I don't believe in complexity in data visualization. I stick with simple charts when you needs to compare measures to add context.

Tableau workbook here.

How many people power the Forbes Cloud 100? Is there a correlation between company rank and employee size? Have they expanded or contracted.

Learn how to build 28 charts in 60 minutes that compare two years.

Join me for live analysis of Zoosk's recent survey, covering 8,793 members' dating drink preferences! 

In this livestream, I'll break down the responses across 7 different drink types, comparing the preferences between men and women. 

Is there a gender divide when it comes to what people choose to drink on dates?  

I'll explore this question and more. 

This week's data set only contained one measure, two years, and country names. This meant that I had to focus on charts that help visualize comparisons.

The midterm elections in the US are next week. If you're able to vote, please do. Democracy is at stake. 

This week's data was about fundraising, spending and debt by people currently in Congress. If you missed #WatchMeViz, I showed 16 different ways to visualize this data set. Hopefully they give you a bit of inspiration for creating your own.

Catch up with the show below. In the end, I went with a bar chart that compares funds raised vs. spent by State. I also have a gantt bar to show the difference between the two. I have a tutorial of that chart here.

Below this video is an image of the dashboard I created. Click on it to see the interactive version on Tableau Public.

This week I iterated through 12 charts and a dashboard in 50 minutes. Once again, I ended up with a bar chart. They're rarely going to let you down. The only other bit I added was the thinner bar showing the change from 2006 to 2016.

My Makeover Monday this week is very similar to the original other than I converted it to a diverging bar chart. Check out #WatchMeViz and the final visualization below. I iterated through 10 charts in about 45 minutes.

In this session I first reviewed the original visualization, then went into data discovery and analysis before finishing with a new visualization.  Watch the video and interaction with the visualization below.

I must admit that I REALLY struggled with this data set. I could have easily just compared males vs. females by country and year, but it seems we've been doing that over and over again. I looked to explore the data and thought a connected scatterplot would look nice, but it didn't.

Fortunately Michel Mahon proposed looking at the lag between the year of HIV infection rate and death. Morbid yet interesting analysis. It took me a while to get the calcs working; I'd recommend you build your view as a table to verify the calcs when you're not sure if they're correct. In the end, thanks to Michel's suggestion, I created a slope graph that compares the lag in years for both men and women.

As the documentation suggested, women die more quickly than men.

Below are both my visualization and the Watch Me Viz session on YouTube. Thanks for tuning in!

For #MakeoverMonday week 43 we analyzed data about apparel exports to the US during the Coronavirus pandemic. In this session of #WatchMeViz, I showed 28 different ways to visualize time series data (there are way more) before we all settled on a viz that's basically the same as the original and looks nicer.

Subscribe to my YouTube channel here to get all of my latest Tableau Tips and notifications for upcoming live streams.

Show Notes / References:

1. Data Set - https://data.world/makeovermonday/2020w43-apparel-exports-to-us
2. Tableau Reference Guide (incredible repository of thousands of Tableau tips) by Jeffrey Shaffer - http://www.tableaureferenceguide.com/
3. How to Shade Between Two Lines with Polygons by Rody Zakovich - http://www.datatableauandme.com/2017/08/how-to-shade-between-2-lines-in-tableau.html
4. Title Capitalization - https://capitalizemytitle.com/ 

Week 39 brought another #Viz5 topic, this time it was about children that are now 20-24 who were married under the age of 18. Child marriage is, of course, horrific and it's a violation of human rights. Unicef has done an excellent job of recapping all of the issues and why these marriages happen on their website here.

As I've mentioned before, I ALWAYS find these Viz5 data sets tought. I'm not sure why; perhaps I have a mental block on them now. This week the data set was three columns: country, female %, and male %. That can't be too tough...right?

Well, I sure made it tough. First, I joined the data to regional mappings from Unicef so that I could possibly look at the data at the regional level; I decided to use medians for each region in the end. Then I went into Tableau and built a bunch of charts to explore the data. I used chart guides to help me think through options and none of them seemed to make any particularly interesting insights pop out.

After about two hours of nothing, I got the idea of simply looking at the % of females that were married under the age of 18. I ended up with a simple bar chart, which turned out to not be too far from the first chart I created a few minutes in.

Here's the #WatchMeViz video and below is the visualization. Thanks to those that watched live and contributed ideas along the way!! It really helps knowing others are there encouraging me.

Eva picked quite an interesting and important topic this week for Makeover Monday week 32 - access to toilets for school girls in India. Coming from a western country, girls and boys have always had equivalent access to restroom facilities, so the topic itself has been a good learning experience for me. I hope this week the Community can make some really impactful vizzes.

Let's have a look at the original:

What works well?

What could be improved?

My Goals

With those goals in mind, here's my Makeover Monday week 32.

On the train this morning I was catching up on some reading and ran across this post from FiveThirtyEight about Trump's approval rating compared to past presidents at the 175-day mark. In the article, there's this table of the ratings:


The table is ok in that it lists the presidents in descending order by net approval rating. However, I thought a visual display would be more effective. I used Google Sheets to import the table and quickly connected it to Tableau and built a slope graph to more effectively display the data.

It only took about 15 minutes to build this, so I'm surprised FiveThirtyEight didn't include a visual. I wonder what their reasoning is for including a chart vs. a table. What do you think? Which view works better for you?

That Emma! She's quite the sneaky stinker! For Workout Wednesday Week 26, she asked us to build a trellis chart full of slope charts that compares election results by political party by constituency for 2015 vs. 2017.

Fortunately, I have my trellis chart calcs safely saved in my notes, so I didn't have to Google those. The tricky bit for me was the sorting. I'm not going to spoil how I did it for you, but you can download my workbook to see how I did it. As usual, Emma and I took very different approaches for the calculations required for the sorting. She used LODs for all of her calcs, I only used one. They both work though! It all depends on how your brain works I suppose.

The data prep parts were pretty straight forward (thank you Emma for alerting us to the need to do this). Emma loves little tricks in the formatting, but I didn't see any this week.

One thing I did different was to provide a "buffer" for the year labels. I place them 10% above the highest value so that they don't overlap the slope chart lines. Emma's year labels sometimes overlap with the slope chart lines. Just a personal preference for me.

Great fun Emma! Thanks! Took me about 90 minutes including this blog post on the train from Frankfurt to Hamburg. Great use of my time! #AlwaysLearning

Click on the image for the interactive version and to download.

Yesterday I intentionally created my own data set for Makeover Monday. Why? Because I wanted to do a step-by-step makeover of the original (FYI, that's kinda what Makeover Monday is about for me).

Today, I had a few spare minutes and knew that Eva really wanted me to create something with the full data set she created for #VizForSocialGood. Also, I was really surprised at the remarkably few slope graphs this week. This is a great data set for slope graphs, whose main purpose is comparing two points.

So, here is my second Makeover Monday this week. Special thanks to Eva for feedback on my iterations. In this viz, I wanted to compare the unemployment percentage for 15-24 year olds by country. Perfect use case for a slope graph...and my 500th Tableau Public viz!! I wonder who has more??

Well, this certainly was a data set I never thought I'd see. Leave it to Eva to surprise us again. I'm really enjoying how she's mixing things up and allowing me to participate like everyone else. I also need to thank her for sending me her great color palettes again.

This week, we looked at the EU potato sector. Seriously! We're creating vizzes about potato production. The original website has things kind of all over the place. First there's this table:


Then there are a few donuts chart, most of them look more or less like this one:


They also included a few bars charts and a line chart. All in all, it's quite colorful.

What works well?

• Donut charts are sorted
• Tables are good for looking up specific values
• Line chart provides context by comparing to an index of 100 to make yearly change easier to understand