Makeover Monday, 2017 #26

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!

The chart in week 26 explored German car production and exports.  Nice clear charts. Not much narrative and for me the hover over was a bit much – possibly this was due to the size and imagery used, but it just seemed to get in the way for me.



After exploring the data and seeing other submissions the dip from the global financial crisis and resulting recession was a clear story. What was also interesting was that the percentage of vehicles exported also dropped in step with the reduction in production. Does this mean that Germans weathered the storm a little better than those they export vehicles to? There is also an intriguing spike in export percentage for Trucks late 2012 / early 2013. With the exploration done it looked like I’d have two charts and some narrative. Great I thought – I can pop this in panels coloured to match the German flag. More on this design choice below, but here’s what it ended up like:


You can also check it out on Tableau Public - although there’s very little interaction, just a bit of hover over.

I quite liked the design choice to mimic a German Flag, but I don’t think it will suit or appeal to everyone. The background colours are obviously quite bright and may not work for those that need a low contrast visual. There has been some interesting discussion amongst the community this week about design and data; when does design detract from the ability to see the stories in the data? I suspect I’ve fallen into the trap of emphasising a design element over the data. If time allowed I’d have a go at toning those colours down, or reversing them so that the colours come out in foreground elements instead.

Makeover Monday, 2017 #25

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

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.



For the makeover I wanted to take on the challenge of showing Turner and other artists in the same viz, without skewing the data. I recalled various art timelines from school days and a Gantt chart seemed like a good way to achieve this. The Gantt chart would show the range of years for each artists work. And when the artists were ordered by start year it would have the feel of a timeline. I then experimented with overlaying semi transparent marks for the actual art works, the aim being to have a denser / more packed overlapping set of marks for Turner than other artists:



The timeline is also available on Tableau Public here, where you get the option to hover over and click a link to see an example piece of art.



There were still too many artists to show so I filtered the list to exclude art not attributed to a year and to focus on the top 25 artists which helped to keep the timeline concise. Highlighting the top 5 also helps the casual reader. Some fiddling around was needed to get labels formatted nicely when including the highlighting. I’ve just employed the usual trick of having two calculations returning a value or empty string in opposite circumstances.

Formatting the Gantt bars was probably the biggest challenge. A Gantt bar will stretch to a point and not beyond, whereas if you plot a shape at that same point it is centred on the point and extends past it by half of it’s size. I wanted to achieve a look where the Gantt bar was simply a box around the collection of points, so to start with I ended up with the start and end points spilling out of the box – definitely not the look I was after.

To get around the marks spilling out of the boxes I created additional calculations that extended the ends of the boxes by enough years to fix the formatting. Is this a fudge? Is it a bad thing to do? To an extent yes, because it misrepresents the data! But in its defence most viewers won’t get (or need?) an accurate idea of the specific start and end year on a first glance anyway, and the actual years are included in the hover over tooltip.

A few participants hit issues using the URLs included in the data set to pull in images of the art work. The conclusion seemed to be that the Tate site didn’t allow it’s content to be iframed. I didn’t try to tackle this and instead just provided a link to click through to some examples.



I think I’ve achieved my goal with the viz. If time allowed I’d work on the option to view the actual pieces of art and more details about them. I briefly toyed with producing ASCII versions of the art work for inclusion in my tooltips; hover over a mark to see the piece of art …. kind of! Could have been a good excuse to create a web data connector maybe. I also wondered whether I could have nested a spark line or histogram within he Gantt chart bars. No shortage of ideas with this weeks data!

Makeover Monday, 2017 #23

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.



I wanted the story to develop from the overall figures (a little like the opening section of the article), via a state-by-state picture, to the detailed data for specific parks. A tile map seemed like a great way to cover the state-by-state picture, but it was hard to get a sense of the numbers so I’ve included the latest recreational visitor count in each tile where applicable:



The viz is also available on Tableau Public where you get a little extra hover over functionality.


Credits and challenges

Thanks to Matt Chambers’ blog post and Brittany Fong’s template for the US tile map approach – well worth a read if you haven’t already.

There were a few challenges over and above the base tile map:

  1. Data blending limitations. I used the Makeover Monday data set exactly as is, so it contains data at a park level which is more detailed than the tile map template. Also the filtered park level data didn’t contain data for all states. A blend in Tableau is like a left outer join - you get all records from the primary data source and any records that link to it in the secondary data source. So from this perspective it seemed best to use the tile map template as the primary. But blends work best when the most detailed data set is the primary data set – I couldn’t get the cases where there were multiple parks per state to display properly; the dreaded * issue. Uh oh. Catch 22? To resolve this I cheated a bit – the parks data actually had data for each state when not filtered to a type of “National Park”. So I removed the filer and added a calculation to just SUM up recreational visitors when the type was “National Park”. Perhaps I should have found a better way to do this – e.g. ensure everything I was displaying was aggregated at state level, like MIN([State]), or just create a suitable data set outside of Tableau?
  2. Adding summary info to each tile. To achieve the summary section at the bottom of each tile I forced the value axis to stretch to a negative value, and then added a fake data point on the last year midway towards this negative value. That data point is plotted as a blank shape and given the label that I wanted. A little bit of fiddling to display a different label (with the same text) for states with no parks so that I could give these a different colour and it was job done. What frustrated me was that I couldn’t put the state abbreviation in the top left whilst having the visitor number in the bottom right. A lack of a guaranteed data point for the earliest year in each state with national parks prevented me from doing the top left label – nothing to hook a fake data point on to.
  3. Getting a different grey background for each part of the tile. I used good old reference bands here. The downside is that you get some spurious info about the reference lines when hovering for tool tips.



I felt that the viz petered out a little. If time allowed I’d like to have experimented with making each state tile act as a filter so that the reader could view the detailed park data for whichever state they wanted. Other than that some graphical content to tie in with the subject of national parks would be good. Overall though I think my makeover achieved my goals and it was a good chance to try out a US tile map … and work through some data blending challenges!

Makeover Monday, 2017 #22

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.



The original visualisation had a headline focussing on the top 5 countries for internet access. The top 5 in 2015 includes three relatively small countries making it a great angle to focus on for what I wanted to do. Rather than just write about the problems with a filled map I wanted to illustrate the issue. Here is the end result – hopefully it speaks for itself?




The interactive viz is also available on Tableau Public.


Final thoughts

I’m not saying filled maps are bad. I’ve used then previously to good effect. I also like the extra context that the maps add to my makeover; the end result is arguably more engaging than the bar chart alone. In addition there have been some really nice map based makeovers this week that serve to highlight some key themes. But map-based visualisations aren’t without their problems, and there are aspects of this weeks data and story that illustrate some of those problems.