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Tableau and Databricks part 1 – getting started

Tue, 23 Dec 2025 11:30:58 +0000

Two data tools I really enjoy working with are Tableau and Databricks. Tableau lets you visually explore and communicate your data. And Databricks is a cloud-based data and AI platform. If you’ve started to learn about or work with Tableau Next, then you’ll be aware that it reboots the Tableau product stack on the power of data cloud and agentic AI (a data and AI platform). But if you’re not fortunate enough to be there yet, or you already work with Databricks, then this series of blog posts could be for you!

Tableau expander tables – 3

Wed, 10 Dec 2025 07:58:43 +0000

In previous blog posts I introduced the use of expander tables in Tableau when needing to handle a single row in multiple ways, along with three specific use cases.

In this post, part three of the series, I’m going to cover three more use cases. Let’s take a look…

4. To create custom sub totals or grand totals

Here I have a view showing sales by country and region. I’ve turned on grand totals and sub totals for rows so that I get the overall total and a sub-total per country:

Tableau expander tables – 2

Fri, 08 Aug 2025 12:07:51 +0000

In a previous blog post I introduced the use of expander tables in Tableau when needing to handle a single row in multiple ways. You may have heard of expander tables before under the more generalised term of “scaffolding”.

In this blog post I’m going to cover some different use cases for expander tables. If you didn’t read part 1 yet, go ahead and have a skim through that…

Tableau expander tables – 1

Thu, 31 Jul 2025 10:31:00 +0000

In a recent VizIt Sydney and Tableau User Group presentation, Fantastic Tableau tricks, and how to avoid them, I talked about expander tables and their benefits when working with data in Tableau. In this blog series I’ll cover:

expander tables in more depth (part 1); and
use cases they can help with (parts 2+)

But first up…

Why might you need to use an expander table?

When working with data in Tableau it’s not unusual to find that you need to treat a single row of data in more than one way in the same view. A common example is where a row has both an opened date and a closed date, and you need to plot items opened and closed per date. Each item, or row, needs to be counted twice. Another example is where you have multiple measures per row, you need to use more than one on the same view, and a dual axis or the built in Measure Names and Measure Values pills aren’t flexible enough.

LOD equivalent of INDEX and RANK (part 2)

Fri, 31 Jan 2025 11:05:24 +0000

In my last blog post I looked at a LOD equivalent of RANK / INDEX table calculations in Tableau. That approach was limited to ranking a very small range of whole numbers, and left me considering other options. This post outlines another LOD approach using spatial functions**!**

WARNING: As before I will stress that you should rarely need a LOD-based equivalent of rank or index, and can often use table calculations when you don’t think that you can. That said, there are scenarios where a LOD equivalent can be useful: onward use of the calc or use in spatial functions being the cases I’ve seen on the Tableau community forums. And it’s also a fun challenge!

LOD equivalent of INDEX

Fri, 20 Dec 2024 10:47:50 +0000

Is there a level of detail expression equivalent of a particular table calculation in Tableau? This question comes up on the Tableau community forums every now and then, and almost always intrigues me.

Often a level of detail (LOD) expression isn’t really necesary, but occaisionally an alternative to table calculations is necesary. And like a mountain to be climbed, or a trail to be explored, I’m fascinated by whether it is even possible to implement LOD equivalents to some common table calcs.

Tableau Prep and #PreppinData 2024 week 13

Sat, 30 Mar 2024 09:16:33 +0000

An Easter themed #PreppinData for 2024 week 13. Preparing sales of products in the 12 weeks running up to Easter to allow for easy comparison of the period across years in Tableau Desktop.

A nice one step solution this week (see screenshot at the end of this post): a FIXED level of detail calc to get the first sale date per year; then date calcs to get the week, day and day order.

Tableau Prep and #PreppinData 2024 week 12

Sat, 30 Mar 2024 09:02:07 +0000

#PreppinData 2024 week 12, graduate student loan repayment calculator. Good to try out the “value ranges from two fields” option within a “new rows” step. Like some others my interest figure is a little different from the supplied output, however the calc appears to be the same. I also shortcutted the join onto repayment info for undergraduates with a filter (down to just the undergrad row), and joiner fields allowing a simple join on 1=1.

Tableau Prep and #PreppinData 2024 week 11

Tue, 19 Mar 2024 11:00:04 +0000

Week 11 of #PreppinData, and the question: what if there were 13 months in a year? Nice concept to have consistent 28 day months, with 4 weeks per month and each month starting on a Monday and ending on a Sunday. As we found out when we expanded the two row data set though … it’s not as neat as it seems, ending up with a spare day (or two in a leap year).

Tableau Prep and #PreppinData 2024 week 10

Tue, 12 Mar 2024 09:42:06 +0000

My solution for the #PreppinData 2024 week 10 challenge follows below. Cool to check out the New Rows step this week to fill in the missing days in the data set.

Transform field per line data with Tableau Prep (2)

Sat, 09 Mar 2024 03:49:56 +0000

A couple of weeks ago I wrote about a Tableau Prep approach to transposing data from a text file that had a field per line, with another line separating records. At the time I noted that the approach wasn’t robust enough to handle optional fields, and that it would be annoying to need a join per field in cases where you had a large number of fields. In this follow up post I look at an alternative that doesn’t have those drawbacks.

Tableau Prep and #PreppinData 2024 week 9

Mon, 04 Mar 2024 08:58:16 +0000

Finishing #PreppinData intermediate-level month with a 1-step solution … fun, but definitely harder and less easy to maintain!

One-step Tableau Prep solutions

Sun, 25 Feb 2024 09:02:23 +0000

This quarter I set myself the goal to learn more about Tableau Prep, and a key part of that has been participating in the weekly #PreppinData challenges. Something I’ve noticed, and have been super intrigued about, is that some participants have been posting one-step solutions. That wasn’t surprising during beginner month, but now I’m seeing one-step flows covering reasonably complex multi-step data transformations. Cool!

This week I took a deeper dive into one of those one-step solutions to learn, and share, how they’re being done.

Transform field per line data with Tableau Prep (1)

Sun, 25 Feb 2024 00:01:08 +0000

I recently answered a question on the Tableau Community Forums about transposing data from a text file that had a field per line, with a line of dashes separating records. I’m not sure what the formal name for this format is, but there are similarities with RecFiles.

Here is an example:

I don’t know of a way to use data formated like that directly in Tableau Desktop. But we can use Tableau Prep to transform it into a more natural row per record format!

Tableau Prep and #PreppinData 2024 week 8

Sat, 24 Feb 2024 11:16:44 +0000

#PreppinData 2024 week 8 – a “what if?” analysis of two different customer loyalty reward systems for Prep Air. Aiming to identify cost and number of customers benefiting.

The “estimated yearly flights” calculation tripped me up for a while, out thinking it with a datediff on days, and only when the flights spanned more than a year. The challenge just required a division by the number of years flown over! I enjoyed expanding the data set throughout the flow (pivoting the benefits, joining onto cost per benefit, and then joining onto those tiers less then or equal to each customer’s tier) to then roll back up at the end.

Tableau Prep and #PreppinData 2024 week 7

Mon, 19 Feb 2024 09:50:38 +0000

#PreppinData 2024 wek 7:

Tableau Prep and #PreppinData 2024 week 6

Sun, 11 Feb 2024 06:02:57 +0000

The #PreppinData 2024 week 6 challenge was to find the latest salary per staff member and summarise their tax position given UK income tax bands.

We’re now into intermediary level challenges and so there are less prescriptive steps, and more options to solve the problem your way. For me the problem had two key parts: (1) get the latest row per staff member; and (2) the various calculations for salary and tax paid based on tax bands.

Tableau Prep and #PreppinData 2024 week 5

Tue, 06 Feb 2024 06:43:18 +0000

The final week of beginner month for #PreppinData involved a bit more complexity around joins, calculations and outputs. On top of Tableau’s getting started tutorial #PreppinData has been a great way to get into Tableau Prep. I’ve invested about 12-15 hours of time and feel like I’ve got a good initial grasp of the product.

The challenge walk through provided less info on the “how”, which in some ways was quite nice as I felt more license to solve the problem my way. On the other hand I wonder if I should have made my flow less complex instead of aiming for one data set that could then be filtered down to the different outputs.

Tableau Prep and #PreppinData 2024 week 4

Sun, 28 Jan 2024 07:38:54 +0000

#PreppinData 2024 week covered using join types, in this case to understand which seats aren’t chosen given a seating plan and booking data.

I had a preconceived idea of the solution here, as I’m used to using LEFT OUTER JOIN in SQL and then having a WHERE clause that returns rows where the result from the right hand table IS NULL. So I was expecting to have a join and then a filter in my flow. However, Tableau Prep has some additional join types that let you return entries where there are only values in the left table, only values in the right table, or even a “not inner” join for entries only in the left or right but not in both. I gave the left only option a go and it did the job nicely! Great how you can click on the segment of the venn diagram representation of the join to select the type too.

Tableau Prep and #PreppinData 2024 week 3

Mon, 22 Jan 2024 10:06:31 +0000

Two lots of Tableau Prep practice this week. A forum question (see end of post) and #PreppinData 2024 week 3. The challenge for #PreppinData was to join targets from a spreadsheet, with a sheet per quarter, to our previous sales figures. And then to calculate difference from target. Similar union and clean up steps to previous challenges to get to the point where there are two data sets to join, and where we have consistent fields in both (first letter of class, and a month number). Then the join is pretty straightforward:

Tableau Prep and #PreppinData 2024 week 2

Sat, 13 Jan 2024 05:09:22 +0000

Week two of getting to grips with Tableau Prep and I decided to countinue with #PreppingData. The team of Carl Allchin, Jenny Martin and Tom Prowse do a great job of picking challenges that gradually introduce you to functionality. This week covered unions, aggregation and reshaping data using pivots. I was particularly interested in pivots, as that’s a frequent challenge people have on the Tableau forums where we talk about data prep being a good option.

Getting started with Tableau Prep

Mon, 08 Jan 2024 06:37:17 +0000

I’ve been meaning to explore Tableau Prep for a while and finally took it for a test drive.

Many data professionals have experienced the need to prepare and clean data prior to analysis in tools like Tableau Desktop. Classic examples are: splitting data out of a single combined field; un-pivoting when each year of a measure is in a separate column; or maybe combining sales data from multiple differently formatted sources.

LOD equivilant of LOOKUP (part 2)

Sun, 15 Oct 2023 06:03:25 +0000

In a previous post I walked through a LOD (level of detail) based alternative to the LOOKUP table calculation. In that example I was looking at sales last month, and in a recent Tableau Forums question someone was asking if it could be extended to determine average sales from the previous three months.

The answer was “yes” and you can check the whole thread on the Forums, including other options. The main difference from my previous post is that we needed to extend the group numbers from two to four, so that any given month falls into 4 “higher level groups” that we can target with a LOD. We then chose which LOD to use based on the index (_i).

Top M within each top N of categories

Sun, 03 Sep 2023 00:56:21 +0000

I recently helped with a Tableau community forums question where the user needed to:

filter to the top N categories based on a measure,
using a dense rank (so that categories with the same value had the same rank),
but list no more than M categories within each rank (based on another criteria)

This screenshot illustrates the requirement where we want to see the top 5 categories based on sales, where categories with the same sales have the same rank. But we only want to see the top 2 within each rank, based on lowest cost:

Tableau tile and hex maps

Sun, 23 Oct 2022 00:15:06 +0000

If you’ve ever built a filled map in Tableau where some of the areas are too small to stand out or label effectively, then you’ve probably come across hex maps as a solution. A hex map means that each area is the same size, regardless of the actual relative sizes.

For example, note how the Nelson (NSN) region in this filled map of New Zealand is relatively small compared to other regions, and hence hard to see and label:

LOD equivalent of LOOKUP

Sat, 23 Jul 2022 12:35:50 +0000

The LOOKUP table calculation in Tableau is really handy when you want to show or use a value from a previous row in the view. For example if you are showing sales per month and need to use the sales figure from the previous month to calculate month-on-month growth.

In the example above our calculation for “Sales last period” is:

What and Why skills vs How skills

Mon, 13 Jun 2022 09:42:47 +0000

John Cutler asked a great question on Twitter; how do we describe less visible skills like qualitative research in comparison to technical skills like software development?

Initially I was intrigued by the parallels with translation vs interpretation in linguistics. I can see similarities between a software developer translating requirements into code. And a design researcher interpreting customer interviews to help produce the right software requirements.

I also liked a response by Tiffany Chang suggesting that one skill is more concrete and the other more abstract. That resonated with human centered design approaches for me. And the idea of not jumping straight from problem to solution:

Depth vs breadth

Wed, 02 Mar 2022 11:22:01 +0000

In a recent sprint review we were asked how our findings, which were based on a relatively small number of customer conversations, could be meaningful. Were they statistically significant?

I’d got used to our stakeholders being familiar with the background to qualitative research and how we don’t try to quantify it as such. And that the selection approach / recruitment matrix mean that we can have confidence in the insights. However staff had come and gone and so it was a good reminder to address the common concern that a survey would have been better and more statistically significant.

Hone your skills with Makeover Monday

Thu, 07 May 2020 09:42:25 +0000

I don’t usually get to attend Tableau User Groups. We don’t (yet?) have one down in the depths of New Zealand’s south island, and it’s a long drive to the nearest one in Christchurch. But with New Zealand and much of the world in some form of lock down, Tableau has encouraged and supported virtual user group meetings. So I was excited to dial into this weeks virtual New Zealand Tableau User Group meeting jointly arranged by Alex, Thabata, Jeff and Paul from the Auckland, Christchurch and Wellington groups. The icing on the cake was being invited to speak about my experience with Makeover Monday!

PREVIOUS_VALUE in Tableau

Fri, 10 Jan 2020 07:47:58 +0000

Late last year I started to actively help out on the Tableau Forums. What a great decision! I’d forgotten how much fun it could be to (1) pick up a discrete challenge; (2) help others out; and (3) learn so much more in the process.

One of the questions I recently chipped in on was about circular references in a sequential calculation. The background to the question is really interesting and I ended up spending a few hours digging into epidemiological models, but that’s a different story! Whilst trying to help I took a fresh look at the PREVIOUS_VALUE function in Tableau. I have to admit, prior to this I had thought that PREVIOUS_VALUE(x) was just the same as LOOKUP(x,-1) … turns out that isn’t the case!

Makeover Monday, 2019 #26

Wed, 26 Jun 2019 10:52:50 +0000

An interesting and deceptively simple data set on alcohol consumption by country for 2019 week 26.

I like the simplicity of the table of data and the factors affecting the top 25 that are discussed in the article. The chart itself would be better as bars not columns in my opinion, allowing the country names to be laid out for easier reading. As Eva noted in her submission showing liters of pure alcohol consumed per capita per year isn’t that easy to relate to. Digging into the definitions for standard drinks / units I was surprised to find that there is quite a range, and that some countries still don’t define a standard drink. I decided to focus on that aspect for my makeover.

Makeover Monday, 2019 #3

Tue, 15 Jan 2019 09:06:45 +0000

Andy Kriebel selected a data set about US workers paid at/below the minimum wage for those choosing to participate in week 3, 2019.

The original viz highlights some of the regional differences for 2017 by showing the data geographically. I like that I can see regional differences, but I found myself wanting to see the trend over time (as it’s available in the data set) to see if the geographical trends are part of an ongoing story.

Makeover Monday, 2019 #1

Fri, 04 Jan 2019 11:12:14 +0000

Makeover Monday 2019 week 1 looks at NHL attendances since the 2000-01 season.

A couple of things emerge from an exploration of the data set provided: firstly there are seasons where labour disputes, or lockouts, dramatically affect attendances. Secondly some teams have different stories to the general trend. I spent most of my time exploring and presenting the lockout story, but added a team selector to allow users to explore average game attendance by team.

Leading with questions

Sun, 07 Oct 2018 00:53:33 +0000

I was preparing for our company celebration of CX Day 2018 on Tuesday and was reminded of this great interview with Warren Berger on the IDEOU site. The interview drills into the power of questions, and how the right question can lead to a breakthrough and real innovation. The bit that sticks out for me is the question that led to the Polaroid instant camera:

Copy and paste text boxes in Tableau

Sat, 01 Sep 2018 10:19:16 +0000

Christina Gorga recently commented on Twitter that she would love the ability to copy or duplicate text boxes on Tableau dashboards.

The tweet attracted favourable attention, with 44 likes. One reason the feature is seen as useful is that it could reduce the time taken to copy formatting throughout a dashboard; styling like fonts, sizes, colours, borders. How much of a pain is it to reapply these to multiple text boxes?

Makeover Monday, 2018 #35

Fri, 31 Aug 2018 10:49:58 +0000

A couple of my colleagues are giving Makeover Monday a go to practice some recent Tableau Desktop training, so I’m back into it too! This week we were given a data set from Figure Eight about wearable tech products, with the challenge to makeover the charts in this article from 2014, about where we are wearing our wearable tech.

Makeover Monday, 2018 #22

Mon, 28 May 2018 12:22:46 +0000

Where is some of the worlds priciest residential property? For week 22 of #MakeoverMonday we look at a World Economic Forum chart trying to answer that question.

On first glance the chart is nice and clear, but is a tree map the right type of chart to use when we’re not looking at parts of a whole? A number of community members have suggested it is not, and for me that detail shouldn’t be left to the chart footnote just in case the chart is used in a standalone setting. The sort order of the areas isn’t super intuitive either, with the most expensive city in the top right.

Makeover Monday, 2018 #21

Sat, 26 May 2018 13:05:14 +0000

How accurate were the Guardian Sports writers’ predictions for the 2017-18 English Premier League? According to this visualisation, which was picked for week 21 of makeover Monday, the predictions were not that great. I decided to have a play with removing inaccurate predictions; after all once you get one wrong you’ll end up with at least one other prediction wrong too right? E.g. getting first and second the wrong way around. I was intrigued to see if the Guardian had more of the sequence correct than it seemed at first glance. Arguably they did have more right – 11 was the number I got to.

“Yes, and”, Cirque du Soleil and innovative design

Thu, 19 Apr 2018 10:51:13 +0000

On a recent holiday we got to go to a Cirque du Soleil show. The show was “O” and you should seriously consider checking it out if you ever get the chance. Absolutely amazing! Aside from being thoroughly entertaining, for me the show reinforced some recent experiences around innovation and creative leadership that I’d picked up from companies like Empathy Design and IDEO.

Makeover Monday, 2018 #13

Tue, 27 Mar 2018 09:44:02 +0000

I’m returning to #MakeoverMonday after a month or two off with family and travelling. After completing all 52 in 2017 I’m pretty relaxed about how many I participate in this year, and hope to pick up on some other community initiatives, like viz for social good. Anyway back to this weeks makeover…

In week #13 the challenge was to makeover the first chart in this infographic about chocolate bar preferences in the UK. I enjoyed the original infographic and found the bump chart interesting. It took me a little while to reconcile that the bump chart plotted preferences across age brackets not years. I like the way the lack of data for some brands has been handled, although that does add to the complexity of the chart. So for my makeover I’ve simplified it down to simple lists of rankings. I’ve coloured the items by manufacturer as I think this tells the story about Cadbury more effectively for the audience.

Makeover Monday, 2018 #2-3

Thu, 18 Jan 2018 12:54:40 +0000

Week 2: What attributes are seen as most preferable in a romantic partner:

.
Week 3: Distributions as a line chart similarly to one or two others, but within a tile map. Each tile shows the distribution relative to all other distributions. Shading highlights the higher proportions for a selected income bracket. I also experimented with a second chart per tile to act as a miniature x-axis and call out the selected income bracket to orientate the viewer, not so sure about this bit … I wanted to show the income bracket too but it was just too dense text-wise! There were a few tricks here – like using a dual axis with area chart to be able to show a different background colour for each tile. Feel free to download the workbook to take a look and let me know if there’s things that could be done more elegantly!

Makeover Monday, 2018 #1

Fri, 05 Jan 2018 11:07:47 +0000

A whole new year of chart makeovers to look forward to! And this year the data is available via data.world too, with integration to a wider set of tools. We’re starting out with a look at per capita poultry consumption in the US since the 1960s based on data from the National Chicken Council; a nice clean line chart that tells the main story. The source data allows us to dive into a little more detail to expand upon the story. It was interesting to look at Turkey and seafood, and also to try to find equivalent data for plant-based proteins.

Makeover Monday, 2017 #52

Tue, 26 Dec 2017 23:40:39 +0000

A “Merry Christmas” makeover to end the year with*, looking at a Statista graph of Christmas tree sales in the US from 2004 to 2016. The original is a nice simple bar chart, which clearly shows the breakdown between sales of real and fake Christmas trees in America. It’s a little hard to see the actual proportions and that’s one thing I wanted to hit in my makeover.

Makeover Monday, 2017 #51

Sat, 23 Dec 2017 02:45:35 +0000

Cruising towards the end of a year of weekly makeovers with a look at over 176 million daily maximum and minimum temperature readings from around the world, over three centuries. As noted by many others, this weeks original visualisation is a tough act to follow – why try to make it better? Well I didn’t! I spent all of my time digging around what was a fascinating data set. In the end my “makeover” is simply a look at how anomalies can just be down to the fact that locations for temperature readings / estimates are introduced over time. The seeming false start for Senegal being a good case perhaps! Equally when temperatures from Antarctica were introduced is it surprising that we see the minimum temperature for the year drop dramatically? What about the impact of elevation of weather stations – over time readings are being taken from more extreme locations and I didn’t even get into looking at that!

Makeover Monday, 2017 #50

Fri, 15 Dec 2017 10:50:57 +0000

This week we take a look at barrier free buildings in Singapore. The original visualisation is part of a site by the Building and Construction Authority in Singapore (BCA). Although the site requires Flash to be available and enabled in your browser, there is a great range of information available if you do have Flash. From the map of an area you can drill into information about individual buildings. We didn’t have quite the data to do that (not having the building ID or the depth of information about each building). Nevertheless is was interesting to attempt to makeover the map to: cover more areas and be a little easier on the eye. Most of the info is available via hover over, but I’ve also added an inset bar chart to show how the selected area compares to the “best” and “worst”.

Makeover Monday, 2017 #48-49

Thu, 07 Dec 2017 10:05:06 +0000

Another double header post! Brief notes on this weeks makeover and last weeks.

Week 48

Last weeks data asked what if the world was made up of just 100 people. The original is visually interesting but hard to read as all categories are combined into one overall circle. A problem with percentages is a lack of perspective of just how many people are affected by something like starvation or malnutrition. There were some great examples from the community showing the actual number of people involved in the real world. I wanted to take this a step further and provide access to a story about just one person. The power of a story over a statistic is really interesting me at the moment as a result of a human centred design project I’m involved in.

Makeover Monday, 2017 #46-47

Sun, 26 Nov 2017 08:20:11 +0000

Week 46:

Original: The world’s top cities for sustainable transport.

Makeover:

Tableau public: link

Notes: nothing particularly special here! The breakdown per continent allow two of the key stories to emerge. Hong Kong is top but there is a high concentration of European cities at the top of the ranking.

Makeover Monday, 2017 #45

Fri, 10 Nov 2017 09:51:16 +0000

Not so much a makeover for me this week. The original chart was a WHO map of life expectancy data. I’ve just looked at a simple comparison of a country of the viewers choice with the two countries with the highest and lowest life expectancy at birth for a given year:

Want to try different countries or years? Click through to the Tableau public version.

Makeover Monday, 2017 #44

Sat, 04 Nov 2017 06:51:55 +0000

For week 44 of #MakeoverMonday Eva selected a Daily Telegraph article mapping the countries with the most public holidays. Nice map – although as ever with filled maps there are data points that get a little lost (the smaller countries). The lists work well to make up for this, although they’re pretty basic and unexciting.

The dataset was quite challenging this week – broader than the original article, lots of variation and some data quality issues. I’ve decided to focus on a specific public holiday, labour day, because it was easier to hone that subset of the data for analysis and visualising. Labour day is also reasonably prevalent and it’s roots are a great reminder to us all about striking a suitable work-life balance!

Makeover Monday, 2017 #43

Fri, 27 Oct 2017 11:36:41 +0000

A tough week for me with a hectic work week and a struggle for #MakeoverMonday inspiration.

The aim was to makeover this Myers Briggs chart. I like the 4×4 grid representation of the original as we are basically looking at a mix of four attributes. I don’t really get a sense of the percentages / proportions though as every segment of the grid is the same size. I also had to flick back and forth between another page on the site to get a more detailed explanation of what the letters mean.

Makeover Monday, 2017 #42

Thu, 19 Oct 2017 07:18:43 +0000

Making over a table of Formula E racing results from the FIA Formula E website this week.

I was interested in how drivers progressed from practice to qualification so went with a bump chart. I’ve highlighted the winner but dashboard actions also allow the user to highlight the driver that they are hovering over. This way they can see how each driver’s performance changes during an event because the bump chart is quite hard to follow otherwise.

Makeover Monday, 2017 #41

Thu, 12 Oct 2017 11:24:48 +0000

Just a brief write up for now! Andy and Eva chose charts relating to adult obesity in America for the 41st makeover with the source data coming from the CDC. The original charts are part of an interactive dashboard and it is well worth checking out.

Similarly to Klaus Schulte I decided to dig into gender differences. Klaus’s makeover is very nice so definitely go take a look at that.

Makeover Monday, 2017 #40

Thu, 05 Oct 2017 09:08:10 +0000

This week was a makeover of a Financial Times article on the UK economy since the Brexit referendum using data from the OECD.

The original

Makeover

I’ve gone with three key elements in my makeover:

Retain the line chart and highlighting of the UK, but show a wider period of time and highlight the referendum date
Show the range of growth across the G7 without showing too many lines. Instead allow the user to choose which G7 country to compare to
Provide a ranking table to highlight the low ranking for the UK in the last two quarters

The viz is also available on Tableau Public.

Makeover Monday, 2017 #39

Thu, 28 Sep 2017 09:58:46 +0000

In August 2016 Nielson released a report what’s in our food and on our mind. Page 8 of the report included a set of spiral bar charts showing restricted dietary requirements around the world:

The charts on this page were chosen for week 39 of Makeover Monday. Whilst the infographic is visually engaging I’m not a big fan of the spiral bars. That and the way the different diets are laid out makes it quite hard to visually compare various categories (e.g. low sodium to vegetarian).

Makeover Monday, 2017 #38

Wed, 20 Sep 2017 12:49:32 +0000

A day at the races! This week we got a wealth of Strava data from Andy and Eva across two recent events that they have competed in. Find out more here. You’ll see that they each had a set of questions for the community to consider. I was busy trying to reproduce some of the graphs I’ve seen produced from sports watch / trackers and got very bogged down in the data. A quick look at the submissions that were coming through on twitter showed me that those submitting were focussing on a subset of the questions and only tackling one person’s data. Phew – it was good to take a step back! Eva’s question about the second kilometer of the run intrigued me. Friends that participate in triathlons have mentioned the initial pain over the first 500 meters but I hadn’t picked up on a lull after the first 1000 meters.

Makeover Monday, 2017 #37

Wed, 13 Sep 2017 09:36:40 +0000

For week 37 of Makeover Monday we’re looking at UK bicycle thefts based on data from data.police.uk.

The reference site linked above has pretty extensive visualisations and I love the way they unfold as you work your way through the site. I’m not going to try to improve upon the site, instead I’m looking for a different angle to show and, given the bleakness of the data (less than 1% of bike thefts resolved!), it’s an opportunity to try a paired back black and white viz.

Makeover Monday, 2017 #36

Wed, 13 Sep 2017 09:14:12 +0000

The UN, #MakeoverMonday and #VizForSocialGood came together to challenge the data visualisation community to visualise results from the MYWorld survey on the UN Sustainable Development Goals. You can read more about the challenge on the Makeover Monday blog.

The design is geared towards a tablet as there were indications that the UN would like to use the finished viz on stands with tablets at various UN events like the forthcoming UN General Assembly in New York.

Makeover Monday, 2017 #35

Fri, 01 Sep 2017 07:09:31 +0000

A quick post for Makeover Monday this week. We had data on NFL player arrests from 2000 to August 2017 with the aim to makeover the interactive visualisation here. Whilst exploring the data I was interested to see the free text outcome data. After grouping this I was surprised to see that the proportion of guilty outcomes seemed to be reducing over time whilst the proportion with an undetermined outcome was increasing . In hindsight you’d expect more recent cases to not yet be determined – perhaps they haven’t yet worked through the system? Still I suspect that there is a story here. Of course that story may not be specific to NFL player arrests and I haven’t checked for similar stats across the wider population.

Makeover Monday, 2017 #34

Fri, 25 Aug 2017 09:53:19 +0000

So apparently there was a pretty exciting solar eclipse this week. I guess that’s why Eva picked a NASA data set on solar eclipses for Makeover Monday! Go check out the original article as it’s got a great map of eclipse paths. The data we got for our makeover didn’t include paths, instead it included one coordinate for each eclipse over 5 millennia along with data on the type and duration. Still, the community produced some amazing and informative visualisations.

Makeover Monday, 2017 #33

Tue, 15 Aug 2017 10:43:54 +0000

Earlier this year the pudding published an excellent analysis into the myth that various events were a trigger for mini baby booms. The analysis was based on CDC data on natality in America. Fast forward to August and the data set was selected by Andy for week 33 of Makeover Monday.

Makeover Monday, 2017 #32

Wed, 09 Aug 2017 10:55:36 +0000

This week a makeover of an article and Tableau chart about sanitation in rural Indian schools, based on data from the ASER Centre. Here is the original chart posted by Eva and Andy:

Made over

A tile map seemed like a good way to retain some of the benefits of the map whilst mitigating the downsides. It was an interesting exercise to make a tile map for India and the end result still retains some similarity to an outline of India. The addition of imagery featuring the Indian flag reinforces the fact that the viz is about India, and I like the analogy of a child putting their hand up to ask to go to the toilet!.

Makeover Monday, 2017 #31

Thu, 03 Aug 2017 09:40:46 +0000

Back into it with a revisualisation of a 2015 Southeast Asian Games infographic. A simple and engaging infographic telling the history of the games. The data provided was a little different; only covering 2007 to 2015. I’ve focussed on a medal table for 2015 and area chart showing medal count trends over the period. Firstly this keeps things simple, secondly it was something I could compare against medal tables on Wikipedia. The reason I wanted to be able to sense check the data against something was due to the structure. For team events both the team medal and individual participants were included so some extra filtering was needed and I wanted to check I had that right.

Makeover Monday, 2017 #27-30

Thu, 03 Aug 2017 09:31:59 +0000

I was on holiday for week 27 to 30 so caught up with these four later in the year…

Week 27: Tourism in Berlin and Brandenburg

For week 27 a makeover of a filled map showing visitor stats for Berlin and Brandenburg. I’ve retained the filled maps but focussed on an angle that interested me; whilst Berlin ranks top for both number of visitors and total number of nights in all years, when you look at average nights per visitor it is one of the regional districts that usually tops the list. Check out the interactive version here on Tableau Public where you can look at different years, and explore how the figures differ for domestic or international tourists.

Makeover Monday, 2017 #26

Thu, 29 Jun 2017 12:54:42 +0000

It’s the half way point for Makeover Monday in 2017: 26 charts selected by Andy and Eva, 26 makeovers submitted and 26 blog posts written. It’s been a tough challenge for me to produce a visualisation every week and tougher still to write about each one. However, both of those aspects have been enjoyable, and the practice and reflection has really helped me get more out of Tableau in my job. I’ve had a few submissions selected in the weekly round up and one was selected in a #VizForSocialGood project for use by Inter American Development Bank. The way the Makeover Monday project works this year with the weekly wrap up lessons and the community input has made a huge difference to me. If you’re reading this, chances are you’ve helped me so thanks!

Makeover Monday, 2017 #25

Thu, 22 Jun 2017 12:48:20 +0000

More Exasol-based data in Makeover Monday week 25. 200 Million+ ozone air quality readings from the EPA and a goal to make over a multi-year air quality tile plot on the EPA website. I spent way too much time exploring the data so only had time for a quick make over in the end and this short blog post. Checkout some of the other participants efforts on twitter or wait for Andy and Eva’s weekly summary to see some really cool visualisations.

Makeover Monday, 2017 #24

Wed, 14 Jun 2017 09:37:25 +0000

Week 24 of Makeover Monday and a fascinating data set of art work in the Tate Collection. Nominally we’re making over charts from an article by Florian Kräutli such as this one:

Thoughts on the original charts

The chart highlighted to Makeover Monday participants was a pie chart showing the proportion of works in the collection by Turner versus all other artists. It works well to illustrate a key point about the data and explain why the author excluded Turner’s works from a number of their beautiful visualisations; when Turner’s works are included they skew the data to the point where other artists can’t be seen. The Tate Collection includes the Turner Bequest of roughly 30,000 works of art. Many of these art works are unfinished or preparatory sketches – e.g. each page in a sketch book is counted as a separate work. Angie Chen’s submission explains this nicely and is well worth checking out along with the original article.

Makeover Monday, 2017 #23

Fri, 09 Jun 2017 10:33:34 +0000

Makeover Monday was live from TCOT in London this week and it was amazing to see some of the output produced in just one hour! The challenge was to redo an already great graph from FiveThirtyEight on US National Park popularity:

Thoughts on the original chart

I like the original chart – actually the whole article is really interesting and the charts engaged me throughout. The chart focussed on above shows the ranking nicely, but not the actual number of visitors over time. So we don’t know how much more popular Great Smoky Mountain has been than Rocky Mountain or Yosemite. It’s hard to follow some of the threads without interactivity – although the colour coding of some of them certainly helps. You also can’t tell which states the parks in question are located in.

Makeover Monday, 2017 #22

Fri, 02 Jun 2017 11:07:48 +0000

Just a quick write up this week. Eva selected a map from Knoema showing what proportion of each country’s population had internet access. I quite like the interactive map, but it suffers from some problems common to filled maps. Eva and Andy have talked about the use of maps a few times in their weekly write ups so this week I thought I’d explore the issue in a bit more detail.

Makeover Monday, 2017 #21

Thu, 25 May 2017 11:31:00 +0000

According to data released by Britain’s Office for National Statistics, and a recent BBC article, Brits drinking habits are changing. Are the British falling out of love with booze? The data and graphs involved were the topic for Makeover Monday this week.

Thoughts on original charts

The data set provided focussed on the first section and chart in the article. It’s a good simple bar chart clearly showing the changes in certain categories between 2005 and 2016. What we can’t see is the pattern in the intervening years. We also cannot see the parts that make up the whole in any given year; we can see the proportion drinking at least once during the week before the survey, and the proportion that do not drink at all, but there is no mention of those that drink but not during the week in question. Finally the response of drinking on 5 or more days of the week is presumably a subset of those drinking at least once during the week. The bar chart does not represent these sets fully.

Makeover Monday, 2017 #20

Fri, 19 May 2017 10:10:54 +0000

For week 20 Makeover Monday is collaborating with #VizForSocialGood and Inter-American Development Bank to look at youth employment trends in Latin America and the Caribbean. Great data set, great cause and a great opportunity for our data visualisations to make a difference. For some reason I also felt an increased sense of responsibility to understand and accurately represent the data!

Makeover Monday, 2017 #19

Wed, 10 May 2017 10:54:01 +0000

We’re redoing a list based on Dutch car registration data this week for #MakeoverMonday. The list doesn’t really do the data set justice, but I don’t speak Dutch so haven’t dug into the rest of the story! The actual figures were hard to reproduce and seemed like a niche part of the data, so I looked for a different story and decided to focus on the most popular makes of car. Headline figures give the reader some context as to how many registrations there were in 2015 and 2016, as well as the general growth rate. The slope chart then shows registrations for the top 5 makes and how these have changed:

Makeover Monday, 2017 #18

Tue, 02 May 2017 11:39:20 +0000

A look at Sydney ferry patronage for week 18 of Makeover Monday based on Transport for New South Wales Open Data.

Thoughts on the original chart

The chart being made over is actually a series of Tableau dashboards within a story (set of tabs). I like the way I can work through the story from an overview of the data to some summary charts and then down to some detail. The card type dashboard interested me. They key story that jumped out to me was that around 70% of trips were made using an Adult Opal Card. I don’t think we need to see this proportion visually per month and then again per line. Perhaps other angles from the data could have been visually represented too? Nice dashboard though and I enjoyed clicking through to the map for some context.

Makeover Monday, 2017 #17

Tue, 25 Apr 2017 10:49:55 +0000

This week Tableau themselves chose the data; a celebration of the importance of data skills based on LinkedIn’s annual Top Skills reports. The Tableau example includes a bump chart and box and whisker plot, whereas the LinkedIn version is a simple list per country in a slide share style:

Thoughts on the original charts

The LinkedIn version is simple and clear. You can see which skills are ranked highest and how that ranking has changed since last year. However it is hard to compare the countries to each other or to the overall global ranking (you have to flick back and forth in the slide deck). Also you can’t see how rankings have changed from years prior to 2015.

Makeover Monday, 2017 #16

Thu, 20 Apr 2017 10:18:16 +0000

Week 16 and it’s back to some big data on EXASOL thanks to Eva and Johannes. This time over 750 million rows of GP practice prescribing data for the UK from 2010 – 2017. The nominal challenge to makeover some of the charts in a House of Commons research briefing based on the data.

Workout Wednesday: practicing level of detail and table calcs in Tableau

Fri, 14 Apr 2017 07:06:31 +0000

I don’t usually participate in Tableau #WorkoutWednesday, but this week the chart we had to reproduce featured my team so I had to give it a go!

Here is the finished chart, which is also available on Tableau Public:

<th>
  Team
</th>

<th>
  Total points
</th>

<td>
  Arsenal
</td>

<td>
</td>

<td>
  Arsenal
</td>

<td>
  3
</td>

So the challenge is to:

Makeover Monday, 2017 #15

Wed, 12 Apr 2017 10:41:03 +0000

Uh oh! Making over a 2009 graph by Andy Kriebel this week. No pressure. The graph looks at the correlation between gold and oil prices with the premise that they’re closely related. You can see Andy’s own makeover here. Another of my favourites is one by Mike Cisneros, which you can read about here. I love the story telling element Mike achieves; something I was aiming for myself.

Makeover Monday, 2017 #14

Wed, 05 Apr 2017 13:19:17 +0000

The Guardian recently reported on the a PWC report into the risk of UK job losses from breakthroughs in robotics and artificial intelligence. Whilst technological unemployment isn’t a new concept and the report and article certainly do comment on job creation too, the percentages at risk in some sectors are quite striking.

Makeover Monday, 2017 #13

Tue, 28 Mar 2017 10:17:32 +0000

I found week 13 of Makeover Monday quite hard. The visualisation in question was an infographic purporting to show the perceived reasons for success by different social strata. The subtitle of the graphic seemed quite inflammatory and so I was keen to trace the data back to source to see what grounds the author had for their claims. Unfortunately, as others found too, it was pretty hard to find the actual Russian survey results used. This University of Illinois blog post has a great critique of the visualisation and is well worth a read.

Makeover Monday, 2017 #12

Sun, 26 Mar 2017 10:06:57 +0000

Learning about March Madness / the NCAA college basketball tournament this week. Is it getting harder to pick the winners at each stage?

Tableau public version available here.

Makeover Monday, 2017 #11

Sun, 26 Mar 2017 10:01:28 +0000

I finally got around to completing week 11 in late June! A redo of the chart in this tweet. It’s a pretty engaging chart, although the colour choice in the icon chart is a little hard to make out. I haven’t really improved on the infographic, instead it was a chance to try something new in Tableau – a radar chart. The viz is also available in Tableau Public.

Makeover Monday, 2017 #10

Wed, 08 Mar 2017 11:57:38 +0000

This week a look at the top 500 gamer channels on YouTube based on this list on socialblade.com. What intrigued me about this week was the immediately interesting disparity between the video views, channel subscribers and the Social Blade ranking (score) and rating (grade). The more influential channels according to Social Blade are not necessarily those with more views or more subscribers. The list itself is pretty handy but not visual engaging and there’s no real story telling or summary.

Makeover Monday, 2017 #9

Tue, 28 Feb 2017 10:21:58 +0000

Makeover Monday week 8 and Andy Kriebel challenged the Tableau community to improve on two graphs of his American Express card expenditure. The graphs are very clean and appear to offer some drill down functionality to view transactions. There doesn’t seem to be an intermediary level of detail, e.g. sub-categories (although that may exist and we’re just not seeing it here). Also the use of a 2016 average may not be as useful as a median given that there were a couple of one off large expenses.

Makeover Monday, 2017 #8

Wed, 22 Feb 2017 09:05:47 +0000

Week 8 is all about potatoes. European Union potato sector stats to be precise. Eurostat provide a very detailed analysis of the EU potato sector on their website:

The article is very detailed and does take some time to read, but the key points are in bold so it can be scanned to get an idea of the main stories. The use of tables provides a lot of background data but again the emphasis is on taking the time to digest the data. For my makeover I’ve tried to focus on three key points – most of the production is in a small number of countries; Germany is the biggest producer; but France achieves the best price. The viz can also be seen on Tableau Public.

Makeover Monday, 2017 #7

Tue, 14 Feb 2017 11:01:15 +0000

Love is in the air this week with a makeover of an infographic on valentines day spending in the US.

The original visualisation is pretty good although some of the key data (like average spend per person) doesn’t necessarily jump out. Also there’s nothing to show changes over time, even though the data source does contain that information.

So for my redo I wanted to focus on a very clean presentation of the main trends over time, whilst still highlighting some key stats. I also wanted to offer the viewer the ability to explore the data a little more – something I haven’t done in many of my makeovers this year.

Makeover Monday, 2017 #6

Tue, 07 Feb 2017 11:49:30 +0000

Great fun exploring 105 million rows of Chicago taxi data for #MakeoverMonday this week using the data underpinning this article. The full data set was provided on a hosted Exasol database, purported to be the fastest in-memory analytic database in the world (and it was pretty fast considering the amount of data I was querying from the opposite side of the world).

Makeover Monday, 2017 #5

Mon, 30 Jan 2017 11:54:36 +0000

A quick redo of the pie charts in this Business Insider article for #MakeoverMonday week 5.

If you’re thinking that something seems dodgy with these charts then you may well be right and should have a read of @ChrisLuv’s comments which are an excellent read.

Makeover Monday, 2017 #4

Tue, 24 Jan 2017 11:18:38 +0000

I spent more time looking into the data than on the visualisation for this weeks #MakeoverMonday because the data related to New Zealand. The task this week was to make over the international and domestic tourism spend charts on figure.nz. The international chart is shown below:

The charts are very clean, but showing each year side-by-side makes it hard to read for me. The key seasonality of tourism spend emerges nicely but also makes it harder to spot trends.

Makeover Monday, 2017 #3

Wed, 18 Jan 2017 09:05:33 +0000

This week’s Makeover Monday challenge was to redo this graphic of the accounts Donald Trump retweeted during his US Presidential election campaign.

The original bubble chart gives an idea of the top accounts being retweeted, but doesn’t cover the depth that the article goes into or allow for easy comparison.

I’ll acknowledge up front that I haven’t improved on the comparability as I wanted to learn how to produce multiple donut charts in Tableau! Depth was added by showing which platform the retweets were made from (which may indicate how much retweeting Trump did himself?) and column charts showing volume of retweets over time (and onward retweeting by others) to see what happened at the point that Trump’s campaign was launched.

Makeover Monday, 2017 #2

Tue, 10 Jan 2017 06:43:31 +0000

A reviz of global iPhone sales over the last decade for week two of Makeover Monday in 2017.

On first glance the only thing I wanted to change from the original chart was the slight 3D affect on the columns, and maybe the background colour. Other than that the chart has a clear and simple title and highlights the data point addressing the question posed.

Makeover Monday, 2017 #1

Wed, 04 Jan 2017 11:09:59 +0000

The first Tableau Makeover Monday for 2017 looked at an article about gender inequality in Australian pay. The article is based on 2013-14 tax year data from data.gov.au. The original article presented the data in two tabular lists which made the comparisons being drawn hard to visualise. Unsurprisingly many of the makeovers represented the gap between male and female taxable income in a selection of occupations. One of the problems with the article, and a number of makeovers, is the assumption that taxable income is the same as pay; that is not necessarily the case as can be seen by digging into the original source data (which seems to cover taxable income from sources other than main occupation). I’ve steered away from mentioning pay in my version and simply tried to represent that in the bulk of cases men will generally have a higher taxable income than their female counterparts. Click on the image to see the interactive version, where hovering over a bubble shows you the detailed figures.

Makeover Monday (#43)

Mon, 24 Oct 2016 05:58:54 +0000

This weeks Tableau Makeover Monday was a challenge to visualise a small amount of data; two data points – total size of US National Debt versus the rest of the world. The original visualisation can be seen on the visualcapitalist website and also include comparisons of the US$ 19.5 trillion debt to thinks like company sizes, oil exports, cash held, etc. The pie chart works well here and the comparisons give some idea of scale.

Makeover Monday (#42)

Tue, 18 Oct 2016 12:02:20 +0000

This weeks #MakeoverMonday was a look at US presidential election forecasting data by Drew Linzer on Daily Kos Elections.

The original charts plot the average percentage being polled by Clinton and Trump over time, along with percentage undecided and other (independents). Personally I wasn’t sure I could improve on the existing charts or some of the community versions (loving the tile maps!) so instead I’ve focussed on a different angle – it wasn’t always easy to see at a glance who was predicted to win the election and why. Particularly with the complexity of the electoral college voting system.

Makeover Monday (#41)

Tue, 11 Oct 2016 11:14:44 +0000

Having a go at Tableau #MakeoverMonday this week, with a reworking of a FT visualisation of European public transportation satisfaction survey results in 2015. A good opportunity to look into ways to visualise Likert scale survey results, and to practice some table calculations in Tableau! Adding the ranking by country along with an indicator of the number of places gained/lost gives a quick idea of how satisfaction has changed.

Visualising LCFCs 2014/15 Season

Thu, 04 Jun 2015 10:52:48 +0000

Leicester City FC defied the odds to avoid relegation from the English Premier League in May. Rock bottom at Christmas and seven points adrift by late March, a resurgence in form saw the foxes to safety with a game to spare. In the following interactive visualisations I look at the club’s results, league position and points over the season, along with player performance data:

LCFC 2014/15 Season (mobile version)

Can an AI algorithm win fantasy football?

Wed, 27 May 2015 11:11:01 +0000

If you’ve heard the term Moneyball, then you’ll know that in 2002 the Oakland ‘A’s Major League Baseball team began to use statistical analysis to identify and sign undervalued players, in order to compete against their richer competitors. The approach is credited with getting them to the playoffs in both 2002 and 2003 and has since been adopted more widely.

In the football world, Brentford FC are reportedly embarking on a similar journey using the data-driven approach proven at their Danish sister club.

Tableau, custom filled map (2)

Sat, 04 Apr 2015 05:10:38 +0000

An embedded, blog and mobile-sized version of the NZ Population map to see how well it works on an iPhone. You can zoom in and out, or choose a territorial authority (e.g. Auckland) to focus in on. See the previous blog post for a link to the full version of the map on Tableau Public.

Tableau, custom filled map (1)

Wed, 18 Feb 2015 10:16:55 +0000

Map of New Zealand showing “usually resident” population at the NZ Stats area unit level, using data from the 2013 census. The map was produced in Tableau and can be interacted with (zoom in to whichever region you are most interest in, etc.) on Tableau Public.

Data sources, the approach used and credits are referenced in the workbook caption.

Intro to HTQL with Python (2)

Sat, 20 Sep 2014 03:18:07 +0000

Following on from part 1, here is an example of using HTQL to pull data from a table on a webpage.

We’ll use the Wikipedia list of most expensive football transfers as our source web page. You can check out the list here. On viewing the page and the HTML source you’ll see that the first row of the table is a header row and that the “player”, “from” and “to” columns contain quite a bit of HTML in order to provide a link to the player/team and a graphical link to their country. Our HTQL will need to cut through this to just get the data that we want.

Intro to HTQL with Python (1)

Sun, 07 Sep 2014 07:03:11 +0000

HTQL – Hyper-Text Query Language – is a language for querying and extracting content from HTML pages. If SQL is a language to get data from tables within a database, then HTQL is a language to get data from webpages on the internet. It is useful when you need to pull data from the web and there is no web service available to use. An example might be to pull population statistics from Wikipedia.

Friendly Islands Kayak Company Website Refresh

Tue, 04 Mar 2014 05:42:35 +0000

Some screenshots from the recently refreshed Friendly Islands Kayak Company website.

The client was keen to refresh the colour scheme, focus in on images on their home page and replace the plain backdrop with one of the home page images.

Add column to SSRS Excel export

Mon, 27 Jan 2014 09:27:04 +0000

A situation arose today where the key fields for an SSRS report just fitted into an A4 landscape page, but additional fields would be useful when exporting to Excel or CSV. Others seem to have had the same requirement and suggest setting the column visibility depending upon the render format. The suggestion works nicely once tweaked to cater for newer versions of Excel!

Right click on the column and change the column visibility to “Show or hide based on an expression”:

IM Infographic from Atlassian

Sat, 11 Jan 2014 09:16:21 +0000

Check out this information management infographic from Atlassian re their Confluence product. Scroll down the page to reveal more of the infographic.

Friendly Islands Kayak Company Website

Mon, 06 Jan 2014 10:22:26 +0000

Some screenshots of the current Friendly Islands Kayak Company website.

A site refresh is currently underway.

Twelfth Day of Christmas

Sun, 05 Jan 2014 04:55:51 +0000

A festive post for the twelfth day of Christmas and one way to avoid taking the decorations down for a while longer!

If you have each gift from the “Twelve days of Christmas” song as an individual record in a SQL Server database table, can you write a query that returns one row for each verse of the song?

Yes you can. This problem is similar to other cases where you need to flatten multiple rows into a single string. In this case for each day we want a comma separated list of the gifts for that day and all preceding days.