Advanced analytics is on every company’s agenda. Those companies leading in the utilization of their data assets with big data analytics are creating better products, improving customer experience, increasing operational efficiency and enabling their employees.
R, the Language of Choice for Advanced Analytics
R is the programming language recommended by data scientists for advanced analytics. This is because of R’s great features:
- R is free and open-source with 8,000+ available packages.
- R has a community of 2.5 million users that continues to grow.
- Statistical methods are easy to implement in R.
- Most of the new research in statistical analysis and modelling is done using R – this means new techniques are available first only in R.
In 2016, IEEE ranked R among the top five programming languages, which is remarkable for a domain-specific language. The four languages above R (C, Java, Python and C++) are general-purpose languages and can be applied for any programming task.
However, despite the extraordinary features R has, enterprises looking to adopt R in production may face several challenges regarding scale and performance, application integration, and data movement.
How Microsoft Is Making It Easy to Use R:
Microsoft has made a family of R-based products to help data scientists, statisticians and analysts leverage the open-source nature of R in their enterprises. We’d like to share four great R-based tools your organization can begin using with R:
1. Microsoft R Server
Microsoft R Server offers an enterprise-class analytics platform for R to support big data statistics, predictive modelling and machine learning. It includes both parallel and chunked processing of data and works on different platforms, including Windows, Linux, Hadoop, Apache Spark and Teradata database.
R Server with RevoScaleR (a collection of proprietary functions in Microsoft R used for practicing data science at scale) helps to boost the performance of open-source R. In addition, Microsoft R Server acts as the deployment engine to allow you to operationalize your analytics easily and in a short period of time.
2. SQL Server In-Database R Services
R Services (In-Database) integrates the R language with SQL Server. Therefore, you can deploy R solutions using convenient and familiar tools, and your production applications can call the R runtime and retrieve predictions and visuals using Transact-SQL.
Using this integration, you can keep analytics close to the data and remove the costs and security risks associated with data movement. You can also get the RevoScaleR to improve the scale and performance of your R solutions.
3. R in Azure Machine Learning
Azure Machine Learning (AML) supports the R language. The functionality of AML can be extended using the “Execute R Script” module. This module helps to create custom data transformations, formulate your own metrics for evaluating predictions and build models using algorithms that are not implemented as standalone modules in AML. RevoScaleR is supported in AML as well.
Additionally, you have the ability to create your own R modules and deploy them into AML to run as custom modules.
4. R in Power BI
There are two different ways to apply R in Power BI. You can use R Script as a method of loading data to Power BI. Use this approach if you want to transform your original dataset using R before loading them into Power BI or if the dataset you need in Power BI is the output of a statistical model built in R. When using R Script, the output of your code should be a data frame.
The second approach is to use R Visual, which, as the name suggests, is for creating visuals in Power BI using R codes. This feature is great for taking advantage of plentiful graphs that can be created using R in Power BI. The output of your code should be a visual when using R Visual.
The advanced analytics opportunities are endless with Microsoft R.
Are you interested in learning more about Microsoft R Services for advanced analytics?
We’ve created a new video series about advanced analytics using Microsoft R. In this 6-part video series, our Data Science team shares how to use R in SQL Server 2016 to create a complete advanced analytics solution.
You can sign for this video series here.