Birthday Probabilities: Heat Map vs. R
Chris Mulligan also tackled this problem with R, but this time using 20 years of Census data from 1969 to 1988. Chris extracted the birthday frequencies using Google BigQuery, and charted the results with the time series below using this R script.
My apologies to Joe, but I much prefer this representation to the heat map. Not only is the February 29 frequency multiplied by 4 (where we see that it’s not a particularly surprising birthday to have given the overall seasonal trend), but the unusual days really stand out (and are annotated). You’re relatively unlikely to find someone born on January 1, July 4 or Christmas Eve or Christmas Day (most likely because fewer Caesarian births happen, or more induced natural births are avoided, on those days). December 30 is a more likely birthday that you’d otherwise expect (maybe this has something to do with getting kids into an earlier school year?). Andrew Gelman shares a model of the seasonal trend that defines these outliers.
chmullig.com: Births by Day of Year
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