I’ve been faced with this scenario many times - I need to sort the top N of a dimension by a measure (e.g., sales), but I want the rest of the members of the dimension to be sorted alphabetically. This is an especially handy trick if you have tons of members in your dimension. It makes it easy to see which members are in the top N, then the rest can be found by looking them up alphabetically.

This is a video tutorial that’s simpler than the method I outlined previously here. In this video, I use sets as recommended by commenters Nelson Davis and Joshua Milligan.

Watching the video, you may also pick up a few other tips along the way, like creating sets and parameters, quickly formatting worksheets, faking headers, etc.

A couple weeks ago, Business Insider published a very simple bar chart showing the total value of all franchises for the four major professional sports in the USA. At the Data School, I’m always stressing context in visualisations.

Business Insider’s chart is lacking context, so in today’s makeover, I walk you through a few simple methods for adding context to a simple bar chart.

I had finally caught up on all of the Dear Data Two postcards, then this extremely difficult topic came up. The problem isn’t necessarily the topic itself, but the data collection. How the heck do you collect data about being nice?

My initial thought was to do a word analysis for Tweets and emails, but that didn’t interest me much. I considered tracking every time I said something nice to someone, but that would be a data collection nightmare.

Instead, I settled on a simple list of some of the people I’m closest to and ways that I can be nicer to them. I borrowed heavily from Giorgia’s categorisation. From there I thought I would create tree-like structures for each person, but when I sent a sample to Jeffrey, his first comment was:

Wow. This looks really awesome. Antenna charts.

So deflated! But drawing the trees meant adding a lot of rows to the dataset…maybe this was a blessing in disguise. I decided to go with the antenna charts idea and straightened out the branches. The Tableau part was pretty simple, since it’s really just a game of “connect the dots”. The tough part was transforming this onto a postcard given my limited drawing skills.

Bonus tip today. This tip started with a request for feedback from The Information Lab’s head honcho Tom Brown. Tom is getting ready to demo a dashboard to a customer and we noticed that he was using automatic sizing on the dashboard he created. This is generally not recommended because Tableau will re-size the dashboard depending on the device size, which can cause your dashboard to not look as you intended.

Below is a dashboard I created for the Transport for London bike scheme. Watch the video on how to get your dashboards to be the “perfect” size. In this video, I used James Dunkerley’s Web Data Connector, which you can find here.

Saturday Down South, which focuses almost exclusively on Southeastern Conference (SEC) football, posted an article back in January about the size of SEC football stadiums. In the article, they have a great line:

The conference’s stadiums are some of the biggest in the world, representing four of the top 10 and seven of the Top 25, many in towns that more than double in population on game days.

They wrap up the article with this basic table:

WORLD’S LARGEST STADIUMS RANKED BY CAPACITY

1. Rungrado May Day Stadium (Pyongyang, North Korea): 150,000
2. Salt Lake Stadium (Kolkata, India): 120,000
3. Michigan Stadium (Ann Arbor, Mich.): 109,901
4. Beaver Stadium (State College, Pa.): 107,282
5. Kyle Field (College Station, Texas): 106,511*
6. Estadio Azteca (Mexico City, Mexico): 105,000
7. Ohio Stadium (Columbus, Ohio): 104,944
8. Neyland Stadium (Knoxville, Tenn.): 102,455
9. Tiger Stadium (Baton Rouge, La.): 102,321
10. Bryant-Denny Stadium (Tuscaloosa, Ala.): 101,821
11. Bukit Jalil National Stadium (Kuala Lumpur, Malaysia): 100,411
12. Darrell K Royal-Texas Memorial Stadium (Austin, Texas): 100,119
13. Melbourne Cricket Ground (Melbourne, Australia): 100,024
14. Camp Nou (Barcelona, Spain): 99,786
15. Soccer City (Johannesburg, South Africa): 94,713
16. Los Angeles Memorial Coliseum (Los Angeles, Calif.): 93,607
17. Sanford Stadium (Athens, Ga.): 92,746
18. Rose Bowl (Pasadena, Calif.): 92,542
19. Cotton Bowl (Dallas, Texas): 92,100
20. Memorial Stadium (Lincoln, Neb.): 91,471
21. Wembley Stadium (London, England): 90,000
22. Ben Hill Griffin Stadium (Gainesville, Fla.): 88,548 23. Gelora Bung Karno Stadium (Jakarta, Indonesia): 88,306
24. Jordan-Hare Stadium (Auburn, Ala.): 87,451
25. Borg El Arab Stadium (Alexandria, Egypt): 86,000

Simple enough, right? The focus of the article is on SEC football stadiums, but their method of emphasising (via bold text) is easy to overlook and comparisons to other stadiums is difficult.

For my makeover, I’ve:

1. Changed the view into a bar chart, which helps see the size variances between the stadiums
2. Used colour to group the stadiums as to whether they are in the SEC, another college football stadium, or something else
3. Used the SEC’s official yellow for the background and blue for the bars of the SEC stadiums
4. Used the News Gothic font, which is the closest I can get to Benton Sans, the font the SEC uses on their website
5. Included a map for the locations of the college football stadiums (this was also my first time creating a custom Mapbox map)

This week I go back to a post I wrote in January that showed how to create a parameter that returns a value, but those values have multiple number formats (e.g., pounds and percentages). I then show how to use custom number formatting to display the metric selected with the proper number format.

Note: This logic only works if all of the numbers you are showing are positive.

The topic for week 22 of Dear Data Two was our past. I immediately knew what I wanted to create in Tableau, and that was a visual resume (which you can view here). The inspiration for the resume comes from Ben Jones, who showed me his resume built in Tableau a couple years ago before he started working at Tableau.

From there, I started working on a couple of different draft versions of my postcard for Jeffrey. I thought I had settled on one and I showed it to my wife. When I asked for her impressions, she thought it was strange how I had stuff going left and right; she thought left looked negative and right positive, so in the final version I incorporated her feedback.

Data collection was pretty straight forward. I used:

• LinkedIn for the dates of my professional history
• Blogger for the dates when I started my various blogs
• The rest was by memory (or what is left of it)

From there, I simply plugged all of the dates into Tableau, categorized them, and built the resume in Tableau.

After last week’s Makeover Monday, several people pointed me to this tweet from World Economic Forum, asking me to immediately give it some love.

How much does each state contribute to the US economy? http://t.co/aykgGdkdMN #economics pic.twitter.com/07RHuVxtK6

— World Economic Forum (@wef) September 7, 2015

It looks like WEF hired howmuch.net to create this tree-map pie chart thingy, as they have a more extensive write up about it here. I’ve used story points to review the visualisation, walk through a series of alternatives, and then conclude with an interesting tidbit about how uninteresting their data is.

Hashu Shenkar from the Data School was rehearsing his Alteryx/Tableau demo this morning and showed us a simple map like this:



One of the problems with maps like this is that the sorting defaults to the data source order, which is this case is the zip code.

What I want the map to do is bring the highest and lowest values to the front. When I look at the sort options on the zip code dimension, I can choose Profit in descending order.


Choosing that only bring the positive values for profit to the front.


To get the positive and negative outliers to the front, I need to create a simple calculated field on Profit that returns the absolute value.


Then in the sort for zip code, choose this measure.


And now you can clearly see the outliers for your map.


If you really want to see the outliers to pop, make one small adjustment to the colour scale. Change it to a palette that has white in the middle of the range.


Download the workbook used to create from Tableau Public here.

I used lollipop Gantt charts in Dear Data Two Week 14 and thought it would be useful to share how to create them. I tend to prefer this look to my Gantt charts instead of standard Gantt charts because I like how to end of the timeline is more prominent.

Several people pointed me to this graphic by Visual Capitalist. It's quite a bad way to represent parts-to-whole. In short, it's basically impossible to make any comparisons. I'm using story points this week to walk you through the makeover.

Just realized this is my 500th post on this blog.  Feels like quite a milestone for some reason.  Anyway, for Dear Data Two | Week 15: Compliments, like Jeffrey, I looked at my content that people liked across social media platforms (assuming that I can take those like, favorites, reshares as compliments).

The topic for week 21 was "Our Cities" and given how Jeffrey loves music, data viz and is a church-goer (amongst other things), I thought I would plan his itinerary for a 5-day holiday in London.

Nearly caught up on the weeks I had to skip. Week 14 was pretty straight forward, but I wanted my gantt chart to look a bit fancier so I put dots on the ends. For this type of lollipop Gantt chart, you need to use both axes. I'll show how to do this for Tableau Tip Tuesday next week.

To collect the data for this week, I combined data from Moves (for places and times), Fitbit (for sleeping) and Sunrise (for my calendar). I entered everything manually into Excel and connected it to Tableau.

Only a couple more weeks to catch up on. Today I got back to week 13, when I tracked desires and dislikes. To track desires, I logged every time I thought about things like: desires, wishes, cravings, requests, asking for, urges, etc.  For dislikes, I logged things like: dislikes, loathes, disgusts, aversions, hates, etc.

In Tableau, a unit chart seemed to make the most sense for visualising the frequency. I then thought about the Facebook "Like" icon and decided to switch to using that symbol as the unit chart. A simple square communicates much more effectively and it much easier to count quickly.

One of my favorite tricks is to create table calculations and make them discrete for showing in the rows and columns. This frees you up to use the other chart types alongside a table.

Back in November, I wrote step-by-step instructions for combining KPIs and sparklines into the same worksheet. With Tableau 9, things have changed a bit in that these are now even easier to create with LOD calcs. In this video tip, I show you how to create these views. Use these and you'll never think about Tableau's lack of synchronous scrolling again.