Played properly, the house advantage will typically be somewhere around 0.5% depending on the rules at the table. In other words, for 20 bets of $10, you can expect to lose only $1. Of course that’s unlikely, and you only approach the house advantage after hundreds or thousands of rounds of cards. In the short run, you’ll be up or down with much bigger swings.

The idea behind basic strategy is simple; you know what cards you have, and the up card of the dealer. That allows you to make a decision based on that information. Here’s the strategy table for a typical ruleset:

And here’s the workbook for testing your basic strategy:

Given what cards you have, you can predict the return. The blue squares show the positive return and the red an expected loss. A negative 45% means that by using basic strategy, on average you will lose 45c per dollar bet. This is data from Wizards of Odds.

Blah blah, standard disclaimer. You can’t sue me if I screwed up.

Therefore, given a minimum you want to bet, some tables and casinos will offer better deal. I found a list of blackjack tables with the house advantage calculated at the excellent Wizard of Odds site. I used this list with permission to generate the interactive visualization below. Hover over the top right button for more information on how to interact with it.

Disclaimer: Don’t be suing me when you’ve lost your money and the house advantage was wrong…

• For the same level of drunkenness, what beer has most calories per 100 ml? (hint, sort by descending calories per alcohol %)
• Which has the least?
• What has the most calories per alcohol percent of the light beers?
• Which brewery has the widest spread of alcohol percent and calories (separate the list by brewery, then highlight each brewery on the scatter plot)
• Which beer would get you the most wasted?

To answer the question about light beers, alcohol, and calories: light beer has 35% less calories, but 24.5% less alcohol. To reach the same level of drunkenness (I know that’s not necessarily the goal…) you are only reducing your calorie intake by 14%.

When I say dearth that’s no exaggeration, there is literally one – Stephen and Eileen McDaniel’s Rapid Graphs with Tableau. As with Tableau, the book is highly visual, I would estimate that half of the page space is taken up with charts and screenshots. The first 50 pages takes you on a mission that most new users will go on – your manager wants you to tell the story hidden behind rows of data. The book is designed so that you follow along in Tableau, using one of the sample datasets that Tableau provides as part of the install.

The rest of the book is also meant for you to read the book with Tableau open, and delves into topics beyond the basics that are critical to understanding data and guiding the end users of the data – other chart types, maps, sorting, calculations, color, and methods of distribution of the end result. Despite considering myself an advanced user of Tableau, I learned some very useful things.

I couldn’t find fault with the book – it would be interesting to watch how a completely novice user works through it – with the “curse of knowledge” I knew where to click, what to do, and I couldn’t force myself into pretending I didn’t know. I suspect they’d be fine. The book is subtitled “Create Intuitive, Actionable Insights in Just 15 Days”. I think that’s a reasonable expectation, but don’t take my word for it, read the first few chapters here.

Disclaimer: I asked Stephen and Eileen if they would like me to review the book. They accepted and sent me a complimentary copy which I thank them for. However, this was not in exchange for a good review – this honestly is a great book for new and more advanced Tableau users.

Creating them however is more of a challenge. While it would seem that with some creative label calculations combined with a line chart would suffice, in this data set (and many I would suspect) the line ends are too close together (see first tab below). In fact Tufte has manipulated the positioning of the line ends and labels manually to provide some spread on the axis. This, of course, provides a challenge if you want to automate the process in any way. I approached this by calculating the start and end positioning of each country using a table calculation (see tab 2), and using this to position the countries (tab 3). Of course, this loses the slope information and only shows the position change. To add the slope back in I use a calculation that combines both the index and original value to ensure that the lines cannot end too close to each other (tab 4). I then manually moved the labels to a better position to create the final slopegraph (tab 5).

Comments on the workbook: 1. Note that the label calculation is has the left() statements because of issues when you use extracts – 46.8 turns into 46.8000000001 or similar. 2. I spent ages trying to get this to work, but as soon as I put the label (country plus value) on the label shelf, it would alter the table calculation (I don’t know why as it’s a string). Making the label an attribute stopped that. 3. This method affects the slope of the lines to some degree. Another option would be shift the correct slopes up and down, thus making the vertical positioning slightly wrong (EDIT: This method now appears on sheet 5a. It turns out that this is really hard to get right – the order messes up, and you end up moving everything around – try changing the parameters yourself and getting the spacing and order correct. Looking back at the original, Tufte changes the slope as per my first method).

Below is the seasonally adjusted data for March 2011, going back over two decades. The index is set so that all cities (regardless of median selling price) are indexed at 100 for the year 2000. There are two tabs on the visualization.

The first shows the location of the 20 metropolitan areas covered by the index with marks encoded for size by the absolute percent change of the index from the year prior, and color by the magnitude of the change (big green = big increase from last year in house prices, big red = big decrease from last year). Use page control to cycle through the years looking at the booms and busts. The sparklines below show the index and the percent change month to month, also encoded by color.You can click on a city on the map and it will filter the sparklines to cities in that region. The year on year value compares values from March 2011 to March 2010. Note the only bright spot of Washington D.C. with a positive gain. Detroit is interesting with one of the smallest decreases – sign of a recovery or that prices just can’t fall any more?

On the second tab are the indexes, and Year on Year charts with all of the cities overlaid. Select a city from the list to highlight it. There are some interesting things here such as Dallas which has had a very shallow change. Compare that to Las Vegas or Phoenix. Unsurprisingly, those that rose the fastest dropped the most.

This workbook has some interesting hacks for Tableau concerning using Measure Values – for example because of my method of flipping between the aggregations means that I can’t show the axis as percent when required, I multiply the field by 100 and put a custom number format of 0.0″%” to fake a percent label. Using this Measure Values method is also useful when you need want the ability to flip between different window partitions.

To understand who is using the oil, where it’s coming from, and what would happen if oil exports ground to a stop I retrieved data from US Energy Information Administration. The visualization is held on 3 tabs. The first allows you to explore where the oil is coming from, and where it goes. Sparklines and sorted tables allow for quick understanding of the data. The second concerns reserves of oil, and the third allows us to see how countries’ use is changing as their population changes over time. Some highlights:

• Many countries decreased the amount of oil imported in the last year of complete data (2009), probably due to the global recession. China and India are among the top importers bucking this trend.
• You can normalize imports/exports by population; this creates some interesting anomalies such as the Virgin Islands which have massive per capita imports of crude (and massive per capita exports of refined) due to the Hovensa oil refinery on St. Croix.
• On the second tab – everyone knows about the Middle East, but Canada, Venezuela, Libya and Nigeria have very significant reserves – selecting just a specific region allows exploration of each country
• If countries had to depend on their own reserves (assuming all oil is accessible, and consumption doesn’t change …) the results are surprising – the US has under three years of oil, whereas Chad has over 2050 left. Select the drop down to switch between years left and total reserves
• The third tab shows how consumption has changed with time and population. You can use the page controls to cycle through the years. Note the general upwards trend. Select just Asia and look at the tracks – how China’s population hasn’t grown as quite quickly as India’s, but consumption has increased much more. The size of the the mark is proportional to the consumption per capita – notice how small China’s is compared to Singapore’s

You can interact with the smaller version of the dashboard below, or go the full size one here.

Edit: Richard Leeke made a Tableau version (see comments below). Tableau Public doesn’t animate it automatically, so download the workbook to see the effect best.