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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Synopsis Market Research is great at compiling the right data, but not so good at making it easy to use. This isn’t about “storytelling”. It’s about the data itself, and clarity on what delivering it actually means. Getting the data out of siloes like tabs and SPSS where it cannot adequately be mined, and out into the big wide world as Tidy Data.
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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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There’s been some great animated maps in the data viz world of late. Most notably this stunner by John Muyskens for the Washington Post, showing the diverted flight paths of planes getting themselves into the line of the recent solar eclipse. What’s more it was made with R and ggplot2! Have a look here: Hundreds of aircraft flocked to the moon’s shadow during Monday’s eclipse. Animation by @JohnMuyskens Data courtesy of @flightradar24 pic.
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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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Background I recently came across Eric Fisher’s brilliant collection of dot density maps that show racial and ethnic divisions within US cities. His work was inspired by Bill Rankin’s Map of Chicago that was made in 2009. Bill makes some salient points in this video about the limitations of choropleth mapping (where boundaries are filled with one colour based on one variable) and how it has a tendancy to “reinforce political ideals of national determination and ethinic homogeneity.
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CULTURE OF INSIGHT

Data Consultancy

London