Lesson 2: Create data features using R and T-SQL
This article is part of a tutorial for SQL developers on how to use R in SQL Server.
In this step, you'll learn how to create features from raw data by using a Transact-SQL function. You'll then call that function from a stored procedure to create a table that contains the feature values.
About feature engineering
After several rounds of data exploration, you have collected some insights from the data, and are ready to move on to feature engineering. This process of creating meaningful features from the raw data is a critical step in creating analytical models.
In this dataset, the distance values are based on the reported meter distance, and don't necessarily represent geographical distance or the actual distance traveled. Therefore, you'll need to calculate the direct distance between the pick-up and drop-off points, by using the coordinates available in the source NYC Taxi dataset. You can do this by using the Haversine formula in a custom Transact-SQL function.
You'll use one custom T-SQL function, fnCalculateDistance, to compute the distance using the Haversine formula, and use a second custom T-SQL function, fnEngineerFeatures, to create a table containing all the features.
The overall process is as follows:
Create the T-SQL function that performs the calculations
Call the function to generate the feature data
Save the feature data to a table
Calculate trip distance using fnCalculateDistance
The function fnCalculateDistance should have been downloaded and registered with SQL Server as part of the preparation for this tutorial. Take a minute to review the code.
In Management Studio, expand Programmability, expand Functions and then Scalar-valued functions.
Right-click fnCalculateDistance, and select Modify to open the Transact-SQL script in a new query window.
CREATE FUNCTION [dbo].[fnCalculateDistance] (@Lat1 float, @Long1 float, @Lat2 float, @Long2 float) -- User-defined function that calculates the direct distance between two geographical coordinates. RETURNS float AS BEGIN DECLARE @distance decimal(28, 10) -- Convert to radians SET @Lat1 = @Lat1 / 57.2958 SET @Long1 = @Long1 / 57.2958 SET @Lat2 = @Lat2 / 57.2958 SET @Long2 = @Long2 / 57.2958 -- Calculate distance SET @distance = (SIN(@Lat1) * SIN(@Lat2)) + (COS(@Lat1) * COS(@Lat2) * COS(@Long2 - @Long1)) --Convert to miles IF @distance <> 0 BEGIN SET @distance = 3958.75 * ATAN(SQRT(1 - POWER(@distance, 2)) / @distance); END RETURN @distance END GO
The function is a scalar-valued function, returning a single data value of a predefined type.
It takes latitude and longitude values as inputs, obtained from trip pick-up and drop-off locations. The Haversine formula converts locations to radians and uses those values to compute the direct distance in miles between those two locations.
Generate the features using fnEngineerFeatures
To add the computed values to a table that can be used for training the model, you'll use another function, fnEngineerFeatures. The new function calls the previously created T-SQL function, fnCalculateDistance, to get the direct distance between pick-up and drop-off locations.
Take a minute to review the code for the custom T-SQL function, fnEngineerFeatures, which should have been created for you as part of the preparation for this walkthrough.
CREATE FUNCTION [dbo].[fnEngineerFeatures] ( @passenger_count int = 0, @trip_distance float = 0, @trip_time_in_secs int = 0, @pickup_latitude float = 0, @pickup_longitude float = 0, @dropoff_latitude float = 0, @dropoff_longitude float = 0) RETURNS TABLE AS RETURN ( -- Add the SELECT statement with parameter references here SELECT @passenger_count AS passenger_count, @trip_distance AS trip_distance, @trip_time_in_secs AS trip_time_in_secs, [dbo].[fnCalculateDistance](@pickup_latitude, @pickup_longitude, @dropoff_latitude, @dropoff_longitude) AS direct_distance ) GO
This table-valued function that takes multiple columns as inputs, and outputs a table with multiple feature columns.
The purpose of this function is to create new features for use in building a model.
To verify that this function works, use it to calculate the geographical distance for those trips where the metered distance was 0 but the pick-up and drop-off locations were different.
SELECT tipped, fare_amount, passenger_count,(trip_time_in_secs/60) as TripMinutes, trip_distance, pickup_datetime, dropoff_datetime, dbo.fnCalculateDistance(pickup_latitude, pickup_longitude, dropoff_latitude, dropoff_longitude) AS direct_distance FROM nyctaxi_sample WHERE pickup_longitude != dropoff_longitude and pickup_latitude != dropoff_latitude and trip_distance = 0 ORDER BY trip_time_in_secs DESC
As you can see, the distance reported by the meter doesn't always correspond to geographical distance. This is why feature engineering is so important. You can use these improved data features to train a machine learning model using R.