Visualizations reference

Imply Polaris provides a wide array of expressive visualizations to enable fast, interactive exploration of your data. Visualizations are not just static charts. You can interact directly with them by clicking on various graphical elements to select data segments for drill-down and deeper analysis.

When you change the shown dimensions, Polaris chooses the best visualization for the selected dimensions automatically.

To choose the visualization you prefer, click on the current visualization type to the right of the filter and split bars. The following example shows Table as the current visualizaton:

The following sections describe the available visualizations.

Overall

Totals is the default visualization. It presents a numeric summary of the currently selected measures, applying any selected filter criteria.

You can select multiple measures and show a time range comparison as well, as shown in the following screenshot:

Table

The table visualization is the default view for a categorical (string) dimension. It presents a table view of the data with shading that aids visualization of measure magnitude.

The table visualization can show multiple dimensions, as well as multiple measures, as columns. You can choose the columns using the Show field dropdown menu:

You can add as many dimensions and measures as you want by adding them to the Rows, Columns, or Measures fields in the right sidebar. To reorder nested rows or columns, click and drag individual dimension or measure names in the right sidebar to move them.

Line chart

The line chart is the natural visualization to show temporal dimensions. This is the perfect visualization to demonstrate a trend over time.

The line chart also has the ability to compare the segments of the first shown dimension to each other over time.

Sparkline

Rather than showing lines for multiple dimensions on a single chart, the sparkline visualization shows multiple line charts, one for each dimension.

Vertical bars

The vertical bars visualization shows each dimension in vertically-oriented time buckets.

Horizontal bars

The horizontal bars visualization shows each dimension in horizontally-oriented time buckets.

Heatmap

The heatmap is a visualization that shows two dimensions as a 'matrix'. The heatmap visualization is similar to the grid visualization and it works particularly well when one or both of the dimensions are continuous.

Spot matrix

The spot matrix shows the distribution of events on a two dimensional matrix using circles, with larger circles representing a greater number of events.

Treemap

The treemap illustrates how the values of a dimension combine to make up the whole. It is particularly suitable for hierarchical dimensions.

Sunburst

The sunburst, also known as a donut or a pie chart with one split, is a visualization that represents the ratios between the values of a dimension. When rendering multiple dimensions, each is subdivided to show proportional representation.

Geo marks

The geo marks visualization is the natural choice for dimensions that represent geographically encoded data. It can work with country encoded data. To use geo-oriented visualizations, modify the data cube configuration to make the country encoded data to be Geo type.

Geo shade

The geo shade visualization, also known as a Choropleth map, is another way to represent geographically encoded data. To use geo-oriented visualizations with country encoded data, set the data type to Geo in the data cube configuration settings.

Stack area

The stacked area chart lets you see the overall trend, while also showing contributions of individual dimensions. Unlike the line chart, the area chart lets you see what the total of values added together.

Records

The records visualization shows the raw data underlying the data cube, allowing you to see all dimensions that are in each record. This view can be useful for certain debugging issues. You can also view your records in groups for specific values of one or more dimensions by adding the dimensions to the show bar.

Sankey

The Sankey visualization shows the flow from one set of values to another. In Sankey diagrams, the width of each connection is proportional to the flow volume between the values.