Data Visualization with TIBCO Spotfire

First, we load all the Northwind sample data in Tibco Spotfire, including:

Order Details.csv, Categories.csv, Customers.csv, Employees.csv, Orders.csv, Products.csv, Suppliers.csv

We examine the columns and their types as we loaded the data:

1. Order Details.csv

• Order ID: String

• Product: String

• Unit Price: Real

• Quantity: Integer

• Discount: Real

2. Categories.csv

• Category ID: String

• Category Name: String

• Description: Name

• Picture: String

3. Customers.csv

• Customer ID: String

• Customer Name: String

• Contact Name: String

• Contact Title: String

• Address: String

• City: String

• Region: String

• Postal Code: String

• Country: String

• Phone: String

• Fax: String

4. Employees.csv

• Employee ID: String

• Last Name: String

• First Name: String

• Title: String

• Title Of Courtesy: String

• Birth Date: String

• Hire Date: String

• Address: String

• City: String

• Region: String

• Postal Code: String

• Country: String

• Home Phone: String

• Extension: String

• Photo: String

• Notes: String

• Reports To: String

• Photo Path: String

• Deleted: Boolean

5. Orders.csv

• Order ID: String

• Customer: String

• Employee: String

• Order Date: Date

• Required Date: Date

• Shipped Date: Date

• Ship Via: String

• Freight: Real

• Ship Name: String

• Ship Address: String

• Ship City: String

• Ship Region: String

• Ship Postal Code: String

• Ship Country: String

6. Products.csv

• Product ID: String

• Product Name: String

• Supplier: String

• Category: String

• Quantity Per Unit: String

• Unit Price: Real

• Units In Stock: Integer

• Units On Order: Integer

• Reorder Level: Integer

• Discontinued: Boolean

• AttributeXML: String

• DateCreated: Date

• ProductGUID: String

• CreatedOn: Date

• CreatedBy: String

• ModifiedOn: Date

• ModifiedBy: String

• Deleted: Boolean

7. Suppliers.csv

• Supplier ID: String

• Company Name: String

• Contact Name: String

• Contact Title: String

• Address: String

• City: String

• Region: String

• Postal Code: String

• Country: String

• Phone: String

• Fax: String

- Now we go to Data Canvas, choose "Orders" table, add columns, choose "Unit Price" from the "Order Details" table and add left outer join.

- Next, we want to remove the comma between the first name and last name of "Employee" column of Orders table, using "Add transformations" option.

- We want to create a table with three columns: Order ID, Sum of Freight from Orders table, Sum of Unit Price from Order Details table using table visualization types.

- A bar chart visualization of top 5 Product Categories from Product table sorted by the highest unit price. Beverages, Confections, Meat/Poultry, Dairy Products, and Condiments are top five highest price product categories.

- A combination chart visualization showing Categories from Product table on x-axis and sum of Unit Price on y-axis with showing the sum of Units on Order as a line chart.

It is clear from the chart that Confections categories has the highest units on order and Meat/Poultry has the lowest units on order.

- Some more visualization.

The pie chart shows the Produce category has the lowest sum of units on order.

The scatter plot shows the distribution of categories with different Unit Price and units in Stock. For example, Row number 75 with Beverage category has the highest Unit in Stock with $7.8 unit price. We can also check if we have outliers in our datasets.

- And a dashboard.

- Let's check sum of freight per Ship City and use the marking and details on demand options to check the data.

- We use Filtering Schemes option to create three (Global, F1, and F2) filters. The global is to filter Ship Country and Ship via, F1 to filter Order Date from Order ID per Employee bar chart. F2 to filter Shipped Date from Order Id per Customer bar chart.

Now, we filter USA, all Order and Shipped Date in September 1997.

- In order to use hierarchy in time charts, we change the Order Date column type from Orders table to Date type.

Now we create a combination of three dynamic charts (Line, Side by side Bar, and Bar Stacked) showing the "Orders" table with "Sum of Freight" per "Employee" columns and color by "Ship via" column. We create Drop down list from property control and use the Iron python script.