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In the dashboard, we created 2 charts:
The first chart, ‘Employee Count by Job Role’ shows the number of employees in each job role:
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The second chart, ‘Employee Count by Department’ shows the number of employees in each department:
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We can now use the second chart, ‘Employee Count by Department’ to filter the first chart, ‘Employee Count by Department’.
If we use a ‘Like’ comparison operator, the first chart will be filtered to only show the employee numbers for the first department in the ‘Employee Count by Department’ chart - Human Resources (a data table has been created for comparison).
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Chart Information, also confirms that ‘Employee Count by Job Role’ has been filtered on ‘Human Resources’.
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data tables:
US State Population
County Population
Our ‘County Population’ chart shows the population for every state in the United States.
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Our second chart, ‘US State Population’ shows the total population for every State in the United States but has been filtered to only show the top 10 most populous states.
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When we use the chart, ‘US State Population’, to filter our ‘County Population’ chart we will only see counties in the top 10 most populous states.
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This works by running the SQL query of the sub-chart, ‘US State Population’, and using the values from this chart to populate the filter for the ‘County Population’ chart.
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If a ‘Like’ filter is used, a single value from the ‘US State Population’ data table will used. This value will be from the first row in the first column in the table which in this case happens to be California.
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If an ‘In’ filter is used, multiple values from the ‘Employee Count by Department’ chart ‘US State Population’ data table will be used. These will be values from the first column of the table - in this case, the 10 top states by population that we filtered this table on.
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