Uncover the R Applications – Why Top Companies are using R Programming
R is one of the latest cutting-edge tools. Today, millions of analysts, researchers, and brands such as Facebook, Google, Bing, Accenture, Wipro are using R to solve complex issues. The applications of R are not limited to just one sector, we can see the use of R in banking, e-commerce, finance, and many more sectors. This article will make you familiar with the real-life analogies of the R programming language.
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What is R Language?
R language is an open-source programming language and software-package maintained by the R core-development team. Also, the R language is used for performing statistical operations and is a command-line driven program.
Nowadays, R is considered as the most popular analytics tool in the world if we compare R, SAS and SPSS. Further, it is estimated that its users range from 250000 to over 2 million.
R is the hands-down the winner if we look at its online popularity. Also, R has more blogs, discussion groups, and email lists than any other tool including SAS. Thus, R was again the top choice in most of the surveys.
Applications of R Programming
Some of the important applications of R Programming Language in the domain of Data Science are:
Data Science is most widely used in the financial industry.
R is the most popular tool for this role. This is because R provides an advanced statistical suite that is able to carry out all the necessary financial tasks.
With the help of R, financial institutions are able to perform downside risk measurement, adjust risk performance and utilize visualizations like candlestick charts, density plots, drawdown plots, etc.
R also provides tools for moving averages, autoregression and time-series analysis which forms the crux of financial applications. R is being widely used for credit risk analysis at firms like ANZ and portfolio management.
Finance industries are also leveraging the time-series statistical processes of R, to model the movement of their stock-market and predict the prices of shares. R also provides facilities for financial data mining through its packages like 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 demonstrate your financial products through vivid and engaging visualizations.
Just like financial institutions, banking industries make use of R for credit risk modeling and other forms of risk analytics.
Banks make heavy usage of the Mortgage Haircut Model that allows them to take over the property in case of loan defaults. Mortgage Haircut Modelling involves sales price distribution, the volatility of the sales price and the calculation of expected shortfall. For these purposes, R is often used alongside proprietary tools like SAS.
R is also used in conjunction with Hadoop to facilitate the analysis of customer quality, customer segmentation, and retention.
Bank of America makes use of R for financial reporting. With the help of R, the data scientists at BOA are able to analyze financial losses and make use of R’s visualization tools.
Genetics, Bioinformatics, Drug Discovery, Epidemiology are some of the fields in healthcare that make heavy usage of R. With the help of R, these companies are able to crunch data and process information, providing an essential backdrop for further analysis and data processing.
For more advanced processing like drug discovery, R is most widely used for performing pre-clinical trials and analyzing the drug-safety data. It also provides a suite for performing exploratory data analysis and vivid visualization tools to its users.
R is also popular for its Bioconductor package that provides various functionalities for analyzing the 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 that are used with R.
Social Media is also a challenging field for Data Science because the data prevalent on social media websites is mostly unstructured in nature. R is used for social media analytics, for segmenting potential customers and targeting them for selling your products.
Furthermore, mining user sentiment is another popular category in social media analytics. With the help of R, companies are able to model statistical tools that analyze user sentiments, allowing them to improve their experiences.
SocialMediaMineR is a popular R package that can take multiple URLs and churn the popularity of their reach on social media. Furthermore, companies use R to analyze the social media market and generate leads for the user.
The e-commerce industry is one of the most important sectors that utilize Data Science. R is one of the standard tools that is being used in e-commerce.
Since these internet-based companies have to deal with various forms of data, structured and unstructured, as well as from varying data sources like spreadsheets and databases (SQL & NoSQL), R proves to be an effective choice for these industries.
E-commerce companies use R for analyzing cross-selling products to their customers. In cross-selling, we suggest additional products to the customer, that complements their original purchase. These types of suggestions and recommendations are best analyzed with the help of R.
Various statistical procedures like linear modeling are necessary to analyze the purchases made by the customers as well as in predicting product sales. Furthermore, companies use R for carrying out A/B testing analysis across the pages of their products.
Manufacturing companies like Ford, Modelez, and John Deere use R to analyze customer sentiment. This helps them optimize their product according to trending consumer interests and also to match their production volume to varying market demand. They also use R to minimize their production costs and maximize profits.
Some More Applications of R
These are some more R applications, which you can use to make better decision making:
- R is primarily used for descriptive statistics. Descriptive statistics summarize the main features of the data. R is used for a variety of purposes in summary statistics like central tendency, measurement of variability, finding kurtosis and skewness.
- R is most widely used for exploratory data analysis. R’s most popular package ggplot2 is considered to be one of the best visualization libraries due to its aesthetics and interactivity.
- R is also used for analyzing both discrete and continuous probability distributions. For example, using the ppois() function, you can draw Poisson distribution. Similarly, with the help of dbinom() function, you can plot the binomial distribution.
- R also allows hypothesis testing to validate statistical models.
- You can find a correlation between the variables in R using the lm() function that is used for establishing linear regression as well as multivariable linear regression.
- Using R, you can make use of the tidyverse package that is used for organizing data and data pre-processing.
- R also provides an interactive web application package called RShiny. With this package, you can develop interactive visualizations that can be embedded on your web-pages.
- Moreover, with the help of R, you can develop predictive models that make use of machine learning algorithms to find the occurrences of future events.
- You can integrate R with Hadoop and HDFS file system to process large datasets like social media data quickly and efficiently.
- R is also useful for developing statistical software packages and to implement analytical processing in other software suites.
Real-Life Use Cases of R Language
R applications are not enough until you don’t know how people/companies are using the R programming language.
- Facebook – Facebook uses R to update status and its social network graph. It is also used for predicting colleague interactions with R.
- Ford Motor Company – Ford relies on Hadoop. It also relies on R for statistical analysis as well as carrying out data-driven support for decision making.
- Google – Google uses R to calculate ROI on advertising campaigns and to predict economic activity and also to improve the efficiency of online advertising.
- Foursquare – R is an important stack behind Foursquare’s famed recommendation engine.
- John Deere – Statisticians at John Deere use R for time series modeling and also geospatial analysis in a reliable and reproducible way. The results are then integrated with Excel and SAP.
- Microsoft – Microsoft uses R for the Xbox matchmaking service and also as a statistical engine within the Azure ML framework.
- Mozilla – It is the foundation behind the Firefox web browser and uses R to visualize web activity.
- New York Times – R is used in the news cycle at The New York Times to crunch data and prepare graphics before they go for printing.
- Thomas Cook – Thomas Cook uses R for prediction and also Fuzzy Logic Systems to automate price settings of their last-minute offers.
- National Weather Service – The National Weather Service uses R at its River Forecast Centers. Thus, it is used to generate graphics for flood forecasting.
- Twitter – R is part of Twitter’s Data Science toolbox for sophisticated statistical modeling.
- Trulia – Trulia, the real-estate analysis website uses R for predicting house prices and local crime rates.
- ANZ Bank – ANZ, the fourth largest bank in Australia uses R for its credit risk analysis.
List of Companies that use R for Analytics
Hope, you got all the answer through this R applications tutorial. Many brands use R programming to design vehicles, monitor user experience, weather prediction, etc. Empire of R language is increasing day by day, many other sectors will be using R for better results.
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