TIL about the
ash function. So,
(defun square (n) (ash 1 (1- n))) is way simpler compared to:
(defun power (n m) (reduce #'* (loop for x below n collect m))) (defun square (n) (power (- n 1) 2))
(Me playing with the CL track at https://exercism.io).
TIL about chemacs, an Emacs profile manager/switcher (à la IPython/jupyter).
P-values are a practical success but a critical failure. Scientists the world over use them, but scarcely a statistician can be found to defend them. Bayesians in particular find them ridiculous, but even the modern frequentist has little time for them. – Stephen Senn, Two Cheers for P-values?
Python is not built with math and statistics in mind, and this doesn’t work without using a package.
If you’re looking to move from R to Python, here are two interesting posts: Python is Weird (an unabashedly biased intro to Python for R users); Programming with Data: Python and Pandas. The first one, from which the above quotation is extracted, provides a side-by-side comparison of some of the features of each language. You might like or not, since R is a DSL and Python is not a good PL to compare. The second one is a complete tutorial on Pandas (including linear regression) in IPython notebooks. Besides, Chris Albon’s Technical Notes On Using Data Science & Artificial Intelligence To Fight For Something That Matters are also worth a look.
If you are a professional writer – i.e., if someone else is getting paid to worry about how your words are formatted and printed – Emacs outshines all other editing software in approximately the same way that the noonday sun does the stars. It is not just bigger and brighter; it simply makes everything else vanish. – https://batsov.com/articles/2011/11/19/why-emacs/
Currently reading a review on Molecular Population Genetics. I have no idea what movie I can watch to occupy the rest of my evening and I’ll probably end up drinking on my couch, which is also my bed. Bad news from the stars…
Significant Pattern Mining for Time Series. I really like such dynamic illustrations.
Introduction to Algorithms, by Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest.
Always interesting to find some gems at QA websites:
Four years, 1400 views, and two dozen upvotes before a review on a site dedicated to code reviews points toward unreviewability as a prominent feature of the code. What hinders reviewability is, I think, the high level of cognitive load the code places on anyone reading it. – https://codereview.stackexchange.com/a/147918
One of the last person, with Dirk and Matt, I find interesting to follow given the recent evolution of the R language. I started with the R Inferno, by Patrick Burns, then discovered Norman’s books, in addition to the MASS book. That was all what I needed to get started. Now, this is illuminating:
R is rapidly devolving into two mutually unintelligible dialects, ordinary R and the Tidyverse. (…) It might be more acceptable if the Tidyverse were superior to ordinary R, but in my opinion it is not. It makes things more difficult for beginners. E.g. the Tidyverse has so many functions, some complex, that must be learned to do what are very simple operations in base R. Pipes, apparently meant to help beginners learn R, actually make it more difficult, I believe. And the Tidyverse is of questionable value for advanced users.
If you’re more versed into Julia these days, see also R vs. Julia.
It’s raining again, and I’m done with the 800th micro-post by now.