Connect to the "Global Superstore" data source and use the Orders table. Create a filter on a product name to find out the top 10 profitable products. Create a crosstab to show these products along with profit value. Now filter this chart to show only the products from the European market. What is the 10th most profitable product?
Drop the Product Name to the filters and Select Top 10 based on Sum of Profit.
Drop the Product Name to the Rows and Profit to the Text.
Drop the Market to the filter and select the Europe.
Your view will look like this:
Here, you are getting only 9 products.
Add Market filter to the context so that the Product Name dimension filter will be executed
after the Market filter.
View:
Connect to the "Global Superstore" data source and use the Orders table. Create a view using Profit and Sales. Find out which sub-category is above the profit median but below the sales median value?
Drop Sales to columns and profit to rows, and Sub-Category to details.
Add two reference lines, one on the sales axis:
Add similarly on profit axis as well.
Out of the following options, only the Binders sub-category is above the profit median but below the sales median value.
Connect to the "Global Superstore" data source and use the Orders table. Find out how many days in the month of May 2012 were profitable?
Here we have created the Fixed LOD expression and fixing the order date. If profit is greater than 0 it means profitable day/month/quarter/year depends on what you need to find. If profit < 0 then not profitable.
Drop the Order Date to the rows and drill-down it till months.
Drop the profitable days field to the Text, aggregated as SUM. So it will find a number of days.
Connect to the "Life Expectancy at birth" data source, combine sheets Data_20th century and Data_21st century. Find the moving average for the average life expectancy for 10 years before 2000 and 10 years after 2000 for New Zealand.
Connect to the 'Life Expectancy at birth' excel file, drag Data_20th Century sheet to the canvas. If you find data columns are not correct.
Use the data Interpreter, it will correct the data.
Select the column 1960 to 2000. And Click on the Pivot option
Rename the two new columns as 'Years' and 'Life Expectancy'
Create a Union by dragging the sheet Data_21st Century to Data_20th Century, once you see the orange block for union.
Now, select column 2001 to 2013 and Select Add data to Pivot.
Select Country and Country Name field, Merge these fields.
Rename the merged field as Country.
Let's create the view.
Drop the country to the Filters and Select New Zealand. Drop the years to the columns and 'Life Expectancy' to the Text marks card. Change the aggregation to Avg (it won't make a difference here).
Right-Click on Life Expectancy -> Quick Table Calc -> Moving Average
Again, right-click on it and edit the table calculation.
Update the values as:
Result is:
Connect to the Olympic Athletes data source, find out how many countries participated in 5 different sports from 2000 to 2012?
First, find the distinct sports for each country:
It will be created as a Measure, move this to Dimension
Get the distinct countries:
drop these columns to the view, final view will look like this:
The X-axis represents a number of distinct sports. As the data set contains data from 2000 to 2012, so even if you don't include the filter, your answer would remain the same.
