Custom Tableau Color Palettes

Tableau comes pre-packaged with many useful color palettes. Tableau also allows you to use a custom sequential or custom diverging palette from withing Tableau. Unfortunately the custom option only allows a two color options. But lets say you see a Tableau packages 3-color-palette you like and you want to make a few adjustments to it and make it lighter or darker.  Here is an example of what I did to the temperature palette, I added 2 lighter lights.


The first think you will need to do is of course take a screen-shot of the tableau palette.  You can then paste that into a paint program and use the eyedropper tool.  I suggest that you download a free tool such as Paint.Net.  That program gives you an eye-dropper tool and gives you the color code in hexadecimal (ie #FFFFFF is white).  Unfortunately Microsoft’s pre-loaded paint program doesn’t give you this info.  It can be converted from the link below though if you don’t want to download

Go to  (I have no idea how to pronounce that, I can only assume its similar to the many ‘L’s after someone scores a goal in soccer), choose paste in the hexadecimal (or choose it from the selector).  Collor will give you a wide range of similar colors.  I was interested in the shades/tones section right on top.  I wanted to take the default temperature palette that Tableau provides and lighten it up for a bit.   Another option for finding similar colors and hex numbers is this website   It could be useful in certain circumstances, but it provides far too many options.  So instead the links below will be via colllor.  I used the eyedropper tool and found that Tableau has these 5 colors for that first temperature palette.

I have gone ahead and picked two sets of lighter Tableau Temperature colors:  “Lighter” and “Lightest”.

Navigate to here:  C:\Users\<username>\Documents\My Tableau Repository\  and edit Preferences.tps in a text editor (make a copy in case).   Between the <preferences> tag add in this XML

<color-palette name=”Temperature_Lighter” type=”ordered-diverging”>
<color-palette name=”Temperature_Lightest” type=”ordered-diverging”>

This will give you two additional temperature palettes.



Tableau Zen Master Joe Mako gave me a incredibly simple tip.  This is exactly why the Tableau Zen masters are recognized by Tableau.  They are actively looking in the community and offering their advice.  Joe advised taking the current temperature palette, then clicking on one of the colors, making no changes to the values, and then pressing ‘add to custom palette.’ This changes the xml in the book and reveals the exact colors Tableau uses for their continuous palettes.  I’m not going to pretend that I regularly look over my Tableau books’ xml but it was fairly easy to locate the palette.  Name your sheet something you will remember then search for that name within the XML.  For instance I named mine ‘CustomDiverg’ and found this line immediately:

<worksheet name=’CustomDiverg’>

under the style header, you should be able to find this line, and the 5 colors under it:

<color-palette custom=’true’ name=” type=’ordered-diverging’>



Here is an animated example courtesy of Joe:


US Economic Progress

So I decided to create an Economic index using data from the Federal Reserve.  I decided on these 6 metrics to monitor our economic progress:

1. Civilian Labor Force Participation Rate
2. Compensation of Employees: Wages & Salary Accruals vs. ½ GDP
3. GDP (x2) vs All Total Debts
4. GDP vs Consumer Price Index
5. Current Real Median Household Income in the United States vs Max
6. M2 Velocity: Velocity of Money.

Link to Tableau Workbook:

Full Detailed Explanation:
1. Civilian Labor Force Participation Rate: This is a better measure than unemployment because it captures actual workers vs the rest of the population. Discouraged workers are included in this. Assumed that higher is better.

2. Compensation of Employees: Wages & Salary Accruals vs. ½ GDP: (Private industries): How much of the American Pie is going to private workers? It is related to metric #1 but captures whether workers are being paid better for the work they are doing. Various factors could structurally alter this number. Obviously automation can and will lower this number. However even in the late 90s it peaked to .4.

3. GDP (x2) vs All Total Debts: A board measure of all debts to all income. Gov’t, Student Loan, Mortgage, Credit, Business etc. to 2x GDP. How leveraged are we? The higher this metric the more risk we have. One can take out debt, and improve the other metrics but it exposes the country to much more risk than before. This metric assumes a 2:1 Debt to income level is ideal.

4. GDP vs Consumer Price Index: CPI is a flawed measure, but still somewhat valid. CPI continues to rise, but how does it track in relation to GDP? If the CPI goes up faster than GDP, we are relatively becoming less rich. If level, then we are holding ground. I’d rising slower than GDP then we are getting richer. More is better.

5. Real Median Household Income in the United States vs max: The household is the basic block of America. How is the average household doing relative to the best year? As long as REAL median household income shows continued gains this metric will be better.

6. M2 Velocity: Velocity of Money. Overall this tells how fast money passes from one individual holder to the next. “If the velocity of money is increasing, then transactions are occurring between individuals more frequently.” according to Wikipedia. Hard to define an ideal, but generally assumed that more is better.

Dualing Chartists. Ebola vs Auto Accidents

The first chart comes from an organization called Sightline , which is a Pacific Northwest sustainability blog.  Sustainability roughly translates to ‘don’t use cars’ for the lay person.  As such their POV is against cars and the deaths and pollution they cause.  The author’s point is that we underestimate risk and the coverage of Ebola is way overblown.

Anyways here is their chart


Source:  Sightline:

Now one of the commentators in the above link contributed this chart:


What I love about this is that neither person is wrong.  They are both properly using bar charts with the axis starting at zero.  The first chart author However the 2nd person is missing the point of the author’s article.  I also love how both as backing up their stories with DATA AND CHARTS!  Properly sourced data and properly used charts to boot!  Now the first chart is showing how we improperly treat risk.  We fear an rare event with a high mortality and completely ignore a very frequent event with a very low mortality.  Regardless though I thought this was a great view of dueling charts.

Algorithm influenced human behavior. The possible dangers of mass customization

A friend of mine clued me into this interesting article:   You should read the article but the gist is that a person ‘liked’ everything they saw on Facebook and then their feeds quickly went to a place devoid of actual human posts.  The resulting posts were especially polarized and extreme and driven not by regular people.  Mobile went that way quicker than the desktop version, probably due to less screen real estate and the higher importance of mobile ads.  Much of FB’s content is through mobile.  It was an interesting experiment that the author: Mat Honan conducted.  It led me to think and then write this post.

I’ll go on a tangent here about the nature of humor.  I think other human experiences are similar, though their mechanisms will necessarily differ.  Not lets take all the things that a person thinks is funny. Lets call this a person’s ‘Humorspace.’ Now this space is greatly influenced by what you thought was funny in the past. Your sense of humor develops over time.  Situational humor is funny for many groups of people because many people experience the exact same situations and the comedian uses that to craft something funny.  Something is funny (there are many theories to this) because in general your brain rewards you for strengthening existing-but-weak neural connections.  Something is funny because the incident is not common, but not so far away as to have no connection (excluding any absurdists).  Two people will think something is funny when their brains share similar strengths of connections for the topic.  Not lets take all the things that a person thinks is ‘funny’ and lets call this a person’s Humor-space.  Now this space is greatly influenced by what you thought was funny in the past.  Your sense of humor develops over time.  We can say if two people think something is funny then there is an intersection of their humor-space at that point in time.


Now go to Pinterest and type in humor in the search bar (or just click  Try it via different IP addresses, or the same IP Address but logged in and not.  Are there differences in the content displayed?  I was able to see completely different information when logged in vs not.  Now what happens if you and I experience different content?  Would not our humor-space drift apart over time?  With such a vast amount of content, it’s improbably that we would have the same content.  Are the algorithms that feed us content divergent, convergent, or simply psuedo-random walks?    Now suppose that we discover a way to live forever, a likely way would be some method of uploading our consciousness to a virtual environment.  We will likely still be able to be fed and consume media content in the immortal age.  After decades and decades, or centuries of time, it will probably be inevitable that our experiences will drift farther from other people.

What would happen to two people after many years.  Would their humor-spaces diverge far from each other so that they are no longer similar. Would we be able to make the other person laugh?


Is Live-Event TV growing?  I’ve heard some chatter off and on about the way that networks are combating streaming TV services, like Netflix, Hulu, and Amazon Prime by offering more live-event TV.  (I use the term Live-Event-TV to include live-TV and simple time-shifted TV viewing like on a DVR).   Think about it for it a minute…  What was the last show you routinely watched and then went into the office/school the next day and said “Hey did you see XYZ last night? Pretty awesome/thrilling/funny huh?”  For me it was some of the earlier seasons of ‘The Office’, which ended in 2013.  It seems to me that networks are driving more content to live-events that cannot be streamed later.  This is a way of converging people towards one set of content and it differs.  It seems to me that many people are interested in this as they sense a drift in their experience space from other people they know.  The Superbowl is still a fun event, even though pro-football doesn’t interest me.  I love it because we have parties and everyone is there sharing the same experience.  Our experience-spaces are becoming more similar and we like it.  Growth of live-event-tv will continue as our media space becomes increasingly balkanized.

I use ‘balkanization’ on purpose.  The definition seems to indicate subdivisions with increasing hostility between them.  The article referenced above indicates that content was driven to hostile and incompatible extremes.