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updated getting started samples
This commit is contained in:
Nellie Gustafsson
+5
-5
@@ -1,4 +1,9 @@
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# Define the connection string
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connStr <- paste("Driver=SQL Server;Server=", "MyServer", ";Database=", "tpcx1b", ";Trusted_Connection=true;", sep = "");
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# Input Query
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input_query <- "
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SELECT
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@@ -35,10 +40,6 @@ FROM
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GROUP BY sr_customer_sk
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) returned ON ss_customer_sk=sr_customer_sk
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"
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# Define the connection string
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connStr <- paste("Driver=SQL Server;Server=", "NELLIELAPTOP", ";Database=", "tpcx1b", ";Trusted_Connection=true;", sep = "");
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# Input customer data that needs to be classified
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customer_returns <- RxSqlServerData(sqlQuery = input_query,
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colClasses = c(customer = "numeric", orderRatio = "numeric", itemsRatio = "numeric", monetaryRatio = "numeric", frequency = "numeric"),
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@@ -56,7 +57,6 @@ head(customer_data, n = 5);
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wss <- (nrow(customer_data) - 1) * sum(apply(customer_data, 2, var))
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for (i in 2:20) {
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xt = kmeans(customer_data, centers = i)
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print(xt$ifault)
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wss[i] <- sum(kms = kmeans(customer_data, centers = i)$withinss)
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}
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plot(1:20, wss, type = "b", xlab = "Number of Clusters", ylab = "Within groups sum of squares")
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@@ -0,0 +1,70 @@
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# Build a predictive model with SQL Server R Services
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This sample provides custom reports for SQL Server R Services that can be viewed from SQL Server Management Studio. The reports can be used to view configuration information, resource usage, execution statistics, active sessions and other information about R Services.
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### Contents
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[About this sample](#about-this-sample)<br/>
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[Before you begin](#before-you-begin)<br/>
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[Sample details](#sample-details)<br/>
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[Related links](#related-links)<br/>
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<a name=about-this-sample></a>
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## About this sample
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Predictive modeling is a powerful way to add intelligence to your application. It enables applications to predict outcomes against new data.
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The act of incorporating predictive analytics into your applications involves two major phases:
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model training and model operationalization.
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In this sample, you will learn how to create a predictive model in R and operationalize it with SQL Server 2016.
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Follow the step by step tutorial [here](http://aka.ms/sqldev/R) to walk through this sample.
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<!-- Delete the ones that don't apply -->
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- **Applies to:** SQL Server 2016 (or higher)
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- **Key features:**
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- **Workload:** SQL Server R Services
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- **Programming Language:** T-SQL, R
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- **Authors:** Nellie Gustafsson
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- **Update history:** Getting started tutorial for R Services
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<a name=before-you-begin></a>
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## Before you begin
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To run this sample, you need the following prerequisites.
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Section 1 in the [tutorial](http://aka.ms/sqldev/R) covers all prerequisites.
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**Software prerequisites:**
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<!-- Examples -->
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1. SQL Server 2016 (or higher) with R Services installed
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2. SQL Server Management Studio
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3. R IDE Tool like Visual Studio
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<a name=sample-details></a>
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## Sample Details
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### PredictiveModel.R
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The R script that generates a predictive model and uses it to predict rental counts
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### PredictiveModel.SQL
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Takes the R code in PredictiveModel.R and uses it inside SQL Server. Creating stored procedures for training and prediction.
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<a name=related-links></a>
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## Related Links
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<!-- Links to more articles. Remember to delete "en-us" from the link path. -->
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For additional content, see these articles:
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[SQL Server R Services - Upgrade and Installation FAQ](https://msdn.microsoft.com/en-us/library/mt653951.aspx)
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[Other SQL Server R Services Tutorials](https://msdn.microsoft.com/en-us/library/mt591993.aspx)
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@@ -0,0 +1,70 @@
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# Build a predictive model with SQL Server R Services
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This sample provides custom reports for SQL Server R Services that can be viewed from SQL Server Management Studio. The reports can be used to view configuration information, resource usage, execution statistics, active sessions and other information about R Services.
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### Contents
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[About this sample](#about-this-sample)<br/>
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[Before you begin](#before-you-begin)<br/>
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[Sample details](#sample-details)<br/>
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[Related links](#related-links)<br/>
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<a name=about-this-sample></a>
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## About this sample
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Predictive modeling is a powerful way to add intelligence to your application. It enables applications to predict outcomes against new data.
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The act of incorporating predictive analytics into your applications involves two major phases:
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model training and model operationalization.
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In this sample, you will learn how to create a predictive model in R and operationalize it with SQL Server 2016.
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Follow the step by step tutorial [here](http://aka.ms/sqldev/R) to walk through this sample.
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<!-- Delete the ones that don't apply -->
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- **Applies to:** SQL Server 2016 (or higher)
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- **Key features:**
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- **Workload:** SQL Server R Services
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- **Programming Language:** T-SQL, R
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- **Authors:** Nellie Gustafsson
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- **Update history:** Getting started tutorial for R Services
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<a name=before-you-begin></a>
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## Before you begin
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To run this sample, you need the following prerequisites.
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Section 1 in the [tutorial](http://aka.ms/sqldev/R) covers all prerequisites.
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**Software prerequisites:**
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<!-- Examples -->
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1. SQL Server 2016 (or higher) with R Services installed
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2. SQL Server Management Studio
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3. R IDE Tool like Visual Studio
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<a name=sample-details></a>
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## Sample Details
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### PredictiveModel.R
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The R script that generates a predictive model and uses it to predict rental counts
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### PredictiveModel.SQL
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Takes the R code in PredictiveModel.R and uses it inside SQL Server. Creating stored procedures for training and prediction.
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<a name=related-links></a>
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## Related Links
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<!-- Links to more articles. Remember to delete "en-us" from the link path. -->
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For additional content, see these articles:
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[SQL Server R Services - Upgrade and Installation FAQ](https://msdn.microsoft.com/en-us/library/mt653951.aspx)
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[Other SQL Server R Services Tutorials](https://msdn.microsoft.com/en-us/library/mt591993.aspx)
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