Tuesday: I only crashed a computer twice today.

Not like I was running stuff that I really wanted finished or anything.

I didn't think I'd have a picture for today, so I took this when I got home.  I totally wasn't trying to avoid the awkward thing of opening my door while my neighbor opens his door.

But, then I saw that this administration continues to be worse than useless, and is planning to investigate to see if white people are being discriminated against in college applications.  Which is dumb, so I did the standard thing of "asking the government about the data, since I'm pretty sure the government has the data" and:

Thanks, competent part of the government!

Lines show fall college enrollment by broad racial category, taken from this table.  Dots show total US demographics, taken from the last table of this wikipedia page, because I'd already parsed and formatted one government table, and this one already had the same broad categories.  The main caveat is that looking at the subsequent section, you can see that younger age categories are less white.  However, I was not able to find a full year/age/race census split, so it's hard to plot a "college age" population.  Still, broadly speaking, white people have historically been over-represented in fall college enrollment, as has the Asian/Pacific Islander category, although that over-representation drops when you note the recently available Two-or-more category.  Conclusion: this administration can't do an hour's worth of work to discover their thesis is likely incorrect.  Alternate conclusion:

I am disappointed that this image makes a return.

R bit (removing all the mistakes, and me learning about the wonder of reshape2.melt, which takes "wide" tables where each column is a category sampled at each index/year, and turns it into a "long" table with each value indexed by the index and category):

p306 = data.frame(read.csv("./tab306_T.P",header=TRUE,sep='\t'))

library(reshape2)

demo = data.frame(read.csv("./demo.dat",header=TRUE,sep='\t'))
demomelt = melt(demo,id.vars="year")

p306X = p306[, c("year", "White", "Black", "Hispanic", "AsianPacificIslander", "AmericanIndianAlaskaNative", "TwoOrMore")]
p306meltX = melt(p306X,id.vars="year")

ggplot()  + geom_line(data=p306meltX,aes(x=year,y=value,color=variable)) + geom_point(data=demomelt,aes(x=year,y=value,color=variable))
ggsave("college_demographics.png")

• Rabbit Rowlet.
• Dogs.
• Weather clustering.  I vaguely remember noting the "large gradient"/"low high temperature" correlation back when weatherspark was a thing.

And tumblr broke xkit again, so my usual evening flow of

1. Read RSS.
2. Save interesting items.
3. Read through saved items, unsaving them as I open in new tab.
4. Like reblog tumblr items as I close them.

doesn't work.