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December 30, 2010

UK University Elitism? Independent vs. Free Meal Students – Part 1


This is part one of a two part analysis of private education in the UK.  The chart below was published on Flickr by li_jacq.

Two simple improvements:

  1. A bar chart would be more effective than a column chart because you don’t have to turn your head to read the labels
  2. The data should be ranked.  This seemingly sporadic display does not provide any insight

I pulled down the data to reconstruct the chart.

UK Elite Universities

As you can see, I’ve made the adjustments I noted above and now you can make more sense of the data.

My curiosity is leading me to look into this further.  My questions include:

  1. Why are only 16 universities listed?  These don’t represent the top 16 for this measure, so what’s their significance versus the others?
  2. What’s the relationship between Independent and Free Meal school students?
  3. Does the region of the UK impact anything?

I’ll answer these questions and more in a future post.

December 24, 2010

You know you’re a viz nerd when…

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…you wife sends you a link to a chart she thinks you’d like.

Author Kevin Drum: “Speaking of tax cuts, I thought everyone should see a nice picture that compares the Obama position with the Republican position. As you can see, under the Obama plan (in blue) everyone gets a tax cut.”

Here’s the original article if you’re interested.

December 22, 2010

The Cola Wars: Don’t Take on Coke

There’s been a buzz around town about the increase in Coke’s (NYSE: KO) stock price over the past several months.  Coke is THE symbol of Atlanta, so naturally there’s a huge interest in their success and the demise of the main competitor.

If I may digress, you can pinpoint the turnaround on March 5, 2009 with Muhtar Kent’s inspiring letter to shareholders.  Mr. Kent’s first job with Coke was in 1978 as a driver and he has risen steadily through the system to CEO and is deservedly compensated quite handsomely ($19.6M in 2008, $14.8M in 2009).  According to the AJC, this could buy you 1.5 personal jets, 109.5 houses and 192.25 luxury SUVs.

When measuring Coke’s performance there’s only one large competitor (yes, I’m intentionally NOT mentioning their name).  I created the dashboard below to do some basic analysis, plus I wanted to get some practice with parameters in Tableau.

Some basic things I see:
  1. The New Coke disaster in 1986 didn’t hurt as much as you would be led to believe by the media (it tasted terrible!)
  2. Coke’s stock is up 85% from a two-year low point in March 2009
  3. Strictly based on price, Coke has been getting killed by its main competitor since 2003, but has effectively closed the gap
  4. When I looked at closing price and shares traded vs. the main competitor, it looks like there is an inverse relationship.  I created the dual axis chart as another way to make this comparison
With Muhtar’s passion and the resurgence of the Coke brand, Coke is poised to stomp the competition!  Don’t be like Richard Branson; he learned the hard way that you don’t take on Coke (anyone remember Virgin Cola?).

Finally, I was given a 6-pack of glass bottles to share over the holidays.  There is a photo contest for the best holiday celebration including these Cokes.  Any ideas?

Shake Up Christmas!

December 9, 2010

It’s cold here in Georgia, but how about Britain?

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According to the Guardian Data Blog on December 1st “The freezing weather, which fell to -20C in parts of Scotland, continues to severely disrupt travel across the country.  The problem is the ground temperature is lower than the air temperature [which] makes thawing difficult.” 

This impacted me since the Manchester United vs. Blackpool match was not shown on Fox Soccer Channel.  Apparently Blackpool doesn’t have an underground heating system to keep the pitch from freezing.  It stinks when you set your DVR and get some other crappy game instead.

The Guardian presented the data via a Google map, which is not surprising since this is such a simple way to present the information.  The problem with presenting the data this way is that you can’t determine how cold it really was nor how much the weather changed; the data is represented merely as points on a map.  If you look at this map, it could mean just about anything: vacation spots, lighthouses, you name it.

Guardian Cold Weather

I wanted to understand how the temperature changed day to day from December 1-3.  I wish they posted the data for more days so that I could see what happened over the weekend as well (to justify the game getting canceled).  I loaded the data into Tableau and changed the measurement to be the change from day to day.  This means that December 1 doesn’t have anything to compare to. 

It’s incredibly clear that there was a bit of a warm up in many places in the UK with the exception of the northwest.  However, the temperature dropped dramatically on December 3, but with a warming occurring in the northwest.  Scroll through the days to see it for yourself.

December 8, 2010

Downloadable US population data by zip

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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

Population Data Sample

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.


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.

December 2, 2010

Movies, movies, movies – The best of all time

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We’re all familiar with the blockbuster movies from our own generation, but what about all time?  I downloaded the data from Box Office Mojo to find out.  The data I’m using is inflation adjusted.

Immediately jumping out to me:

  • There was a large increase in the number of movies in the top 100 from the 1930s to the 1970s.  This isn’t very surprising given the growth of the industry as a whole.  Click on the 1970s on the bottom-right bar chart; there were some awesome movies that decade.
  • There was a big dip in the 1980s.  Did the awful music of the decade play a part?  It’s a good thing Steven Spielberg and George Lucas were pumping out movies.  They basically owned the decade.
  • Interesting in the 2000s is that most of the movies in the top 100 have lots of special effects
  • From the sparkline I see that all but five years between 1963 and 1984 are below the average gross earnings.  But, what’s most surprising to me is that this list of five includes "The Empire Strikes Back”, “Return of the Jedi” and “Raiders of the Lost Ark”.  I would have never guessed those would be below the average.
  • In the top 5, I still haven’t seen “Gone with the Wind”, “The Sound of Music” or “The Ten Commandments”.  I fall asleep every time I start watching them.

What do you see?

NOTE: See a similar post by Ross Perez on Tableau’s blog for the highest grossing movies since 2001.

December 1, 2010

Tableau: The perfect blend of productivity, happiness and features


Today I attended the Information Management webinar “The End of BI as We Know It: A fresh look at what business analytics means for today's organizations” and Mark Madsen from Third Nature presented a fabulous slide that I just had to share.  For any of you that use Tableau, I know you’ll agree with where Tableau sits in my mind.  (Note that I added Tableau to the chart, not Mark.  I’d hate to get him in trouble.)

Fullscreen capture 1212010 125511 PM.bmp

UPDATE (12/1): I have posted the presentation from the webinar -

UPDATE (12/2): The webinar recording is now available -

November 25, 2010

STDs in the USA: Who should you avoid and where?


The CDC publishes an annual report on Health in the United States and included in the report is a “Chartbook”.  It’s 574 pages long, but you can skip to page 32 for the start of the charts.  There are some quite horrendous charts, especially the pie charts, that you will get a kick out of. 

You can download the data on CDC Wonder.  Once you create your query, you get a spreadsheet of the results, a map, and a bar chart.  The bar chart is particularly poor and only allows you to pick two dimensions.

I have downloaded the data and produced an interactive dashboard via Tableau Public.  Within this dashboard you can filter by Gender, Age, State and Disease.  In the end, I have included all of the views from CDC Wonder, plus much more. 

Some observations:

  • The infection rate for the total US has continued to climb for all diseases combined.  This is largely due to Chlamydia.
  • Syphilis infection rates declined from 1996-2001, but have continued to climb since.  Particularly concerning is the rate in Washington, DC.
  • In fact, Washington, DC has the highest infection rate for all three diseases.
  • Alaska’s overall infection rate in twice the national average, with the Chlamydia rate 86% higher than the national average.  This is definitely worth looking into.
  • The overall infection rate for females is more than double that for males.
  • Females between the ages of 15-24 are most likely to get infection, while males are most likely between the ages of 20-24.

There are many more observations and insights to be gleaned from this dashboard.  It is considerably quicker to identify outliers and trends with a simple dashboard like this than with CDC Wonder.  Imagine how much more useful the “Chartbook” would be if the CDC used Tableau.

What other observations can you make?

November 24, 2010

Failed Banks in the US: Popping the Bubbles

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Simon Rogers from the Guardian created a visualization of failed banks in the US using Many Eyes.  The article can be found here.  Before I critique the visualization, take a minute to interact with the bubbles.

A quick word about data integrity.  The article on the Guardian references the data going back to 1935, and in fact, the data compiled does go back to 1935.  I made the assumption that the viz created by Simon went back that far as well, but then I couldn’t find a year filter.  If you look at the data that built the bubble chart, it only covers 2008-2010 and all three years are combined.  But 2010 isn’t even a complete year.  Come on already!  This can be extremely misleading and should be clearly noted, but it’s not! 

The initial view above is assets in failed banks as dollars per person. 

  1. The huge bubble for Nevada surely stands out, but why not a simple bar chart? 
  2. Notice that Total is listed as a State; that doesn’t make any sense.
  3. Which state is #2?  How about #5?  It takes some work.
  4. Do we really care about all 50 States or maybe just the top 10? 
  5. How much bigger is Nevada than the #2 state?

It’s so much easier to compare the size of the bars than the size of the bubbles.  From the bar chart, you can easily see the rank and the relative size of each bar.  It turns out that Nevada is 20x larger than Alabama.  There’s absolutely no way you can identify that in the bubble chart.

Change the Bubbles Size option to Number of failed banks.  Holy smokes!  What state is collapsing?  Oh, it’s not a state; it’s that pesky Total again.  The Total completely distorts the view and makes all other comparisons impossible.  Again, a simple bar chart will suffice.

Finally, since there are two data points that are highlighted by the article (assets per person and number of failed banks), a scatter plot provides one of the best means of seeing the relationship between the two.  In this view, you immediately see the five outliers I have labeled below.

Scroll through the other filters and you continue to see that including Total as a State completely wrecks any insight that could be gleamed from the viz.

The viz below was built with Tableau Public and it includes the data all the way back to 1935.  However, I decided to only focus on the last 20 years; this time period represents the most volatility since the Great Depression. 

NOTE: 2005 and 2006 are not included since there were not any bank failures listed on the FDIC website for those years.  I also excluded 2010 since the year is not complete.

There are three visualizations included.  The line chart (and size) represents the number of bank failures.  The color indicates the estimated loss (adjusted to the value of the dollar as of 31 Dec 2009).  When you choose a Year, you will get the corresponding map and bar charts.  The map and bar chart are sized and colored in the same manner as the line chart.

Naturally, I went straight to 1989.  Texas had 224 bank failures!  Then I went to the surrounding years and Texas was at the top of the list again.  It turns out that there was a banking collapse in Texas in the middle 1980s to early 1990s. 

According to the Dallas Morning News: “In the state's 1980s collapse, an energy bust and a subsequent real-estate wreck leveled hundreds of Texas banks, including longtime pillars of the economy.”

Sound familiar?

November 19, 2010

The Sally Field problem

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via Seth's Blog by Seth Godin on 11/17/10
It doesn't really matter if we like you.

It matters if we like your work.

[Surprisingly, the converse of this rule also works].

Sometimes it seems as though people who are really concerned about one would be better off focusing on the other.


Great advice for a consultant, don't you think?

November 16, 2010

A more effective display of’s hourly forecast

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My daughter had a soccer game last weekend across town early in the morning and the weather was predicted to be quite cold.  Naturally I went to to check the hourly forecast, but this time something struck me.


Notice the vertical scale.  It’s not zero-based.  Sure, it’s simply showing the changes in temperature, but as I scrolled through the pages, the axis values changed, that is, the range did not stay consistent.  I also noticed that 12am is repeated, that’s kind of odd.  Fusion Charts is their tool of choice.

I would have used Tableau to create a simpler chart.  Unfortunately I lose the nice pictures across the top of each hour, which I really like, and the gentle shading (though why use gold for night hours…doesn’t gold mean sunny?), but I gain a zero-based scale and a line that I can color based on temperature, with the mid-point at 32 degrees.  Below 32 = red, above 32 = green.


In this view the variances in the temperatures are even easier to see.  You can see the huge change from 6am to 3pm and then the dramatic drop as sunset approaches.  Which view works best for you?

Coming to Atlanta - Tableau 6.0 Tour

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The Tableau 6.0 tour stops in Atlanta this Thursday, November 18th. Be sure to let me know if you'll be there; it'd be nice to meet some of you and chat data viz.

+ 2:00 – Registration
+ 2:30 – The End of BI as You Know it
+ 3:00 – Tableau 6.0: Speed, Power and Style
+ 4:00 – Wrap-up
+ 4:30 – Reception: Cocktails, Networking and Hands-on Demos
Registration is FREE!! Click on the image below to learn more.

November 13, 2010

If the glove doesn’t fit, you must acquit!

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The November Atlanta Tableau User Group (ATUG) meeting included over 30 people from industries including transportation, social media, consumer packaged goods, and data visualization consulting, just to name a few.  Over half the group had downloaded Tableau 6.0 the same day as the meeting. 

Our last three user group meetings have all included hands-on exercises and this time we challenged the groups to come up with a dashboard within 30 minutes based on 50 years of crime data.  That might seem like a short amount of time, but that’s the point.  We want the membership to realize the power of Tableau to let you gain rapid-fire insights.  Almost half the group was new users, so having them work with Tableau and putting the power in their hands is the best way to sell the product.

We formed three team of five (yes, I know that doesn’t add up to 30; there were people that left and others than hovered) and told the teams that the best viz would win a prize…t-shirts donated by Tableau.

First place went to Team 2 (as voted by their peers).

Team 1 came in a close second place with their dashboard that contains action filters on each sheet.


Well done to each team.  I’m looking forward to our next meeting on January 20th.  Remember to bring a friend.

November 12, 2010

Is it a cherry pie?

If you've been a follower of this blog for a while, you are well aware of my dislike of pie charts. I criticized a pie chart from a poll conducted by Digital Photography School in a blog post back in April. Today they published the results from another polls, but this time, I'm not too unhappy with their use of a pie chart.

In this case, their use of the pie chart is somewhat acceptable because:
  1. There are only three data points.
  2. The chart starts at the zero position.
  3. The largest slice is first (though it would be better if they were in descending order all the way around).
  4. The results can be easily discerned.
It truly hurts me to say a pie chart doesn't irk me, but I'll let this one slide, mainly because it's my favorite photography website.

November 6, 2010

What is a reverse time-series line chart with a non-zero axis?

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I never knew such a chart existed, but alas I found one, and I hope it becomes extinct! Occasionally I scan through Many Eyes visualizations for ideas and/or blog inspiration. Let's review this simple line chart of Estimated Median Age at First Marriage. Click on the image below to get started.

When I first saw this I thought "Wow! What a huge variance over the years!" But then I looked a bit closer and saw that:
  1. The years are backwards. A time-series line chart should nearly always start with the oldest time period on the left. I can't even think of a way to interpret time backwards. Maybe the DeLorean from Back to the Future could help.

  2. The Y axis does not start at zero. This creates a misleading variance. It appears there has been a 700% variance from highest to lowest, but really it's only 35%.

  3. The Y axis should be rounded to a whole number; this is unnecessary precision.

  4. I find myself having to refer back to the legend to remind myself which sex is represented by which color. They are way too close in hue. Why not use blue for men and pink for women?

  5. The Years on the X axis are at an angle and squished together. If you must show all of the years, the turn them a full 90 degrees. In the end though, I believe the purpose of the chart is to show a trend, so I don't need to see all of the years, just enough so that I know it's a regular interval.

  6. One more thing. It's very subtle. This is NOT a regular interval after all. Between 1890 and 1940, there is only one measure per decade. Only beginning in 1947 is there data for every year. I would only display 1947-2003.
To address all of these problems, the chart could have been created like this.

November 5, 2010

Voter Motivation Made Simple

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Possibly the best, simplest summary of voter intentions ever made.

Via the Indexed blog.

November 3, 2010

REMINDER: Register for the November Atlanta Tableau User Group Meeting

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The next ATUG meeting will be November 30 from 1-4PM ET

Who - All ATUG members and guests

What – The November in person hands on meeting

Where - Norfolk Southern building located at 1200 Peachtree St NE. Atlanta, GA 30309 -Peachtree room


  1. The Greatest Show on Earth - Tableau 6.0
  2. Team project – If the glove doesn't fit, you must acquit!
  3. Open discussion – 2011 plans

-- This will be a hands on session - Bring your laptop and Tableau with you --


November 2, 2010

Transparency International: Corruption Perceptions Index

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On October 26, 2010 the Guardian published the latest Corruption Perceptions Index from Transparency International which is the world's most credible source for measuring corruption.

According to Transparency International:
    The 2010 Corruption Perceptions Index shows that nearly three quarters of the 178 countries in the index score below five, on a scale from 10 (highly clean) to 0 (highly corrupt). These results indicate a serious corruption problem.
Download the data here.

To summarize the 2010 results:
  • Denmark, New Zealand and Singapore are tied at the top of the list with a score of 9.3, followed closely by Finland and Sweden at 9.2.
  • The most corrupt country is Somalia with a score of 1.1. Only slightly less corrupt are Myanmar and Afghanistan, with a score of 1.4, and Iraq at 1.5.
View the map produced by Transparency International here. While their version only contains data for 2010, my version of the map allows you to filter by continent, country or year.

Immediately obvious to me are that:
  • The rankings haven't changed much over the past three years.
  • You should avoid nearly all of Africa and Asia.
  • Western Europe, particularly the Scandinavian countries, are relatively devoid of corruption.
I suspect that the level of corruption could be related to poverty levels, but would need to prove it with the data.

If you want to see a horribly create bubble chart from which you cannot infer anything, go to Many Eyes.

October 27, 2010

National Survey of Sexual Health and Behavior

FlowingData is running a visualization contest this month based on data from the National Survey of Sexual Health and Behavior study. You can find the data here. Here is my entry:

Note that I concatenated Sex & Behavior to determine which acts are the most frequent and by whom. I chose a red-blue pallet since our sexual activity goes hot and cold. Displaying the data in the manner, I was able to quickly identify that:
  1. Men masturbate their whole lives
  2. The age ranges from 20-39 are clearly the peak sexual activity periods, with 25-29 standing out the most
  3. There is very little woman on woman and man on man oral sex
  4. We run out of steam in our 70s
Contest entries are due today, October 27, but if you don't make the deadline, feel free to leave a link as a comment to this post. And, as Nathan said, keep it tasteful.

Download the workbook from Tableau Public here.

Poverty in America - A Visualization

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I first saw the visualization below on the Chart Porn blog, which linked to the original article on The Huffington Post. This is an excellent visualization that effectively uses colors to emphasizes the highest poverty rates and provides information in a user-friendly format when you mouse over the map. The only thing I wish it would do is allow you to click on a state and drill down into the county-level data.

Immediately popping out to me are the poverty rates around the lower Mississippi River and Eastern Kentucky/Central Appalachia regions. A quick Google search turned up a documentary by Diane Sawyer that aired on ABC's 20/20 in February 2009.
    The oldest mountains in America are rich in natural beauty with their raging creeks, steep hollows and old pines. They are also one of the poorest, most disadvantaged regions in America. Central Appalachia has up to three times the national poverty rate, an epidemic of prescription drug abuse, the shortest life span in the nation, toothlessness, cancer and chronic depression.

From The Huffington Post:
    In 2009, poverty among Americans reached its highest level in 51 years. The states hardest hit include Louisiana, Mississippi and District of Columbia. States with the lowest poverty statistics include, Wyoming, Hawaii, New Jersey and Minnesota.

Roll over each state to see its poverty rate.

October 26, 2010

Improving on the Blogger Stats Dashboard

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Recently Google added the Stats feature to Blogger blogs. It's basically a dashboard very similar to Google Analytics, just stripped down (like Tableau Public vs. Tableau Desktop). There are four tabs, three of which are well done (Overview, Posts and Traffic Sources). However, the Audience dashboard is poorly designed.

This is the Google version of my blog stats from May 2010 - October 2010:

The map is well done, as you would expect, though it's tough to see the light green on the countries with less pageviews. I like the table below the map as a reference.

My issue is with the pie charts on the right. The tables are sufficient for my needs, especially since it has the total pageviews as well as the % of total. The pie charts are shown in descending order, but you have to work to find the starting point; as I've said in the past, pie charts should always start at the 0 degree mark when used.

If I were to visualize this dashboard, I would create it like this.

I've made the following changes/improvements:
  1. The left half of the dashboard the same, except that I was limited to bubbles on the map since I built this with Tableau's standard features. I prefer the filled in countries, and I know there are workarounds in Tableau, but I think this map makes the variance easier to see since I used an orange-brown color pallet.
  2. I did not change the reference table below the map.
  3. On the right, I have combined the pie charts and tables into a single view. The bar charts are listed in descending order by % of total pageviews and labeled with the # of pageviews. It's much easier to make sense of the bar charts than the pie charts.
I'm fairly certain at some point in the near future that Google will begin following visualization best practices. They certainly have the money to pay for the expertise (hint, hint).

October 25, 2010

November Atlanta Tableau User Group Meeting

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The next ATUG meeting will be November 30 @ 1PM ET

Who - All ATUG members and guests

What – The November in person hands on meeting

Where - Norfolk Southern building located at 1200 Peachtree St NE. Atlanta, GA 30309 -Peachtree room


  1. The Greatest Show on Earth - Tableau 6.0
  2. Team project – If the glove doesn't fit, you must acquit!
  3. Open discussion – 2011 plans

-- This will be a hands on session - Bring your laptop and Tableau with you --


October 23, 2010

Party like it's 1999

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Have you heard of the Tea Party? Interested to know how many people in your area are members?

The Tableau Public blog focused recently on a map of Tea Party membership created by IREHR. Find the original post here.

Naturally when I see a map like this the first thing I do is zoom into my area. However, the IREHR visualization makes it difficult to do so quickly. The city and state data are already available, so I put it to use.

  1. The map is slow to load and update. I suspect this is due to number of points the map is drawing.
  2. The IREHR viz did not include Alaska, Hawaii and Guam, while it did included lots of members that did not have a location listed. I have filtered out the unknown locations and grouped the rest of the data more logically.
I made the following changes/improvements in my visualization:

  • Added filtering by Region, State or City to allow you to quickly zoom into an area of interest
  • Removed the size legend and replaced it with a caption on the map
  • Add a link to the data source instead of just listing it
  • Changed the membership numbers you see when you hover over a point to a whole number
  • Changed the Faction filter to a slider instead of a single select option (saves space)
  • Washed out the Faction color legend so that it's easier to see overlapping points

Which visualization is easier for you to use? I think it is easier and quicker to find what you need in my viz, but then again, I'm biased.

October 21, 2010

Proved Natural Gas Reserves: My Take

I read a post today by Russian Sphinx in which she used Tableau Public to visualize proved natural gas reserves. For this post, I thought I would create my own take on her visualization.
Improvements made include:
  • Making the views interactive -- You can filter each of the views or use the radio button to select a region. You can also use the highlighting feature of Tableau.
  • Changing the diamonds used to represent the countries to circles and colored them by region -- To me, the circles are "softer" to the eye.
  • Replaced the pie chart by region with a bar chart by region by country -- I also corrected the hierarchy in the data set.
  • Changed the color pallet for the regions to Tableau's standard color pallet
  • Added a note to the bottom to define "proved reserves"
I like how she included a link to the data source. I used that as well...thanks for the idea!

October 15, 2010

Many Eyes hurts my eyes

The goal of Many Eyes is to "democratize visualization and to enable a new social kind of data analysis." This is a fantastic goal, however, many of the options available to create visualizations are poorly used. Let's look at two examples.

The data used for these visualizations can be found here, but note that the source is unknown, so I have no idea if the data is reliable or not.

The trouble with critiquing a visualization is that sometimes there are so many problems you become overwhelmed. Here are just a few:
  • A bar chart is a much better way to display this data than a pie. chart
  • There are way too many data points in the pie chart. While pie charts should be avoided whenever possible, if you MUST use them, limit them to no more than three slices.
  • The pie does not start at the 0 degree mark.
  • The slices, while ordered descending, are shown counter clockwise. Why?
  • The interactivity (click on the slices) provides no value (i.e., chart junk).
Here is the second visualization create for this data set:

For the most part, this map is well done. Some thoughts:
  • There needs to be bit more contrast through the colors.
  • Iraq clearly stands out from the rest as it should. My focus goes immediately there.
  • I like the shading of the countries in their entirety. This one feature of Tableau that I wish they would add as a standard choice. Yes, I know there are workarounds.
  • I like the option to switch between the shaded countries and bubbles, but again, the color choice could be better.
I took this same data set (and added continent for filtering) and created this visualization with Tableau Public. You can download the Tableau Packaged Workbook here.

I had a few goals I wanted to accomplish. How did I do?
  1. Create a greater contrast between the colors in order to make those with the highest number of casualties stand out more.
  2. Convert the pie chart to a bar chart to make the comparisons easier to detect.
  3. Create a Pareto chart to highlight the most important set of factors.
  4. Make the map and bar charts interactive. If you click or lasso (i.e., select multiple) on countries, the map and bar charts automatically filter each other and the countries are highlighted on the Pareto chart.
  5. Add a filter (at the top right) for Continent so that you can easily zoom in.
I have been having three problems publishing this workbook from Tableau Desktop 5.3 that maybe you can help me with. I don't recall running into these problems with 5.2.
  1. When I first publish the workbook to Tableau Public, the blue-red color range looks exactly as I created it. However, once I interact with the Tableau Public workbook above (e.g., lasso several countries), the color range changes to red only; the blue converts to red.
  2. The axis on the Pareto chart would not display the tick marks. It looks fine in Desktop.
  3. Since the tick marks would not display, I decided to add reference lines. Again, those display perfectly in Desktop, but you can't see them in Public.
Very odd! Has anyone else run into these problems?

POST PUBLICATION NOTE: The blue-red color range has already changed to red only, even though I have yet to interact with the data.

October 12, 2010

Hans Rosling: The good news of the decade?

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About this talk
Hans Rosling reframes 10 years of UN data with his spectacular visuals, lighting up an astonishing -- mostly unreported -- piece of front-page-worthy good news: We're winning the war against child mortality. Along the way, he debunks one flawed approach to stats that blots out such vital stories.

October 11, 2010

Questions or answers

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via Seth's Blog by Seth Godin on 9/20/2010

You can add value in two ways:

  • You can know the answers.
  • You can offer the questions.

Relentlessly asking the right questions is a long term career, mostly because no one ever knows the right answer on a regular basis.

October 8, 2010

ATUG Project - Rapid-fire BI in Action

At the last Atlanta Tableau User Group (ATUG) meeting we had everyone participate in a team project we called "We Hate to Fly and it Shows!" We broke everyone into groups of 5 (counted off randomly so they'd have to work with people they don't know, the whole networking thing), gave them 30 minutes (they took 45) and told them: "Here is a data set. Go build a dashboard."

The primary purposes of the team exercise were:
  1. Introduce new or prospective users to Tableau
  2. Prove that you can indeed build a dashboard incredibly quickly with Tableau (as Tableau professes)even though it's unfamiliar data
  3. Demonstrate the ability to find insights very rapidly
  4. Increase the capabilities of the user group. Each group presented their dashboard and feedback was provided.
  5. Learn tips and tricks
Here is an example of a dashboard built by the team led by Deborah Chan of Rubicon. I took the liberty of resizing it so that it could be published on Tableau Public.

While this isn't a perfect dashboard, you can clearly see how much detail and quality you can build into a dashboard in just minutes with Tableau. I think this is an excellent design given the very limited amount of time.

Some of the features built into this dashboard include:
+ Filtering by airport using the drop down
+ Using the map as an action filter
+ Airports on the map are sized by the average arrival delay and colored by the % of arrivals delayed
+ Stacked bar charts to show the proportion of each type of delay to the total
You can download the workbook here.

This exercise/group activity was such a hit that the group request we do it again in our November 11th meeting. Of course, they want more time so they can really impress everyone.

I'm not going to provide the data ahead of time...I have something interesting in mind.

Tableau 6.0 Tour: Speed, Power and Style

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I believe this is the first time Tableau has done a launch party for a new version and if what I have heard from those that attended the customer conference I true, then we're all in store for an industry altering event. I have been using the beta version and have been extremely impressed and I'm sure I haven't been using it in the most efficient way.

I'm headed to the Atlanta tour stop on November 18th, hopefully with lots of my ATUG friends. It's only a 2-3 hour commitment, so I would highly recommend you make the time to attend your local tour stop.

+ 2:00 – Registration
+ 2:30 – The End of BI as You Know it
+ 3:00 – Tableau 6.0: Speed, Power and Style
+ 4:00 – Wrap-up
+ 4:30 – Reception: Cocktails, Networking and Hands-on Demos
Registration is FREE!! Click on the image below to learn more.

September 29, 2010

Where are guns used in crimes coming from?

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From Mayors Against Illegal Guns:

    "September 27 – A new report by Mayors Against Illegal Guns reveals a strong connection between weak gun laws and interstate gun trafficking. The report, which examines comprehensive crime gun trace data provided by ATF to Mayors Against Illegal Guns, finds that the states with the weakest gun laws are the top suppliers of the guns recovered in out-of-state crimes and are also the source of a greater proportion of likely trafficked guns."

My home state of Georgia isn't making me too proud:
  • Georgia exported 2,781 guns recovered in connection with crimes in other states. That put Georgia atop a list of "exporters" of "crime guns" in 2009.
  • Adjusting for population, Georgia ranked 10th, exporting 28.3 guns per 100,000 inhabitants
  • Of the ten key laws that curb illegal gun trafficking, Georgia only has two in place
  • 27.6% of Georgia's guns are recovered in a crime within two years of original sale — a strong indicator of gun trafficking (5% higher than the national average)
  • Georgia's crime gun export rate is more than double the national average (28.2% per 100,000 inhabitants vs. 14.1% nationally)

There is a fantastic interactive map to explore the data. Click the image below.

View the full report titled "Trace the Guns: The Link Between Gun Laws and Interstate Gun Trafficking" here.

September 16, 2010

NameVoyager: Baby Name Wizard Graph of Most Popular Baby Names

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NameVoyager is an excellent example of a simple line graph that provides quick, meaningful information. Not only that, it's really cool too!
  1. Type a name and the graph updates.
  2. If there is more than one name, it will show all of the choices.
  3. Type a name and hit enter to show just that name.
  4. Mouse over the graph and you can see the rank in each decade.
Try it right here.

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Here's how my family turned out:

It looks like four of the six of us, those with the less popular names, had much more common names at the turn of the 20th century. What can I say? We're an old-fashioned family.

September 14, 2010

Tableau vs. Xcelcius: Internet Usage Dashboard

SAP has been hosting an event they're calling Reportapalooza - "a festival of reporting and dashboard challenges sponsored by SAP Crystal solutions."

They challenged five of their "experts" to use SAP Crystal solutions to create an everyday dashboard. According to SAP: "All of them were great." I beg to differ. Maybe they're all cute, but not great examples of dashboards. Shame on SAP!

One of the runner's up was the dashboard Internet Usage by Brian Durning. Here's a screen shot:

You really should check it out for yourself. Some of the more serious design flaws include:
  • The giant instructions/welcome message at the capture your immediate focus. Place the instructions, if needed at all, in a less prominent position.
  • The map is at a strange angle. Many of the northern countries at the top look much smaller than those of similar size to the south.
  • There is no way to compare the countries. You have to mouse over each of them individually. Why not shade them?
  • The stock ticker in the "Internet Users by Continent" section is very difficult to read unless you mouse over it, which freezes it. However, when you freeze it, you can't see all of the continents simultaneously.

I have created a workbook using Tableau Public. You can interact with the data using the three controls at the top right.
  • Scroll through the years to see how the number of users and the % of the population in each country has changed (it's interesting to watch the changes over time)
  • Focus in on a continent using the Continent filter
  • Pick a country to filter even farther

Do you see anything interesting? I do.
  1. There has been explosive growth in China, even though they got a late start. However, there is still room for tremendous growth. In 2008, only 22% of the population was using the internet.
  2. Oceania has the 2nd highest % of users. This surprised me, although I know very little about that part of the word. My initial guess was that Europe would be 2nd.
  3. 33% of Moroccans use the internet (leads Africa)
  4. Vietnam has grown from 0% in 2000 to 24% in 2008. I've been to Saigon three times and saw internet cafes everywhere. The source data says "Internet users are people with access to the worldwide network." That has potential to be misinterpreted and not measured consistently across the world.

Let me know what you come up with. You can download the workbook from the dashboard above or you can download it here.

Indicator: Internet users (per 100 people)
Description: Internet users are people with access to the worldwide network.
Source: International Telecommunication Union, World Telecommunication Development Report and database, and World Bank estimates. Note: Please cite the International Telecommunication Union for thirdparty use of these data.

Indicator: Population, total
Description: Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. The values shown are midyear estimates.
  1. United Nations Population Division. 2009. World Population Prospects: The 2008 Revision. New York, United Nations, Department of Economic and Social Affairs. Available at
  2. Census reports and other statistical publications from national statistical offices
  3. Eurostat: Demographic Statistics
  4. Secretariat of the Pacific Community: Statistics and Demography Programme
  5. U.S. Census Bureau: International Database
  6. World bank estimates based on the data from the sources above, household surveys conducted by national agencies, Macro International, the U.S. Centers for Disease Control and Prevention, and refugees statistics from the United Nations High Commissioner for Refugees.

David McCandless: The beauty of data visualization

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About this talk: David McCandless turns complex data sets (like worldwide military spending, media buzz, Facebook status updates) into beautiful, simple diagrams that tease out unseen patterns and connections. Good design, he suggests, is the best way to navigate information glut -- and it may just change the way we see the world.

Some quotes that stuck with me:
  • Relative figures are needed to connect to other data so we can see a fuller picture that can lead to us changing our perspective.
  • Data can change your perspective and change your mind midstream.
  • Design is about solving problems and providing elegant solutions. Information design is about solving information problems.
  • Visualizing information can give us a very quick solution to problems. We can get clarity or the answer to a simple problem very quickly.

Pass it along!

September 13, 2010

Energy Generated by a Pie

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Our power company, Cobb EMC, sends out a monthly community magazine and it always provides nice stories on how they support the community and how they utilize their energy resources.

In the September edition of Georgia Magazine, they published a pie chart of the fuel sources of energy generated (see p. 15).

When I first looked at this I thought Coal was the #1 fuel source. Why? Because Coal was on the top of the pie and it just looks like it was the largest. Fortunately there are labels that let me know otherwise. If I was forced to use a pie chart (i.e., if the purpose was to establish a parts-to-whole relationship), I would present it like this:

Some improvements include:
  1. Displaying the data largest to smallest starting at the 0 degree mark.

    This allows you to quickly see that a great, great majority of the fuel comes from two sources. It's much harder to see in their pie chart.

  2. The 3D view has been removed.

  3. The spaces between the slices have been removed.

If I were to present this data, I would present it in a ranking relationship with a bar chart. I would want to convey to the reader the fuel sources that provide the most energy, not necessarily the contribution to the total.

To me, it's much easier and quicker with the bar chart to see how much more energy comes from nuclear and coal than from natural gas and hydroelectric sources. I'm going to write to the editor.

September 9, 2010

September Atlanta Tableau User Group Meeting

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The next ATUG meeting will be September 30 @ 1PM ET

Who - All ATUG members and guests

What – The September in person hands on meeting

Where - Norfolk Southern building located at 1200 Peachtree St NE. Atlanta, GA 30309 -Peachtree room


  1. UPS: Tightening the Ship (as presented at the 2010 Tableau Customer Conference) - Chris Cushman, Martin Click, Dave Gorman
  2. Team project – We Hate to Fly and it Shows!
  3. Debrief – 2010 Tableau Customer Conference
  4. Open discussion – future meetings, topics, next steps, etc. (bring your comments, suggestions, concerns, etc.)

-- This will be a hands on session - Bring your laptop and Tableau with you --

Corda Sales Dashboard

I received the September 2010 email newsletter from Dashboard Insight today and the sponsor for the month is Corda. Corda provides data visualization software, one of their specialties being dashboard development with the tool CenterView.

I was looking at their demo dashboards and ran across this one (click on the image to go to the source).

Does it look familiar? It's indentical to the sample sales dashboard Stephen Few presents in his book Information Dashboard Design (see Figure 8.1 on page 177).

When I read Stephen's book I wondered which tool he produced the dashboard from. Now I know, at least I hope it's from Corda, otherwise it's a complete ripoff. I suspect it's the former. Nice work!

September 3, 2010

Tableau Tip: Creating a Waterfall Gantt Chart

I keep up with the website eagereyes regularly and recently they ran a three-part series titled "A Protovis Primer" in which they introduced the tool Protovis. Protovis is an open-source tool that allows you to create data visualizations.

Part 3 of the series was dedicated to instructions for creating a waterfall chart. If you take a quick look at the tutorial (well, there is no way to read it quickly), you will see that the method they have for creating a simple waterfall chart is in fact quite complicated and requires a lot of coding.

There's a much simpler way to do it...use Tableau. Here's the step by step way to do it create a Gantt chart using the same Presidential data eargeyes used (I added data from different polls that ranks all of the Presidents). Get the data here.

Step 1 - Add Inauguration to the column shelf and # and President to the row shelf. You need to have the # field so that the Presidents are listed in order from Washington to Obama.

Step 2 - Right-click on the YEAR(Inauguration) field and choose "All Values". This is required otherwise the time scale will not allow you to connect the start and end dates.

Step 3 - Right-click on the # field and uncheck "Show Header". This will hide the # column, yet still use it for sorting purposes.

Step 4 - Create a calculated field named Length of Presidency and drop it on the Size shelf (this gives the bar its length)

Step 5 - Create three calculated fields (Life, Time in Office, Age @ Inauguration) and place them on the Level of Detail shelf.

The end result should be a waterfall Gantt chart like this (I removed the grid lines):

That's it! Very simple, especially after you do it a few times.

Going a step farther, I created a "timeline" that highlights the times each political party was in office. Here are all of the settings:

I put it all together in a dashboard. I made the Political Party field a global filter and highlighting is enabled when you click on the color of Political Party on the right. Finally, I published the workbook to Tableau Public.

Give it a shot. Download the Tableau Packaged Workbook.