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Tonight Eric Hannell invited me to speak at the data+visual meetup he runs here in London about Makeover Monday. I’ve been doing makeovers since my very first blog post in August 2009, yet this was the first time I’ve actually spoken about the project, why I do it, the goals, why Andy Cotgreave and I joined forces, and the impact it’s having on the data visualisation community.

This meetup group isn’t a Tableau meetup; it’s about data visualisation, making it great way to meet many new people. For nearly everyone, this was their first exposure to Makeover Monday and, even for many, their first data viz meetup. My favorite part was, of course, the Q&A. There were questions about how decide what to makeover, how we make design choices, my favorite vizzes, etc. I think there may even be some new participants this week, despite it being a bank holiday on Monday.

So. Much. Fun.

You can flip through the slides below and get a good feel for what I talked about. Enjoy!

Inspired by the fabulous VizClub projects that the folks in Leicester have been doing, a few of us decided to give it a shot here in London. The VizClub normally meets at a pub, but given the noisiness of pubs in London, we decided to meet at The Data School. The great Sophie Sparks of the Tableau Public team started a Twitter chat and invited Graeme Wiggins, Emily Chen, Matthew Nixon, Waseem Ali, Eric Hannell and me. But Sophie had a surprise in store for us, she brought along Andy Cotgreave (sound the groans). However, she brought beer and pizza so we let Andy stay.

We had been discussing using the data from the Open Beer Database to try to build something that would let people identify the beers they might like best. Graeme had warned us that the data wasn't particularly exciting, but we marched on anyway, blind to his advice.

Emily did a fabulous job of joining all of the various datasets using Alteryx and quickly got us a clean data set we could visualise. And boy was Graeme right; there was absolutely nothing interesting about the data. All it had was a list of beers, their ABV and IBU and their location. That's it. So we built a map, then another map, then a bar chart and we all were quickly bored.

On to Plan B. Andy C mentioned that he had been wanting for a long time to have a crack at making over this chart called Horizontal History (click on the image to view a larger version):

Sweet! This looked like a fabulous idea, yet like most projects, finding the data quickly became a problem. We ended up finding a great data set by MIT as part of their Pantheon project. So exciting! Until we looked at the data and realized it only included birth years.

To build a timeline-like viz, we would need death dates for those no longer living. Ugh!!! Back to Google we went and this time we found this data set that included many more people and also their death dates. We download this file (which was in JSON format) and Emily began combing them in Alteryx. This took way, way longer than we expected because we couldn't figure out how to get the JSON Parse tool in Alteryx to behave like we expected. We wasted a good hour here.

While Emily was working on that, I decided to see if anyone had already built a tool to convert a JSON to CSV and low and behold I found this great little tool. A few minutes later I had a CSV and we were able to join this CSV with the TSV from Pantheon within Tableau.  Phew! That took was too long.

By this time, it was about 9:30pm (we started at 6:15) and the team needed to get going. So we started playing with the data, built a simple timeline. Then we started playing with some of the dimension that we get from the Pantheon dataset.

For example, only about 14% of the famous people in the list are women. What??? That's sad.

Note: Not all women are shown (this is merely a screenshot)

Ok, what occupations are associated with these women?

Note: Top 15 occupations only

On we went with several more iterations and the questions were flying about. Fortunately Tableau makes answer all of these questions at a super fast pace possible. At this point we needed to build something, anything so we could get home. Since Andy C left, we decided (well, I decided) to pick on him. We all know his great love for pies, and who doesn't love a good donut, so we build a donut chart of all of the historical figures sorted by their name and used the Cyclic color palette. We wanted to make sure Andy could see it well, so we stuck him in the middle of the chart like a donut chart.


Then someone proposed sorting the names by birth year and then changing the fonts to Comic Sans and Papyrus, really only in an effort to troll Andy for leaving. Yes, this was it! Have a look at the tooltips (hover outside of Andy's pretty face)...fabulous!

Don't worry, you'll have a chance to improve this in a future Makeover Monday.

Here’s the situation: You have multiple measures that you want to view across time, but you don’t want to see all of the measures at the same time. Instead you want the user to be able to pick the measure to view.

Parameters to the rescue!

Andy Cotgreave blogged about user built views in his Data Studio days. In this blog post I will provide the step-by-step directions.  The charts types you can create with the technique are seemingly endless, but I’ll go through a simple scenario.

Step 1 – Right-click anywhere in the Measures or Dimensions windows and choose Create Parameter.  Create a parameter named “Choose a measure” with the settings below.  Note that I am giving this a name that will instruction the user as to what to do.

Step 2 – Find the parameter you just created in the Parameters window.  Right-click on it and choose “Create Calculated Field”.

Step 3 – Use a CASE statement to build the calculated field.  This field is telling Tableau what measure to use based on the value chosen in the “Choose a measure” parameter.

Step 4 – Add Order Date to the column shelf and your new “Measure chosen” measure to the row shelf. For this example, I’ve expanded the Order Date field to Quarter.

Notice how the y-axis is labeled “Measure chosen”.  We’ll clean that up in a bit.

Step 5 – Right-click on your “Choose a measure” parameter in the Parameter window and choose “Show Parameter Control”.  It should appear on the upper-right of the window. 

Make different selections in the parameter control and notice how the y-axis and the chart change dynamically.

Step 6 – Drag the “Choose a measure” parameter to the row shelf.  Again, make different selections in the parameter control and watch the label change.

We’re almost done.  Just a bit of formatting remains.

Step 7 – Clean up the chart.

1. Right-click on the field that shows the “Choose a measure” value and select “Rotate Label”
   
   
   
2. Right-click on the row label and choose “Hide Labels for Rows”.
   
   
   
3. Double-click on the “Measure Chosen” axis to bring up the Edit Axis window.  Delete the title.

That’s it.  You’re final viz should look like this:

If you’re like me, once you saw this technique, you began thinking of all of the possible uses.  Play around with lots of different use cases.  Try scatter plots, dual-axis charts, bubble charts, etc.  You can control ANY of the shelves using parameters.  Imagine the guided analysis you can provide your users and the exploration they’ll be able to do on their own.  It’s almost like creating a pivot table for them. 

If you want a slightly more complex version, I created a scatterplot of NBA franchise values that allows you pick the x-axis, y-axis and size, all with parameters.

Parameters are quite powerful.  Leverage them!

Download the sample workbook here.

I’ve been gone for a few weeks enjoying some much needed time off before changing jobs and moving to the west coast.  I’m hopping back into the blog saddle with a series of posts about different charts type, their strengths and weaknesses, when to/not to use them, etc. 

The posts will include step-by-step instructions for creating the charts in Tableau.  The instructions for many of these charts have been written before and I will reference the authors whenever I know about their work.

I will typically use a chart I’ve found on the internet in order to provide variety of examples and also to provide a forum for discussing the strengths and weaknesses of their chosen designs.

Let’s get right to it with this lollipop chart from The Washington Post (via Chart Porn):

I like the clean design, the use of simple colors, and the excellent use of data-ink ratio of this chart, but there are few issues that immediately stand out to me.  First though, what is a lollipop chart?

I couldn’t find an exact definition, but I think of a lollipop chart as a combination of a bar chart and a dot plot.  Lollipop charts are great for giving you a sense of both length (bars) and precision (dots). 

However, it only makes sense to use the stick of the lollipop when you’re range starts at zero. 

In the example above, the bars start at 60, therefore including the bars could mislead the reader into thinking the retirement age in Malta is five times lower than Austria. In this example, the bars should be removed, which turns the chart into a dot plot.

Another problem with this chart is that there is no particular rationale to the sort.  At first I thought it was ranked buy retirement age from youngest to oldest, but then Spain was listed before the United States.

For the purpose of the rest of this blog post, we’re going to focus on the retirement age only.

Taking the issues above into account, the data could be represented as a simple dot plot:

Or alternatively as a lollipop chart:

Hopefully at this point you understand when and why you would use a lollipop chart.  In the end, it’s basically a bar chart with a bit more emphasis on the exact value of the bar.  CAUTION: Don’t avoid a bar chart simply because a lollipop chart looks cute.  I would nearly always prefer a bar chart over a lollipop chart.

So now onto the instructions for how to build this chart.  (Credit to Andy Cotgreave, who back in his days at The Data Studio wrote similar instructions)

Step 1 – Drag the Country dimension onto the Rows shelf and the Retirement Age measure onto the Columns shelf.  The result is a bar chart.  Click the sort ascending button.

Step 2 – Drag the Retirement Age measure to the Columns shelf again.  The results is the same bar chart side-by-side.

Step 3 – Right-click on the 2nd Retirement Age measure on the Columns shelf and choose Dual Axis

Step 4 - Right-click on the 2nd Retirement Age measure on the Columns shelf and choose Synchronize Axis

Step 5 - Right-click on the 2nd Retirement Age measure on the Columns shelf and uncheck Show Header

Step 6 – Drag the right side of the chart to the left to shrink the view

Step 7 – On the Marks card, click on the triangle (a.k.a. carrot) on the upper-right and choose Multiple Mark Types

The Marks card should now show “All” at the top and there are now arrows for moving left and right through the measures on the Column shelf, e.g., the two Retirement Ages measures.

Step 8 – Click the right arrow on the Marks card once, then:

1. Change the format of the chart from Automatic to Bar
2. Move the Size slider all the way to the left to make the bars as small as possible
3. Remove the Measure Names field from the Color shelf
4. Change the color of the bar by clicking on the colored square.

Step 9 – Click the right arrow on the Marks card again to move to the 2nd Retirement Age field, then:

1. Change the format of the chart from Automatic to Circle
2. Remove the Measure Names field from the Color shelf
3. Drag the Retirement Age measure onto the Label shelf
4. Format the Label to the font color of your choice and set the horizontal alignment to Center to place the label in the middle of the circle
5. Change the color of the circle by clicking on the colored square
6. Resize the circle

Voila! You’re lollipop chart is complete.  After you practice these steps a few times, you’ll be able to build it in under one minute…guaranteed!

Download the Tableau Workbook here.