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Create Visualizations and Looks

Analytics makes it easy to create graphics and charts based on the results of a query. This tutorial introduces you to Analytics’s data visualizations. When you finish, you’ll be able to create and configure charts.

Quick Guide

You can add an eye-catching chart to any query result set on the Explore. This page shows how to create visualizations that best show off your data.

  1. Create and run your query.
  2. Click the Visualization tab to start configuring your visualization options.
  3. Select the type of visualization that best displays your data. For more options, click to the right of the displayed visualization options.
  4. Click Edit to configure the visualization option settings, such as naming and arranging chart axes, choosing the position and type of each data series, or modifying the chart color palette.

You can further customize your visualization by specifying which dimensions and measures you want to be included in the visualization. If your data is missing key values, you can tell Analytics to fill in those values on the appropriate part of your visualization.

Your Good Looks Should Look Good!

The Explore page in Analytics lets you immediately add an eye-catching chart to any query result set. Analytics keeps query details and visualization configuration data together, so when you share a query, people get the picture as well as the data.

Let’s go with an ecommerce store example, and create a graphic for a sales dashboard. We’ll query the total items sold (ORDER ITEMS Count), grouped by date (ORDERS Created Date) and pivoted on item category (PRODUCTS Category Name). The query is shown below. We filter by ORDERS Created Date and PRODUCTS Category Name, to limit our results to 90 days of sales for a set of categories that are interesting to the business.

Visualizations Bring Data to Life

On the Explore page, click the Visualization tab to configure visualization options for the current query. Use the chart buttons to pick the visualization that’s right for the data.

You can see a list of Analytic's visualization options on the Visualization Types page. From there, click on a visualization type to see details about all of that visualization type’s settings.

Fine-Tuning Your Visualizations

Learn how to customize your visualizations with the Analytics features below.

Customizing Visualizations with Chart Settings

You can customize a visualization to make the data more readable and to add visual styling. Click Edit to see the visualization options, then change the settings to get a result that suits you.

To see the visualization options available for a particular visualization type, click that type on the Visualization Types documentation page.

The example below shows some of the visualization settings chosen for an area chart with stacked series.

Including Multiple Visualization Types on a Single Chart

You can also create charts that include more than one visualization type:

  1. Click the Edit button to show the chart customization options.
  2. Click the Series tab.
  3. In the Customizations section, you’ll see an entry for each series in the chart. Click the arrow next to the series you want to change to display its customization options.
  4. In the Type box, select the type of visualization to use for that series.

Charts with multiple series types always layer line and scatter series in front, then they layer area, column, and bar series.

You can alter the layering order of column, bar, and area series by changing the series’ positions in the data table and clicking the Run button. The leftmost series will layer on top and the rightmost series will layer on bottom.

Creating Stacked Charts with Multiple Visualization Types

You can include stacked series in a chart with multiple visualization types. All the series of the same type as the chart overall will be stacked together; series of other types will not stack. For example, the chart below is a column chart, so the columns stack, but the line series do not stack.

To create a stacked chart that uses multiple y-axes, drag any series to a different axis in the Y menu. The stacked series will appear together, but all other series can be moved independently, including individual series within a pivot.

Filling in Missing Dates and Values

Some data sets have values, such as dates, that follow a predictable pattern. A user might pull data by a time frame and find that some dates, weeks, months, or other date types don’t have any corresponding value. By default, the data table and the visualization will display dates returned from the query and skip any dates that are missing. Analytics’ “dimension fill” option lets you display the missing dates or other values in the data table and on the corresponding axis of the query’s visualization. This option is found in the dimension’s gear menu in the Data section of an Explore.

For example, this accident data from 1990 shows only a few dates in which an accident occurred:

If you do not dimension fill, Analytics connects the data points it has, resulting in a potentially misleading graph:

Turning on dimension fill adds the missing dates and makes the graph more informative:

To use dimension fill simply choose the Fill in Missing Dates or Fill in Missing Values option from the gear menu of the appropriate dimension:

Dimension fill is available for dimensions with yes/no values and most date types. It can also be applied to any dimension based on a list of values.

Dimension fill will turn on automatically for queries that run with a single dimension and/or a single pivot, just as long as you haven’t applied filters to any measures.

There are a few cases when you will not be able to dimension fill:

  • Dimensions that have a filter applied to them and also have a fixed number of values, such as yes/no, days of the week, days of the month, etc.
  • Drilling into a pivoted dimension.

Configuring Visualizations to Add Polish

Customizing Visualizations with Chart Settings

Let’s go with a stacked bar chart to show the SLA fulfillment over time. We can customize it to make the data more readable and to add visual styling. Click the Edit button to configure the visualization parameters, then play around to get a result that suits you.

Specifying Fields to Include in the Visualization

All dimensions and measures are automatically added to any visualizations you use. However, sometimes you won’t want to display every dimension or measure in the chart. In the example below, note that the measure SHIPMENTS % is displayed, but not in the visualization:

To hide a column from the visualization, select the gear icon at the top right corner of the column, then select Hide from Visualization:

This will hide the column from the visualization. Table calculations can also be hidden from a visualization, as described on the Creating Table Calculations page.

Drilling Into Visualizations

Analytics makes it possible to “drill” into the data on a visualization, to get more specific information about a specific data point. To do so, simply click on the part of the visualization about which you’d like more information.

You can choose which type of drill to perform when you click on the item of interest. In the example below, we’re clicking on the “Search” slice of the “Female” donut chart on the left. Doing so gives you two different options. You can choose to see row level data for all 1,438 females who found us via search, or you can choose to filter the visualization to females only.

If you chose the Filter on “Female” option, you’d arrive at a visualization like this one:

The options that appear to you will change depending upon the data and visualization you’re using.

Viewing Visualizations on Mobile Devices

When viewing a visualization on a mobile device, Analytics has the following touch options to make it easier to view information about your data:

  • Tap a data point on the visualization to show information about that data point.
  • Press and hold a data point to drill into the data behind the data point.
  • Press and drag across the visualization to show information about each data point as you move over them.

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