Book Image

SQL Server 2017 Machine Learning Services with R

By : Julie Koesmarno, Tomaž Kaštrun
Book Image

SQL Server 2017 Machine Learning Services with R

By: Julie Koesmarno, Tomaž Kaštrun

Overview of this book

R Services was one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to do additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing Machine Learning Services;maintaining, deploying, and managing code;and monitoring your services. Delving more deeply into predictive modeling and the RevoScaleR package, this book also provides insights into operationalizing code and exploring and visualizing data. To complete the journey, this book covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power.
Table of Contents (12 chapters)

Understanding SQL and R data types

Before we dive into exploring data using R in T-SQL, let's get started with understanding data types to store data in R. The first and most important data type to be familiar with when working with R in T-SQL is data frame. The input and output parameters of sp_execute_external_script in SQL Server received and sent from R are data frames. Other data types that are important to know for data munging, and that are very similar to data frame, are matrix and data table, which are beyond the scope of this chapter.

Aside from data frame, R supports a limited number of scalar data types such as character, complex, date/time, integer, logical, numeric, and raw. Thus, when you provide data from SQL Server in R Scripts, when necessary the data will be implicitly converted to a compatible data type in R. When a conversion cannot be performed automatically...