Introduction – R Programming Language

For the last couple of decades and until now, R programming has been one of the most in-demand skills in many industries. Reasonably, R has been mentioned among the best languages for statistical programming, visualization, and analytics. Small and big companies in different fields are using the R programming language. Using various packages of this language, you can create elegant, dynamic and flexible data-heavy reports. Besides that, the language is highly customizable. Hence, it accommodates a wide range of application demands from different fields.

Application of R Programming in the Real World

Analysts looking to grow and make a mark in the world of analytics should consider R a must-have competence. For a better understanding of what R programming entails, look at the following real-world applications of the language.

1. Predicting sales volumes:

Analysts use R to fit autoregressive models to product sales. For example, at Google, such models are applied to predict retail, automotive, and home sales, as well as passenger arrival rates. These analytics help the company to determine the number of ads to put online for effective marketing.

2. Banking:

Since R is highly flexible, it can adapt to new financial modeling situations and efficiently produce summaries, predictions, etc. Big banks all over the world are using R to fit models for different finance-related functions such as mortgage loss and investment portfolio returns.

3. Weather forecasting:

National weather services use R to make weather-related predictions such as river flooding. R programming also enables the generation of representative graphics for a real-time hydrologic ensemble.

4. Manufacturing:

Predictive analytics using R play a crucial part in optimization processes. In manufacturing, it is a general way of using this kind of analytics to optimize operational efficiency. Often, data mining, exploration, and cleaning are a challenge in data analytics. With R programming, however, you have a variety of tools to perform multivariate data exploration and analysis without any difficulty.

5. Healthcare:

The healthcare industry generates large amounts of data through various operations including its record keeping and compliance procedures and processes. R has been one of the most common tools for using this data to improve the quality of healthcare. Programming using R has supported many medical and healthcare functions and lead to the improvement of the overall health management practice.

6. Journalism:

It’s amazing how R finds data-oriented solutions in any industry. In journalism, R is mostly used for visualization. Reputable media establishments such as the New York Times are known to be the frequent users of this powerful and praise-worthy data analysis and visualization tool.

Conclusion

R has about 5000 packages. Most of these packages are committed to particular applications. Additionally, R is easy to use and is platform-independent meaning you can use it with any operating system. The language is free. Therefore, you don’t need a license to use it. Another important attribute of R is that it is open-source. You can examine the code to understand what it is doing, add features, and fix bugs.

The biggest advantage of R is provided by its extensibility. As a developer, you can code your own software and distribute it as an add-on. The good thing about the R language is that you don’t necessarily have to be an R programming pro to start developing your applications.