My goal for week 9 was to take this original matrix table...

Venn diagrams (also called Set diagrams or Logic diagrams) use overlapping circles to illustrate the logical relationships between two or more sets of items. They depict how things relate to each other within a particular segment. 

For example, how many customers made purchases in the East region, in the West region, and in both regions? 

Where the circles overlap, you display text or a value the represents the value associated with the relationship.

I created a Venn diagram for my Visual Vocabulary and while teaching how to create it, I thought of a much simpler way to create them. All of the versions you see online (and the version in the Visual Vocabulary) require you to:

1. Create sets
2. Evaluate how many things are in each set as an aggregate
3. Align the circles according to these values
4. Create a calc to display the text for the overlap with a computed set

People love maps. Putting dots on a map gives a sense of precision. There are lots of methods for displaying maps. In this video, I show you four:

1. The exact locations
2. Density map
3. Using the ROUND function to generalize the points
4. Using the HEXBIN functions to generalize the points, but at a level controllable by the user

Rody brought back a classic technique for Workout Wednesday week 11. Get all of the requirements here. Almost exactly two years ago, he wrote a guest post on this blog about how to create the unit charts he's challenging everyone to make.

One thing he mentioned in the blog is that now you can use unicode characters in 10.5, however, I've been using them for a long time. I got to this site, find the character I want to use, and paste it into the calculation or field name.

The trickiest part for me on this challenge was getting the dots in the right place, that is, starting at the bottom right. I had to swap some of the logic of the calcs around and it was done. As for the headers above each set of dots, well, I'll leave that to you to figure out. Here's my tip: don't overcomplicate it; it's quite simple.

Again, find all of Rody's requirements here. Click the image below for the interactive version.

Week 41: Music was probably the easiest from a data collection perspective so far. In week 32, I tracked everything I listened to, so I didn't want to do that again. Inspired by Andy Cotgreave's great post about his music trends I thought I would do an analysis on the music that's in my iTunes playlist. I've never thought about looking at the type of music I listen to, how long songs are, nor the bands and their songs that I prefer, so this provided me with the perfect opportunity.

This time, I started my analysis in Vizable and after a few minutes I had a good feeling for the overall patterns in the data.

Having fun today using @VizableApp to understand my music data for #DearDataTwo Week 41 w/ @HighVizAbility pic.twitter.com/z23jE9Mcxl

— Andy Kriebel (@VizWizBI) January 21, 2016

Using Vizable this way definitely helped speed up my analysis in Tableau. I like how Vizable keeps me from overthinking the analysis. It encourages me to play rather than overdiagnose. That being what it is, I was quite surprised to learn how much alternative music I listen to. I suppose that goes back to when I was in college and that genre first became more popular.

I also had a playlist of the songs that are in my primary running playlist, so I used that throughout the analysis as well to help me see if what I listened to in general was the same music I listen to when I run.

For the final visualisation, I accidently created this radial pie chart thingy. I would never create something like this if it weren't for this project. I consider this more "data art" than data visualisation.


Click through the story below to see how I conducted my analysis and to see the postcard I created.

Last week I tracked the rather depressing topic of negative thoughts, so it was great to turn the page this week and begin taking in the things around me that I find beautiful. I started the week tracking every little thing I found beautiful, which made the data collection overwhelming. So I stepped back and decided to only track those things where in my head I said "Wow! That's stunning!"

This week really helped me appreciate the exceptional beauty that is around me every day, whether it be our wonderful parks, the people I see, or the amazing architecture London offers. I had this week off from work, so I was able to spend a lot of time with my family going on walks, visiting museums, playing games, etc. So the topic, combined with the time I spent with my family, really made for a wonderfully positive week after a rather crappy Christmas week.

My analysis of the data started much like every other week: exploring what each dimension I tracked offered, looking for stories, trying to find patterns. I didn't learn a whole lot about myself, but trying to emulate what Giorgia did in her postcard for this week helped me continue to learn about using Tableau as a drawing canvas.

The trouble I run into time and time again is that I have so many dimensions that I want to put in a single view, but if I add them all in Tableau, the view quickly becomes impossible to comprehend. However, I'm really beginning to appreciate the freedom that drawing on a postcard gives me. I can incorporate as many dimensions as I'd like through the use of symbols and marks in a way that doesn't clutter the visualisation and aids in understanding.

I'm not sure why, but I feel like I'm starting to grow as a data artist, particularly when it comes to pen and paper. My ideas are flowing at the moment. Let's hope I can continue the momentum for the remaining 13 weeks.

Explore my week below by click through the story points. Enjoy!

Christmas...it's supposed to be the most wonderful time of the year, at least that's what the song says. Coincidentally, for week 38 (Dec 21-17), the topic was negative thoughts. Given the level of stress that I felt all week, I'm actually happy this was the topic because it helped me understand what and who was "causing" my negative thoughts.

I tracked the date, what was on my mind, the general topic, where I was, who I was with, the stress level and what I was doing.

I tend to always look at what Giorgia and Stefanie produce each week as a way of looking for inspiration. This week, I really liked how Stefanie created piles for her negative thoughts.

Stefanie included a classification of inward/outward for each negative thought then split them up so you could see how much more there was on one side vs. the other. So I went back to my data and added a similar classification. More on this in a minute.

Here are some of the highlights from the data analysis I performed on my data:

1. My inward negative thoughts more than doubled my outward negative thoughts. This means that I was keeping my negativity to myself more than expressing it to others. For me, this is good because I have always tended to just say whatever is on my mind. Does this mean I am learning to control myself a bit more?
2. Most of the negative thoughts were me by myself. Otherwise, Beth was second most involved. This really isn't surprising given it was Christmas week, we went shopping together, and every year I get stressed about how much we spend on Christmas.
3. 75% of my negative thoughts were low stress...phew!!

Explore the entire week in the Tableau story below, including my thought process for how I decided which data to put in the final viz.

For week 36 (Dec 7-13), I tracked every major indecision. I didn't want to track every little decision that went through my head, only those that really required a significant decision. I tracked a bunch of metadata for each occurrence as well:

• Who I was with? 
• What stress level did it introduce?
• What was the major topic? 
• How long will it take to resolve?
• Was it resolved by the end of the week?

UPDATE: Thanks to a reader for noticing that I had the years in my data set backwards.  I have corrected the data and updated this blog post.

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Consider this recent chart by Business Insider that attempts to compare data across time and contributions to the whole with multiple pie charts.

It’s not worth words to review how horrible this chart is given the message it’s trying to convey.  Instead, consider slope graphs.  Download the data for this example here.

I’m assuming that you know how to connect to the data in Tableau.

Step 1 – Drag Measure Names to the Columns shelf

Step 2 – Drag Measure Values to the Rows shelf

Step 3 – Drag the Number of Records measure off of the Measure Values card.

Step 4 – Change the mark type to Line

Step 5 – Drag the Shopping Category dimension to the Level of Detail shelf

You’ve just created a slope graph in Tableau in about 20 seconds.  Does this make the data easier to compare?  Sort of.  You see some lines that go up and some that go down, but the chart need more detail to make it easier to understand.

Step 6 – Format the measures and axis to be percentages.  Also remove the “Value” title from the axis while you’re at it.

Step 7 – Drag Shopping Category to the Label shelf and set the labels to show only on the start of the line.

Step 8 – Create a calculated field to identify the categories that increased or decreased.

Step 9 – Drag your calculated field to the Color shelf and adjust the colors to your preferred color scheme.

Step 10 – Add markers to the end of the lines

Does a slope graph communicate the message better than the pie charts?  Absolutely! 

You can easily see the winners and losers:

• Retailers have increased significantly, nearly double the time spent as the previous year.
• Online shopping and Daily Deals appear to have lost most of their mobile time spent shopping to Retailers.

Are these relationships seen as easily in the pie charts?  No chance!

Download the Tableau 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.

The chart below comes from a recently released study by Nielsen.  With some simple fixes, this data can communicate much more effectively.

I find this chart hard to read and interpret because:

1. The data is not aligned vertically, making comparisons across categories for the same country difficult.  For example, your eyes are constantly pinging left to right to left trying to compare the UK values.  It kind of makes me feel like I’m in a tennis match.
2. The bubbles are not sized according to their percentage, making comparing bubble sizes meaningless and inaccurate.  You can’t tell me that the orange Italy bubble for Downloaded Music is 1/3 the size of the US bubble.
3. There doesn’t seem to be any logic to the order of the categories.  At first I thought they were sized by the US percentages, but that’s not it.  Maybe they’re ordered by the total?  Nope.  I have no idea!

There are two better alternatives for presenting this data.  First, if you like the bubbles, then a viz like this works. 

With this viz, it’s so much easier to compare values both across and down.  It’s easier to compare bubble sizes, you don’t have to lookup the colors since they’re organized in columns, and the bubble sizes are relative to each other.  Look at Downloaded Music in Italy now: 20% now looks like it’s a bit less than 1/3 the size of the US bubble (62%).

Note that I would normally have ordered the categories alphabetically, but I sorted them in the same order as the Nielsen viz so that you could compare the mine and their’s more easily.

A second alternative would be a simple bar chart like this.

This chart also addresses the comparison problems.  The gridlines make it especially easy to compare categories within the same country, though your eyes do have to skip over three other bars before getting to the next one.

Bar lengths are much, much easier to compare than bubble sizes, but the bar chart feels a bit more cluttered to me than the bubble chart.  In this situation, I would use the bubble chart I created. 

This goes to show that there’s more than one way to skin a cat.