What we’re reading: Finals week edition

Mid-May in Portland – skies are grey and tempermental, and all of campus is feeling the effects of nine months of academic rigor. Take a break from your usual with these links and questions:

All you data/visualization folks, we can do better. What is misleading about these numbers, and how would you clarify that? If you had to re-do this graphic, what would you change? (Behind the Numbers: Food For Thought | Wall Street Journal)

Where you grow up affects when you are likely to marry — not surprising, methinks. What else might be behind this pattern? What is troublesome¬†about mapping this data at the scale of counties, and why do you think the authors made that choice? (How Your Hometown Affects Your Changes of Marriage | The Upshot @ New York Times)

Looking to hone your R and/or data science skills this summer? Some suggested analytical tools/R packages, and a brief tutorial on cluster analysis, both from R Bloggers.

Data visualization in action: the UK elections, visualized — now featuring at least one of the Simpsons. (The Two Winners and Two Losers of the UK Elections | Visualising Data)

30 seconds of entertainment: watch US county boundaries evolve … and then go sit on the front lawn and congratulate yourself on the end of the 2014-2015 school year. (Animated history of US county boundaries | Flowing Data)

Wait. You can’t sit on the front lawn because it’s raining? Very good, then. Take your data-critical brain and review this NY Times article on the effects of working mothers on children. What else do you want to know about these numbers? What do you believe? (Mounting Evidence of Some Advantages for Children of Working Mothers | The Upshot @ New York Times)

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