Top 10 Examples of "vega-embed in functional component" in JavaScript
Dive into secure and efficient coding practices with our curated list of the top 10 examples showcasing 'vega-embed' in functional components in JavaScript. Our advanced machine learning engine meticulously scans each line of code, cross-referencing millions of open source libraries to ensure your implementation is not just functional, but also robust and secure. Elevate your React applications to new heights by mastering the art of handling side effects, API calls, and asynchronous operations with confidence and precision.
}
}
let schema = schemaMap[plotType[0]];
if (!schema) app.alertError("invalid plot type");
// console.log(schema);
let flip = plotflip(plotType);
json.vars = nodeNames;
facetSpecifications[i] = fillVegaSchema(schema, json, flip);
}));
let specification = facetSpecifications.length === 1
? facetSpecifications[0]
: {vconcat: facetSpecifications, config: {axisY: {minExtent: 30}}}
vegaEmbed('#plot', specification, {width: 800, height: 600});
}}
},
"mark": { "type": "line", "tooltip": { "content": "data" } },
"selection": {
"selector016": {
"bind": "scales",
"encodings": [
"x",
"y"
],
"type": "interval"
}
}
}
try {
vegaEmbed(document.querySelector('#chartplace'), chartData);
} catch (ex) {
console.warn('Could not create vega chart:', ex);
}
}reloadImage (config) {
config.data = {"values": this.values}
vegaEmbed('#' + this.source, config, {tooltip: {offsetY: -110}, mode: 'vega-lite',})
}
}useEffect(() => {
if (spaceChart.current && subspaceList.length > 0) {
embed(spaceChart.current, {
data: {
values
},
vconcat: [
{
mark: 'rect',
selection: {
dim: {
type: 'single',
on: 'click',
encodings: ['y']
}
},
encoding: {
x: { field: 'measureName', type: 'nominal' },
y: {'mark': {'type': 'text', 'baseline': 'middle'},
'encoding': {
'text': {
'condition': {
'test': 'datum["noFill"] == true',
'field': 'diagCount',
'type': 'nominal',
},
'field': 'count',
'type': 'nominal',
},
}
});
}
await embed(drawArea, spec, embedOpts);
return Promise.resolve();
}processed = processed.filter(point => point['variable'] === eventdata.aggregationHeadersLabels['dyad'][0]);
vegaSchema = {
"$schema": "https://vega.github.io/schema/vega-lite/v4.json",
"data": {"values": processed},
"mark": "line",
"encoding": {
"x": {"field": eventdata.aggregationHeadersLabels['date'][0], "type": "temporal", "axis": {"format": "%Y-%m-%d"}},
"y": {"field": preferences['melt'], "type": "quantitative"},
"color": {"field": "value", "type": "nominal"}
}
};
}
}
vegaEmbed('#plotResults', vegaSchema, {actions: false, width: width, height: height});
}
}'text': {'fontSize': options.fontSize},
'legend': {
'labelFontSize': options.fontSize,
'titleFontSize': options.fontSize,
}
},
'data': {'values': values, 'name': 'values'},
'mark': 'bar',
'encoding': {
'x': {'field': 'index', 'type': xType, 'axis': xAxis},
'y': {'field': 'value', 'type': yType, 'axis': yAxis}
}
};
await nextFrame();
const embedRes = await embed(drawArea, spec, embedOpts);
instances.set(drawArea, {
view: embedRes.view,
lastOptions: options,
});
}createDataChart () {
const { testData, trainingData, predictions, showTestData } = this.props;
const trainXs = trainingData.xs.dataSync();
const trainYs = trainingData.ys.dataSync();
const testXs = testData.xs.dataSync();
const testYs = testData.ys.dataSync();
const trainValues = Array.from(trainYs).map((y, i) => ({trainX: trainXs[i], trainY: trainYs[i], pred: predictions[i]}));
const values = showTestData ?
trainValues.concat(Array.from(testYs).map((y, i) => ({testX: testXs[i], testY: testYs[i]})))
: trainValues;
return renderChart(this.dataChart, createDataSpec(values, showTestData), { actions: false });
}vegaEmbed('#plotMax' + plt + count, vegaSchema, {actions: false, width: width, height: height});
}
}
for (var plt in eventdata.discoveryData[eventdata.selectedDataset]["data"]["min"]) {
for (var count in eventdata.discoveryData[eventdata.selectedDataset]["data"]["min"][plt][0]) {
let vegaSchema = {
"$schema": "https://vega.github.io/schema/vega-lite/v2.0.json",
"data": {"values": [...melt(eventdata.discoveryData[eventdata.selectedDataset]["data"]["min"][plt][0][count]["data"], ["date"])]},
"mark": "line",
"encoding": {
"x": {"field": "date", "type": "temporal", "axis": {"format": "%Y-%m-%d"}},
"y": {"field": "value", "type": "quantitative"},
"color": {"field": "variable", "type": "nominal"}
}
};
vegaEmbed('#plotMin' + plt + count, vegaSchema, {actions: false, width: width, height: height});
}
}
}
}return {'x': xvals[i], 'y': yvals[i]};
});
const spec = {
'$schema': 'https://vega.github.io/schema/vega-lite/v2.json',
'width': 300,
'height': 300,
'data': {'values': values},
'mark': 'point',
'encoding': {
'x': {'field': 'x', 'type': 'quantitative'},
'y': {'field': 'y', 'type': 'quantitative'}
}
};
return renderChart(container, spec, {actions: false});
}