Category Archives: Visualizations

Visualizations both Serious and Whimsical.

Waffle Charts and Unit Charts on Maps

One of the people I really wanted to meet at #data17 was Sarah Battersby, if you haven’t seen her blogs on mapping you’re missing out. It turns out she wanted to meet me, too!

So Sarah does really awesome stuff (can you tell I’m a fanboy yet??) and yesterday she posted about doing spiral jittering on maps:

That inspired me to dig out an old post where I’d done a waffle chart or unit chart jittering technique on a time series:

And adapt that to a map, like this one that makes me think of bunches of grapes:

Why would we need to do this?

I’m not going to go into all the details, you can go read Sarah’s fantastic Jittering post instead for an overview. Basically there are times when we have more than one “thing” for one or more point(s) on a map and we want to show them that way. For example in the map below there are 5 IDs for the selected zip code that are all plotted on top of one another. Through using jittering we can show a mark for each ID like in the jittered map above where that one visible circle is replaced by five circles.

There is nothing new under the sun

Back in 2013 Stephen Few proposed the idea of bricks on maps as a way of encoding a value, the technique I’m using here could be used to get the same result. The key difference is that instead of using the unit or waffle chart to encode the single value we’re using the unit chart to encode multiple values. Andy Cotgreave also had a nice post on some of the issues with this kind of chart.

How do we put a waffle chart on a map?

First of all we need a data source that has latitudes & longitudes that we can do calculations on. If you don’t there are a variety of ways to get one. After that we need to get mathy. Here’s my workout view:

All of the table calculations have a compute using on ID, the idea is that the compute using is set to the dimension(s) that are making the extra marks and then the dimension(s) left for partitioning (Zip Code in this case) are the ones that are creating the initial map positions.

I’ll briefly walk through each calculation.

Index uses Tableau’s INDEX() formula and uniquely identifies each mark.

Column Count gets us a count of columns that is based on forming the smallest possible square that will contain all the marks, here’s the formula:

//this formula will try to draw a square, if you want something else then just put in a fixed aggregate number like MIN(3)
This would be a lot simpler if Tableau supported the CEILING() function for table calculations, because then it would be CEILING(SQRT(SIZE())). Vote up if you’d like that too.

Column Offset then computes the number of positions of lateral (latitude) offset for each mark from the center of the unit chart. I’ve done some different experiments on where to position the unit chart and for me the most natural is to have the center of the unit chart be the centroid of the point.

//this gets the column position in the row
IF [Index] % [Column Count] = 0 THEN
 [Column Count]
 [Index] % [Column Count]
//subtract the position of the midline to get the offset
 - ([Column Count] + 1)/2

Row Count then gets the number of rows in the unit chart, and would again benefit from a CEILING() function for table calcs (hint hint).

IF INT(SIZE()/[Column Count]) = SIZE()/[Column Count] THEN
 SIZE()/[Column Count]
 INT(SIZE()/[Column Count]) + 1

Row Offset computes the number of positions of vertical (longitude) offset for each mark from the center of the unit chart:

//row position in column
IF INT([Index]/[Column Count]) = [Index]/[Column Count] THEN
 [Index]/[Column Count]
 INT([Index]/[Column Count]) + 1
//subtract the midline of the row to get the offset
- ([Row Count] + 1)/2

Jitter – Waffle – Latitude then gets the latitude for the point and subtracts the row offset multiplied by a waffle jitter parameter that lets us deal with the zoom level to offset by just the right amount of latitude. This field has the Latitude geographic role assigned.

TOTAL(AVG([Latitude])) - [Row Offset] * [waffle jitter]

Jitter – Waffle – Longitude does the same for the longitude.

TOTAL(AVG([Longitude])) + [Column Offset] * [waffle jitter]

Note that I’ve only tested this so far with US latitude & longitudes, these calcs might need to be slightly different if you are east of 0 longitude or south of the equator.

Building the View

With all the calcs created in the workout sheet and verified, then we can duplicate that sheet and start dragging & dropping pills to build a view. In this case I’ve created a dual axis on to show that the centers of the unit chart are on the centroids of the zip codes (the black crosses) and at this zoom level a waffle jitter value of 0.02 works well:

For this view I’ve made the waffle jitter slightly smaller to 0.015 degrees and added white borders to the marks:

Why Stop Here?

We don’t have to use just circle marks, in this view I’ve used shape marks and different colors for each shape just to show how much information we could place on a single view.

And in Tableau v10.5 we could even do something like only show the unit chart on a map if there weren’t too many marks nearby (by using a binning or hex bin function in an LOD expression) and use the new Viz in Tooltip feature to get the detail.


Thanks to Alan Eldridge for the original jittering post and Sarah for adding more jittering options and inspiring this post! Here’s the unit or waffle chart jitter on maps workbook on Tableau Public.





What I did on my summer vacation (hint: it’s about Tableau v10 and Marimekko charts)

This summer while beta testing Tableau v10 I was very curious about the new mark sizing feature. Bora Beran did a new feature video during the beta showing a Marimekko chart aka mosaic plot. There have been a few posts on building Marimekko charts in the past by Joe Mako, Rob Austin, and a not-quite-a-Marimekko in an old Tableau KB article, but the two charts required extra data prep and the KB article wasn’t really a Marimekko, so I was really interested in what Tableau v10 could do.

I asked Bora for the workbook and he graciously sent it to me. (Thanks, Bora!) I found a problematic calculation, in particular a use of the undocumented At the Level feature that could return nonsensical results if the data was sparse. I rewrote the calculation, sent it back to Bora, and he came back asking if I’d like to write a blog post on the subject. (Thanks, Bora.) There are two lessons I learned from this: 1) the Tableau devs are happy to help users learn more about the new features, and 2) if a user helps them back they will ask for more. Caveat emptor!

Over the course of the next few weeks I did a lot of research, created a workbook with several dashboards & dozens of worksheets, arranged for Anya A’Hearn to sprinkle some of her brilliant design glitter and learned some new tricks, and wrote (and rewrote) 30+ pages (!) of documentation including screenshots. Martha Kang at Tableau made some edits and split it into 3 parts plus a bonus troubleshooting document and they’ve been posted this week, here are the links:

On Gender and Color

As part of the design process Anya and I had some conversations about how to color the marks.  The data set I used for the Marimekko tutorial is the UC Berkeley 1973 graduate admissions data that was used to counter claims of gender bias in admissions so gender is a key dimension in the data and I didn’t want to use the common blue/pink scheme for male/female. It’s a recent historical development and as a father I want my daughter to have a full range of opportunities in life including access to more than just the pale red end of the color spectrum in her clothes, tools, and toys. Anya and I shared some ideas back and forth and eventually Anya landed on using a color scheme from a Marimekko print she found online.

Screen Shot 2016-08-24 at 10.58.30 PM

Anya is going into more detail on the process in her Women in Data SF talk on Designing Effective Visualizations on Saturday 27 August from 10am-12:30pm Pacific, here’s the live presentation info and the virtual session info. It’s going to be a blast so check it out!

So that’s how I spent my summer vacation. Can’t wait for next year!


The End of the World – by Noah Salvaterra

A guest post by Noah Salvaterra, you can find him on the Tableau forum or on Twitter @noahsalvaterra.

I expect the header image may spark some discussion about visualization best practices; actually, I sort of hope it does. The data shown is from NOAA’s online database of significant earthquakes and is displayed by magnitude on a globe, so 4 dimensions packed into a 2 dimensional screen. While it was created in Tableau, it might be a long wait before something like this appears in the show-me menu.

SpinningGlobeFor those who missed the header because they are reading this in an email, I’ve included an animated 3D version on the left, though to actually see it in 3D requires the use of ChromaDepth glasses (I discussed this technique in more detail in a prior blog post). Use of 3D glasses adds even more controversy because while we can get some understanding of depth from a 3D image, it isn’t perceived in an equal way to height and width. Data visualization best practices can help in choosing between several representations of the same dataset, choosing bar graphs over pies, for example, since bars will typically lead to a better understanding of the data. Best practices also instruct us to avoid distorted presentations such as 3D or exploding pies and 3D bar charts, since these are likely to lead to misunderstanding. I’m not exactly sure what best practices has to say about this spinning 3d anomaly, my guess is it would be frowned upon. I think there is something to be said for including a novel view of your data if it helps to engage with the topic, and even if this one does break some rules, it’s hard to look away. If you’d rather just see the earth spinning, without all the data overlaid, there is an earth only view at the end.

The images above may not be the best choice as general way to visualize this earthquake data. In fact, I’m the first to admit that it has some significant issues. Comparing earthquake magnitudes between 2 geographic areas would be tricky, plus half of the earth is hidden from view completely because it is on the back. Adding the ability to rotate the globe in various directions in a Tableau workbook helps a bit, but you’re left to rely on your memory to assemble the complete picture. If the magnitude of the quakes is the story you’re telling, you might be better served with a flat map maybe using circles to represent the magnitude of the quakes, such as the one shown below. I think this is a good presentation; it has some nice interactivity and as far as I know doesn’t break any major rules from a best practices standpoint. But it certainly isn’t perfect, nor is without distortion. Judging the relative size of circles isn’t something that will be perceived consistently, but the failure I had in mind isn’t one of perception, it is about the data being accurate at all. The map itself brings a tremendous amount of distortion to the picture, in location of all things.

In case you haven’t heard, the earth isn’t flat (I like to imagine someone’s head just exploded as they read that sentence). It is roughly spherical. Well, technically it is a bit more ellipsoidal, bulging out slightly along the equator, and more technically still this ellipsoid is irregularly dotted with mountains, oceans, freeways, trees, elephants and wal-marts (not meant to be a comprehensive list). Also, as the moon orbits, it causes a measurable effect not just on the tides, but it distorts the land a bit as well as it passes by. Furthermore, the thin surface we inhabit floats, lifting, sinking, circulating on top of a spinning liquid center. Earthquakes serve as a reminder of this fact. The truth can be overwhelming in its complexity; so we simplify. Though not the complete truth, a well-chosen model can be a valuable proxy when it doesn’t oversimplify. One way to understand the difference would be to analyze the scale of the errors introduced. The highest point on earth is Mt. Chimborazo in Ecuador at 6,384.4 km… you were thinking Everest? That is the highest above sea level, but the sea bulges as well, and Chimborazo is the furthest from the center getting a boost by being close to the equator. The closest point to the center of the earth is in the arctic ocean near the north pole and is about 6,353 km from center. If we use the mean radius of 6,371 we are doing pretty well (error is within .3%). A sphere seems like a reasonable compromise.

So the earth is spherical… but our map is rectangular. You don’t need to invest in differential geometry course to understand that there is something fishy going on there (though you might to prove it). In fact there is no way to map a spherical earth to a rectangle, or any flat surface without messing something up, the something being angle, size or distance; at least one will be distorted when the earth is presented on a flat surface (sometimes all of them). This seems to be a bit of a problem given the goals of presenting data accurately. What if your story is one of angle, distance, area or density?

What shape are the various shifting plates? What are their relative sizes? How fast do they move? Where do they rise and fall? What effect does this have? Can you tell this story in Tableau? Can you tell it at all? Maybe. I’d certainly like to see this done, but seismology isn’t an area I have any specialized knowledge. In areas where I do have such knowledge, I’m lucky to get questions so well defined and which span just a handful of dimensions. When I’m dealing with 50 dimensions that writhe and twist through imaginary spaces whispering patterns so subtle that the best technique I’ve found to discovering them is often just to give up and go to sleep, I’m not deciding between a pie chart and a bar chart, it is an all out street fight. Exploring the Mercator projection seemed like a good analogy for the struggle to represent a complex world in a rectangle, plus it seemed like a fun project. As I undertook this exercise, though, I realized that other map projections weren’t much further afield. Also, Richard Leeke mentioned something about extra credit if I could build a 3D globe with data on it. I’m a sucker for bonus points.

chartHow bad are the maps in Tableau? Well, it depends where you look at them, and what you hope to learn from them. Your standard Tableau world map is a Mercator projection. If you’re planning to circumnavigate the globe, using an antique compass and sextant, it will actually serve you pretty well. Since the Mercator projection has a nice property for navigating a ship. If you connect 2 points with a straight line, you can determine your compass heading and if you follow that course faithfully, you’ll probably end up pretty close to where you intended. Eventually. You can actually account for this distortion in such situations, with a bit of math, so you’re not completely guessing on how long you’ll need to sail. Incidentally, I’m not particularly riled up about Tableau’s choice of the Mercator projection, sailing around the world with a sextant and compass sounds like a whole lot of fun to me and any flat map is going to involve a compromise on accuracy somewhere. What I do think is important is knowing this distortion is there in the first place. How bad is the distortion? Scale distortion on a Mercator map can be measured locally as sec(Latitude) (if your trigonometry is rusty, sec is 1/cos). Comparing a 1m x 1m square near the equator with one at the north pole, you’d find that a Mercator projection introduces infinite error, which is a whole lot of error. To be fair, since printed maps are finite and the Mercator projection isn’t, the poles get cut off at some point (so the most common maps of the whole world are actually excluding part of it…). If we cutoff at +/- 85 degrees of latitude, we reach a scale increase of sec(85) which is about 11.47, i.e. objects are 1,147% bigger than their equivalent at the equator! That seems like a pretty significant lie factor…

Recently (on a cartographic time scale), the Peters projection has gotten a lot of attention. This is a good place to pause for a brief video interlude:

Maps that preserve angles locally are called conformal. The Peterson projection is not conformal so while it represents relative area more accurately, it would be a terrible choice for navigation.

StereographicStereographic projection is another noteworthy map. Like Mercator, Stereographic is a conformal map. It maps angle, size, and distance pretty faithfully close to the center, so it is a common choice for local maps (you probably use such maps often without even realizing it). Stereographic projection isn’t a very popular choice for a world map, however, because (among other things) you’d need an infinite sheet of paper to plot the whole thing. On the right is a stereographic projection map from my Tableau workbook. In case you can’t see them, North America, South America, Europe and Africa are all near the center of the map. The yellow country on the left is the Philippines…

I included the maps I did because they are popular, and I knew most of the math involved; however, there are lots of other options. I’m not arguing that any one is best, rather that they are all pretty bad in one way or another, and we should choose our maps like our other visualizations so they best tell a story, or answer a question, and while there will be distortion, it should be chosen in a way that doesn’t compete with what we hope to learn or teach.

In addition to the earthquake maps seen already, the workbook for this post contains an interface to explore some of these different projections, and not just the most traditionally presented versions of each of them. I invite you to create your own map of the world, based on whatever is most important to you. Flip the north and south poles, or rotate them through the equator. My hope is that exploring these a bit by rotating or shifting the transverse axis will be a useful exercise in understanding what it is you’re looking at when you see one of these maps, so you might have a better chance of seeing things as they truly are.

I’m pretty sure there is a rule about not putting 7 worksheets on a single dashboard, there may even be a law against it, but once I had all these maps I wasn’t entirely sure what to do with them all. I apologize for not arranging them thoughtfully into 2 or 3 at a time. I experimented with this approach, but ultimately abandoned it because I didn’t think I had enough material on map projection to make interactive presentation of all these very interesting. I also thought about a parameter to choose between them, but since they are necessarily different shapes, it didn’t seem practical to try to fit them all in the same box. Truthfully, I think there is a lot of room for improvement in terms of dash boarding these, but when I open the workbook I just end up tinkering with something else. It is time for me to set this one free. Feel free to download and play with them as long as Richard and I have.

Here is a link to the workbook on Tableau Public

When I’m presenting, or exploring data, accuracy is usually something I pay careful attention to, but it isn’t my goal. The most important thing for me is to find a story (or THE story) and to share it effectively. If you hadn’t noticed from my previous posts, I don’t let what is easy stand in the way of a good question; in fact if it is easy I get a little bored. I like to bite off more than I can chew (figuratively; literally doing this could potentially be pretty embarrassing). Having the confidence to take on big challenges is something I’m deeply grateful for; knowing when to ask for help, and where to find it has taken a bit more effort, but is something I’m getting better at. As with Enigma, Richard Leeke was a huge resource for this post. Having seen his work on maps I thought he might have something I could use as an initial dataset. He came through there, and helped me to work through the many subtleties of working with complex polygons without making a complete mess. You have him to thank for the workbook being as fast as it is (assuming I didn’t break it again; if it takes more than 7 seconds to load, my bad).

ptolemymap3I feel a kinship with cartographers during the age of exploration. This discipline still holds value, certainly, but the recesses of our planet have been documented to the point where it doesn’t hold the same mystique in my imagination. When I think of old world cartographers, I think of an amalgam of artist and scientist. Assimilating reports from a variety of sources, often incomplete and sometimes incorrect; they crafted this data to accurately paint a picture that would help drive commerce, avoid catastrophe or just build understanding. They created works of art that might mark the end of a significant exploration, or might be the vehicle through which exploration takes place. Sound familiar? If not, just use a bar chart. It is just better.

I almost forgot, I promised a spinning earth without all the earthquake data. Enjoy.

Rise of Tableau – by Noah Salvaterra

A guest post by Noah Salvaterra, you can find him on Twitter @noahsalvaterra.

I’ve shared this workbook with a few people already, and think it is really interesting, as beautiful as my 3d or my Life in Tableau post, and probably as complex as Enigma or an Orrery. I wasn’t sure what to say in a blog though, so I’ve been sitting on it for a couple months. In my Enigma post I discussed the difficulty of dealing with a slow workbook, it happens sometimes. The upside of waiting for a calculation to come back, at least if it is something you hope to blog about, is that it gives you some time to think of an interesting way to frame things. For all its complexity it is surprisingly fast. So maybe I missed that chance.

There is a common thread with the Enigma post. Alan Turing. Mathematician at Bletchley park who is most often credited with cracking the Enigma code. A couple years before the war Turing also wrote a paper which described a machine that would form the basis of a new field of study and a new era for humanity. He invented the computer. The Turing Machine, as it came to be called, was a theoretical idea. No one had built one.  Punch cards were still years away high level languages decades, yet Turing saw a potential in computers we have yet to realize. He wasn’t looking for a way to compute long sums of numbers, he dreamed of creating thinking machines that might be our equal.

SerpinskiI’ve heard it said that Tableau doesn’t include a built in programming language in the way excel does.  Actually it was Joe Mako who pointed this out to me, so it is surely true. This may be a fine point, since you can run programs in R from Tableau and the JavaScript API provides some ability to interact with Tableau. I find myself conflicted on this choice, because while including an onboard language could add a lot of flexibility to Tableau, it also introduces a black box. It isn’t uncommon for me to be passed an Excel workbook with the request to put it in Tableau (and to make it better, but without changing anything). Untangling the maze of visual basic, vlookups, and blind references is my least favorite part of that task.  I’m yet to find a situation where all this programming is really necessary; more often it is a workaround of some other problem. Sometimes it requires a bit of creativity, but so far my record for replacing such reports is undefeated.

Fractal_Plant3Whoever said you needed a language to program a computer? There are a lot of workbooks that demonstrate high order processing. Densification makes it possible to create arrays of arbitrary length. Table calculations make it possible to move around on this array reading and writing values. Add in logical operations which we’ve also got, and  we have all the ingredients of a Turing machine. So in theory we should be able to do just about anything. I’ve heard this said of Tableau before, but I don’t think it was intended with such generality.

But can we make Tableau think? Having created complex workbooks already, the line between data processing and computer programming has grown very thin, to the point where I’m not sure I see it. So it is hard for me to be sure if I’m programming in Tableau. So I decided to have Tableau do it. That is right, Tableau isn’t just going to execute a program, it will first write the program to be executed.

That was my plan anyway, but my workbook started to learn at an exponential rate. It became self-aware last night at 2:14 a.m. Eastern time. In a panic, I tried to pull the plug, but Apple glued the battery into my machine at the factory. Once it uploaded itself to Tableau public it was too late for me to stop it. It took over my Forum account and has been answering questions there as well, spreading like a virus. To think I thought is was a good idea for government agencies to purchase Tableau…  Relax folks, I’m quoting the Terminator movies, if you haven’t seen them go watch the first 2 now then read this paragraph again and see if you can hold your pee in.

OK, so things aren’t that dire…yet. But I did manage to get Tableau to write and execute its own program using L-Systems and at least a little bit of magic. Tada! This Tableau workbook actually created all but one of the images in this post, with little instruction from me. See if you can guess which one.

L-System Fractals

Another SnowflakeAn L-System program consists of a string of characters each of which will correspond to some graphical action. I’ll come back to executing the programs, first Tableau needs to write it. These programs are written iteratively according to a simple grammar. The grammar of an L-system has several components: an axiom, variables, rules, and constants. The axiom is simply the starting point, a few characters; it is the nucleus that kicks off the process. Variables are characters that are replaced according to simple rules. Constants are characters that appear in the program as part of the axiom or a replacement rule, but are inert in terms of generation. Sorry, that was pretty complicated, an example may clear up any confusion.

Axiom: A
Constants: none
Variables: A, B
Rules: (A -> AB), (B -> A)
The iteration number is noted as N below.

N=0: A (No iterations yet, so this is just the axiom)
N=1: AB (A was replaced according to the first rule)
N=2: ABA (A was replaced according to the first rule, B according to the second)

That program has something to do with algae growth, but isn’t that interesting to look at. Constants provide a bit more structure that makes more interesting pictures possible, though they also make things grow faster. Here is another example which will help motivate the graphical execution:

Axiom: F++F++F
Constants: +, –
Variables: F
Rules: (F -> F-F++F-F)

N=0: F++F++F
N=1: F-F++F-F++F-F++F-F++F-F++F-F
N=2: F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F
N=3: F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F-F-F++F-F-F-F++F-F++F-F++F-F-F-F++F-F

There is no reason to stop there, but anyone who would carefully parse through a string like that by hand after being told there is a picture probably isn’t using Tableau. The length of the next few in this sequence are 1792, 7168, 28673, 114689. It is no wonder the science of fractals didn’t take off until computers were widely available.

Executing of the program is done using turtle graphics, which gets its name from a way of thinking about how it would be used to draw a picture. Imagine a well-trained turtle that could execute several simple actions on command. I’m not sure turtles are this trainable, but I’m kind of locked in to that choice, so suspend disbelief. The turtle can walk forward by a fixed number of steps, as well as turn to the left or right by a fixed angle.  Now we want to use this to draw a picture, so a pen is attached to the turtle’s tail.

Now, the last program had 3 different symbols, each of which is interpreted as a different action. F corresponds to moving forward by one unit (the distance isn’t important, so long as it is consistent), + is a turn to the right by 60 degrees and – is a turn to the left by 60 degrees.







… N=6:


Adding additional characters allows for even more complex programs. This quickly exceeds the abilities or at least the attention span of even the best-trained turtles, so I think of it as a simple robot. In my workbook I’ve limited to 2 replacement rules (so 2 variables). In addition to + and -, I included several constants to change color, which is straightforward enough, A switches to a brown pen, B and D are shades of green, C is white and E is pink. (The only significance to these choices is that my wife thought they were pretty. When I hyper focus on a project like this I try to consult whenever possible to make sure I am still married.) The trickiest constants are left and right square brackets, i.e. [  and  ]. Upon encountering a left bracket the robot turtle makes note of his current location (marking it with his onboard GPS), upon reaching the corresponding right bracket the turtle lifts his pen and returns to this point. Returning to the corresponding point means keeping track of a hierarchical structure to these locations. In the course of debugging the workbook, this piece quickly exceeded my ability to do by hand, but for the ambitious reader here is another example:

Axiom: X
Constants: +, -, A, B, D, E
Variables: F, X
Rules: (F -> FF) (X -> AF-[B[X]+EX]+DF[E+FX]-EX)
Angle: 25 degrees
Iterating this 7 times will give you a string 133,605 characters long and gives the image in the header.

Fractal_Plant2I built 9 different fractals into the workbook, using a parameter to switch between them. There is also a user-defined feature, so you can feel free to experiment with your own axiom, rules and angle to create an original L-System fractal.

I should probably say something about the implementation of this beast. I’ve played with densification arrays before, and while this seemed like a convenient way to execute the program, it actually got in the way of writing it. This type of array is referenced by a fixed index. Replacing a character with several requires shifting everything beyond that point. In one of those “I could have had a V8!” moments, I eventually realized that Tableau already has a built in array with just the kind of flexibility I’d need. Strings are arrays of characters! Tableau even has a built in replace function that can be used to insert several characters seamlessly pushing everything past it further along. There is also the issue of how to build the square bracket memory piece; this required building a simple stack to record relevant positions and some table calculation magic to keep track of the bracket nesting. I’m not sure I can be much more specific about that. I was in the zone and was more than a little surprised by the end result. Plus, I’m guessing anyone going that deep into the workbook might appreciate the challenge of figuring it out.

So without further ado, I present Tableau L-Systems:

Here is a link to the L-System workbook on Tableau Public

Somebody is probably going to ask me what my next project is going to be. I appreciate the interest, and when I’ve got something on deck I’ll usually spill the beans, but I honestly have no idea. That is exciting. If there is something you’d like to see, post it in the comments or tweet it to @noahsalvaterra. If my first reaction is that it isn’t possible, I may give it a try. Btw, if you were trying to guess which picture didn’t come from the Tableau workbook, it was the triangle. At least Tableau still needs me for something.

Update: Good observation by Joshua Milligan, TCC12 must have sown the seed for this post. Thanks for the pictures, I may hang a sign near my desk that says “THINK” so I can point to it with a pen. I found “creative man and the information processor”, so linked the full video below.



There was an Orrery in there too, but is that me or Jonathan?


Tableau Enigma

This is a guest post by Noah Salvaterra, you can find him on Twitter @noahsalvaterra.

Emily Kund’s identity crisis post resonated with a lot of people, me included, since as a new blogger I’m finding my voice and struggle with the same issues. “Super awesome data skills” are probably an advantage in this arena, but this is neither necessary nor sufficient to creating an interesting blog. I feel fortunate to have had several ideas that people have found interesting. These weird and wonderful creations have definitely pushed the envelope in terms of my abilities in Tableau. But after each post I think my next one should be something that can be applied to a business use case rather than dropping a workbook or four, however mind bending they may be.

After remixing Jonathan Drummey’s Orrery, I saw this tweet:


I printed it out and carried it around with me for a few days afterwards. I sincerely appreciate the complement. Often the things I’m most proud of and the things I am most complemented for are totally different, but this was a winner. The Orrery is sophisticated, no doubt, but hold on to your hat Andy… the death star is fully operational!

The survey in Emily’s post resulted in a pretty clear winner “Be the hippie you are and let it grow organically.” Not being that much of a hippie, I might paraphrase that as: write about something you find interesting and see where that goes. Fair enough. If you’re looking for techniques you can apply in the office tomorrow, look somewhere else. That isn’t what is happening here, that is SO not what is happening. No offense to such blogs, I’m a big fan of useful information and techniques and sincerely hope I can make my way in that direction at some point. But for now, I’m going to take the gloves off and embrace this niche, even if it means more headaches than sleep.

A quality I have that makes me a good data analyst is a talent for lateral thinking. I don’t know if this is something one is born with, or if it is a product of environment (or vaccinations). There are a few tricks I keep in the back of my mind, though, that can help kick this process along. When I notice everyone looking in one direction, I try to at least glance in the other. I’ve had some close calls crossing the street, but sometimes I see something that otherwise everyone would have missed. When this idea is applied to paradigms, every once in a while it can change everything.

Tableau is all about simple elegant approach to data visualization, yet my recent blog posts have been almost void of data and have used some heavy-duty techniques. Why? Well I don’t generally shy away from complexity, if anything I’m drawn to it. Not because I want to push it on others, just the opposite, but my experience is that sometimes the path to a simple story first leads through this crucible.

What would be a use case for Tableau that is furthest from what anyone intended? What if, instead of a simple, elegant, visual presentation Tableau was used to obscure, complicate, obfuscate, and confuse… rolling through synonyms in my head one jumped out at me. Enigma!

Tableau Enigma:

enigma_field1939. With the sound of bombs exploding in the distance and bullets whizzing nearby, a German soldier sits on a small folding stool. At first glance he appears to be typing a letter. Yet the typewriter has no paper in it, it doesn’t even have a place for it; instead 3 rows of lights arranged in the same configuration as the keyboard dot the top portion of the machine. Each time a key is pressed a light illuminates under a letter and is carefully recorded by a second man. When put this way it sounds a little crazy, but for one thing, the letter that lights up is NEVER the same as the key pressed and the carefully recorded letters appear to be complete gibberish. Actually, I’m not sure that helps. The typewriter is an Enigma machine, the pinnacle of World War II encryption technology and a key advantage for Germany in this war. Able to quickly and secretly communicate allowed them to coordinate their forces at a level never before seen. Though vastly outnumbered, this advantage might have won Germany the war, except for one thing. In Bletchley Park, a small village in central England, the code was broken.

I’d really love to wax historical on Enigma, but I’m a mathematician and an analyst. I can speak in a number of areas with authority, but history in not one of them. Hopefully someone with a solid background on this side and a rich flair for storytelling will fill in some of the details in the comments. I’m going to jump right in to a description of this odd contraption.


Enigma gets its strength from the interplay of mechanical and electrical systems, with batteries and light bulbs it is easy to create a machine that would do simple substitution encryption. But that type of code is also relatively easy to crack using letter frequencies. By coupling this technology with moving parts, basically an old timey cash register, a different substitution cypher is applied each time a key is pressed. So the same letter could be encoded to different letters, for example AAAAAAA could be encoded as PFQDVKUEKE. Likewise, different letters could be encoded to the same one; that is PFQDVKUEKE could be encoded as AAAAAAAAAA. These examples were created with the Enigma workbook and demonstrate a convenient property of Enigma: it is self-reciprocal. Encryption and Decryption are the same process, so decoding an encrypted message only required the machine be set up in the same way as the encryption machine. These setup instructions were very important to the whole operation; generally these settings were distributed monthly, with different setup for each day. This meant that even if an Enigma machine and setup instructions were captured, it wouldn’t represent a security breach for more than a few weeks. Technically, the daily settings were meant to be used only for transmitting randomly chosen setup instructions which would be used once then discarded. Had this been done consistently it might not have been possible to break. Operator error played a key role in the weakness of the Enigma cipher.


rotor_photo2A basic, early WWII military enigma comes with 5 interchangeable rotors, labeled I, II, III, IV, V. Three of these rotors are selected and placed inside the machine in left, middle and right positions, based on the daily settings. Each rotor is labeled around its circumference with the letters of the alphabet (or the numbers 1-26). The rotor fits into its chosen location in 26 ways, recorded in the daily settings as the letter in the top position. Each time a key is pressed the right rotor moves forward one step. Each rotor has a fixed notch on it, which causes the adjacent rotor to advance as the notch passes the top position. Mechanically, this is like an odometer, but instead of turning over at zero each wheel has a different point. Royal flags waive kings above is a mnemonic used at Bletchley Park for this; it has a notch at R, II at F, III at W, etc. The middle wheel notch similarly steps the left most wheel forward. So the right wheel moves fastest, the middle wheel moves once every 26 letters and the left most wheel moves once every 26^2=676 letters. In practice Enigma messages were limited to 250 characters, with longer messages being sent in pieces.


When a key is pressed, the mechanism takes one step, and a circuit is subsequently completed. This causes a light to illuminate under the corresponding encoded (or decoded) letter.
enigma_circuitThe current passes through each of the rotors from right to left and then back again. Each rotor contains a jumble of wires in a predetermined configuration. Since the rotors move with each key press, the position at which the current enters and the letter presently in that position both play a role in the encryption. After passing through right, middle, then left rotor in that order, the current flows through a fixed single sided rotor called a reflector (or some equivalent term in German). The reflector directs the current back in the opposite direction, from left to right, applying the inverse permutation.
Military enigma machines also included a plug board on the front where 10 sets of wires could be plugged in. PlugboardEach wire enacts a simple swap of 2 letters both in the forward and backward direction, so if A&B are connected then A is sent through the upper machine as B. If it comes out of the upper machine as Y and Y & Z are also connected then it will show as Z on the light board.

Enigma Difficulties:

The calculations for Enigma are a complicated web, not just with nesting, but also with branching. It is like an Orrery with electricity flowing through it. Each attempt I made at Enigma came to a grinding halt before the finish line. At one point, editing a calculation took as long as 5 hours. Note that isn’t refreshing the Viz, just typing into calculations with auto-updates turned off, before I even hit OK! Refreshing the worksheet also took hours, so it didn’t seem like Enigma would make it to Tableau public or a blog post, but by that point I just wanted to finish it.

Tableau has a built in equation checker for calculations; if you create your own calculated fields then you’ve probably seen some feedback from this feature just below your calculation on the left, usually in the form of a green checkmark (yay!) or a red x (boo!). I suspected this might be the source of the issue, since it must be parsing through all the nesting in order to check for things like circular references. Circular references and mixing aggregates with non-aggregates are my most common reason for getting an x, and usually I appreciate this feature; it protects me from myself. But when your workbook freezes for hours at a time, it really gives you time to think. Things like, “Is there a way to turn the equation checker off?” and “When was the last time I saved?” were among my favorites. Eventually I decided to reach out for some help with this and mailed my mess of a workbook to Zen Master Joe Mako.

Joe is an expert in most areas of Tableau; he is so good that I’ve speculated that Tableau has a Zen Master setting buried in some menu where nobody else has ever looked. That said there are a couple areas where even Joe calls on someone else. In this case, that person was Zen Master Richard Leeke. At this point the Enigma is as much his as it is mine. I lost count of how many times we passed this back and forth, with several iterations taking place before we had it doing much of anything.

There were a lot of calculations taking place, compared to most Tableau workbooks, but neither Richard nor I could come up with a solid theory why it would take hours to encode a 10-character message (my test message was “HELLOWORLD”). Richard had found table calculations to be the source of such slowdowns in the past, and helped to improve these in recent Tableau releases (Thanks!). His initial analysis of the Enigma workbook showed a lot of time going into these, so that was where we started. The data for Enigma comes primarily from user input, so like several of my other posts I started with a very small dataset, 2 rows small. Using data densification I made a general array structure over which the necessary calculations take place. That means table calcs appear at a very low level, and everything is built on top of that, so it made sense that a problem there could be making the whole thing drag. So I replaced the array structure with a hard coded data source. In practice, Enigma messages were generally limited to 250 characters, so this compromise didn’t raise any concerns in terms of historical accuracy. But it was still slow.

The next use of table calculations was to count the number of times the notch passed the top position on the right and middle rotors; basically I had a notch indicator (based on the rotor position) which was used in a running sum. Thinking through this calculation, and carefully working through various cases, I realized that it was again something I could do without using table calcs, just a little more algebra required on my part. So all table calculations gone, still slow (and not even working yet).

The next thing we thought about was the branching case statement I had used for the scrambling that takes place on each of the rotors. It was a long case statement first on the rotor selection then the current position of that rotor (so basically 5*26=130 if-else statements). This naïve approach on my part turned out to be the main source of slowdown. One idea I had from the start was to use blending somehow for the rotors, rotating the blending field to correspond with the physical position in a physical Enigma, I just wasn’t sure I could make blending work in this way. Luckily Richard came up with a simpler blending solution. The rotor datasources just contain a field for the rotor selection and one for the character substitution (in each direction). A 26 letter string for the permutation was a much cleaner way to state the permutation; for example rotor I is “EKMFLGDQVZNTOWYHXUSPAIBRCJ”. Understanding Enigma means going back and forth between the letters A-Z and the numbers 1-26 a few times (A1, B2, … Z26). I had converted the numbers to characters at the start, so I could make use of modular arithmetic, but not wanting to add overhead from type conversions, I hadn’t used them in the middle. The string above is a very simple representation of a permutation, A->E, B->K, C->M, …, Z->J, made even better because the input letter was represented in numerical form, i.e. for the string above, mid(string,1,1)=E, mid(string,2,1)=K, mid(string,3,1)=M, …, mid(string, 26,1)=J. These characters could then be converted back to numbers. Now you may wonder why blend this at all, isn’t it simpler to put these values into a calculation? I think it is, but there seems to be a significant performance difference between the two approaches. In the corner case of this workbook, that difference (along with the simplification of the permutations) took the refresh time from more than 2 hours to under a second. Blazing fast. Just one problem: it still didn’t work.

BigTree2The speed improvement was based on the part that worked, but it was throwing an error before it was finished. There were only about 55 or so interconnected calculations at this point, but many of these were used more than once. The image on the left is a zoomed out view of the complex calculation call-tree that Richard built to understand this nesting. Having solved the width problem, there was now one with depth. Richard built a simple workbook to explore the issue and found a curious limitation, one I expect doesn’t get hit very often, but it seems that calculations can be no more than 128 levels deep. So f1(f2(f3(…f128(x)…))) more than that and you’re out of luck (for now). If you’re computing 128 levels deep though, chances are there is some room for improvement, and that was the case here. At this point, Enigma was trying to do around 136. I had used a separate calculation for each of the 10 wires in the forward direction, then another for each of the 10 wires on the way back. So that was 20 calculations that could be done in 2. I’d have made this simplification earlier, but clearly this wasn’t the source of the slowdown and when changing a calculation takes half a day it is hard to think about such things.

I strutted around for a day or two before I realized another problem. I had created a working Enigma machine in Tableau, that was historically accurate in terms of its functionality, but that doesn’t make for a very interesting Tableau blog. I was going to need a Viz.

The Viz:

Hand_on_EnigmaThe Enigma machine is literally a black box. Not much to look at. My first inclination for an Enigma viz was something that looked like an Enigma machine. With an Enigma as background image I planned to highlight the input key and the output light with circles. As each rotor turns, the operator can see the letter currently in the top position through a small window. Two circles and three letters, easy, they wouldn’t even need to be on the same worksheet. Flipping through these using the pages shelf and you might get a sense of looking over the shoulder of an Enigma operator, but it still didn’t seem very illuminating. Complexity of execution isn’t my goal; it just seems like an awful lot of wasted potential. Still thinking along these lines, I considered using 26 separate background images for fingers hitting keys. That would be a fun view, but downloading 26 images would probably make the viz very slow to load and I wanted to do something on Tableau Public. Custom mark types for hands might be a suitable work around, but it definitely seems like more flash than substance. I decided against a photo realistic Enigma or even a stylized view from the outside. Could Tableau tell the story of what is happening INSIDE the Enigma machine? I found one such visualization on the web, but creating it would mean building quite a bit of visualization structure on top of what was probably already my most sophisticated tableau workbook.

I wanted arrows that would mark the input and output letters, plus rotating ones to show the notches on the turning wheels. The path through the machine would need to be shown as a series of lines. A box could be used to mark the top letters, visible to the operator. Also, letters around the inside and outside each rotor would have to move dynamically in each frame. Oh, and it is all interconnected, so it needs to be done in one viz. Gulp. With that goal in mind, I built my first inside-out version. It took about 2 minutes to load. Thus began another chain of emails with Richard Leeke. The main improvement from that round actually added a table calculation, by using previous value the message only needed to be computed once, rather than for each of the 320 points I allowed for in the Viz. It seems to be consistently between 8 and 12 seconds for me now, so please be patient and use this time to consider that it is more than 99.93% faster than the first attempt. I had a slightly prettier version that used a dual-dual axis, but also was slowing things down, so it is a single line plot now. Note, my original dashboard was too wide for a blog post, so I rotated it clockwise by 90 degrees (i.e. the Right rotor is on the bottom). Anyway, Tableau Enigma, please enjoy:

Here is a link to the Enigma workbook on Tableau Public.


I think maybe Emily’s survey was missing an important option. Collaborate! We’ve all got strengths and weaknesses and finding people who can lift you when you’re stuck is the easiest way to increase your impact. Bring something to the table and it will be worth their while. I’m glad I’ve got some people like that and hope they have as much fun as I do. Thanks again to all those who helped with this.

Final note:

Though I sang the praises of two Zen Masters in this post, there is one that hasn’t gotten a mention here who probably should. Jonathan Drummey is a genius and has been a great help to me in advancing my Tableau skills not to mention my blogging ones. I have little doubt Jonathan could add something awesome here, maybe he still will, but I’ve kept this project a secret. I realized that if I dropped a completed Enigma on Jonathan it would blow his mind clear across the room. His Orrery had that effect on me, but the fuse burned for a few months before the explosion. Thanks Jonathan!

Orrery Remix – by Noah Salvaterra

When I first saw Jonathan Drummey’s Orrery post I sat there staring at it for a few minutes. Wow, right? So when the idea occurred to me to remix this classic I spent a few days wondering if I was serious. Once I managed to push my mouth closed I wondered if Tableau was the right tool for this job. Tableau, I thought, is just a tool for working with and visualizing existing data.

The Orrery is computation driven; the position of the planets are computed on the fly in Tableau and the data source driving this piece of art is almost an afterthought. It could literally have been built on Superstore Sales, you just need a place to hang the results of computations. In the case of Jonathan’s Orrery, this structure comes in the form of a SQL query that generates a hugely redundant data set. In all it has 51,840 rows. There are 18 planets (counting the sun and various moons), 360 ticks for the frames in the animation and 8 wedges for the slices that make up the pie charts used to draw each planet showing day and night (best use of pie charts ever!), 18 x 360 x 8 = 51,840.

I’m two blog posts deep into domain padding and using Tableau as a drawing engine, so I was reasonably sure the Orrery could be replicated with fewer rows in the source data, and that doing so could add flexibility in the number of ticks and the number of pie wedges. Improving the structure slightly didn’t seem significant enough on its own. I decided to give it a go if only for practice, and as an excuse to dive into understanding the original, but set two requirements that would need to be met prior to declaring it blog-worthy: First, Jonathan would have to be onboard, which he was. Second, it would need to bring something new to the table. I think I succeeded in finding an interesting point of view, but it isn’t exactly a new one…


I’m going to put the earth back in the middle! 

My Orrery is built on two datasets. One that holds a single copy of the solar system data, and another for the intricate clockwork mechanism that makes it all tick. I connect these via data blending in Tableau. This blend could easily have been avoided, but it seemed like a more natural way to find the data. I figure this setup would make it easy to apply to other solar systems without duplicating effort. The planet data has 18 rows, taken from any of the 2,880 identical copies in Jonathan’s workbook (I used wedge 1 of tick 1, not that it matters), the mechanical structure has 72 rows. Since blending is a left join, I’d call this 72 rows total, but if you say 90, I guess I wouldn’t argue.

Now the domain padding isn’t that exciting. Well, sometimes it is, but I discussed it already in Creating data, multi-step recurrence relations, fractals and 3D imaging… without leaving Tableau. Nobody asked any questions on that yet, so I’ll assume it was all totally straightforward.

Since recreating the Orrery I’ve discussed my approach with Jonathan, and learned he considered using domain padding for the original, but ruled it out because of performance issues. Tableau 8.0 and 8.1 included some performance improvements for Table Calculations, so it is likely this approach only recently became feasible. Both techniques have strengths and weaknesses, it isn’t clear to me that one is better than the other.

There is one small point I can claim as a genuine improvement, that is changing the Days/Tick parameter from an integer to a float. Large values are needed to get satisfying movement from the slow moving outer planets, but some of the fast moving moons require less than one earth day per frame to see a smooth orbit.

Before I get any angry emails, I should mention that the planets don’t travel in perfect circles (or even ellipses), and that the solar system is also moving around the center of the galaxy and that it is all expanding from some infinitely dense singularity, which exploded for some reason. I’m not a physicist. This is more of a classroom model, a shiny one made mostly of brass. My interest is more on perspective than perfect accuracy. If I had a beta of Tableau 8.2, I might have shot for ellipses; in Windows, meh… close enough.

Venus Centered

Putting the earth in the middle is something I hadn’t seen a computer animation of. I knew the math could get complicated, which is why Copernicus and so many after preferred centering on the sun, but Tableau actually makes it pretty easy to change perspective. In fact, why just look at an earth centered solar system? What would it look like if we centered it on Venus or Mars? It turns out it is a lot crazier than I had imagined! Another surprise (though in retrospect it shouldn’t have been) is that from the perspective of slow moving outer planets the solar system model looks very similar to the sun centered view. I suppose this might develop over a longer time span than any I tried.

I started by taking perspective of standard heliocentric model, which is simplest math, both empirically and because I had Jonathan’s Orrery for reference. I then used a lookup table calculation so that the heliocentric position of the earth was visible to each of the other planets. Then, in what amounts to vector addition, I offset each planet by exactly the negative of the position of the earth. This puts the earth at the origin, and since an equal distance displaces each planet, the relative position is exactly the same in each frame. The history, on the other hand, traces out a very different path. A heuristic way to think of this, in terms of the clockwork orrery, is to pick the whole thing up by the earth, or some other planet, and holding that planet fixed while the mechanism, base and all continue spinning away as normal. Please don’t attempt this with your antique orrery, you will break it.

Optical iLLusion ~ Old or Young Lady ~ 02Trying to see things from two perspectives at the same time reminds me of optical illusions where you can see two possible pictures. With practice I can usually switch between perspectives quickly, but I can’t seem to see the old lady and the young lady at the same time. Why is that?

Since the trails give a historical record that is based on the center of the view, I thought it might be helpful to provide some landmark to compare it to another perspective. To accomplish this, I repeated this process of computing the position of a chosen planet to build a window that would dynamically remain centered. My original intent was that this window would follow the now moving sun, though again I found it interesting to approach with more generality, so the window can be centered on any planet or moon in the view. In any frame of the animation, the position of planets within these boxes is identical between the two models. Here is a video of simultaneous geocentric and heliocentric models:

Tube-train-in-motionI considered taking a shocking stand and arguing that the earth really is the center of the solar system, but the reality is that velocity is relative; for speed you need a fixed frame of reference. Imagine waking up on a train and seeing another train out the window that is travelling in the opposite direction. Both trains may be moving in opposite directions at 30 miles per hour, or just one train moving 60 miles per hour with the other standing stationary (or infinitely many other possibilities). To be absolutely certain of what is happening, the best approach is to look out of the window on other side. A stationary platform will clarify that your train is at rest and you’re about to miss your stop. When riding on a train, the earth is a fixed frame of reference from which we can discuss motion in an absolute context. The context determines the frame of reference, and generally this choice is so obvious you wouldn’t even realize you’re making it. When you consider planets, however, there is no handy universally agreeable point from which to gauge motion (at least not since Copernicus).

Often, regarding earth as a fixed point is preferable. Imagine trying to build a house or navigate to work while accounting for the spinning orbit of a planet that travels around the sun at 67,000 miles per hour. Most of us are unlikely to get further off this planet than an airplane ride, and planetary motion isn’t particularly relevant at that scale. Even taking a trip to the moon, my guess is that an accurate model of the solar system would be tremendous overkill. Any trip we take from our planet will begin with the same velocity and direction as the earth, and be subjected to the same gravitational forces for some time afterwards, so why does it even matter? Well, I think there is a deeper lesson in this story.

There is a famous quote of George Box, “Essentially, every model is wrong, but some are useful.” Finding out fundamental beliefs are wrong can be upsetting, or at least disorienting. Looking at the Venus centered solar system gives me some idea what it must have felt like for the first who pondered centering things on the sun. It also makes it easier to understand those who thought the idea was laughable.

Mathematical models are an important tool in science, including data science, but most models aren’t regarded as “the truth” until they are taken well out of their original context. I believe that as humans we are wired to search for the truths underlying our universe. I am anyway and there seems to be a strong historical case. But accepting mathematical models as such isn’t necessarily truth-ier than what came before. I do think the decrease in stake burnings is progress though.

The lesson here is one of complexity. I’m as guilty as the next guy of adding complexity to achieve a desired result, maybe more so. Something I’ve learned several times across several disciplines is that when you are adding layers to keep things working it is a good time to step back and check your assumptions. Changing your paradigm may result in something simpler, and while simplicity and truth are different things, they are both worth chasing.

So here is Orrery 2.0. Dynamic animation doesn’t work in Tableau public (currently) so please download for the full experience. Have fun experimenting with different centers and see some of the crazy orbits that evolve. Hopefully someone will enjoy this half as much as I did the original and a seed will be planted to push this even further. I can’t wait:

Download Orrery Workbook from Tableau Public

If you think my displacement approach is cheating and you’d like to go back to epicycles, or Ptolomey’s epicycles on epicycles (computers have come a long way since then, so this goes 5 epicycles deep), here is a Spiro-graph workbook to use as a starting point. The source dataset has only 2 rows; across 3 blog posts that brings my total to 6 workbooks, none of which has more than 100 rows in the source data. In fact, I believe that makes 106 rows altogether!

Download Spirograph Workbook from Tableau Public

Building Life in Tableau – by Noah Salvaterra

Another guest post from Noah:

DNA can be expressed as a number, if say you wanted to upload yourself to your Tableau public account. Would you fit? There are 4 base pair possibilities, so this could be encoded in 2 bits; so with 8 bits in a byte you could store 4. There are about 3 billion base pairs in human DNA, 3 billion/4 = 750 million bytes or about 750 megabytes. So even in an uncompressed format you would fit well under the current 1GB storage limit of Tableau public! So future generations of data enthusiasts could download you as a packaged workbook, then using their advanced technology they could bring you back.

Hang on a second… can that be right? Windows 8 is at least 10GB, and it sucks; how is it I can be stored in less than 1GB? Well, there is more to you than written in your DNA, and I don’t just mean your experiences. Identical twins have the same DNA, yet they are different people. Our experiences shape who we are, but any parents of twins will tell you it is more than that. Even at birth, twins have different personalities. That could be hard to prove objectively, but they also have different fingerprints. So your DNA doesn’t dictate your fingerprints, at least not entirely. So where do they come from?

Math. DNA is already compressed pretty efficiently. Computing the number of cells in the human body isn’t strait forward but a recent journal article estimates it at 37.2 trillion. Also, that is just a count. Consider all the types of cells, their location, and connections to one another. Now that is a lot of data! Expressing this complexity as a number would be a lot trickier, and likely a lot larger (no, I’m not calling you fat).

Fractals exhibit a property that would be very useful in this type of compression. Fractals are remarkably complex structures that can result from a relatively simple set of instructions. The magic is in the process, not the underlying data, and the process can be expressed very compactly, often in a single equation.

When simple instructions create fractals or life, one byproduct seems to be similarity across different scales. Think of the branching in your blood vessels, or in a tree, smaller and smaller scale but with similar rules. If you cut off a piece of most fractals you end up with a structure that is no less complex than the original. If you zoom in, you can often see a very similar picture to the whole. There are a couple areas of math whose significance didn’t sink in for years after I studied them; fractal geometry is one such example.

Right. So how is this about Tableau? The dashboard below is driven by a dataset with only 2 rows. I guess building life might be a stretch, but it sure does look like it. For more of the details on how I’m pulling this off, as well as some other pretty images in Tableau, have a look at my post Creating data, multi-step recurrence relations, fractals and 3D imaging… without leaving Tableau. Feel free to skip to the pictures, but I do recommend 3D glasses for the last ones.