Let me be completely honest with you. I am writing this post in the aftermath of the 2019 Rose Bowl where the University of Washington lost to Ohio State so my views and opinions are most certainly biased. It’s not a surprise to me that the Huskies lost. I think Ohio State was the better team. Rather, it was a decision in the 4th quarter by the Huskies that surprised me…
Nope.
If you need to pivot data from a SQL table for use in Tableau, Tableau Desktop’s default pivot feature can’t help you.
You might never have heard of a self join but you might need one. Occasionally, tables in a database are structured so that it makes sense to join a copy of a table to itself…
Sometimes you need to use data in Tableau that isn’t in a clean, denormalized format. It might have been exported from an application or prepared by a coworker in a way that Tableau doesn’t like.
Totals in Tableau are notoriously rigid. You can’t add two totals lines; one for summarized values and one for averaged values, in the same worksheet. You can have one, the other, or allow Tableau to use a field’s default aggregation for totals.
Imagine you are working on a project where you want to allow users to only see data that’s applicable to them. A simple example of this is a restaurant chain. You might create a sales report where you want a General Manager to only see the data for their store but not others.
If you use a parameter to swap between two worksheets in a Tableau dashboard, you lose the ability to use the worksheets’ default color legends effectively. Check out this video to learn a trick you can use to create a dynamic color legend which always shows an accurate data range despite which worksheet is showing.
I frequently hear the question, “Can Tableau show my missing data?”. Generally when I get this question, people want to either see a 0 or a blank where there should be missing data.
Showing the relationship between two values over a variety of categories or time periods is always a challenge.
It’s more complicated than you think. Let me explain.
In my first sample data set I have just the two columns of data below and 365 rows, one for each day of 2018.
I’ll be honest, when Tableau Dashboard Extensions first released with version 2018.2 earlier in the summer, I took a quick glance and moved on. I didn’t have time to dig into it. I kept hearing they “allow you to do the impossible” with dashboards and didn’t understand what they actually did. Fast-forward and I’ve had some time to dig. Here are a few practical things Tableau Dashboard Extensions can do.
When using Tableau, you might occasionally create a worksheet that uses dimensions only. Imagine you created a worksheet displaying an organizational hierarchy that looks like this:
When embarking on a data communication project, you might not always have all the data you need to create a prototype in a timely manner. I often generate realistic, placeholder data sources so I can design a dashboard and get feedback, even if the actual data isn’t ready for display yet.
Have you ever found that Tableau Desktop took a long time to load a worksheet or apply a filter? You might have found yourself wondering “Is my data source too large for Tableau?” The answer is…“maybe”.
Yes, but it takes some wrangling. Unlike Alteryx, there is no “fuzzy match” tool in Tableau Prep, but there is a method you can employ which will help (though, like all fuzzy matching, it isn’t perfect).