Tableau Dynamic Maps with Parameters: A COVID Dashboard Breakdown

Operation “Reverse Engineer” a Tableau Zen Master dashboard is back in full effect. You know the drill by now, I spent weekend hours analyzing an impressive dashboard put together by Tableau Zen Masters Anya A’Hearn, Tamas Foldi, Allan Walker, and Jonathan Drummey.

In this video I will demonstrate to you how they use parameters to dynamically change the measure that is displayed on both a map and bar chart. Accurate data is made possible through the use of a context filter to equalize the data that is displayed between the United States and all other countries (U.S. data lags by one day).

I should mention that we are using the carefully curated data offered at the Tableau’s COVID-19 Data Hub.

What’s in it for You?

You will learn a neat little trick that encapsulates multiple measures into one calculated field. By using two parameters we can update our visuals to display the correct measure based upon user selected options. This even applies to the size of our marks on a map. You have to love the dynamic nature of Tableau!

In order to understand how we work with the current Tableau COVID-19 data file, you should watch the first video as a prerequisite.

Also Make Sure to Watch this Additional Video Series

Make sure to also check out this extremely useful tutorial on building a COVID-19 Dashboard from scratch. It’s perfect for your first Tableau project with step by step instruction.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Do Great Things With Your Data

-Anthony B. Smoak

Build Advanced Tableau KPIs: A COVID-19 Dashboard Breakdown

You want to build an advanced Zen Master level KPI BAN using Tableau’s latest COVID-19 data? Well you’re in luck as I spent a lot of weekend hours analyzing an impressive dashboard put together by Tableau Zen Masters Anya A’Hearn, Tamas Foldi, Allan Walker, and Jonathan Drummey.

Specifically I was intrigued how they put together the KPI BAN from the dashboard below that highlights either NEW or CUMULATIVE Positive cases and the percentage difference from the previous day.

Official Tableau COVID Tracker

The official Tableau COVID-19 tracker database can be found here.

In breaking down their approach I renamed some calculations to better help me organize and understand how they come together to create the KPI.

What’s in it for You?

From a learning standpoint, there is a good mix of parameters, filters, context filters and Level of Detail (LOD) calculations that work in concert to deliver the desired outcome.

In the video you’ll learn how I simplified some of the back-end aspects to be a tad more approachable for beginner to moderate Tableau learners. Of course if you want to see the whole dashboard in context with the original back-end naming conventions and layout you can go download the official workbook and deconstruct it for yourself.

It’s all about learning! I encourage you to make use of workbooks that others have shared for bettering yourself and appreciating skills that are at the next level. Of course, always cite your sources and inspirations!!

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Make Sure to Watch this Additional Video Series

Make sure to also check out this extremely useful tutorial on building a COVID-19 Dashboard from scratch. It’s perfect for your first Tableau project with step by step instruction.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Do Great Things With Your Data

– Anthony B. Smoak

Learn the New Tableau Set Control (Workout Wednesday 2020 Week 20 Solution)

Tableau 2020.2 introduced a handy new feature called set control. According to Tableau:

“The set control allows users to dynamically change the members of a set using a familiar, quick filter-like interface. End users can change set membership with both a single and multi-select dropdown, and the set control automatically refreshes its domain so that the data stays fresh.”

As with all new features I had to figure out what this new capability entailed and how best to learn it. Luckily, the hardworking crew over at Workout Wednesday had the perfect challenge.

Sean Miller (@HipsterVizNinja) created a dashboard that enables the user to select a US state, which then adds that state to a set. Three proportional bar charts update at the top of the viz. A right side bar area displays all of the selected states and selecting a state will remove the state from the set, side bar and the map.

Observe the following gif from my solution:

WOW 2020 Wk 20 GIF

  1. Take a look at the challenge here.
  2. Attempt to re-create the dashboard.
  3. If you give up, (or after you complete your solution), take a look at how I approached the dashboard in this solution video, or take a look at the Tableau Public interactive version here.
  4. Don’t just recopy the steps from this solution and post a viz to LinkedIn and/or Tableau Public. You’re better than that, but if you do, make sure to definitely credit Sean Miller and optionally credit me if you used my approach.

Remember, this is just my approach, there are multiple ways to solve any problem.

This was the first Workout Wednesday challenge that I’ve ever done and I’m sure I’ll tackle a few more.

Do some great things with your data!

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Anthony B. Smoak

Build a Tableau COVID-19 Dashboard

I hope everyone is safe and staying indoors during this challenging time. Like most of you, I find myself with an abundance of weekend time to spend indoors. I’ve used some of this time crafting a dashboard series leveraging the outstanding COVID-19 data hub provided by Tableau.

I did not expect the series to be as popular as it turned out to be, but it is one of my most viewed lessons on YouTube!

Tableau COVID Dashboard GIF

In this set of videos you will learn how to use Tableau and the Johns Hopkins data set which tracks COVID-19 cases across the globe, to assemble a dashboard. The great part about this dashboard is that it can be put together without reliance on overly complex calculations or the need to be a graphic designer, and it looks amazing if I do say so myself.

This dashboard utilizes the Tableau pages functionality to enable animation; as dates change the dashboard updates to reflect the current number of confirmed cases and deaths at that point in time.

Another cool trick is the use of containers to swap visualizations on the same dashboard. I use this functionality to switch between a linear and logarithmic scale for confirmed cases and deaths. You will need at least Tableau 2019.2 to use the sheet swapping functionality.

The first video provides an overview of the Tableau data-set and touches upon the visualizations required to build out the dashboard.


By popular demand, the second video goes more in-depth on the formatting and color scheme of each of the visualizations.


In my opinion the best part of the series is the 3rd video. I spend a full 93 minutes demonstrating various topics on dashboard refinement.

  1. Eliminating the hard-coding and manual sorts using a level of detail calculated field
  2. Detailed formatting with containers (applicable to all dashboards)
  3. Tableau sheet swapping using containers
  4. Making a Tableau Data Connection


When you get through with the first three videos you can opt for bonus material that teaches you how to implement a “bar chart race” aspect to the countries.

Instead of the same countries remaining static, they will move up and down depending upon the number of cases or deaths associated with a particular date.

Tableau COVID Dashboard Pt4 Gif Proj

Learn the Tableau “bar chart race” effect in Part 4 here:



Feel free to interact with the original viz or the Bar Chart Race version on Tableau public:

As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

All views and opinions are solely my own and do NOT necessarily reflect those my employer.

Tableau Dashboard Tutorial: Dot Strip Plot

In this video tutorial I describe a dashboard that I put together that displays the distribution of various NBA player statistics. I use the always handy parameter to enable the user to choose which statistics are displayed on the dashboard. Although I’m showing sports statistics measures in this dashboard, it could easily be repurposed to show the distribution of a variety of business related metrics.

I break the dashboard up into three areas: histogram, dot strip plot, and heat map. In the second part of the video, I describe in detail how to build out a jittered dot strip plot. The benefit of the jittered dot strip plot is that the marks representing NBA players obstruct each other much less as compared to the linear dot strip plot.

Techniques used in the dashboard were previous outlined in my Ultimate Slope Graph and How to Use Jittering in Tableau (Scattered Data Points) posts.

Feel free to head to my Tableau Public page and download the workbook for yourself. Drop me a line in the comments or on YouTube if you learned something.

As always, do great things with your data!

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Create Rounded Bar Charts in Tableau

Part 1: How to Make Rounded Bar Charts in Tableau

In this post you’re getting two videos for the price of one (considering they’re all free for now, that’s a good thing). I put together a relatively simple dashboard to help illustrate a few intermediate level concepts. In this first video I take a look at the number of total assists by NBA players during the 2017-2018 season. In case you were wondering, Russell Westbrook led the league in assists during that season. If you don’t know who Russell Westbrook is, then skip this Tableau stuff and watch the last video immediately (and then come back to the Tableau stuff).

In the first Tableau dashboard video, you’ll learn two concepts:

  • How to make rounded bar charts;
  • How to filter the number of bar chart marks via use of a parameter;

Part 2: Apply Custom Sorting in Tableau

In the second video I build upon the dashboard built in the first video by showing you how to add a custom sort. The custom sort relies upon the creation of a parameter and a calculated field. The parameter and calculated field enable the user to select either a dimension (e.g., Player Name) or a measure (e.g., sum of assists) from a drop down box and the visualization will sort ascending or descending as requested.

The calculated field relies upon the RANK_UNIQUE function.

In this context, RANK_UNIQUE returns the unique rank of each player’s assist total. The key with RANK_UNIQUE is that identical values are assigned different ranks. As an example, the set of values (6, 9, 9, 14) would be ranked (4, 2, 3, 1), as no tied rankings are allowed.

Part 3: Interact with the Dashboard

Bonus: Russell Westbrook on the Attack

For those of you who do not know who Russell Westbrook is, I’ve got you covered. These aren’t assists but in these situations, he didn’t need to pass!


Thanks to both the Tableau Magic blog for outlining the concept of rounded bar charts and the VizJockey blog for the custom sort methodology. Check out and support these  blogs!

As always, do great things with your data!

If you find this type of instruction valuable make sure to subscribe to my Youtube channel.

Row and Column Highlighting in Tableau

In this post you’ll learn how to highlight values in your Tableau table using set actions. The dashboard in this video displays the number of total points scored by NBA teams by position in the 2017-2018 season. I will give you step by step instructions on how to implement row and column highlighting on this dataset downloaded from

I’ve only made a few minor tweaks but this technique was developed by Tableau Zen Master Matt Chambers. You can check out his blog at and follow him at Big shout out to Matt for sharing this technique with the Tableau community!

You can interact with my visualization on Tableau Public:

If you find this type of instruction valuable make sure to subscribe to my Youtube channel!

My Submission to the University of Illinois at Urbana-Champaign’s Data Visualization Class

I’m a huge fan of MOOCs (Massive Open Online Courses). I am always on the hunt for something new to learn to increase my knowledge and productivity; and because I run a blog, MOOCs provide fodder for me to share what I learn.

I recently took the Data Visualization class offered by the University of Illinois at Urbana-Champaign on Coursera. The class is offered as part of the Data Mining specialty of six courses that when taken together can lead to graduate credit in its online Master of Computer Science Degree in Data Science.

Ok enough with the brochure items. For the first assignment I constructed a visualization based upon temperature information from NASA’s Goddard Institute for Space Studies (GISS).

Data Definition:

In order to understand the data, you have to understand why temperature anomalies are used as opposed to raw absolute temperature measurements. It is important to note that the temperatures shown in my visualization are not absolute temperatures but rather temperature anomalies.

Basic Terminology

Here’s an explanation from NOAA:

“In climate change studies, temperature anomalies are more important than absolute temperature. A temperature anomaly is the difference from an average, or baseline, temperature. The baseline temperature is typically computed by averaging 30 or more years of temperature data. A positive anomaly indicates the observed temperature was warmer than the baseline, while a negative anomaly indicates the observed temperature was cooler than the baseline.”

Interpreting the Visualization

The course leaves it up to the learner to decide which visualization tool to use in order to display the temperature change information. Although I have experience with multiple visualization programs like Qlikview and Power BI, Tableau is my tool of choice. I didn’t just create a static visualization, I created an interactive dashboard that you can reference by clicking below.

From a data perspective, I believe the numbers in the file that the course provides is a bit different than the one I am linked to here but you can see the format of the data that needs to be pivoted in order to make an appropriate line graph.

All of the data in this set illustrates that temperature anomalies are increasing from the corresponding 1951-1980 mean temperatures as years progress. Every line graph of readings from meteorological stations shows an upward trend in temperature deviation readings. The distribution bins illustrate that the higher temperature deviations occur in more recent years. The recency of years is indicated by the intensity of the color red.

Let’s break down the visualization:

UIUC Top Portion

Top Section Distribution Charts:

  • There are three sub-sections representing global, northern hemisphere and southern hemisphere temperature deviations
  • The x axis represents temperature deviations in bins of 10 degrees
  • The y axis is a count of the number of years that fall between the binned temperature ranges
    • For example, if 10 years have a recorded temperature anomaly between 60 and 69 degrees, then the x axis would be 60 and the y axis would be 10

UIUC Distribution Focus.png

  • Each 10 degree bin is comprised of the various years that correspond to a respective temperature anomaly range
    • For example in the picture above, the year 1880 (as designated by the tooltip) had a temperature anomaly that was 19 degrees lower than the 30 year average. This is why the corresponding box for the year 1880 is not intensely colored.
    • Additionally, the -19 degree anomaly is located in the -10 degree bin (which contains anomalies from -10 to -19 degrees)
    • These aspects are more clearly illustrated when interacting with the Tableau Public dashboard
  • The intensity of the color of red indicates the recency of the year; for example year 1880 would be represented as white while year 2014 would be indicated by a deep red color

Bottom Section Line Graph Chart:

UIUC Bottom Portion

  • The y axis represents the temperature deviation from the corresponding 1951-1980 mean temperatures
  • Each line represents the temperature deviation at a specific geographic location during the 1880-2014 period
  • The x axis represents the year of the temperature reading

UIUC Gobal Average

In the above picture I strip out the majority of lines leaving only the global deviation line. Climate science deniers may want to look away as the data clearly shows that global temperatures are rising.

Bottom Line:

All in all I thought it was a decent class covering very theoretical issues regarding data visualization. Practicality is exclusively covered in the exercises as the class does not provide any instruction on how to use any of the tools required to complete the class. I understand the reason as this is not a “How to Use a Software Tool” class.

I’d define the exercises as “BYOE” (i.e., bring your own expertise). The class forces you to do your own research in regards to visualization tool instruction. This is especially true regarding the second exercise which requires you to learn how to visualize graphs and nodes. I had to learn how to use a program called Gephi in order to produce a network map of the cities in my favorite board game named Pandemic. The lines between the city nodes are the paths that one can travel within the game.

UIUC Data Viz Week 3

If you’re looking for more practicality and data visualization best practices as opposed to hardcore computer science topics take a look at the Coursera specialization from UC Davis called “Visualization with Tableau”.

In case you were wondering I received at 96% grade in the UIUC course.

My final rating for the class is 3 stars out 5; worth a look.

Ranking Banks by Number of Complaints

I recently downloaded a dataset from the Consumer Finance Protection Bureau (CFPB) in order to construct a handy visualization. The CFPB maintains a database that houses a collection of complaints on a range of consumer financial products and services that are sent to companies for a response.

Per the CFPB, “the database also includes information about the actions taken by the company in response to the complaint, such as, whether the company’s response was timely and how the company responded.”

Although the database is updated daily, I chose to visualize information from the complete year of 2017. In fairness to the financial institutions, company level information should be considered in context of company size and/or market share.

Financial institutions analyze this information frequently as a way of understanding and continuously improving their customer service.

I highly recommend “The Big Book of Dashboards” by Jeffrey Shaffer, Andy Cotgreave and Steve Wexler. The book contains a number of visualization examples that provide guidance on dashboard creation for any number of business use cases. In this Tableau Public dashboard I relied heavily on the visual guidance for their Complaints Dashboard as you can observe.

Screen Shot 2018-06-03 at 10.02.14 PM

Complaints Dashboard from “The Big Book of Dashboards”

Click on the picture link to view the dashboard on Tableau Public (not optimized for mobile).

Dashboard 1

If you’re interested in Business Intelligence & Tableau subscribe and check out my videos either here on this site or on my Youtube channel.

Coursera Review: Creating Dashboards and Storytelling with Tableau

Discounts Harm Profits

I recently finished the “Creating Dashboards and Storytelling with Tableau” course on Coursera. The course was taught by adjunct faculty at the University of California Davis. Although it is the fourth course of five in the “Data Visualization with Tableau” specialization, it is only the third course that I have taken. I skipped the very basic first course and will concentrate next on finishing the capstone. 

If you do take this course be prepared to put in a fair amount of work on weeks three and four when the dashboard and story project are respectively due. I put in at least five hours of effort on each individual assignment not including watching videos, reading materials and taking quizzes.

I found the storytelling course to be informative and worthwhile. Unlike a Udemy course on Tableau that wades right into the applied aspects of clicking and dragging items, Coursera courses offer more of an academic background on the subject matter.

The point of this course is to hammer home that stories provide context and meaning that can’t be matched by a list of facts. We’re informed that stories engage more of your brain than simply absorbing a list of facts.

We learn that you should always try to make your stories relatable to the viewer so that they personally connect or identify with some aspect of the story. You should find a specific story of a person who exemplifies the larger narrative rather than starting with a lot of general facts and figures.

Politicians employ this tactic all of the time. Instead of spouting off a list of facts about their particular issue, the politician will first paint a picture regarding Joe the small businessman or Jill the single mom. They’ll then discuss how legislation (or lack thereof) will affect their constituents particular situations; in the hope that the listener will relate to the individuals. This is an exercise in using the particular to illuminate the general.

Here are a few of the tips I learned in regard to telling stories with data:

  • Use time based trends and consider a line or bar graph depending upon the data;
  • Use rank ordering (e.g. use a bar graph to rank salespersons by sales);
  • Use data comparisons where appropriate (e.g. polling data showing candidate support over a period of time);
  • Use counter intuitive visualizations (e.g. most people are surprised to learn that the United States has the highest incarceration rate by far amongst OECD countries);
  • Tell stories through relationships (e.g. use scatterplots to illustrate the relationship between sales and profits);
  • Check your facts;
  • Focus on a key statistic or intriguing piece of information;
  • Make your story insightful; don’t leave the audience guessing on what you want them to take away form your presentation;
  • Make your story relatable;

By all means check out my submission for the final project. I illustrated the relationship between discounted orders and profits to show that discounted orders are by far less profitable. This was accomplished by creating a set in Tableau to identify all discounted orders.

Until next class!

See also:

Coursera Final Assignment: Essential Design Principles for Tableau

Coursera Final Project: Data Visualization and Communication with Tableau