Importance of statistical Programming Languages
Broadly speaking, data science would not be as effective as it is without the help of statistical programming languages such as R and Python. Data scientists rely heavily on statistical programming languages to execute algorithms that generate knowledge and insights from big data. Statistical programming languages provide an optimum environment for processing big data by providing all essential tools to process data. Additionally, statistical programming languages provide a standard method that is applied by data scientists from across the world to generate statistical outputs. As a result, it is relatively easy to collaborate data mining tasks as long as the stakeholders are aware of the programming language being used. Modern-day advanced programming languages such as SAS, R, Python, and SQL allow data scientists to verify the accuracy of their findings by comparing results.
Advantage of R programming Language
Open-source – data scientists, can acquire the source code of R studio for free and without a license. R language allows anyone to process data as long as they have the knowhow to operate the R studio.
R programming Language has features such as dplyr and readr that provide support for data wrangling. Other main statistical programming languages do not provide such services.
Compatibility – R programming language is highly compatible with existing technologies and programming languages such as Python, C, and C++.
Disadvantages of R programming Language
Speed – when compared to other main programming languages such as Python, R is generally slower.
Complexity – Python uses a programming syntax that is similar to that of the natural language. On the other hand, most people find it challenging to learn R due to the complexity of its programming syntax.
Security – R offers inferior underlying security and lacks essential security features.