The Psychopath Code is a short fun read. Hintjens’ model of the world doesn’t match with mine. I sum it up with “not everybody is your friend” plus the good faith assumption once lost can’t be regained.
Only mothers love unconditionally. Understanding circumstances and motives whether it’s sex, work, power, or money makes life easier. It’s a hard thing to know thy self.
Dan Lyons’ Disrupted: My Misadventure in the Start-Up Bubble is a hilarious and insightful look into the world of fake.
My former professor, Norm Matloff, wrote “The Art of R Programming” and NoStarch Press was kind enough to send me a review copy.
The Art of R Programming is a straight forward explanation of R for programmers who are reasonably familiar with programming in another language. Matloff makes no assumptions of expertise in C or algorithms and his explanations are succinct and easy to follow.
If you’re aren’t familiar with R, it is a statistical programming language, with some similarities to Matlab.
The big advantages of R are (1) it’s high level, (2) reasonably easy to read, (3) functional in nature, (4) simple syntax. If you’re familiar with Python, it has a similar feel. Compared to complex languages such as C++, Java, etc, R is a breadth of fresh air due to the lightness of its syntax. That said as a programming language Python is nicer. R has a few annoyances (for me at least) that make it less pleasant to write in than Python.
A couple of those are:
- Non-standard assignment operator e.g. to assign 5 to x in R we use “x <- 5" instead of the normal "x = 5" used in other languages. This is annoying because a significant amount of programming is doing assignments and a two character assignment operator is twice as much typing. Contrast this with Python which uses the plain "x = 5".
- Vector creation using “c(1,2,3,4)”. Vectors in R are similar to lists in Python, it would be more natural to add a little syntactic sugar and use “[1,2,3,4]” for vector creation i.e. the same syntax as Python and many other languages.
The real reason to use R are its statistical libraries, it’s very widely used for statistics and is the most pleasant environment to work in.
The areas Matloff covers are:
|1 Why R?
||2 Getting Started
||6 Data Frames
|7 Factors and Tables
||8 R Programming Structures
||9 R Functions
|10 Doing Math in R
||12 Object-Oriented Programming
||15 Writing Fast R Code
|16 Interfacing R to Other Languages
||17 Parallel R
||18 String Manipulation
|19 Installation: R Base, New Packages
||20 User Interfaces
||21 To Learn More
Much of the material is available online in tutorials such as John Cook’s, R Language For Programmers. The real gems are the chapters “Writing Fast R Code”, “Interfacing R to Other Languages”, and “Parallel R”. These chapters have great information that is not easily discoverable otherwise.
“The Art of R Programming” is a fun read, albeit somewhat specialized. If you need to do statistical work as a programmer I highly recommend buying it and spending an afternoon browsing it.