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.
So you want to add some spice to your bland looking Sparklines in Tableau? You have come to the right place (start by watching the video above). Let’s talk about how a Sparkline is defined per Wikipedia:
“A sparkline is a very small line chart, typically drawn without axes or coordinates. It presents the general shape of the variation (typically over time) in some measurement, such as temperature or stock market price, in a simple and highly condensed way. Sparklines are small enough to be embedded in text, or several sparklines may be grouped together as elements of a small multiple. Whereas the typical chart is designed to show as much data as possible, and is set off from the flow of text, sparklines are intended to be succinct, memorable, and located where they are discussed.”
Here are a few examples of Tableau specific sparklines in action (with latest complete month bubble indicators and reference lines): Notice how I do not include any data axes, but you can clearly recognize the data trends in the visuals.
Here is an example of how I used the sparklines demonstrated in the video to build a out a classic yet refined looking Tableau dashboard.
For reference purposes I am going to list three formulas used in the completion of the sparklines, you’ll have to watch the video to learn how to put them together.
In this exercise I am using that standard Tableau Superstore data set which you can perform a Google search to find if you are using Tableau Public.
Calculated Fields
Calculated Field #1 (Name: SPRK_CircleMonths)
This calculated field puts a circle on the penultimate month data points. Penultimate is just a fancy SAT word way of saying “next to last”. When the month of the data point on the line chart (Order Date) equals the next to last order date month in the dataset, then return the Order Date.
//IF THE MONTH OF THE DATE ON THE LINE CHART EQUALS THE MONTH-1 OF THE MAXIMUM DATA POINT
// THEN RETURN THE DATE
If DATEPART('month',[Order Date]) = DATEPART('month',dateadd('month',-1,{MAX([Order Date])}))
Then [Order Date] END
Calculated Field #2 (Name: SPRK_CircleMonths)
This logic will be applied to the circles generated by the previous calculation SPRK_CircleMonths. Only the next to last month will meet the TRUE condition (which will be colored as red).
// IS THE MONTH OF THE CHART DATE EQUAL TO THE MOST RECENT DATE MONTH MINUS 1 MONTH
// E.G., NOV 2018 = NOV 2020 WILL RESOLVE TO TRUE DUE TO MATCHING MONTHS
DATETRUNC('month',[Order Date]) = DATEADD('month',-1,DATETRUNC('month',{max([Order Date])}))
Calculated Field #3 (Name: SPRK_RefLine Profit)
This logic will return the profit associated with the next to last month in the dataset to display on the reference line.
// RETURNS A VALUE USED FOR THE REFERENCE LINE
// IF THE MONTH OF THE DATE = THE MONTH OF THE MAXIMUM DATE MINUS 1 MONTH (GET A COMPLETE FIRST MONTH)
if DATETRUNC('month',[Order Date])
= DATEADD('month',-1,DATETRUNC('month',{max([Order Date])}))
THEN [Profit] END
When you put all the functions together in a manner according to the video, you end up with a more refined sparkline in my opinion. Big shoutout to the Data Duo for the inspiration on the dashboard I created and this technique. If you haven’t checked out any of their work make sure to do so.
In this video I will teach you how to perform a multiple keyword search in Tableau. I’ll show you how to perform both an OR search & an AND search against keywords you type into your dashboard parameter.
In order to accomplish these tasks, we need to use regular expressions. As I mention in the video, regular expressions can be indecipherable, akin to hieroglyphics or something out of a Dan Brown novel to the average person. You can go find this information on the Tableau knowledge base, but I want you to be the person that understands WHY something works as opposed to just copying and pasting a solution that you cannot explain to yourself or others.
The use case for multiple keyword search is for those times when you need to filter your data by a comment, description or other free form blocks of text that are not necessarily subject to the most stringent data governance rules, because let’s face it, these types of fields exist in abundance. In the video I create a dashboard that illustrates the power that this type of search can bring to your dashboard.
You can interact with the dashboard from the video on my Tableau Public page.
OR SEARCH
1. CREATE PARAMETER
Create a parameter named Search Terms.
Data type: String
Current value: keep it blank.
Allowable values: All
2. CREATE CALCULATED FIELD 1
Create a calculated field named Regex String (OR) including the following formula:
"(" + REPLACE([Search Terms], ',' ,'|') + ")"
Let’s breakdown what is going on with the apparent gibberish seen above (there is a method to the madness here). I’ll assume you want to perform a search against a field that contains the terms “paper” OR “Paper”; the case of the spelling is a factor in the results (case sensitive). To adhere to proper regex formatting, you could write a valid expression as such:
(paper|Paper)
This pattern indicates that you want to return a match on the characters “paper” OR “Paper” literally. You can test this out at https://regex101.com/
Although for some reason the official Tableau knowledge base article says to use the REGEX_REPLACE formula in lieu of REGEX, it is not needed since we are doing a simple replace and not using a regex formula to aid our replacements.
In the Tableau calculated field above, we are using the REPLACE function against the parameter named “[Search Terms]” to replace every instance of a comma with the the ‘|’ pipe symbol.
Think about it, if you type the terms “paper, Paper” into the a dashboard’s search term parameter as seen below, the calculated field will reformat your list into the proper regex format by replacing the comma with a pipe and then enclosing the term within parenthesis; i.e., (paper|Paper).
3. CREATE CALCULATED FIELD 2
Next create a calculated field named Regex OR Filter that contains the following formula:
REGEXP_MATCH([Product Name],[Regex String (OR)])
Let’s breakdown what is going on with the formula seen above. The REGEXP_MATCH formula is evaluating a properly formatted regex expression which is contained within the [Regex String (OR)] calculated field we created in step 2. This calculated field will contain the formatted expression (paper|Paper).
REGEXP_MATCH returns TRUE if a substring of the specified string matches the regex pattern. In our case
[Regex String (OR)] = (paper|Paper)
Thus the REGEXP_MATCH function will evaluate the regex expression (paper|Paper) from the second argument against the [Product Name] field in the first argument. The function will return either TRUE or FALSE depending upon whether a match is located within this field.
3. DRAG FIELDS TO ROWS AND FILTERS SHELF
Place [Product Name] to rows and [Regex OR Filter] to filters, then select True.
AND SEARCH
In order to create the AND search for your dashboard, you will follow very similar steps to the OR search. 1. Create the same parameter named Search Terms as in the OR Search above.
2. CREATE CALCULATED FIELD 1
Next create a calculated field named Regex AND Filter including the following formula:
This may look like something out of a Dan Brown novel, but don’t let it intimidate you. In a similar fashion to the OR search where we replaced commas with a pipe symbol, we are simply replacing commas with the following characters between the double quotes “)(?=.*”
The proper regex pattern to indicate an AND search against the terms paper and Paper within a body of text would be as such:
(?=.paper)(?=.Paper).*
Knowing the proper regex format should make the contents of the calculated field above more clear. We are using the REPLACE function against the parameter named [Search Terms] to replace every instance of a comma with the the “)(?=.*” symbols. Thus “Paper, paper” values typed into the [Search Terms] parameter becomes reformatted into(?=.paper)(?=.Paper).*
Placing .* at the end of the string means that any combination of characters can be placed after the search terms and still yield a TRUE result.
3. CREATE CALCULATED FIELD 2
Next create a calculated field named Regex AND Filter including the following formula:
REGEXP_MATCH([Product Name],[Regex String (AND)])
4.DRAG FIELDS TO ROWS AND FILTERS SHELF
Then place [Product Name] to rows and [Regex OR Filter] to filters, then select True.
CONCLUSION
That wasn’t so horrible was it? If you’re a visual learner like me, then go ahead and watch the video to hammer home the concepts. Hopefully this post will help you understand how these regex patterns work and how they can add value to your next dashboard design! As always if you learned something drop a comment on the YouTube video and let me know.
Are you looking for the next viz to showcase on your Tableau Public page? In this video I will teach you a technique that spices up the humble stacked bar chart with dynamic totals (using Tableau Set Actions). We will build out the viz step by step with Superstore data.
Stacked Bar Chart with Dynamic Totals
This chart is powered by Tableau Superstore data which is a data set that is readily available on the internet and is packaged as the default data set with Tableau. As you select the three legend categories at the top of the visual, the stacked bar chart sections will appear or disappear. The totals will also automatically update based upon your selection.
How cool is that!!??
I have to give a shoutout to Dorian Banutoiu for originating this technique. A few years ago, Dorian used this technique in a Makeover Monday exercise and it recently caught my attention when I was checking out his Tableau Public page. I immediately attempted to reverse engineer the technique (which admittedly took some effort).
Because I wanted to enable everyone with Tableau and/or Tableau Public to duplicate the chart, I used Tableau Superstore data as my foundation. Make sure to give Dorian a Twitter follow at @canonicalizedco.
What’s In it for You?
By following along with the video, you will utilize multiple Tableau elements such as:
Practice makes perfect so this will be a good opportunity for you to practice multiple Tableau elements with the creation of one visualization. You can click the links on the list above to see additional videos that cover respective areas.
Give Credit
If you do reproduce this visualization step by step or leverage the technique for your Tableau Public page or Linkedin Page, please link to this post or the YouTube video and place “Inspired by Dorian Banutoiu & Anthony Smoak” somewhere on the viz and post text.
Interact with the Finished Visualization
You can interact with the finished visualization on my Tableau Public page here:
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.
In this video I kickstart the #VizznessFirst initiative where I describe a Tableau Dashboard I constructed using multiple resources from the Tableau #datafam community. Invest your valuable time in watching the videos as it will be worth the investment if you’re looking to pick up some new skills.
I describe the dashboard, show you the resources and then you attempt to build it. This is a perfect intermediate/advanced dashboard idea starter for students looking to improve their Tableau skills.
Background
Let’s start with a little background on how this initiative came together. I am a big fan of the Real World Fake Data (i.e., RWFD) project run by Mark Bradbourne at Tableau. I wanted to finally get involved and put together a dashboard and hopefully learn something new.
Because the particular data set used (Week 5: Help Desk) had very few measures to sum or aggregate; this left counting rows as the most informative means to squeeze some meaning from the data.
I stumbled upon an excellent blog post from Tableau Zen Master Lindsay Betzendahl where she explained a technique to highlight when a “filter” has been activated with a small indicator. I reversed engineered her dashboard to try and tease out how to pull this off with the RWFD data set.
In addition to this technique, I mashed up some other techniques from various members of the Tableau community. One of the great things that I enjoy about the Tableau community is that there are many creators who put out great content for others to learn from; whether it be videos, blog posts, PowerPoint slides, etc.
Once I had a dashboard I was proud of sharing. I figured I would release it as a series to try and teach others some of these techniques. The 3 videos in this series is the culmination of that effort.
VIDEO 1: Overview of the Project and Resources Required
Here are the references I used to put together the dashboard.
Data File: REAL WORLD FAKE DATA DATSET #5 (HELP DESK)
Make sure to watch Video 2 above because this is where I explain the main technique required to complete the dashboard.
VIDEO 3: STEP BY STEP INSTRUCTIONS (TABLE TRICKS)
Video three rounds out the series by demonstrating a cool trick that enables you to build a filter button that opens a detailed table dashboard populated with only the records of interest from the first page. Yes, it uses a filter action but there is twist. Make sure to check it out.
Operation “Reverse Engineer” a Tableau Zen Master dashboard is back in full effect. You know the drill by now, I spent weekend hours analyzing an impressive dashboard put together by Tableau Zen Masters Anya A’Hearn, Tamas Foldi, Allan Walker, and Jonathan Drummey.
In this video I will demonstrate to you how they use parameters to dynamically change the measure that is displayed on both a map and bar chart. Accurate data is made possible through the use of a context filter to equalize the data that is displayed between the United States and all other countries (U.S. data lags by one day).
I should mention that we are using the carefully curated data offered at the Tableau’s COVID-19 Data Hub.
What’s in it for You?
You will learn a neat little trick that encapsulates multiple measures into one calculated field. By using two parameters we can update our visuals to display the correct measure based upon user selected options. This even applies to the size of our marks on a map. You have to love the dynamic nature of Tableau!
In order to understand how we work with the current Tableau COVID-19 data file, you should watch the first video as a prerequisite.
I spent a lot of time breaking down how #Tableau put together this 1 KPI in their #COVID19 Cases Starter Workbook so you don't have to. Thank you to the Zen Masters who enabled this KPI for great dashboard design and data. https://t.co/bUjKT6E9Y8pic.twitter.com/d4DdgJCDJ5
Also Make Sure to Watch this Additional Video Series
Make sure to also check out this extremely useful tutorial on building a COVID-19 Dashboard from scratch. It’s perfect for your first Tableau project with step by step instruction.
You want to build an advanced Zen Master level KPI BAN using Tableau’s latest COVID-19 data? Well you’re in luck as I spent a lot of weekend hours analyzing an impressive dashboard put together by Tableau Zen Masters Anya A’Hearn, Tamas Foldi, Allan Walker, and Jonathan Drummey.
Specifically I was intrigued how they put together the KPI BAN from the dashboard below that highlights either NEW or CUMULATIVE Positive cases and the percentage difference from the previous day.
The official Tableau COVID-19 tracker database can be found here.
In breaking down their approach I renamed some calculations to better help me organize and understand how they come together to create the KPI.
From a learning standpoint, there is a good mix of parameters, filters, context filters and Level of Detail (LOD) calculations that work in concert to deliver the desired outcome.
In the video you’ll learn how I simplified some of the back-end aspects to be a tad more approachable for beginner to moderate Tableau learners. Of course if you want to see the whole dashboard in context with the original back-end naming conventions and layout you can go download the official workbook and deconstruct it for yourself.
It’s all about learning! I encourage you to make use of workbooks that others have shared for bettering yourself and appreciating skills that are at the next level. Of course, always cite your sources and inspirations!!
As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.
Make Sure to Watch this Additional Video Series
Make sure to also check out this extremely useful tutorial on building a COVID-19 Dashboard from scratch. It’s perfect for your first Tableau project with step by step instruction.
Tableau 2020.2 introduced a handy new feature called set control. According to Tableau:
“The set control allows users to dynamically change the members of a set using a familiar, quick filter-like interface. End users can change set membership with both a single and multi-select dropdown, and the set control automatically refreshes its domain so that the data stays fresh.”
As with all new features I had to figure out what this new capability entailed and how best to learn it. Luckily, the hardworking crew over at Workout Wednesday had the perfect challenge.
Sean Miller (@HipsterVizNinja) created a dashboard that enables the user to select a US state, which then adds that state to a set. Three proportional bar charts update at the top of the viz. A right side bar area displays all of the selected states and selecting a state will remove the state from the set, side bar and the map.
If you give up, (or after you complete your solution), take a look at how I approached the dashboard in this solution video, or take a look at the Tableau Public interactive version here.
Don’t just recopy the steps from this solution and post a viz to LinkedIn and/or Tableau Public. You’re better than that, but if you do, make sure to definitely credit Sean Miller and optionally credit me if you used my approach.
Remember, this is just my approach, there are multiple ways to solve any problem.
This was the first Workout Wednesday challenge that I’ve ever done and I’m sure I’ll tackle a few more.
Do some great things with your data!
As always, If you find this type of instruction valuable make sure to subscribe to my Youtube channel.
All views and opinions are solely my own and do NOT necessarily reflect those of my employer.
By now you’ve probably heard that the Los Angeles Lakers were a pretty solid dynasty in the latter half of the 90’s. I was never a Michael Jordan and Bulls fan during their reign of terror in the 90’s. It all started with the Bulls first title at the expense of Lakers’ fans back in 1991.
So while I must admit that “The Last Dance” was a well executed documentary focused on a team I didn’t care for, it did evoke nostalgia for the 90’s.
Although we suffering Lakers’ fans had to wait our turn, we did get the last laugh as “The Next Dance” revolved around a young Kobe Bryant and prime era Shaquille O’Neil.
I built a ribbon chart visualization in Power BI showcasing the top scorers from 1995 to the three peat years ending in 2002. Thank you Spencer Baucke for the ingenious web scraping technique!
Follow along in the video and make a ribbon chart for your favorite NBA team.
Smoak Signals | Data Analytics Blog 