Statistical Programming Languages
Introduction
Statistical programming entails the usage of programming language to write codes for data analysis, through the application of mathematical concepts. Statistical Programming languages have proven beneficial to data scientists over the years (Braun, & Murdoch, 2016). The various existing programming languages, however, have both advantages and disadvantages over one another.
Why are statistical programming languages important to data scientists?
Programming languages have proven beneficial in the construction of statistical models. Programming languages aid in data representation. These languages of programming also facilitate and enhance data manipulation. Statistical programming languages have also given great help in the handling of large data sets (Kishore & Reddy, 2017).
What are some advantages and disadvantages the R programming language has over the other main statistical programming languages (i.e. Python, SAS, SQL)?
Advantages
R programming offers packages for numerous statistical applications, for example, CRAN, which hosts over ten thousand packages, which allow for secure contribution by the users.
R is equipped with outstanding visualization libraries, for example, ggplot2.
R allows very capably, for standalone analyses of data, which occurs in individual servers.
R also efficiently provides for a large community platform, with support via mailing lists and also documentations contributed to by the users themselves.
Disadvantages
In comparison to other rival programming languages like Python, R is generally slower, in terms of data handling.
Package maintenance on R is dependant on its users’ goodwill, hence, should the goodwill of a user be withdrawn from the support of a particle package, the package in question is subjected to probable depreciation (Vechev, & Yahav, 2016).
R portrays relatively weak scaling when it comes to the handling of large data sets.
Outstanding discipline is required for maintenance of a coding-standard which is proper, lack of which makes R-code maintenance harder, especially as the code grows larger.
Conclusion
Programming languages have been very crucial in the field of statistical programming, by helping data scientists in statistical models’ construction, data presentation and manipulation and even the handling of large data sets. Programming languages such as R, Python and SAS have been the most embraced and utilized programming languages. Each of the programming languages has its own merits and demerits, dependent on the type of data being handled. This fact makes it very difficult to point out, for sure, which programming language is the best. It, therefore, suffices to say that generally, the varied types of programming languages have played vital roles in the lives of data scientists, in the field of statistical programming.
References
Braun, W. J., & Murdoch, D. J. (2016). The first course in statistical programming with R. Cambridge University Press.
Vechev, M., & Yahav, E. (2016). Programming with “big code”. Foundations and Trends® in Programming Languages, 3(4), 231-284.
Kishore, G. D. K., & Reddy, M. B. (2017). Comparison of Programming Languages for Human Gender Classification. i-Manager’s Journal on Computer Science, 5(2), 34.