by Joseph Rickert
John Chambers opened UseR! 2014 by describing how the R language grew out of early efforts to give statisticians easier access to high quality statistical software. In 1976 computational statistics was a very active field, but most algorithms were compiled as Fortran subroutines. Building models with this software was not a trivial process. First you had to write a main Fortran program to implement the model and call the right subroutines, and then you had to write the job control language code to submit your job and get it executed. When John and his Bell Labs colleagues sat down on that May afternoon to work on what would become the first implementation of the S language they were thinking about how they could make this process easier. The top half John’s famous diagram from that afternoon schematically indicates their intention to design a software interface so that one could call an arbitrary Fortran subroutine, ABC, by wrapping it in some simplified calling syntax: XABC( ).
The main idea was to bring the best computational facilities to the people doing the analysis. As John phrased it: “combine serious computational challenges with convenience”. In the end, the designers of both S, and its second incarnation, R, did much better than convenience. They built a tool to facilitate “flow”. When you are engaged in any mentally challenging work in (including statistical analysis) at a high level of play, you want to be able to stay in the zone and not get knocked out by peripheral tasks that interrupt your thought processes. As engaging and meaningful as it is in its own right, writing code is not doing statistics. One of the big advantages of working with R is that you can do quite a bit of statistics with just a handful of functions and the simplest syntax. R is a tool that helps you keep moving forward. If you want to see something then plot it. If the data in the wrong format, then mutate it.
A second idea that flows from the idea of S as an interface is that S was not intended to be self sufficient. John was explicit that S was designed as an interface to the “best algorithms”, not as a “from the ground up programming language”. The idea of being able to make use of external computational resources is still compelling. There will always be high-quality stuff that we will want to get at. Moreover, as John elaborated: “unlike 38 years ago there are many possible interfaces to languages, to other computing models and to (specialized) hardware”. The challenge is to interface to applications that are “too diverse for one solution to fit them all”, and to do this “without loosing the R that works in ‘ordinary’ circumstances.
John offered three examples of R projects that extend the reach of R to leverage other computing environments.
- Rcpp – turns C++ in to an R function by generating an interface to C++ with much less programming effort than .Call
- RLLVM – enables compiling R language code into specialized forms for efficiency and other purposes
- H2O – provides a compressed, efficient external version of a data frame for running statistical models on large data sets.
These examples, chosen to represent each of the three different kinds of interface targets that John called out, also represent projects of different scope and levels of integration. With a total of 226 reverse depends and reverse imports, Rcpp is already a great success. It is likely that ready access to C++ will form a permanent part of the R programmers mindset.
RLLVM is a much more radical and ambitious project that would allow R to be the window to entirely different computing models. As best I understand it, the central idea is to use the R environment as the system interface to “any number of new languages” perhaps languages that have not yet been invented. RLLVM would “Use R syntax for commands to be interpreted in a different interpreter”. RLLVM seems to be a powerful idea and a direct generalization of the original XABC() idea.
The RH2O package is an example of providing R users with transparent access to data sets that are too large to fit into memory. It is one of many efforts underway (including those from Revolution Analytics) to integrate Hadoop, Teradata, Spark and other specialized computing platforms within the R environment. Some of these specialized platforms may indeed be longed lived, but it is not likely that all of them will. From the point of view of doing statistics, it is the R interface that is likely to survive and persist, platforms will come and go.
An implication of the willingness of R developers to embrace diversity is that R is likely to always be a work in progress. There will be loose ends, annoying inconsistencies and unimplemented possibilities. I suppose that there are people who will never be comfortable with this state of affairs. It is not unreasonable to prefer a system where there is one best way to do something and where, within the bounds of some pre-established design, there is near perfect consistency. However, the pursuit of uniformity and consistency seems to me to doom designers to be at least one step behind, because it means continually starting over to get things right.
So what does this say about the future of R? John closed his talk by stating that “the best future would be one of variety, not uniformity”. I take this to mean that, for the near future anyway, whatever the next big thing is, it is likely that someone will write an R package to talk to it.
Some links regarding S and R History: