Using Data Grid (Table) Widget Plot Types: Best Practices

What is a Data Grid Widget Plot Type?

The data grid is a type of data widget that produces a table display of the data (see the image below).

When creating a data grid widget, you are given five plot type style options. Each result in a different visual effect for the given data:

1. Default
2. Heat Map
3. Classic
4. Conditional Map
5. Grouped Column

The remainder of this article discusses which use cases are best suited to which plot type. To learn about styling the grid/table widget, see How to Style the Grid/Table Widget.

Best Use Cases for Each Plot Type

Default

The Default plot is a new data grid style with options for font size, font color, font style, font weight, grid display/hide, wrap text, and alternating row colors and backgrounds. Use this by default when you have no particular table style requirements.

Heat Map

The Heat Map grid style enables users to select a background color for each column in order to display the dataset's metric values in a range of gradations. You can see in the images below the effect of toggling on the gradient option. Use this to get a sense of relative values within a column.

Off

On

Classic

The Classic plot is TapClicks' grid style for the old UI. It is preserved for backward compatibility, which is the only reason to use it.

Conditional Map

The Conditional Map plot is a data grid style with the ability to apply color to a cell depending on whether the numerical value in the cell meets a specified logical condition (greater than a number, less than a number, or between two numbers).  You can see in the image below, where a cell meets the logical condition, there is a user-specified color. Use this to quickly see which cells meet a certain condition.

Grouped Column

Grouped Column plot is a new data grid style with the ability to create multiple groupings of metrics by column and label them. Grouped Columns are styled with different background colors for better legibility and emphasis. Use this when you want to quickly zero in on multiple columns.