Data science in C?

Coursera is offering a Data Science specialization taught by professors from Johns Hopkins. I discovered it via one of their courses which is about reproducible research. I have been watching some of the video lectures and they are very interesting, since they combine data processing, programming and statistics.

The courses use the R language which is free software and is one the reference tools in the field of statistics, but it is also very much used for other kinds of data analysis.

While I was watching some of the lectures, I had some ideas to be implemented in some of my tools. Although I have used GSL and VXL1 for linear algebra and optimization, I have never really needed statistics libraries in C or C++, so I ducked a bit and found the apophenia library2, which builds on top of GSL, SQLite and Glib to provide very useful tools and data structures to do statistics in C.

Browsing a little bit more, I found that the author of apophenia has written a book “Modeling with data3, which teaches you statistics like many books about R, but using C, SQLite and gnuplot.

This is the kind of technical book that I like most: good math and code, no fluff, just stuff!

The author sets the stage from the foreword. An example from page xii (the emphasis is mine):

The politics of software

All of the software in this book is free software, meaning that it may be freely downloaded and distributed. This is because the book focuses on portability and replicability, and if you need to purchase a license every time you switch computers, then the code is not portable. If you redistribute a functioning program that you wrote based on the GSL or Apophenia, then you need to redistribute both the compiled final program and the source code you used to write the program. If you are publishing an academic work, you should be doing this anyway. If you are in a situation where you will distribute only the output of an analysis, there are no obligations at all. This book is also reliant on POSIX-compliant systems, because such systems were built from the ground up for writing and running replicable and portable projects. This does not exclude any current operating system (OS): current members of the Microsoft Windows family of OSes claim POSIX compliance, as do all OSes ending in X (Mac OS X, Linux, UNIX,…).”

Of course, the author knows the usual complaints about programming in C (or C++ for that matter) and spends many pages explaining his choice:

“I spent much of my life ignoring the fundamentals of computing and just hacking together projects using the package or language of the month: C++, Mathematica, Octave, Perl, Python, Java, Scheme, S-PLUS, Stata, R, and probably a few others that I’ve forgotten. Albee (1960, p 30)4 explains that “sometimes it’s necessary to go a long distance out of the way in order to come back a short distance correctly;” this is the distance I’ve gone to arrive at writing a book on data-oriented computing using a general and basic computing language. For the purpose of modeling with data, I have found C to be an easier and more pleasant language than the purpose-built alternatives—especially after I worked out that I could ignore much of the advice from books written in the 1980s and apply the techniques I learned from the scripting languages.”

The author explains that C is a very simple language:

Simplicity

C is a super-simple language. Its syntax has no special tricks for polymorphic operators, abstract classes, virtual inheritance, lexical scoping, lambda expressions, or other such arcana, meaning that you have less to learn. Those features are certainly helpful in their place, but without them C has already proven to be sufficient for writing some impressive programs, like the Mac and Linux operating systems and most of the stats packages listed above.”

And he makes it really simple, since he actually teaches you C in one chapter of 50 pages (and 50 pages counting source code is not that much!). He does not teach you all C, though:

“As for the syntax of C, this chapter will cover only a subset. C has 32 keywords and this book will only use 18 of them.”

At one point in the introduction I worried about the author bashing C++, which I like very much, but he actually gives a good explanation of the differences between C and C++ (emphasis and footnotes are mine):

“This is the appropriate time to answer a common intro-to-C question: What is the difference between C and C++? There is much confusion due to the almost-compatible syntax and similar name—when explaining the name C-double-plus5, the language’s author references the Newspeak language used in George Orwell’s 1984 (Orwell, 19496; Stroustrup, 1986, p 47). The key difference is that C++ adds a second scope paradigm on top of C’s file- and function-based scope: object-oriented scope. In this system, functions are bound to objects, where an object is effectively a struct holding several variables and functions. Variables that are private to the object are in scope only for functions bound to the object, while those that are public are in scope whenever the object itself is in scope. In C, think of one file as an object: all variables declared inside the file are private, and all those declared in a header file are public. Only those functions that have a declaration in the header file can be called outside of the file. But the real difference between C and C++ is in philosophy: C++ is intended to allow for the mixing of various styles of programming, of which object-oriented coding is one. C++ therefore includes a number of other features, such as yet another type of scope called namespaces, templates and other tools for representing more abstract structures, and a large standard library of templates. Thus, C represents a philosophy of keeping the language as simple and unchanging as possible, even if it means passing up on useful additions; C++ represents an all-inclusive philosophy, choosing additional features and conveniences over parsimony.

It is actually funny that I find myself using less and less class inheritance and leaning towards small functions (often templates) and when I use classes, it is usually to create functors. This is certainly due to the influence of the Algorithmic journeys of Alex Stepanov.

Footnotes:

1

Actually the numerics module, vnl.

2

Apophenia is the experience of perceiving patterns or connections in random or meaningless data.

3

Ben Klemens, Modeling with Data: Tools and Techniques for Scientific Computing, Princeton University Press, 2009, ISBN: 9780691133140.

4

Albee, Edward. 1960. The American Dream and Zoo Story. Signet.

6

Orwell, George. 1949. 1984. Secker and Warburg.

7

Stroustrup, Bjarne. 1986. The C++ Programming Language. Addison-Wesley.