Making data analysis convenient and customizable through an open-source R programming language
R programming language is one of the most popular tools that is currently being widely adopted for statistical work. It is a very important tool used in Data Science. It is an open-source programming language developed by a wide community of avid developers across the globe. It is a combination of various packages and graphical libraries which gets continuously added and upgraded by the developers and available for free at the R, https://jp-seemore.com/ project website cran.r-project.org. This resource provides over 10,000 packages for programming in R. The interface of R is called R Studio which is a comprehensive environment that provides the ability to handle data, code, perform statistical modelling, and for developing outcomes in graphical or textual format. The R console takes the commands as input and is evaluated and executed subsequently. R language cannot automatically detect auto-formatting characters such as quotes and dashes, hence whenever a code is used from external sources the user should discreetly use those in the R environment.
Important Features of R-programming for dissertation work
Dissertations which involve a large span of data, available both on public domains as well as extracted from various sources, have found immense use of R programming from data mining to create statistical models and visuals. The application of R in data science is immense, and one can use it to perform simple data mining, statistical analysis to machine learning techniques. The user can create objects, functions and packages in R. It is also supported by most operating systems and as it comes as an open-source licensing and hence can be installed and used by anyone. For its free availability, it is very commonly used in the academic world but also has lately found its presence in various industries working in the data science field.
R combines both the procedural programming as well as object-oriented programing involving generic functions and is therefore called as a comprehensive programming language. As there are already over 10,000 built functions in R hence it provides convenience for easier programming using these functions to the coder. R is an interpreter-based language and hence can be portable independent of the machine. Thus, it is also easy to debug an error in the code. It can handle complex operations involving arrays, vectors, data frames and other objects with variable s