This map shows the origin and destination of the African slave trade. Source: https://t.co/SJeBMCPq6v pic.twitter.com/4y6KUBaExN
— Simon Kuestenmacher (@simongerman600) May 27, 2019
This map shows the origin and destination of the African slave trade. Source: https://t.co/SJeBMCPq6v pic.twitter.com/4y6KUBaExN
— Simon Kuestenmacher (@simongerman600) May 27, 2019
Which cities are the best deals? Quality of life vs. cost of living around the world. #datavizhttps://t.co/RhDZIQNyGA pic.twitter.com/H53hXERbYv
— Randy Olson (@randal_olson) May 25, 2019
Life expectancy by country charted over time against health spending per person. Obvious to see how messed up the US system is. Money wasted without getting decent returns! Source: https://t.co/fnIRCKVAec pic.twitter.com/0nHLFs7AYQ
— Simon Kuestenmacher (@simongerman600) May 24, 2019
Sweet 📦 by @jacobandrewlong w/ nice vignettes!
— Mara Averick (@dataandme) May 23, 2019
🧰 "interactions: Comprehensive, User-Friendly Toolkit for Probing Interactions" https://t.co/fF5fLLYvq4 #rstats #dataviz pic.twitter.com/EGrjzwOkBN
Rising cost of college https://t.co/vYs29XPpGs pic.twitter.com/zcAErjvy5z
— Nathan Yau (@flowingdata) May 22, 2019
.@IMDb episode rating trajectories for a selection of TV shows with famous (or infamous) endings, including #GameOfThrones. #dataviz
— Randy Olson (@randal_olson) May 22, 2019
Source: https://t.co/Gxz8H9NSul by @boknowsdata pic.twitter.com/g1DtY2gOuj
CleverHans blog post with @nickfrosst: we explain how the Deep k-Nearest Neighbors (DkNN) and soft nearest-neighbor loss (SNNL) help recognize data that is not from the training distribution. The post includes an interactive figure (credit goes to Nick): https://t.co/aajpf8NOib pic.twitter.com/MKKc4WX8Rp
— Nicolas Papernot (@NicolasPapernot) May 21, 2019
🎨 Fun idea, and 👍 post, too!
— Mara Averick (@dataandme) May 16, 2019
📺 "Introducing {tvthemes}: ggplot2 palettes & themes from your favorite TV shows!" by @R_by_Ryo https://t.co/XzOLDnnYnh #rstats #dataviz pic.twitter.com/nphoEiMc0T
From @KevinSimler, how things spread in a network demonstrated with interactive simulations https://t.co/4mKqgDt0sJ pic.twitter.com/o25MOcGddh
— Nathan Yau (@flowingdata) May 15, 2019
More info about those features: pic.twitter.com/zBBNwibzev
— Randy Olson (@randal_olson) May 14, 2019
Bernie Sanders railed against the campaign-finance system in 2016. This time around, he’s benefiting from it. https://t.co/3STjEH74me via @WSJ pic.twitter.com/BaQf9bnNhm
— WSJ Graphics (@WSJGraphics) May 13, 2019
Becoming a yearly tradition: predicting #Eurovision based in Google search data https://t.co/0U9VgBxAnr
— Maarten Lambrechts (@maartenzam) May 13, 2019
This time we take a look back, and apart from the weird map, the piece also contains lots of scatterplots (the last one is HUGE 😉) pic.twitter.com/n39G4zT47w