Does Nazi Political Speech Have a Place in the Marketplace of Ideas?
Substack.com substitutes slogans for really thinking about it
Newsletter
Some say the world will end … in AI
Some say the world will end … in AI Chorus: Prelude Fire and Ice BY ROBERT FROST Some say the world will end in fire, Some say in ice. From what I’ve tasted of desire I hold with those who favor fire. But if it had to perish twice, I
On Ambiguity Tolerance
When I enrolled in a small, West-coast liberal arts college in 1965 under rapidly disabused notions that I would be following a sweetheart, the greeting included the notion of ambiguity tolerance. Supposedly the ability to hold two contradictory thoughts in mind at the same time was something to be cultivated.
Eddie Feather
a very short portrait Eddie Feather knows the odds, how to play the players and who will be an ugly drunk. No one looks at him twice, a big soft-spoken man with living eyes in a death mask face. You wouldn’t think him wealthy by the cut of his
Without comment
I, Richard Careaga, represent and warrant that the image above is a true, correct and complete copy of an electronic photograph taken by me on September 14, 2014 in King County, Washington at the QFC Grocery on Juanita Drive and that the same has not been edited or altered by
Visages
Faces in photographs. Stylized selfies. Posed weddings. Red carpet mannikin faces. Say cheese! Leibowitz staged. Avedon psychological dissections. And candids. People unaware.
Composition
Just as visualizing tensors degrades as n > 3, juggling pieces in an R script can get jiggly when they do not %>% neatly. A post recently reminded me so. Despite strides in deconstructing R to make it more commercially attractive no obvious point (aside from the richness of statistical
A minor lubridate snip
Dates can be fussy. (Especially when they come to you mangled by manual formatting in spreadsheets.) Sometimes it’s easier, when there are consecutive dates, simply to create them with seq. For ts, don’t even bother, just use object <- ts(source, start = c(2018,1 ) ...) The date
Look before you count
Often, it is possible to back into a number from other numbers, basic K12 learning; we got it pretty well, not knowing any better. Like all learning, though, it is perishable, as I learned preparing a recent short take. This all began with a question about data wrangling and how
Even more wrong
Woody Hayes was supposed to have run up the score 42-12 late in the fourth quarter and went for a two-point conversion, shortly before the clock ran out. Asked why, he was said to have answered There are no 3-point conversions! In the same spirit, I posted the other day
Not even wrong
A recent tweet “There is no bubble” via DB pic.twitter.com/MnMSpE38q2 — Daniel Lacalle (@dlacalle_IA) December 30, 2019 was commented on by Nassim Nicholas Taleb. The plot was attributed to Deutche Bank Research. Our ever-helpful friend Fred has two data series, SP500_PCH and WALCL_PCH that cover
What happened to the streak? rle: run length encoding
A common task is to isolate records when some condition changes. Failure analysis is an obvious example. When working in a tidy environment, this isn’t always easy because of its strong column bias. Looking back at previous rows is clunky, at best. The sometimes overlooked which() provides one part
HSL in R, Chapter 1
An R Companion to Chapter 1 of Hosmer, Lemeshow and Sturdivant’s Applied Linear Regression This post is an R illustration of the first chapter of Hosmer, David W., Stanley Lemeshow, and Rodney X. Sturdivant. Applied logistic regression. Hoboken, New Jersey: Wiley, 2013, the standard text (HLS). Chapter 1 introduces
Logistic regression
Why learn about logistic regression? Because the outcome, \(Y\), is binary, rather than continous, we choose logistic regression, rather than linear regression. Hosmer, David W., Stanley Lemeshow, and Rodney X. Sturdivant. Applied logistic regression. Hoboken, New Jersey: Wiley, 2013, the standard text (HLS). HSL HSL doesn’t have code examples,
Metadata for datasets
Where does this dataset come from? Is this the original revised data or the revised revised data? Keeping track of the provenance of data can be a challenge, especially when drawing on published sources. Keeping a record of the origin, the date accessed, the transformations applied (e.g., converting from
Missing Inaction--locating data holes
## Note: Using an external vector in selections is ambiguous. ## ℹ Use `all_of(interest_vars)` instead of `interest_vars` to silence this message. ## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>. ## This message is displayed once per session. Your hands can’t hit what your eyes can’