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Line And Area Charts

With Popily, you focus on the relationships in your data that you care about. Popily does the heavy lifting to give you beautiful, interactive, best-practice visualizations of that relationship. in your data that you care about, and Popily the heavy lifting to return beautiful, interactive, best-practice visualizations of that relationship. On the previous page we looked at the relationship between sales, product categories, and types of customers. Now, let us look at average sales and date. All you need to do is change the columns you care about to sales and order date and Popily's analytic engine does the rest.

var insightOptions = {
    source: 'sales-data',
    columns: ['Sales', 'Order Date'],
    calculations: [
      {
        column: 'Sales',
        calculation: 'average'
      }
    ]
};

popily.chart.getAndRender('#chart-1', insightOptions);

Popily performed the calculations necessary, restructured the data as required, and returned a chart visualization the requested relationship. But what happens when we add a new column, perhaps customer segment? Again, all it takes is adding the column we care about to our request:

var insightOptions = {
        source: 'sales-data',
        columns: ['Sales', 'Order Date','Customer Segment'],
        calculations: [
          {
            column: 'Sales',
            calculation: 'average'
          }
        ]
    };

    popily.chart.getAndRender('#chart-2', insightOptions);

And if we want a different relationship? Say, the sum total of sales? We just ask Popily for that relationship and the analytics engine intelligently determines the best visualization for that relationship and creates it.

var insightOptions = {
    source: 'sales-data',
    columns: ['Sales', 'Order Date','Customer Segment'],
    calculations: [
      {
        column: 'Sales',
        calculation: 'sum'
      }
    ]
};

popily.chart.getAndRender('#chart-3', insightOptions);

Notice a difference between the two charts above? Popily's analytics engine recognized the need to run a different calculation, and more importantly, to restructure the data appropriately. This gives you a different type of visualization that best describes the relationship.

What happens if we only care about the relationship when average sales are greater than $1500? Just tell Popily's analytics engine and see that relationship visualized.

var insightOptions = {
    source: 'sales-data',
        columns: ['Sales', 'Order Date','Customer Segment'],
    calculations: [
      {
        column: 'Sales',
        calculation:  'average'
      }
    ],
    transformations: [{
        column: 'Sales',
        op: 'gt', // Greater than
        values: ['1500']
        }
    ]
};

popily.chart.getAndRender('#chart-5', insightOptions);

As you can see, Popily's relational analytics engine allows users to focus on finding the relationships they care about rather than wasting time and energy on converting that relationship into a best practice visualization. With Popily, ask for the relationship you care about and receive a visualization of that relationship.

This is interesting, but Popily analytics engine can do much more.