R is a popular language for statistical analysis and visualizations. This course teaches you how to install R packages on SQL Server, use functions from Microsoft’s R distribution, and execute R packages using a variety of data platform options.
The Microsoft data platform now supports the integration of analytics into your on-premises solutions by using R scripts. This course, Getting Started with R in the Microsoft Data Platform, explores the various options available across the data platform for using R. After learning how to configure your SQL Server environment to support R, you learn how to implement R scripts as stored procedures, store R predictive models in tables, and configure security for execution of R scripts. Next, you learn how to execute R scripts to get, transform, or score data using Integration Services and Power BI. Finally, you learn how to add R visualizations into Reporting Services and Power BI reports. By the end of this course, you will know how to integrate R into your Microsoft data platform workflows.
Stacia Misner Varga is a consultant, instructor, author, mentor, BI Partner for SQLSkills, and principal of Data Inspirations specializing in Microsoft business intelligence technologies for over 10 years. She is a frequent speaker at the PASS Summit, IT/Dev Connections
conferences, and various SQL Saturday and Microsoft-related events
Course Overview Hi everyone, my name is Stacia Varga and welcome to my course Getting Started with R in the Microsoft Data Platform. I'm a consultant, classroom instructor, and author of several technical books, and I work at Data Inspirations, a company I founded way back in 2006. R is a popular language for business analysts and data scientists and although it's been around for 20 years, it was only relatively recently introduced into the Microsoft data platform, in 2016. This course explains how to take advantage of the features related to R that are now available in the Microsoft data platform, from the SQL Server database engine to Integration Services and Power BI. Some of the major topics that we are going to cover include getting the development and server environments ready to use with SQL Server, using the RevoScaleR functions that are provided in the Microsoft distribution of R, executing R scripts by calling T-SQL stored procedures, creating predictive models using data from SQL Server and sing them to score data, and visualizing data with R functions and reporting services and Power BI. By the end of this course you'll know how to adapt your own R scripts for obtaining, transforming, analyzing, and visualizing data for use in the SQL Server and Power BI environments. Which means, before beginning the course you should be familiar with developing R scripts. Some general familiarity with SQL Server Integration Services, Reporting Services, and Power BI is also helpful, although you do not need to be an expert in these tools. You can find many complimentary courses here at Pluralsight to fill in any gaps in your knowledge of these technologies. Now I hope you'll join me on this journey Getting Started With R in the Microsoft Data Platform at Pluralsight.
Getting Started with R in SQL Server 2016 Hi, I'm Stacia Varga. Microsoft now supports R script execution across its data platform. Beginning in SQL Server 2016, you can now run R scripts right in the database engine, which has significant implications for how you manage data workflows and how you manage your technical infrastructure. In this module, we'll explore the steps that you need to take to get started using R in SQL Server. But first we'll explore why you'd ever want to use R inside your SQL Server environment in the first place, then we'll look at the various components necessary to develop and test your R scripts and what you'll need to install and configure on SQL Server itself. Last we'll review the steps that you'll need to perform to ensure the necessary R packages and their dependencies are available on SQL Server for production ready R scripts to run successfully.
Exploring SQL Server Data with RevoScale R Functions Hi, I'm Stacia Varga and welcome to Exploring SQL Server Data with RevoScaleR Functions. In the previous module we set up the client and server environments in preparation for using R on our data. In this module we'll focus on ways that we can start working with R as an exploration tool. To do this, we'll review the interplay between the client and the server during R script development, and we'll take a closer look at specific RevoScaleR Functions that we can use to explore our SQL Server data. Now SQL Server and R handle data types differently, so we'll take a moment to review the data types that you might be using SQL Server that R is not going to recognize. And after we develop some R scripts, we'll go through the process of deploying them to the SQL Server for production use. And then we'll see how we can use an application to call and execute R script on the SQL Server.
Building Predictive Models in SQL Server with RevoScaleR Functions Hi, I'm Stacia Varga. Now that we've set up our environment and explored data with RevoScaleR functions, let's continue our exploration of functions in this module, Building Predictive Models in SQL Server with RevoScaleR Functions. To learn about these functions, we'll use the data that we explored in the previous module and we'll go through the recommended methodology for building predicting models in SQL Server. And then we'll look at the types of predictive modeling that are supported in the RevoScaleR functions. Next, we'll review the process for generating models, storing the generated models in a SQL Server table, and then retrieving the model and using it to perform a prediction task. Last we'll explore the scoring mechanisms that are built into SQL Server 2017, native and real time scoring. These give you some streamlined options for scoring tasks.
Calling R Scripts in SQL Server Integration Services Hi, I'm Stacia Varga, and in this module of the Getting Started with R in the Microsoft Data Platform course, we're going to shift gears away from our focus in the R development environment and SQL Server to a new focus on another component in SQL Server, Integration Services, which allows us to automate the execution of R scripts. More specifically, we're going to start this module by reviewing why we would use R in Integration Services instead of using native Integration Services components to get and transform data. And we'll look at three different ways that we might use R in an Integration Services package. First we can use it in an execute process task and second we can use it in an execute SQL task, and third we can use it in a data flow task.
Using R Scripts in Power BI Hi, I'm Stacia Varga, and welcome to the final module of this course in which I show you how to use R scripts in Power BI, another component of the Microsoft data platform. Although it's technically independently of SQL Server, Power BI is part of the broad array of tools available from Microsoft for managing data, and just like the other data platform tools we covered in the previous modules, Power BI also includes support for R. As with the earlier modules, my assumption is that you already have a working knowledge of R and need to know the specifics of using your R scripts in Power BI. And if you're new to Power BI, I do have a course here on Pluralsight called Getting Started with Power BI to help you learn more about this tool. One of the key capabilities in Power BI is importing and wrangling data into a model so that you can perform analysis and create visualizations. Power BI provides a lot of flexibility and, well, power. But you can take that flexibility to new levels with your own R scripts. Wrangling data is not the end game in Power BI though. The reason we go through all that trouble is to create all kinds of visualizations. Power BI has a lot of built in visuals and provides the capability for you to develop your own, but sitting in between these two options is the ability to use R visuals. Another popular feature of Power BI is the ability to publish content to the cloud using the Power BI service. If your Power BI desktop file contains R script, there are some limitations that you need to know about before publishing to the Power BI service.