The Death of Flash SAP XCelsius was the Ford Fiesta of the BI industry. Inexpensive to run, simple to implement. Businesses could get dashboards up and running in days or hours. We’ve helped clients design over 1,000 XCelsius dashboards over time, so we know how practical and adaptable they were.
But then the industry went and killed Flash. Old XCelsius dashboards, which worked perfectly well last month and posed no threat to anyone’s IT security, are now downloaded and treated as high-risk by browsers, or blocked outright.
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Happy New Year to one and all! As data scientists/analysts/researchers/programmers/anything else on that crazy data science Venn diagram, I’m assuming all of our new years resolutions involve visualising our data with more sophistication and finesse. So with that in mind, I thought it was high time for a post about the joys of modularizing your shiny app code.
New Year, new improved workflows with emphasis on efficiency & reproducibility, amiright?
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A Dive Into Some Global Flooding Data
I always like to keep a look out for interesting open data sets. One great resource for such things is Jeremy Singer-Vine’s Data is Plural weekly newsletter that brings together a collection of “useful, curious datasets” for us all to enjoy and wrangle with.
One that cropped up last week was The Dartmouth Flood Observatory’s Global Archive of Large Flood Events.
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Background I really liked this blogpost by Peter Ellis that was recently brought to my attention by everyone’s favourite #rstats tweeter, Mara Averick:
???? code-through: “Inter-country inequality and the World Development Indicators” by @ellis2013nz https://t.co/zIjgqjPqKc #rstats #dataviz pic.twitter.com/h1sUfO2PPJ — Mara Averick (@dataandme) July 22, 2017 In the post, Peter recreates some of the charts from Branko Milanovic’s highly acclaimed book ‘Global Inequality: A New Approach for the Age of Globalization’ using World Development Indicator data from the World Bank.
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TL;DR If you’re looking for a tool to scrape all the posts in facebook page/group with a link and have the data presented to you in a searchable, filterable table then check out the shiny app I made for this purpose by clicking on the image below (very niche market, I know).
If however, that’s not why you’re here, and would like to look at some interesting ways of visualising social media data (or any kind of events over time data), please read on.
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UPDATE: We’ve just done an update to this tutorial on our new blog using R Spatial’s new simple features package! It’s a much nicer workflow than the one below so head over to Multivariate Dot-Denisty Maps in R with sf and ggplot2 now to check it out. Cheers!
Background I recently came across Eric Fisher’s brilliant collection of dot density maps that show racial and ethnic divisions within US cities.
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