How Did this Language Become Usable for Statistical Computing?
R is a GNU based project that is similar to the S language developed by John Chambers and colleagues at Bell Laboratories. R programming is a computer language used for different statistical and graphical techniques including classical statistical tests, classification, clustering, linear and nonlinear modelling, etc. There could be various reasons behind using the R language for statistical computing and graphics. The top three reasons are explained below:
Free and open-source
There are few programming languages that charge a high amount to use them but R programming is open-source and free to download. This language is licensed by the GNU General Public License. Moreover, most R programming packages work under the same license, thus it can be used even in commercial applications.
R language supports all the platforms such as Linux, Windows, and Mac. For example – if you write code on one platform, you will be easily accessible to port it to another platform easily without any hurdles. Cross-platform interoperability can is an important feature behind using R for statistical computing and graphics.
R is popular and still increasing popularity
R programming language is one of the top 5 most used languages in the field of data science and machine learning. According to IEEE top programming languages 2019 report, R is ranked 5th after Python, Java, C, and C++.
Applications of R Programming in Real-World
R is one of the best pioneering tools being used by various companies and brands such as Bing, Accenture, Facebook, Google, etc. The applications of R is not just restricted to a single sector, it can be seen that R is being used in banking, finance, e-commerce, and other sectors as well. So, let’s read about them below:
As we have discussed above that R is one of the most popular and advanced programming languages used in industries like data science, machine learning, etc. R comes with a rich package including over 9100 packages. With the use of different R syntax, researchers are allowed to quickly import, analyse, and clean data from different resources. It also allows users to create a chart that can help plot data and create exciting visualisations.
Students who want to begin their career in R and Data Science, social media can be a data playground for them. Platforms like Sentiment Analysis and other types of social media data mining are the examples of statistical tools utilised by R. Social Media can be a challenging field for Data Science because the established data on social media sites is amorphous.
Manufacturing companies like Modelez, Ford, and John Deere are utilising R to examine their customer’s sentiment. The use of R helps manufacturing companies to optimise products as per their consumer interests and market demand. Moreover, using R reduces production costs and enhances profits.
The use of R in the healthcare industry is in great demand as it helps crunch data and information process and gives an essential backdrop for data processing and analysis. Additionally, R is being widely used in the processing of advanced drug discovery to perform activities like pre-clinical trials, examining the drug-safety data, and more.