SQL Server 2017 Machine Learning Services with R

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Contributors

About the authors

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Preface

Who this book is for

What this book covers

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Download the example code files

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Conventions used

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Introduction to R and SQL Server

Using R prior to SQL Server 2016

Microsoft's commitment to the open source R language

Boosting analytics with SQL Server R integration

Summary

Overview of Microsoft Machine Learning Server and SQL Server

Analytical barriers

The Microsoft Machine learning R Server platform

Microsoft R Open (MRO)

Microsoft Machine Learning R Server

Microsoft SQL Server Machine Learning R Services

R Tools for Visual Studio (RTVS)

The Microsoft Machine Learning R Services architecture

R Limitations

Performance issues

Memory limitations

Security aspects

Language syntax

Summary

Managing Machine Learning Services for SQL Server 2017 and R

Minimum requirements

Choosing the edition

Configuring the database

Configuring the environment and installing R Tools for Visual Studio (RTVS)

Security

Resource Governor

Installing new R packages

Package information

Using R Tools for Visual Studio (RTVS) 2015 or higher

Using R.exe in CMD

Using XP_CMDSHELL

Copying files

Using the rxInstallPackages function

Managing SQL Server R Services with PowerShell

Getting to know the sp_execute_external_script external procedure

Arguments

Summary

Data Exploration and Data Visualization

Understanding SQL and R data types

Data frames in R

Data exploration and data munging

Importing SQL Server data into R

Exploring data in R

Data munging in R

Adding/removing rows/columns in data frames

More data munging with dplyr

Finding missing values

Transpose data

Pivot / Unpivot data

Example - data exploration and munging using R in T-SQL

Data visualization in R

Plot

Histogram

Boxplot

Scatter plot

Tree diagram

Example – R data visualization in T-SQL

Integrating R code in reports and visualizations

Integrating R in SSRS reports

Integrating R in Power BI

Summary

RevoScaleR Package

Overcomming R language limitations

Scalable and distributive computational environments

Functions for data preparation

Data import from SAS SPSS and ODBC

Importing SAS data

Importing SPSS data

Importing data using ODBC

Variable creation and data transformation

Variable creation and recoding

Dataset subsetting

Dataset merging

Functions for descriptive statistics

Functions for statistical tests and sampling

Summary

Predictive Modeling

Data modeling

Advanced predictive algorithms and analytics

Deploying and using predictive solutions

Performing predictions with R Services in the SQL Server database

Summary