![]() The filter logic we are going to apply is fairly straight forward. To isolate just the dates that are appropriate for each support ticket, we will create a filter using the step option. Now if you look at the data we have at this point, because we have used an append, we have every single date, against every single support ticket. With the sample data used in this post, this process will create the following table…Īnd my flow is as follows (note I have removed the ‘Append’ fields from both data streams in the join menu. The process itself is outlined in a blog here. We will then append the datasets together, using the join tool, using the now common field in both datasets ‘append’. We can do this using a step, for each stream, where we create a calculated field called Append, and the value will be ‘JOIN’. The next step is to use a neat trick to append the date scaffold table, to the support tickets table. Now we can open Tableau Prep, and input our two files, out date scaffold data, and our support ticket data. I will then save my file as a xlsx file, and refer to it as ‘Date Scaffold’ from this point forward. You may want to extend the range into the future so that your solution is a future proof against a later end date.įor the sample dataset above, my date file would look something like this… To do this you may use Tableau desktop to understand the min start date and the max end date. We need to create a single column table with every date that is possible in our range. So let’s start with how we can do this with Tableau prep.Īs with Andy’s custom SQL solution, we need to do some ground work in Excel before. ![]() So we need to transform our data from looking something like this… Well, without a mark for every date, in Tableau this is difficult to do. We get many support desk tickets, and I want to visualise, over time, how many tickets and open at any given point in time. ![]() when both the support desk agent, and the end-user decided that the problem had been resolved). when the ticket was raised by the end-user), and an end date (i.e. We run a support desk, and our tickets are logged with a start date (i.e. It’s fantastic, and still works, but this year Tableau launched Tableau Prep, and I wanted to show you how we can transfer the logic shown here into a Tableau prep file, or for those of you with Alteryx, an Alteryx designer workflow. | Ben Moss How to fill a date range with Tableau Prep or Alteryx Designerīack in 2016, my colleague Andy Pick created this great blog on how to use custom SQL to ‘fill in’ a date range, in order to effectively show metrics using a time-series chart. ![]()
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