What is R analytics?

R analytics (or R programming language) is a free, open-source software used for all kinds of data science, statistics, and visualization projects. R programming language is powerful, versatile, AND able to be integrated into BI platforms like Sisense, to help you get the most out of business-critical data.

These integrations include everything from statistical functions to predictive models, such as linear regression. R also allows you to build and run statistical models using Sisense data, automatically updating these as new information flows into the model.

The language is built specifically for statistical analysis and data mining. R analytics is not just used to analyze data, but also to create software and applications that can reliably perform statistical analysis. In addition to the standard statistical tools, R includes a graphical interface. As such, it can be used in a wide range of analytical modeling including classical statistical tests, lineal/non-lineal modeling, data clustering, time-series analysis, and more.

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Statisticians like using R because it produces plots and graphics that are ready for publication, down to the correct mathematical notation and formulae. It’s quite popular for its visualizations: graphs, charts, pictures, and various plots. BI analysts can use these types of visualizations to help people understand trends, outliers, and patterns in data.

Another reason for its popularity is that its command-line scripting allows users to store complex analytical methods in steps, to be reused later with new data.

Instead of having to reconfigure a test, users can simply recall it. This also makes it useful for validation and confirmation purposes. Researchers can explore statistical models to validate them or check their existing work for possible errors. Even though it’s known as a more complex language, it remains one of the most popular for data analytics.

Sisense uses R for its analytics products, see it in action:

Investment Portfolio- Financial Dashboard

Get the most out of data analysis using R

R, and its sister language Python, are powerful tools to help you maximize your data reporting. Instead of using programming languages through a separate development tool like R Studio or Jupyter Notebooks, you can integrate R straight into your analytics stack, allowing you to predict critical business outcomes, create interactive dashboards using practical statistics, and easily build statistical models. Integrating R and Python means advanced analytics can happen faster, with accurate and up-to-date data.

What can I use R analytics for?

There are multiple ways for R to be deployed today across a variety of industries and fields. One common use of R for business analytics is building custom data collection, clustering, and analytical models.

Instead of opting for a pre-made approach, R data analysis allows companies to create statistics engines that can provide better, more relevant insights due to more precise data collection and storage.

More importantly, using R as opposed to boxed software means that companies can build in ways to check for errors in analytical models while easily reusing existing queries and ad-hoc analyses. In order to get the most out of your data, R, and its sister language, Python, should be a part of your analytics stack. They can be integrated in a way that makes them as easy to use as SQL.

In academia and more research-oriented fields, R is an invaluable tool, as these fields of study usually require highly specific and unique modeling.

As such, organizations can quickly custom-build analytical programs that can fit in with existing statistical analyses while providing a much deeper and more accurate outcome in terms of insights.

Even when it comes to social media or web data, R can usually provide models that deliver better or more specific insights than standard measures like page views or bounce rates.

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