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‎docs/machine-learning/tutorials/demo-data-nyctaxi-in-sql.md‎

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description: Create a database containing the New York City taxi sample data. This dataset is used in R and Python tutorials for SQL Server Machine Learning Services.
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ms.prod: sql
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ms.technology: machine-learning-services
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ms.date: 10/31/2018
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ms.date: 11/02/2022
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ms.topic: tutorial
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author: WilliamDAssafMSFT
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ms.author: wiassaf
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This article explains how to set up a sample database consisting of public data from the [New York City Taxi and Limousine Commission](http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml). This data is used in several R and Python tutorials for in-database analytics on SQL Server. To make the sample code run quicker, we created a representative 1% sampling of the data. On your system, the database backup file is slightly over 90 MB, providing 1.7 million rows in the primary data table.
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To complete this exercise, you should have [SQL Server Management Studio](../../ssms/download-sql-server-management-studio-ssms.md?view=sql-server-2017&preserve-view=true) or another tool that can restore a database backup file and run T-SQL queries.
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To complete this exercise, you should have [SQL Server Management Studio (SSMS)](../../ssms/download-sql-server-management-studio-ssms.md?view=sql-server-2017&preserve-view=true) or another tool that can restore a database backup file and run T-SQL queries.
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Tutorials and quickstarts using this data set include the following:
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## Download files
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The sample database is a SQL Server 2016 BAK file hosted by Microsoft. You can restore it on SQL Server 2016 and later. File download begins immediately when you click the link.
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The sample database is a SQL Server 2016 BAK file hosted by Microsoft. You can restore it on SQL Server 2016 and later. File download begins immediately when you open the link.
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File size is approximately 90 MB.
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::: moniker range=">=sql-server-ver15||>=sql-server-linux-ver15"
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>[!NOTE]
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>To restore the sample database on [SQL Server Big Data Clusters](../../big-data-cluster/big-data-cluster-overview.md), download [NYCTaxi_Sample.bak](https://sqlmldoccontent.blob.core.windows.net/sqlml/NYCTaxi_Sample.bak) and follow the directions in [Restore a database into the SQL Server big data cluster master instance](../../big-data-cluster/data-ingestion-restore-database.md).
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>To restore the sample database on [SQL Server Big Data Clusters](../../big-data-cluster/big-data-cluster-overview.md), download [NYCTaxi_Sample.bak](https://aka.ms/sqlmldocument/NYCTaxi_Sample.bak) and follow the directions in [Restore a database into the SQL Server big data cluster master instance](../../big-data-cluster/data-ingestion-restore-database.md).
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::: moniker-end
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::: moniker range=">=azuresqldb-mi-current"
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>[!NOTE]
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>To restore the sample database on [Machine Learning Services in Azure SQL Managed Instance](/azure/azure-sql/managed-instance/machine-learning-services-overview), follow the instructions in [Quickstart: Restore a database to Azure SQL Managed Instance](/azure/azure-sql/managed-instance/restore-sample-database-quickstart) using the NYC Taxi demo database .bak file: [https://sqlmldoccontent.blob.core.windows.net/sqlml/NYCTaxi_Sample.bak](https://sqlmldoccontent.blob.core.windows.net/sqlml/NYCTaxi_Sample.bak).
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>To restore the sample database on [Machine Learning Services in Azure SQL Managed Instance](/azure/azure-sql/managed-instance/machine-learning-services-overview), follow the instructions in [Quickstart: Restore a database to Azure SQL Managed Instance](/azure/azure-sql/managed-instance/restore-sample-database-quickstart) using the NYC Taxi demo database .bak file: [https://aka.ms/sqlmldocument/NYCTaxi_Sample.bak](https://aka.ms/sqlmldocument/NYCTaxi_Sample.bak).
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::: moniker-end
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1. Click [NYCTaxi_Sample.bak](https://sqlmldoccontent.blob.core.windows.net/sqlml/NYCTaxi_Sample.bak) to download the database backup file.
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1. Download the [NYCTaxi_Sample.bak](https://aka.ms/sqlmldocument/NYCTaxi_Sample.bak) database backup file.
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2. Copy the file to C:\Program files\Microsoft SQL Server\MSSQL-instance-name\MSSQL\Backup folder.
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2. Copy the file to `C:\Program files\Microsoft SQL Server\MSSQL-instance-name\MSSQL\Backup` or similar path, for your instance's default `Backup` folder.
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3. In Management Studio, right-click **Databases** and select **Restore Files and File Groups**.
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3. In SSMS, right-click **Databases** and select **Restore Files and File Groups**.
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4. Enter *NYCTaxi_Sample* as the database name.
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4. Enter `NYCTaxi_Sample` as the database name.
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5. Click **From device** and then open the file selection page to select the backup file. Click **Add** to select NYCTaxi_Sample.bak.
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5. Select **From device** and then open the file selection page to select the `NYCTaxi_Sample.bak` backup file. Select **Add** to select `NYCTaxi_Sample.bak`.
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6. Select the **Restore** checkbox and click **OK** to restore the database.
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6. Select the **Restore** checkbox and select **OK** to restore the database.
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## Review database objects
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Confirm the database objects exist on the [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] instance using [!INCLUDE[ssManStudioFull](../../includes/ssmanstudiofull-md.md)]. You should see the database, tables, functions, and stored procedures.
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![rsql_devtut_BrowseTables](media/rsql-devtut-browsetables.png "rsql_devtut_BrowseTables")
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|**Object name**|**Object type**|**Description**|
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|----------|------------------------|---------------|
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|**NYCTaxi_Sample** | database | Creates a database and two tables:<br /><br />dbo.nyctaxi_sample table: Contains the main NYC Taxi dataset. A clustered columnstore index is added to the table to improve storage and query performance. The 1% sample of the NYC Taxi dataset is inserted into this table.<br /><br />dbo.nyc_taxi_models table: Used to persist the trained advanced analytics model.|
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|**NYCTaxi_Sample** | database | Creates a database and two tables:<br /><br />`dbo.nyctaxi_sample` table: Contains the main NYC Taxi dataset. A clustered columnstore index is added to the table to improve storage and query performance. The 1% sample of the NYC Taxi dataset is inserted into this table.<br /><br />`dbo.nyc_taxi_models` table: Used to persist the trained advanced analytics model.|
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|**fnCalculateDistance** |scalar-valued function | Calculates the direct distance between pickup and dropoff locations. This function is used in [Create data features](r-taxi-classification-create-features.md), [Train and save a model](r-taxi-classification-train-model.md) and [Operationalize the R model](r-taxi-classification-deploy-model.md).|
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|**fnEngineerFeatures** |table-valued function | Creates new data features for model training. This function is used in [Create data features](r-taxi-classification-create-features.md) and [Operationalize the R model](r-taxi-classification-deploy-model.md).|
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|**Stored procedure**|**Language**|**Description**|
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|-------------------------|------------|---------------|
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|**RxPlotHistogram** |R | Calls the RevoScaleR rxHistogram function to plot the histogram of a variable and then returns the plot as a binary object. This stored procedure is used in [Explore and visualize data](r-taxi-classification-explore-data.md).|
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|**RPlotRHist** |R| Creates a graphic using the Hist function and saves the output as a local PDF file. This stored procedure is used in [Explore and visualize data](r-taxi-classification-explore-data.md).|
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|**RxTrainLogitModel** |R| Trains a logistic regression model by calling an R package. The model predicts the value of the tipped column, and is trained using a randomly selected 70% of the data. The output of the stored procedure is the trained model, which is saved in the table nyc_taxi_models. This stored procedure is used in [Train and save a model](r-taxi-classification-train-model.md).|
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|**RxPlotHistogram** |R | Calls the RevoScaleR `rxHistogram` function to plot the histogram of a variable and then returns the plot as a binary object. This stored procedure is used in [Explore and visualize data](r-taxi-classification-explore-data.md).|
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|**RPlotRHist** |R| Creates a graphic using the `Hist` function and saves the output as a local PDF file. This stored procedure is used in [Explore and visualize data](r-taxi-classification-explore-data.md).|
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|**RxTrainLogitModel** |R| Trains a logistic regression model by calling an R package. The model predicts the value of the `tipped` column, and is trained using a randomly selected 70% of the data. The output of the stored procedure is the trained model, which is saved in the table `dbo.nyc_taxi_models`. This stored procedure is used in [Train and save a model](r-taxi-classification-train-model.md).|
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|**RxPredictBatchOutput** |R | Calls the trained model to create predictions using the model. The stored procedure accepts a query as its input parameter and returns a column of numeric values containing the scores for the input rows. This stored procedure is used in [Predict potential outcomes](r-taxi-classification-deploy-model.md).|
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|**RxPredictSingleRow** |R| Calls the trained model to create predictions using the model. This stored procedure accepts a new observation as input, with individual feature values passed as in-line parameters, and returns a value that predicts the outcome for the new observation. This stored procedure is used in [Predict potential outcomes](r-taxi-classification-deploy-model.md).|
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## Query the data
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As a validation step, run a query to confirm the data was uploaded.
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1. In Object Explorer, under Databases, right-click the **NYCTaxi_Sample** database, and start a new query.
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1. In Object Explorer, under **Databases**, right-click the **NYCTaxi_Sample** database, and start a new query.
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2. Run some simple queries:
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```sql
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SELECT TOP(10) * FROM dbo.nyctaxi_sample;
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SELECT COUNT(*) FROM dbo.nyctaxi_sample;
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```
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The database contains 1.7 million rows.
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3. Within the database is a **nyctaxi_sample** table that contains the data set. The table has been optimized for set-based calculations with the addition of a [columnstore index](../../relational-databases/indexes/columnstore-indexes-overview.md). Run this statement to generate a quick summary on the table.
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3. Within the database is a `dbo.nyctaxi_sample` table that contains the data set. The table has been optimized for set-based calculations with the addition of a [columnstore index](../../relational-databases/indexes/columnstore-indexes-overview.md). Run this statement to generate a quick summary on the table.
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```sql
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SELECT DISTINCT [passenger_count]
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ORDER BY AvgFares DESC
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````
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Results should be similar to those showing in the following screenshot.
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![Table summary information](media/nyctaxidatatablesummary.png "Query results")

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