Data Viz Done Right

November 22, 2017

Workout Wednesday: Fun with Formatting

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The week 47 challenge builds upon this viz from Zen Master Rody Zakovich which has you taking 100 rows of data and displaying it in a single viz as five columns. Sounds simple right? It's not.

The data this week is the top 100 global brands, which you will need to download here. The data is only two columns: (1) the name of the company and (2) it's 2017 value.

Here are the requirements:

  1. Everything MUST be done in a single worksheet. Yes, the title, subtitle, footer, etc. all must be done in one sheet.
  2. Put the worksheet in a dashboard before publishing that is 1200x650, but DO NOT add anything else to the dashboard.
  3. Display the data in 5 columns, with each column being a grouping of 20 brands. 
  4. The brands must be displayed in order based on their value.
  5. Include the ranking next to each name. These need to be left aligned.
  6. Match the tooltips (easy)
  7. Make sure every line you see in mine is in yours.
  8. The title should be 24pt Tableau Semibold. The subtitle is Tableau Book 9pt.
  9. The footer is 12pt.
  10. The column headers are 14pt Tableau Medium.
  11. Everything else is Tableau Book 9pt.

Click on the image below for the interactive version. Post your solution to Twitter and tag @EmmaWhyte and @VizWizBI. Most importantly...have fun!

That's it! Good luck! 

November 21, 2017

Tableau Tip Tuesday: How to Create a No Whisker Box Plot

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In a recent Makeover Monday viz review, I mentioned how I find the whiskers on box plots distracting at times. This is only helpful advice if there's a way to create a no whisker box plot. In the box and whisker settings in Tableau, there's no way to NOT show the whiskers. So this week's tip shows you how to work around that.


November 20, 2017

Makeover Monday: Snapchat is tops with American teens

I have three teens, so this week's dataset had me particularly curious. While my daughter prefers Snapchat, my boys prefer Twitter. One big missing piece with this dataset is the lack of demographic information. I'm curious as to how my teens compare to those surveyed.

The original viz comes to us from Business Insider:

What works well?

  • Catchy title that quickly tells the story of the viz
  • Bar charts are simple to understand and they show the pattern well
  • Sorting the apps by the most recent value
  • Including the axis, otherwise we wouldn't know what the labels mean on the top of the bars

What could be improved?

  • The fading colors across the time periods are unnecessary. Include a time axis instead.
  • Change the numbers above each bar to percentages
  • The color legend doesn't match any of the bar charts and should be removed.

My Ideas

I started by looking at slope graph comparing the starting and ending periods.

This tells the story simply, but also doesn't show enough of the change over time. Next, I took the original and turned it into a line chart, labeling only the start and the end and also changing the colors to match the official colors of each app.

I think the line charts help make the change and trends much more obvious than the bar charts in the original. From there, I decided to look at the change since the starting period (spring 2015) to make the growth or decline of each app easier to understand. And with that, here's my Makeover Monday week 47.

November 17, 2017

America's Love Affair With Guns

Having lived abroad for more than 2 years now has given me a certain perspective about the perception of guns in America. I can't think of anyone that I've met here in the UK that DOESN'T think Americans love their guns. People assume everyone in America has multiple guns in their house and they're dumbfounded as to why Americans would need them in the first place.

Another debate for another time.

I bring this up because I saw this really striking viz from Danne Woo, Founder of data.visual. I met Danne a few years ago when I was working at Facebook and we've stayed in contact since. I really like the work he and his team create.

I reached out to Danne because I wanted to recreate his chart in Tableau for practice. He gladly sent me the data and encouraged me to do so. I designed it slightly differently, while maintaining the thematic colors Danne chose.

What did I change?

  • I put the title in the form of a question.
  • I included a BAN for the total number of background checks to give the viz (and the problem) some overall scale.
  • I removed the gridlines (thanks to Jonathan MacDonald for that suggestion).
  • I made the white line thicker than the red line to make it stand out more.
  • I made the scale all the same number type. The original mixes K and M.

The best way to improve it to practice. Learning is never done.

November 15, 2017

Workout Wednesday: Top N Customers with a Twist

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For Workout Wednesday week 46, Emma had a nice little twist on the standard top N scenario. Head over to her blog for the requirements.

I learned a bit about calculated sets and mixing them with parameters in calculations. Overall, not too bad. Good luck!

November 13, 2017

Makeover Monday: Sustainable Cities Mobility Index

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For week 46, Eva chose for us to have a go at making over this visualisation from Statista about the "Sustainable Cities Mobility Index".

What works well?

  • Ranking the countries from highest to lowest
  • Good title and sub-title that explain the viz
  • Including the city names
  • Noting how the index was calculated
  • Including the data source
  • Choosing colors that go well together

What could be improved?

  • Make the bar charts bar charts. Making them look like the front of a train is cute, but it's also distracting.
  • It's limited to the top 10. What are the other 90 cities? How can I find the ranking of cities in my region?
  • The icons off to the right are unnecessary.

My goals

  • Keep it simple; I only had around 30 minutes to work on this
  • Allow the user to pick a region
  • Use the design from last week's Workout Wednesday so I could practice it some more. I was excited to find a dataset that allowed me to use this technique again.
  • The vizzes on the Arcadis website are pretty good. Can I build upon those? Can I use their colors?
  • Include a description of the index
  • Create a mobile version

With those goals in mind, here is my makeover for week 46.

November 8, 2017

Workout Wednesday: Stock Portfolio

For #WorkoutWednesday week 45, your challenge is to build this new style of visualising a stock portfolio. This idea came from Jeffrey Shaffer, which was a fun challenge to rebuild, and for your challenge, you will have to work with a bigger data set.

Here's what you need to do:

  • Download the data here. You will most likely need to do a bit of data prep like pivoting, at least I needed to.
  • Match the title
  • Match the legend
  • Match the tooltips
  • The black bars represent the 52-week low and 52-week high for each stock and the text below each bar also shows these values.
  • The blue dot represents the latest price.
  • Size should be 375x667 to fit on a mobile device. If it doesn't display properly on my phone, then it's not right. Use Device Designer.
  • Rebuild everything to look identical to mine.

Good luck!

November 7, 2017

Data Science Go - The Next Big Data Analytics Conference?

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I head to San Diego this afternoon to speak this weekend at the first Data Science Go conference. As a thank you to their team for inviting me to speak, I've recorded a short video about why I'm excited about this conference in particular and what I'll be speaking about.

If you're nearby and want to come, you can still attend. In fact, get 50% OFF using the coupon ANDY50OFF.

See you in San Diego!

November 6, 2017

Makeover Monday: Life Expectancy at Birth, 1960-2015

This data set was originally supposed to be use for Makeover Monday Live at TC17, but given what had just happened, we decided to postpone it thinking it would have been poor taste. I'm really excited about this week because this is one of those data sets (much like the global warming data set last year) that is quite simple, yet provides for so many different visualisation options.

I went a bit overboard this week. Before I get to that, let's review the original viz:

What works well?

  • Nice interactivity across the charts
  • Maps are always easy to understand and audiences recognize them quickly
  • Using simple colors for the color legend
  • Simple, easy to use filtering

What could be improved?

  • The bar chart should start at zero.
  • The bars are in reverse order.
  • The map makes comparing small countries nearly impossible.

What I did

I found a great post by Nathan Yau back in January based on this same data set. In it, he created 25 different vizzes with this data, which helped me draw inspiration. I had also been planning a table calcs training class for The Data School, so I spent a lot of time on this over the past month or so building lots of vizzes. In the end, I've come up with 10 alternatives.

The first viz is my the favorite of everyone I've shown these to, so I'll use this as my entry for this week. Farther down, you'll see the other nine as images, which you can click on to go to the live version.

November 3, 2017

Using Alteryx & Tableau to Visualize London Crimes by the Square Mile

At the Data School, more often than not when Alteryx needs to be taught, I bring in people way better than me. Last week though, I wanted to give teaching spatial analytics a go. I'd been practicing with some exercises, felt like I was improving, and wanted to test my learning by teaching. If you don't know me, then it's important to understand that I am a massive believer that teaching a topic is the best way to learn it.

One of the exercises we went through was taking two data sources, one with shapes and one with points, and creating a grid map. This requires the following as my workflow below demonstrates:

  1. Import shape files for each London borough
  2. Create 1x1 mile grids within each Borough (which turned out really cool because Alteryx accounts for the edges of each Borough)
  3. Import the London crimes data
  4. Create spatial points from the latitude and longitude fields
  5. Use the spatial match tool to determine the Borough in which each crime occurred
  6. Summarize the crimes
  7. Export as shape files
  8. Import into Tableau

Practical exercises make concepts stick and this one worked for me. From this point, I created a simple interactive Tableau dashboard so people can find the areas near them with the most crimes.

November 1, 2017

Workout Wednesday: Customer Cohorts

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Quite amazing timing that the day Emma chooses to challenge us to understand cohorts for Workout Wednesday, I happen to be running LOD exercises at The Data School about calculating cohorts. Lucky me!

For the detailed requirements, head over to Emma's blog.

How did I find the exercise?

  • Designing the charts was pretty simple.
  • Create the cohorts was simple as I practice this A LOT.
  • This turned into container hell on my dashboard, so at the end I was sure to remove all the extra containers that got added along the way.
  • The trickiest part was getting the actions to do the excluding. Emma and I actually did it the same way for once!
  • Emma and I took a different approach to calculating the "Leaving Date". I think my method is much simpler, but both work, so that's all that matters.

Some things I changed

  • I included a divider line between the top two bar charts to make it clear they were separate.
  • I made the scales on the two bar charts the same.
  • I made my viz 800x700 so it would fit better on my blog.

Another fun week and eight more to go!

Tableau Tip Tuesday - The Information Lab Zen Master Webinar Series - Part 3

Today was the final day of my 3-part tips series for The Information Lab. There are two more webinars coming up from Chris Love and Craig Bloodworth. Sign up for those here.

Hopefully you've enjoyed the series. I'd love to hear your feedback on how useful the video are and things I could do differently. Enjoy!