Makeover Monday, 2019 #26

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.

Interactive version on Tableau Public: here.

DASH

Makeover Monday, 2019 #3

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.

So for my makeover I’ve kept things pretty simple and separated the different regions and sub-regions. Adding the overall line for the US and differentiating values above / below this in different colours helps to tell the story. A state highlighter allows users to focus in on one state if they want to – this is quick built in functionality for Tableau (right click a dimension and set as highlighter). I spent a lot of time in the depths of SQL Server geography queries for last week’s makeover, so it was refreshing to step back to simple built in Tableau functionality for week 3!

Interactive viz: here on Tableau Public.

Static image:

US-MIN-WAGE

 

Makeover Monday, 2019 #1

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.

Interactive version on Tableau Public is available here.

DASH

Copy and paste text boxes in Tableau

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?

The good news is that there is a Tableau feature request (idea) to copy and paste objects in a dashboard, and we can vote for that to try to get it onto the product roadmap! Like any product development team I’m sure Tableau have to prioritise their investment, and up voting ideas gives them an idea of what to focus on.

In the meantime, if you’re willing to take some risks in a non-critical Tableau dashboard, there is already a way to copy and paste text boxes. I’ve seen this idea mentioned on the forums by Andy Cotgreave, and he quite rightly points out that it is likely to be unsupported. So if you’re going to try it, then take a back up of your workbook first. I’ll tell you more about why this is important towards the end of this post! For now trust me and take a backup.

Right let’s work through the how to guide. Please do excuse the awful dashboard design; it’s purely to illustrate the approach.

How to copy and past text boxes in Tableau

Note that this approach is for floating layouts!

Step 1 is to setup an outline dashboard and add your template text box – this is the text box format you want to copy throughout. In this example it’s the text in the top left and I’d like to duplicate the style in the bottom left and bottom right.

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Step 2 is to close your workbook and open up the .twb in a text editor (like notepad) instead of Tableau Desktop. The file is mainly metadata about how to transform and display the actual data in your data sources, and is encoded in XML. XML is generally human readable and, importantly to us in this case, human editable. Once open in a text editor find the section that starts “dashboards”. Within this section you should find a section for your particular “dashboard”. I’ve highlighted the section for my dashboard below:

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I’ve also added some annotations to draw your attention to two parts. A is a zone of type=”text”. As you can see part way down and to the right, it includes the text that I placed at the top of my dashboard. You can see other layout and formatting elements and attributes in this overall section like x and y coordinates, height and width and some styling. One aspect not included here is text alignment. You can see that in the section of XML I’ve marked B.

Step 3 is to copy that <zone … id=”X” …>…</zone>  element of XML (in my case X=1, but your case will likely differ). I’m going to paste that block back in twice (as I want two more text boxes) and I’m going to paste at the bottom of the zones section, just before the </zones> closing tag, as you can see in the next screenshot:

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What you’ll see I’ve also done is

  • updated the id=”…” for each of the copies I’ve pasted in to be the next highest id number based on the preceding zones.
  • updated the x=”…” and y=”…” coordinate values appropriately (I made my life easy here by having a 2×2 grid where I’d already added elements to two spots, so I can just copy the appropriate x and y value from preceding zones, and I didn’t need to edit width and height). Don’t be phased by the x, y, width and height values not looking like the corresponding pixel values in Tableau Desktop. You can always grab your calculator and work out what you need from other values, or just offset enough from other zones that you can subsequently fix it up in Tableau.
  • finally I updated the text in the formatted-text > run elements. You don’t have to do this here in the text editor though, as you’ll be able to edit it in Tableau Desktop too.

Cool. Save those changes, close your text editor and …

Step 4: Reopen the workbook in Tableau Desktop. You should see that the pasted text boxes show up with pretty much the same styling:

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There is one slight problem though - the text alignment isn’t the same. We can fix this!

Step 5 in this guide is to close Tableau Desktop, reopen the .twb in a text editor and add a bit more XML. Obviously if or when you are doing this for real you’ll do step 5 at the same time as step 3 above. We need to copy and paste the text- and vertical-align format styles too as illustrated here:

copy-paste-05



You’ll see that I’ve had to derive the relevant id=”dash-text_X” value. The X matches the id chosen for the previously pasted in zones. Save your changes again.

Step 6 is to reopen in Tableau Desktop and you should see correctly aligned text:

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There you go. Copy and pasting text boxes in Tableau!

Yeh, but…

I went on to try copy and pasting a non-text box zone. I copied a chart zone and edited the id, and changed the name to another unused chart from my workbook. When I reopened the workbook in Tableau Desktop I got an error. The error told me to contact support. I’m pretty sure Tableau Support don’t want to hear from me after I’ve hacked the underling XML. And I’m pretty sure I know what one of their first questions would be; “do you have a backup you can revert to?”!

I’ve used this approach to copying and pasting text boxes for a couple of Makeover Monday submissions, but not on work projects. Nevertheless I believe I’ve learnt a bit from the underlying XML behind my workbooks, and it makes me think that if the idea referenced above is up voted enough it wouldn’t be a big stretch for the development team to iterate some potentially very well received functionality.

PS. if you want know more about XML, then check out this resource.

Makeover Monday, 2018 #35

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.

The charts are simple, clear bar charts. For me it could be made clearer that the charts don’t indicate what products sell well, and hence what tech is actually worn most, and where. Also we don’t get to see the inter-relationships; are lifestyle products worn on a different part of the body to health products or entertainment products? For my makeover I wanted to take a look at these angles whilst retaining the simplicity of the bar charts. I’ve minimised styling because one of the team is keen to see how to move away from Tableau defaults for fonts, grid lines, etc.

The makeover follows below. Or you can click through to the interactive version where the highlight picker at the bottom lets you explore the inter-relationships (e.g. try picking entertainment to see where those devices are worn and who produces them).

DASH

Makeover Monday, 2018 #22

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.

I felt that areas worked well for the topic – square meters of real estate – but have overlaid them to allow the different cities to be more easily compared. This approach also removes the issue of not showing parts of a whole. I’ve tried for a blue-print like look and feel. Picking courier new to complement that. In hindsight that perhaps doesn’t work with a theme of wealth and costliness.

Tableau public version.

Pricey Property

Makeover Monday, 2018 #21

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.

Tableau public version here.

GuadianEPL

Makeover Monday, 2018 #13

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.

Available on Tableau Public here.

DASH

Makeover Monday, 2018 #2-3

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

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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!

Available on Tableau Public here.

DASH3

 

 

Makeover Monday, 2018 #1

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.

My version of the chart follows and is also available on Tableau Public here.

DASH