Book Image

SQL Server 2017 Machine Learning Services with R

By : Toma≈æ Ka≈°trun Kaštrun, Koesmarno
Book Image

SQL Server 2017 Machine Learning Services with R

By: Toma≈æ Ka≈°trun Kaštrun, Koesmarno

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)

Functions for descriptive statistics

Descriptive statistics give insights into understanding data. These are summary statistics that describe a given dataset by summarizing features and measures, such as central tendency and measure of spread (or variability). Central tendency includes calculation of the mean, median, mode, whereas measures of variability include range, quartiles, minimum and maximum value, variance and standard deviation, as well as skewness and kurtosis.

These statistics are covered byrx- functions in RevoScaleR package, which means that you can use all the computational advantages of the package by calling: rxSummary, rxCrossTabs, rxMarginals, rxQuantile, rxCube, and rxHistogram, without worrying about the performance, out of memory exceptions, or which R package holds the right function.

We will be using the[Sales].[vPersonDemographics] view in the AdventureWorks...