Abstract:
The R language is an anomaly when compared to other tools in computing. As a descendant of languages developed in Bell Labs like S and Scheme, creators Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand developed R to be an object-oriented programming language with lazy functional features [1]. As a result of this unique configuration and overall design, R is characterized as a hard language to learn [2]. Even though there may be some difficulties in determining all of the nuances of R, the overall configuration of the language and the fact that it is open source are what lead to this R’s rise in popularity.
Infrastructure:
Despite the languages steep learning curve, R has still immersed as a vital tool in statistical computing. This notion is support by the fact that a myriad of individuals in fields that range from Computational Biology to the Social Sciences use R to run analysis [1]. Many factors contribute to the overall popularity of R; for instance, in R, to have less paging problems, memory is allocated once the language starts up [2]. Regarding scope, R allows the functions to have direct access to the variable once it is defined [1].
To further elaborate on R’s functionality, the language is a dynamic like JavaScript. However, R’s underlying data type is a vector [1]. This basic data type allows the language to be optimal for analyzing large data sets. Also, R has a plethora of data structures which makes it a better opinion then using traditional statistical packages or a spreadsheet [2]. Lastly, since R is a language, you can make the language preform mundane task that may seem repetitive [2].
Open Source:
In addition to its functionally, what also contributes to R rising popularity is that it is open source. During the late 1990s, R began to grow in popularity, mostly by word of mouth [3]. As a result, the R mailing list was introduced. The mailing list would accelerate the overall development of R. The original developers of R could not make enough changes at a rate that would satisfy the R community. As a result, the language was made open source. The concept of “free software” was essential to R progression because now the language has a chance to be edited and improved by a larger talent pool and an accelerated rate [1]. Since S was around, it was rebranded as S – PLUS, a commercial version of the language while R is the free an open source version of the product.
Every day, R is improving as well as increasing in popularity. The previous statement is supported by the notion that R ranked 12th on the TIOBE index. R is at the forefront of data analytics. Like all great inventions, the R programming language was initially designed as an experiment, with no intentions of it being one of the primer tools for data analytics [3].
References
Ihaka, R. (1998). R: Past and Future History. Interface. doi:https://www.stat.auckland.ac.nz/~ihaka/downloads/Interface98.pdf
Morandat, F., Hill, B., Osvald, L., & Vitek, J. Evaluating the Design of the R Language. Objects and Functions For Data Analysis. doi:http://r.cs.purdue.edu/pub/ecoop12.pdf
Burns, P. (2006). R Realtive to Stastrical Packages: Comment 1 on Technical Report Number 1 (Version 1.0) Strategically ussing General Purpose Statistics Packages: A Look at Stata, SAS, SPSS. Statistical Consulting Group UCLA Academic Technology Services, 1(1)