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!

References:

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.

Advertisements

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.

Free SQL Data Profiling Tool: Idera SQL Data Profiler

In this tip I will provide an overview of a completely free data profiling tool (at least as of the time of this post) that is easy to use if you are in the Microsoft database stack. You can download the IDERA SQL Profiling tool and immediately put it to work to perform basic column profiling and analyses. The tool will display a summary of the data contained in a selected table and each of its columns. 

Use this tool on the following systems:

  • Microsoft SQL Server: 2008 R2, 2012, 2014, 2016; 2017 Windows & Linux (provisional); Express, Standard, Enterprise editions
  • Microsoft Azure SQL Database
  • Amazon Relational Database Service (RDS)

IDERA SQL Data Profiler has some minor quirks but you can’t beat the price. Check out my review in the video above.

Just remember that data profiling should always be done initially before you start analyzing a new dataset or designing a new visualization. Always start with the basics.

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

Filter Top N Values with a Slicer in Power BI

In this video you will learn how to filter the top N values shown in your bar chart visualization using a slicer.

  1. This technique uses one measure that generates a number 1-10, that will be applied to a slicer.
  2. Another measure will basically rank all of the values associated with your data bars and only return the values that are less than or equal to the number you select in the slicer.

The comments that I apply to the DAX function should help make it easy to understand. I have to give a shoutout to GilbertQ from the PowerBI community for coming up with the  initial approach which I tweaked for the video.

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

How to Compare Actuals vs. Forecast in Tableau

 

Forecasting in Tableau uses a technique known as exponential smoothing. This where 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!