Imagine you are building a line graph and want to visually call out the highest and lowest values for your end user. To do that, we can create a dual axis chart where circles representing the MIN and MAX points is overlaid on the line graph.
How can we calculate the highest and lowest values in a Tableau view? These values need to be dynamic (as we filter out info, the values need to change), and they need to be integrated into what we’ve already built. But how can we do that?
If you want to create the best user experience in Tableau, odds are you want to convert your data connections into extracts. Tableau Data Extracts (.hyper) provide the most efficient way to query data in Tableau. Extracts are local snapshots of your data which have been optimized for usage in Tableau.
Extracts can be customized to filter data, aggregate data, and even hide unused fields. Server and cloud-based data sources can support automated extract refreshes on Tableau Server and Tableau Cloud.
Navigating around the dashboards you’ve built can be difficult. How do you build a good homepage? How can you easily toggle from one dashboard to the next without it feeling clunky? Is it even possible for Tableau dashboards to feel easy to navigate?
In today’s video, we answer all those questions. We’ll build a great homepage that allows you navigate to all your dashboards, using icons and buttons. We’ll create dropdown, hamburger menus and a home button to help your users navigate to other dashboards, or go straight back to the homepage.
These skills can make your dashboards feel slick, professional, and user-friendly!
How do you approach a new data set in Tableau? Do you spend a while studying the fields and data types? Or do you Google what other people have built using similar data? Maybe you like jumping straight into building to see how things shake out?
I lean toward the latter. Building visuals allows you to learn the data quickly, develop insights, and expose data issues (let's be honest, there are almost always wrinkles to work out of the data).
Sets are a powerful Tableau tool that can be used in innumerable ways. One of the most useful (and eye-catching!) ways to use sets, is in combination with Dashboard or Worksheet Actions.
This combination allows us to update the values of our sets simply by interacting with our dashboards and worksheets. We can add values to our sets, assign values, or remove values, just based on our clicks.
In Excel, we can use Text to Columns to split a string. But what about in Tableau?
Tableau has a built-in function called SPLIT() that allows us to split strings into multiple columns. We’ll take a look at how to indicate to Tableau the number of columns we want to see and how to understand where to split the string.
Want to see how to split names, email address or phone numbers to isolate the meaningful information from the text string? Check out these examples!
Effective data dashboard design is an art. It requires meticulous attention to detail. It includes visualizing the right data, displaying the appropriate level of granularity, designing an efficient layout, and making a number of minute design decisions so the data hits your user "just right".
If you're looking for a checklist of design elements to review before your next dashboard rollout, you've came to the right place! In this video, we discuss the following 15 design best practices:
Do you want to use Tableau to look at running headcount over time? For example, you work with employee level data which has Start Dates and End Dates and you'd like to be able to see how the total active headcount at the company has changed over the last 8 quarters.
That's something Tableau can do, but it isn't going to work out of the box. It's going to take some creative data structuring and calculations to get things working.
A reference line is a useful worksheet element. It’s a simple tool - just a line based on a single value that we want to reference in our views.
But, did you know that you can change the value of a reference line using a parameter?
An adjustable reference line turns your static reference line into an interactive tool that you and the end-users of your dashboards can use in a variety of ways. You might run basic forecasts, explore outcomes using 'what if" scenarios, or change a comparison threshold.
In 2020, Tableau unveiled a new way to combine multiple tables of data in Tableau Desktop: a Relationship. Billed as "smart joins", relationships provide the capability to merge multiple tables of data horizontally on shared dimensions (like a join). However, they don't duplicate data when the tables being combined have different levels of granularity.
So, are they better than joins? Not always! Relationships can reduce data duplication and improve performance, but a data set that is generated from a relationship might present blind spots if the data isn't perfectly clean.
I was recently working on a project for a client that had a lot of dimension filters - anywhere from 5-10 on each dashboard. This allowed us to drilldown to the level of information we needed!
But a problem arose when we wanted to reset the filters and select new values. How could we do that? Did we really need to reset each filter individually? That’s so inefficient!
Do you ever have a data set with US State names and it would be really handy to save some space in your visualizations by displaying state abbreviations instead of full state names? Or how about a situation where you want to display country abbreviations instead of the full country name? This happens to me often!
It feels like every year or two I run across a need for this and end up writing the same calculation from scratch over and over again. No more! This video shows how to write calculations which convert state and country names to their abbreviations and provides a Tableau Workbook with the calculations you can copy and paste and never have to write them from scratch again!
Does your data set have multiple date fields? For instance, maybe you have both an "Order Date" and a "Delivery Date". Or maybe you have both a "Hire Date" and a "Termination Date".
Sometimes, you need to filter on both date fields at once to answer questions like, "How many people were hired and how many people were terminated in the Marketing Department this year?"
Sometimes, you have too many values in a dimension on the rows shelf in your Tableau worksheet and end up with a scroll bar. Annoying, right? Scroll bars mean your users are less likely to see the data at the bottom of your worksheet because it it out of sight and out of mind.
One idea I've heard discussed is, what if you could break the data into multiple columns? For example, instead of displaying 50 states as a single column of 50 rows, could you display it at 2 columns or 25 rows?
We're looking at Tree Maps this week! If you're trying to show breakdowns of the whole in Tableau, you need to have Tree Maps in your tool kit.
Tree Maps are amazing because:
They're particularly good at representing data with long tails.
They can represent data in a hierarchical structure (we can build Tree Maps within Tree Maps)!
They're space-efficient, and allow us to visualize many dimensions or measures in one view.
Tableau Prep is a powerful data preparation tool which allows you to do complex data transformation in a "no code" environment. No need to learn Python, SQL or R to shape your data.
While Tableau Prep has come a long ways in its first five years of existence, it's still a young tool with limitations. For example, it's not strong at crafting multi-row formulas (e.g. referencing the value from the previous row in the next row).
