The Wait is Over. Watch this Tableau Dashboard Container Layout Video!
This video was a long time coming. If you’ve ever dealt with layout containers in Tableau (which are about as intuitive as a Thomas Pynchon novel) you know they can leave you scratching your head if you’re new to the tool. And yes, I do own a copy of Gravity’s Rainbow.
Luckily for you I was up I was up at 2:30 a.m. on a Sunday recording the second part of my popular Tableau dashboard tutorial, because that’s how passionate (and stubborn) I am about providing you educational videos of value. Quality over Quantity!
Watch the preview video above, you’ll observe that I’m teaching you a step-by-step breakdown of how I laid out the visual components for an advanced Tableau dashboard using layout containers. This is part two of the series, and while you can absolutely follow along here, the full experience (with all the dashboard elements, charts, and finishing touches) is available to members of my YouTube channel.
I demonstrate a floating container technique that I learned from Curtis Harris’s excellent video on the subject (look up “Things I Know About Tableau Layout Containers” on YouTube).
Before I forget, I want to give a shout out to Dmitry Shirikov, whose original design I reverse engineered and rebuilt (with his permission), and Murilo Cremon for original overall inspiration.
Let’s get into what I actually did.
What You’ll Miss If You Don’t Watch the Full Video
There’s much that you won’t fully grasp until you see it in action in the video. Things like:
How I embedded a YouTube link in an image element
The way I sequence container stacking to prevent the annoying TILE factor
How to work around Tableau’s quirks when resizing and aligning objects
What I do when Tableau auto-adds objects I don’t want
And most importantly, how all the complex pieces come together to form a professionally polished dashboard
If you’ve been struggling with dashboard layouts, or you’ve watched my first Tableau dashboard build and you’ve been waiting forever for part 2, here’s what I recommend:
Rewatch Part 1 of this tutorial series (it’s free on my channel).
Becoming a member supports the channel and helps me keep making high-quality Tableau content. Tutorials like this take hours of research, recording, editing, and production. I don’t mind putting in the work because I know how much value it provides (thank you for all your wonderful comments, I read them all) but your support goes a long way.
Thanks for rocking with me all these years; now get out there and do some great things with your data!
-Anthony Smoak
I appreciate everyone who has supported this blog and my YouTube channel.
Since Tableau Public was recently upgraded to enable local saving (It’s about time), I decided to look and see what new dashboards were out there that I could learn from, and subsequently teach others to rebuild. My mantra is that you learn the most, by teaching others.
I did find the perfect dashboard built by Dmitry Shirikov on his Tableau Public page! There are also design elements from a dashboard created by Murilo Cremon so I tip my cap to him as well.
I reached out to Dmitry via Linkedin and asked for his permission to use this dashboard as a teaching tool for video lessons and he graciously gave me permission. This further demonstrates the collaborative spirit that thrives on Tableau Public. I offer a big thank you to Dmitry!
What I like about his dashboard is the use of the standard Tableau standard superstore dataset combined with interactive/dynamic year over years metrics and thoughtful design.
It contains deceptively simple KPIs, intermediate level metric swapping and a button selector process that relies on parameter actions. Parameter actions update the value of a parameter based on a user interacting with the marks in the view (in this case, the Sales, Profit or Orders dimensions).
I spent a great deal of after-work and weekend time researching how to put this dashboard back together and rebuilt it piece by piece so I could understand what was done.
It also took some time to record and edit this video as it is coming in at over an hour in length! I can’t believe I put this much time and effort into a free product!
My hope is that by dissecting the dashboard and teaching you how to build the chart elements, you can gain some valuable dashboard building insights to add to your repertoire.
If you think a part 2 video regarding the actual dashboard layout will be valuable, please leave a comment on my YouTube channel!
Let’s learn how to build a histogram in Excel with some interesting NBA data. Personally, I use the histogram in my data analyses to help me understand how my data is distributed and to identify any outliers or extreme values that warrant further investigation. In this blog post, I’ll use Kobe Bryant’s playoff career scoring game log as our data source to create a histogram in Excel. Sound fun? Of course, it does. Let’s go!
Inserting a Histogram Chart in Excel
I advocate that you watch the video above for more detail, but you’ll find this blog post equally informative. To create a histogram in Excel, we’ll need a column of numerical data to analyze. In this case, I have Kobe Bryant’s playoff career scoring game log, which shows how many points he scored in each of his playoff games during his career. I referenced this data from basketballreference.com.
The first step is to highlight the data column and press [Ctrl + Shift + Down] to select the entire range. Then, go to the Insert tab and click on the Histogram icons as shown below. This will insert a histogram chart based on your data.
Formatting the Histogram Chart in Excel
The default histogram chart may not look very attractive or informative. Thus, we will use my personal favorite technique to kick start the formatting (the “easy” way) which is the selection of “Layout 2” as a chart style.
Now this option may be easy (if you’re using Excel 2016 or greater) but you must know which additional options to change in order to make the histogram look more presentable.
The Layout 2 style does an excellent job of removing the horizontal grid lines and vertical axis (so we can keep the Edward Tufte style “chart junk” to a minimum). It also adds data labels to our bar charts as well for interpretation clarity.
Formatting the Histogram Chart in Excel
We can further improve our histogram’s appearance by applying some formatting options. For example, we can:
Delete the grid lines and the vertical axis that we don’t need (already performed by Layout 2)
Increase the font size of the data labels
Change the fill color of the bars
Add a chart title
To access the formatting options, right-click on any element of the chart and select “Format”. Alternatively, we can hit [Ctrl + 1] to open the Format pane.
Adjusting the Bin Size and the Overflow/Underflow Options in Excel
One of the most important aspects of any histogram is the bin size, which determines how the data is grouped into intervals. The bin size affects the shape and the interpretation of our histogram. We can adjust the bin size by selecting the horizontal axis and changing the “Bin width” option in the Format pane.
The default bin size for this data set is 5.7, which means that the data is grouped into intervals of 5.7 points. For example, the first bin includes the values from 0 to 5.7, the second bin includes the values from 5.7 to 11.4, and so on.
However, this bin size may not be very intuitive or meaningful. A better option for our histogram is to use a bin size of 5, which means that the data is grouped into intervals of 5 points. For example, the first bin includes the values from 0 to 5, the second bin includes the values from 6 to 10, and so on. This makes the histogram easier to read and understand.
Another option that we can adjust in the histogram chart is the overflow and the underflow bins. These are special bins that capture the values that are above or below a certain threshold.
For example, we may want to create an overflow bin that includes all the games where Kobe scored more than 40 points, and an underflow bin that includes all the games where he scored less than 10 points. To do this, we can select the horizontal axis and change the Overflow bin and the Underflow bin options in the Format pane.
After applying the overflow and the underflow options, the histogram chart looks like this:
Histogram Axis Notation
You’ll notice the histogram’s horizontal axis includes both brackets “[” and parentheses “(“. I will quote the Microsoft blog to explain this notation.
“In our design, we follow best practices for labeling the Histogram axis and adopt notation that is commonly used in math and statistics. For example, a parenthesis, ‘(‘ or ‘)’, connotes the value is excluded whereas a bracket, ‘[‘ or ‘]’, means the value is included. “
In our histogram for example, the notation (20, 25] indicates that the respective bar includes any value greater than 20 but less than or equal to 25.
Interpreting the Histogram Chart and Finding Outliers in Excel
Our new histogram isn’t just pretty, it’s equally informative allowing us to answer questions that we couldn’t easily determine from a wall of numbers in spreadsheet form. The histogram easily helps us understand Kobe’s playoff scoring distribution. We also gain an understanding of his outlier games. For example, we can now:
..locate the mode, which is the most frequent value or interval. In this case, the mode is the interval from 20 to 25, which means that Kobe Bryant scored between 20 and 25 points in most of his playoff games.
..find the range, which is the difference between the maximum and the minimum values. In this case, the range is 50, which means that Kobe Bryant’s playoff scoring varied from 0 to 50 points.
..find the skewness, which is the asymmetry of the distribution. In this case, the distribution is right-skewed, which means that the longer tail of values is on the right side of the distribution. This indicates that Kobe Bryant’s playoff scoring was more concentrated in the lower values. This makes perfect sense as it is much harder to score more points as opposed to lesser points.
..find the outliers, which are the values that are far away from the rest of the data. In this case, the outliers are the point score values that are greater than 40.
I hope you enjoyed this blog post and learned how to create a histogram in Excel to analyze data distribution and outliers!!!
Let me leave you with this highlight package of Kobe’s best dunks:
I was recently teaching someone how to use the decomposition tree in Power BI and it clicked that this topic would make for a great video lesson. What I love about the decomposition tree is that it enables data analysts to conduct root cause analyses, identify patterns and discover insights that are not readily apparent. For example, if we want to understand the contributing factors to our small business profits based upon the data at hand, this visual fits the need to a tee.
Specifically, the decomposition tree lets you visualize data across multiple dimensions and enables drilling down into your dimensions in any order. As a bonus, it’s also an artificial intelligence (i.e., A.I.) visualization, so you can ask it to find the next dimension to drill down into based on certain criteria.
This A.I. also had the “answers”
The picture below illustrates how our Smoaking Coffee Company Profits can be subdivided by dimensions across the top of the visual. Massachusetts apparently likes their coffee (Smoaking Coffee is hypothetically better than Dunkin’).
Absolute vs Relative AI Splits
Another benefit of the decomposition tree is the ability to choose between two types of AI splits: absolute and relative. AI splits are the automatic breakdowns that Power BI suggests based on your data.
Absolute AI splits show you the highest or lowest contributors to the measure you are analyzing. In my example, if we are looking at profit by market size, the absolute AI split for high value will show us the market size that has the highest profit, in this case the Central region with $59,337 in profit.
Relative AI splits shows us the most interesting or unusual contributors to the measure we are analyzing. For example, if we switch the Analysis type to Relative from Absolute and perform the same analysis, the relative AI split will show us the product category that has the highest profit compared to its expected value based on the other categories. In this instance it is the Colombian coffee with $44,131 in profit. This number is lower than the absolute value of $59,337, but relative to it’s other product peers, it stands out.
You can switch between absolute and relative AI splits by going to the format visual pane and selecting the analysis option. You can also choose whether you want to see the high or low values by clicking on the arrow next to the plus sign on the actual decomposition tree values.
Drill Through to Details
The decomposition tree is a great way to get a high-level overview of your data, but sometimes you may want to see the details behind the numbers. For example, if you are looking at profit by region, you may want to see the individual transactions that make up the profit for a specific region.
Power BI allows you to drill through to another page that shows the details of your data. To do this, you need to have a detail page that has the same measure as the one you are using in the decomposition tree. For example, if you are using profit as your measure, you need to have a detail page that has profit as well.
To drill through, you need to right-click on a node in the decomposition tree and select drill through to your detail page.
This will take you to the detail page and apply the filters based on the path you followed in the decomposition tree. For example, if you drilled through to the product value of Colombian, you will see the details of the profit transactions for products noted as Colombian.
This is a very useful feature that allows you to see the underlying data behind the summary. You can also use the back button to go back to the decomposition tree and explore other paths.
Use Bookmarks to Save and Share Your Analysis
Another cool feature of the decomposition tree is that it fully supports bookmarks. Bookmarks are a way to save and share your analysis with others. You can use bookmarks to capture the state of your report, including the filters, slicers, and visuals.
To create a bookmark, you need to go to the view tab and select bookmarks pane. Then, you need to click on the add button to create a new bookmark. You can give it a name and a description to make it easy to identify.
You can also link your bookmarks to buttons or images on your report. This way, you can create interactive scenarios that allow you to switch between different views of your data. For example, you can create a button that shows you the decomposition tree for the lowest state profit value and associated region.
To link a bookmark to a button or an image, you need to select the button or the image and go to the action option in the format shape pane. Then, you need to turn on the action and select bookmark as the type. You can then choose the bookmark that you want to link to the button or the image.
Using bookmarks, you can create dynamic and engaging reports that showcase your analysis and tell a story with your data.
Some Final Tips
Before I conclude, I want to share some final tips on how to use the decomposition tree in Power BI.
You can rename your dimensions in the decomposition tree by selecting them in the Visualizations pane “Explain By” area. Simply right click on a value and select “Rename for this visual”. This can help you to customize the labels and make them more meaningful.
You can lock the values in the decomposition tree by selecting the area to the left of the dimension name at the top of the decomposition tree visual (select the light bulb if the dimension was placed in the visual by AI). This will prevent the users from changing the nodes or the AI splits. This can be useful if you want to fix the analysis and avoid confusion.
The maximum number of levels and data points that can be displayed in the decomposition tree are 50 levels and 5000 data points. I hope you never get to that point, however if you’re at that point, just start over; your visual is way too cluttered.
Conclusion
Watch the video to understand how you can use the decomposition tree in Power BI to analyze your data and conduct root cause analyses.
Yes I did get carried away with AI in the thumbnail picture for this video. I was always a fan of the John Stewart version of Green Lantern so I had to play around with AI to get a close approximation of me as a Lantern. May the decomposition tree work for you in brightest day and darkest night!!
In this video, we’re going to tackle an interesting little challenge – adding dynamic totals to stacked area charts in Tableau. While this may not be a technique for production-ready charts, it’s perfect for those one-time presentations or exports to PowerPoint.
Recently, I had a fantastic 90-minute private data tutoring session with someone who contacted me via this website. During this session, we worked together to address three different data issues, one of which was the quest to display totals for stacked area charts. With some creative thinking and a dash of Tableau magic, I found a solution that I’m excited to share with you.
Use the timestamps below to navigate directly to your desired point in the video.
Historically, creating Sankey charts in Tableau has been a time-consuming process, often requiring the use of complex templates. However, the team at Tableau Public has introduced a game-changing functionality that allows us to create Sankey charts effortlessly. This feature, currently in beta and available for a limited time (like the McRib of data visualizations), enables us to author and publish Sankey charts directly to our Tableau Public profiles.
In this blog post, I’ll briefly walk you through the process of creating one using Tableau Public.
What is a Sankey Chart?
Before we delve into the specifics of this new Tableau Public feature, let’s take a moment to understand what a Sankey chart is and why it’s such a powerful visualization tool. A Sankey chart is a flow diagram that illustrates the movement of data, be it goods, energy, or even money. With a Sankey chart, you can effortlessly compare different data points and identify patterns that might remain hidden in traditional charts or tables.
Testing Out the New Feature
Assuming you already have a Tableau Public profile (and if you don’t, I highly recommend creating one—it’s an incredible platform for sharing your data visualizations with the world). You’ll need to create a visualization directly from your Tableau Public page.
Once you’ve created your visualization, navigate to the “Connect to Data” section. As we’re uploading data from our computer, select the “Upload from Computer” option. Choose the dataset you want to work with—I’ll be using the “Sample Superstore” dataset for this example. After confirming that your data has been successfully imported, select the “Sankey” chart type.
Now, here’s where the magic happens. You’ll notice a “Level” and “Link” section that appears. To define the flow in the Sankey chart, let’s select a dimension like “Segment” and drag it into the “Level” area. Next, grab another dimension—I’ll choose “Region”—and place it in the “Level” area as well. Finally, to quantify the flow, let’s choose a measure like “Sales” and place it in the “Link” area.
Voila! With just a few clicks, we’ve created a Sankey chart. Impressive, isn’t it? You’ll notice the flow between the segments and regions instantly come to life. But we’re not done yet—let’s keep the party going by adding another level.
For the sake of experimentation, let’s grab a dimension like “Ship Status” and drop it into the visualization. Now we have an additional sub-level in our Sankey chart. To avoid overcrowding, we can uncheck the “Allow Labels to Overlap” option, ensuring our chart remains clean and legible.
Keep Innovating for The Analytics Core Audience
Tableau Public’s decision to incorporate this feature highlights their commitment to democratizing data visualization. While the addition of features like Sankey charts to Tableau Public is fantastic, it’s essential that the overlords at Salesforce remember Tableau’s core audience—the general analytics users who are generally decoupled from Salesforce usage. Let’s keep hoping for bigger and better things to come with the tool. This beta signals that they’re heading in the right direction.
(Note: The mentioned feature and availability were accurate as of the blog post’s publication date, but please refer to the Tableau Public documentation for the latest updates and information.)
Have you ever wanted to disable the default Tableau highlighting effect when you select a mark on your chart and then remove the filter? Even when the filter is removed via the “Remove All Filters” process, it can be confusing for the user experience when all values remain “greyed out”, tricking the user into thinking that their filter is still applied. This video will help you remedy this issue and improve your dashboard user experience.
Fortunately, there is a solution to this problem that is simple and easy to implement. In this video I will show you how to use a simple calculated field and highlight action to remedy the issue. This should be default behavior in Tableau, (help us out here Tableau!)
The solution approach involves creating a boolean calculated field and setting it initially to TRUE. Then, placing this calculated field on the detail of the chart that has a filter applied. Next, adding a highlight action to the same chart that you want to remove the “greyed out” effect for. In the “Add Highlight Action” pop-up box, the Source Sheet and the Target Sheet should be the same and the Selected Fields option should have the boolean calculated field checked.
By following these steps, you will be able to remove the greyed out effect on your Tableau chart when the “Remove All Filters” process is applied.
This not only improves the appearance of your dashboard but also makes it easier to understand the data.
★☆★ THESE ADDITIONAL FILTERING VIDEOS IN TABLEAU ARE WORTH YOUR TIME ★☆★
Don’t let the greyed out effect on your Tableau charts hold you back any longer. Watch the video and follow the steps outlined in this blog post to improve the appearance and functionality of your Tableau dashboards.
You can also follow my dapper data adventures on Instagram.
I was recently inspired by some really great tile-maps that have been created in the Tableau community (e.g., see beautiful work by Chimdi Nwosu and Michael Dunphy). Thus, you know I had to come up with a way to construct a simplified map in this style with some data and share with my followers. In these two videos, I’m going to walk you through how to prepare the necessary data file in Tableau Prep Builder and then we’ll build out the tile-map in the second video, step by step.
This is a good intermediate level portfolio project for you to follow along with in order to increase your Tableau Desktop and Tableau Prep skills. We’ll use CDC data, specifically United States COVID-19 Cases and Deaths by State over Time, to build the tile-map.
The advantage of a tile-map is that it represents geographic regions (like states) at equal sizes. Thus, the distortions and biases introduced by differences in sizes are eliminated. In the case of the United States, data for smaller regions like Washington D.C. can be interpreted on equal footing with data for a much larger region like California.
Tableau Prep Builder helps to greatly simply the data shaping process. My only wish is that Tableau would integrate Prep into Tableau Desktop for one seamless data tool to rule them all, but I digress. The process below illustrates how simple it is take some data from an input file, and subsequently clean and pivot the data into a new file. Watch the first video, to learn how to build out this simple flow in Tableau Prep. If you do not have a copy of Tableau Prep, you can complete this lesson on a 14 day trial license of the tool, which you can download here.
Watch the second video for the step by step instructions to build out the tile-map above.
In this video I will provide a method in which you can place your bar chart labels above the bars in Tableau. This technique is based off Adolfo Hernandez’s technique with a little more explanation and additional alternatives for the zero line. Make sure to add this to your bar chart repertoire!
If you want to follow-along with the video, you can download the data at this link:
I worked hard to create a Tableau dashboard packed with multiple features that any beginner or intermediate user should know how to complete. Use this dashboard as an inspiration regarding techniques to learn for your next Tableau dashboard.
Here are a few of the features included in this dashboard:
Parameters
Dynamic Titles
KPIs
Filters
Context Filters
Top 5 by Dimension
Highlight Actions
Filter Actions
Ranking
Show/Hide Containers
Image Buttons
Parameter Driven Chart Swap
Maps
Shape Files
Reset All Filters
Combo Chart / Dual Axis Chart (Bar in Bar)
Quick Table Calculations
Bullet Chart
Animations
Containerized Dashboard Layout
Because I love to teach in my relatively spare time, I am considering offering 1 on 1 training to learn how to put together this sample dashboard. As I mention in the video, leave a comment with your thoughts on how much of an investment you think someone would make for 3 hours of 1 on 1 training to build this together. Someone would definitely impress their manager or future hiring manager if they had the knowledge to build this type of front end reporting.
Smoak Signals | Data Analytics Blog 