Four tips for creating interactive visualisations with Shiny

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I’ve recently presented a toy Shiny app at the Edinburgh Data Visualization Meetup to demonstrate how Shiny can be used to explore data interactively. In my code-assisted walkthrough, I began by discussing the data used: a set of records detailing customer purchases made on Black Friday (i.e., each customer was given a unique ID, which was repeated in long format in the case of multiple purchases). Both the customers and the items purchased are described along various dimensions (e.

Dealing with many dimensions in historical data: Tracking cooperation & conflict patterns over space and time in R

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For this post, I’ve managed to find some extremely interesting historical event data offered by the Cline Center on this page. As you will see, this dataset can be quite challenging because of the sheer number of dimensions you could look at. With so many options, it becomes tricky to create visualisations with the ‘right’ level of granularity: not so high-level that any interesting patterns are obscured, but not too detailed and overcrowded either.

LA maps of crime: Using R to map criminal activity in LA since 2010

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I’ve recently come across — a huge resource for open data. At the time of writing, there are close to 17,000 freely available datasets stored there, including this one offered by the LAPD. Interestingly, this dataset includes almost 1.6M records of criminal activity occurring in LA since 2010 — all of them described according to a variety of measures (you can read about them here). Using information like the date and time of a crime, its location (longitude & latitude), and the type of crime committed (among other things), you can come up with some pretty interesting visualizations.