Imply provides a wide array of expressive visualizations that enable fast, interactive exploration of your data. The visualizations are not just static charts. You can interact directly within them by clicking on various graphic elements to select data segments for drill-down and deeper analysis.
When you change the dimension splits, Imply will automatically guess the best visualization for the selected dimensions.
You can you choose the visualization you prefer by clicking on the button highlighted in the image below, which is located to the right of the filter and split bars.
The visualizations are described below.
This is the overview visualization that’s shown when there is no dimension selected to be shown. It presents a numeric summary of the currently selected measures, applying any selected filter criteria.
The table visualization is the default to show 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.
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. Note the different colored lines in the chart below.
The Grid table is a visualization that allows you to compare two dimensions to each other.
The bar chart is selected for numeric visualizations. It naturally acts as a histogram - showing the frequency distribution of occurrences over numeric buckets.
The heatmap is a visualization that shows two dimensions as a 'matrix'. It works particularly well when one (or both) of the dimensions are contentious.
The sunburst (also known as the donut/pie chart with only one split) is a visualization that represents the ratios between the values of a dimension.
When there are multiple dimensions being rendered each subdivides its slice to show proportional representation.
The map visualization is the natural choice for dimensions that represent geographically encoded data.