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R programming language is also an environment that is used for statistical and data analysis as well as computing. Ross Ihaka and Robert Gentleman developed this programming language in the year 1993. R comes with a huge repertoire of statistical as well as graphical tools and techniques. It is a development on the S programming language with considerable improvements and its name is word play both in terms of its developers first names and the s programming language. R is primarily programmed in C and FORTRAN as well as R itself, which is why R is also one of the few self hosting programming languages.
R is not only popular in the academia, but is also the entrusted programming language of some of the biggest corporations such as Facebook, Google, Uber etc. Despite primarily having an interface that is command line, R can also host and support several third part GUIs such as Jupyter and R studio. R is an environment in the true sense of the word as far as programming language vocabulary goes because it is an intricately planned and truly coherent system. R is not merely a statistical system as considered by many, but it is an environment where various statistical operations can be implemented efficiently and with ease. R integrates almost all of basic C features in it and is very compatible with a number of programming languages. Another added advantage of R is its free availability online subject to GNU’s General Public License’s terms. R is compatible with both Windows OS and Linux and the latest version of the programming language,3.6.1 was released in July 2019. We also provide Statistics Homework Help and SPSS Homework Help
R is available online free of cost and easy to learn with a very simple coding paradigm and user friendly interface. Also, users can add various packages to improve and add functionalities to R. R has very strong capabilities when it comes to graphics. This makes it the go to programming language when it comes to data visualization. It has a vast library providing interactive graphical representations such as flow charts, pie charts, graphs etc.
R being an open source software has a very active and supportive user community that is growing by the day. R, like C is a programming language that is object oriented and its coherent and inclusive environment facilitates software development and computing in equal measures. R being an interpreted language can function without a compiler adding to its ease of use. R can interface with databases through certain packages like Roracle, RmySQL etc. R is extremely efficient with various data operations such as storage, manipulation, modeling etc and is highly compatible with other programming languages. R’s impressive vector arithmetic capabilities mean that R can directly operate on its vectors eliminating the need for excessive looping. However, R also has a couple of disadvantages.
This programming language’s origin from an older one called S means it cannot support three dimensional graphics. R has a rather weak origin. Unlike programming languages such as Python, R has a lack of basic security. Compared to Matlab and Python R is much slower and a more complicated language to learn making it harder for people without prior knowledge of basic programming to pick up the language. R being spread across various packages, beginners struggle to implement algorithms in R.
R is taught in almost all the major universities in the undergraduate level computer science and computer engineering courses. Careers are mostly related to data science and business analysis. R is a high demand popular programming language that is currently dominating the analytical, statistical as well as data science industries. R being a simple programming language, the users need only have basic math and programming knowledge as a prerequisite.
R has over two million users spanning across industries and firms. R finds a huge range of applications especially in industries involving heavy data and statistical analysis. Some of the major industry applications of R are briefly discussed.
Finance: Data science is a crucial element in the finance industry and R is the most popular and in-demand programming language when it comes to data sciences. R facilitates various prediction and visualization operations so fundamental to the finance industry.
Banking: R is used along with tools such as SAS for processes such as Mortgage Haircut Modeling and other risk analytics operations. Also it is combined with Hadoop for analyzing customer quality and retention analysis.
Healthcare: R finds extensive application in various healthcare domains such as drug development, pre clinical trials of the same, epidemiology, genetics in statistical modeling and data visualizations relating to the aforementioned.
Social Media: R is a boon to social media giants in that it is an efficient tool in various social media analytics such as sentiment analysis and data mining. R is also used in various social media to perform market analysis.
E Commerce: This is one of the leading industries that makes extensive use of R programming language. R has, in the recent times become one of the most essential and go-to to tools of the e-commerce industry. R comes in especially handy in cross selling and statistically analyzing and recommending various suggestions that these websites present the users with.
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