More on Tableau Public

Yesterday’s post on Tableau Public generated a surge of traffic so I thought I should add more examples and practical information for people interested in the software.


Here’s a quick one on health, based on OECD Health at a Glance:

click to interact

Just select two indicators, and you see how one influences the other. Or rather, is correlated because correlation doesn’t imply causation!

Here are links to more example done with Tableau Public.

Another Paris-based intergovernmental organisation is using Tableau – the UNESCO.

These 2 have been done by PAHO to describe the situation in Haiti (the 2nd is really powered by Tableau Server, but it’s close enough)


There are further examples on the Tableau blog.

Now more about Tableau Public and the Beta.

Tableau Public doesn’t exactly allow you to do everything that Tableau does from the web. To prepare the views which are going to be published on the web, you need to use a software that runs on your computer.  It lets you do whatever you can do with the regular Tableau Desktop, with a couple of limitations: you have to stick to basic source file types (access, excel, and text file, no exotic database) and you are limited to 100,000 records of data. One other difference with the regular Tableau Desktop  is that you can’t save your work locally: you have to save it on the web, in your private space on Tableau servers. However, there are the same analytical and visual features in Tableau Public than in Tableau Desktop.

When your work is published, users don’t have access to all the tools you had when creating the view: they can’t move dimensions around, create exotic filters or calculations. They really see the chart as you intended it to be seen. There are a certain number of interactions built-in, however: users can select, highlight, sort and filter. If you are publishing a dashboard, the different tables and charts of the dashboard can be linked, meaning that an action (such as highlighting one dimension) in one place will be replicated elsewhere, or not. The underlying data can also be downloaded. So there is a great deal of interactivity, but not enough to twist your display beyond recognition. That being said, other Tableau Public users can download your workbook and manipulate it with the client software.

About the Beta: currently, Tableau Public is in closed beta. It will be in open Beta in February, as far as I know. To get a spot in the close beta, you need to write to the people of Tableau.


Using Tableau Public: first thoughts

I am currently beta testing Tableau Public. Essentially Tableau Public let you bring the power of Tableau analysis online. With Tableau public, your audience doesn’t need to download a workbook file that they can see in an offline, software client – they can see and interact with your work directly on a web page.

There are quite a few examples of the things you can do with Tableau public. These are the examples you are given when you start the product:

Tracking Economic Indicators by FreakalyticsA Tale of 100 Entrepreneurs by Christian ChabotBird strikes by airport by CrankyflierInteractive Running Back Selector by CBS sports

And there are always more on Tableau’s own blog. I’ve done quite a few which I’ll share progressively on this blog and on my OECD blog,

So that’s the context. What’s the verdict?

1. There is no comparable data visualization platform out there.

There are many ways to communicate data visually. Count them: 1320, 2875… and many more.

However these tools have a narrower focus than Tableau, or require the user some programming ability. For instance, Many Eyes uses a certain number of types of data visualization which can be set up in seconds, but which cannot be customized. Conversely, Protovis is very flexible but requires some knowledge of Javascript. And even for a skilled developer, coding an interactive data visualization from scratch takes time.

By contrast, Tableau is a fully-featured solution which doesn’t require programming. It has many representation types which can be deeply customized: every visual characteristic of a chart (colour, size, position, etc.) can depend on your data. Several charts can also be combined as one dashboard. On top of that, data visualization done in Tableau comes with many built-in controls, with an interface to highlight and filter data, or to get more details on demand. For dashboards, it is also possible to link charts, so that actions done on one chart (highlighting records, for instance) affect other charts.

2. The solution is not limitless.

Tableau enables you to do things which are not possible using other packages. But it doesn’t allow you to do anything. That’s for your own good – it won’t allow you to do things that don’t make sense.

There are many safety nets in Tableau, which you may or may not run into. For instance, you can’t make a line chart for data which don’t have a temporal dimension – so much for parallel coordinates. However, the system is not fool-proof. Manipulating aggregates, for instance, can lead to errors that you wouldn’t have to worry about in plain old Excel, where the various steps through which data are computed to create a graph are more transparent (and more manual). Compared to Excel, you have to worry less about formatting – the default options for colours, fonts and positions are sterling – and be more vigilant about calculations.

3. Strength is in numbers.

Over the years, many of us grew frustrated with Excel visual capacities. Others firmly believed that anything could be done with the venerable spreadsheet and have shown the world that nothing is impossible.

The same applies to Tableau. The vibrant Tableau community provides excellent advice. “Historic” Tableau users are not only proficient with the tool, but also have a better knowledge of data visualization practices than the average Excel user. Like any fully-featured product, there is a learning curve to Tableau, which means that there are experts (the proper in-house term is Jedis) which find hacks to make Tableau even more versatile. So of course, it is possible to do parallel coordinates with Tableau.

The forum, like the abundant training, available as videos, manuals, list of tips,or online sessions with an instructor, doesn’t only help the user to solve their problems, but it also a fantastic source of inspiration.

With the introduction of Tableau Public, the forum will become even more helpful, as there will be more questions, more problems and more examples.


Health statistics

In the last days of 2009, this chart has been published by the National Geographic blog:
the cost of care

The chart has since been debated and criticized, among others, by Jon Peltier, Andrew Gelman, and Evan Falchuk – which all made valid points. For instance, to show correlation and outliers, a scatterplot does a much better job. That being said, it’s difficult to see the country names with a scatterplot. On the substance, the number of doctor visits is not the most relevant variable to bring into this picture, mostly because this number directly depends on the compensation mode of these doctors, not on their efficiency. The notion of “universal coverage” is also quite arbitrary. France, for instance, which had what could be called universal coverage since 1945, got an even more “universal” one in 2000. And still, some people can’t receive the healthcare they need.

The chart is based on OECD data, from a recently released book: OECD Health at a Glance.  For the release of the book, I had worked on 2 presentations, which we remained unpublished. Since they were not formerly published by OECD the standard disclaimer apply – they do not commit the organization and do not necessarily represent its point of view and that of its members.

Anyway, for anyone interested in health statistics in general and in USA healthcare specifically, here they are in their slideshare glory:

Mortality data with Tableau Public

Last month I saw this infographic chart put together by GE and GOOD magazine:

While the look and feel is pleasing I was bothered by a few choices of design.

First, homicides and accidental deaths are not taken into account. I suspect that for some demographic categories, they represent a significant proportion of the deaths.

Second, the table doesn’t give an indication of the differences in mortality between the different age groups. For instance, there are over 15,000 deaths per 100,000 people over 85 years old, but only about 130 / 100,000 for young people aged  15-24. So the last item in the right-most column corresponds to much more deaths than the top item in the left-most column, although they have the same visual weight.

Coincidentally, I got to try Tableau Public Beta and thought it would be a good exercise to give it a spin.
The data source is the same. I got my data through the wonder service of the CDC.
Here goes:

By playing with the filters you can see the ranking of the causes of death. For instance, we can see that accidents and homicide are precisely the leading causes of death of young people aged 20 to 24. Now what if you want to see the demographic categories that one given cause of death affects most? Here’s a second visualization:

You can see that certain causes of death, for instance, only affect one gender or the other (such are certain forms of cancer).
I’ve made that last one to illustrate the evolution of mortality with age. No one would be surprised to learn that older people have a higher probablity of dying but by what proportions?