Explore R Applications - Why Top Companies Use R Programming
Published
R is one of the most modern tools. Today, millions of analysts, researchers, and brands like Facebook, Google, Bing, Accenture, and Wipro use R to solve complex problems. The applications of R are not limited to any particular sector but are also used in banking, electronic commerce, finance and many other areas. This article will familiarize you with the real-life analogies of the R programming language.
What is the R language?
The R language is an open source programming language and software package maintained by the R development team. The R language is used for performing statistical operations and is a command line driven program.
Nowadays, R is considered the most popular analytical tool in the world when comparing R, SAS and SPSS. It is estimated that there are between 250,000 and over 2 million users.
Looking at its online popularity, R is the clear winner. Additionally, R has more blogs, discussion groups, and email lists than any other tool, including SAS. That's why R was the first choice in most surveys.
Applications of R programming
Some of the most important applications of the programming language R in the area of Data science are:
1. Finance
Data Science is most commonly used in the financial industry.
Those: hslu.ch
R is the most popular tool for this role. The reason for this is that R provides an advanced statistical suite that can be used to perform all necessary financial tasks.
With the help of R, financial institutions are able to measure downside risks, adjust risk performance, and use visualizations such as candlestick charts, density plots, drawdown plots, etc. R also provides tools for moving averages, autoregression, and time series analysis, which are the core of financial applications. R is used extensively for credit risk analysis at companies such as ANZ and for portfolio management.
The financial industry also uses R's statistical time series methods to model the development of the stock market and predict stock prices. R also offers options for evaluating financial data with its packages such as quantmod, pdfetch, TFX, pwt, etc. R makes it easy for you to extract data from online assets. With the help of RShiny, you can also display your financial products through clear and attractive visualizations.
2. Banking
Like financial institutions, banks also use R for credit risk modeling and other forms of risk analysis.
Banks often use the mortgage discount model, which allows them to take over the property in the event of a loan default. Mortgage Haircut Modeling includes sales price distribution, sales price volatility, and expected default calculation. For these purposes, R is often used together with proprietary tools such as SAS.
R is also used in conjunction with Hadoop used to facilitate analysis of customer quality, customer segmentation and customer loyalty.
Bank of America uses R for financial reporting. With the help of R you can Data Scientist Analyze financial losses at BOA and use R's visualization tools.
3. Healthcare
Genetics, bioinformatics, drug discovery, and epidemiology are some of the areas in healthcare where R is used extensively. With the help of R, these companies are able to condense data and process information that provides essential background for further analysis and data processing.
For advanced processing such as drug discovery, R is most commonly used for conducting preclinical studies and analyzing drug safety data. It also offers its users a suite for exploratory data analysis and clear visualization tools.
R is also known for its Bioconductor package, which provides various functions for analyzing genomic data. R is also used for statistical modeling in the field of epidemiology, where data scientists analyze and predict the spread of diseases.
4. Social media
For many beginners in Data Science and R, social media is a data playground. Sentiment analysis and other forms of social media data mining are some of the important statistical tools used with R. Social media is also a challenging field for data science because the data on social media websites is usually unstructured. R is used for social media analysis to segment potential customers and target them to sell your products.
Another popular category of social media analysis is user sentiment mining. With the help of R, companies are able to model statistical tools that analyze user sentiment and allow them to improve their experiences.
SocialMediaMineR is a popular R package that can determine the popularity of their social media reach from multiple URLs. In addition, companies use R to analyze the social media market and generate leads for the user.
5. E-Commerce
The e-commerce industry is one of the most important areas where data science is used. R is one of the standard tools used in e-commerce.
Since these internet-based companies deal with various forms of data, structured and unstructured, as well as from different data sources such as spreadsheets and databases ( SQL & NoSQL ), R proves to be an effective choice for these industries.
E-commerce companies use R to analyze cross-selling products to their customers. When cross-selling, we suggest additional products to the customer that complement their original purchase. These types of suggestions and recommendations are best analyzed using R.
Various statistical techniques such as linear modeling are required to analyze purchases made by customers and predict product sales. Additionally, companies use R to perform A/B testing analysis on their product pages.
6. Manufacturing
Manufacturing companies like Ford , Mondel and John Deere use R to analyze customer sentiment. This helps them optimize their products according to current consumer interests and adapt their production volume to fluctuating market demand. They also use R to minimize their production costs and maximize their profits.
