DV Posts

As usual, the reading pointers below I borrowed from my Google+ microblogs “Data Visualization” (https://plus.google.com/111053008130113715119/posts , 7090+ followers) and “Data Visualization with Tableau” (https://plus.google.com/112388869729541404591/posts , almost 1000 followers). Again, sometimes the reading is more important then doing or writing.

Map of Scientific collaboration:



MicroStrategy vs. Tableau:


Brain Capacity: http://www.forbes.com/sites/jvchamary/2016/01/28/brain-capacity


Qlikview 12 finally released:



Looker: http://www.looker.com/docs/exploring-data/visualizing-query-results

Amazon QuickSight: https://aws.amazon.com/quicksight/


Engagement: http://www.perceptualedge.com/blog/?p=2197

American Panorama: http://dsl.richmond.edu/panorama/

Statistica 13:


Wolfram Community:


Recreation of Statistical Atlas:



Social Colors: https://www.materialui.co/socialcolors

Pantone’s Language of Color:


Urban Growth:



How many people ever lived:

Stephen Curry: http://fivethirtyeight.com/features/stephen-curry-is-the-revolution/

Free book: http://web.stanford.edu/~hastie/StatLearnSparsity/index.html

Errol Morris: How Typography Shapes Our Perception of Truth



Animation and Visualization:


Visualizing Sentiment and Inclination


Compare JS libraries: http://www.jsgraphs.com/

TabJolt, part 1: http://tableaulove.com/the-mondo-tabjolt-post/

TabJolt, part 2: http://tableaulove.com/the-mondo-tableau-server-tabjolt-series-part-2/

Plus 1000 in 2016:


Correlations in Tableau:


Unions in Tableau:


Mapbox and Tableau:






This is the Part 2 of my post about Tableau’s history. Part1 “Tableau self-intro: 2003-7” was published on this blog earlier. The text below is based on Tableau’s attempt to re-write own history, version by version. What is below is said by Tableau, but interpreted by me. Part 1 “Intro” covers 2003-7 from version 1 to 3, Part 2 (this article) “Catching-up” covers 2008-10 from versions 4 to 6. Recent Q3 of 2015  ($171M revenue) financial results showing that Tableau keeps growing faster than anybody in industry, so interest to its history remaining high among visitors of my blog.

In 2010, Tableau reported revenue of $34M, $62M in 2011 (82% YoY), $128M in 2012 (106% YoY). The company’s 2013 revenue reached $232M, an 81% growth over 2012’s $128M.  2014 revenue exceeded $413M (78% YoY) and in 2015 Tableau expected $650M revenue (57% YoY), more than QLIK:


In Multi-line Chart above (data are from Morningstar, for example: http://financials.morningstar.com/ratios/r.html?t=MSTR) the width of the each line reflects the value of Year-over-Year growth for given company for given year (Tableau is blue, Qliktech is green and Microstrategy is orange; unfortunately Spotfire sales data are not available since 2008, thanks to TIBCO). Here is Tableau’s revenue for last 5 quarters:


Tableau’s success has many factors but in my opinion the 5 main contributors are:

  • In 2007 TIBCO bought Spotfire, making it incapable to lead;
  • Both Spotfire and Qliktech left their R&D in Sweden while scattered other offices in US;
  • Release of free Tableau reader in 2008 – brilliant marketing move;
  • Release of free Tableau Public in 2010 – another brilliant marketing move;
  • Gift from Qliktech in 2011-2015 (more about that in Part 3 or 4 of this blog post).

4.0. 2008. Integrated Maps added: “Data elements such as city, state and country are now automatically recognized as mappable dimensions, and users can also assign geospatial rules to selected dimensions. Once maps are created, users can also change the way data is presented and drill down into the underlying information without a need to understand map layers or complex geographic parameters”.

“Other upgrades in Tableau 4.0 include support for embedding visualizations within Web applications, Web sites and portals such as Microsoft SharePoint. Conversely, Web applications can also be embedded into Tableau”.

In 2008 Tableau released the free Tableau Reader and enables server-less distribution of visualization with full Windows UI experience. “Getting an unlimited free trial into the hands of thousands of people raises awareness among people who are interested in analyzing data, while at the same time training them in its use”. Also see old video here:

5.0. 2009. Tableau enables Views and Dashboards to act Visual Filters, which improves tool’s ability to drill-down data. Such actions can be local and global. Tableau Server now is capable of multi-threading and it can be distributed among multiple hardware boxes or virtual machines, greatly improve scalability and performance.

New Data sources and connectors introduced: Postgres 8.3, Oracle 11g, MySQL 5.1, Vertica v3, Teradata 13, DB2 v9.5 ; Tab, Space, Colon and Pipe delimited flat files, custom geocodes.

5.1. 2010. Added reference lines, bands and distributions, added bullet charts and box-and-whisker charts, expanded set of available pallets, enabled the customization of titles, annotations, tooltips, dashboard sizes,


actions and filters. Tableau 5.1 extended the support for Teradata and Essbase.

Tableau Public. 2010. In its 2nd brilliant marketing move (1st was the release of free Tableau Reader in 2008) the free Tableau Public was released and that instantly made Tableau as the leader in Data Visualization field.

6.0. 2010. The evil Data Blending was introduced in version 6 due an inability of Tableau to join tables from multiple databases and datasources. This architectural bug will be partially fixed in 2016 (Tableau 9.2 or later – it was not clear from TC15 announcement), but real solution can be achieved only when Tableau will implement own internal in-memory DBMS (preferably capable to support columnstore).

Data Engine was introduced as the separate process, which in theory is capable to optimize the creation of Data Extracts and the usage of available RAM as well as take advantage of available disk space so Data Extract can be larger than available RAM. Among new features are improved server management; parameters, which can accept user’s input; suite of table calculations; and drag-and-drop UI for creating Ad-hoc hierarchies.

Below is a screenshot of my drill-down dashboard I did originally in Qlikview and then redid in Tableau 6 to prove that Tableau can do as much drill-down as Qlikview can (using Tableau’s Dashboard Actions):


Image above has an interesting “story”: since it was published on this blog more than 4 years ago it was copy-pasted (in many cases “stolen” without credit to me!) and used as the showcase for Tableau by many blogposts, articles and other publications and “authors”, who disrespect the elementary quoting/crediting rules since internet allows copy/paste operations and leaving up to those people to be polite or disrespectful.

The indirect prove of the brilliancy of Tableau’s marketing moves (free Tableau Reader and free Tableau Public) in 2008-2010 is the volume of the internet searches (thanks to Freakalytics.com) for Tableau and its 6 nearest competitors in 2009-14:


In follow-up I am planning the Part 3: Tableau competes, 2011-13 and Part 4: Tableau the leader, 2013-15.

I was accused by many that I like Tableau too much. That is wrong: in fact I love Tableau but I will try to show below that love can be “objective”. Tremendous success of TC15 (with 10000+ attendees, unmatched by any competitor; 1st conference in 2008 attracted only 187 people) convinced me to return to my blog to write about Tableau’s history – it is interesting how it came to be.


Tableau was spun out of Stanford in 2003, from project Polaris, led by professor Pat Hanrahan and Chris Stolte. It was originated at Stanford as a government-sponsored (DoD) research project to investigate new ways for users to interact (including VizQL) with relational and OLAP databases. In 2004 Tableau got $5M from VCs. In 2005, Hyperion (now Oracle owns Hyperion) began to offer a Tableau under the name “Hyperion Visual Explorer“.

By end of 2010 Tableau had 4 products: Tableau Desktop ($1999 for Pro edition), Tableau Server ($10000 for 10 users), Tableau (free) Reader and Tableau (free web service) Public. In 2010 Tableau had about $34M revenue and was one of the fastest growing software companies in the world (123% YoY). Even in Q3 of 2015 Tableau’s revenue was $171M, 64% up from Q3 of 2014 and it was twice more than entire Tableau’s revenue over period of 2003-10. Overall for last 5 years Tableau had explosive (and unsustainable by industry standards) 75% or above growth; that YoY revenue growth (and Tableau expects $650M for entire 2015) presented in bar chart below:


The text below is based on recent Tableau’s attempt to re-write own history, version by version. Also I reused some posts from this blog – I already covered in my blog versions 6.0 (in 2010) and then 6.1, 7.0, 8.0, 8.0 Desktop, 8.0 server8.1, 8.2, Tableau Online, Tableau Reader, Tableau Public.

I will follow this pattern with one exception (and I promise to avoid the marketing BS like “revolutionary innovation”). I will start with something which is still is not here yet at the end of 2015. Noted by me before: No MDI, no re-sharing of workbook infrastructure with other workbooks, no internal DB (ugly data blending instead), no in-memory columnstore, wrong approach to partners etc.

What is below is said by Tableau version by version, but interpreted by me (my blog, my opinions, my interpretation). Part 1 “Intro” covers 2004-7 from version 1 to 3, Part 2 “Catching-up” covers 2008-10 from versions 4 to 6, Part 3 “Competition” covers 2011-13 from version 6 to 8 and Part 4 “Leading the field” covers 2013-15 from version 8.1 to 9.1, including Tableau Online.

1.0. 2004.

Introduction of VizQL allowed less coding (but required a lot of drag-drops, clicks, resizing and other gymnastics with mouse, which seems more acceptable to wider population – Tableau insists on “anyone, anywhere”). Tableau 1.0 can access to Access, Excel, Microsoft Analysis Services Cubes!), MySQL, SQL Server 2000. Data from multiple tables have to be denormalized (this proved overtime to be the weakest part of the tool) into one table before importing into Tableau.

I am not sure why even in 2015 the Tableau insists on its own self-assessment that it works as fast as you can think – that is offensive to thinkers.


Tableau 1.0 was available in three editions. The $999 Standard Edition can connect to Microsoft Excel, Microsoft Access, or plain text files. The $1299 Professional (MySQL) edition adds MySQL to the list of supported data sources, while the $1799 Professional edition extends the list to include Microsoft SQL Server and SQL Server Analysis Services.

2.0. 2006.

Tableau 2.0 added the ability to join tables in the same database. Added the ability to create Data Extracts and work offline without live connection to data. New features: Distinct Counts and Median aggregations, new “Size” shelf (marks will be sized proportionally to the value of the field in that shelf), new “Page” shelf (useful for animations, see example of it I did a while ago):

Here the content of this video as the presentation with 24 Slides:

Tableau 2.0 also added optional trend and reference lines, calculated fields (can be used with formulas, with all functions and with custom SQL and MDX expressions). 3 Screenshots below preserved for us by Stephen Few in his reviews of Tableau 2.0 and 3.0.


Tableau 2.0 is priced at $995 for the standard edition and $1,799 for the professional edition, including one year of software maintenance and unlimited technical support.

3.0. 2007.

Tableau Server introduced so people can see visualizations through browser over intranet or internet. When visualization application is published from Windows Desktop to Tableau Server (which is in fact, application server), it will be converted to web application: no downloads, plugins or coding required and all related data-sources will be published on that server.

Among other new features: new Sorting “shortcuts”,


as well as Ad-hoc grouping, Auto-calculated reference lines, annotations and most importantly, dashboards with global filters. Tableau missed the opportunity to introduce the MDI into multi-view dashboards and this design bug persisted even now in 2015 – tool still using non-MDI containers (panels) instead of MDI child-windows for each chart. Another problem (in Tableau 3.0) was that views in dashboard updated sequentially and not in-parallel.


By 2007 Tableau employed just 50 people but it was just a beginning:


In 2007 the Tableau Software company got lucky, because TIBCO bought Spotfire that year and it greatly restricted the ability of Spotfire to lead Data Visualization field. Another luck for Tableau was a strategic mistake by both Qliktech and Spotfire to leave development teams in Sweden while placing their HQs, sales, marketing etc. elsewhere in multiple US locations. Tableau got lucky one more time later thanks to gift from Qliktech but I will discuss it later in Part 3 or 4 of this blog-post. As mentioned above, I am planning the Part 2 of this post: Tableau is catching-up, 2008-10, then Part 3: Tableau competes, 2011-13 and finally the Part 4: Tableau the leader, 2013-15

Reading pointers below I borrowed from my Google+ microblogs “Data Visualization” (https://plus.google.com/111053008130113715119/posts , 7000+ followers) and “Data Visualization with Tableau” (https://plus.google.com/112388869729541404591/posts , almost 1000 followers). Sometimes the reading is more important then doing or writing. The reading on the beach (like below) can be even more…


  1. How Scalable Do Analytics Solutions Need to Be? http://www.perceptualedge.com/blog/?p=2097
  2. The Data Visualization Catalogue. http://blog.visual.ly/the-data-visualization-catalogue and http://datavizcatalogue.com/
  3. The Evolution of SQL Server BI, https://www.simple-talk.com/sql/reporting-services/the-evolution-of-sql-server-bi/
  4. Abela’s Folly – A Thought Confuser. http://www.perceptualedge.com/blog/?p=2080
  5. TIBCO Spotfire Promotes an Insidious Myth. http://www.perceptualedge.com/blog/?p=2035
  6. User Ideas Turned into Product Features: https://www.tableau.com/about/blog/2015/8/community-contributes-again-ideas-released-91-41812
  7. Is Data Is, or Is Data Ain’t, a Plural? http://blogs.wsj.com/economics/2012/07/05/is-data-is-or-is-data-aint-a-plural and http://www.theguardian.com/news/datablog/2010/jul/16/data-plural-singular?es_p=662983
  8. Talk: How to Visualize Data, https://eagereyes.org/talk/talk-how-to-visualize-data#more-8879
  9. Pillars Of Mapping Data To Visualizations, http://global.qlik.com/us/blog/authors/patrik-lundblad
  10. Radar Chart can be useful(?), https://www.tableau.com/about/blog/2015/7/use-radar-charts-compare-dimensions-over-several-metrics-41592
  11. Visualization Publication Data Collection, http://www.vispubdata.org/site/vispubdata/
  12. Visual Representation of SQL Joins, http://www.codeproject.com/Articles/33052/Visual-Representation-of-SQL-Joins and http://www.theinformationlab.co.uk/2015/02/05/joining-data-tables-tableau-alteryx/


  1. Example of stupidity of the crowd: http://about.g2crowd.com/press-release/best-business-intelligence-platforms-summer-2015/
  2. Reviving the Statistical Atlas of the United States with New Data, http://flowingdata.com/2015/06/16/reviving-the-statistical-atlas-of-the-united-states-with-new-data/
  3. Exploring the 7 Different Types of Data Stories: http://mediashift.org/2015/06/exploring-the-7-different-types-of-data-stories
  4. Set Your Own Style with Style Templates: http://www.tableau.com/about/blog/2015/6/saving-time-style-templates-39932
  1. A Look at Choropleth Maps , http://visualoop.com/blog/84485/a-look-at-choropleth-maps
  2. Mountain Chart for different categories (profiles) of web visits: https://public.tableau.com/profile/andrei5435#!/vizhome/MountainChart/MountainChart


  1. To the point: 7 reasons you should use dot graphs, http://www.maartenlambrechts.be/to-the-point-7-reasons-you-should-use-dot-graphs/
  2. Rant: A Tableau Faithful’s View On Qlik , http://curtisharris.weebly.com/blog/rant-a-tableau-faithfuls-view-on-qlik
  3. Too Big Data: Coping with Overplotting, http://www.infragistics.com/community/blogs/tim_brock/archive/2015/04/21/too-big-data-coping-with-overplotting.aspx
  4. Too much data to visualize? Data densification in Tableau 9 , https://www.linkedin.com/pulse/too-much-data-visualize-densification-tableau-9-kris-erickson
  5. The Architecture of a Data Visualization, https://medium.com/accurat-studio/the-architecture-of-a-data-visualization-470b807799b4 , also see https://medium.com/@hint_fm/design-and-redesign-4ab77206cf9
  6. Filter Views using URL Parameters , http://kb.tableau.com/articles/knowledgebase/view-filters-url


  1. Building a Visualization of Transit System Data Using GTFS , http://datablick.com/2015/05/05/building-a-visualization-of-transit-system-data-using-gtfs/
  2. A Look At Box Plots , http://visualoop.com/blog/32470/a-look-at-box-plots
  3. Custom Tableau Server Admin Views , http://ugamarkj.blogspot.com/2014/08/custom-tableau-server-admin-views.html
  4. Circular and Hive Plot Network Graphing in Tableau , http://datablick.com/2015/04/13/circular-and-hive-plot-network-graphing-in-tableau-by-chris-demartini/
  5. Hexbins in Tableau , http://www.theinformationlab.co.uk/2015/05/12/hexbins-in-tableau/
  6. Tableau Public Goes Premium for Everyone; Expands Access to 10 Million Rows of Data , http://investors.tableau.com/investor-news/investor-news-details/2015/Tableau-Public-Goes-Premium-for-Everyone-Expands-Access-to-10-Million-Rows-of-Data/default.aspx


2 year ago the IPO instantly created almost $3B of market capitalization for Tableau Software Inc. and since then it almost tripled, making Tableau the most “valuable” Data Visualization company (click on image to enlarge):


Tableau more then doubled the number of its Full-Time employees (almost 2200 now, roughly the same (or more?) as QLIK has) and more then doubled its Revenue (again, roughly the same as QLIK has). Tableau’s YoY growth still in range of 77%-100% per year, which is far, far more then any competition:


Combination of that growth with technological progress and new features of Tableau’s products led to huge growth of its share price – it reached in 1st week of June 2015 $115, while Qlik’s share price is hovering around $37 or even below (click on image to enlarge):


Visitors to this blog kept asking me of what is most impressive (for me) about Tableau and what are my concerns. I will list just 3 of each:

  • most impressive: YoY (Year-over-Year growth ratio); migration to 64-bit (finally) and performance improvements; and  increasing capacity of Tableau Public to 10 million rows and 10 GB storage.
  • concerns: rumors that price of Tableau Server will be increased (I heard doubled; that can slow down the growth and the popularity of Tableau); moving CEO to Europe away from HQ (repeating of mistake of Spotfire and Qliktech, who had/have R&D in Europe – away from american HQ);  and limited capacity of Tableau Online (basically it can be good only for small workgroup).

Not all of its huge success can be contributed to Tableau itself:

QLIK for example did not release Qlikview version 12 for last 4 years (but kept updating the last version, recently with release 11 (!) of Qlikview version 11.2). Another help Tableau got from TIBCO, who kept Spotfire inside strict corporate cage and went private with little change for Spotfire to be a spin-off. As a result, competition for Tableau during last 2 years was weaker then before its IPO and we are witnessing a massive migration to Tableau from competitive products.

Don’t assume that Tableau is slowing down: I visualized (using Tableau Public of course, see it here: https://public.tableau.com/profile/andrei5435#!/vizhome/Data2Months/TableausMarketCap ) the Tableau’s Market capitalization during last 52 business days and it keeps growing at least as fast as last 2 years:

Tableau's Market Cap

Update 6/7/15: finally, just check the number of Job Openings @Tableau – 344 (as of today 6/7/15), @QLIK – 116 (3 times less then Tableau!), and only 1 (ONE!) opening for Spotfire… If you still think that Microstrategy can compete with Tableau, then please keep this in mind: as of today Microstrategy’s total number of Job Openings is … 50.



My best wishes in 2015 to visitors of this Data Visualization blog!

2014 was very unusual for Data Visualization Community. Most important event was the huge change in market competition where Tableau was a clear winner, QLIK lost it leadership position and Spotfire is slowly declining as TIBCO went private. Pleasant surprise was Microsoft, who is finally trying to package Power BI separately from Office. In addition other competitors like Microstrategy, Panorama and Datawatch were unable to gain bigger share in Data Visualization market.

2014 again was the year of Tableau: market capitalization exceeded $6B, YoY growth was highest again, sales approaching $0.5B/year, number of employees almost the same as @QLIK, LinkedIn index exceeded 90000, number of Job Openings increased again and as of today it is 337! I personally stopped comparing Data Visualization products for last few months, since Tableau is a clear winner overall and it will be difficult for others to catch-up unless Tableau will start making mistakes like QLIK and Spotfire/TIBCO did during last few years.

2014 was very confusing for many members of QLIK community, me included. Qlik.Next project resulted in new Qlik Sense Product (I don’t see too much success for it) and Qlikview 12 is still not released, while prices for both QLIK products are not public anymore. Market Capitalization of QLIK is below $3B despite still solids sales (Over $0.5B/year) and YoY growth is way below of Tableau’s YoY. Qlikview’s LinkedIn index now around 60000 (way below Tableau’s) and Qlik Sense’s LinkedIn index is only 286…  QLIK has only 124 Job opening as of today, almost 3 times less then Tableau!

Curiously, BI Guru Mr. Donald Farmer, who joined QLIK 4 years ago (a few months before the release of Qlikview 11) and who was the largest propagandist of Qlik.Next/Qlik Sense, was moved from VP of Product Management position to new “VP of Innovation” @QLIK just before the release of Qlik Sense and we hear much less from Donald now. Sadly, during these 4 years Qlikview 12 was never released, and QLIK never released anything similar to free Tableau Reader, free Tableau Public and Tableau Online (I am still hoping for Qlikview in Cloud) and all Qlikview prices were unpublished…

As a member of Spotfire community, I was sad to see the failure of Spotfire (and its parent TIBCO) to survive as public company: on December 5, Vista Equity Partners completed the acquisition of TIBX for $4.3 billion. I estimate Spotfire sales around $200M/year (assuming it is 20% of TIBCO sales). LinkedIn index of Spotfire (is way below Tableau’s and Qlikview’s) is around 12000 and number of Job Openings is too small. I hope Vista Equity Partners will spinoff the Spotfire in IPO as soon as possible and move all Spotfire’s Development, Support, Marketing and Sales into one American location, preferably somewhere in Massachusetts (e.g. back to Somerville).

Here is a farewell Line Chart (bottom of Image) to TIBX symbol, which was stopped trading 3 weeks ago (compared to DATA and QLIK Time Series (upper and middle Line Charts) for entire 2014):


While on Cape Cod this summer and when away from beach, I enjoyed some work-unrelated fun with Tableau. My beach reading included this article: http://www.theinformationlab.co.uk/2014/03/27/radar-charts-tableau-part-3/ by Andrew Ball and I decided to create my own Radar. When I show it to coworkers later, they suggested to me to publish it (at least the fun with Polygons, Path and Radars) on my blog. I may reuse this Radar chart for our internal Web Analytics. CloudsOverAtlantic

Natural Order of Points and Segments in Line.

Many visualization tools will draw the line chart, its datapoints and connecting line segments between datapoints in natural progressing order – repainting them from left to right (horizontal ordering by Axis X) or from bottom to upside
(vertical ordering by Axis Y) or vice versa.

Path as the method to break the Natural Order.

Some demanding visualizations and users wish to break the natural repainting and drawing order and Tableau allows to do that by using the Path as the method to order the datapoints and line segments in Lines and Polygons. A Collection of increasing Ordering Numbers (Pathpoints) for each Datapoint in Line defined a Path for drawing and connecting datapoints and segments of that Line (or Polygon). Each Pathpoint can be predefined or calculated, depends on mplementation and business logic.
Changing the Natural Order can create “artificial” and unusual situations, when two or more datapoints occupying the same pixels on drawing surface but have very different Pathpoints (example can be a Polygon, when Line ends in the same point it starts) or when two or more Line Segments intersecting in the same Pixel on screen (example can be the Center of the letter X ).


Radar Chart has 2 parts: Radar Grid (background) and Radar Polygons (showing repetitive Data Patterns, if linear timeline can be collapsed into circular “timeline”). Radar Grid has Radials (with common Center) and Concentric Rings.
Polygons optionally can be filled with (transparent) color. For future Discussion let’s use the RMax as the maximal possible distance between the Center of Radar Grid (in case of Radar Grid) or the Center of Radar Polygon (in case of Radar
Polygon) and the most remote Datapoint shown in Radar Grid or Polygon respectively. We will use the “normalized” statistics of Visits to typical Website to visualize the hourly and daily (by day of the week) patterns of Web Visitations. By
normalization we mean the removal of insignificant deviations from “normal” hourly and daily amounts of Web Visits. For complete obfuscation we will assume for Demo purposes that RMax = 144.

Radar Radial Grid.

Radial Grid contains a few Radiuses (equidistant from each other) and we will draw each Radius as 3-point line where Starting and Ending points of each line are identical to each other and collocated with the Center of Radar. For Demo Web Visitation Radar we will use Radial Grid with 8 Radiuses, corresponding to the following hours of the complete 24-hours day: 0, 3, 6, 9, 12, 15, 18, 21:
For example see the Radius, corresponding to HOUR = 3 (below in light brown, other Radiuses greyed out on that image):
And for that Radius we are using (redundantly) the following 3 datapoints:

Concentric Rings for Radar Grid.

For Demo Radar we will use 4 Concentric Rings, corresponding to 25%, 50%, 75% and 100% levels of maximum visitation per hour:
Each ring is a line with 25 datapoints, where Starting and Ending Points collocated/equal. For example, dataset for external Ring (red line above) looks like this:
When Radials and Concentric Rings collocated and overlaid they represent the Radar Grid, ready to be a background for Radar Chart:

Radar Polygons.

For Demo purposes we use only 2 Polygons – one (largest) representing average Hourly Visits during Weekday and 2nd Polygon representing average Hourly Visits during Weekend day. For Websites which I observed the minimum number of visits happened around 1 AM, so you will see both Polygons are slightly rotated clockwise and slightly shifted up from the Center of Radar Grid to reflect the fact that the minimum number of visitors (even around 1 AM) is slightly more then 0. Each Radar Polygon (in our Demo) has 25 Data Points with Starting and Ending Points collocated at 1AM. Here is a Weekday Polygon, overlaid with Radar Grid:
Here are the data for Weekday Polygon: 


Here is a Polygon for Weekend day, overlaid with Radar Grid:


Radar Chart.

When Radar Grid and Radar Polygons overlaid (Polygons transparent but on top of
Grid) we will get the Radar Chart. Please note that Centers of Radar Grid and Radar
Polygons can have different locations:



I published Tableau workbook with this Demo Radar Chart and Radar Data here: 


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