The R core-development team is responsible for maintaining the open-source programming language and software package known as R. Statistical processes are also carried out using the R language, which is a command-line driven application.
If we compare R, SAS, and SPSS now, R is regarded as the most widely used analytics programme in the world. The number of users ranges from 250000 to over 2 million, according to estimates.
If we consider how popular R is online, it is clearly the winner. Also, compared to other tools, like SAS, R has more blogs, discussion groups, and email lists. R therefore again received the most votes in the majority of the surveys.
Applications of R Programming
The following are some significant R programming language applications in the field of data science:
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Finance
The financial sector is where data science is most frequently applied.
The commonly utilised tool for this function is R. This is due to the fact that R offers a sophisticated statistical package capable of doing all required financial functions.
Financial institutions can use visualisations like candlestick charts, density plots, drawdown plots, etc., execute downside risk measurement, and alter risk performance with the use of R.
The core of financial applications, time-series analysis, moving averages, and autoregression are all supported by R. R is frequently used for portfolio management and credit risk analysis at companies like ANZ.
Banking
The banking sector uses R for credit risk modelling and other types of risk analytics, just like financial institutions do.
The Mortgage Haircut Model, which enables banks to seize the property in the event of loan defaults, is frequently used by banks. Mortgage Haircut Modeling includes calculating the predicted shortfall as well as the sales price distribution and volatility. R is frequently used in conjunction with exclusive tools like SAS for these goals.
Healthcare
The use of R in the healthcare industry is widespread, especially in the areas of genetics, bioinformatics, drug discovery, and epidemiology. These businesses can process information and crunch data with the aid of R, giving a crucial foundation for additional analysis and data processing.
R is primarily used for pre-clinical studies and drug-safety data analysis for more complex processing, such as drug development. Additionally, it gives users access to a package of tools for undertaking exploratory data analysis and striking visualisations.
Social Media
Social media serves as a data playground for many rookies in Data Science and R. Among the essential statistical tools utilised with R are sentiment analysis and various types of social media data mining.
Because most of the data found on social media platforms is unstructured, social media is also a difficult area for data science. R is used for social media analytics, segmenting and focusing on potential customers for product sales.
E-Commerce
One of the most significant industries to use data science is e-commerce. One of the tools that is frequently used in e-commerce is R.
R proves to be a successful solution for these industries because these internet-based businesses must deal with a variety of structured and unstructured data types, as well as from multiple data sources including spreadsheets and databases (SQL & NoSQL).
Manufacturing
R is used by manufacturers like Ford, Modelez, and John Deere to examine client sentiment. This enables them to adapt their product to changing consumer interests and to match the volume of their output to shifting market demand. R programming is also used by them to reduce production costs and increase revenue.
