Just how fast is Usain Bolt?

He completed the 100m Sprint in 9.69 seconds. Running some simple arithmetic we know that his average speed over the course of the race is 10.32 meters per second. We can convert this metric speed to a more familiar (to Americans) 23.09 MPH. Would it surprise you that in Bolt’s winning 200m race that he was slightly faster at 23.18 MPH? According to Sports Endurance, Bolt’s fastest 10 meter split in the 100 was 0.82 seconds which works out to a peak speed of 27.27 MPH. Here’s a list of animals that are faster than Bolt.

• Cheetah (70 MPH)
• Pronghorn Antelope (61 MPH)
• Wildebeast (50 MPH)
• Lion (50 MPH)
• Thomson’s Gazelle (50 MPH)
• Quarter Horse (47.5 MPH)
• Elk (45 MPH)
• Cape Hunting Dog (45 MPH)
• Coyote (43 MPH)
• Gray Fox (42 MPH)
• Hyena (40 MPH)
• Zebra (40 MPH)
• Mongolian Wild Ass (40 MPH)
• Greyhound (39.35 MPH)
• Whippet (35.5 MPH)
• Domestic Rabbit (35)
• Mule Deer (35)
• Jackal (35)
• Reindeer (32)
• Giraffe (32)
• White-Tailed Deer (30)
• Warthog (30 MPH)
• Grizzly Bear (30 MPH)
• Domestic Cat (30 MPH)
• Usain Bolt (27.27 MPH)
• Elephant (25 MPH)

Let’s review, here’s a sampling of things from above that are faster than Usain Bolt — Wile E Coyote, Thumper, Bambi and Whiskers the cat. Not to mention Bears, Lions and Rudolph the Red-Nosed Reindeer.

Nature by Numbers from Cristóbal Vila on Vimeo.

Infoviz pr0n

Look! An excuse to embed a Hulu video on the blog – w00t! (If you go full screen on the video, you can see the element under discussion.)

So, Hulu (a favorite of mine) rolled out a new version of their online video player. Kudos to the team! Generally speaking I like the new player – it’s cleaner than previous (not sure how they made it more sparing than it was before) and the dynamic bit rate is pretty damn awesome but there are a few things that I don’t like (the new loading spinner, the inauthentic-feeling "is this ad relevant to you?" and this new fangled thing they call a "Heat Map".

Here’s what wikipedia has to say about heat maps:

A heat map is a graphical representation of data where the values taken by a variable in a two-dimensional map are represented as colors. A very similar presentation form is a tree map. The term is also used to mean its thematic application as a choropleth map.

Not a bad definition, especially for my purposes here. Two aspects of the definition are key here:

1. “…values taken by a variable in a two-dimensional map…”
2. “…are represented as colors.”

So the basic structure of the map is two dimensional – perhaps a Cartesian coordinate system and the values of the variable are represented as a color. What does something like that look like? Here is an example:

We have a basic x (horizontal) – y (vertical) coordinate system and then blobs of color represent where users spend the most attention (as measured by eye-tracking). The deep, red colors indicate high-attention areas while the cooler, more shallow colors (green) represent less attention.

Here’s what Hulu is calling a heat map:

Wait … what? Hulu says this is a heat map? Unfortunately, it only has one dimension (time) for it’s variable (Popularity) and it is monochromatic – a stepped gray scale. It has failed all the tests contained in the heat map definition above. Oh, and its a Bar Chart.

Going back to our favorite source of definitions, Wikipedia has this to say about Histograms

A bar chart or bar graph is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can also be plotted horizontally.

Bar charts are used for plotting discrete (or ‘discontinuous’) data i.e. data which has discrete values and is not continuous. Some examples of discontinuous data include ‘shoe size’ or ‘eye colour’, for which you would use a bar chart. In contrast, some examples of continuous data would be ‘height’ or ‘weight’. A bar chart is very useful if you are trying to record certain information whether it is continuous or not continuous data.

In this case, we are probably looking at a simplified Histogram — a bar chart — where the bar height equals the frequency of the interval as opposed to the area as described above.

Here’s an example of a bar chart:

This looks a lot more like what Hulu is doing in their so-called heat map.

So why call it a heat map when its clearly a bar chart?

If its a naming thing, why not call it the Popularity Curve? I get that bar chart is business-boring. But, “heat map” is an established visualization concept with concrete rules and its nothing like a bar chart.

Now, I’m not saying that the underlying concept here is bad – I think it may be quite interesting (more on that later).

All I’m saying is why call something that it isn’t when on the one hand that misnomer already exists as its own unique concept and on the other hand the original thing is probably the best solution anyway? This isn’t a Shakespearean issue (calling a rose by any other name … I’m paraphrasing here). A heat map is not a bar chart and no amount of wishing will make it so. They are designed to solve different problems.

So, Hulu, let’s call things what they are shall we? What you’ve got there is a bar chart and no amount of wishing will make it a heat map. The bar chart is a good solution – go with it and spend a little more time coming up with a better name for the product than a misapplied name for a completely different visualization technique.

So what’s the purpose of this thing anyway? It’s seemingly obvious, show users where the most attention is paid in a particular show. Straightforward.

But, why?

Content discovery.

If you’ve never seen Glee before, this tool will (hopefully) show you where all the good bits are – according to all the other viewers. Or, if you missed Saturday Night Live with Betty White over the weekend and need to be prepared to talk about it at the water cooler on Monday then the popularity curve will show you all the skits that folks found funny and maybe you can save some time by only watching those sketches instead of the whole episode. This encourages both new and regular viewers to watch more content and to find new content that they enjoy. Pretty cool right?

So what should Hulu call this utility (assuming that you agree that “heat map” is the wrong name for it)?

Yesterday, the Alley Insider published the chart at left as their “Chart of the Day”. The chart shows trends in the number of people employed by the Newspaper industry from 1947 – September 2009 as estimated by the Bureau of Labor Statistics. At first blush it seems ok, but the title (The End of Newspapers) begs for closer inspection.

The issue here, if you haven’t already spotted it, is that the y-axis starts at a non-zero value (200K). While it is acceptable, under certain conditions, to use a non-zero y-axis this isn’t one of them.

Using a non-zero y-axis on a trendline is typically used to expose patterns in the trend that otherwise would not be visible and the absolute value of any given point on the line is not that important. This trendline has a pronounced pattern so a truncated axis does not expose any new information. If you don’t intentionally look at the values on the y-axis then you might assume that where the trendline starts on the left is zero so the employment numbers for newspaper publishers are quickly approaching bottom. That is simply not true, so the chart is being used to lie to you.

Now, I’m not actually arguing that the newspaper business isn’t in trouble. Circulation is down, ad revenue is down, yada yada yada. Looking at the graph, we can see that the most recent months have the same employment levels as those of the early 1950s. But again, without some context, the number of people employed in the industry probably isn’t the best indicator of sector health.

So here’s how I’d look at this single measure of the Newspaper industry:

The two sets of graphs show monthly and annual employment estimates and for the Newspaper industry from 1947 to 2009. The first chart in the monthly or yearly block simply trends the number of employees from period to period across the entire set. The variable width of the line is also the number of employees and is used to re-enforce the relative weight of one period compared to another. The yellow band on the employment trend charts covers the minimum number of employees to the maximum – basically the vertical height covered by the Alley Insider chart. You can see that there is quite a bit of white space underneath the yellow band and thus the chart lie. Changing the start value of the y-axis doesn’t expose any new insight about the trend, exaggerates the line slope and makes it seem as though the employment numbers are bottoming out – which they are not.

The second chart in each block shows the percent (%) change period-over-period (M/M or Y/Y) in number of employees. In this case, the yellow band shows the normal variation (mean +/- 1.96 standard deviations). For the monthly chart, the gray line is the actual M/M values and the red line is the 6 month moving average. It’s interesting that when looking at the monthly M/M trend that the vast majority of points are within the normal variation. It’s not until November 2007 that we see a strong downward pressure in the M/M numbers. In the yearly chart, the yellow band is still normal variation as defined above and the gray line is the actual Y/Y values. In this case, the red line represents the 5 year moving average of Y/Y change.

Taking the ‘higher’ view of year-over-year employment numbers, we can see that the decline in Newspaper employment started in 1986 (24 years ago)! That is not to say that the rapid expansion of internet use this past decade hasn’t had an impact on the Newspaper industry, but if the sole basis for health of an industry is the number of employees it has, then the newspaper business has been in trouble for nearly a quarter of a century and there are larger forces at play than citizen journalism and Google et. al. “stealing” newspaper content.

So, what conclusions do the data support? Newspaper sector employment peaked (on an absolute basis) in 1990. On a percentage growth basis, employment peaked in the mid 80s and has been declining ever since. The rate of loss in employment has accelerated in the last 2-3 years. It’s unclear from the data, since it is a single measure, what is driving the changes described. It’s also unclear, despite the link-baiting headline given to the Alley Insider chart whether or not this is the end of the newspaper business. From an employment basis, the sector has contracted to early 1950 levels but we lack the proper context to understand what the implications of that contraction are.

In the visualization below ages are grouped into ‘Age Groups’ (basically minor, adult and middle-aged +) then into more typical age bands (familiar to marketers I’m sure) and down to individual ages. The top ‘row’ of visualization shows the projected population in 2030 compared to the population in 2005, visualized as bullet charts. On these bullet charts, the darker gray background as well as the black reference lines mark the number of 2005 while the bullets show the projected population in 2030. Most folks are probably familiar with the story that America is aging  — that our population is growing in the elder segments. You can see in the bullets for Age Groups that those 55 years and older show the most growth both in absolute terms (from 67 million in 2005 to nearly 111 million in 2030) and on a percentage basis – +65% vs +8% for people ages 18-54 and +16% for those aged 0-17. Looking at the Age Band bullets (horizontal) we can see that elderly growth is most concentrated in the 65-74, 75-84 and 85+ bands. In other words, most of the population growth is in retirement-aged people!

Ok, so now try clicking on the bullet (red bar) for people ages 55-64. Notice that the ‘Ages Detail Trend’ chart filters for that age band. The chart itself shows the percentage growth of a given year compared to the first year reported (e.g. % growth of population in year 2015 compared to 2005). If you are looking at ages 55-64 in the trend, notice the highly stratified trend lines. The younger end of that age band (55-58) shows relatively tame growth over the 25 year period compared to those aged 59-64.

FYI, this interactive visualization was built with Tableau Public, which is currently in private beta, and uses new features of Tableau 5.1 (also in beta) like native support for Bullet Charts – Hooray! Feel free to share the visualization by clicking the ‘Share’ button at the bottom of the vis and grabbing the embed code.

The Problem: Splitting a measure into discrete, sub-measures based on the values of a dimension.

The Why: Lets say you have a measure ‘Sales’ and a dimension ‘Product Type’ (this should sound familiar as its from the ‘Coffee Chain’ sample DB). Further assume that you want to do some parts-to-whole comparison which could be achieved via a table calculation but there are a couple of practical issues with that. One, the table calculation is basically a virtual object and so you can’t use it in downstream calculations and two, if you only want to display a specific dimension-value — ‘Coffee’ for instance – I can’t filter for ‘Coffee’ and still show its part-to-whole relationship correctly. By splitting the measure into discrete sub-measures we can perform operations and calculations on the data that otherwise might not be possible.

Here’s what the post on the Tableau Forum said by way of further illustration:

[H]ave a look at the attached xls file. You see that I have a column called "Indicator". I now would like to make a scatter plot with KPI_1 on the x-axis, KPI_2 on the y-axis, and using KPI_3 to size the circles.

I thus somehow need to split my dimension "Indicator" into 3 specific components for each of the KPI. Can this be done in Tableau ? Or is it mandatory to have basically one column in your data source, per dimension?

The solution is fairly simple although it may not be particularly intuitive. We’ll leverage the LOGICAL function ‘IIF’ which tests whether a condition is TRUE or FALSE and returns a specific response depending on the condition. So, using the Coffee Chain sample Access DB…

1. Right-mouse click on ‘Product Type’ in the Dimensions shelf
2. Select ‘Create Calculated Field…’
3. Name the Calculated Field “Coffee Sales”
4. In the formula pane (which should be pre-populated with [Product Type], add the following formula: IIF([Product Type = “Coffee”,[Sales],NULL)
   
5. Click ‘OK’

You should now have a calculated measure in the Measures shelf called “Coffee Sales”. Repeat the above process for each of the other three values in the ‘Product Type’ dimension (Espresso, Herbal Tea and Tea).

Use ‘Duplicate Measure’ and ‘Edit…’ to speed up the process. All you will need to do is change the targeted Dimension value

Now that we’ve created four measures based on splitting values in the dimension of Product Type, we can create an example Scatter Plot to address the question from above.

On a blank worksheet, drag ‘Coffee Sales’ from the Measures shelf to the column shelf – make sure it is set to ‘Continuous’. Drag ‘Espresso Sales’ to the row shelf and make sure it’s set to ‘Continuous’. You should now have a scatter plot with one mark. Probably, we want more detail than a single mark – try right-mouse clicking the date dimension and dragging it to the ‘Level of Detail’ shelf and select ‘Date (Continuous)’. This ought to give you 24 marks. They probably appear to be tightly grouped with little discernable pattern wise, but let’s work with it for a moment.

Try putting ‘Profit’ on the size shelf. Now we get an indication of where the high profit versus low profit intersects are but the data is still highly clustered making pattern recognition difficult.

See? Still a jumbled mess, we’re not learning anything yet but this is exploratory analysis so it’s ok! Let’s try putting ‘Market Size’ onto the ‘Shape’ shelf.

Ok, so Profit isn’t really telling us anything that we couldn’t have logically concluded from ‘Major’ to ‘Small’ market differences. Let’s create a % Net Margin measure. In this case, be sure to use SUM otherwise the % Margin calculation may have unintended values. The formula should look like this: SUM([Profit]/SUM([Sales]).

Still not a lot to see here. What if we drag ‘State’ onto the Color shelf?

Now we’re getting somewhere! Adding State-level detail gives us a lot more marks to work with, but the overlapping points are still making it harder to read than is necessary. Let’s make some formatting changes to see if we can make things a little more clear. Start by increasing the transparency of the mark colors – I ended up at about 50% transparent. That definitely helps to see ‘through’ the overlapping points as well as the density of marks. Make sure that the scales on the x and y axes are the same – it’s a scatter plot afterall. I also changed the pane background to black (yes, black) and added light gridlines. In this case, I think both help make discernment easier.

Enable highlighting from the Color legend, then you can focus on a particular state in the visualization. Most states exhibit what most of us would be ‘normal’ patterns – either no particular relationship between Coffee and Espresso or as Sales in one grows, so does the other. A couple of outliers though are apparent though. Try selecting Ohio. In the ‘Small’ markets product sales are binary – that is, either Espresso is sold or Coffee is sold but not both. On the other hand, in the Ohio ‘Large’ markets, we revert to one of the ‘normal’ patterns. Now select ‘Massachusetts’. What? There appears to be a downward trend in Espresso sales as Coffee sales increase. This would seem to indicate a more graduated preference pressure on Product Type than we saw in Ohio. Further analysis of Massachusetts would be necessary to understand the graduated sales and if that pressure can be used to improve sales or profits.

Alright, so that’s it. A tutorial on how to create dimension-value specific measures that turned somewhat into a discussion of exploring data to find interesting patterns. All that’s left is the discussion…

FILE ATTACHMENT: Split Dimension Tutorial Packaged Workbook

Adobe is more than Flash. Heck, before Adobe acquired Macromedia it wasn’t anything approaching Flash.

Don’t get me wrong, Flash is important. How much of online video is distributed via Flash? A significant majority I imagine. Flash is also used for interactive web pages and light gaming. Oh, it’s also developing into a desktop connected-application platform via Flex.

Integrating Omniture (or any other measurement technology) into Flash or Flex, what’s the difference? There largely isn’t one. And Flex desktop apps are proliferating. How valuable will it be for those developers/companies to have a solved, stable integration of analytics into those apps? And of course, since Flash serves SO MUCH of the video content online, how will Adobe’s acquisition apply pressure to the standards that are used to measure video?

Adobe has not one, but two development and publishing platforms (Dreamweaver (ok, who still uses Dreamweaver?) and Cold Fusion)

What if some of the components of Insight and/or Test & Target are integrated directly into Cold Fusion?

What about Shockwave/Director for rich gaming experiences?

In-game analytics don’t really exist, not on a large scale anyway. What if Adobe-ture can solve that?

And of course the standards: Photoshop, Illustrator, Acrobat.

Holy Crap! Adobe has an end-to-end services & platform suite for developing connected applications, web-sites etc.

With the addition of Omniture to the fold, Adobe can now add adjunct services to their platform.

• Basic Measurement (Site Catalyst), customized for specific environments
• Data Integration & Data Mining (via Discover, Insight, etc)
• Testing & Targeting via –wait for it- Test & Target

And don’t take “adjunct” the wrong way, we all know that the worst kept secret of online is better analysis and synthesis right? The data and the insight they drive are as important as the products/sites/apps that drive the data.

Once you step past the WTF moment, you start to realize that this may be less of an eBay-Skype thing and might just be more of a Google-Applied Semantics kind of thing. It will take careful execution of a well-thought out integration plan but it could be a very interesting play.

What if Adobe dusts off HBX (or offers a trimmed down version of Site Catalyst) for free to publishers (especially video publishers) in return for aggregated and anonymous video usage data? What if they push something like that through partners like BrightCove that provide video players to publishers?

Again, once you move past the shock and think about a bit, this acquisition might just make more sense than you thought, or at least the possible reasons for it begin to emerge.

What do you think? Why did Adobe do this? Why did Josh et. al. agree to it? Are the reasons above worthwhile? Are there other more important factors? Please leave a comment and let me know!

One last random thought, does this help Adobe in positioning against HTML 5? My understanding is limited but it seems to me that the HTML 5 standard is, in some ways, a direct shot at Flash.

BTW, Gary Angel over at SEMPhonic has a very thoughtful post on this deal

Adding a sudo reference line in Tableau from Joe Mako on Vimeo.

NOTE: Joe directed me to his tutorial after I was asking some questions about reference lines on Twitter yesterday, I’m simply passing along his great little tutorial. Please enjoy!

Well, here is your first peek at that project – Tableau Online!

Essentially, it will allow anyone with Tableau Desktop to publish their workbook online and embed the visualization in their website.

Tableau Online is currently in private beta, but you can sign up for updates here, or click on the logo below the visualization.

Before we get to the visualization itself, here’s what I’ve learned about the project from Christian and Ellie Fields – Director of Product Marketing.

Christian says that the vision for Tableau Online is for it to be “the YouTube of information visualization…” which I interpret as the platform of choice for publishing rich, interactive visualizations online.
Additionally, during a recent discussion with Ellie Fields, I uncovered some of the more nitty-gritty details…

1. It currently only works with local data – basically you need an extract from any source locally on your machine in order to use the visualization with Tableau Online (in all likelihood you will upload a packaged workbook)
2. There have been discussions around an API so that data might be transmitted ‘over-the-wire’, but there are no specific plans for getting that done
3. This is a very early look, the hardware is still undergoing performance testing so be patient if you get an error with the visualization or it takes a while to load; and hey, leave a comment describing what happened!

What follows is a proof of concept based on the calendar view I had developed.

This calendar specifically, and Tableau Online generally is built on the new version 5 platform (there have been some examples creeping out on the Tableau blog). I’ve been working with the version 5 beta so let me tell you about the features from 5 that are important here. Sheetlinks and highlights have been repackaged – and improved – to a new function called ‘Actions’. In this visualization, the calendar filter for year is also applied to the small multiples sparklines below the calendar. This is now very easy to do compared to how sheet links work in version 4. Also, if you click on any particular day within the calendar, that date is highlighted on the small multiples below – enabling this is as easy as hitting a switch (which is basically what you do).

Many improvements and new options have been added to quick filters – but, so far, the biggest improvement IMHO is the addition of the search feature! (Although I wish that the Tableau team had built in direct feedback for when a search fails)

Here are some of the things you can do in this proof of concept.

1. Filter on Year for the whole visualization
2. Select a particular month for the calendar view
3. Highlight any given day in both the calendar and the small multiples by selecting a day in the calendar (you can also select multiple days)
4. Highlight a day in the small multiples only by clicking on a plotted point (or drag select multiple days)
5. Highlight a particular month by selecting the month name in the small multiples column header
6. Lowlight inactive days in the calendar for a particular product category by selecting the category name in the small multiples row header (you can select multiple categories by selecting one category and then either SHIFT or CTRL clicking another category)

The Tableau team and I have discussed many changes to this visualization, but we weren’t able to get them done in time for this post as each change currently requires extensive testing on the new platform, but those changes should be coming soon. Discussed changes include changing the color encoding to profit instead of total sales, and doing a daily sparkline only for the currently selected month and then bar charts aggregated by month for the rest of the months in the currently selected year.

What else would you change/remove/add to this visualization?

Finally, there will be a smattering of sneak peeks going up across the infoviz community so keep your eye on out here and the Tableau blog as those new visualizations go up!

I don’t necessarily disagree with Tableau’s argument, but I think they made several errors in how they chose to communicate it.

Error #1: Bad Blogging Etiquette: The post provides no link to the Media Cloud project and the specific item under discussion. There is no native way for the reader to go back to Media Cloud and investigate the visualization on their own. I wasn’t familiar with Media Cloud so I actually had to Google it to get there.

Error #2: Poor Graphic Use: Tableau chose to use just a thumbnail (the same thumbnail that is provided by Media Cloud) of the infographic. Furthermore, they covered it up with their “Viz Police” badge making it impossible to get a decent view of the graphic. Finally, the way they incorporated their badge into the Media Cloud graphic (it looks like someone screen-shot a layered graphic out of Photoshop or similar) presupposes the bad nature of the graphic. In other words, poor execution of including the badge on the Media Cloud graphic gives the reader the, possibly false, impression that the map is a bad graphic.

Error #3: Lack of a Full-Size Graphic: Tableau did not provide a full-sized version of the Media Cloud graphic. Now, I run hi-res (1928×1208) so I’d argue that the original graphic is too small anyway. But, when you look at the full-sized graphic sans “Viz Police” badge, the errors are not quite as egregious as indicated by Tableau. In the full-sized graphic the Area-bias still exists, but it’s clear that the UK is more saturated than the U.S. on the BBC graphic – when you spend some time looking at it.

Error #4: Misdirection. In attempting to show how Tableau’s solution (circles vs. density) is better, they bring in a completely different data set – “Net Internal Migration by State”. Now Media Cloud does not provide the data behind their graphic so if you wanted to create a comparative graphic in another tool you’d presumably have to jump through some hoops to either get them to provide the data or to estimate the data, but in either case you are not comparing apples and steel ingots ( apples & oranges are, in fact, too similar for the old adage to work) as Tableau is asking you to do in their post.

Error #5: Using area to encode value. In an error similar to the map-density one they are arguing against, Tabeau’s example uses the area of the circles to encode some value – which is not even explained via a legend (the legend only explains the color encoding)! For those that may not know, we humans are not good at estimating area, it’s not what our visual systems are built to do. We tend to over-estimate large areas and underestimate small areas – remember, this is the basic argument against pie charts.

Error #6: Chart Junk. At best, chart junk obfuscates your data making it difficult to understand. At worst, it causes bias or error in judgment of the data. Well, in the Tableau map, the circles have a light-colored border. This border is more evident on some of the plot points than others, creating the misrepresentation that those points are somehow more important. Is the migration to Maine somehow more important than the migration to Minnesota? I don’t really know because they are roughly the same size and color BUT the border on the Maine circle is much clearer than the one on Minnesota – what does that mean?

Error #7: Breaking map conventions. I can’t speak for the rest of the world, but here in the U.S. a circle on a map generally means a population center – a city – and the size of that circle may indicate how big the city is, or all circles are the same size (e.g. no value encoded on the area). So, when I’m looking at a map like this I/we expect the circles to reference a city and these circles do not, they reference a state – Tableau has just broken your mental model of a map! When you use a convention unconventionally and break the standard mental model you typically end up creating cognitive dissonance. I’m not saying that it should never be done, but you have to be very careful. If the confusion throws something into sharp relief that might otherwise be obscured, ok you’ve got a case to do it, but if all it does is create a buzzing between the ears that makes processing the information more difficult you are better off not doing it.

So what? Why am I all in a huff? It’s not because I dislike Tableau – quite the opposite, I am an avid user. I do dislike poorly executed arguments. If the argument is not made cogently, it has holes in it. It looks sloppy and therefore is less effective. Tableau has an excellent point about the pitfalls of area-based information graphics but they’ve shot themselves in the foot with how they argue it and that makes it less likely their readers will understand and trust the argument which well might lead them to not using the learning in their design efforts.

To be honest, I find the whole argument a bit disingenuous. The post argues against a specific type of area encoding – density encoding on geographic areas but Tableau not only allows area encoding on plot types up to and including the ignoble pie chart but their geographic visualizations allow density encoding via the data layers.

BTW, this is a follow-on to the comment I posted on Tableau’s blog. I wasn’t particularly happy with my comment so I rewrote it as a post here rather than editing the comment there.

Source: Media Cloud A Harvard Law | Berkman Center Project

Source: Media Cloud a Harvard Law | Berkman Center project

Source: Tableau

One of the simplest things to do is limit the time frame for the report. For instance, maybe the report you’re working on only needs the last 30 days of data or the last 6 months or the last year.

But how do you do this in Tableau?

Beginners, myself included, will just drop the date field of choice onto the filter shelf and manually set the date range required. The problem is that every time you open the report you have to manually adjust your date filter so it’s not helping you as much as it could be.

Enter DATEDIFF. This function – as the name implies – calculates the difference between two dates at a specific granularity. That’s right, it will calculate the difference in dates by number of days, weeks, months, etc. How cool is that?

Ok, so how does that help create a filter that returns only the last N days of data?

It’s so simple you might think the above is a trick question some how.

Before I get started on directions, I’m going to assume that you’re already in your workbook and connected to your data source. For the purpose of this exercise, I’ll be using our friend – SuperStore Data.

1. Start by opening the ‘Create Calculated Field’ dialog box
2. Let’s call this new field ’30day-Filter’
3. Tab down (or click in) the ‘Formula Field
4. Type in DATEDIFF(
5. The first argument in DATEDIFF is the granularity to calculate with (day, week, month, etc). For our 30day-Filter we’ll use ‘day’
6. So far you should have DATEDIFF('day'
7. Arguments in a function are separated by commas so add a comma after ‘day’
8. The next argument is the start date (e.g. the oldest date) which will be the date from your data source record. In this case, the field we want is ‘Order Date’
9. Our new function should look like this so far: DATEDIFF('day',[Order Date],
10. For the end date, we’re going to use another function – TODAY(). TODAY() takes no arguments and returns the current date from your data source. If you’re connected to a remote data server TODAY() will reflect the date on the server. If you’re working with a local data source (text file, excel, etc) then TODAY() will reflect the time on your local machine
11. So far we have DATEDIFF('day',[Order Date],TODAY()) This is a valid function all by itself and will calculate the difference between the two dates with ‘day’ granularity, but it’s not a filter yet
12. In this case we want a filter that evaluates to ‘TRUE’ or ‘FALSE’ and really, it’s very simple. Just add ‘<=30' to the end of the function
13. DATEDIFF('day',[Order Date],TODAY())<=30
14. Save the calculated metric

notice that in the Dimension shelf your new metric has an icon of T/F. Now we're ready to use the filter! Go ahead and add it to your filter shelf.

A couple of last points on using this filter to make your report dynamic in terms of the date range. Make the filter global - remember the date filter should encompass all of the data you need for the report, and making it global makes it apply to all worksheets automatically. Add the filter to your context - this limits the data Tableau is working with on the server side which should improve the speed of all subsequent queries (less rows to traverse for an answer).

This is an exceptional piece. Beautiful and informative. Make sure to check out Charlene’s post describing the project with more photos.

BTW, see all the contest glory at Information Aesthetics

This a video history of the Internet using Picol icons – what do you think?

We’ve been using Tableau at work for about three months during which I had something of a rare experience (or at least rare to me). Still being a neophyte with Tableau it was surprising to me that building a simple calendar view of data hadn’t occurred to the Tableau team. Purists will argue (and who’s to disagree) that calendars don’t make the best data visualizations (lack of ability to see trends, low data to ink ration, blah blah blah) but when you have a set of users that prefer a calendar AND everyone is already trained to use/read/interpret a calendar, it’s a compromise I can live with.

Long story short, it was easy enough to build the calendar framework in Tableau but I was still new enough to struggle with getting the data into the calendar in the way that I wanted. So, I reached out to the Sales Engineering team at Tableau (Thanks Ty!) and they he helped me out. The surprising part was the mini-storm of attention that this created on the Tableau team. Apparently this type of visualization hadn’t occurred to them and it created some excitement. What follows is a combination of what I learned on my own and what Ty helped me to create.

For this exercise, we’ll be relying on some of the training data that comes along with Tableau – the ‘superstore data’.

This post is pretty in-depth (and long) but it assumes that you have a working knowledge of Tableau and SQL. If you are new to either or both, some of this may not make sense.

Set 1: Getting Set Up

1. Open Tableau
2. Start a new workbook
3. Connect to “Sample -Superstore Sales” Excel file

You may see some different dimensions and measures in here than what you see in your workbook. There are two reasons for this, there are a couple of custom dimensions and measures that we need to create along the way AND you may have a different version of this file than I do.

Step 2: Building the Calendar Framework

1. The first thing we need is a custom dimension, not because we’re going to use it right away but we will need it later for building in some interactivity and it’s part of the calendar framework. this dimension will be called “YYMM” and is a simple concatenation of the 2-digit year and 2-digit month from the Order Date dimension.

    

    

    

    

    

   This simple dimension is simple a string and will return values like ’0801′ (e.g. January 2008)

2. Setting the stage…
   1. Right-mouse click and drag the “Order Date” dimension to the Columns pane. This should prompt you to select the type of aggregation to use – select “WEEKDAY(Order Date)”. This will get you your days of the week running across the top.
   2. Now drag “YYMM” to the Rows pane, followed by “YEAR(Order Date)” (which is the default aggregation for date dimensions), “MONTH(Order Date)” (remember that right-mouse drag?) and “WEEK(Order Date)”

      Right now you have a table that should look something like the following:

    

    

    

    

    

    

    

3. Making the table into a calendar
   1. In the “Marks” pane, change the Marks drop down to “Square”
   2. Make a new custom dimension called “Day” with the formula DAY([Order Date]) (Make sure that day is in the Dimensions pane and not the Measures pan after creating it)
   3. Drag “Day” to the “Level of Detail” box in the “Marks” pane

      Now the Tableau stage probably looks something like this:

      

       

       

       

       

       

       

       

   4. Let’s put in a quick filter so that we can look at just one month, we’ll remove the filter later. Control drag (CTRL+Drag) “YYMM” from the Columns pane to the “Filters” pane. Put a check mark on “Exclude” and then scroll to the end of the list and take the check mark off of “200812″. Now we’re just looking at December 2008. Still doesn’t look like much of a calendar does it? But it’s in there.
   5. We just have to manually adjust the size of the stage and the cells to get what we are looking for.
      1. Find the up/down handle on the Week of Order Date cell and drag it down so that the row height is about 1.5″
      2. Do the same for the Day of Week Columns. We’re now getting closer, you can probably start to see the calendar format:

          

          

          

          

          

          

          

          

      3. Add borders to enhance the calendar effect. Go to Format –> Borders
         1. Add a black, narrow border to Cell under the Default section of the Sheet–>Borders pane
         2. Ass an added bonus you can now fine tune your column width and row height to a square

         Your Tableau stage should now look an awful lot like a calendar, albeit an ugly blank one:

          

          

          

   6.  

       

Step 3: Adding Data and Visual Analysis Cues

So now we have our basic calendar structure but there is no data in it and let’s face it, it’s U-G-L-Y. Let’s start by adding data.

1. For the purpose of this exercise, we’re going to assume that we want to keep track of total sales (Gross Revenue) and Profit. We also need to add the day of the month to each cell. Initially, I had gone down the road of using Tableau annotations to create this but it’s cumbersome and breaks whenever the dates change. So the biggest thing that Ty in Sales Engineering helped me with was a custom measure that contained the Day of the Month, the total Sales and the Profit.
   1. So, we’re going to create a custom measure called “Day Calc” that concatenates the day of the month, the Sales total and the Profit total. The formula looks like this:

      str(MIN(DAY([Order Date])))+”

      s: “+str(round(SUM([Sales]),2))+”
      p: “+str(round(SUM([Profit]),2))

   2. Now, drag “Day Calc” from the Measures pane to the “Text” field in the Marks pane
   3. Right-mouse click a date cell and go to Format. Set the vertical alignment to “Top”. Horizontal alignment is your choice, as long as it’s left or right

       

       

       

       

       

       

       

       

       

       

       

      Ok, now we have actual data in the calendar, but what’s up with the color? The blue ain’t helping – maybe we should do something about that?

2. Use color as an indicator of health
   1. Drag “Sales” from the Measures pane to the “Color” field on the Marks pane
   2. In the new “Colors” pane that appears below Measures, click the down arrow and select “Edit Colors”
   3. In the new dialog box, change the palette from “Automatic” to “Red-Green Diverging
   4. Put a check on “Stepped Color” and change the number of steps to two (2)

       

       

       

       

       

       

       

       

       

       

       

      The colors, which are splitting on the average revenue in this scenario, are still quite a bit saturated though.

   5. Change the opacity of the cell colors
      1. Back in the Marks pane, drag the opacity slider under Color to the left to increase the transparency of the color in order to desaturate the display. Find a level you’re happy with it and go!

Step 4: Cleaning It Up

Now we have a calendar that gives us useful information and visual cues to the health of a particular day – hooray! But there is still a lot of stuff chart junk showing that we don’t necessarily need to show.

Things that can probably be hidden (not removed because they are either needed to maintain the calendar framework or we will need them later to create interactivity).

1. YYMM can be hidden
2. Week of Order Date can be hidden

   These two items can be hidden by clicking on the dropdown arrow in their respective lozenges (yes their called lozenges) in the Row shelf and taking the check mark off of “Show Header”

3. Additionally, we can remove the row headers. Right-mouse click on “Order Date” in the stage and select “Hide Field Labels for Columns” – Order Date should disappear. Right-mouse click on either “Year of Order Date” or “Month of Order Date” and select “Hide Field Labels for Rows” and both of those should now be hidden.
4. You may want to resize the Year and Month columns at this point but keep them visible because in the next post we’ll be making this calendar interactive so you want to make sure and indicate which month and year is being viewed
5. VIOLA! A calendar based view in Tableau:

Stay tuned for the next post in this series where we’ll discuss taking this view and making it interactive!

Here’s an updated version of the calendar that I did when speaking to the Atlanta Tableau User Group:

Happy Holidays

Here’s some Excel chart goofiness for your warm and fuzzy holiday season!

From the article:

…As impressive as her statistics were, Nightingale worried that Queen Victoria’s eyes would glaze over as she scanned the tables. So Nightingale devised clever ways of presenting the information in charts. Statistics had been presented using graphics only a few times previously, and perhaps never to persuade people of the need for social change. In doing so, she ignored the express advice of her mentor, Farr. “You complain that your report would be dry,” he wrote to her. “The dryer [sic] the better. Statistics should be the dryest [sic] of all reading.”…

They have what I’m sure is a fun little interactive graphic of her “coxcomb” info-graphics but I’m not sure it’s helpful.

What do you think?

So very exciting! The library of congress is publishing photos on Flickr.

Here’s a screenshot:

Flowing Data