by Karl-Kuno Kunze
The novice in the R ecosystem may face quite a challenge to find her way through the jungle of R providers as well as licensing and pricing models. In addition, it helps to understand the different components that make a productive R environment.
In the following I give my view of the present situation. However, the R ecosystem is rather dynamic. So, should I have left something out, don’t hesitate to write a comment.
What is R?
R is a free software environment for statistical computing and graphics. This is what the website of the The R Project says – which you should more or less always visit in the first place when traveling to R-land. You see the logo in the title.
Initially, R was developed by Robert Gentleman and Ross Ihaka of the Statistics Department of the University of Auckland. By now, many people are involved in the R project. However, there is a special group of 20 developers that constitute the Core Group.
In addition, R is also a programming language. In contrast to other languages, such as C, there is no such thing as a language definition, which is standardised as, for example ANSI C. So, R is what the program R understands in order to create calculations or graphics.
In 2015 some big players in the software market have founded the R Consortium. This institution aims at developing and maintaining R to a degree that reflects the growing demand of companies and organizations that rely on R for their business critical processes. The R Consortium is by far no closed shop. You may consider to join or at least support them.
R is an interpreted language. Therefore R consists of an interpreter, that understands and executes your commands at runtime. In the most down to earth installation you have the console to work with. No graphical user interfaces (GUI) are needed. However, a simple GUI comes with the installation package for MAC and Windows.
R by The R Project is govered by the license conditions of the GNU GENERAL PUBLIC LICENSE, which essentially grants you using and spreading the software free of any charge. If you change or use it to make a new software product, you are obliged to note as such. The result of your work is also compulsively governed by the above license.
In addition to the core installation of R there are about about 8.000 packages available as of now. These packages are freely accesible and make an invaluable advantage of R as compared to similar products.
Which alternative R Interpreters do exist?
Just as there are several compilers for different languages, provided by different manufacturers, there are quite some alternatives to the initial R by The R Project around.
For Developers, Data Scientists and Researcher
Probably the most followed product is Microsoft R Open. This interpreter is essentially governed by the same license conditions as R by The R Project. For your first steps with R, you may want to turn to R Fiddle. If you open the page, you land immediately on an R editor and you can enter your first commands.
For Large Data Sets and Organizations
There have been quite some efforts to let R cope with large data sets in the sense that they exceed usual working memory resources. Interpreters for this special field of application are Aster R by Teradata, Microsoft R Server by Microsoft and Renjin (R Engine) by BeDataDriven.
These are conceived for extremely large data sets and very demanding performance restrictions. Probably not surprisingly these products are governed by more restrictive and at least partly fee-based license models.
That’s it for the introduction to R and a first look at the present choice when it comes to selecting an interpreter. In the next article of this series you will read about different ways to communicate with R.