Dylan vs. Python vs. R vs. Ruby Comparison Chart

It is dynamic while providing a programming model designed to support efficient machine code generation, including fine-grained control over dynamic and static behaviors. Describes the Open Dylan implementation of the Dylan language, a core set of Dylan libraries, and a library interchange mechanism. The core libraries provide many language extensions, a threads interface, and object finalization, printing and output formatting modules, a streams module, a sockets module, and modules providing an interface to operating system features such as the file system, time and date information, the host machine environment, as well as a foreign function interface and some low-level access to the Microsoft Win32 API.

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.