What’s up on the internet in February? Here is a collection of Safari saved tabs, Twitter favs and some byproduct of my lost memory.

The Handbook of Graph Drawing and Visualization (Roberto Tamassia, Ed.) is a great collection of 26 chapters on graph drawing. From the preface, the handbook “covers topological and geometric foundations, algorithms, software systems, and visualization applications in business, education, science, and engineering.”

Agile Data Science by Waclaw Kusnierczyk. Nice looking slides on Agile methodology and its application to the field of Data Science. Agile Data Science “focuses on delivery of insight and predictions ef ciently; creates research plans to build MVPs; prefers simplistic but ef cient models to elaborate but slow ones; uses off-the-shelf tools to the extent possible; evaluates the results against business objectives.” For a more complete coverage, see Agile Data Science by Russell Jurney (more to come in a future blog post).

Sadly, bitbooks appears to be defunct, but I am pretty sure I would have loved this Sinatra-based framework to deploy online textbooks.

The Joy of Haskell I wish there were more ressources like this to learn new languages. This is the second book that Julie Moronuki wrote about Haskell. Surely it will be a great one like the first that I bought some year ago.

Is it helpful to learn Lisp? Interesting yet poor question. All but one answer to this thread just confirmed that I do not want to waste my time there anymore. This really is a common problem with QA websites: Questions with no definite answer would be closed on Stack Exchange (yet, you will have to properly define what a question with no definite answer really is, but that is another story), but on Quora there seems to be no such “How to ask” guidelines; you may happen to find really good questions and answers, but also really poorly worded questions and just junk replies. If you are interested in functional programming and are looking for good content, consider visiting Atabey Kaygun, Alexis King, or Greg Hendershott’s blogs.

Some “Public Fonts for Minority Languages of Russia”: Nice heading! Here is the “Paratype” PT font, available in Serif, Sans and Mono typeface, right on your OS or through Google Web Fonts.

A collection of R demos and additional notes for the book *Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin.

Observable is up and running. If you follow this blog, you probably remember that I mentioned a [talk by Mike Bostock at the CSV conf]https://aliquote.org/post/live-coding/ which prefigured his move to developing a new JS framework. That’s it! And here is one of the latest cool think that happened on the live platform: Introduction to Apache Arrow.

Statistical Rethinking with Python and PyMC3 This is a GitHub repository with Python translation for the R+Stan code that is used in Richard McElreath’s book. On a related note, here is a revamped version of R code for Bayesian Data Analysis (3rd ed.), by Aki Vehtari.

If you want to learn about string encoding, specifically in R, String Encoding and R by Kevin Ushey is the way to go.

Fonts for Complex Data (h/t Bob Rudis) In which we learn that ScreenSmart fonts can help compensate “scale effect” (whereby smaller-than-text footnote become hard to read on web forms), that narrow typefaces (e.g., Gotham Narrow) may be better than condensed ones, and of course that we should use tabular figures, “the most vital part of any composition that includes numbers.”

David Drukker started blogging again on the Stata blog. This is a followup to the previous series on Programming an estimation command. Stay tuned! Meanwhile, here are two new books that were just published in February: The Mata Book: A Book for Serious Programmers and Those Who Want to Be, Survey Weights: A Step-by-Step Guide to Calculation.