Personally, my main encounter with plotting was in Python. I'm not a big fan of matplotlib, I got the impression that with increasing plot complexity, code complexity grew exponentially. Then there's bokeh [0], which I preferred to matplotlib, due to it being more declarative. HoloViews [1] is more declarative than both matplotlib and bokeh, and boasts that "usually [you can] express what you want to do in very few lines of code, letting you focus on what you are trying to explore and convey, not on the process of plotting". I've not used HoloViews yet.
Then I've heard of R's ggplot [2], which is based on (or inspired by?) The Grammar of Graphics [3]. This books is definitely something I want to check out.
Vega [4], an “assembly language” for visualization, is neither here nor there as far as this discussion goes, but nonetheless I just stumbled upon it and I'm quite optimistic about the initiative. Maybe someone will not have heard of it.
[0] https://bokeh.pydata.org/en/latest/docs/user_guide/concepts.... [1] http://holoviews.org/ [2] http://r4ds.had.co.nz/data-visualisation.htm [3] https://www.amazon.com/Grammar-Graphics-Statistics-Computing... [4] https://vega.github.io/vega/about/
and Hadley Wickham wrote about it in http://vita.had.co.nz/papers/layered-grammar.pdf.
I'm no expert, but I think that one of the main ideas is to separate the elements of making a plot from the way that the data is presented. For example, in ggplot2, you have the data that will go into the graph, the type of plot (or "geometry") that defines how the data are presented (scatterplot, bar plot, etc.), and then various "layers" that can be added that affect style.
In order to split a plot into subplots, you simply define how it is to be faceted (what column should be used to define groups). Grammar-of-graphics moves plotting away from the "turtle graphics" model and lets you specify what should be done. Then ggplot figures out how to do it, kind of like SQL vs. writing for loops to retrieve information.
# Elements of Programming
https://www.amazon.com/Elements-Programming-Alexander-Stepan...
This book proposes how to write C++-ish code in a mathematical way that makes all your code terse. In this talk, Sean Parent, at that time working on Adobe Photoshop, estimated that the PS codebase could be reduced from 3,000,000 LOC to 30,000 LOC (=100x!!) if they followed ideas from the book https://www.youtube.com/watch?v=4moyKUHApq4&t=39m30s
Another point of his is that the explosion of written code we are seeing isn't sustainable and that so much of this code is algorithms or data structures with overlapping functionalities. As the codebases grow, and these functionalities diverge even further, pulling the reigns in on the chaos becomes gradually impossible.
Bjarne Stroustrup (aka the C++ OG) gave this book five stars on Amazon (in what is his one and only Amazon product review lol).
This style might become dominant because it's only really possible in modern successors of C++ such as Swift or Rust, not so much in C++ itself.
https://smile.amazon.com/review/R1MG7U1LR7FK6/
# Grammar of graphics
https://www.amazon.com/Grammar-Graphics-Statistics-Computing...
This book changed my perception of creativity, aesthetics and mathematics and their relationships. Fundamentally, the book provides all the diverse tools to give you confidence that your graphics are mathematically sound and visually pleasing. After reading this, Tufte just doesn't cut it anymore. It's such a weird book because it talks about topics as disparate Bayesian rule, OOP, color theory, SQL, chaotic models of time (lolwut), style-sheet language design and a bjillion other topics but always somehow all of these are very relevant. It's like if Bret Victor was a book, a tour de force of polymathical insanity.
The book is in full color and it has some of the nicest looking and most instructive graphics I've ever seen even for things that I understand, such as Central Limit Theorem. It makes sense the the best graphics would be in the book written by the guy who wrote a book on how to do visualizations mathematically. The book is also interesting if you are doing any sort of UI interfaces, because UI interfaces are definitely just a subset of graphical visualizations.
# Scala for Machine Learning
https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...
This book almost never gets mentioned but it's a superb intro to machine learning if you dig types, scalable back-ends or JVM.
It’s the only ML book that I’ve seen that contains the word monad so if you sometimes get a hankering for some monading (esp. in the context of ML pipelines), look no further.
Discusses setup of actual large scale ML pipelines using modern concurrency primitives such as actors using the Akka framework.
# Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...
Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn.
# Basic Category Theory for Computer Scientists
https://www.amazon.com/gp/product/0262660717/ref=as_li_ss_tl...
Not done with the book but despite it's age, hands down best intro to category theory if you care about it only for CS purposes as it tries to show how to apply the concepts. Very concise (~70 pages).
# Markov Logic: An Interface Layer for Artificial Intelligence
https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...
Have you ever wondered what's the relationship between machine learning and logic? If so look no further.
# Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
https://www.amazon.com/gp/product/0262018020/ref=as_li_ss_tl...
Exhaustive overview of the entire field of machine learning. It's engaging and full of graphics.
# Deep Learning
https://www.amazon.com/gp/product/0262035618/ref=as_li_ss_tl...
http://www.deeplearningbook.org/
You probably have heard about this whole "deep learning" meme. This book is a pretty self-contained intro into the state of the art of deep learning.
# Designing for Scalability with Erlang/OTP: Implement Robust, Fault-Tolerant Systems
https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...
Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own.
# Practical Foundations for Programming Languages
https://www.amazon.com/gp/product/1107150302/ref=as_li_ss_tl...
Not much to say, probably THE book on programming language theory.
# A First Course in Network Theory
https://www.amazon.com/First-Course-Network-Theory/dp/019872...
Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.
> To be honest, matplotlib seems a good contender to me (http://matplotlib.org/).
They're quite different, though, and I can see why many prefer ggplot. It's a declarative, domain-specific language that implements a Tufte-inspired "grammar of graphics" (hence the gg- in the name; see section 1.3 of [1], and [2,3]) for very fast and convenient interactive plotting, whereas matplotlib is just a clone of MATLIB's procedural plotting API.
[1] http://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis...
[2] http://www.amazon.com/The-Grammar-Graphics-Statistics-Comput...
I was planning to read Interactive Data Visualization for the Web http://ofps.oreilly.com/titles/9781449339739/index.html I think that's mostly an introduction to the tool (D3) but maybe has some info on visualization itself.
Also, this tutorial was an easy intro to D3: http://code.hazzens.com/d3tut/ Hopefully, the author continues to add to it.
Mike Bostock (author of D3) has some interesting blogs at: http://bost.ocks.org and also some great visualizations at http://bl.ocks.org/mbostock
A PHD in my office recommended The Grammar Of Graphics http://www.amazon.com/The-Grammar-Graphics-Statistics-Comput... It seems much more technical and statistical but could be an interesting set of ideas to synthesize. I'm planning on borrowing his copy.
Mike Bostock's stuff also led me to this page on Hive Plots which is a pretty cool description of how to do network visualization better: http://www.hiveplot.com
And I like Flowing Data as a blog: http://flowingdata.com/
This is all of course prior to actually having studied it. I'm hoping using these resources and playing with some toy projects will lead me down further paths. (Assuming I have time which I, of course, probably won't.)
The The Grammar of Graphics (Statistics and Computing) by Leland Wilkinson is also great, though more focused on building graphing systems. I believe it is the inspiration for a lot of d3.js http://www.amazon.com/The-Grammar-Graphics-Statistics-Comput...
EDIT: I thought Tufte did it, I was wrong. https://www.amazon.com/Grammar-Graphics-Statistics-Computing...