As Lorna mentions in the week 11 challenge, the key is in the data prep. Once you have that, the visualization is really simple.
I then renamed Pivot Field Names to "Year" and changed the data type to Number (whole) and also renamed Pivot Field Values to "Values".

This week, Eva chose a viz/data from the London Datastore about population projections. Here's the original viz:

SOURCE: London Datastore

What works well?

• It's a simple bar chart, which is very easy to read.
• Uses a single color; we often see the bars double encoded with the same value as the length of the bar.
• The axis starts at zero.
• Simple, clear axis labels

We've done it! Another year in the books for Makeover Monday, the most fun project I've been a part of and the largest in the Tableau community (perhaps in data viz as well??). From year one with Andy Cotgreave, to years two and three with Eva Murray, to Makeover Monday the Book, this has been an incredible journey.

I believe Charlie Hutcheson is the only community member to complete all 156 weeks, though Simona Loffredo has only a couple of weeks to catch up on before the end of the year to join Charlie (and me) in the 100% club. As of this writing, Charlie has 307 vizzes on his Tableau Public profile, while Simona has 197. That's an incredible achievement and a testament to their dedication to improve week by week.

It's nearly Christmas Day, so Eva picked a Christmas-themed data set. Let's have a look at the viz:

Original viz by Statista

What works well?

What could be improved?

What I did

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

Another fun Workout Wednesday challenge from Emma this week. She asked us to build a butterfly chart, something I'd never done before.

Conceptually, I knew exactly what I needed to do:

1. Create separate measures for males and females
2. Create additional measures to make them percentages by dividing by the population estimate
3. Create an LOD for the national average for males and females
4. Create additional measure to make them percentages
5. Make the male axis reversed
6. Set all of the axes scales to be equivalent (Emma didn't do this, but I think the axes should be the same on each side of the center)
7. Create the calcs for the rows and columns of the trellis
8. Add a filter for local authority and add that to the title
9. Throw is all together in a dashboard size 1000x800

Following on from last week’s discussion about Makeover Monday and what might be keeping people from participating, Andy and I are committing ourselves to keeping our visualisations simple and quick. The data set provided this week has A LOT of census information included, but I focused on the data from the original post. Here’s the original from Rhiannon Fox, a graphic designed based in Bermuda.

I spoke to Rhiannon about her work and I’m hoping she becomes part of our teaching curriculum at the Data School. She’s very excited to see what everyone comes up with.

What works well?

• I love the colors
  
• Simple, small multiples layout
  
• Great fonts
  
• Great labeling
  
• Consistent use of shapes for the unit charts
  
• Neatly organized in a Z-pattern from oldest to most recent
  
• Population growth is easy to see
  

What doesn’t work well?

• A unit chart sacrifices accuracy for engagement.
  
• The overall pattern is harder to see than it needs to be.
  
• It’s too difficult to compare females from year to year.
  

Given what I like and don’t like, and sticking to the one hour time limit, I decided to go with a diverging bar chart, also known as a back-to-back bar chart or a bikini chart or a population pyramid.

Back in August I critiqued and improved a viz created with Many Eyes.  Some of my comments included:

1. The map incorporates both color and size, which are set based on the measures picked on the right.
2. Brushing a State(s) in any chart highlights the State(s) on the other charts.
3. Hovering over a bubble or bar reveals the details of the stats chosen.  The stats update based on the selections made.
4. I’m able to use the color scale consistently through all of the charts.

Now that I have learned how to use simple state polygons in Tableau, I realized that what I previously created was unnecessarily complex.  Here’s take 2:

I made a few easy to implement improvements:

1. States are now polygons instead of bubbles
2. The option to choose a size has been removed as it no longer would add any value
3. The table on the right is now shows only the top and bottom 10 (I learned how to do this today via a discussion on the Tableau Forum that, not surprisingly, included a sample workbook for the solution from Joe Mako)

I also didn’t lose this set of functionality:

1. Brushing a State(s) in any chart highlights the State(s) on the other charts.
2. Hovering over a state or bar reveals the details of the stats chosen.  The stats update based on the selections made.
3. The color scale is consistent through all of the charts.

In the end, I’ve reduced the viz from four charts to two and made it much easier to interpret the results.

See, I can practice what I preach and I love learning something new everyday.

It’s been a while since I’ve checked out Many Eyes, but this one below has been on my list to review for a while now. Click around to get a flavor for their presentation. 

I’ve seen lots of vizzes done well on Many Eyes.  The viz above has many good qualities:

1. The mouse-over feature is smooth and quick. I like the format of the text and how the state becomes outlined.
2. You can easily switch between metrics with the drop-down box.
3. You can show one, two or all maps at the same time to make comparisons more easily.
4. Initially, when you show more than one map, the color scales are unique to each map, but there is a simple checkbox that aligns the scales.
5. The brushing/highlighting feature works well when you show more than one map, which makes comparisons between individual states even easier to make.
6. I like the choropleth maps, a feature we’re all still waiting for in Tableau.
7. Switching from choropleth maps to States represented as bubbles can be accomplished in one click.  This would be another great feature to add to Tableau once/if they add choropleth maps.
8. I particularly like how you can crop Alaska and Hawaii to make the overall map bigger without sacrificing those States.  It’s a bit annoying that you can’t do this directly in Tableau, however, you can work around it in a dashboard.

However, there are some improvements that I would make (and attempted to below):

1. There’s not enough variance in the colors, making small variances between States harder to distinguish.
2. While the choropleth maps are nice, they should be used with caution because the larger States can easily stand out simply due to their size (e.g., Texas)
3. For some reason, most of the States are labeled, but not all of them.  Not sure why that is especially considering the unlabeled States in the Southeast.
4. Many Eyes limits you to one view (i.e., a map or a line chart, but not both).  In other words, you can’t create a dashboard.
5. If you view the map as bubbles, you can’t add another dimension or measure as a color.
6. The default bubbles sizes make variances between States indistinguishable.  There’s a scale on the lower-left, but it doesn’t automatically adjust to the data in the view.  The only measure that is well represented by the size is the number of births.

Here’s my take, using Tableau:

With Tableau I’ve been able to make many of the improvements recommended as well as add vizzes that allow for quick insights:

1. The map incorporates both color and size, which are set based on the measures picked on the right.
2. Those same parameters update the other three charts.
3. Brushing a State(s) in any chart highlights the State(s) on the other charts.
4. Hovering over a bubble or bar reveals the details of the stats chosen.  The stats update based on the selections made.
5. You can easily see that Utah has a small population, but the highest fertility rate.  Maybe it’s their religion? 
6. I’m able to use the color scale consistently through all of the charts.

I made this viz with Tableau 6.1, which now allows hyperlinks on images.  This certainly simplifies directing people to your social networks and data sources.

How would you improve my viz?  Please share.

I’ve been working on a project that would benefit greatly if only I could get my hands on population data by zip.  The new data blending feature is a fabulous way to combine data across disparate data sources. It took me a while, but I found what I was looking for and have uploaded it to share with you all.

You can download the data via Google Docs or Dropbox.

Fields included: ZCTA, Zip, Population, Housing Units, Land Area (Sq Mtr), Water Area (Sq Mtr), Land Area (Sq Mi), Water Area (Sq Mi), Latitude, Longitude

The data is courtesy of the Census 2000 U.S. Gazetteer Files.  There’s lots of other useful information from the 2000 census on this site as well.

Enjoy!

P.S. Data blending in Tableau has rocked our world!  The time this one feature alone has saved us has more than paid for our licenses.  Download a great on-demand training video here or go to page 17 in the “what’s new” PDF.