In the field of computer science, programming languages form the spine of software development. Statistical programming languages are those which have their focus on core functionality constructed on statistical concepts. Data science combines domain information, programming competencies, and knowledge of arithmetic and statistics to draw meaningful conclusions from a given set of data. Data scientists develop algorithms using statistical tools and apply them to all forms of data to create artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence and effort. In turn, these systems generate insights, which analysts and business users can interpret to make crucial decisions.
As data Scientists should administrate, clean, and analyze a huge amount of data regularly, doing it without the assistance of any software would be very tough. Statistical programming languages are hence essential to data scientists for providing an efficient method for calculating typical statistical outputs. Instead of using an inappropriate programming language and often having to develop workarounds, the data scientists can quickly process their data with the available functions, modules, and libraries offered by these languages. Some of the most common statistical programming languages that are used in the data science field are R, Python, Java, Scala, SQL, SAS, Julia, Matlab and so on. Among these, for their specificity, generality, popularity and performance, R and Python are more widely used.
R-Language:R is one of the most preferred data analysis languages, used by data scientists for statistical analysis, predictive modeling and visualization. It is a comprehensive programming language that assists in procedural programming involving functions as well as object-oriented programming with generic functions. Its massive repository and sheer versatility make R one of the most popular and standard statistical languages. Since it is a constantly evolving programming language, updates are provided whenever a new feature is added to it.Advantages of using R language:R language’s ability to address complex linear algebra makes it ideal for not just statistical analysis but also for neural networks, in contrast to its alternative SQL, whose analytical capabilities are rather limited. A single R command can perform complex statistical operations on data like calculating mean, median and standard deviation, unlike several lines of code in other languages. As it is an interpreted language, R produces a machine and platform independent code that doesn’t require a compiler and is portable in nature. It additionally allows easy debugging of errors in the code.R is cross-platform compatible and its packages can be installed and used on any operating system in any environment without having to make much changes.
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With over 10,000 packages in the open-source repository of CRAN for various disciplines like astronomy, biology, and so on, R caters to many statistical applications and facilitates easier programming when compared to other languages like Python.
While most of its functions are written in R itself, C, C++ or FORTRAN can be used for computationally heavy tasks. R is compatible with other programming languages like Java, .NET, Python, C, C++, and FORTRAN to manipulate objects directly if needed.R facilitates quality plotting and graphing. The popular libraries like ggplot2 and plotly advocate for aesthetic and visually appealing graphs that set R apart from other programming languages.R can handle simple as well as complex operations with vectors, arrays, data frames as well as other data objects of a wide variety and of varying size data sets. Since it can perform operations directly on vectors, it doesn’t require too much looping.
R is open-source software environment with growing number of contributors. Since it is free of cost and can be adjusted, adapted and easily integrated with other applications, it is widely spread. Due to a large number of researchers and leading statisticians using it, new ideas and technologies often appear in the R community first.
Using feature like Shiny and R Markdown, attractive reports that combine plain text with code and visualizations of the results, can be easily created.In distributed computing, tasks are split between multiple processing nodes to reduce processing time and increase efficiency. It has packages like ddR and multiDplyr that enable it to use distributed computing to process large data sets.R provides various data modeling and data operation facilities due to its interaction with databases. It has several built-in packages and R based environments like RStudio has enabled R to interact with databases like Roracle, Open Database Connectivity Protocol, RmySQL, and so on.R can be used for machine learning as well. The best use of R when it comes to machine learning is in case of exploration or when building one-off models.R with its extensive library of tools can be used for data manipulation and wrangling, which is the process of cleaning complex and inconsistent data sets to enable convenient computation and further analysis.R is integrated with all the formats of data storage due to which data handling becomes easy. It can pull data from APIs, servers, SPSS files, and many other formats.Unlike Python, which is a dynamically typed language, type errors do not cause issues when programming with R language.
Limitations of R language:R is not an easy language to learn. It has a steep learning curve and the algorithms in R language are spread across different packages, programmers without prior knowledge of those packages may find it difficult to implement algorithms.R is not an ideal general purpose programming language which means that it is not useful for tasks other than statistical programming.
Since R shares its origin with a much older programming language “S”, its base package does not have support for dynamic or 3D or animated graphics, unless packages like ggplot2 and so on are used. Unlike its alternative languages like Python, R stores the objects in its physical memory and as a result, large computations involving vast data utilize more memory, which doesn’t make it an ideal option when dealing with Big Data. However, with data management packages and integration with Hadoop possible, this is easily covered.R packages and the R programming language are much slower than other languages like MATLAB and Python. The quality of some packages in R is below par and needs improvement. Also, there’s no customer support of R Language, unlike other languages that can offer a solution quickly if something goes wrong.
R commands are hardly concerned about memory management, and so R can consume all the available memory, which can cause performance issues. R lacks basic security. This feature is an essential part of most programming languages like Python. Because of this, there are several restrictions with R as it cannot be embedded into a web-application without use of packages like Shiny server. R has a few unusual features that might catch out programmers experienced with other languages. For instance: indexing from 1, using multiple assignment operators, unconventional data structures and so on.
