In the screen shot below, months 7, 11 and 12 are the top 3 months for 2008. Instead of showing the highest costs for months 1 to 6, the pivot table shows the highest months overall, for each year. Now the pivot table shows the 3 months with the highest costs, but the Label filter was removed. ![]() To do that, apply a Top 10 filter on the MthNum field, based on the Cost. Next, within those filtered months, you’d like to see the 3 months with the highest costs for each year. Show Items for which the label is less than 07.To compare the first six months of each year, use a Label Filter on the month number field (MthNum). In this example, the pivot table has data from January 2008 to June 2010. You’ll see all 3 types of field filters, and how to use them separately, or together. It’s possible to apply multiple pivot field filters at the same time, and the steps to do that are shown below. So, if you apply a Value filter on a pivot field, then try to add a Label filter, the first filter is removed. However, you can only use those field filters one at a time, with the default pivot table settings. The pivot field filters are easy to use, and you can quickly change the pivot table report for different needs. Use Label, Value, and Manual filters on the pivot fields, to narrow the focus.Add Report Filters at the top of the pivot table, to limit what’s summarized in the pivot table data.Here are the types of pivot table filters that you can use: See which types of filters are available, and learn how you can apply more than one filter on pivot table field at the same time. westcalifornia), then reference the calculated field in the over function.One of the best features of a pivot table is filtering, which allows you to see specific results in your data. If the objective is to find aggregate information over unique pairs, for instance sales of all employees in a specific region and state, first create a calculated field that concatenates the two dimensions together (i.e. In the example, every time the record “west” is mentioned, the sales value in the row is summed to other rows that have “west”.Īs a result, the column created will have the same value, in this case sum of sales, for all rows that have a region ID of “west”. The dimension field that is referenced in the parenthesis of the over() function means that every unique value in the dimension field is considered for the aggregate. For instance a region ID can be considered a measure if we are counting how many IDs exist, but is a dimension if we break down sales by region. ![]() ![]() These classifications are dependent on how the column or field is used and are not inherent to the column itself. Measure: a calculation input a value that the user would want to add, count, etc.ĭimension: a means of segmenting data or a category In the pop up window, I then select inserted column, and a box appears to enter a custom expression.Įxample syntax is as follows: sum(sales)over(region) or aggregationdunction(measure_column)over(dimension_column). This is achieved by selecting “edit” on the top toolbar, then “column properties”. Generally in Spotfire, I insert a column into the table to first calculate the values and then troubleshoot. I would like to rank the regions by total sales volume. I would like to show each employee’s sales next to the region’s average sales to see whether the employee is selling more or less than average. For an example let’s think of employee sales in different regions: An over function allows a person to aggregate (by this I mean perform a sum, count, max, min, etc) information in a column next to detail or “drilled down” information.
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