Are you a developer interested in staying current in your field? Most employers recognize that employees and potential job candidates who are able to and interested in learning new technologies and continuing their education are better able to stay competitive in the market and provide increased value for the companies they work for. Today’s article will discuss the latest programming language, known as “R” which is used for statistical computing and graphics, and will help you decide whether it is worth the investment.
The Rise of R
While not as popular as other programming languages such as Java and C++, R has seen an ongoing and steady rate of adoption since it came on the scene. It is continuously noted as more ubiquitous than other specialized languages, including PL/SQL or even MATLAB, and this is possibly due to the open-source and multiplatform nature of the language. R can be used on Windows, Mac and Linux systems and is presented as a conveniently small download.
Furthermore, R is considered to be more than just a programming language but an Integrated Development Environment (or IDE). This provides comprehensive facilities that consist of source code editors, build automation tools and debuggers. R is designed to run programs, perform calculations, and handle data in a fully inclusive manner. There are almost 8,000 packages currently available via the Comprehensive R Archive Network, and new releases are made available multiple times per year.
How R Is Used
Given that the default IDE for R is considered to be more on the basic side than not, finding a high-quality IDE can make a big difference in using R. Even technology frontrunner Microsoft has taken an interest in the specialized language with a unique plugin for Visual Studio forthcoming. An example of a good IDE for R is RStudio which provides a pretty full-featured user experience. Other IDEs that work well with R include Vim with Tmux, Eclipse, and RKWard.
The simplest uses for R allow for basic arithmetic, package fetching, file system navigation, and running of R files. Various built-in data sets come with R, which can be accessed by typing data (data set name). The par function is used to set or query graphical parameters and can be used to draw histograms in row or column order. Linear and nonlinear modeling, classical statistical test, time-series analysis, classification, clustering, and other statistical techniques are all within the scope of R programming.
These, in addition to other functionalities of the language, make R a helpful software environment for statistical computing and graphics. Data miners in particular are finding R to be helpful in developing statistical software and doing data analysis. All things considered, while R seems to have a bit of a learning curve, overall the programming language has a good deal of usefulness especially in the age of big data and a growing need to understand and analyze quantitative information in quick and efficient ways. If you are considering learning a new program language, R might be a great option based on your specific skills and interests.
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