VizWiz

This week Eva chose a data set about the amount of land it takes to make one gram of protein. For example:

1. A 9oz/251g steak contains 62g of protein. 
2. Multiply that by 1.02m² (the amount of land needed to produce 1g of steak) 
3. It takes 63.25m² of land to produce ONE STEAK. That's 678ft² for my American friends. 

SERIOUSLY! WTF! 

That's like a one bedroom apartment for every 10oz steak. That's ridiculous and a major reason why I went vegetarian 18 months ago.

Let's have a look at this week's chart:

What works well?

What could be improved?

What I did

I'm a bit behind again on Workout Wednesday, you know, this whole life thing. Today I had a few minutes to look at week 48. As it was a waterfall chart with a couple of other tricks thrown in, I thought it would be pretty straightforward, and it was...thank goodness.

The basic requirements:

• Create a waterfall chart showing the top 5 sub-categories by sales. Include an other category for all other categories.
• Show a bar on the far-left of the waterfall that shows all sub-categories. Label it all.
• Add dashed lines that “connect” each bar.
• Add a dashed line that connects the All bar with the Other bar.
• Make sure each line looks as a single continuous dashed line. 
• Label each sub-category above the bottom line of each bar.
• Label each bar with the profit ratio below the bottom line of each bar.
• Add a filter by region. Make sure the filter is centered on the dashboard above the chart.
• Color the bar gray if it above zero or pink if it is below zero.

We've had quite a few Makeover Mondays this year focusing on gender disparities. This week, we're analyzing the gender composition of constructors of New York Times crossword puzzles. The original viz come from XWord Info:

What works well?

What could be improved?

What I did

I'm going back to Workout Wednesday week 16. Why? Because I really struggled with it. I was so close for so long, but couldn't quite get my date calculations correct. After writing them down on paper and building tables in Tableau to verify I had them correct, the rest was pretty straightforward.

This use case is super useful in a business context. I like Workout Wednesday challenges that you can employ later. The most important requirements:

1. Use a date parameter to select a select end date, limit it to all days in 2017.
2. Use a parameter to select the period type (day, week, or month).
3. Use a parameter to select the number of periods to go back (limit from 1 to 12).
4. Create a bar chart that show the total sales for the last complete period. 
5. Add sales for the same period as a label on the end of the bars.
6. Compare the selected period to sales over the same period from the prior year.
7. Add a blue arrow up if sales are up compared to prior year.
8. Add a red arrow down if sales are down compared to prior year.
9. Show the difference in sales over these time periods. Make sure to show no negative signs. The arrows will indicate the change.

For Makeover Monday week 48, Eva chose this visualization from Thrillist (created by Statista):

What works well?

What could be improved?

What I did

20 November 2018: This post now includes the recording from my repeat session on BrightTalk. The record is below the presentation slides. Thank you to all of those that attended the talk and for asking great questions. I hope you found it insightful and inspiring. ~Andy

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On the last day of TC18, I had the honor of presenting at Fanalytics, the post-TC wrap up about the Tableau Public Community. The team asked me to present about the FT Visual Vocabulary that I built in Tableau, my motivations for doing so, and my thoughts about learning in general. For those that missed it, here are my slides. Enjoy!

SLIDES

WEBINAR

For #WorkoutWednesday2018 week 46, I required people to use table calculations to:

1. Show the percentage of the total population for each age group for each year
2. Show labels on the outside ends of the lines
3. Plus some other requirements

This week's chart to makeover is nothing short of hideous; I mean like really, really bad!

What works well?

What could be improved?

What I did

This is the 150th week of #MakeoverMonday as a Community project. Congrats to those that have completed every week so far. I'm sure you have quite an incredible portfolio. For week 46, Eva chose this visualization from Information is Beautiful:

What works well?

What could be improved?

What I did

Week 45 continued the post-2018.3 set actions theme. This week, Rody gave us a fairly simple, yet extremely useful challenge. We had to create a simple table that highlighting the rows and columns for the selected cell that you hover over.

The highlighter is very fickle. It seems you have to hover over a very specific spot to get it to work. I was able to make it work by using a boolean calculation, but the highlight set action was going crazy. So I went with Rody's technique of making it a continuous field so that it would actually stick.

As this week is election week in the United States, I thought it would be fun to give everyone a data set about population projections by race, sex, origin, age and year. It's a fascinating data set to explore.

The original visualization is from the US Census Bureau:

What works well?

What could be improved?

What I did

This being the first Workout Wednesday since TC18, Ann Jackson decided to spoil us by letting us test out set actions. Set actions allow the user to dynamically update a set based on actions in a dashboard. They're wickedly powerful and a game changer for Tableau.

Here are the basic requirements (full list here):

1. Create a line chart of monthly sales that drills into the months you select
2. Create a treemap of sales by category that has the ability to drill to sub-category and product name upon clicking
3. When selecting months in the line chart, the treemap should also filter.
4. Create dynamic labels (and tooltip) for the treemap that display based on the level of detail shown (category/sub-category/product name)

With Tableau 2018.3 comes density heatmaps, a feature I've been playing with quite a bit and love it for when I have a dense concentration of points and a regular scatterplot doesn't work well. Transparency can help with dense dots, but I think the heatmaps work much better.

To give it a test, I downloaded every pitch for Clayton Kershaw and Justin Verlander (two of the best pitchers in Major League Baseball) from 2008-2018 from the great stats website Baseball Savant. Every time I look at baseball data, I'm amazed at the detail of the stats covered; the data far exceeds anything that is covered in other sports.

After downloading the data, I built the small multiples view below for each pitcher so that I could see their progression through the years. Click on the images for the interactive versions. I love how the data shows me how each pitcher has gotten better with their "misses" through their careers. For example, when they throw sliders for balls, they now tend to miss below the strike zone. This is a great sign that they have command of their pitches and are less likely to miss in an area where the batter can take advantage.

The density heatmap feature will most likely be used by most people on maps, which makes sense, but consider looking at it as an alternative whenever you need to plot x/y coordinates and have lots of points to display.

When Eva told me about this week's data set, all I could do was chuckle. Being a huge fan of the squatty potty (and the dad of four kids), I knew this was a data set that was ripe for analysis. Unsure of what the squatty potty is and WHY YOU NEED IT, watch this video...it's indeed magical!

Anyway, onto the viz to critique:

What works well?

What could be improved?

My Goals

This week's Workout Wednesday was a live edition at TC18 hosted by Ann Jackson and Luke Stanke. You can get the challenge requirements here. I worked with Lorna Eden (former Data Schooler) and we nearly finished in the allocated time; the only part we could get quite right was the shapes.

I headed back to my hotel room after the session because I had to finish it (plus I have a presentation to work on). What we did wrong was quite a dumb oversight. In the calculation to create the shapes, we were using the wrong dimension. How dumb of us!

Anyway, we're done now. It was really fun to try to get it done in 50 minutes. So close!