Typically, programming languages are widely used by software developers to develop applications and software systems. However, statistical programming languages such as R, SQL, SAS, Python and MATLAB are widely used by data scientists and data analysts to develop algorithms to process data. Statistical programming languages and visual programming languages such as RapidMiner play a pivot role in enabling data scientists and analysts process data. These statistical programming languages allow the processing of all forms of data such as structured, unstructured and semi-structured data (Chen & et al., 2005). Having robust analytical skills demands that the data scientists must have extensive knowledge in using one or more statistical programming languages.

One of the primary advantages of statistical programming languages is that they enable data scientists to develop algorithms to process the data. Most of the statistical programming languages are coded and require the data scientists and analysts to have knowledge of the syntax of the language. Others such as RapidMiner are visual platforms that allow the analysts to develop algorithms using drag-and-drop techniques.

Statistical programming languages also provide the means and techniques to process data. For instance, they provide algorithms to conduct classification, clustering, regression analysis and association rule mining. Advancement in technology and the embracement of artificial intelligence and machine learning in data mining has led to the automation of analytical algorithms. As a result, analytical tasks such as predictive modelling can be executed using automated systems, thus reducing the amount of time and energy required to be dedicated by the data scientists in analytical tasks (Chen & et al., 2005). Such platforms also help in the modelling and visualization of the results generated. This means that the data scientists can easily and effectively present and describe the results to the target audience since the results are presented in graphs and charts. In the long run, the statistical programming languages help data scientists to turn massive amounts of data into meaningful and actionable information.

The digitization of the current business environment has enabled people to collaborate in the execution of tasks. Similarly, data scientists and analysts from across the globe can collaborate in analytical tasks since the statistical programming languages provide a standardized platform to process data. Data scientists can also similar or different languages to determine the accuracy of analytical results (Raychev & et al., 2014). These capabilities have significantly improved the effectiveness and suitability of data analytical in the business world. On an overall note, both coded and visual statistical programming languages play a fundamental role in data analytics by providing the means to process and convert data into knowledge and useful information.

Advantages of R

R is a statistical programming language that is typically developed and edited using R Studio. The tool is one of the most applied statistical programming languages, and extensive popularity can be attributed to the tool's numerous advantages. One of the notable benefits of R is that it is open-source software. This means data scientist can use the platform freely and can review its source code to understand its functionality as well as determine ways to improve it. R is a free platform, thus eliminating the need to pay for license or subscription fee when working with it or an integral part of R (Matloff, 2011). another significant advantage of R is that it is highly compatible with other technologies.

R is platform-independent, meaning that it does not rely on the underlying hardware and software resources when being installed (Matloff, 2011). Additionally, R can be used directly from the internet without installing it on a computer. R is also highly compatible with other database management systems and data processing technologies. The open-source nature of nature enables developers and data scientists to point out errors and areas where the platform could further be improved. As a result, R is under constant improvement, with every version of R addressing the challenging the challenges that limited the functionality of the previous versions. R is still an immature technology, and continuous development promise to deliver a platform that has embraced all emerging data mining technologies.

Disadvantages of R

R is a coded programming language that requires data scientists and analysts to understand its language's syntax. R has a complicated syntax, thus making it difficult to learn, especially for inexperienced developers. As a result, many data scientists and analysts prefer other simpler languages such as Python whose syntax is similar to that of the natural language. Another significant disadvantage is that R is relatively slower compared to other statistical programming languages (Matloff, 2011). R is, therefore not suitable in analytical tasks that process data and generate results in real-time. R also requires more memory space compared to other analytical tools since it stores data in more objects. R does not also provide enhanced security features that determine whether a statistical programming platform is suitable for use in web applications. Future advancements of R are anticipated to address these challenges.