Want to Learn Table Calculations? Here’s How! Going beyond Tableau’s built-in Quick Table Calculations can be challenging, and help you progress along the path of the Tableau Jedi. I’ve been sharing these links about table calculations a lot via email, here’s an annotated post of them: Beginner Intermediate – Richard Leeke’s awesome tutorial on tuning table calculation performance. Advanced Have any suggestions? Like this: Like Loading... Related Partitioning by Table Calculations Here's a issue on the Tableau forums that shows up at least once per month or so: someone has a data set that they want to compute a measure over, and the measure requires table calculations. October 31, 2012 In "Tips and Techniques" Upcoming Presentations I've been a little quiet the last couple of weeks as I've been head-down in a number of projects. May 6, 2013 At the Level - Unlocking the Mystery Part 1: Ordinal Calcs. RHSD - The Anatomy of a Nested Table Calculation.
The twittersphere & inter-webs are abuzz with help understanding Tableau Table Calcs. Among the more recent, these are my favorites: And fully two years ago now, Jonathan started to compile this list of resources for all skill levels: Want to Learn Table Calculations? Here’s How! With such a trove of useful information available, this post of mine is brief & tactical: The Anatomy of a Nested Table Calc, i.e. what does it look like? If data is our paint, then Table Calcs are the Tableau artist's wooden palette: the place where we can "mix & match our pigments". For more detail on the difference between Table Calcs & Calculated Fields, see the Master Tableau Approach. To annotate the Anatomy of a Nested Table Calc, I'll piggy-back upon the example recently narrated by Joe & Matthew in their 12 minute TRL video Ratio of Ratios. First, a run-down of their example. The Sum of Sales Overall has NO partition, and must therefore compute addressing both Category and Weekday.
OK! Word Count: 690. Moving average of running total table calculation. Breakthrough. After I modified Shawn's version of the sales workbook with my forumlas, I found the results were almost the same. Then I realized that's because there are order events for every day, and the date format is set to date, which means the columns of the table are exactly days, so the window calculation units are days. Whereas in my data, my events are plotted in datetime rather than date, which means there is not a 1:1 relationship between columns and days, hence the need for all my formulas.
When I switched my data from datetime to date, everything got smoother and I had events for almost every day so my units are almost 1:1. My formulas are still required for when there are days with no data, though if this is rare, and you're only concerned with window averages, the effect is minimal. Here's what my data looks like now, compare to the first picture I posted above. Thanks for all your help Shawn, I've learned lots.
Jonathan's Teachings re: Table Calculations &am... This came from a thread I didn't want to lose: Taking a step back here, the question is about comparing across records in the underlying data in order to filter out Email IDs that belong to list 123, then count the remaining Email IDs per List ID. This kind of problem is a great practice for me to explain, so I'll take a little extra time to walk through the steps involved to reach a couple of solutions. A distinct record (row) in the data is made of the Email Address and List ID. So if we want to exclude certain Email IDs based on the presence of a List ID for that record, we have to look across records. In order to make Tableau do that (there's no single magic function to do that kind of filtering, like the other BI tool you mentioned, Roopa) we need to work with the three calculation levels in Tableau: - Record (or row) level.
. - Aggregate. . - Table calc. 1) Does a row-level calculation to turn the List ID into a string. 2) Wraps that in the MIN() aggregation. This is in the CF #3 view. Jonathan's Teachings re: Table Calculations &am... Calculating sum of requests within a zip code r... Hi Florian, To understand what is going on here requires some understanding of how Tableau computes totals. Joe Mako shared an nice perspective on this with me and it is a very helpful perspective for this example, so I will do my best to paraphrase (I pinged him just in case I got something wrong). Think of the grand total as a separate worksheet, where the only difference is that the dimension you are totaling across isn't present. In your situation, that means zip code isn't there. The problem with that your calculations, in particular the filters, depend on the full granularity of zip code. So what happens if you remove zip code from the view?
If zip code isn't there, then the filter aren't meaningful since the distance in the aggregate becomes the distance between the average location and the average location, which is zero hence you get the total from the whole dataset. My approach to forum questions sometimes reminds me a bit of the tv show Mythbusters. GROUPBY DISTINCT MAX Tableau 9 | Tableau Support Community. For more info on the three levels of calculation, I suggest you watch this Think Data Thursday on Setting up for Table Calculation Success, most of it is very applicable to working with Level of Detail calcs: Fundamentally, there are three levels of calculation in Tableau: - record level: The calc is computed for each and every record in your data source. - aggregate: This is SUM(), MIN(), MAX(), etc. and the calculation is computed at a specific granularity.
In the case of a view, the granularity uses the dimensions in the view (this is a simplification when it comes to blending) - in SQL terms, all your dimensions are part of the GROUP BY of the aggregation. For a conditional or top filter or computed Set, the granularity is the chosen dimension. For LOD calcs the dimensions used for the granularity depend on the FIXED/INCLUDE/EXCLUDE and what's in the view, along with what's done with the field (is it aggregated or not).
Cheers, Jonathan. Table Calculations - Why apply them 'By Cell' A question I was recently asked by an in training Tableau User was ‘Why would I apply a table calculation by cell?’. To answer this I have produced a simple example of a by cell Table Calculation. Before I get to it I would like to run through some Table Calculation basics. Calculation Direction One of the key principles in Table Calculations is the direction of the calculation. Consider the following table which shows the ‘Running Sum’ table calculation running ‘Table Across’: As you can see this running sum is running horizontally left to right and restarting on each row. If you ran the calculation again, this time ‘Table Down’ the calculation would work vertically down the table restarting for each column. ‘Across then down’ runs first horizontally and then carries the result to the next row running against the entire table: Down then across runs vertically but carries the result to the next column of data, working across the whole table.
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Top 10 Tableau Table Calculations. This page shows 10 powerful examples of Tableau's Table Calculations. Most require writing a simple formula of some kind. Each example contains a live example and instructions in a tabbed view. You can download any workbook for a deeper look. In order to view and re-create these table calculations, you will need a copy of Tableau Desktop.
You can get a free 14 day trial here. Table Calculation Basics Table Calculations rely on two types of fields: addressing and partitioning fields. Partitioning fields do what it sounds like they do: they partition your data into separate buckets, each of which is acted on by the calculations.Addressing fields define the “direction” that you want your calculation to take. In the example to the right, we have a running total of sales. 1. With Table Calculations you can calculate the percent change from an arbitrary value. 2. You may want to see data from a common starting point rather than over an absolute timeline. 3. 4. A classic bump chart. 5. 6. 7. 8. RHSD - Master Tableau Approach. In an earlier post, titled Master Tableau Concepts, I have summarized and added color to a talk by Joe Mako in which he highlighted the advanced concepts that a master practitioner can use to achieve flow by working with instead of against Tableau.
This was an excellent talk which he also gave as a repeat at TC14. In this new post, Master Tableau Approach, I’ll summarize & add color to a TC14 session from Bethany Lyons and Alan Eldridge, titled Jedi Level Calculation Techniques. Today's topic is the thought process used by a master practitioner when deciding which technique to employ when answering a complex question using Tableau. While introducing his own TC14 session, Dashboarding at the Edge of Impossible, Dustin Smith described the Tableau Jedi not as the person who knows every tip and trick. He instead described a Jedi as the person who knows how to best approach the problem. So a common aim of 4 separate Jedi level sessions at TC14 was to teach not tactic, but strategy.
Null. Rank Question. True. It is always interesting to see several solutions to a problem. This is curious because of the different partitioning required. Using a table calc across date field turns on domain completion (densification) so you get the same set of dates for every employee. This is a problem (and often a very confusing one), since the max date is then the same for every employee but since this isn't necessarily a record in the original datasource you end up getting a null for sum([Result]).
Matthew's solution works around this by formally including the Employee dimension in the partitioning, but restarting the calculation for each employee. Mine allows the densification to take place, but the total calculation seems to rely only on the source data, so it isn't impacted. For the tie breaker, I added a third solution that uses the same partitioning as Matthew's, and also uses Rank. A Jedi (Filter and Table Calc) Trick. Let’s say we would like to rank our Sub-Category of Products by Sales and have the ability to remove/hide certain Sub-Categories without this affecting our rank calculation or average reference line.
Well, this is not as straight-forward as it seems! Try it yourselves in the below viz, if you de-select the Office Machines from the filter, the rank and the average sale reference line will both recalculate. This is actually working as intended as when we remove the item from the Quick Filter we are actually removing it from the query that Tableau sends to the datasource. Do not panic though, thanks to the Tableau Force we can use a filter and a Table Calculation to achieve exactly what we want. Here are the results: Now whenever I de-select a Sub-Category in the Quick Filter the item will be removed (or hidden) in the view and the rank and average reference line will not recalculate. Why does this work? So how did I do this? Average Number of Records per Hour per Weekday. Hi Bill, I'm working on my Think Data Thursday presentation for next week and found your post and it's a really good example of some things I'll be presenting, so I'm going a little long in my write-up here.
I also do appointment analysis, so I'm not unfamiliar with this kind of problem. In Sheet1 of your view you had turned off Aggregate Measures, so what Tableau is doing in SQL terms is something like: SELECT weekday(date), hour(date), row_num, Number of Records FROM your data So there's no aggregation actually happening and SUM(Number of Records) and AVG(Number of Records) return the exact same value, 1, and since Tableau by default stacks marks in a bar chart what you're seeing for Monday at 8am is 8 records with a value of 1.
A clue is in the lower left, where you can see that Tableau is drawing 1571 marks, another is that when you click on bar segment, it's only selecting a tiny segment of the bar and a different row_num is showing up in the tooltip for each segment. Hope this helps! At the Level – Unlocking the Mystery Part 1: Ordinal Calcs. There was a Tableau forums thread on At the Level awhile back where Matthew Lutton asked for an alternative explanation of this somewhat puzzling table calculation configuration option, and I’d promised I’d take a swing at it. Plus, I’ve been deep into book writing about shaping data for Tableau, and a taking a break to write about obscure table calc options sounds like fun! (Yes, I’m wired differently.)
Read on for a refresher on addressing and partitioning and my current understanding of uses of At the Level for ordinal table calculations such as INDEX() and SIZE(). Part 2 will cover LOOKUP(), and Part 3 will cover WINDOW_SUM(), RUNNING_SUM(), and R scripts. When we set the Compute Using for a table calc, we are setting the addressing—Compute Using and addressing are synonyms.
All of the dimensions in the view that are not part of the addressing are automatically part of the partitioning. What if I want to generate a set of results for each Year & Region? Seeing Results Like this: Table Calculation Fundamentals.pdf. Tip0911.pdf. Want to Learn Table Calculations? Here’s How! Transition Matrix. I'm with Noah. Doing the work outside of Tableau to give yourself a Row ID that you could join on or even to set up some slightly-complicated SQL to do the same using a date will make life so much easier in Tableau that I believe it's worth the effort. I'll explain how the LOOKUP() is doing what it does in your Table Calc view, then explain why a working table calc solution is more difficult to put together. Tableau makes working at different levels of granularity really easy, right up until the point that we need to pay attention to them and then it's not much help. In the Table Calc view, there are two levels of granularity that are important, the granularity of the raw data (where Date & Name are the dimensions that determine what makes a unique record) and the granularity of the view (the distinct combinations of values of the dimension(s) are on Rows, Columns, Pages, and the Marks Card).
The Table Calc view has only Current Bucket as a dimension. Jonathan. Want to Learn Table Calculations? Here’s How! How to Have Sets with Your Secondary (Data Sources) Tableau Sets can turn incredibly complicated interactions into a few mouse clicks that can reveal patterns in your data. However, if you’ve ever created a Set in a secondary data source and tried to use it across a blend in a primary, this is what you see: Lots of grayed out items that can’t be touched. It’s like you’re at a party and have just met the hot-babe-of-appropriate-gender-of-your-dreams and they are totally into you, but only at the party, and not back at your place. However, like many things in Tableau, with a little creativity you can get what you need, and there is a way where you can have your Sets anywhere you want.
If you’re not familiar with sets, then I suggest you start with Hot. For this post I’ll attempt to avoid any more jokes about sets and show you how you can get jiggy with use sets from blended secondary data sources, and probably learn a bit more about data blending on the way. And here’s a view of that Coffee Chain data, where there are only 20 states: Table Calculation Fundamentals.pdf. Table Calculation Functions. Table Calculations. Help me understand "At the Level" and "Restarti. Help me understand "At the Level" and "Restarti. Version 6 table calcs - understanding a few basics. Add Sparklines to a Tableau Table | Steiner Analytics. Want to Learn Table Calculations? Here’s How! Comparing Each Against Each Other: The No-SQL Cross Product. Getting the Total for Count Distinct.
Table Calculations. Quick Table Calculations. Addressing and Partitioning. Groundwork for Custom Table Calculations - InterWorks - Tableau Software & Business Intelligence Consulting. Space: Forums. Top N, with everyone else as "Other" Question on how to create a product velocity ca... Addressing and Partitioning.
Table Calculations. Top 10 Tableau Table Calculations. Table Calculations. Advanced Training - Table Calculations.