Databases Reference
In-Depth Information
Right-click on the ShoesZip3 dataset; select “Aggregate rows”.
In the “Aggregate On” box of the “Aggregate Dataset Definition” form,
check Shoe and Zip3.
In the “Aggregations” box check “Row Count”. Each row in the original
dataset represents a shoe sale of one pair. “Row Count” will contain the
total shoe sale count for each Shoe and Zip3 combination.
Enter a name of “SalesByZip3”.
Click “OK”.
View the summary statistics for the newly created dataset.
The “SalesByZip3” dataset contains one row for each combination of three-
digit zip and shoe - 154 three-digit zips times three shoe lines
¼
462 total rows.
Viewing the data in the boundary plot would be the logical viewer choice.
However, there is still more preparatory work to be completed. The boundary
plot requires that the row identifier (key) of the dataset be one of the supported
boundary identifiers (state, county, three-digit zip). However, the row identifier
of our new dataset is a combination of Zip3 and Shoe.
View SalesByZip3.csv in a parallel plot.
Adjust the “Shoe” filter slider to include only the “Praia” line.
Hide the “Shoe” column. (Right-click on the “Shoe” axis.)
Make a dataset named PraiaSales from the filter. (Right-click on any of the
filter sliders.)
The resulting PraiaSales dataset contains two columns: Zip3 and RowCount
(sales of the Praia shoe line summarized by Zip3). In other words, almost
500,000 rows, filtered by “Praia” and aggregated by three-digit zip, are reduced
down to 154 rows.
View PraiaSales in the boundary plot.
In what geographic areas of the Western States' Zapata market are the Praia
shoes selling best?
Exercise 3.3
Use the previously created SalesByZip3.csv dataset.
a.
In the same way that the PraiaSales dataset was created in the previous
tutorial, create datasets for Montanha and Fazenda shoe sales.
