Introduction to Displayr 6: Automated and Reproducible Reporting

Introduction to Displayr 6: Automated and Reproducible Reporting

This is the final blog post in our series introducing Displayr’s functionality. It focuses on reporting.



Creating a report by exporting the document

A Displayr document is in and of itself a report accessible by yourself and the team you work with. To share this with people outside your team, you can export the document.  A range of options for exporting are available on the Export tab of the ribbon.

Displayr supports the four main formats used in reporting: Excel, PowerPoint, PDF, and exporting as a web page (i.e. a dashboard). See Using Displayr to filter data, analyses, and whole reports for an example of a simple dashboard.



Exporting parts of a document

You can export single outputs, such as a chart or a table, as an Image. They can also be included in HTML documents if you choose to Export > Embed them. Once embedded, you can press Export > Re-Publish to update them with refreshed data.



A reproducible report for “free”

If you have read through the previous five posts in this series, you will know that one of the key benefits of Displayr is that you are building a report at the same time as you are conducting your analysis. Considerable time savings result from omitting the whole stage of converting analysis outputs into pretty charts and tables.

A less obvious, but in some ways more important, benefit is getting a free reproducible report as a side-effect. That is, the final document is directly linked back to the data which eliminates the chance of data transcription errors. This reproducibility is automatic. You don’t need to do anything special. There is no need to diligently record all your steps or document your work processes. Displayr remembers everything and it is really smart about how it does it. Rather than log each step performed, Displayr instead tracks the changes made relative to any raw data that was imported.

Because of the way that Displayr works, you can import revised data, and Displayr will automatically re-perform your calculations on the new data. You can even get Displayr to reword text-based conclusions too, if you don’t mind getting your hands dirty with a bit of programming.



Automatic updating

Displayr documents are designed to be automatically updated with new data. For additional background information, read Introduction to Displayr 2: Getting your data into Displayr.



“Manual” updating

If you click on a data set in the Data tree and press Update in the object inspector on the right of the screen, you can choose a new data file. All the calculations that depend on this data set will automatically update along with charts and tables.



Updating via URLs

If you wish to have your data updated at regular intervals, you first save your data somewhere on the web (e.g. using Dropbox: you would need to have a process in place that updates your file in your chosen online location).  Then, in Displayr, you provide the URL and schedule a time interval for updating (Insert > Data > Data Set > URL).



Updating via SQL

SQL can be used as an alternative to a URL. This allows a fully automated process using SQL queries, and so does not require any manual steps. Because SQL does not support metadata, this method is recommended for use with data that doesn’t rely on large volumes of metadata (e.g. survey data).



Updating via R

If you insert message(“R output expires in 3600”)  into any R code in Displayr, it will cause the R code to recompute every 3600 seconds (i.e., hour). You can change this to other values. See the wiki for more information.

About 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, 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.