Why R Programming Language
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R is a free and robust statistical programming environment. It is a powerful tool for statistics, statistical programming, and visualizations; it is prominently used for statistical analysis. It has evolved from S, developed by John Chambers at Bell Labs, which is a birthplace of many programming languages including C. Ross Ihaka and Robert Gentleman developed R in the early 1990s.
Roughly around the same time, bioinformatics was emerging as a scientific discipline because of the advent of technological innovations such as sequencing, high throughput screening, and microarrays that revolutionized biology. These techniques could generate the entire genomic sequence of organisms; microarrays could measure thousands of mRNAs, and so on. All this brought a paradigm shift in biology from a small data discipline to one big data discipline, which is continuing till date. The challenges posed by this data shoot-up initially compelled researchers to adopt whatever tools were available at their disposal. Till this time, R was in its initial days and was popular among statisticians. However, following the need and the competence of R during the late 90s (and the following decades), it started gaining popularity in the field of computational biology and bioinformatics.
The structure of the R environment is a base program that provides basic programming functionalities. These functionalities can be extended with smaller specialized program modules called packages or libraries. This modular structure empowers R to unify most of the data analysis tasks in one program. Furthermore, as it is a command-line environment, the prerequisite programming skill is minimal; nevertheless, it requires some programming experience.
In recent years, there have been significant advances in genomics and molecular biology techniques, giving rise to a data boom in the field. Interpreting this huge data in a systematic manner is a challenging task and requires the development of new computational tools, thus bringing an exciting, new perspective to areas such as statistical data analysis, data mining, and machine learning. R, which has been a favorite tool of statisticians, has become a widely used software tool in the bioinformatics community. This is mainly due to its flexibility, data handling and modeling capabilities, and most importantly, due to it being free of cost.