.to_standalone_html(chart)`