How big is a 12% defection rate?

There's this story going around about how 12% of the people who voted for Bernie Sanders in the primary voted for Trump in the general, and this was sufficient to swing the vote in Trump's favor.  The Political Wire report is behind a paywall, but the raw data are here.

So, how big is a 12% defection rate?

At first glance, 12% seems huge, but how does it compare to other defection rates?  Well, Trump lost roughly 34% of Kasich voters, 3% of Cruz voters, 12% of Rubio voters, and 2% of Trump primary voters to Clinton.

4.5% of 2016 primary voters for Clinton voted for Trump.  If those voters had gone for Clinton in the general, they would be sufficient to swing the vote in her favor**.

Using this data to understand how people voted in an effort to understand how to reach those people in future elections could be useful.  Using this data to fan flame wars between Bernie and Hillary supports seems like a huge waste of time.  


** Brian Schaffner breaks down the Bernie defectors by state.  I'm a little nervous about breaking down the numbers by state, because there are a large number of voters with "na"  values for State.  I don't know what these values mean, except that we would need to take care when talking about individual state results based upon this data.

EDIT (8/28/17): I only just saw Schaffner's statement to NPR that "I found basically no Clinton primary voters who voted for Drumpf."  I'm looking at the source data and see the following weighted breakdown.  This shows 4.5% of Clinton voters defecting to Trump, so I'm not certain how Schaffner arrived at that conclusion.

Here is the R code that creates this table, from the raw RData downloaded from the CCES Dataverse:

library(data.table)
library(magrittr)
library(dplyr)
load("CCES16_Common_OUTPUT_Jul2017_VV.RData")
x %>% subset(select=c("CC16_328","CC16_410a","commonweight_vv_post")) %>%          setnames(old=c("CC16_328","CC16_410a","commonweight_vv_post"),  new=c("Primary2016","General2016","weight")) %>% 
 group_by(Primary2016,General2016) %>% filter(Primary2016 %in% ("Hillary Clinton")) %>% 
 summarise(weightedN=sum(weight)) %>% print()

Focusing on the specific, rather than the general, in child rearing and monument building

I saw this comic on monuments at the Nib today, and for whatever reason, it reminded me that as a parent, you learn that it's important to praise children for specifics, rather than generalities.  I'm beginning to feel like this advice is applicable to monuments.  Perhaps they should commemorate something specific a person has done and placed in a location linked to that action, rather than a person in general.  

For example, the statue of Tesla in Niagara Falls makes perfect sense because Tesla was instrumental in building the world's first hydroelectric power plant there.

At the other end of the spectrum, Robert E. Lee never set foot in Louisiana, and so the monument to him in New Orleans is pure cult of personality.

The saddest day of 2017 (according to the Hedonometer)

The Hedonometer is a cool project that tracks the happiness of the Twitterverse over time, using mechanical Turk-derived "happiness scores" for each word.  So far, last Saturday was the saddest day of 2017, and the single day view of August 12 is perfect for showing how it currently works and where future development is needed.

That Saturday was sadder than the previous seven days because of a greatly increased incidence of "negative" words like hate, violence, and terrorism, with no counterbalancing increase of "positive" words (or decrease of "negative" words) until we hit the word white.

This is where things get interesting, because out of context, white is considered a "positive" word, but in the context of the events in Charlottesville, white was likely a part of phrases that would definitely be negative.  The Hedonometer team is aware of this, and in the frequently asked questions on the About page, note the following:

“How will you deal with context?”
We are currently developing a principled method to identify relevant phrases, for example to deal with the multitude of both positive and negative uses of profanity. We expect to be scoring phrases instead of words, where appropriate, in the near future.

Maintenance victory, of a sort

Problem: Venerable Patton High Velocity Air Circulator (henceforth referred to as "fan") stopped working. Took it apart, cleaned off years of dust. Showed the kids the copper coils that generate the electromagnetic field that causes the disc to rotate so that the blades turn. Diagnosed that, for tribological reasons I can't determine, the generated EM field is no longer strong enough to overcome the initial inertia of the disc, even after applying lubricant.

Solution: keep a screwdriver handy to give the fan a little push, and it will keep the blades moving after that.

Newly insured in the U.S., 2010-2015

FlowingData recently pointed to a report on the number of people in the U.S. who gained health insurance as a result of the ACA (and thus most at risk if the ACA is repealed without replacement), along with the following map:

I think this could be more useful by grabbing some U.S. census data.

The following map makes the potentially troubled areas immediately clear.  Bubble size indicates the total number of newly insured, and color indicates the ratio of newly insured to the population under 65.