Using Displayr
| 31 January 2017 | by Tim Bock

Using Displayr to Filter Data, Analyses, and Whole Reports

Gague and Sankey plot in Displayr

Displayr’s approach to filtering is uniquely powerful. Filters can be applied to tables, charts, complex visualizations, advanced analyses, and text (with a bit of care). You can even filter an entire report, which means that filters are great when implementing automated reporting.

 


 

Example: Filtering Google NPS Data

Net Promoter Score (NPS) is a metric for evaluating the popularity of a brand.  The screenshot below shows a dashboard of Google’s NPS among men.  The cool thing about it is that this dashboard was created for the entire database, men and women combined.  Once it was finished, a single mouse click applied the Males filter to the whole page.  The charts and title updated and the decision tree was re-estimated.  You can view the original dashboard in Displayr and collapse, expand and zoom in and out of the decision tree to see it better.

googlenpsmales

Here is the same dashboard again. This time it has been filtered to show the data for Females. As you can see, all the numbers and the decision tree have changed.

googlenpsfemales

 


 

How to apply a filter in Displayr

  1. Select whatever you want to filter. In the example above, I am selecting the entire page (from the list of pages at the top-left of the screen in Displayr.
  2. On the Home tab in the ribbon, select Data Selection > Filter.
  3. Choose the filter or filters that you want.

applyingfiter

When choosing your filters, note that filters are grouped according to their variable set. Where two filters are in the same variable set, if they are both selected, it is treated as an OR operation. For example, if you were to choose Detractor and Passive from the list above, the data would be filtered to show people that are either Detractors or Passives. By contrast, where filters are in different variable sets, they are treated as AND operations. Thus, if you were to select Males and Females in the example above, the filtered data would contain nobody (as nobody is both male and female, in this data example).

You can examine this example in more detail by inspecting it in Displayr.

To learn more about all the different ways of creating filters, read the blog post 5 ways to create a filter in Displayr.

Author: Tim Bock

Tim Bock is the founder of Displayr. Tim is a data scientist, who has consulted, published academic papers, and won awards, for problems/techniques as diverse as neural networks, mixture models, data fusion, market segmentation, IPO pricing, small sample research, and data visualization. He has conducted data science projects for numerous companies, including Pfizer, Coca Cola, ACNielsen, KFC, Weight Watchers, Unilever, and Nestle. He is also the founder of Q www.qresearchsoftware.com, a data science product designed for survey research, which is used by all the world’s seven largest market research consultancies. He studied econometrics, maths, and marketing, and has a University Medal and PhD from the University of New South Wales (Australia’s leading research university), where he was an adjunct member of staff for 15 years.


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You can examine this example in more detail by inspecting it in Displayr.

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