What is R analytics?
R analytics is data analytics using R programming language, an open-source language used for statistical computing or graphics. This programming language is often used in statistical analysis and data mining. It can be used for analytics to identify patterns and build practical models. R not only can help analyze organizations’ data, but also be used to help in the creation and development of software applications that perform statistical analysis.
With a graphical user interface for developing programs, R supports a variety of analytical modeling techniques such as classical statistical tests, clustering, time-series analysis, linear and nonlinear modeling, and more. The interface has four windows: the script window, console window, workspace and history window, and tabs of interest (help, packages, plots, and files). R allows for publication-ready plots and graphics and for storage of reusable analytics for future data.
R has become increasingly popular over many years and remains a top analytics language for many universities and colleges. It is well established today within academia as well as among corporations around the world for delivering robust, reliable, and accurate analytics. While R programming was originally seen as difficult for non-statisticians to learn, the user interface has become more user-friendly in recent years. It also now allows for extensions and other plugins like R Studio and R Excel, making the learning process easier and faster for new business analysts and other users. It has become the industry standard for statistical analysis and data mining projects and is due to grow in use as more graduates enter the workforce as R-trained analysts.
What are the benefits of R analytics?
Business analytics in R allows users to analyze business data more efficiently. The following are some of the main benefits realized by companies employing R in their analytics programs:
Democratizing Analytics Across the Organization: R can help democratize analytics by enabling business users with interactive data visualization and reporting tools. R can be used for data science by non data scientists so that business users and citizen data scientists can make better business decisions. R analytics can also reduce time spent on data preparation and data wrangling, allowing data scientists to focus on more complex data science initiatives.
Providing Deeper, More Accurate Insights: Today, most successful companies are data driven and therefore data analytics affects almost every area of business. And while there are a whole host of powerful data analytics tools, R can help create powerful models to analyze large amounts of data. With more precise data collection and storage through R analytics, companies can deliver more valuable insights to users. Analytics and statistical engines using R provide deeper, more accurate insights for the business. R can be used to develop very specific, in-depth analyses.
Leveraging Big Data: R can help with querying big data and is used by many industry leaders to leverage big data across the business. With R analytics, organizations can surface new insights in their large data sets and make sense of their data. R can handle these big datasets and is arguably as easy if not easier for most analysts to use as any of the other analytics tools available today.
Creating Interactive Data Visualizations: R is also helpful for data visualization and >data exploration because it supports the creation of graphs and diagrams. It includes the ability to create interactive visualizations and 3D charts and graphs that are helpful for communicating with business users.
How can R analytics be implemented?
While R programming was originally designed for statisticians, it can be implemented for a variety of uses including predictive analytics, data modeling, and data mining. Businesses can implement R to create custom models for data collection, clustering, and analytics. R analytics can provide a valuable way to quickly develop models targeted at understanding specific areas of the business and delivering tailored insights on day-to-day needs.
R analytics can be used for the following purposes:
- Statistical testing
- Prescriptive analytics
- Predictive analytics
- Time-series analysis
- What-if analysis
- Regression models
- Data exploration
- Text mining
- Data mining
- Visual analytics
- Web analytics
- Social media analytics
- Sentiment analysis
R can be used to solve real-world business problems by turbocharging an organization's analytics program. It can be integrated into a business’s analytics platform to help users get the most out of their data. With an extensive library of R functions and advanced statistical techniques, R can be used to apply statistical models to your analysis and better understand trends in the data. It can help predict potential business outcomes, identify opportunities and risks and create interactive dashboards to gain a comprehensive view of the data. This can lead to better business decisions and increased revenue.
Why should you get started with R analytics?
R is an open source coding language and has an extensive community which means there are many sample codes available for a wide range of data analysis projects. It’s also free and therefore a great starting point for experimenting with analytics at your company. Oftentimes, R is used for testing out a new analytics project or Proof of Concept to demonstrate the benefits of further investment by management. With over two million users, there are countless R forums and tutorials available to help analysts learn the language on their own.
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