Netherlands: Software

Introductie van Micorosoft SQL Server 2016

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117 C H A P T E R 6 | More analytics You can also use graphical objects such as a histogram to explore your data. Figure 6-17 shows the results of the rxHistogram function, which plots the count of observations in your data for each distinct value of fare_amount under 50 (to ignore outliers) by using the following code: rxHistogram(~(fare_amount), data = inDataSource, title = "Fare Amounts Under $50", endVal=50) Figure 6-17: Viewing the plot created by executing the rxHistogram function. Note To achieve the results shown in Figure 6-17, the code shown in Example 6-5 was modified to select the top 100,000 rows from the table and then executed again prior to executing the rxHistogram function. You can further fine-tune the appearance of the histogram by adding arguments to apply formatting to the axes and configure other style settings. For more information, refer to the function documentation at /functions/rxHistogram. Spatial data can be plotted on a map as another option for exploring data. Because a common security practice is to prevent SQL Server from accessing the Internet, you cannot perform the complete operation on the server. Instead, you use the local context to make a geocoding call to Google Maps to obtain a graphical layer for the map and send it to the server context to get the plot points for individual locations. To start the process, create a custom function to get the plot points, as shown in Example 6-5. Example 6-5: Creating a custom function to plot spatial data mapPlot <- function(inDataSource, googMap){ library(ggmap) library(mapproj) ds <- rxImport(inDataSource) p <- ggmap(googMap)+ geom_point(aes(x = pickup_longitude, y =pickup_latitude ), data=ds, alpha =.5, color="darkred", size = 1.5) return(list(myplot=p)) } In this example, you use the function function to define a custom function and supply an argument list in parentheses. The arguments in this case are inDataSource, which is the RxSqlServerData object

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