Tableau Conference is headed to Vegas shortly, so I thought for Makeover Monday week 45 we should look at data about visitors to Las Vegas. The original viz comes from Calculated Risk:

SOURCE: Calculated Risk

What works well?

• The time goes from oldest to latest.
• The colors are easy to distinguish.
• The axes are well labeled.
• Including the caveat for 2019 since it's not a complete year in the data.

Nielsen recently published a report on economic recession sentiment, which included this summary chart:

At first glance, you might think “This is simple enough, how could Nielsen screw this one up?”  Fortunately for me, they continue to make a mess of even the simplest charts. 

This chart makes North America and Europe negative sentiment look so much higher than any other region or the global average.  However, that’s only because the scale does not start at zero, nor do they call that fact out.  Look at Latin America.  Since when is 53% about 1/4 of 75%?

I wanted to see how far off their chart was so I built a simple bar chart in Tableau as a first alternative.

This is a very different perspective.  Europe, Middle East/Africa and North America are all pretty close.  And the change by quarter does not look nearly as severe.

A simple bar chart is an effective way to present this data, and there are other alternatives that work as well.  Let’s consider two slope graphs. 

The technique used to build these slope charts is very similar to the one I outlined in my previous blog post.  Notice that the lines are colored by whether the sentiment increased (got worse) or decreased (got better).  For this coloring, I used a technique recommended by reader Santiago Restrepo in which we calculate the quarterly change as a discrete measure.

First, you need to create a calculated field:

By default, the table calculate is set to compute along the Table (Across).  For the slope graphs, we want the calculation to calculate by Quarter.  To do so, click on the Default Table Calculation link on the Calculated Field window and choose Quarter.

The first slope graph looks at all Regions together:

The second option splits out each region across the table.

Which of the three options do you prefer?  The bar chart or maybe one of the slope graphs?

Do you want to build something to share?  Download the data here and/or the Tableau workbook here and leave a comment with a link to your work.

Nielsen has done some really poor work in the past, but this time they may have outdone themselves.  There are times, many of them, that I wonder what Nielsen is thinking.  I hate donut charts even more than pie charts and donuts inside donuts are an even more profound mistake.

Here’s a chart from their Q3 2012 Consumer Confidence Report.

The report includes some additional facts about change from Q2 2012 (except for North America which they only provide vs. Q3 2011 for some inexplicable reason).  That gives us two interesting and simple data points: (1) current sentiment and (2) change from previous.

I would represent the data as a bar chart colored by % change, with a reference line for Global.

My version makes it much easier to do a few things:

1. Compare relative differences between regions (bars work better than slices or donuts)
2. Quickly see which regions are improving, which regions are declining, and the magnitude of the change (via color)
3. Compare the regions to the global average

I was considering having another bar for the Global average, but decided to use the reference line because it makes the comparisons significantly simpler and my interpretation from Nielsen’s charts was that you should be comparing to the Global average.

Also, their title is misleading.  They make a blanket statement that “Recessionary Sentiment Grew”, yet it didn’t grow everywhere.  I can only assume that they’re referring to the global average.  My title makes it more clear.

For as long as I’m in this line of work, I will hold out hope that Nielsen will incorporate more best practices into their work.  Until that time, I will continue to leave them comments and suggestions.

In an August issue of Time Magazine (I can't recall exactly which) there was a discussion contrasting economic indicators that show we're in a Bear market against other indicators which show we're in a Bull market.

The first, which supports the notion that we're in a Bear market, looks like the following:



The second, which supports the notion that we're in a Bull market, looks like the following:



The image that I found most interesting and wanted to duplicate in Tableau is the Manufacturing trend show in the middle of the Bull market graphic. Here it is enlarged:



A couple of things I noticed:

Here's how I did it.

First, I created the line chart you see below (I won't go into each of the individual steps).



The important part of this step is to add a Reference Band with the following settings, which provides the shading of everything between the axis (50% and above indicate growth) and the line.



The key settings are:

The trick to shading only the growth areas is to add a Reference Line with the following properties:



The key settings are:

And viola, only the growth areas are shaded.



Of the issues I saw, here's what I fixed:

If you would like to play with the workbook yourself, you can download it here. There are many more measures/indicators to use in your analysis. Let me know what else you find; post your analysis as a comment.

Time Magazine blogger Justin Fox took a wonderful jab at a chart that Nancy Pelosi's office posted about the current recession. The Pelosi chart below makes it look like the sky is falling, but it looks at the data in terms of total job losses.



Justin did a great job of creating a different, perhaps more realistic, comparison of the current recession to past recessions by looking at job losses as a percent decline.



What an incredible difference if you just look at the data slightly differently.

It's kind of scary that there isn't someone in Pelosi's office that would know to at least consider looking at the data as a percent decline. This spins the data in a more positive light, or maybe I should say, a less negative light.

Read Justin's full post here.