Well, we already know that there are different programming languages available nowadays. It becomes difficult for analysts and researchers to choose one among them. The R programming language is also one of them. Therefore, today we will learn some uses of R. It will help you to decide why you should choose R for your specific work.
However, R is the latest cutting-edge tool. Millions of analysts, researchers, and companies like Facebook, Google, Bing, Accenture, and Wipro use R. Moreover, it is not limited to the industries. We can also use R in banking, e-commerce, finance, and a variety of other fields. To learn the uses of R in more detail, read the blog carefully. In this blog, you will find exciting applications of the R programming language. So, keep scrolling to learn more. But, first, let’s discuss R.
R Programming Language
R is an open-source computing language. The R core development team maintains this software package. Also, the R is a command-line-driven software. It helps in different statistical computations.
When comparing R, SAS, and SPSS, R is now the most popular analytics tool in the world. Furthermore, the number of users is likely to range from 250000 to over 2 million.
Moreover, when it comes to internet popularity, R is already the true winner. R also has several blogs, discussion forums, and email lists of any program, including SAS. Therefore, R was once again the most popular choice in most of the studies.
Now, let’s learn some common applications of R.
Common Uses Of R
The following are some of the most useful applications of the R in the field of data science:
Data science is commonly used in the field of finance. However, for this purpose, data scientists use R. This is because of the advanced statistical package. It can do all of the relevant financial operations.
Financial organizations can use R to monitor downside risk and manage risk performance. Also, they can use visualizations like candlestick charts, density plots, and drawdown plots.
Moreover, R also has tools for moving averages, autoregression, and time-series analysis. These tools are important in financial applications. Also, companies like ANZ, commonly use R for credit risk analysis and portfolio management.
In addition, the finance industry is also using R’s time-series statistical technique. It helps them to control their stock market and estimate share values. Quantmod, pdfetch, TFX, pwt, and other R packages offer tools for financial data mining. Also, R makes it simple to extract data from web resources.
Just like the finance sector, banks also use R for credit risk modeling and other types of risk analytics.
Banks generally use the Mortgage Haircut Model. It allows them to seize property in the case of loan default. This model includes:
- The sales price distribution
- Also, the volatility of the sales price
- The estimation of the expected shortage
Moreover, R is also used in connection with Hadoop. It helps to check customer quality, customer segmentation, and retention.
Bank of America uses R for financial reporting. The data scientists at BOA also examine financial losses and use R’s visualization features.
Healthcare is also one of the common uses of R. It is widely used in the sectors of genetics, bioinformatics, drug discovery, and epidemiology in healthcare. These firms can crunch data and process information with the help of R. Also, they can form the basis for future analysis and data processing.
Furthermore, R helps in performing pre-clinical tests and assessing drug-safety data. Also, it helps in complex operations such as drug development. It provides various tools to its users for doing exploratory data analysis and vivid visualization.
For many newcomers to Data Science and R, social media is a data playground. There are various vital statistical techniques that are used with R. For example, sentiment analysis and other kinds of social media data mining.
Moreover, the data on social media websites is usually unstructured. That is why Social Media is also a difficult topic for Data Science. Therefore, social media analytics uses R for segmenting and targeting prospective buyers for sales activities.
In addition, user sentiment mining is another important category in social media analytics. Companies use model statistical tools that examine user attitudes with the help of R. R helps them to enhance their experiences.
SocialMediaMineR is a popular R program that can mine the popularity of many URLs’ social media reach. Also, it helps businesses to study the social media industry and produce leads for users.
One of the most important industries that use Data Science is e-commerce. However, R is also one of the programming languages in the field of e-commerce.
R is a good fit for this field. Because these internet-based businesses must deal with both structured and unstructured data. Also, they have to manage data from a variety of sources. For example, spreadsheets and databases (SQL and NoSQL).
Moreover, R helps e-commerce organizations to study cross-selling possibilities for their consumers. It is the ideal tool for analyzing these kinds of ideas and recommendations.
Furthermore, there are several statistical approaches required to analyze customer purchases and estimate product sales. For example, linear modeling. Also, R helps businesses to do A/B testing across the pages of their products.
It is also one of the uses of R. Several manufacturers use R to study customer feedback. For example, Ford, Modelez, and John Deere. This allows businesses to modify their product according to consumer preferences. Also, they adjust production volume to changing market demand. R also helps reduce manufacturing costs and increase revenues.
Moving further, let’s learn some real-life applications of R.
Real-Life Uses Of R
R applications are useless until you understand how people and organizations use the R programming language.
Facebook uses R to update its social network graph and status. Also, it R helps in forecasting the interaction with colleagues.
Ford Motor Company
Ford uses Hadoop. It also uses R for statistical analysis and data-driven decision-making.
R helps Google to estimate the return on investment (ROI) on advertising campaigns and forecast economic activity. Also, it increases the influence of social media marketing.
This is another real-life uses of R. R is a key component of Foursquare’s famed recommendation engine.
John Deere statisticians use R for reliable and accurate time series modeling and geospatial analysis. The data is then integrated into Excel and SAP.
Microsoft uses R for the Xbox matchmaking service. Also, R helps Microsoft with the Azure ML framework’s statistical engine.
It is the backbone of the Firefox web browser. It uses R to show online activity.
New York Times
The New York Times uses R in its news cycle to analyze data and generate visuals before they go to print.
National Weather Service
R is used by the National Weather Service’s River Forecast Centers. Consequently, they easily can make flood predicting visualizations.
For advanced statistical modeling, R is a part of Twitter’s Data Science toolkit.
Trulia uses R. It is a real-estate research website. R helps it to estimate home prices and local crime statistics.
Australia’s fourth-largest bank, ANZ uses R for credit risk analysis.
To sum up, we have discussed the several uses of R in the above blog. We have gone through from common to real-life applications of R. As we can see, some various big companies and firms use R. For example, Facebook, Google, Microsoft, ANZ bank, etc. Moreover, it helps in developing automobiles, analyzing user experience, forecast weather, etc. It shows the popularity and importance of R in data science. So, in the end, I hope this blog will be useful for you in the learning of various R uses.