Lorna set this week’s challenge inspired by a challenge first set in 2018 that was a ‘combo’ challenge with Prep : use Prep to generate the data set, then visualise. In this instance, we’re doing all the data calculations in Desktop itself (and adding on a few extra too).
Building out the calculations
We need to find the first purchase date per customer, so we use a FIXED LOD for this (we’ll be using a lot of these 🙂 )
First Purchase Date
{FIXED [Customer ID]: MIN([Order Date])}
We then want, for each customer, then next purchase date, which is the earliest order date, where the date is after the first purchase date
IF SUM([First Purchase Sales]) >= SUM([Second Purchase Sales]) THEN ‘First Purchase Higher than Second Purchase’ ELSE ‘Second Purchase Higher than First Purchase’ END
Let’s put all this into a table
To determine the type of outlier, we need more fields. We need to get a value for the average of all the First Purchase Sales
Avg First Purchase Order Value
{AVG([First Purchase Sales])}
note this is a short notation for {FIXED:AVG([First Purchase Sales])} and is an instruction to average across the whole data set, as we want a single value that is the same for all the rows of data. We also need
Avg Second Purchase Order Value
{AVG([Second Purchase Sales])}
Pop these into the table too
and we need to know what the standard deviation is for each value, which again are essentially ‘a constant’ across the whole data set
First Purchase Std
{FIXED :STDEV([First Purchase Sales])}
and
Second Purchase Std
{FIXED :STDEV([Second Purchase Sales])}
And now we can determine the outlier type for each customer
Outlier Type
IF SUM([First Purchase Sales])>=10000 OR SUM([Second Purchase Sales]) >= 10000 THEN ‘High Purchase Outlier’ ELSEIF SUM([First Purchase Sales])> (SUM([Avg First Purchase Order Value])+ (3*MIN([First Purchase Std]))) AND SUM([Second Purchase Sales]) > (SUM([Avg Second Purchase Order Value]) + (3*MIN([Second Purchase Std]))) THEN ‘Outlier’ ELSEIF SUM([First Purchase Sales])> (SUM([Avg First Purchase Order Value])+ (3*MIN([First Purchase Std]))) THEN ‘First Purchase 3STD Outlier’ ELSEIF SUM([Second Purchase Sales]) > (SUM([Avg Second Purchase Order Value]) + (3*MIN([Second Purchase Std]))) THEN ‘Second Purchase 3STD Outlier’ ELSE ‘Within Range’ END
Add the Outlier Type into the table and also add to Filter and show the filter, and do some checks on the different types
We’ve got all the data, now we can build.
Building the chart
ON a new sheet, add First Purchase Sales to Columns and Second Purchase Sales tp Rows. Add Customer ID to Detail. Add Purchase Diff to Shape and adjust accordingly. Hide the null indicator
Add Difference Between Purchases to Size and adjust mark size range to suit
Add Outlier Type to Colour, and then add another instance of Purchase Diff to Detail, then click on the ‘detail’ icon to the left of the Purchase Diff pill on the marks card, and change it to Colour, so 2 pills are on the Colour shelf. Adjust colours to suit.
Add Outlier Type to Filter and show the filter and uncheck the High Purchase Outlier option. Then add Customer Name to Tooltip and adjust to suit.
To make the diagonal ‘reference line’, add another instance of First Purchase Sales to Rows. This creates another marks card. Remove all fields from this card, except Customer ID and change the mark type to Line. Remove all text from the Tooltip for this marks card.
Make the chart dual axis and synchronise the axis.
Format the chart by
hide the second axis (uncheck show header)
set the row and column dividers to not display on the header sections (but to display on the pane sections)
Set background of worksheet to grey, but set the pane background to white
And that should be the build
Create dashboard, and use a horizontal layout container. In the right hand side, add the viz. In the left side, add a vertical container and use text objects to display the information, and add the filter control into this section too. Use a blank object to make a vertical divider, and add a border around the ‘parent container’.
It’s Tableau Conference week, so this week’s challenge, set by Yoshi, was presented as part of the live session at #TC26. Yoshi set 3 levels of the challenge, to represent session data from TC25. I managed to get through the Basic and Bonus challenges
Building the Basic KPI sheet
After connecting to the data set, create a new field to determine if the session is AI related or not
AI-related
CONTAINS([Title], “AI”) OR CONTAINS([Title],”Agent”) OR CONTAINS(IFNULL([Topic],”),”Agentic Analytics”) OR CONTAINS(IFNULL([Topic],”),”Artificial Intelligence”)
Add this to the Text field on a new sheet, then adjust the text to include the rest of the words, adjusting the font style and colour as required.
Building the basic calendar sheet
Format Start Time and End Time to custom date format of hh:nn, and format Date as mmm dd
On a new sheet add Date as a discrete exact date (blue pill) to Columns and Start Time as a continuous exact date (green pill)to Rows, and edit the Start Time axis so it is reversed.
Add ID to Detail and change the mark type to circle. Create a new field
Index
INDEX()
and add this to Columns
Add AI-related to Colour and adjust accordingly. The edit the Index table calculation so it is computing by Id and AI-related only and sorted by the min value of AI-related descending, so the AI related sessions are listed first
Format the Start Time axis, so the Scale is formatted as h AM/PM and change the font style to larger, blue and bold
Format row and column dividers in the pane only to be thicker blue lines, with no dividers on the headers
Add thin blue gridlines to the Rows and remove column gridlines and zero lines
Edit the Start Time axis again, and set the Tick Marks to start at 08:00 and display every 2 hours
Remove the axis title. Hide the Index axis (right click, uncheck show header). Format the Date header fields (large, blue, bold) and remove the Date label (right click – hide field labels for columns).
Add Title, Description, Start Time and End Time to Tooltip and adjust accordingly.
Add both sheets onto a dashboard, and add an additional Text object to contain the additional explanation.
As before, add this to the Text field on a new sheet and adjust text to contain the additional wording and formatting.
Building the Bonus Calendar Sheet
Start by duplicating the basic calendar sheet. Add Session Duration to Size.
The sessions need to be sorted by the longest durations first, while still be grouped with AI -related sessions listed before non AI session. Create a new field
Sort
[Session Duration] + (INT([AI-related])*100)
I’m basically creating a numeric value to sort by, based on the duration. adding 100 if the session is AI related, ensuring the Sort value for even the shortest AI session (20 mins) will be larger than the longest non AI session (90 mins).
Adjust the Sort property on the Index field table calculation, to now sort by the minimum of Sort descending
Adjust the tooltip to include the Session Duration, and you should be done.
Duplicate your basic dashboard, then use the ‘object swap’ feature to change the 2 vizzes
For this week’s challenge, Kyle took the challenge I posted the previous week about building a radar chart with map layers, and used a viz extension to build the chart instead – a stroke of genius!
If you want to use this method within your organisation for use on Tableau Cloud or Tableau Server, you’ll need to factor in licensing costs for the extension, and you also might need to agree access with your security team. The map layer solution I blogged about here, works as part of the product itself.
After connecting to the dataset (I used the Engagement Survey Data – Filled sheet), click Add Extension from the marks card dropdown
search for radar in the resulting Add an Extension dialog, and then select the Radar option by LaDataViz. and then select Open on the resulting screen
The presented sheet will give some prompts to get started, but due to the requirements of this challenge, we need to build some calculations first.
The user need to be able to select the business function (Area) to display, so right click Area and then Create Parameter to create
pSelectArea
string parameter defaulted to Whole Company. As the parameter is created from a field, the list of available options is pre-populated. Delete the Benchmark entry from this list.
We only want the selected Area and the ‘Benchmark’ option to display on the chart, so create
Area to Display
[Area]=’Benchmark’ OR [Area] = [pSelectArea]
Add this the Filter shelf and set to True
The chart displays the ‘Benchmark’ as an area bounded by a dashed line. This is achieved by setting the Benchmark value as a target, so we need to define that value in its own calculation
Target
IF [Area]=’Benchmark’ THEN [Value] END
set this to % with 0 dp.
We also want to isolate the values associated with the selected area too
Selected Value
IF [Area] = [pSelectArea] THEN [Value] END
set this to % with 0 dp too.
Add Themes to the Spokes shelf, Selected Value to the Values shelf and Target to the Target shelf
We’ve got all the info we need, now just to format the display, so select Format Extension and apply the following properties
Radar tab
Line Style = Linear
Fill opacity = 10
Range: uncheck auto, set min =0, max = 1
Marker = None
Expand the Target section, and set Fill Opacity = 2
Grid tab
#Lines = 10
Spacing = 0
Style = Straight
Labels tab
Spokes section : Orientation = Horizontal
Labels section : check the Labels checkbox, Font style = bold
Number format : Style = Percentage
Expand the Axis section, check the Axis Labels checkbox,
Line: uncheck the Show Line checkbox
Number Format : style = percentage
Finally, adjust the Tooltip, and then that should be it – just need to add to dashboard and show the pSelectArea parameter
For this week’s community challenge, we’re recreating radar charts using fellow Tableau Ambassador and Visionary, Johan de Groot‘s, map layers technique, which he has blogged about here.
Modelling the data
I provided 3 sets of data in one excel workbook
Gridlines : a basic template to help build the radial gridlines
Engagement Survey Data – Filled : for use with the filled area chart radar display
Engagement Survey Data – Line: for use with the non-filled line chart radar display
The Engagement Survey Data – Line data contains an extra ‘Dummy’ theme which has values that exactly match the values associated to Theme ID 1. This helps ensure the lines ‘join up’ once plotted.
Each Engagement Survey Data needs to be related to the Gridlines data by a custom calculation of 1 = 1
Building the Filled Area Radar Chart
Model the data as described above using the Engagement Survey Data – Filled data set.
Create a new parameter
pArea
string parameter, which can be populated as a list using the Add values from option, and then removing the Whole Company and Benchmark options. Default to Finance
Drag Point to the Detail shelf. Change the mark type to Polygon and add Theme ID to the Path shelf.
Set opacity on Colour to 30% and add a black border. Set the the Background Layers (Maps > Background Layers) to show nothing: untick all options. Also remove all map options ( (Maps > Map Options -> uncheck all options.) Remove row/column dividers.
Create a new field
Area to Display
IF Area = ‘Benchmark’ OR [Area] = ‘Whole Company’ OR [Area] = [pArea] THEN [Area] END
and add to Colour. Exclude the Null option that display (right click -> exclude; this will automatically add the pill to the Filter shelf). Adjust colours as required.
Add this to the view as a new marks layer (drag onto canvas and drop when you get the option displayed)
This will create a new marks card – add Theme ID to the Detail shelf.
Adjust the size and colour of the spokes as required.
Create a new field
Outer Points
MAKEPOINT(cos([Angle]), Sin([Angle]))
Add this as another marks layer. Change the mark type to circle. Reduce the opacity to 0% and size to as small as possible. Add Themes to the Label shelf.
(If you wish, you can format the text to wrap a bit, by creating
Label – Theme formatted
REPLACE([Themes],” “, “ “)
and putting this on the Label shelf instead
You can adjust the labels using the label alignment options, but you may need to manually move the labels to get them positioned in an acceptable place.
Create a new field
Radar Labels
IF [Theme ID]= 1 AND [Position] <=10 THEN MAKEPOINT(cos([Angle])*[Position], Sin([Angle]) * [Position]/10) END
and add as another marks layer and change mark type to circle. Again reduce opacity to 0% and size as small as possible.
Create a new field
Value Axis
IF [Position]%2 = 0 THEN [Position] * 10 END
and add to the Label field of the Radar Labels marks card as a Continuous dimension (unaggregated green pill)
To add gridlines, create
Grid Lines
IF [Position]<=10 THEN MAKEPOINT(COS([Angle])*[Position]/10, SIN([Angle]) * [Position]/10) END
and add as another marks layer. Change the mark type to polygon, and add Position to Detail and Theme ID to Path. Set the colour to pale grey at 5% opacity and add a darker grey border.
Finally add Value to the Tooltip of the original Point map layer marks card and adjust tooltip. Then make all the other may layers ‘disabled’ so the can’t be clicked on.
Show the pArea parameter and change the values to see the areas change. This is now the core ‘filled’ radar, which you can add to a dashboard.
If you duplicate the sheet for the radar chart you’ve just made, and change the original Points marks card from Polygon to Line, you will find that the lines don’t join up. This is why we need to adjust the source data to introduce a ‘Dummy’ theme with the values that match the first theme.
Model the data as described above using the Engagement Survey Data – Line data set.
Create all the same fields as detailed above, but some are adjusted as follows :
There is a slight difference here, in that 1 is subtracted from the count of themes, as we don’t want to be counting the ‘Dummy’ theme, otherwise we get an extra ‘spoke’.
Label – Theme Formatted
IF [Theme ID]<> 10 THEN REPLACE([Themes],” “, “ “) END
adjusted to only return labels that aren’t related to the Dummy theme.
When you add the Point field as the first map layer, change the mark type to line instead of polygon to get the display below.
Then continue to build as described above. Adjust the colourings and amount of opacity as you see fit.
This week’s WOW challenge for community month, saw Sean partner with Tableau Visionary and ‘Hall of Famer’, Linsdsay Betzendahl to deliver this double challenge – creating a Mekko chart with staggered labels AND a stacked bar chart with staggered labels.
There was quite a lot going on in this challenge, and I had utilised all the concepts in the past, but did still need a refresher to remind how to get things done. So I did draw on the links provided in the challenge, as well as blogs I’ve written for other WOW challenges, specifically
The link in the challenge downloaded a CSV file to connect to. Once connected, the instruction was to pivot the data, so all the ‘types of death’ columns were transposed from rows into columns. In the data source pane, multi-select all the ‘Total Dead xxxx’ columns then right-click and select pivot
I then renamed the Pivot Field Names column as Disaster Type Raw and Pivot Values as Decadal Deaths.
In the Data pane, I then created a group against the Disaster Type Raw field, called Disaster Type. Every value was grouped, even if it only contained a single entry, just so I could display the name of the disaster in the way that matched the solution
Building the calcs for the Mekko chart
A Mekko chart is based on % – in this instance, the height of the chart is split based on the % of deaths by type per decade, while the width is based on the % of total deaths per decade. For this we’ll need some table calculations, so building out a tabular view to check the values is always something I find very valuable.
Change the Year field to be discrete, then add to Columns. Add Disaster Type to Rows and Entity to Filter and restrict to World. Add Decadal Deaths to Text.
Add this into the table and then adjust the table calculation so it is computing by Disaster Type only, which essentially means based on the layout of this table view, that each column should be adding up to 100%
This is giving us the value we need to plot on the Y-Axis of the chart (ie the height)
To determine the value to plot on the X-Axis, we need several calculations. First we need to know the total deaths per decade
Total Deaths per Decade
{FIXED [Year],[Entity]:SUM([Decadal Deaths])}
Add this into the table. The value should be the same for every row for the same year.
Then we need to know what this value is as a % of all the deaths across all the decades
% Total Deaths per Decade
SUM([Total Deaths per Decade]) / TOTAL(SUM([Total Deaths per Decade]))
Add this into the table, and adjust the table calculation so that it is computing by Year, which means the values per row should add up to 100%
To build a Marimekko chart, which is a variable width chart, we need to use the cumulative value of this %, so we plot points from the start up to 100%. So we need
Running Sum % Total Deaths per Decade
RUNNING_SUM([% Total Deaths Per Decade])
Add this into the table, verify the table calc is computing explicitly by Year as described above for both the nested calculations, and we should see the values increasing up to 100%
Now typically this would be enough for the chart and is what is mentioned in the referenced blog posts for building these charts. However, due to the staggered labelling requirement, we need to go a step further, as the value we plot on the X-Axis needs to be used for both the Mekko chart and the ‘pointers’ associated to the staggered labels. As these labels are centred, we need to plot our point midway
Mekko – Plot Point X Axis
[Running Sum % Total Deaths Per Decade] – ([% Total Deaths Per Decade]/2)
ie take the running sum value, but subtract half of the % value for the decade.
As before add to the table, setting all nested table calculations to be computing by Year.
Now we have all this, we can start to build out the chart
Building the Mekko chart
On a new sheet, add Entity to Filter and restrict to World. Add Year to Detail and Disaster Type to Colour, and adjust colours to suit.
Then add Mekko – Plot Point X-Axis to Columns. Adjust the table calc, so both next calculations are computing by Year only. Then add % Decadal Deaths to Rows and adjust the table calc to be computing by Disaster Type only. Change the mark type to bar and adjust the sort on the Disaster Type field to sort by field Decadal Deaths ascending
Then add % Total Deaths per Decade to Size. Adjust the table calc so it is computing by Year only. Then click on Size and change the option from Manual to Fixed and align Centre, so the width of the bar is stretched equally, either side of the central plot point. Add a white border via the Colour shelf.
Add Decadal Deaths and Total Deaths per Decade to Tooltip and update as required.
Add Disaster Type to Label, then adjust the Label settings so the label is only displayed based on the maximum value of the Year field, and match the mark colour.
At this point, while the labels are displayed, they aren’t in the required location. To resolve this, start by extending the X axis to be fixed to end at 1.2 (right click axis > Edit)
This extends the axis, giving some ‘space’ in the chart for the labels. Trying to adjust the alignment of the labels doesn’t work though, so move them manually. Click on the label so the cursor changes to a cross, and then drag the label to the side to reposition it.
Do this for all the labels displayed in the solution, doing your best to align them vertically. Once done, then in the label settings, uncheck the ‘allow labels to overlap other marks’ option, so the other values disappear.
Now we have the Mekko chart, we now need to handle the staggered labelling.
Mekko – Label Position YAxis
IF INDEX()%2 = 1 THEN -0.1 ELSE -0.3 END
We’ll index the year from 1 to 13, and every odd number index will have the label positioned at -0.1 and even at -0.3
Add this to Rows which will create a 2nd marks card. Remove all the fields except Year and Total Deaths per Decade from the card.
Adjust the table calc to explicitly compute by Year, and then change the Size from Fixed to Manual and reduce the slider to as small as possible. You should now have your label ‘pointers’
Move the Year and Total Deaths per Decade pills onto the Label shelf, then set the Label settings, so All labels are shown, they can overlap other marks, and adjust the alignment, so they’re not skewed (you may need to find you have to click the sideways position first before you can get the alignment working)
Adjust the formatting of the label, then edit the Mekko – Label Position Y Axis axis so that it is fixed to start at -0.5, and the title and tick marks are removed, so the axis looks to disappear.
Finally tidy up by removing all gridlines, row and column dividers and hiding the Mekko – Plot Point X Axis axis.
Add to a dashboard, and include the requested citation to the data source. I added this within a floating text box with a show/hide button.
Building the calcs for the Bar chart
The bar chart employs similar principals as the Mekko chart, we need to calculate a position to plot the point on the X-axis, but the bar has a fixed height and not split. Let’s start with getting the calcs we need again.
Build a table with Entity on Filter (filtered to World), Year on Rows and Disaster Type on Columns. Add Decadel Deaths onto Text and then add % Decadel Deaths into the table, and adjust the table calc so it is computing by Disaster Type. Due to the layout of this table, the values in each row now add up to 100%.
The % Decadal Deaths defines the ‘width’ of each segment in the bar chart, so one again we need a cumulative value to help determine the plot point.
Running Sum % Decadal Deaths
RUNNING_SUM([% Decadal Deaths])
Add to the table and ensure all the table calcs for the nested calcs are all computing by Disaster Type.
And as before, as we need a ‘central’ point to plot, create
Bar – Plot Point X Axis
[Running Sum % Decadal Deaths] – ([% Decadal Deaths]/2)
Add to the table and ensure all the table calcs for the nested calcs are all computing by Disaster Type.
There is an additional requirement for this chart, to restrict what is displayed based on the Year selected. Create a parameter based off of the Year field
pYear
integer parameter, defaulted to 2020 (formatted without thousand separators), that lists the values
Then create a field
Show Last 3 Decades
[Year]>= [pYear]-20 AND [Year]<=[pYear]
and add to the Filter shelf and set to True.
For the label pointer position, we also need a similar field as before, but only want labels to show when the % is over 4%, so create
Bar – Label Position Y Axis
IF [% Decadal Deaths] > 0.04 THEN IF INDEX()%2 = 1 THEN -0.1 ELSE -0.3 END ELSE NULL END
format to show 2dp, then add to the table, and ensure all table calcs are computing by Disaster Type.
Values will only display if the % Decadal Deaths value is >4%, and the value alternates based on the index position.
Building the bar chart
The steps are very similar to the Mekko chart, except this time, we need the Year dimension on Rows and we can ‘hard code’ a ‘y-axis’ by double clicking into Rows and typing MIN(0.5). This axis is then fixed from 0 to 1 to provide some spacing. Bar – Plot Point X Axis is on Columns and % Decadal Deaths is on Size with the size once again Fixed and centred. This time to Disaster Type field is sorted by Decadal Deaths descending.
Add Bar – Label Position Y Axis to Rows to create the 2nd marks card, and again apply similar steps as above to end up with labelled pointers. I set the axis of this measure to be fixed from -1
The you just need to tidy up the formatting by removing the gridlines, axis lines, row/column dividers and hiding the various axis. Then add to a dashboard and show the parameter.
For this week’s challenge we’re using the Superstore data set to simple averages with weighted averages
Connect to the data and on a new sheet add Order Date to Filter and select the last 3 months in your data set (I had the 2025.3 instance of Superstore, so the data was filtered to Oct, Nov & Dec 2025.
Simple Average
In the data we have, a single Order ID can have multiple order lines (ie Products), and the quantity of each product can differ. Different discounts can be applied to each product. In the example below, we are at the lowest ‘grain’ of the data, ie each row in the table is equivalent to a single row in the raw data set. As a result the SUMs and the AVG values are identical. This order has 5 order lines. (Note, the Discount field has been formatted to a % with 1 dp).
If this data is summarised just at the Order ID level, the SUM and AVG differ, as you would expect.
The AVG([Discount]) is automatically being computed by Tableau as SUM(Discount) / number of rows associated to the Order ID, which is 280/5 = 56%.
This is the ‘simple’ average and is taking no account of the Quantity associated to each order line.
Weighted Average
We need to get the average based on the fact that in this example, there are actually 26 ‘items’ ordered across the 5 order lines, so we need the following
At the raw line level, the Discount is being multiplied by the Quantity (so a weighting is being applied per line as the quantities and discounts can vary). This is then being aggregated (ie summed up) and then the average is determined by dividing by the total number of items ordered. So based on the example we have (2*0.2) + (3*0.8) + (5*0.8) + (9*0.8) + (7*0.2) = 15.4 which is then divided by 26 = 0.592 or 59.2%
Building the Viz
Add Discount to Columns and change the aggregation to Average. Then add Quantity to Rows and change that aggregation to Average too. Add Order ID to Detail. Change the mark type to circle.
Create a new field
Difference from correct metric
ABS([Weighted Avg] – AVG([Discount]))
Add to Tooltip and then create
Is difference?
ROUND([Difference from correct metric],3)<>0
note – I am rounding here as I was getting some anomalies to Erica’s solution due to very very small differences
Add to Colour and adjust to suit. Reduce the opacity and size as desired. Add Discount toLabel (aggregated to Average). Set the Label to display only when highlighted.
Add Difference from correct metric to Filter
Remove row/column dividers and adjust Tooltip as required.
Name the viz Simple Avg or similar and then duplicate the sheet. Replace the AVG(Discount) pills on the Label and Columns shelf with the Weighted Avg pill. Adjust tooltip and rename axis title. Name this sheet Weighted Avg or similar.
Creating the Viz in Tooltip
On a new sheet, add Order ID, Category to Rows and then Quantity and Discount into the table aggregated at the Average level. Remove row banding and column dividers and adjust row dividers. Name the sheet Order Details or similar.
On the Simple Avg sheet, edit the Tooltip and insert the Order Details sheet, adjusting the width if required.
Repeat on the Weight Avg sheet.
Creating the Dashboard
Using layout containers, create the desired arrangement. My hierarchy is below
I used blank objects with a grey background to create the horizontal and vertical ‘lines’, using outer padding to help. Here’s an example of the top horizontal line. The height of the object is 134 pixels, but I’ve added 130 px top padding, making the ‘line’ 4px wide.
Add a highlight dashboard action that applies when a mark is selected
And that should then be done! My published viz is here.
This week’s #WOW2026 challenge was inspired by the annual viz challenge held at the Japanese TUG. It took me a bit longer than 10 minutes to build though!
I’ve built this using 3 sheets – a sheet for the section of the calendar above the displayed bar chart, the bar chart and then the section of the calendar below the bar chart.
Building the Calendar Sheets
On a new sheet, add Order Date at the month-year level to Filter and select December 2025. Then add Order Date at the Weekday level to Columns and Order Date at the Week (number) level to Rows. Additionally add Order Date at the month-year level to Columns as a discrete (blue) pill positioned first.
Create a parameter
pSelectedDate
date parameter defaulted to 10 Dec 2025
and show this parameter on the view.
Create a new field
Is Selected Date
[pSelectedDate] = [Order Date]
Change the mark type to circle then add Is Selected Date to colour. Adjust colours to suit and add a dark border to the circle. Set the sheet to entire view. Add Order Date at the Day level to Label and align middle centre.
We want the calendar to show ‘no selected date’, so will utilise 01 Jan 1900 as a ‘null’ date. We also want the calendar to only show the dates related to the week of the day selected and any previous in the month. So create
Show Top
[pSelectedDate] = #1900-01-01# OR WEEK([Order Date])<= WEEK([pSelectedDate])
and add to the Filter shelf and set to True
We also need all the days of the week to display, even if there aren’t sales on those dates. Eg, set pSelectedDate to 03/12/2025 and we lose the ‘Sunday’ column.
To resolve this, select the Show Empty Columns from the Analysis > Table Layout menu
Adjust Tooltip to suit.
Hide the Order Date column heading (right click > hide field labels for columns). Adjust the format (font size) of the other labels. Hide the WEEK(Order Date) row heading (right click, uncheck Show Header). Hide row & column dividers.
Create fields
True
TRUE
False
FALSE
and
Date for Param
IF [Order Date] = [pSelectedDate] THEN #1900-01-01# ELSE [Order Date] END
Add all these to the Detail shelf, setting the Date for Param field to be a discrete (blue) exact date.. They’ll be needed for the interactivity later.
Name this sheet Top or similar, then duplicate the sheet and change the Show Top filter to be False. Then hide the header labels (uncheck Show Header). Remove the True & False fields form the Detail shelf. Name this sheet Bottom or similar.
Building the bar chart
On a new sheet add Is Selected Date to the Filter shelf and set to True. Add Sales to Columns and Region to Colour and add Region to Label. Expand the width to make the bar wider.
Add Order Date to Tooltip and adjust Tooltip to suit. Set the background colour of the worksheet to dark grey, then format axis to have white font. Remove gridlines.
Building the dashboard
Use a vertical container, add the Top sheet and then then Bottom sheet underneath. Remove all padding from these sheets, and remove the title
Then add a horizontal container between the 2 calendar sheets. Add a text object and adjust text as required and reference the pSelectedDate parameter. Then add an image object, and select the image you hopefully downloaded from the link in the challenge page. Then add the bar chart, removing the title and setting to fit entire view.
Remove all padding for each object in this container and set the background colour of the text and image objects to the same dark grey as the bar chart.
Change the colour of the font in the text box, and I set some inner padding on the text object and the bar chart (10 pts all round) just for some extra breathing room. If need be, set the background colour of the bar chart object too.
Change the date in the pSelectedDate parameter and watch the bar chart move
Adding the interactivity
Add a dashboard parameter action
Select Date
on select of the Top or Btm sheets, set the pSelectedDate parameter by passing the value from the Date For Param field.
Also add a dashboard filter action
Unhighlight Top
On select of the Top sheet on the dashboard, target the Top sheet directly, explicitly setting the True field to False. Show all values when selection cleared.
Finally, we need to make the horizontal container disappear when no date is selected (ie the pSelectedDate parameter is set to 01 Jan 1900).
Create a new field in the data set
Is Not Null Date
[pSelectedDate] <> #1900-01-01#
then back on the dashboard, select the whole horizontal container, and from the Layout pane, check the option to control visibility using value, and select the Is Not Null Date field
When the date is 01 Jan 1900, this whole section will now disappear. You should be able to click dates now, and get the functionality as expected.
Once done, you can tidy up the dashboard by removing the container with all the filters and legends, adding a title, and adding some extra padding and background colours as required.
For this week’s challenge, Yoshi got us creating gauge charts using Tableau’s radial viz extension.
Modelling the data
Yoshi provided a version of the Superstore dataset along with a Budget csv. After downloading, I related the 2 files within Desktop using the following relationships pictured below
Building the gauge chart
On a sheet, select the Add an extension option from the mark type dropdown and then select the Radial (by Tableau) option, and click Open on the resulting screen
Add Measure Names to Ring and Measure Values to Angle and then add Measure Names to filter and retain the Sales and Budget options only.
Click Format Extension and then set the options as follows
Total angle (degrees) : 180
Starting angle (degrees) : 270
Ring padding : 30
Segment padding : 5
Segment labels : Ring and Angle Values
Font : size 9
Centre size (%) : 50
Show centre label: on
Automatic font size: on
Edit colours and select appropriate colours
Adjust the order of the Sales and Budget pills in the Measure values pane if required
Add Region, Segment, Category and Order Date at the month-year level to the Filter shelf. Set Category to Furniture and Order Date to December 2025.
Format the Measure Values pill on the Angle shelf so that it displays the numbers as $ with 0 dp.
Create a new field
Acheivement %
(SUM([Sales])/SUM([Budget])) / 2
format this to % with 1 dp and add to the Centre shelf. Note, the division by 2 is necessary due to their being 2 measures (and therefore 2 marks) displayed and the number duplicating itself.
Create another field
Tooltip – Achievement %
(SUM([Sales])/SUM([Budget]))
Format this to % with 1 dp and add to the Tooltip shelf along with Sales, Budget and Category. Update the Tooltip to suit. Edit the title to reference the Category field, and then name this sheet Gauge – Furniture or similar.
Then duplicate the sheet. Change the Category filter to Office Supplies and name the sheet Gauge – Office. Repeat to create a version for Technology. So you should end up with 3 sheets, one for each Category. Apply the Region, Segment and Order Date filters to be shared across all 3 workbooks.
Building the bar chart
Create a field called
Sales Rank
RANK(SUM([Sales]))
and change it to be discrete (right click > convert to discrete)
Add Category to Columns and Sales Rank to Rows and Sub-Category to Detail. Set the table calculation associated to Sales Rank to be computing by Sub-Category only.
Double click into Columns and manually type MIN(1) to create a ‘fake axis’. Change the mark type to bar and edit the axis to be fixed from 0 to 1. Widen each row slightly. Then move Sub-Category to Label
Then via the Colour shelf, reduce the opacity to 0% and remove the border
Now add Sales to Columns. and on the Sales marks card, move Sub-Category back to Detail, and rest the opacity to 100%
Colour the bars as required. Add Budget to the Detail shelf, then add a reference line to the Sales axis, which shows the Budget per cell as a line with a fill below of light grey.
From one the gauge sheets, set the Region, Segment and Order Date filters to also apply to this sheet.
Add Achievement % to Tooltip and adjust the Tooltip on the Sales marks card only. Remove all the text from the Tooltip on the MIN(1) marks card.
Then format the sheet by
editing Sales axis and removing the axis title
editing the MIN(1)axis and removing the title and tickmarks
hide the sales Rank header (right click pill > uncheck show header
hide the Category header
remove row & column dividers
Then add the Gauge sheets into a horizontal container on a dashboard, setting the container to ‘distribute contents evenly’. Add the bar chart underneath, but I ‘floated’ it into position as each gauge chart object takes up more vertical space than necessary (the size of the object doesn’t adapt to the fact you’re only showing half a circle).
Lorna wanted us to practice layout containers this and also sprinkled in a bit of new functionality – rounded corners! As a result you’ll need to use Tableau Desktop v2026.1 to complete this challenge.
Lorna provided a starter workbook with all the required sheets – we had to build the dashboard.
Describing how to do this is tricky, so I’m going to show a picture of my item hierarchy, and then describe some key points.
Lorna challenged us to use no more than 11 containers. I have 12 as I’ve included my standard ‘footer’ in a horizontal container, which isn’t part of the solution. When working with containers, it’s often useful to add blank objects as placeholders to help ensure the layout. and while you add and position the actual objects. The blank objects then get deleted. Padding is very helpful to reposition objects and add whitespace, but getting the values right can take a bit of trial and error and lots of tweaking.
I have renamed all the containers and numbered them, so you can see the number there are. I’ve named them with an ‘h’ or ‘v’ prefix depending on whether the container is vertical or horizontal.
When dealing with tricky layouts, I always start with a floating container that is positioned at point 0,0 and has the height and width of the dashboard. In this case, this is the container labelled 1.vBase. When I add other containers/objects into this base container, they are set to be tiled. I remove the automatic ’tiled’ container that exists initially, and if any reappear as I’m adding objects, I delete them too.
To generate the bordered shadow effect around the Superstore Overview title, the 2.hHeader container has the following properties:
no border
background set to light grey
corners set to a radius of 5
outer padding all 0
inner padding all 0
The Superstore Overview title Text object that sits inside this container, then has the following properties
no border
background set to white
corner radius all 0
outer padding: left 5, top, bottom, right all 2
inner padding all 15
A similar principal is applied to the 11.hRightPane container and the Scatter plot object contained within. The 11.hRightPane container does additionally have a left outer padding of 5 and a bottom padding of 10.
The 4.vLeftPane container just has a right outer padding of 5. This along with the 5 left outer padding of the 11.hRightPane container above gives the 10 pixel spacing between the 2 columns of data in the main body of the chart. There will ultimately be 3 ‘KPI’ rows within this container, so the 4.vLeftPane should be set to distribute contents evenly (only apply once you’ve built all the rows as described below).
Each KPI ‘row’ is constructed in the following manner; I’ll describe the top row, but just repeat for the other rows:
5.hTopKPI-Outer has the following properties
no border
background of light grey
corner radius of 5 all round
outer padding all 0
inner padding all 0
The 6.hKPI-Inner then has the properties
no border
background is white
corner radius all 0
outer padding: left 5, top, bottom, right all 2
inner padding all 0
Having the 2 horizontal containers set up this way creates the bordered, shadowed effect.
The Total Sales CY vs PY sheet is then set to a fixed width of 140 and inner padding all round of 10.
The Sales % Change sheet is then set with a fixed width of 130, corner radius of 20 all round, and outer padding of left 15, top 80, right 15 and bottom 20. Note, I also reduced the label text on the sheet itself from 20 to 15 pt.
Finally the Sales over Time sheet is set to have inner padding of 15 all round.
Hopefully all this helps, though as mentioned, I can imagine they’ll be a bit of fiddling to get things right. My published viz is here.
This week’s challenge uses data I collated from the stats vest my daughter wears during her football matches and training sessions. The data provided is already pre-filtered to the 2024-25 season and her Reading U15 team.
If you want to watch a video which demonstrates the concept then this video by Andy Kriebel will help. But if you prefer to read, then carry on 🙂
Building out the calculations
In creating this chart we need to normalise all the different stats we want to show; that is we want to convert every measure into a spread between 0 and 1. To do this we calculate the difference between the value and the minimum value as a proportion of the full range of values (that is the difference between the min and max values). We use table calculations for this.
Total Distance (km)
(SUM([Distance – Total (KM)]) – WINDOW_MIN(SUM([Distance – Total (KM)]))) / (WINDOW_MAX(SUM([Distance – Total (KM)])) – WINDOW_MIN(SUM([Distance – Total (KM)])) )
format to 4 dp
Distance / Min (m)
(SUM([Distance per Min]) – WINDOW_MIN(SUM([Distance per Min]))) / (WINDOW_MAX(SUM([Distance per Min])) – WINDOW_MIN(SUM([Distance per Min])) )
format to 4 dp
HSR Distance (m)
(SUM([HSR – Total (M)]) – WINDOW_MIN(SUM([HSR – Total (M)]))) / (WINDOW_MAX(SUM([HSR – Total (M)])) – WINDOW_MIN(SUM([HSR – Total (M)])) )
format to 4 dp
HI Distance (m)
(SUM([HI Distance – Total (M)]) – WINDOW_MIN(SUM([HI Distance – Total (M)]))) / (WINDOW_MAX(SUM([HI Distance – Total (M)])) – WINDOW_MIN(SUM([HI Distance – Total (M)])) )
format to 4 dp
Max Speed (m/s)
(SUM([Speed – Max (m/s)]) – WINDOW_MIN(SUM([Speed – Max (m/s)]))) / (WINDOW_MAX(SUM([Speed – Max (m/s)])) – WINDOW_MIN(SUM([Speed – Max (m/s)])) )
format to 4 dp
NOTE – in my solution I called these fields Norm – xxxx . After putting on the chart, I aliased them to the names above. Creating with the actual names I used is the simpler solution 🙂 I don’t know why I didn’t just rename them…
On a new sheet, add Game ID to Rows, and then add all the original key measures and their associated normalised values into the table, so each original measure is next to its normalised value. If you sort by one of the original measures from largest to smallest, you should see that the associated normalised value is distributed from 1 at the top of the list to 0 at the bottom.
The other key requirement for this viz is to identify a selected opposition. For this we create a parameter based on the Opposition field. Right click on Opposition > Create > Parameter and the following dialog with the list of opposition teams pre-populated will display. I set the default as Chelsea Ladies U14
Opposition Parameter
The create a new field
Highlight Match
[Opposition] = [Opposition Parameter]
Show the Opposition Parameter control and add Highlight Match to Rows.
Now we can start building the viz.
Building the chart
On a new sheet, add Game ID to Detail, then add Total Distance (km) to Rows. Adjust the table calculation of the field to be computing by Game ID.
Then add Distance / Min (m) onto the sheet, by dragging the field and dropping it on the y-axis when the 2-column icon appears
This will automatically add Measure Names and Measure Values into the display, including a Measure Values ‘box’
Adjust the table calc of the Distance / Min (m) field to compute by Game ID. Reorder the fields in the Measure Values box, and then change the mark type to line. Set to Fit Width.
Add the other 3 normalised fields into the view, by adding the field into the Measure Values box, and adjusting the table calc each time.
We now have the core viz, we just need to format it and add the additional features.
Show the Opposition Parameter control. Add Highlight Match to the Colour shelf. Adjust colours and re-order so True is listed first. Adjust all the table calcs in the 5 measures so they are also now computing by both Game ID and Highlight Match.
Now add H/A to the Detail shelf and change to be an Attribute (this means by default it is excluded from the table calcs, so we don’t need to make all those adjustments again). Then change the icon to the left of the pill from Detail to Colour, so there are now two fields on the Colour shelf. Adjust to suit.
Add Highlight Match to the Size shelf, and then adjust the size to be reversed, so the True lines are thicker.
Create a new field
Label: Opposition
IF [Highlight Match] THEN [Opposition] END
And add this to the Label shelf and set to be an Attribute. Adjust the Label property so it’s just labelling the line ends and label end of line. Ensure labels set to overlap to, and set font to bold and to match mark colour and align top right.
Alias the field H/A (right click field > aliases) so they display as Home and Away. Then add the following fields to the Tooltip : Category, Cup/League, H/A, Opposition, Round and Date. Also add the following measures to the Tooltip : Distance – Total (KM), Distance per Min, HI Distance – Total (M), HSR – Total (M), Speed – Max (m/s). Then adjust and format the Tooltip
To create the vertical lines, add another copy of Measure Values to the Rows which will create a Measure Values (2) marks card. Remove all the fields from this card except the Game ID. And then move Game ID to the Path shelf which has the effect of creating the vertical lines
Adjust the Colour of the line and then add Measure Names to the Label shelf. Set the label to Min/Max by pane, for the Measure Values field. Align centrally.
Set the chart to dual axis and synchronise the axis. Hide all the axis, and the Measure Names labels at the bottom (uncheck show header against the pills). Remove all row/column dividers and gridlines/zero lines etc. Set the background colour (if desired).
Adding the interactivity
Add the chart to a dashboard then create a dashboard parameter action
Set Opposition
On select of the viz, set the Opposition Parameter parameter, with the value from the Opposition field. Set the value to <empty> when cleared.
Adding the information panel
Add a floating Text object onto the dashboard. Copy and paste the information from the challenge page. Resize and reposition the text box as required. Set the background of the text object and add a border to make it prominent. Then from the context menu of the object, select the Add Show/Hide Button option. The Show/Hide object will appear as an X. Move this into the required position and re-adjust the text object to suit too.
And that should then be it. My published viz is here.
Donna + DataViz 