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9. Time Series

In this Session…

Before you begin…

  • Using Filters to display and animate time series.

  • Using the f(x) Transform to reformat data.

  • Using Parametric and Geometric layouts and the Quick Layout menu with Filters.

Download the starting data view and snapshots library:



How To GraphXR 9. Time Series


Before You Begin…

Ideally, you’ll have worked through Module 8. Layouts and Pinning. If you’re starting here, and you want to follow along, you’ll need to:

  • Log in to GraphXR, create a Project, and open its graph space.

  • Download starting data (HowTo_09_START.graphxr and the snapshot library HowTo_09_START.graphxrsnapshots) for this module and drag and drop it onto the graph space.  


With the data loaded we’ll look at changes over time in the television series. Open an Episodes Table to see that our Episodes nodes include an episodeAirDate, as well as properties like millionViewers and totalLines.


To visualize time series, we’ll use geometric or parametric layouts to order and caption nodes by date-time property values, then use filters to select data from specific time periods and animate changes over time.


First we’ll lay out Episodes nodes in a grid arranged by episodeAirDate in ascending order. In the legend, click the Episodes category to select all its nodes, and open the Geometric layout panel.


Click Order by Property, choose Episodes and episodeAirDate and click Ascend
Then click Grid. Use left-click drag to move the selected grid, and Expand on the Quick Layout menu (or right-click Layout menu) to expand it.


Next we’ll scale the nodes by the millionViewers property value, and set episodeAirDate values as captions. Click the Episodes colored dot in the legend to open the style setting dialog. In the Size tab select millionViewers.


In the Caption tab, select episodeAirDate, and dismiss the style setting dialog. Zoom in to see that the captions and sizes are applied.


For visual clarity, we’ll lay out Characters nodes in a spiral and House nodes in a line sorted by houseName.


Now ctrl+left click in the legend to select both Character and House categories and use left click+ drag to move those nodes away from the Episodes grid.


Now open the Filter panel and select episodeAirDate in the Node Property menu to filter Episode nodes by date. Notice that we can apply multiple filters at once for any date, numerical, or categorical (string) property. We’ll filter on one set of property values for now.


Let’s filter on a 2-month date range. Use the handles to set a range, or click the
Min or Max fields to select a precise date and time. Select Lock Range to filter data in 2-month intervals.


Scrub through the values using the handles, or use the playback controller to play through values or a locked range. The live chart shows the distribution of nodes as you play through the timeline. Let’s click the Chart checkbox to hide it.


We use the filter’s playback controller to animate our timeline filter. We can set the duration and direction of play, and play the animation on continuous loop. To share the animation with other users, go to the Project=>Data tab and save a view.a View.


Click the trash can icon to clear a filter and display all the data again. Notice that if you first click Select Visible Nodes, filtered nodes remain selected.


This is useful when you want to temporarily tag or hide filtered nodes, or invert the filtered data.


Now let’s build a time series by filtering a parametric layout. Open the Layout=>Parametric tab.  Select episodeAirDate and millionViewers for X and Y axes and totalLines for the Z axis, then click Apply.


Next we apply a filter to isolate nodes with specific date property values. 
Open the Filter panel and select episodeAirDate in the Node Property menu. 


Manually scrub through the values using the timeline handles, or use the playback controller to animate the view. Again, to share the animation with other users, go to the Project=>Data tab and save the graph as a view.


Next, in Module 10. Algorithms, we’ll look at finding paths and measuring the degree of connectedness or grouping of nodes in a graph.

Next Steps…

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