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SQL Server 2017 Machine Learning Services with R

You're reading from   SQL Server 2017 Machine Learning Services with R Data exploration, modeling, and advanced analytics

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787283572
Length 338 pages
Edition 1st Edition
Languages
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Authors (2):
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Julie Koesmarno Julie Koesmarno
Author Profile Icon Julie Koesmarno
Julie Koesmarno
Toma≈æ Ka≈°trun Kaštrun Toma≈æ Ka≈°trun Kaštrun
Author Profile Icon Toma≈æ Ka≈°trun Kaštrun
Toma≈æ Ka≈°trun Kaštrun
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Table of Contents (12) Chapters Close

Preface 1. Introduction to R and SQL Server 2. Overview of Microsoft Machine Learning Server and SQL Server FREE CHAPTER 3. Managing Machine Learning Services for SQL Server 2017 and R 4. Data Exploration and Data Visualization 5. RevoScaleR Package 6. Predictive Modeling 7. Operationalizing R Code 8. Deploying, Managing, and Monitoring Database Solutions containing R Code 9. Machine Learning Services with R for DBAs 10. R and SQL Server 2016/2017 Features Extended 11. Other Books You May Enjoy

Variable creation and recoding

Using rxGetVarInfo will expose the information about the data.frame to the sp_execute_external_script output. It is obvious that some of these functions were never designed for presenting the output to data.frame, but were designed only for exploring the dataset. Some of these functions-for example, rxGetVarInfo-will give a nice output in the R environment, but will be hard to manipulate in data frames for outputting in the SQL Server database:

EXEC sp_execute_external_script
      @language = N'R'
      ,@script = N'
                  library(RevoScaleR)
                  df_sql <- InputDataSet        
                  var_info <- rxGetVarInfo(df_sql)
                  OutputDataSet <- data.frame(unlist(var_info))'
      ,@input_data_1 = N'
                  SELECT 
                   BusinessEntityID
        ...
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