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Setting data types (Vue)

By default, WebDataRocks can auto-detect basic data types such as strings, numbers, and dates, but you can use all available data types to enrich your visualization. To set types explicitly, use the metadata object in JSON or data type prefixes in CSV.

After setting field data types, see the guides about connecting to the JSON or connecting to the CSV data.

Available data types

The following data types are available in WebDataRocks:

• Number
• String
• Date
• Time
• Month
• Weekday
• Level (only for JSON)
• Hidden (only for JSON)

Number

This field type is used to store numbers.

Available aggregations: all.

See how to set this type for a field in your data source:

"Price": {
  "type": "number"
}

-Quantity, Product
3, Cars
4, Bikes
2, Components
2, Bikes

String

This field type is used to store strings.

Available aggregations: Count, Distinct Count.

See how to set this type for a field in your data source:

"Product": {
  "type": "string"
}

Quantity, +Product
3, Cars
4, Bikes
2, Components
2, Bikes

Date

WebDataRocks offers a wide range of date types. Review the options in this section to select the most suitable one.

 Read how to specify date fields in JSON or CSV data.

Year, Month, Day subfields

The date is divided into 3 string subfields: Year, Month, Day.

This is the default format for all date fields unless a different type is specified in the metadata object or prefixes.

Available aggregations: Count, Distinct Count.

See how to set this type for a field in your data source:

"Date": {
  "type": "date"
}

d+Invoice Date, Quantity, Country
2018-05-15, 3, France
2018-05-16, 4, Italy
2018-05-17, 2, Spain
2018-05-12, 2, Japan

Year > Month > Day multilevel hierarchy

The date is represented as a multilevel hierarchy: Year > Month > Day.

Available aggregations: Count, Distinct Count.

See how to set this type for a field in your data source:

"Date": {
  "type": "year/month/day"
}

D+Invoice Date, Quantity, Country
2018-05-15, 3, France
2018-05-16, 4, Italy
2018-05-17, 2, Spain
2018-05-12, 2, Japan

Year > Quarter > Month > Day multilevel hierarchy

The date is represented as a multilevel hierarchy: Year > Quarter > Month > Day.

Available aggregations: Count, Distinct Count.

See how to set this type for a field in your data source:

"Date": {
  "type": "year/quarter/month/day"
}

D4+Invoice Date, Quantity, Country
2018-05-15, 3, France
2018-05-16, 4, Italy
2018-05-17, 2, Spain
2018-05-12, 2, Japan

“dd/MM/yyyy” format

The date is displayed as a string in the "dd/MM/yyyy" format.

Available aggregations: Min, Max, Count, Distinct Count.

The format can be changed using the datePattern option.

See how to set this type for a field in your data source:

"Date": {
  "type": "date string"
}

ds+Invoice Date, Quantity, Country
2018-05-15, 3, France
2018-05-16, 4, Italy
2018-05-17, 2, Spain
2018-05-12, 2, Japan

“dd/MM/yyyy HH:mm:ss” format

The date is displayed as a string in the "dd/MM/yyyy HH:mm:ss" format.

Available aggregations: Min, Max, Count, Distinct Count.

The format can be changed using the dateTimePattern option.

See how to set this type for a field in your data source:

"Date": {
  "type": "datetime"
}

dt+Invoice Date, Price, Country
2018-05-15T18:30:00, 329, France
2018-05-16T06:20:00, 139, Italy
2018-05-17T13:45:00, 429, Spain
2018-05-12T04:50:00, 559, Japan

Time

This field type is used to store time intervals, such as duration.

Available aggregations: Min, Max, Count, Distinct Count.

See how to set this type for a field in your data source:

"Duration": {
  "type": "time"
}

t+Duration, Movie, ReleaseYears
7020, Blade Runner, 1980-1990
5220, The Lion King, 1990-2000
7560, Jurassic Park, 1990-2000
9120, The Dark Knight, 2000-2010

Month

Fields of this type store months. Natural sorting is applied to the field members: from January to December.

Available aggregations: Count, Distinct Count.

See how to set this type for a field in your data source:

"Month": {
  "type": "month"
}

m+Month, Price, Country
Apr, 329, France
Feb, 139, Italy
Jan, 429, Spain
Dec, 559, Japan

Weekday

Fields of this type store the days of the week. Natural sorting is applied to the field members: from Sunday to Monday.

Available aggregations: Count, Distinct Count.

See how to set this type for a field in your data source:

"Week Day": {
  "type": "weekday"
}

w+Week Day, Quantity, Country
Tuesday, 3, France
Wednesday, 4, Italy
Thursday, 2, Spain
Saturday, 2, Japan

Level (only for JSON)

This field type means that a field is a level of a multilevel hierarchy.

To mark the field as a hierarchy level, set the type property of the field’s metadata object to "level".

Available aggregations: none.

See the example.

Hidden (only for JSON)

This field type is used to hide fields from the report.

To mark the field as hidden, set the type property of the field’s metadata object to "hidden".

Available aggregations: none.

Note. Another way to hide fields from the report is to exclude them from the metadata object.

Automatic type detection

If you exclude the field’s type in JSON metadata object or CSV prefixes, the component detects the data type based on the field’s first value. The following data types can be auto-detected:

• Number
• String
• Date

See the example of automatic type detection in JSON.

The results of the automatic detection may be unexpected when:

• The first field value is null. In this case, the field type cannot be detected automatically.
• The first field value is formatted differently from other values. For example, the first value is a number while the others are strings.
• Field values are not formatted correctly. For example, a number was specified in hexadecimal form instead of decimal.
• Field values may be confused with values of other types. For example, time values are processed as numbers if the "time" type is not explicitly specified.

See also

• Connecting to JSON
• Connecting to CSV
• Metadata object for JSON
• Data type prefixes

You can hide the spreadsheet headers with column and row numbers, which are displayed by default on the grid.

To hide column and row numbers

Set the grid.showHeaders property to false in the Options Object.

There are several ways to apply this setting, depending on whether the pivot table is configured during report initialization or at runtime:

const report = {
  options: {
    grid: {
      showHeaders: false
    }
  }
};

this.$refs.pivot.webdatarocks.setOptions({ grid: { showHeaders: false }});
this.$refs.pivot.webdatarocks.refresh();

Example

This tutorial explains how to connect WebDataRocks to a third-party charting library, such as Chart.js, ApexCharts, ECharts, or D3.js.

WebDataRocks can be integrated with charting libraries using the getData() API call, which returns data displayed on the grid. Then, you need to preprocess the data to a format required by your charting library and pass it to the chart.

In this tutorial, we will integrate with Chart.js, but the steps are similar for other libraries.

Note that if you are integrating with amCharts, Highcharts, FusionCharts, or Google Charts, use our ready-to-use connectors that will automatically process the data. Check out the list of available tutorials.

Step 1. Add WebDataRocks to your project

Step 1.1. Complete the integration guide. Your code of the component with WebDatarocks should look similar to the following:

<script setup>
  import { Pivot } from "@webdatarocks/vue-webdatarocks";
  import "@webdatarocks/webdatarocks/webdatarocks.css";
</script>

<template>
  <div>
    <Pivot
     toolbar
    />
  </div>
</template>

Step 1.2. Create a report for WebDataRocks — connect to the data source and define which fields should be displayed in rows, columns, and measures:

<script setup>
  import { Pivot } from "@webdatarocks/vue-webdatarocks";
  import "@webdatarocks/webdatarocks/webdatarocks.css";

  const report = {
    dataSource: {
      filename: "https://cdn.webdatarocks.com/data/data.csv",
    },
    slice: {
      rows: [
        {
          uniqueName: "Country",
        },
      ],
      columns: [
        {
          uniqueName: "Measures",
        },
      ],
      measures: [
        {
          uniqueName: "Price",
          aggregation: "sum",
        },
      ],
    },
  };
</script>

<template>
  <div>
    <Pivot
     toolbar
     v-bind:report="report"
    />
  </div>
</template>

The fields you’ve specified in the report will be shown on the chart.

Step 2. Get a reference to the WebDataRocks instance

Some of WebDataRocks methods and events are needed to create a chart. Using a reference to the WebDataRocks instance, we can access WebDataRocks API.

Get the reference as follows:

<script setup>
  // ...
  import { ref } from "vue";
  
  const pivotRef = ref(null);

  // ...
</script>

<template>
  <div>
    <Pivot
     ref="pivotRef"
     toolbar
     v-bind:report="report"
    />
</template>

Now it’s possible to interact with the component through pivotRef.value.webdatarocks.

Step 3. Add a charting library

Note that this tutorial integrates WebDataRocks with Chart.js. The following steps may vary depending on your charting library.

Step 3.1. Install the npm package with your charting library. For example:

npm install vue-chartjs

Step 3.2. Import the charting library into your component:

import { PolarArea } from "vue-chartjs";
import {
  Chart as ChartJS,
  RadialLinearScale,
  ArcElement,
  Tooltip,
  Legend,
} from "chart.js";

ChartJS.register(RadialLinearScale, ArcElement, Tooltip, Legend);

Step 3.3. Add a container for your chart in the template of the component:

<PolarArea/>

Step 3.4. Create a reference to store the chart data:

const chartData = ref({
  labels: [],
  datasets: [
    {
      data: [],
    },
  ],
});

We will pass the data from the component to chartData in step 4.3.

Step 3.5. Pass the chart data to the chart container:

<PolarArea v-bind:data="chartData" />

Step 4. Integrate WebDataRocks with the charting library

In this step, we will use the getData() API call. This method was added especially for integration with third-part charting libraries; using it, you can request data from WebDataRocks and pass it to a charting library.

Step 4.1. If we call the getData() method before WebDataRocks is fully loaded, it will return an empty result. To know when WebDataRocks is ready to provide data for the chart, handle the reportcomplete event:

<script setup>
// ...

function reportComplete () {
  // Unsubscribing from reportcomplete
  // We need it only to track the initialization of WebDataRocks
  pivotRef.value.webdatarocks.off("reportcomplete");
  createChart();
}
</script>

<template>
  <div>
    <Pivot
     ref="pivotRef"
     toolbar
     v-bind:report="report"
     v-bind:reportcomplete="reportComplete"
    />
    <PolarArea v-bind:data="chartData" />
  </div>
</template>

Step 4.2. Implement the createChart() function using the getData() API call:

function createChart() {
  if (pivotRef.value) {
    pivotRef.value.webdatarocks.getData(
      {},
      // Function called when data for the chart is ready
      drawChart,
      // Function called on report changes (filtering, sorting, etc.) 
      drawChart
    );
  };
};

Step 4.3. Implement the drawChart() function specified in the previous step. This function will initialize the chart, set all the configurations specific to this chart type, and fill it with the data provided by WebDataRocks:

function drawChart(rawData) {
  const { labels, values } = prepareData(rawData); // This function will be implemented in step 5

  // Configuring the chart and filling it with data
  chartData.value = {
    labels: labels,
    datasets: [
      {
        data: values,
        backgroundColor: [
          "rgba(255, 99, 132, 0.5)",
          "rgba(54, 162, 235, 0.5)",
          "rgba(255, 206, 86, 0.5)",
          "rgba(75, 192, 192, 0.5)",
          "rgba(153, 102, 255, 0.5)",
          "rgba(255, 159, 64, 0.5)",
        ],
      },
    ],
  };
};

Step 5. Prepare the data

getData() API call returns an object (e.g., rawData) that contains the data from the grid and its metadata. The metadata includes a number of fields in rows and columns in the slice, an array of format objects, etc. Read more about the getData() response.

Here is an example of the rawData.data array returned by getData(). This data is based on the slice that was defined in the step 1.2:

data: [
  { v0: 6221870 }, // Grand total
  { r0: "Australia", v0: 1372281 },
  { r0: "France", v0: 1117794 },
  { r0: "Germany", v0: 1070453 },
  { r0: "Canada", v0: 1034112 },
  { r0: "United States", v0: 847331 },
  { r0: "United Kingdom", v0: 779899 }
]

This raw data now must be transformed to match the data format required by your charting library, in our case — Chart.js. Let’s create a function that will preprocess the data, for example, prepareData().

We are passing data to the data.labels and data.datasets properties, where data.labels contains field members and data.datasets contains values. Read more in the Chart.js documentation.

The prepareData() function for Chart.js will look similar to the following:

const prepareData = (rawData) => {
  const labels = rawData.data.filter((rec) => rec.r0 && rec.v0).map((rec) => rec.r0);
  const values = rawData.data.filter((rec) => rec.r0 && rec.v0).map((rec) => rec.v0);

  return { labels, values };
};

prepareData() must return an object with the data that can be used by your charting library. The example shows the returned object for Chart.js:

{ 
  labels: ["Australia", "France", "Germany", "Canada", "United States", "United Kingdom"], 
  values: [1372281, 1117794, 1070453, 1034112, 847331, 779899] 
}

Step 6. Run the project

Run your project with the following command:

npm run dev

Open http://localhost:5173/ in the browser to see how the pivot table looks in combination with Chart.js.

Try filtering the data, changing measures, and adjusting aggregation functions — the chart will be updated at once.

Check out the full code

After completing this tutorial, the full code of the integration should look as follows:

<script setup>
  import { Pivot } from "@webdatarocks/vue-webdatarocks";
  import "@webdatarocks/webdatarocks/webdatarocks.css";
  import { ref } from "vue";

  import { PolarArea } from "vue-chartjs";
  import {
    Chart as ChartJS,
    RadialLinearScale,
    ArcElement,
    Tooltip,
    Legend,
  } from "chart.js";

  ChartJS.register(RadialLinearScale, ArcElement, Tooltip, Legend);

  const pivotRef = ref(null);

  const chartData = ref({
    labels: [],
    datasets: [
      {
        data: [],
      },
    ],
  });

  const report = {
      dataSource: {
        filename: "https://cdn.webdatarocks.com/data/data.csv",
      },
      slice: {
        rows: [
          {
            uniqueName: "Country",
          },
        ],
        columns: [
          {
            uniqueName: "Measures",
          },
        ],
        measures: [
          {
            uniqueName: "Price",
            aggregation: "sum",
          },
        ],
      },
    };

  function reportComplete () {
    // Unsubscribing from reportcomplete
    // We need it only to track the initialization of WebDataRocks
    pivotRef.value.webdatarocks.off("reportcomplete");
    createChart();
  };

  function createChart() {
    if (pivotRef.value) {
      pivotRef.value.webdatarocks.getData(
        {},
        drawChart, 
        drawChart
      );
    };
  };

  function drawChart(rawData) {
    const { labels, values } = prepareDataFunction(rawData);

    chartData.value = {
      labels: labels,
      datasets: [
        {
          data: values,
          backgroundColor: [
            "rgba(255, 99, 132, 0.5)",
            "rgba(54, 162, 235, 0.5)",
            "rgba(255, 206, 86, 0.5)",
            "rgba(75, 192, 192, 0.5)",
            "rgba(153, 102, 255, 0.5)",
            "rgba(255, 159, 64, 0.5)",
          ],
        },
      ],
    };
  };

  const prepareDataFunction = (rawData) => {
    const labels = rawData.data.filter((rec) => rec.r0 && rec.v0).map((rec) => rec.r0);
    const values = rawData.data.filter((rec) => rec.r0 && rec.v0).map((rec) => rec.v0);

    return { labels, values };
  };
</script>

<template>
  <div>
    <Pivot
       ref="pivotRef"
       toolbar
       v-bind:report="report"
       v-bind:reportcomplete="reportComplete"
    />
    <PolarArea v-bind:data="chartData" />
  </div>
</template> 

See also

• getData()

This step-by-step tutorial will help you integrate WebDataRocks with amCharts. Our tutorial is based on the V4 of amCharts.

Supported chart types

WebDataRocks Connector for amCharts takes the data from the pivot table and prepares it according to the structure of an array of objects required by amCharts. As a result, any chart type will work with WebDataRocks Pivot Table.

Here is the list of demos that show how to integrate different chart types with WebDataRocks:

• Column Chart (demo)
• Line Chart (demo)
• Stacked Column Chart (demo)
• Bar Chart (demo)
• Clustered Bar Chart (demo)
• Stacked Bar Chart (demo)
• Radar Chart (demo)
• Bubble Chart (demo)
• Pie Chart (demo)
• Semi-circle Pie Chart (demo)
• Donut Chart (demo)
• Nested Donut Chart (demo)
• Radar Chart with switched axes (demo)

Follow the steps below to start creating interactive data visualizations.

Step 1. Add WebDataRocks to your project

Step 1.1. Complete the integration guide. Your code of the component with WebDatarocks should look similar to the following:

<script setup>
  import { Pivot } from "@webdatarocks/vue-webdatarocks";
  import "@webdatarocks/webdatarocks/webdatarocks.css";
</script>

<template>
  <div>
    <Pivot
     toolbar
    />
  </div>
</template>

Step 1.2. Create a report for WebDataRocks — connect to the data source and define which fields should be displayed in rows, columns, and measures:

<script setup>
  import { Pivot } from "@webdatarocks/vue-webdatarocks";
  import "@webdatarocks/webdatarocks/webdatarocks.css";

  const report = {
    dataSource: {
      filename: "https://cdn.webdatarocks.com/data/data.csv",
    },
    slice: {
      rows: [
        {
          uniqueName: "Country",
        },
      ],
      columns: [
        {
          uniqueName: "Measures",
        },
      ],
      measures: [
        {
          uniqueName: "Price",
          aggregation: "sum",
        },
      ],
    },
  };
</script>

<template>
  <div>
    <Pivot
     toolbar
     v-bind:report="report"
    />
  </div>
</template>

The fields you’ve specified in the report will be shown on the chart.

Step 2. Get a reference to the WebDataRocks instance

Some of WebDataRocks methods and events are needed to create a chart. Using a reference to the WebDataRocks instance, we can access WebDataRocks API.

Get the reference as follows:

<script setup>
  // ...
  import { ref } from "vue";
  
  const pivotRef = ref(null);

  // ...
</script>

<template>
  <div>
    <Pivot
     ref="pivotRef"
     toolbar
     v-bind:report="report"
    />
</template>

Now it’s possible to interact with the component through pivotRef.value.webdatarocks.

Step 3. Add amCharts

Step 3.1. Install the amCharts 4 npm package with the following command:

npm install @amcharts/amcharts4

Step 3.2. Import amCharts into your component:

import * as am4core from "@amcharts/amcharts4/core";
import * as am4charts from "@amcharts/amcharts4/charts";
import am4themes_animated from "@amcharts/amcharts4/themes/animated";

// Apply the imported theme
am4core.useTheme(am4themes_animated);

Step 3.3. In the template of the component, add a <div> container for your chart:

<template>
  <div>
    <Pivot
       ref="pivotRef"
       toolbar
       v-bind:report="report"
    />
    <div id="amchartsContainer" style="width: 100%; height: 300px"></div>
  </div>
</template>

Step 3.4. Create a shallowRef variable to store the chart:

import { ref, shallowRef } from "vue";
// ...

const chartRef = shallowRef(null);

Step 4. Show the data from the pivot table on the chart

Step 4.1. Import the WebDataRocks Connector for amCharts:

import "@webdatarocks/webdatarocks/webdatarocks.amcharts.js";

The Connector provides the amcharts.getData() method, which gets data from WebDataRocks and converts it to the format required by amCharts.

Step 4.2. If we call the amcharts.getData() method before WebDataRocks is fully loaded and ready to use, it will return an empty result. To know when WebDataRocks is ready to provide data for the chart, handle the reportcomplete event:

<script setup>
// ...

function reportComplete () {
  // Unsubscribing from reportcomplete
  // We need it only to track the initialization of WebDataRocks
  pivotRef.value.webdatarocks.off("reportcomplete");
  createChart();
}
</script>

<template>
  <div>
    <Pivot
     ref="pivotRef"
     toolbar
     v-bind:report="report"
     v-bind:reportcomplete="reportComplete"
    />
    <div id="amchartsContainer" style="width: 100%; height: 300px"></div>
  </div>
</template>

Now the chart will be created only when the data is loaded and the report is ready.

Step 4.3. Implement the createChart() function. This function will use the amcharts.getData() method from the Connector:

function createChart() {
  pivotRef.value.webdatarocks.amcharts.getData(
    {},
    // Function called when data for the chart is ready
    drawChart,
    // Function called on report changes (filtering, sorting, etc.) 
    drawChart
  );
}

Step 4.5. Create a function to draw the chart. drawChart() initializes the chart, sets all the configurations specific for this chart type, and fills it with the data provided by WebDataRocks:

function drawChart(chartConfig, rawData) {
  // Create chart instance
  // The selector must match the id of the <div> container for the chart
  let chart = am4core.create("amchartsContainer", am4charts.XYChart);

  // Add data processed by Flexmonster to the chart
  chart.data = chartConfig.data;

  // Create category axis for a column chart
  let categoryAxis = chart.xAxes.push(new am4charts.CategoryAxis());
  categoryAxis.dataFields.category = pivotRef.value.webdatarocks.amcharts.getCategoryName(rawData);
  // Create value axis
  chart.yAxes.push(new am4charts.ValueAxis());
  // Create and configure series
  let series = chart.series.push(new am4charts.ColumnSeries());
  series.dataFields.categoryX = pivotRef.value.webdatarocks.amcharts.getCategoryName(rawData);
  series.dataFields.valueY = pivotRef.value.webdatarocks.amcharts.getMeasureNameByIndex(rawData, 0);

  chartRef.value = chart;
}

Learn more about the amcharts.getCategoryName() and amcharts.getMeasureNameByIndex() API calls.

Step 4.6. Set the autoDispose global option to true. As a result, the chart will be automatically disposed when it is necessary:

am4core.useTheme(am4themes_animated);
am4core.options.autoDispose = true;

Step 5. Run the project

Run your project with the following command:

npm run dev

Open http://localhost:5173/ in the browser to see an interactive dashboard with WebDataRocks and FusionCharts. The column chart shows the data from WebDataRocks and reacts instantly to any changes in the report.

Learn more about available chart configurations in the amCharts Documentation.

Check out the full code

After completing this tutorial, the full code of the component should look as follows:

​​<script setup>
  import { Pivot } from "@webdatarocks/vue-webdatarocks";
  import "@webdatarocks/webdatarocks/webdatarocks.css";
  import { ref, shallowRef } from "vue";

  import * as am4core from "@amcharts/amcharts4/core";
  import * as am4charts from "@amcharts/amcharts4/charts";
  import am4themes_animated from "@amcharts/amcharts4/themes/animated";

  import "@webdatarocks/webdatarocks/webdatarocks.amcharts.js";

  am4core.useTheme(am4themes_animated);
  am4core.options.autoDispose = true;

  const pivotRef = ref(null);
  const chartRef = shallowRef(null);

  const report = {
    dataSource: {
      filename: "https://cdn.webdatarocks.com/data/data.csv",
    },
    slice: {
      rows: [
        {
          uniqueName: "Country",
        },
      ],
      columns: [
        {
          uniqueName: "Measures",
        },
      ],
      measures: [
        {
          uniqueName: "Price",
          aggregation: "sum",
        },
      ],
    },
  };

  function reportComplete () {
    pivotRef.value.webdatarocks.off("reportcomplete");
    createChart();
  }

  function createChart() {
    pivotRef.value.webdatarocks.amcharts.getData(
      {},
      drawChart, 
      drawChart
    );
  }

  function drawChart(chartConfig, rawData) {
    let chart = am4core.create("amchartsContainer", am4charts.XYChart);

    chart.data = chartConfig.data;

    let categoryAxis = chart.xAxes.push(new am4charts.CategoryAxis());
    categoryAxis.dataFields.category = pivotRef.value.webdatarocks.amcharts.getCategoryName(rawData);
    chart.yAxes.push(new am4charts.ValueAxis());
    let series = chart.series.push(new am4charts.ColumnSeries());
    series.dataFields.categoryX = pivotRef.value.webdatarocks.amcharts.getCategoryName(rawData);
    series.dataFields.valueY = pivotRef.value.webdatarocks.amcharts.getMeasureNameByIndex(rawData, 0);

    chartRef.value = chart;
  }
</script>

<template>
  <div>
    <Pivot
     ref="pivotRef"
     toolbar
     v-bind:report="report"
     v-bind:reportcomplete="reportComplete"
    />
    <div id="amchartsContainer" style="width: 100%; height: 300px"></div>
  </div>
</template>

See also

• amcharts.getData()
• getData()

If you want to pick up where you left while working with the previous report, you can load it into the pivot table:

• Via the Toolbar
• Programmatically

Loading the report via the Toolbar

To load a local report

• Go to the Open tab () on the Toolbar.
• Select Local report.

To load a remote report

• Go to the Open tab () on the Toolbar.
• Select Remote report.
• Enter the URL of the remote report.

Loading the report programmatically

Load your report while embedding WebDataRocks by specifying a path to your report file:

<script setup>
  import { Pivot } from "@webdatarocks/vue-webdatarocks";
  import "@webdatarocks/webdatarocks/webdatarocks.css";
</script>

<template>
  <div>
    <Pivot 
      report="https://cdn.webdatarocks.com/reports/report.json"
      toolbar
    />
  </div>
</template>

WebDataRocks Toolbar is designed to make your web reporting experience easier and more convenient. You can customize the Toolbar in the following ways:

• Add new custom tabs.
• Remove the tabs you don’t need.
• Reorder/change the existing tabs.
• Set your custom icons for tabs.

Important Before customizing the Toolbar, ensure it is enabled.

Let’s see how to customize the Toolbar in practice.

All customization can be made using the beforetoolbarcreated event which is triggered before the Toolbar is created. The event’s handler will be responsible for changing the Toolbar.

How to remove tabs

Step 1. Assign a handler (e.g., customizeToolbar) to the beforetoolbarcreated event. The handler function has the toolbar as a parameter that contains information about the Toolbar.

Step 2. Inside the handler (e.g., customizeToolbar), retrieve all the tabs using the toolbar.getTabs() method. It returns an array of objects that describe tabs’ properties.

Step 3. To remove a tab, delete a corresponding object from the array of tabs.

Your code should look similar to the following example:

<script setup>
import { Pivot } from "@webdatarocks/vue-webdatarocks";
import "@webdatarocks/webdatarocks/webdatarocks.css";

const customizeToolbar = (toolbar) => {
  // Get all tabs from the Toolbar
  let tabs = toolbar.getTabs();
  if (tabs.length > 0) {
    // Delete the Connect tab
    tabs = tabs.filter(tab => tab.id !== "wdr-tab-connect");
  }
  toolbar.getTabs = () => tabs;
};
</script>

<template>
 <div>
  <Pivot
    toolbar
    :beforetoolbarcreated="customizeToolbar"
  />
 </div>
</template>

How to add new tabs

Step 1. Assign a handler (e.g., customizeToolbar) to the beforetoolbarcreated event. The handler function has the toolbar as a parameter that contains information about the Toolbar.

Step 2. Inside the handler (e.g., customizeToolbar), retrieve all the tabs using the toolbar.getTabs() method. It returns an array of objects that describe tabs’ properties.

Step 3. Add a new tab to the array of tabs.

Your code should look similar to the following example:

<script setup>
import { Pivot } from "@webdatarocks/vue-webdatarocks";
import "@webdatarocks/webdatarocks/webdatarocks.css";

const customizeToolbar = (toolbar) => {
  // Get all tabs from the Toolbar
  let tabs = toolbar.getTabs();
  toolbar.getTabs = () => {
    // Add a new tab
    tabs.unshift({
      id: "fm-tab-newtab",
      title: "New Tab",
      handler: yourCustomFunction,
      icon: toolbar.icons.open,
    });
    return tabs;
  };
};

const yourCustomFunction = () => {
  // Your functionality
}
</script>

<template>
 <div>
  <Pivot
    toolbar
    :beforetoolbarcreated="customizeToolbar"
  />
  </div>
</template>

Even more advanced customization

In this guide, we mentioned how to remove the Toolbar tabs and how to add a new one. For further customization, you can reorder the tabs, set the custom icons for the tabs, or implement new functionality. We recommend investigating the existing code to understand how the Toolbar works:

1. Open the source code of the Toolbar – webdatarocks.toolbar.js file.
2. Find a prototype of the toolbar.getTabs() method.
3. Investigate how this method works.

You can also change the appearance of the Toolbar by changing the component theme.

See also

• Language localization
• Changing component theme
• Conditional formatting

We use only open-source components in WebDataRocks. Here is a list of these libraries and links to their respective licenses:

• html2canvas v1.0.0-rc.1 – MIT License
• FileSaver.js v1.3.1 (modified by Flexmonster) – MIT License
• jsPDF v1.5.3 – MIT License
• JSZip v3.1.5 – MIT License

See also

• License agreement

This page contains information about browsers compatible with WebDataRocks.

Starting from version 1.4, WebDataRocks follows the ES6 standard. As a result, the component will work correctly only in browsers that fully support ES6. You can find these browsers and their versions in the below table:

*To work with WebDataRocks in Internet Explorer, use WebDataRocks versions earlier than 1.4. The component’s version history is available on npm. See how to install a specific package version from npm.

WebDataRocks allows visualizing the data from the pivot table using 3rd-party charting libraries. Feel free to use the following tutorials:

• Integration with amCharts
• Integration with Highcharts
• Integration with Google Charts
• Integration with FusionCharts
• Integration with any charting library