I have written a few blogs recently on the subject of data scaffolding. Let me make a summary of them.

Data scaffolding is a technique to artificially create a data structure for the purpose of visualization. It will either reshape the original data or blend multiple data sources in such a way for better visualization.

The technique is pioneered by Tableau Zen Master Joe Mako.

The general methodology is as follows
1.Create a table of pure dimensions to act as the primary data source. It sets up the structure necessary for visualization.
2.Create measures from secondary data sources by blending.

There are two major use cases for scaffolding: Data Reshaping and Data Blending

Data Reshaping: single data source
The original data structure is altered through the scaffolding, such as unpivoted, for better computation or better visualization.

Scaffolding Video Lecture by Joe Mako
Data Reshaping via Scaffolding

Data Blending: multiple data sources
In the regular blending, there could be data loss in secondary data sources, because it's kind of like a left join.

In many cases, no one data source is more primary or secondary than the other sources. Thus we need a third party to act as the primary to all the actual data sources. This third party is a data scaffolding that is created artificially.

Blending Data Via Multi-Dimensional Scaffolding
Lossless Data Blending via Scaffolding
Blending Dates via Scaffolding

Examples of Scaffolding around the web:
Facebook Jeopardy: Create a Single Sheet Waterfall Chart in Tableau
GOOGLE ANALYTICS IN TABLEAU: BLENDING DATA FROM MULTIPLE ACCOUNTS
Basic Monte Carlo Simulations in Tableau


Posted by
0

Add a comment

  1. (Refresh the page if you want to view the gif image multiple times. Or go to Tableau Public and click the button at the top-right corner.)

    Jake and I collaborated on a dashboard. He told me that he learnt a way to create an in-place help page in Tableau. He first saw it at a conference somewhere and couldn't recall who the speaker was. So I am blogging here about it but the credit goes to somebody else. If anyone knows who the original creator is, leave a comment below.

    The key idea is to float a semi transparent worksheet on top of the dashboard, where a help text box is strategically placed on top of each chart. This way, we can explain how to view each chart and what data points are important, etc. This worksheet is collapsible by a show/hide button. 

    Below I would like to show how this worksheet can be constructed.

    1. Sheet with a single data mark.

    • Double click the empty space in Marks panel and add two single quotes. Make the null pill a text label. This creates a single null mark.
    • Set the view as "Entire View"

    2. Create an show/hide button

    • Go to the target dashboard
    • Drag a floating vertical container to the dashboard, making it cover all the area of interest.
    • Drag the Single Null Mark sheet and drop it into the above container. Hide the sheet title.
    • Create an open/close button for the container and place the button at the top-right corner.

    3. Add annotations

    • Format the sheet background opacity as 70% in the layout manager             
    • Select area annotations and place them anywhere of interest. 
    • Write help text and format it to highlight important messages.  
    • The text can serve as functional guide and/or insight guide.

    Here is an example. Feel free to download the workbook and explore. Click the "i" button at the top-right corner to view the in-place help. 

    Posted by
    0

    Add a comment

  2. (Addendum: Jonathan Drummey has a much better Tableau-only solution that I missed from his presentation. I only caught later part of the presentation. You might ask him about it if you know him.)

    In a recent presentation, Tableau visionary HOF Jonathan Drummey talked about a solution for a variable row heights in a text table. The question apparently came from a perfectionist tableau designer. Tableau is not really made for text processing. But for someone visually sensitive, he or we may wish to design a text table beautifully.

    Here is what it is about. 

    Problem Statement

    We assumed that the text has been properly line-broked to accommodate a certain width.

    In Tableau we usually have a text table like this:

    Note that, when the row heights are equal, it leaves a lot of white spaces. What we want is actually like this:

    The row height is variable, removing unnecessary blank spaces.

    The Tableau Solution

    Jonathan got a solution using Python to preprocess the data. I tried using Tableau only and found a solution that doesn't need extra tools. 

    1) First, we can calculate the number of new rows per each Row ID, by counting the number of line breaks. (per Jonathan's courtesy)

    2) Secondly, let's perform the self-union of the data source. Note that the data source is in the 'breaks' sheet of an Excel file. In the Tableau data source editor, drag the break sheet to the editor canvas first. Then drag it again to the lower side of the first sheet, an orange rectangle of Drag table to union will appear. Drop the sheet there and we will have a self-unioned data source.
    3) After self union of the data source, we rename the data source as "Text table with linebreaks SU". Now we create a new field "Row Index" as follows.
    This will help create Index of rows per Row Id, that is, the index for the new rows as inserting line breaks. 

    4) Right click "Row Index" and create bins named as "Row Index (bin)"
    Using this field "Row Index (bin)", we create a new table with indexes within each Row Id. In the Text String field, the string is the same one per Row Id with multiple line breaks. Note that there are three dots "..." at the end of each string.
    5) For technical reasons, we can't use "Row Index (bin)" in our calculations, neither with Index() function based on the bins. (For the curious ones, Tableau's string functions don't accept aggregated fields)

    So, to keep the indexes which are absolutely necessary, let's export the data to a .csv file to create a new data source. The new one has a new index field. Note that we are using Tableau to do some data processing and reshaping.

    Go to the menu Analysis>View Data which will open the view below. Download the table into a new .csv file. And our data preparation is finally done.
    6) Import the new .csv file into Tableau. Rename "Row Index (bin)" as "Row Index". Create a new field "Text Row" as follows.
    This formula will extract each of the new rows by line breaks per Row Id. See the table below.
    By hiding the Row Index column, we obtain the table with variable row height. We leave it unhide for your understanding.

    Voila we just turned a table of equal row height into one of variable row height in Tableau.

    Download the above workbook here.

    This is one approach to handle text wrapping in a text field. I have written another post before. Tableau doesn't do it well. That's why we have to go through some acrobatics to achieve it.  



    Posted by
    0

    Add a comment

  3. [Forward: I asked ChatGPT o1-mini who then wrote this. Hope it helps. All the credit and the blame go to ChatGPT.

    I went over the plan and it looked decent. Whether it can be done in 30 days or not, it depends on the person and the time he spends on it. By the way, ChatGPT can be a really good study buddy. Ask it questions whenever you have any.]

    This comprehensive 30-day plan is designed to take you from a Tableau beginner to an advanced user. Each day includes specific learning tasks, recommended resources, and practical exercises to build and reinforce your Tableau skills.

    Week 1: Introduction and Basic Concepts


    Day 1: Introduction to Tableau


    • Topics:

    • Overview of Tableau and its applications.

    • Differences between Tableau Desktop, Tableau Public, and Tableau Online.

    • Resources:

    • Tableau Official Getting Started Guide

    • YouTube Video: Tableau for Beginners

    • Exercise:

    • Install Tableau Public (free) or Tableau Desktop (trial version).

    • Explore the Tableau interface and navigate through menus and toolbars.


    Day 2: Connecting to Data


    • Topics:

    • Types of data sources (Excel, CSV, SQL databases, etc.).

    • Live vs. Extract connections.

    • Resources:

    • Tableau Help: Connect to Data

    • YouTube Video: Connecting to Data in Tableau

    • Exercise:

    • Connect Tableau to multiple data sources like Excel and a sample SQL database.

    • Explore and familiarize yourself with data connection options.


    Day 3: Basic Data Manipulation


    • Topics:

    • Understanding data types and field properties.

    • Renaming, sorting, filtering, and grouping data.

    • Resources:

    • Tableau Tutorial: Basic Data Preparation

    • YouTube Video: Data Manipulation in Tableau

    • Exercise:

    • Use the Superstore dataset to rename fields, sort data, apply filters, and create groups.


    Day 4: Creating Basic Visualizations


    • Topics:

    • Building bar charts, line charts, pie charts, and tables.

    • Understanding Marks card and encoding data visually.

    • Resources:

    • Tableau Official Visualization Guide

    • YouTube Video: Creating Basic Charts

    • Exercise:

    • Create a bar chart for sales by region, a line chart for sales over time, and a pie chart for sales by category using the Superstore dataset.


    Day 5: Working with Filters


    • Topics:

    • Applying dimension and measure filters.

    • Using quick filters and filter actions.

    • Resources:

    • Tableau Help: Filtering Data

    • YouTube Video: Using Filters in Tableau

    • Exercise:

    • Apply filters to your visualizations to display sales data for specific regions or time periods.

    • Create a dashboard with interactive filters.


    Day 6: Aggregation and Granularity


    • Topics:

    • Understanding data granularity.

    • Aggregation functions: SUM, AVG, COUNT, MIN, MAX.

    • Resources:

    • Tableau Tutorial: Understanding Aggregation

    • YouTube Video: Aggregation in Tableau

    • Exercise:

    • Create visualizations showing total sales, average profit, and count of orders by different dimensions.


    Day 7: Review and Mini Project


    • Review:

    • Revisit all topics covered during the week.

    • Consolidate your understanding through summary notes.

    • Exercise:

    • Build a simple dashboard summarizing key metrics like total sales, profit, and average order size.

    • Publish your dashboard to Tableau Public (if using Tableau Public) and share it.


    Week 2: Intermediate Tableau Skills


    Day 8: Advanced Calculated Fields


    • Topics:

    • Creating calculated fields using functions and formulas.

    • Introduction to Level of Detail (LOD) expressions.

    • Resources:

    • Tableau Tutorial: Calculated Fields

    • YouTube Video: Calculated Fields in Tableau

    • Exercise:

    • Create calculated fields for profit margin and year-to-date sales using the Superstore dataset.


    Day 9: Working with Dates


    • Topics:

    • Date functions and custom date formats.

    • Creating date hierarchies.

    • Resources:

    • Tableau Help: Date Functions

    • YouTube Video: Date Calculations in Tableau

    • Exercise:

    • Analyze sales trends by creating monthly, quarterly, and yearly views.

    • Calculate Year-over-Year (YoY) growth using date functions.


    Day 10: Joins and Data Blending


    • Topics:

    • Joining multiple tables within Tableau.

    • Difference between data blending and data joining.

    • Resources:

    • Tableau Help: Joins and Blends

    • YouTube Video: Data Blending in Tableau

    • Exercise:

    • Join the Superstore dataset with a custom customer dataset.

    • Create visualizations that combine information from both datasets.


    Day 11: Parameters in Tableau


    • Topics:

    • Creating and using parameters.

    • Parameter controls for interactive dashboards.

    • Resources:

    • Tableau Help: Parameters

    • YouTube Video: Using Parameters in Tableau

    • Exercise:

    • Create a parameter to switch between different measures (e.g., Sales vs. Profit).

    • Implement the parameter in a dashboard to allow dynamic data views.


    Day 12: Creating Maps and Geographic Visualizations


    • Topics:

    • Building geographic maps using latitude and longitude.

    • Custom territories and map layers.

    • Resources:

    • Tableau Tutorial: Creating Maps

    • YouTube Video: Mapping in Tableau

    • Exercise:

    • Create a map showing sales by state or country.

    • Enhance the map with color gradients to represent different sales volumes.


    Day 13: Dual-Axis and Combination Charts


    • Topics:

    • Creating dual-axis charts.

    • Combining different chart types for richer insights.

    • Resources:

    • Tableau Tutorial: Dual-Axis Charts

    • YouTube Video: Dual-Axis in Tableau

    • Exercise:

    • Create a dual-axis chart showing sales and profit on the same graph.

    • Experiment with combination charts to display multiple data dimensions.


    Day 14: Review and Intermediate Project


    • Review:

    • Go over all intermediate topics covered during the week.

    • Clarify any doubts and revisit complex concepts.

    • Exercise:

    • Develop an intermediate dashboard incorporating calculated fields, parameters, and geographic maps.

    • Share your dashboard on Tableau Public or present it as a portfolio piece.


    Week 3: Advanced Tableau Techniques


    Day 15: Table Calculations


    • Topics:

    • Understanding table calculations like running totals, moving averages, and percent of total.

    • Advanced table calculations (rank, percent difference).

    • Resources:

    • Tableau Help: Table Calculations

    • YouTube Video: Table Calculations in Tableau

    • Exercise:

    • Implement running totals and moving averages in your sales dashboard.

    • Use table calculations to rank products by sales performance.


    Day 16: Advanced Visualizations


    • Topics:

    • Creating heat maps, highlight tables, and bullet graphs.

    • Using dual-axis and combination charts for advanced insights.

    • Resources:

    • Tableau Blog: Advanced Visualizations

    • YouTube Video: Advanced Charts in Tableau

    • Exercise:

    • Develop a heat map to display sales density across regions.

    • Create a bullet graph to compare actual sales against targets.


    Day 17: Dashboards and Stories


    • Topics:

    • Building interactive dashboards.

    • Creating data stories to convey insights effectively.

    • Resources:

    • Tableau Tutorial: Dashboards

    • YouTube Video: Creating Dashboards in Tableau

    • Exercise:

    • Combine multiple visualizations into a cohesive dashboard.

    • Use actions (filter, highlight, URL) to add interactivity.

    • Create a story by sequencing dashboards to narrate a data-driven narrative.


    Day 18: Data Blending and Relationships


    • Topics:

    • Deep dive into data blending techniques.

    • Understanding Tableau’s data relationships (logical vs. physical).

    • Resources:

    • Tableau Help: Data Relationships

    • YouTube Video: Data Relationships in Tableau

    • Exercise:

    • Blend multiple data sources to enrich your analysis.

    • Create visualizations that leverage data from different sources seamlessly.


    Day 19: Advanced Calculations and LOD Expressions


    • Topics:

    • Mastering Level of Detail (LOD) expressions for complex calculations.

    • Using FIXED, INCLUDE, and EXCLUDE in LOD expressions.

    • Resources:

    • Tableau Tutorial: LOD Expressions

    • YouTube Video: LOD Expressions in Tableau

    • Exercise:

    • Create LOD expressions to calculate average sales per customer across regions.

    • Use FIXED LOD to compare individual performance against overall metrics.


    Day 20: Parameters and Dynamic Calculations


    • Topics:

    • Advanced use of parameters in dynamic calculations.

    • Creating user-driven scenarios and simulations.

    • Resources:

    • Tableau Tutorial: Advanced Parameters

    • YouTube Video: Dynamic Calculations with Parameters

    • Exercise:

    • Develop a parameter-controlled dashboard where users can adjust targets and see real-time impact on performance metrics.


    Day 21: Review and Advanced Project


    • Review:

    • Recap all advanced topics covered during the week.

    • Ensure understanding of complex concepts like LOD expressions and advanced calculations.

    • Exercise:

    • Build an advanced dashboard that incorporates table calculations, LOD expressions, and dynamic parameters.

    • Present your dashboard as a case study in your portfolio.


    Week 4: Special Topics and Capstone Project


    Day 22: Tableau Prep and Data Cleaning


    • Topics:

    • Introduction to Tableau Prep for data cleaning and transformation.

    • Building and optimizing data flows.

    • Resources:

    • Tableau Prep Getting Started

    • YouTube Video: Tableau Prep Basics

    • Exercise:

    • Use Tableau Prep to clean and prepare a messy dataset for analysis.

    • Perform tasks like removing duplicates, splitting columns, and creating calculated fields.


    Day 23: Advanced Mapping Techniques


    • Topics:

    • Custom geocoding and spatial files.

    • Enhancing maps with layers and advanced settings.

    • Resources:

    • Tableau Tutorial: Advanced Mapping

    • YouTube Video: Custom Maps in Tableau

    • Exercise:

    • Create a custom map using latitude and longitude data.

    • Add multiple layers to your map to show different data dimensions.


    Day 24: Integration with R and Python


    • Topics:

    • Connecting Tableau with R and Python for advanced analytics.

    • Using calculated fields with R and Python scripts.

    • Resources:

    • Tableau Help: R Integration

    • YouTube Video: Tableau and R Integration

    • Exercise:

    • Set up R or Python integration with Tableau.

    • Create a visualization that uses an R or Python script for advanced statistical analysis.


    Day 25: Performance Optimization


    • Topics:

    • Best practices for optimizing Tableau workbook performance.

    • Reducing load times and improving interactivity.

    • Resources:

    • Tableau Performance Guide

    • YouTube Video: Optimizing Tableau Performance

    • Exercise:

    • Analyze and optimize an existing dashboard for better performance.

    • Implement techniques like data extracts, indexing, and efficient calculations.


    Day 26: Storytelling with Tableau


    • Topics:

    • Crafting compelling data stories.

    • Using Tableau’s Story feature to present data narratives.

    • Resources:

    • Tableau Tutorial: Story Points

    • YouTube Video: Storytelling in Tableau

    • Exercise:

    • Create a Tableau Story that guides viewers through a data-driven narrative.

    • Use multiple dashboards and visualizations to support your story.


    Day 27: Tableau Server and Online Sharing


    • Topics:

    • Publishing dashboards to Tableau Server or Tableau Online.

    • Managing permissions and user access.

    • Resources:

    • Tableau Help: Publish to Tableau Server

    • YouTube Video: Publishing to Tableau Server

    • Exercise:

    • Publish your dashboards to Tableau Public or Tableau Online.

    • Share your visualizations with others and gather feedback.


    Day 28: Introduction to Tableau Extensions and APIs


    • Topics:

    • Enhancing Tableau with extensions and APIs.

    • Using Tableau’s JavaScript API for custom integrations.

    • Resources:

    • Tableau Extensions

    • YouTube Video: Tableau JavaScript API

    • Exercise:

    • Explore and integrate a Tableau extension into your dashboard.

    • Experiment with basic API calls to customize dashboard functionality.


    Day 29: Capstone Project Planning


    • Topics:

    • Selecting a real-world dataset.

    • Planning your capstone project workflow.

    • Resources:

    • Kaggle Datasets

    • Tableau Public Gallery for Inspiration

    • Exercise:

    • Choose a dataset that interests you (e.g., sales, healthcare, finance).

    • Outline the objectives and key metrics for your capstone project.


    Day 30: Capstone Project Execution and Presentation


    • Exercise:

    • Develop a comprehensive Tableau dashboard based on your chosen dataset.

    • Incorporate various techniques learned over the past 29 days (calculated fields, parameters, advanced visualizations, interactivity).

    • Document your process and insights in a report or Tableau Story.

    • Present your project to peers, mentors, or online communities for feedback.


    Additional Resources:


    • Books:

    • “Learning Tableau” by Joshua N. Milligan

    • “Tableau Your Data!” by Daniel G. Murray

    • Online Courses:

    • Coursera: Data Visualization with Tableau Specialization

    • Udemy: Tableau 2023 A-Z: Hands-On Tableau Training for Data Science

    • Communities and Forums:

    • Tableau Community Forums

    • Tableau Subreddit

    • Practice Platforms:

    • Makeover Monday

    • Tableau Public Gallery


    Tips for Success:


    1. Consistency: Dedicate a specific time each day for learning and practicing Tableau.

    2. Hands-On Practice: Apply what you learn immediately through exercises and projects.

    3. Join Communities: Engage with Tableau communities to seek help, share your work, and gain inspiration.

    4. Seek Feedback: Regularly share your dashboards with others to receive constructive feedback.

    5. Stay Updated: Tableau regularly updates its features. Keep an eye on the latest releases and incorporate new functionalities into your learning.


    By following this structured 30-day plan, you’ll develop a strong proficiency in Tableau, enabling you to create insightful and impactful data visualizations and dashboards. Happy learning!

    Posted by
    0

    Add a comment

  4. Mundane charts are those basic ones that all data visualization beginners can create, possibly with Show Me in Tableau. They are the boring ones at times because many people tend to create fancier ones just to show off. 

    I actually like the mundane ones a lot because they are not only easy to create but also easy to be read by the stakeholders.

    Pareto chart is a very powerful tool, providing great insights into the data set and into the business at stake. It shows how a majority of outputs may come from a high concentration of inputs. It may bring risk to business as described by Sharon.

    Pareto is usually created as a standalone chart. Here I would like to present a simple technique that allows us to embed Pareto insights into some rather mundane charts that everyone can create. This way we all can use simple charts to show great insights to our stakeholders.

    Bar Chart with Pareto Insights

    Let's build a bar chart showing the sales by state using the Superstore data set. Nothing extraordinary.

    1. Sort the State dimension by Sales, descendingly.
    2. Create the following formula, compute over the State and place it on the Color shelf.
    Voila. By simple coloring, we just added Pareto insights to the bar chart, thus showing the top 15 states (by sales) that contribute ~80% to the total sales. The top 15 states make up ~30% of all US 50 states.

    In addition to the common ranking by sales, we now have Pareto stats: 80% of sales by 30% of states.

    This may make the chart more valuable to the stakeholders as an analytical tool. For the viz designer, it doesn't cost anything.

    More Mundane Charts with Pareto Insights

    In the same token, we can add Pareto stats to other mundane charts as below which includes Horizontal Bar chart, Map, Bubble chart, Treemaps, Pie chart and a table. The map with Pareto insights is particularly interesting. It visualizes where the top states by sales are located, which make up 80% of national sales.



    You can download the workbook to look into the implementation details.

    More Pareto Details

    We may add more Pareto stats to the chart by placing them in the tooltips. You can get the formulas from the workbook. 
    Leave comments if you have questions.
    Posted by
    0

    Add a comment

  5. A while ago, Sharon came to me asking a question regarding Pareto Chart Multiples. That is, per each category, there is a Pareto chart. And we need to create Pareto charts for all the categories. This chart allows us to quickly view the few most important factors that matter to the majority of output in each category. 

    Vilfredo Pareto (1848-1923) is the father of the 80/20 rule: 80% of output are produced by 20% of input. It works magically well through all the years. Using the Superstore data, I found that the top 23% of products accounts for 80% of sales as shown below.


    The vertical axis is based on the cumulative sum of sales over the total sales, computing along the product dimension which is sorted by sales.

    The horizontal axis is the cumulative number of products over the total number of products what are sorted the same way.

    Both axis are normalized to be percentage based up to 100%.

    We use this formula to determine the top % of sales and the rest. Once we set the reference % to be 80%, we can get the top % of products that contributed to the 80% of sales.

    For the Pareto multiples, it's really simple to build them. Just place the Category pill on the Rows shelf. Voila we get it.

    From the chart, we can easily see that the 80% of sales are from different % of products for different categories. In Binders, top 11% of products contribute to 80% of sales. In Chairs, it requires the top 48% of products.

    So each category is different. But the combination of all the categories still follow the Pareto rule. 

    Following such an analysis, we may proceed to optimize product mix and supply chain.

    Note that in each category, the sales and the # of products are different from other categories. Some categories are more important than others to the total sales. The optimization also needs to be prioritized for the most important ones. We provided sorting by sales and by the # of products in the final dashboard.

    BTW, we added a US tiled map of Pareto chart by State, where we can view the Pareto distribution of Sales vs Products in each state, and how much % of products are needed to reach 80% of sales. Hover the chart to view the details in tooltips.

    The workbook can be downloaded here.
    Posted by
    0

    Add a comment

  6. [Update: The product manager Wilson Po alerted me that the Viz Extension is still a work in progress. It will not be part of the incoming version 2024.1. Instead, it will be released later in 2024. Just be patient]

    Tableau 2024.1 is coming. I got a chance to test drive it. As I wrote a bunch of posts on Sankey chart tutorials in the past, I am most excited by the new Sankey chart type. Here I would like to share what I learnt. This is a quick preview. Your comments are welcome.

    I created both dark mode and light mode of a 4-level Sankey chart. The design is a breeze as I did it before.

    1.The new chart type needs to be imported as an add-on, aka Viz Extension. This opens the door to many new bespoke chart types by 3rd parties. WoW!

    This is actually following the same philosophy behind Tableau's product design for many years. Any dashboard can be a chart type because we can get a copy of the workbook and plug in our own data by replacing data sources. Voila, in theory, we can create our own version of the same dashboard in no time.

    Now new chart types can be created and shared at a much lower level or lower granularity of a dashboard. I can foresee that it will spawn a new wave of creativities and provide a wide array of design options.

    2.The new chart type is only available on the web editor. So we won't see it in Tableau Desktop.

    For the moment, the web editor still lag behind the desktop in terms of functionalities. For example, the formatting on the web editor has much less options.

    3.I can't format the headers of column bars in the Sankey chart

    4.There is no way to add tooltips to the column bars. 

    5.There is no choice of custom color palettes. The web editor will assign its own color palette to the Sankey chart. We need the ability to color the bars and curves according to our own taste.

    6.I downloaded the workbook from Tableau Cloud server. But I could not display the chart in Tableau desktop 2024.1. It seems I need the Sankey Viz extension in the desktop. I wonder if we will ever have one.

    7.I also tried to download the images of the dashboard. But it doesn't work. The downloaded images are blank. The images in the above are screenshots.

    8.A great feature is that each path can be highlighted easily.

    9.The hierarchy of Category and Sub-Category is kept even after I deleted it. I tried to sort Category and Sub-Category independently by deleting the hierarchy. Still sorting the Sub-Category is always nested in Category.

    All the above will be fixed or improved sooner or later. It will definitely popularize the utility of a beloved chart type. Great job, Tableau team!

    Posted by
    Labels: Sankey Chart
    0

    Add a comment

  7. Buzzfeed recently asked Midjourney to draw images of people in 50 US states.  So the AI drawing tool created 50 images of couples that represent its perception of the people in each state.

    I just put the images into a tiled map in Tableau. Each image is added as a background in each tile.

    And also I added Viz-in-tooltips to enlarge an image to look at more details.
    Feel free to download the workbook and explore it.

    Posted by
    1

    View comments

  8. The folks at Business Expert had a brilliant idea. They asked AI's perception on UK banks as a dog. I am inspired to do the same on US banks.

    ChatGPT is asked to confess its perceptions on top US banks as a dog. Then Midjourney is tasked to generate the images. Check out what dog is matched to your favorite bank.


    All are put together into a single-sheet Tableau dashboard. Feel free to check it out.

    Posted by
    0

    Add a comment

  9. Through my previous post on the new Sankey chart type, I got in touch with Wilson, the product manager leading the development of this new chart type. I made some comments on creating multi-level Sankey via cascading of single Sankey's. He told me it can be done already by dropping more dimensions into the Level card. How could I overlook that functionality when exploring? Maybe it's because there are only two level bars in the Level card icon? Or did I have a fixation on building multi-level Sankey via a Lego approach?

    Oh well, I am really happy that we can build multi-level Sankey charts with a few drag and drops!

    Below are a few varieties of multi-level Sankey chart built with the new chart type.

    1.Multi-Level Sankey via Chart Type

    We only need 5 drag-n-drops to create this chart: 4 dimensions and 1 measure.

    2.Multi-Level Sankey with Level Padding

    In the Level card, we can set the option to add vertical space between members of a level dimension. This is called Level Padding.

    3.Multi-Level Sankey without Level Bars

    In the Level card, we can set the option to hide/minimize the vertical bars.

    4.Multi-Level Sankey Showing Labels when Selected or Highlighted

    We can set the option in the Label card to show labels on links that are selected or higlighted.

    Comments

    Overall, the creation of multi-level Sankey chart is a breeze.

    My only observations are as follows.
    • Can we have more options in setting the colors of links and bars? 
    • Can we support Funnel chart through this chart type? People ask me about Funnel chart often.
    • In my experience, one decimal percentage is enough.
    The above chart can be viewed here. And it can be explored in web edit. The workbook can't be downloaded because it's a pilot program.

    Posted by
    Labels: Sankey Chart
    0

    Add a comment

  10. As an enthusiastic user of Sankey charts, I am excited to learn that a Sankey chart type is being piloted in Tableau Public (Web Edit only). I wrote about Sankey chart design in multiple posts. Sankey chart may appear in different forms depending on applications. 

    I played a little with it just to evaluate it. Here are my initial findings and comments.

    1. The basic Sankey

    I can quickly create a Sankey with 2 dimensions and 1 measure. This is great! 

    I noticed that two new marks cards are being used: Level and Link. The two dimensions are placed in Level card and the one measure is placed in Link card.

    Comments:

    - The text labels are showing by default and not editable. I understand the design choice. More details can be put in the tooltips. Note that the measure is showing in percentage and not the measure itself. That's fine in Sankey. But I hope we can format the percentage. Currently we can't.

    - The Label card is no longer the same as in other chart types. The labels have option to show on links/branches that are selected or highlighted or both.

    - The Color card provides options to a number of color palettes. We as user can't assign color to each member of a dimension. It's pretty rigid. The coloring of the links follows that of the left level/dimension with a lower opacity. I wonder if we should have option to use the colors of either left or right level/dimension for those of the links.

    2. Sankey with Level Padding

    We can add vertical spaces between the members in sidebars. This is what we have been doing when creating Sankey charts. The option is in the Level card as shown below. This is being called Level Padding. The padding size can be adjusted appropriately. 

    3. Sankey without Sidebars

    In the Level card, we can minimize the sidebars by adjusting the Level Width.

    Comment:

    I would ask that we have the option to minimize/hide either of the sidebars. The need arises in the building of multi-level Sankey chart, where we cascade single Sankey's. Then one of the two sidebars in a single Sankey would be redundant. We need to be able to hide/minimize it. 

    This way, we will expand the utility of the Sankey chart type to building multi-level Sankey chart.

    4. Sankey Label Showing when Selected or Highlighted

    In the Label Card, we have the option to only show labels when a branch is selected or highlighted. This is neat, saving some effort of creating action filters.

    5. Further expectations

    Funnel chart is an important chart. It can be derived from multi-level Sankey chart. This can be done with a few more hiding options and cascading. The hiding is for a subset of members in a sidebar level/dimension.

    6. Summary

    Overall, the new Sankey chart type is easy to use. I am very happy about its pilot trial.

    With a few additional tweaks, I think it can be applied to a wider area of use cases such as multi-level Sankey chart and Funnel chart.

    The above example charts can be viewed on Tableau Public and explored in Web Edit. But you can not download it because the pilot is in web edit only.

    Posted by
    Labels: Sankey Chart
    0

    Add a comment

Blog Archive
Blog Archive
Loading
Dynamic Views theme. Powered by Blogger. Report Abuse.