R programming is a computer language that is open-sourced and easy to use. It is supported by the R Foundation of Statistical Computing that is used to develop a software environment for statistical graphics and computing. This programming language is mostly used by statisticians and data miners in order to analyse data and statistical software. Moreover, the career prospects of R Programming have been increased in the past few years. After successful completion of R programming language courses, students have a great opportunity to work in the Data Science industry. There are many companies including Google, Twitter, and Facebook using R to accomplish their systematic goals. In this blog, we have covered the two fundamental topics of R – its usage and application in the real world. It is certain that you must have a tight grip over the basic of R programming before heading toward writing your assignments. So, read on, and get to polish your R programming fundamentals.
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.