Business Intelligence

How to Use Jittering in Tableau (Scattered Data Points)


In this video I will explain the concept of jittering and how to use it to scatter your data points in Tableau. In a normal box plot Tableau data points are stacked on top of each other which makes it more difficult to understand positioning. By using this simple tip combining a calculated field a parameter, you will be on your way to gaining a better understanding of your data points. We’re going to get our “Moneyball” on by analyzing average NBA player points per game in the 2016 season.



Add a “Reset All Filters” Button to Your Tableau Dashboard

Help users navigate your Tableau dashboard with less effort. In this video I will show you how to create a “Reset All Filters” button on a Tableau dashboard. We achieve the desired effect by using a Tableau action that runs on select of a mark.

The data I am using for illustration purposes is primarily sourced from and is loosely based upon data from an actual client of mine. All vendor names, dates, amounts and other data are changed substantially from original form. Feel free to contact me if you need an analysis of your Accounts Payable ERP data from PeopleSoft, JD Edwards or any other source!

Create a Hex Map in Tableau the Easy Way

There are may different ways to create a hex map in Tableau. The hex map helps visualize state geographic data at the same size which helps to overcome discrepancies that make smaller states harder to interpret. Also, larger states (e.g. Alaska) can overwhelm a traditional map with their size.

I’ve found that the quickest and easiest way to build a hex map is to leverage a pre-built shape file. Shape files can be found at various open data sources like or

In this video I will use a shape file created by Tableau Zen Master Joshua Milligan who runs the blog He has a blog post where you can download the shape file I reference. Hats off to Joshua for creating and sharing this great shape file!

Tableau Quadrant Analysis Part 2: Dynamic Quadrants

There are a couple of tweaks that can be made to the Quadrant Analysis video I showed you earlier. We can enhance upon the first iteration of the analysis by making the visualization interactive. I will create parameter driven quadrants where the reference lines are not static at a 50% intersection.

You can tweak the instructions to suit your actual visualization as necessary, but the concepts will remain the same.

We’re going to create two new parameters and have those parameters dynamically control the placement of our reference lines. Then we’re going to update the calculated field which defines the color of each data point or mark, with the parameters we created. In this manner, the colors of each mark will dynamically update as the references lines are adjusted.

To put this in English, as you change the parameter values, the reference lines will move and the mark colors will update.

Watch the video above and/or follow along with the instructions below.

Remove Existing Reference Lines:

Step 1:

  • Remove all existing reference lines from the original quadrant analysis. Simply right click on a reference line and select “Remove”.
  • Also remove the annotations from the 4 quadrants.

Create Parameters

Step 2:

  • Create a parameter named “Percentile FG Pct” (without quotes). Select the dropdown triangle next to “Find Field” icon and choose “Create Parameter”.


Make sure your parameter is setup as a “Float” and the Range of values reflects the picture below. The Display Format will be set as “Percentage” with zero decimal places.


Step 3: Duplicate Your Parameter

  • Right click on your new parameter and select “Duplicate”.
  • Right click on “Percentile Wins” and select “Edit”.
  • Name the new parameter “Percentile Wins”.

Step 4: Show the Parameters Controls

  • Right click on each parameter and select “Show Parameter Control”.
  • Right click on each drop down triangle in the upper right corner of the Parameter Control and select “Slider”.


Step 5: Add Reference Lines

  • Right click on the Percentile of FG% Axis at the bottom of the viz. Select “Add Reference Line”. The Line Value should refer to the X axis parameter (i.e. Percentile FG Pct). For the Line Formatting I choose the third dashed lined option.


  • Right click on the Percentile of Wins Axis on the left side of the viz. Select “Add Reference Line”. The Line Value should refer to the Y axis parameter (i.e. Percentile of Wins).

At this point you should have two parameter controls that adjust the placement of the respective reference lines on the visualization.

However, you’ll notice that the colors of the marks do not change as the reference lines move in increments.

Step 6: Edit the original calculated field to use parameters instead of hardcoded percentage values

Right click on the calculated field (i.e. “Color Calc” in my case), select “Edit” and change all references of “.5” to the corresponding parameter name.

  • The original calculated field:







Is edited to become:

IF RANK_PERCENTILE(SUM([FG%])) >= [Percentile FG Pct] AND RANK_PERCENTILE(SUM([Wins])) >= [Percentile Wins] THEN ‘TOP RIGHT’

ELSEIF RANK_PERCENTILE(SUM([FG%])) < [Percentile FG Pct] and RANK_PERCENTILE(SUM([Wins])) >= [Percentile Wins] THEN ‘TOP LEFT’

ELSEIF RANK_PERCENTILE(SUM([FG%])) < [Percentile FG Pct] and RANK_PERCENTILE(SUM([Wins])) < [Percentile Wins] THEN ‘BOTTOM LEFT’

ELSEIF RANK_PERCENTILE(SUM([FG%])) >= [Percentile FG Pct] and RANK_PERCENTILE(SUM([Wins])) < [Percentile Wins] THEN ‘BOTTOM RIGHT’



In the above formula both [Percentile FG Pct] and [Percentile Wins] are parameter values that have replaced the hardcoded values of “.5”.

Final Result:

As you change your parameter values on the parameter control, the corresponding reference line moves and the color of each mark changes automatically to fit its new quadrant.


Before w/ Static Quadrants

Notice how the marks are colored according to their respective quadrant in the screen print below.


After w/ Parameter Driven Quadrants

I hope you enjoyed this tip. Now, get out there and do some good things with your data!

Anthony Smoak

Quadrant Analysis in Tableau

Release your inner Gartner and learn how to create a 2×2 matrix in Tableau. In this video I will perform a quadrant analysis in Tableau using NBA data to plot FG% vs Wins. Since the data points will be compact, we’ll use percentiles to expand the data and create a calculated field to color the data points per respective quadrant.

Make sure to check out part 2 of this series where I will show you how to make the quadrant boundaries interactive.

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

Advanced Bar Chart Labeling in Tableau

Here is a quick and easy, yet advanced tip for placing your labels just to the inside of your bar chart. This tip will provide you another alignment option in addition to the default ones. Credit to Andy Kriebel for the tip.

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Create A Barbell/DNA Chart in Tableau with NBA Data


A Barbell, Dumbbell or DNA chart should be considered when you want to illustrate the difference or degree of change between two data points. In this video I will use NBA data from the 2016-2017 season (courtesy of to illustrate the difference between team wins and losses.

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

Tableau Filtering Actions Made Easy

This is a guest post provided by Vishal Bagla, Chaitanya Sagar, and Saneesh Veetil of Perceptive Analytics.

Tableau is one of the most advanced visualization tools available on the market today. It is consistently ranked as a ‘Leader’ in Gartner’s Magic Quadrant. Tableau can process millions of rows of data and perform a multitude of complex calculations with ease. But sometimes analyzing large amounts of data can become tedious if not performed properly. Tableau provides many features that make our lives easier with respect to handling datasets big and small, which ultimately enables powerful visualizations.

Tableau’s filtering actions are useful because they create subsets of a larger dataset to enable data analysis at a more granular level. Filtering also aids user comprehension of data. Within Tableau data can be filtered at the data source level, sheet level or dashboard level. The application’s filtering capabilities enable data cleansing and can also increase processing efficiency. Furthermore, filtering aids with unnecessary data point removal and enables the creation of user defined date or value ranges. The best part is that all of these filtering capabilities can be accessed by dragging and dropping. Absolutely no coding or elaborate data science capabilities are required to use these features in Tableau.

In this article, we will touch upon the common filters available in Tableau and how they can be used to create different types of charts. After reading this article, you should be able to understand the following four filtering techniques in Tableau:

  1. Keep Only/Exclude Filters
  2. Dimension and Measure Filters
  3. Quick Filters
  4. Higher Level Filters

We will use the sample ‘Superstore’ dataset built in Tableau to understand these various functions.

1. Keep Only/Exclude Filters in Tableau

These filters are the easiest to use in Tableau. You can filter individual/multiple data points in a chart by simply selecting them and choosing the “Keep Only” or “Exclude” option. This type of filter is useful when you want to focus on a specific set of values or a specific region in a chart.

While using the default Superstore dataset within Tableau, if we want to analyze sales by geography, we’d arrive at the following chart.


However, if we want to keep or exclude data associated with Washington state, we can just select the “Washington” data point on the map. Tableau will then offer the user the option to “Keep Only” or “Exclude”. We can then simply choose the option that fits our need.


2. Dimension and Measure Filters

Dimension and measure filters are the most common filters used while working with Tableau. These filters enable analysis at the most granular level. Let’s examine the difference between a dimension filter and a measure filter.

Dimension filters are applied to data points which are categorical in nature (e.g. country names, customer names, patient names, products offered by a company, etc.). When using a dimension filter, we can individually select each of the values that we wish to include or exclude. Alternatively, we can identify a pattern for the values that we wish to filter.

Measure filters can be applied to data points which are quantitative in nature, (e.g. sales, units, etc.). For measure filters, we generally work with numerical functions such as sum, average, standard deviation, variance, minimum or maximum.

Let’s examine dimension filters using the default Tableau Superstore dataset. The chart below displays a list of customers and their respective sales.


Let’s examine how to exclude all customers whose names start with the letter ‘T’ and then subsequently keep only the top 5 customers by Sales from the remaining list.

One way would be to simply select all the customers whose names start with ‘T’ and then use the ‘Exclude’ option to filter out those customers. However, this is not a feasible approach when we have hundreds or thousands of customers. We will use a dimension filter to perform this task.

When you move the Customer Name field from the data pane to the filters pane, a dialogue box like the one shown below will appear.


As shown in the above dialogue box, you can select all the names starting with “T” and exclude them individually. The dialogue box should look like the one shown below.


The more efficient alternative is to go to the Wildcard tab in the dialogue box and select the “Exclude” check box. You can then choose the relevant option “Does not start with”.


To filter the top 5 customers by sales, right click on “Customer Name” in the Filters area, select “Edit Filter” and then go to the “Top” tab in the filter dialogue box. Next, choose the “By Field” option. Make your selections align to the following screenshot.


After performing the necessary steps, the output will yield the top 5 customers by sales.

top 5 customers by sales

Let’s move on to measure filtering within the same Tableau Superstore dataset. We’re going to filter the months where 2016 sales were above $50,000. Without a measure filter applied, our sales data for 2016 would look like the following:


To filter out the months where sales were more than $50,000, move the sales measure from the data pane to the filter pane. Observe the following:


Here, we can choose any one of the filter options depending upon our requirement. Let’s choose sum and click on “Next”. As shown below, we are provided with four different options.


We can then choose one of the following filter options:

  • Enter a range of values;
  • Enter the minimum value that you want to display using the “At least” tab;
  • Enter the maximum value that you want to display using the “At most” tab;
  • From the Special tab, select “all values”, “null values” or “non-null” values;

Per our example, we want to filter for sales that total more than $50,000. Thus, we will choose the “At least” tab and enter a minimum value of 50,000.


In the output, we are left with the six months (i.e. March, May, September, October, November, December) that have a sum of sales that is greater than $50,000.


Similarly, we can choose other options such as minimum, maximum, standard deviation, variance, etc. for measure filters. Dimension and measure filters make it very easy to analyze our data. However, if the dataset is very large, measure filters can lead to slow performance since Tableau needs to analyze the entire dataset before it filters out the relevant values.

3. Quick Filters

Quick filters are radio buttons or check boxes that enable the selection of different categories or values that reside in a data field. These filters are very intuitive and infuse your visualizations with additional interactivity. Let’s review how to apply quick filters in our Tableau sheet.

In our scenario, we have sales data for different product segments and different regions from 2014 to 2019. Our data looks like the following:


We want to filter the data by segments and see data for only two segments (Consumer and Corporate). One way to do this would be to use a dimension filter, but what if we want to compare segments and change the segment every now and then? In this scenario, a quick filter would be a useful addition to the visualization. To add a quick filter, right click on the “Segment” dimension in Marks pane and choose “Show Filter”.


Once we click on “Show Filter”, a box will appear on the right side of the Tableau screen. The box contains all constituent values of the Segment dimension. At this point, we could choose to filter on any segment value available in the quick filter box. If we were to select both Consumer and Corporate values, Tableau will display two charts instead of three.


Similarly, we can add other quick filters for region, country, ship status or any other dimension.


4. Higher Level Filters

Dimension, measure and quick filters are very easy to use and make the process of analyzing data hassle free. However, when multiple filters are used on a large data source, processing becomes slow and inefficient. Application performance degrades with each additional filter.

The right way to begin working with a large data source is to initially filter when making a connection to the data. Once the data is filtered at this stage, any further analysis will be performed on the remaining data subset; in this manner, data processing is more efficient. These filters are called Macro filters or Higher-Level filters. Let’s apply a macro level filter on our main data source.

We can choose the “Add” option under the Filters tab in top right corner of the Data Source window.


Once we click on “Add”, Tableau opens a window which presents an option to add various filters.


Upon clicking “Add” in the Edit Data Source Filters dialogue box, we’re presented with the entire list of variables in the dataset. We can then add filters to the one we select. Let’s say we want to add a filter to the Region field and include only the Central and East region in our data.


Observe that, our dataset is filtered at the data source level. Only those data points where the region is either Central or East will be available for our analyses. Let’s turn our attention back to the sales forecast visualization that we used to understand quick filters.



In the above window, we observe options for only “Central” and “East” in the Region Filter pane. This means that our filter applied at the data source level was successful.

Hopefully after reading this article you are more aware of both the importance and variety of filters available in Tableau. However, using unnecessary filters in unorthodox ways can lead to performance degradation and impact overall productivity. Therefore, always assess if you’re adding unnecessary options to your charts and dashboards that have the potential to negatively impact performance.

Author Bio:

This article was contributed by Perceptive Analytics. Vishal Bagla, Chaitanya Sagar, and Saneesh Veetil contributed to this article.

Use Parameters in Tableau to Enhance Your Tables

When you receive a requirement to make a boring Excel style table in Tableau, consider spicing up the table by incorporating parameters. One clever use of parameters enables you to incorporate user defined rows and columns into a Tableau table layout. As a user selects a parameter value (representing a column or row), the table is dynamically updated to show the column or row that was selected.

“Parameters are useful when you want to add interactivity and flexibility to a report, or to experiment with what-if scenarios. Suppose you are unsure which fields to include in your view or which layout would work best for your viewers. You can incorporate parameters into your view to let viewers choose how they want to look at the data.

When you work with parameters, consider the following two things that are important in making them useful:

They need to be used in calculations.
The parameter control needs to be displayed so that viewers can interact with it.”

In this video I will show you how to infuse an otherwise boring table with some parameter driven interactivity. Enjoy!

Reference: Tableau Online Help

Benford’s Law Visualization in Tableau

Benford’s law, also called the first-digit law, is an observation about the frequency distribution of leading digits in sets of numerical data. The law states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small [1]. For example, in sets that obey the law, the number 1 appears as the most significant digit about 30% of the time, and the percentages decrease all the way down to a leading digit of 9, which appears 4.6% of the time.

Why Run This Analysis?

When fraudsters are fabricating data, they may not know to manufacture fake data in a manner that conforms to Benford’s Law.  Constructing a Benford’s Law visualization in Tableau can help you determine if your numerical data is fake or at least raise doubts about its authenticity.

In short, remember that one isn’t always the loneliest number!

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