The Ultimate Tableau Slope Graph Video

In this video I tackle the subject of slope graphs also known as slope charts. I had some fun putting together this dashboard that illustrates the changes in wins for NBA teams during the 2016-2017 and 2017-2018 seasons. From the video you’ll discover that Chicago, Atlanta and Memphis are on a Hindenburg-like trajectory, while trusting the process in Philadelphia led to huge season gains in overall wins.

Here’s what you will learn from this video:

  • How to create a parameter that enables a user to select which win statistic measure to visualize;
  • How to use Table calculations like, LOOKUP(), FIRST() and LAST() to calculate period over period change;
  • How the impact of Mike Conley’s injury affected the Memphis Grizzlies last season;

Click the pic to interact with the Tableau Public visualization, also download the workbook and data to dissect as needed.

For your convenience the calculated fields that I used to create the measures are listed here. Note that [Selected Measure] is a parameter that you need to create that lists all of the measures.

Calc Select Measure
CASE [Selected Measure]
WHEN “Home Losses” Then [Home Losses]
WHEN “Home Wins” Then [Home Wins]
WHEN “Overall Losses” Then [Overall Losses]
WHEN “Overall Wins” Then [Overall Wins]
WHEN “Road Losses” Then [Road Losses]
WHEN “Road Wins” Then [Road Wins]
WHEN “vs East Conf Losses” Then [vs East Conf Losses]
WHEN “vs East Conf Wins” Then [vs East Conf Wins]
WHEN “vs West Conf Losses” Then [vs West Conf Losses]
WHEN “vs West Conf Wins” Then [vs West Conf Wins]

END
Better or Worse
IF [Selected Measure] = “Home Wins” OR
[Selected Measure] = “Overall Wins” OR
[Selected Measure] = “Road Wins” OR
[Selected Measure] = “vs East Conf Wins” OR
[Selected Measure] = “vs West Conf Wins”
THEN
//WIN MEASURES: Negative delta treated as “WORSE”, Positive delta treated as “BETTER”
(IF [Delta] < 0 THEN “WORSE” ELSEIF [Delta] = 0 THEN “SAME” ELSE “BETTER” END)
ELSE
//LOSS MEASURES: Positive delta treated as “WORSE” (more losses are worse), Negative delta treated as “BETTER”
(IF [Delta] > 0 THEN “WORSE” ELSEIF [Delta] = 0 THEN “SAME” ELSE “BETTER” END)
END
Delta
LOOKUP(SUM([Calc Select Measure]),LAST()) – LOOKUP(SUM([Calc Select Measure]),FIRST())
Delta ABS Value
ABS(LOOKUP(SUM([Calc Select Measure]),LAST()) – LOOKUP(SUM([Calc Select Measure]),FIRST()))
ToolTip
<Team> Trend: <AGG(Better or Worse)> by <AGG(Delta ABS Value)>
During the <Season> Season, the <Team> had <SUM(Calc Select Measure)> <Parameters.Selected Measure>.

I have to give thanks to Ben Jones at the Data Remixed blog for the inspiration!

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

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How to Compare Actuals vs. Forecast in Tableau

Forecasting in Tableau uses a technique known as exponential smoothing. This is when an algorithm tries to find a regular pattern in your data that can be continued into the future.

In this video I’ll share some helpful tips to help you determine which options you should select that will enable Tableau to make the most predictive forecast for your data. By the end of the video you will be able to differentiate between an additive and multiplicative data pattern and to evaluate MASE to measure the accuracy of the forecast.

I’m not talking about this Mase:

Harlem World

Rather, you’ll learn about the mean absolute scaled error (i.e., MASE) and how it helps you judge the quality of the model.

In addition, you’ll also also learn how to compare your actual data to the Tableau forecast in order to judge if the model is doing its job.

If you’ve used the forecasting capabilities in Tableau without knowing about these concepts, you might have generated an inaccurate error riddled forecast. Don’t just set a forecast and forget it. Watch this video and generate better forecasts in Tableau!

Here is additional reading from Tableau on the forecast descriptions (including MASE).

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

How to Generate a Forecast in Power BI

In this video I’ll demonstrate how to use the forecasting analytics option in Power BI. Although Power BI’s forecast algorithm is a black box, it’s more than likely using exponential smoothing to generate results. At a very high level, exponential smoothing is an algorithm that looks for patterns in data and extrapolates that pattern into the future. To help exponential smoothing perform at an optimal level, it is very important to pick an accurate seasonality estimation, as this will have an outsized effect on the time series forecast.

If your data points are at the daily grain, then you’d use 365 as your seasonality value. If your data points are at a monthly grain, then you’d use 12 as your seasonality value. Generally, the more seasonality cycles (e.g., years) that you provide Power BI, the more predictive your forecast will be.

Without giving away the whole video, here is a pro and a con of using forecasting in Power BI.

Con: As I stated earlier the exact algorithm is a black box. Although based upon a Power View blog post, we can reasonably assume exponential smoothing is involved. Furthermore, the results cannot be exported into a spreadsheet and analyzed.

Pro: The ability to “hindcast” allows you to observe if the forecasted values match your actual values. This ability allows you to judge whether the forecast is performing well.

Check out the video; I predict you’ll learn something new.

As always, 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 basketballreference.com.

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 sirvizalot.com 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!

How to Drill into Data Using Set Actions in Tableau

Drilling with Set Actions

If you’ve ever tried to use the default drill functionality within Tableau, you know that it could be a more user friendly experience. The default table drill functionality opens all of the options at the next drill level which can force a user to lose sight of the data upon which they’re focusing. A more user-friendly option enables the user to only drill into a specific selected value where focus and attention can be maintained. This is otherwise known as asymmetric drill down.

Fortunately as of version 2018.3, Tableau has added Set Actions as a new functionality. At a high level, developers can take an existing set and update its values based upon a user’s actions in the visualization. The set can be employed via a calculated field within the visualization, via direct placement in the visualization or on the marks card property.

In lay terms this means empowering a user with more interactivity to impact their analyses.

In this first video, I’ll demonstrate a use of set actions on an NBA data set. We’ll drill from Conference to Division to Team to Player. This tip will be easily applicable to your Tableau data. And with the bonus tree-map tip you’ll release your inner Piet Mondrian.

Feel free to interact with the set action example on Tableau Public and then download and dissect the workbook.


Drilling with Level of Detail (LOD) Calculations
If you want to stay with a classic approach, a nice Level of Detail (LOD) workaround can be employed to drill into the next level. Here is a tip that accomplishes a similar outcome where I demonstrate a technique originally presented by Marc Rueter at Tableau Conference 2017.

Now that I’ve equipped you with the knowledge to incorporate customized drilling functionality into your analyses, go forth and do some great things with your data!

References:

https://onlinehelp.tableau.com/current/pro/desktop/en-us/actions_sets.htm
https://www.tableau.com/learn/tutorials/on-demand/set-actions
https://www.basketball-reference.com/leagues/NBA_2018.html
https://www.youtube.com/watch?v=d22A4XVoUEs

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Yet Another Market Basket Analysis in Tableau

This video represents part two in my Market Basket Analysis series.

The steps in the post were inspired by the book Tableau Unlimited written by former co-worker of mine, Chandraish Sinha. I wasn’t planning to construct another market basket analysis video but when I saw the approach outlined in his book, I felt like it warranted sharing with my readers and followers.

In this version we’ll use default Tableau Superstore data to show the relationship between sub-categories on an Order; all without using a self table join. The visualization and analysis is driven by a user selection parameter.

Once the user selects a sub-category, the bar chart visualization updates to reflect the number of associated sub-category items on the same order.

Sample Superstore Data 2

Watch the video and as always get out there and do some great things with your data!

Feel free to also check out Part 1 here where we create a simpler correlation matrix version that shows all the sub-category relationships in one visual.

 

 

 

Market Basket Analysis in Tableau

 

A favored analysis technique employed by retailers to help them understand the purchase behavior of their customers is the market basket analysis. When you log on to Amazon, most likely you’ve noticed the “Frequently Bought Together” section where Jeff Bezos and company would like to cross-sell you additional products based upon the purchase history of other people who have purchased the same item.

Market Basket Analysis influences how retailers institute sales promotions, loyalty programs, cross-selling/up-selling and even store layouts.

If a retailer observes that most people who purchase Coca-Cola also purchase a package of Doritos (I know they’re competing companies), then it may not make sense to discount both items at once as the consumer might have purchased the associated item at full price anyhow. Understanding the correlation between products is powerful information.

In this video, we’ll use Tableau Superstore data to perform a simple market basket analysis.

Sample Superstore Data 2

Feel free to interact with this market basket analysis on Tableau Public and then download and dissect the workbook.

Watch the video and as always get out there and do some great things with your data.

Feel free to also check out Part 2 here where we’ll create an analysis driven by a user selection parameter.