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Transformations: Rename Regression Analysis to Trendline (#108631)
* Rename regression analysis transformation * fix a couple translations * remove extra word * Fix tests * Change frame name to use regression
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‎docs/sources/panels-visualizations/query-transform-data/transform-data/index.md

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@@ -1483,17 +1483,19 @@ If you have multiple types it will default to string type.
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{{< figure src="/media/docs/grafana/transformations/screenshot-grafana-11-2-transpose-transformation.png" class="docs-image--no-shadow" max-width= "1100px" alt="Before and after transpose transformation" >}}
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### Regression analysis
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### Trendline
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Use this transformation to create a new data frame containing values predicted by a statistical model. This is useful for finding a trend in chaotic data. It works by fitting a mathematical function to the data, using either linear or polynomial regression. The data frame can then be used in a visualization to display a trendline.
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There are two different models:
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- **Linear regression** - Fits a linear function to the data.
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- **Linear** - Fits a linear function to the data.
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{{< figure src="/static/img/docs/transformations/linear-regression.png" class="docs-image--no-shadow" max-width= "1100px" alt="A time series visualization with a straight line representing the linear function" >}}
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- **Polynomial regression** - Fits a polynomial function to the data.
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- **Polynomial** - Fits a polynomial function to the data.
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{{< figure src="/static/img/docs/transformations/polynomial-regression.png" class="docs-image--no-shadow" max-width= "1100px" alt="A time series visualization with a curved line representing the polynomial function" >}}
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> **Note:** This transformation was previously called regression analysis.
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[Table panel]: ref:table-panel
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[Calculation types]: ref:calculation-types
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[sparkline cell type]: ref:sparkline-cell-type

‎public/app/features/transformers/docs/content.ts

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@@ -1584,25 +1584,27 @@ ${buildImageContent(
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},
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},
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regression: {
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name: 'Regression analysis',
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name: 'Trendline',
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getHelperDocs: function (imageRenderType: ImageRenderType = ImageRenderType.ShortcodeFigure) {
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return `
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Use this transformation to create a new data frame containing values predicted by a statistical model. This is useful for finding a trend in chaotic data. It works by fitting a mathematical function to the data, using either linear or polynomial regression. The data frame can then be used in a visualization to display a trendline.
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There are two different models:
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- **Linear regression** - Fits a linear function to the data.
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- **Linear** - Fits a linear function to the data.
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${buildImageContent(
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'/static/img/docs/transformations/linear-regression.png',
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imageRenderType,
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'A time series visualization with a straight line representing the linear function'
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)}
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- **Polynomial regression** - Fits a polynomial function to the data.
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- **Polynomial** - Fits a polynomial function to the data.
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${buildImageContent(
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'/static/img/docs/transformations/polynomial-regression.png',
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imageRenderType,
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'A time series visualization with a curved line representing the polynomial function'
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)}
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> **Note:** This transformation was previously called regression analysis.
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`;
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},
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},

‎public/app/features/transformers/regression/regression.test.ts

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import { ModelType, getRegressionTransformer, RegressionTransformerOptions } from './regression';
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describe('Regression transformation', () => {
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describe('Trendline transformation', () => {
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const RegressionTransformer = getRegressionTransformer();
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it('it should predict a linear regression to exactly fit the data when the data is f(x) = x', () => {
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name: 'Linear regression',
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fields: [
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{ name: 'time', type: FieldType.time, values: [0, 1, 2, 3, 4, 5], config: {} },
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{ name: 'value predicted', type: FieldType.number, values: [0, 1, 2, 3, 4, 5], config: {} },
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{ name: 'value', type: FieldType.number, values: [0, 1, 2, 3, 4, 5], config: {} },
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],
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length: 6,
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}),
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name: 'Linear regression',
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fields: [
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{ name: 'time', type: FieldType.time, values: [0, 1, 2, 3, 4, 5], config: {} },
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{ name: 'value predicted', type: FieldType.number, values: [1, 1, 1, 1, 1, 1], config: {} },
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{ name: 'value', type: FieldType.number, values: [1, 1, 1, 1, 1, 1], config: {} },
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],
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length: 6,
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}),

‎public/app/features/transformers/regression/regression.ts

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export const getRegressionTransformer: () => SynchronousDataTransformerInfo<RegressionTransformerOptions> = () => ({
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id: DataTransformerID.regression,
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name: t('transformers.regression.name.regression-analysis', 'Regression analysis'),
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name: t('transformers.regression.name.trendline', 'Trendline'),
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description: t(
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'transformers.regression.description.create-new-data-frame',
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'Create a new data frame containing values predicted by a statistical model.'
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fields: [
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{ name: xField.name, type: xField.type, values: predictionPoints, config: {} },
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{
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name: `${getFieldDisplayName(yField, predictFromFrame, frames)} predicted`,
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name: `${getFieldDisplayName(yField, predictFromFrame, frames)}`,
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type: yField.type,
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values: predictionPoints.map((x) => result.predict(x - normalizationSubtrahend)),
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config: {},

‎public/app/features/transformers/regression/regressionEditor.tsx

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help: getTransformationContent(DataTransformerID.regression).helperDocs,
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imageDark: darkImage,
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imageLight: lightImage,
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tags: new Set([t('transformers.regression-transformer-editor.tags.regression-analysis', 'Regression analysis')]),
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};
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};

‎public/locales/en-US/grafana.json

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"create-new-data-frame": "Create a new data frame containing values predicted by a statistical model."
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},
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"name": {
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"regression-analysis": "Regression analysis"
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"trendline": "Trendline"
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}
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},
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"regression-transformer-editor": {
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}
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},
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"regression": "regression",
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"tags": {
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"regression-analysis": "Regression analysis"
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},
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"tooltip-number-of-xy-points-to-predict": "Number of X,Y points to predict"
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},
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"rename-by-regex-transformer": {

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