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)

Variable creation and data transformation

Variable creation and data transformation are two processes when defining data munging and data wrangling tasks. These tasks are important for proper data preparation and make it easier to analyze data for future tasks.

The functions that we will be exploring are as follows:

  • Variable creation and recoding
  • Data transformation
  • Handling missing values
  • Sorting, merging, and splitting datasets
  • Aggregate by category (which means sums), which is similar to T-SQL aggregations and Windows functions

This part will cover some of the following functions, mainly focusing on data transformation, handling missing values, and splitting datasets:

RxDataSource, rxDataStep, rxDataStepXdf, RxFileSystem, rxFindFileInPath, rxFindPackage, rxFisherTest, RxForeachDoPar, rxGetInfo, rxGetInfoXdf, rxGetJobInfo, rxGetJobInfo, rxGetOption, rxGetVarInfo, rxGetVarNames...