Filtering dimensions vs. measures, limiting data
The difference between filtering dimensions and filtering measures
Filtering dimensions: Restricts raw data before calculations
When you filter on a dimension, you are restricting the raw data before any calculations are made. For example, if you want to see Pulse data for a subcategory each day, you could create an Explore with a filter for that subcategory, with Date as a dimension and Total Pulse as a measure.
Then you decide you only want to count Pulse measured in California, so you add a filter on the Date dimension. You will see the results for each day, but the Pulse scores will be smaller. All of the Pulse data from outside California will be removed from the data, and what remains is counted by the measure for each day.
Filtering measures: Calculates first, then restricts the results
When you filter on a measure, however, you are restricting the results after the measure has been calculated. For example, start from the same Explore used in the prior section, with a filter for a specific subcategory, and Date as a dimension and Total Pulse as a measure.
This time, add a filter on the Total Pulse measure to see only the days where Pulse exceeded 5,000. All of the Pulse data is calculated for each dat and then the filter is applied. The filter removes days that had a Pulse of less than 5,000, leaving the remaining days where Pulse exceeded 5,000.
Sometimes you want to see only a subset of the complete results from your query. You can do this in Centricity by setting a row limit, a column limit, or both.
Without a set row limit, Centricity supports up to 5,000 rows. Centricity supports an unlimited number of columns for unpivoted queries, although we recommend that you have 50 or fewer columns for browser performance. Centricity supports up to 200 columns for pivoted queries but sets a default column limit of 50 columns.
When you set a row limit, Centricity will only display up to the number of rows you have set. Centricity will warn you if you might be hiding data by setting a row limit that is too low. Your sort order is important in these situations; Centricity first applies the sort, and then applies the limit. For example, if you only want to see the top five states by number of orders sold, make sure you’re sorting by orders.
If you reach a row limit, you will not be able to sort row totals or table calculations.
If you’ve added a pivot to your report, you can also apply a column limit of up to 200. Centricity will warn you if you might be hiding data by setting a column limit that is too low. Again, the sort order of your pivot is important, because Centricity first applies the sort, and then applies the limit. For example, if you want to see the five most recent months when orders were created, make sure you’re sorting by the order created month.
Dimensions, dimension table calculations, row total columns, and measure table calculations outside of pivots are not counted toward the column limit. Pivoted groups each count as one column toward the column limit.