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Showing posts with label quadrant chart. Show all posts

February 25, 2026

How to Build a Quadrant Chart in Tableau | Step-by-Step Tutorial

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In this lesson, I walk through how to build a quadrant chart (4-quadrant scatterplot) in Tableau.

Quadrant charts are one of the most effective ways to compare performance across two measures and quickly identify outliers, opportunities, and risk areas.

In this example, we’re analyzing:

- Profit Ratio
- Average Discount

At first glance, a scatterplot shows the relationship.

But once you add quadrant segmentation, the insight becomes much clearer.

You can instantly see:

- High profit / Low discount performers
- High profit but heavily discounted items
- Low profit / Low discount products
- Low profit / High discount problem areas

Inside the video, I’ll walk you through:

- Creating a Profit Ratio calculation
- Building the base scatterplot
- Adding average reference lines
- Splitting the view into four quadrants
- Categorizing marks based on performance
- Two different ways to build the quadrant chart

This is a foundational analysis technique you can apply to product performance, customer segments, sales teams, marketing channels, and more.

Download the Workbook + Dataset

You can download the workbook and dataset used in this lesson here:
👉 https://nlt.kit.com/quadrant

If you want to go deeper than individual chart techniques, this is exactly the kind of real-world training we focus on inside Next-Level Tableau.

Live classes.
Real use cases.
Advanced techniques you can apply immediately.

Learn more here:
👉 https://www.nextleveltableau.com/


March 11, 2022

How to Include Multiple Dimensions on the Color Shelf

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In this tip, I show you how to include more than one field on the color shelp. 

A few notes:
  1. You can have more than two dimensions on the color shelf. Simply repeat the steps for adding a second color.
  2. This only works for dimensions, as I show in the video.
  3. You do not have to use Sets for this to work; I used that in my example for simplicity.

September 29, 2021

#MakeoverMonday 2021 Week 39 - MLB All-Time Offensive Wins Above Replacement

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This is the final installment of Makeover Monday as a community project and I decided to go back to the very first week of Makeover Monday in 2016 to use as our viz to makeover this week. It's looking at a complicated stat in baseball called Wins Above Replacement, which is basically a measure of how good a player is that an average player that would replace him.

There are several, even more confusing stats included. I thought the easiest way to compare them would be with a scatterplot. So I made an interactive scatterplot that highlights players that are above the average of the top 200 players. 

If you watch the Watch Me Viz video (below), you'll see how I used table calcs to highlight those players and also count the number of players in that quadrant. You'll learn how to use parameters to create a dynamic scatterplot, plus some other bits along the way.

Thanks for coming along on this 300 week journey with me. I hope you have developed your skills and become better at your work. Until next time...

April 27, 2021

How to Create a Dynamic Quadrant Chart Using a Set Action

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A quadrant chart, by definition, is a scatter plot with a background split into four equal sections. Typically, though, the sections are divided up as to how they compare to the average of both axes. 

In this video, I show you how to make the axes you want to compare to dynamic. I show you how to use a set action to change the reference point to which each quadrant is compared.

April 22, 2021

How to Create a Dynamic Quadrant Chart Using a Parameter Action

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A quadrant chart, by definition, is a scatter plot with a background split into four equal sections. Typically, though, the sections are divided up as to how they compare to the average of both axes. 

In this video, I show you how to make the axes you want to compare to dynamic. I show you how to use a parameter action to change the reference point to which each quadrant is compared.

November 5, 2019

#TableauTipTuesday: Using Level of Detail Expressions to Count Items Exceeding a Threshold

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In this tip, I show three examples of using Level of Detail expressions to count members of a dimension in a view. I also show how parameters can be used for counting members based on thresholds.

I ended up babbling quite a bit as I created more examples; sorry for that, but I was on a roll.

May 28, 2019

#TableauTipTuesday: Create an Interactive Quadrant Chart with Parameter Actions

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Parameter actions are now out in the wild with Tableau 2019.2. These have me going through A LOT of charts I've made with parameters before, evaluating which ones would benefit from Parameter Actions.

The first chart I wanted to try was a quadrant chart. A quadrant chart colors each quadrant based on thresholds set for each axis in a scatter plot. Previously, I created two parameters and the user had to type in numbers to adjust the view. However, with parameter actions, I can now enable to use to update the quadrants by simply hovering over a dot.

And here's the video...enjoy!

May 6, 2019

#MakeoverMonday: Major League Baseball's Most Cost Effective Players

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Since the Makeover Monday Community seemed to enjoy sports data two weeks ago, I thought I would provide some data about Major League Baseball this week. First, here's the original visualization to makeover:


What works well?

  • The title and subtitle explain what the viz is about.
  • Dividing the viz into two sections by using different background colors on the scatter plots
  • Consistent scales for the salaries across the charts for each section
  • Using gridlines to help the audience understand the approximate values of each point
  • Only labeling the type of stat once by putting the label between the players and teams charts

What could be improved?

  • There's no data source listed.
  • I have no idea why these players or team are highlighted; an explanation is needed. At first, I thought it was highlighting the most effective player/team, but it's not (at least that's what I see).
  • The logos are meaningless for people that aren't familiar with the teams.
  • What does the big logo on the upper right represent? Is that the author?
  • The data should be filtered to players that meet certain criteria, like at bats in a season. This would then filter out many players near zero.

MY APPROACH

I liked the idea of using a scatter plot like the original, but I wanted to focus on a metric the better measures "effective". There are so many sophisticated metrics now in baseball. I didn't want to use any of those because they're hard for people to understand. I decided to use on base percentage which is the number of times a player reached base (H + BB + HBP) divided by at bats plus walks plus hit by pitch plus sacrifice flies (AB + BB + HBP + SF).

Why did I choose OBP? Ryan Kelley sums it up best in a post on Quora:

Outs are an extremely scarce resource in the economy of a baseball game, each team has 27 to use (in a 9-inning game) while trying to score as many runs as possible. Every time a batter makes an out therefore, the expected number of runs his team will produce will decrease (assume runs are also a limited resource for now).

A batter's job is to get on base--not make an out in other words. A batter fails to do his job when he makes an out, this failure percentage  is 1 - OBP. The success percentage is OBP. If every batter had a perfect 100% OBP, their team would score an infinite amount of runs before every making an out. 

Now, because you're talking about value specifically. OBP alone isn't effective in measuring value. You can make it a better indicator of value by giving it context. That context depends on what kind of value you're talking about. 

You could tie OBP  to a player's salary. This would give you an indicator of how value that batter was to his team in the context of a labor market. After all, baseball players are just employees of franchises in the end. Their jobs are to produce wins. A hitter's job is to produce wins via producing runs. Franchises make money by selling those wins to fans as entertainment.

Each team has a fixed amount of payroll to spend on wins, so the more payroll a batter's salary takes up, the less valuable he is to his team. A good way of illustrating a player's value would be OBP/$ of Salary.

Based on Ryan's explanation, I decided to use OBP as my proxy for batter effectiveness (y-axis). For the x-axis, I wanted to use salary for comparison. However, the data does not adjust salaries for inflation, so a salary in 1985 is not listed in 2016 value. Instead, I came up with a way to normalize the data across all of the seasons.

I created a calculation that compares a player's salary to that of the average salary of the entire league for each season. I made this a percent difference so that the data would then be normalized. Therefore, a player that was 10% above a 1985 salary would be comparable to players that was 10% above a 2016 salary.

Here are my calculations:

  1. Season average salary: { FIXED [Season] : AVG([Salary]) }
  2. △ to Season Average Salary: (AVG([Salary]) - SUM([Season avg Salary])) / SUM([Season avg Salary])

BUILDING THE VIZ

  1. First, I applied some filters to only include what I deemed "eligible" players. These are noted at the bottom of the viz.
  2. Now that I have the x-axis (salary variance from season average) and the y-axis (OBP), I created a scatter plot and added a point for each player for each season.
  3. I added reference lines for the average of each axis.
  4. The players on the upper left are the most cost efficient players. That led me to a quadrant chart, but I only wanted to highlight the most cost effective. I created a calculation to determine the points in that quadrant and place it on the color shelf.
  5. The problem now was that it was basically impossible to find a player in the viz. I thought about using a set action to drill in to a player, but that loses all of the context of the other players. Therefore, I create a parameter to allow the user to highlight a player and I show that players as a connected scatter plot.

SOME THINGS I FOUND

  1. Players tend to be more cost effective earlier in their careers. That makes sense since they are on rookie contracts for the first few years of the career. 
  2. Once players sign their first big contract, they tend to either move to the upper right (high OBP, high relative salary) or the bottom right (low OBP, high relative salary). 
  3. Some players can sustain that for the rest of of their careers, but that's rare. Typically it's the superstars that follow this pattern (like Barry Bonds or Chipper Jones).
  4. For many of the other players, as they approach the end of their career, they tend to move either to the lower right (high relative salary, low OBP) or the lower left (low relative salary, low OBP). Neither of these are particularly good for the team.

And here's my final product. I had never thought of combining a scatter plot and a connected scatter plot before. I'm quite pleased with how this turned out.

December 4, 2017

Makeover Monday: Comparing the cost of food and drinks at Wetherspoons

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Week 49 is here and with it and interesting data set about the cost of food and drink in a chain of restaurants in the UK called Wetherspoons. The fine folks at the Financial Times conducted a fun experiment traveling across the country, ordering the same items at every restaurant with the idea to compare the prices. The FT normally does top notch data viz, but this articles is quite a poor effort.


What works well?

  • Binning the prices helps cluster the restaurants
  • Distinct enough colors

What could be improved?

  • The viz is desperately trying to be a map, but failing miserably.
  • The dots are so big that they are causing massive overlapping.
  • There's no interactivity, so I have no idea which restaurant is which.
  • The title doesn't tell me anything.
  • What do the dots represent?

My Goals

  • Explore the data, particularly focusing on price comparisons.
  • Are there regional differences?
  • Do large metro areas charge more?
  • Many restaurants make a lot of their money from drinks. Does Wetherspoons?
  • Elaborate on the price buckets idea from the original. How are the prices distributed?
  • Use FT-themed colors (which you can find here)
  • Practice highlight actions so that a restaurant can be more easily found on the map
  • Create a custom Mapbox map based on what I learned from our webinar last week.

With these goals in mind, here's my creation for Makeover Monday week 49.

July 5, 2017

Workout Wednesday: The Quadrant Chart

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Nothing too complicated this week. The idea for this week's challenge came from a tweet I received.


This week your challenge is to build a quadrant chart, with a few tricks thrown in of course. Download the data here (it's the same version of Superstore Sales I've used for other Workouts).

  • Match the tooltips
  • Match the title (note that it changes as you change the options on the right)
  • Match the colors
  • Match the formatting
  • Include a Region filter
  • Include an option for the user to pick the level of detail in the view
  • Include lines to aid in breaking up the quadrants

Good luck!

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