Can you visualise survey data?

It was Sean’s first challenge of 2023, and he asked us to recreate this chart . Sean referred to it in the requirements as a floating bar chart, others may refer to it as a diverging bar chart or likert chart.

Modelling the data

Fake survey data was provided which was structured with each question in a column

The first thing we need to do is to pivot the data, so we have 1 row per question, per respondent. On the data source tab in Tableau Desktop, multi-select the question columns (Ctrl-click), then click the context menu for one of the selected columns and select Pivot

Due to the width of the screen and the number of columns, you may have to do this in multiple steps. Just multi-select the next set of columns, and select Add Data to Pivot from the context menu instead.

Once complete you should have a data set with 3 columns

Rename Pivot Field Names to Question and Pivot Field Values to Response.

Building the calculations

All the questions have 5 options to respond to ranging from Strongly Disagree -> Disagree -> Neutral -> Agree – > Strongly Agree. Sean hinted we would need to group the questions based on their sentiment. Some questions were worded in a positive way, which meant ‘agree’ responses could be taken as a ‘good thing’. While other questions were worded in a negative way, meaning agree’ responses were actually a ‘bad thing’.

So the first thing to do was to group the questions. I used the group functionality (right click on Question -> Create -> Group).


All the questions where I deemed an ‘agree’ answer was a favourable response to the product I grouped under Positive.

All the question where I deemed an ‘agree’ answer was a non-favourable response to the product I grouped under Negative.

I then wanted to bucket the responses into 3 groups – Disagree, Neutral and Agree – based on the combination of the actual Response, and the Sentiment grouping.

Response Group

IF [Sentiment] = ‘Positive’ THEN
IF CONTAINS([Response],’Disagree’) THEN ‘Disagree’
ELSEIF CONTAINS([Response], ‘Agree’) THEN ‘Agree’
ELSE ‘Neutral’
ELSEIF [Sentiment] = ‘Negative’ THEN
IF CONTAINS([Response],’Disagree’) THEN ‘Agree’
ELSEIF CONTAINS([Response], ‘Agree’) THEN ‘Disagree’
ELSE ‘Neutral’

With this I am essentially swapping disagree responses against questions of a negative sentiment into the agree/positive grouping and vice versa.

Finally I need another couple of calculations just to sense check were we are

Count Respondents


Question – Display

TRIM( SPLIT( [Question], “-“, -1 ) )

This removes the text that precedes the ‘-‘ from the Question string. I actually created this field using the Split functionality. Right click on Question -> Transform -> Custom Split. Use the separator – and split off the Last 1 columns

This will generate the field int he dimensions pane which I then renamed.

If we put all these out into a table, we can see how the data is shaping up

The next step is to work out the NPS values. When I’ve dealt with NPS in the past, I’ve referred to terms such as Promoters and Detractors, with an NPS score being computed as (Number of Promoters – Number of Detractors)/Total No of Respondents.

In this case a Promoter is anyone who is in the agree bucket.


IF [Response Group]=’Agree’ THEN 1 ELSE 0 END

while a Detractor is anyone who is in the disagree bucket


IF [Response Group]=’Disagree’ THEN 1 ELSE 0 END

The total number of respondents is

Total Respondents


So I can now calculate


(SUM([Promoters])-SUM([Detractors]))/[Count Respondents]

Format this to percentage with 0 dp.

Let’s put this into another table so we can validate the data.

Ok, so this gives us the data required to plot the circle marks.

But we need to work out what’s need to plot the bars. And for this we need to know the respondents who are neither Promoters or Detractors.


IF [Response Group] = ‘Neutral’ THEN 1 ELSE 0 END

We are going to plot 2 bars for each row. 1 bar that represents the proportion of respondents who are in the positive side and 1 bar to represent the proportion of respondents who are in the negative side. The positive side mainly consist of the promoters, while the negative is the detractors. However, we need to consider the neutrals too, and we do this by halving the number and making half positive and half negative. Let’s see what I mean

# Respondents – Positive

(SUM([Promoters]) + (SUM([Neutrals])/2))/SUM([Total Respondents])

format this to percentage with 0 dp.


# Respondents – Negative

-1 * ((SUM([Detractors]) + (SUM([Neutrals])/2))/SUM([Total Respondents]))

format this to percentage with 0 dp.

In this case the result is multipled by -1 as we want the bar to be plotted on the negative side of the axis.

Let’s pop these into the table too.

Ok. So now we have the core building blocks required to start building the viz.

Building the Likert Chart

Add Question – Display to Rows and # Respondents – Positive to Columns.

Click and drag # Respondents – Negative onto the canvas and drop the pill on the bottom axis when you see the 2 green columns icon appear.

This will automatically add the pill onto the same axis, and the view will update to use Measure Names and Measure Values

Remove Measure Names from the Rows and the bars will all appear on the same row. Sort the Question – Display field by NPS descending.

Add Response Group to the Colour shelf and adjust colours accordingly (using the Nuriel Stone palette). I also set the transparency to about 70%. You may need to reorder the values of Response Group – just do this manually in the colour legend if need be.

Increase the width of each row a bit, expand the Question – Display column, and remove the tooltips from displaying. Format the Question – Display column so it is aligned left and the font is a bit bolder/darker. Remove the Question – Display column heading (right click label > hide field labels for rows).

Format the axis to display as percentages to 0 dp, then edit the axis and

  • remove the title
  • fix the axis from -1.05 to 1.05
  • fix the tick marks to display every 0.5

Adjust the row banding to show, and remove any column/row dividers. Remove all gridlines. Adjust the column axis rulers and tick marks to be a solid dark line. Set the zero line for the columns to be a wider solid white line – it should just about be visible through the viz.

Now we need to label the agree and the disagree sections only. For this we need additional fields

Label – Positive

If MIN([Response Group]) = ‘Agree’ THEN [# Respondents – Positive] END


Label – Negative

If MIN([Response Group]) = ‘Disagree’ THEN [# Respondents – Negative] END

Add both these fields to the Label shelf and arrange side by side. Manually adjust the colour of the font to a dark grey.

This is the core design of a likert chart. It could be bult using dual axis too, but that wasn’t an option in this instance, as we need to add the additional NPS circles to the chart.

Adding the NPS circles

Add NPS to Columns. On the NPS marks card, remove all the fields, and change the mark type to circle. Add NPS to the Colour shelf, and adjust the colours so they range from the same yellow/green to blue/teal colours already used. The easiest way to do this is to click on the coloured square at each of the edit colour dialog and use the pick screen colour option to select the colours already used.

Set the colour to be 100% opacity and add a white border around the circles (via the Colour shelf)

Set the chart to be dual axis and synchronise the axis. Change the mark types back to bar and circle accordingly if they changed.

Now we can see some of the labels on the bar chart are positioned where the circles are/very close to them. Manually move those affected – click on the section of the bar to highlight it, then click on the label and when the cursor changes to a crosshatch, drag the label to where you want. Note – this isn’t something I would typically do if the data was to change behind this viz without any manual involvement.

Hide the top axis (uncheck show header) and remove all row/column dividers.

Now we just need to label the circles. I need additional fields for this. Firstly, the NPS field is on the wrong format – it’s actually stored as a decimal, formatted to a percentage. Secondly, I ended up with two label fields, due to the colouring. Sometimes when you add a label to a mark, the colour of the text may display black or white depending on the contrast with the underlying colour. Tableau automatically does this, and it isn’t something you can really control. As soon as you start manually adjusting any colours, it can be hard to get back to the right state. I fiddled around trying to get things to work properly for a while using the auto colouring (which involved rebuilding by starting with the NPS dot plot and then adding the bars), but it still wasn’t a ‘cast-iron’ solution. So I ended up creating

Label -NPS-Black

IF [NPS] * 100 <=70 THEN [NPS] *100 END


IF [NPS]*100 >70 THEN [NPS]*100 END

I just chose an arbitrary ‘cut off’ for when the colour might change.

Add both these fields to the Label shelf of the NPS marks card, and arrange them side by side. Edit to font so the Label-NPS-White field is coloured with white font, and the other black. Set the alignment of the text to be middle centre. Adjust the size of the circles if required. Verify tooltips don’t exist on any marks.

Stopping the chart from being clicked

In the solution, if you try to click on the bar chart, you get no interaction – nothing highlights, like it does if you click on the axis or the questions. On a dashboard, this is managed by adding a floating blank object and carefully placing it over the top of the section you don’t want to be clickable.

And that should be it! My published viz is here.

I have to admit I did have a few false starts when building this out, and its highly probable this could have been created without all the fields I ended up with. It’s sometimes just the path your brain follows, and there’s nothing wrong with that!

Happy vizzin’!



Calculating Year-on-Year Percentage Change

In this week’s #WOW2023 challenge, Erica asked us to show the data for the selected year for a set of EU countries, but within the tooltip, provide additional information as to how the data compared to the same month in the previous year.

A note about the data

For this we needed to use the EU Superstore data set, a copy of which was provided via a link in the challenge page. Since part of validating whether I’ve done the right thing is to have the same numbers, I often tend to use any link to the data provided, rather than use any local references I may have to data sets (ie I have so many instances of Superstore on my local machine due to the number of Tableau instances I have installed). I did find however, that using the data from the link Erica provided, I ended up with a data set spanning 2015-2018 rather than 2016-2019. However I quickly saw that the numbers for each year had just been shifted by a year, so 2018 in Erica’s solution was equivalent to the 2017 data I had.

The viz is also just focussed on a subset of 6 countries. I chose to add a data source filter on Country to restrict the data to just those countries required (right click data source in the data pane -> Add data source filter).

Building out the required data calculations

The data will be controlled by two parameters relating to the Year and the Country


integer parameter, defaulted to 2017, displayed using the 2017 format (ie no thousand separators). 3 options available in a list : 2016,2017,2018


string parameter defaulted to Germany. This is a list parameter and rather than type the values, I chose the option Add values from -> Country

We need to use the pYear parameter to determine the data we want to display, rather than simply apply a quick filter on Order Date, as we need to reference data from across years. Simply filtering by Order Date = 2017 will remove all the data except that for 2017, and so we won’t be able to work out the difference from the previous year. Instead we create

Sales Selected Year

ZN(IF [pYear] = YEAR([Order Date]) THEN [Sales] END)

Wrapping within ZN means the field will return 0 if there is no data.

Format this to € with 0 dp.

We can then also work out

Sales Prior Year

ZN(IF [pYear]-1 = YEAR([Order Date]) THEN [Sales] END)

which then means we can work out

Diff From PY

(SUM([Sales Selected Year]) – SUM([Sales Prior Year])) / SUM([Sales Prior Year])

custom format this to +0.0%;-0.0%;0.0%

This will display a positive change in the format +12.1%, a negative change as -12.1% and no change as 0.0%

Let’s pop all this information out in a tabular view along with the Country and Order Date to sense check the numbers

This gives us the core data to build the basic viz.

Core viz

Add Order Date at the Month date part level (blue pill) to Columns and Sales Selected Year to Rows and Country to Colour. Make sure it’s a line chart (use Show Me) if need be. Adjust the colours accordingly.

Amend the Order Date axis, so the month names are in the abbreviated format (right click on the bottom axis -> format)

Identifying the selected country

We need to change the colours of the lines to only show a coloured line for the selected country. For this we need

Is Selected Country


Add this field to the Detail shelf . Then click on the small icon to the left of the Is Selected Country pill, and select the Colour option.

This will mean that both Country and Is Selected Country are on the Colour shelf, and the colour legend will have changed to a combo of both pills

Move the Is Selected Country pill so it is positioned above the Country pill in the marks card section, and this will swap the order to be True | Country instead. Modify all the colours in the legend that start with False to be ‘grey’. Change the pCountry parameter and check the right colour combinations are displayed.

Change the Sort on the Is Selected Country pill so it is sorted by Data source order descending. This will ensure the coloured line is in front of the grey lines.

Adding the circles on the marks

We need a new field that will just identify the Sales for the selected country and selected year.

Sales Selected Year & Country

IF [Is Selected Country] AND [Year Order Date]=[pYear] THEN [Sales] END

Add this to Rows, then make the chart dual axis and synchronise the axis. Change the mark type of the Sales Selected Year & Country marks card to a circle, and adjust the Size to suit.

Finalising the line chart

Add Diff From PY onto the Tooltip shelf of the All marks card.

Create a new field

Month Order Date

DATENAME(‘month’, [Order Date])

and also add this to the Tooltip shelf. Adjust the tooltip to match the required formatting.

Hide the right hand axis (uncheck Show header).

Edit the left hand axis and delete the title, fix the axis from 0 to 50,000 and verify the axis ticks are displaying every 10,000 units.

Hide the 60 nulls indicator (right click -> hide indicator).

Remove the row & column dividers. Hide the Order Date column heading (right click -> hide field labels for columns)

Create the Country name for the heading

On a new sheet

  • Add Country to Rows
  • Add Is Selected Country to Filters and set to True
  • Add Country to Colour and then also add Is Selected Country to colour in the way described above.
  • Add Country to Label
  • Adjust the formatting of the Text so it is much larger font.

Hide the Country column (uncheck show header), and remove all row/column dividers. Ensure the tooltip won’t display.

Putting it all together

I used a horizontal container placed above the core viz. In the horizontal container I added blank objects, a text object, and the Country label sheet. I adjusted the size of the objects to leave space to then float the parameters. The parameters were resized to around 25 pixels so they just displayed the arrow part of the parameter. All this was a little bit of trial and error, and I did find that after publishing to Tableau Public, I had to adjust this section again using web edit.

My published viz is here.

Happy vizzin’!


Can you visualise orders?

For Luke’s first challenge of 2023, he asked us to recreate this visualisation which showed multiple chart types (donut charts and bar charts) within a tabular layout, all built on a single sheet.

As with most challenges, I’m going to first work on building out the calculations needed to provide the data being presented.

Building the calculations

Firstly we need to identify the number of orders, based on unique Order IDs

Total Orders

COUNTD([Order ID])

and then to get the average order value we need

Avg Order Size

SUM([Sales]) / [Total Orders]

format this to $ with 0 dp.

In order to build the donut chart, we will need two measures for each region; one which shows the % number of orders for that region, and one which shows the % number of orders across the remaining regions.

% Orders for Region

[Total Orders]/SUM({FIXED:COUNTD([Order ID])})

Format this to % with 0 dp

and then

% Orders for not Region

1 – [% Orders For Region]

format this to % with 0 dp.

Pop all these out in a table along with Sales formatted to $ with 0 dp, and Sort the Region field based on Total Orders descending.

Building the table view

Add Region to Rows and sort by Total Orders descending.

All the columns are going to be managed by what I refer to as a ‘fake axis’. Double click into the Columns shelf and type in MIN(0). This will create an axis.

Change the mark type to Text and add Total Orders to the Text shelf. Remove all the text from the Tooltip dialog box.

This is our 1st ‘column’ in the table.

Create another column by adding another instance of MIN(0) to Columns.

Change the mark type of this 2nd MIN(0) marks card to Pie Chart and remove Total Orders from the text shelf.

Add Measure Names to the Filters shelf, and ensure only % Orders for Region and % Orders for not Region are selected.

Add Measure Values to the Angle shelf of the pie chart, and add Measure Names to Colour. Adjust the colours to suit and ensure a white border is added (via the Colour shelf options). Again remove all the text in the Tooltip dialog.

Now add another instance of MIN(0) to Columns.

Change the mark type of this marks card to Circle and set the colour to white. Add % Orders for Region to the Label and align centre.

Now make this Min(0) pill to be dual axis and synchronise the axis. Reduce the size of the circle mark type marks card, so some of the pie chart is visible. Remove Tooltip text again.

Add another instance of MIN(0) for our ‘3rd’ column. Set the mark type to Text and add Sales to the Text shelf, and remove the tooltip text.

For the final column, add Avg Order Size to Columns. Change the mark type to bar. Reduce the size and adjust the Colour to suit. Add Avg Order Size to the Label and remove all tooltip text.

Remove all gridlines zero lines and column dividers. Add row dividers at the highest level so you have a divider per row. Uncheck Show Header on the first MIN(0) pill to hide the axes, and right click the Region column label and Hide field labels for rows.

This gives you the viz you need to add to a dashboard. Use a horizontal container with a blank and 4 text boxes positioned above the viz to provide the label heading to the columns.


you can do what I did, and having not noticed the comment about the textboxes, used further instances of MIN(0) to make every ‘column’ a dual axis so I could use the axis label for the secondary axis displayed at the top to provide the required labels.

I set the mark type of these additional MIN(0) axis to polygon, and ensured no other pills existed on the marks shelf. The tick marks on all the top and bottom axes had to be set to none, and the title on all the bottom axis had to be deleted.

The benefit of using the text boxes option is that you aren’t left with a ‘blank’ space underneath the chart. However wide your axis is at the top to display the labels, will be repeated as ‘blank space’ underneath the chart too (vote up this idea to prevent this).

My published viz is here.

Happy vizzin’!


Can you use Image Role & Dynamic Zone Visibility?

New year means only one thing – a change to the WorkoutWednesday hashtag! We’re now #WOW2023, and it was Lorna’s turn to kick the next year of challenges off with a focus on the latest Tableau features. This challenge uses the image role function introduced in 2022.4 and the dynamic role visibility feature introduced in 2022.3 (so you need to make sure you’re using at least 2022.4 to complete this).

Setting up the data source

Lorna provided an excel based data sheet which I downloaded. It contains several tabs, but the two you need to relate are Player Stats and Player Positions, relating as Lorna describes on both the Game ID and the Player Name

The data needs to be filtered to just the men’s competition, so I did this by adding a data source filter (right click on the data source -> Edit Data Source Filter) where Comp = Rugby League World Cup. Doing this meant I didn’t have to worry about adding fields to the Filter shelf at all.

Building the scatter plot

For the basic chart, simply add All Run Metres to Columns and Passes to Rows, then add Game ID and Name to the Detail shelf.

We need to identify which player and game is selected when a user clicks, so we need parameters to capture these.


integer parameter defaulted to 0


string parameter defaulted to nothing/empty string/ ”

Show these parameters on the canvas, and then manually enter Brandon Smith and 21.

We now need calculated fields to reference the parameter values

Is Selected Player

[Name] = [pSelectedPlayer]

Is Selected Game

[Game ID] = [pSelectedGameID]

Both return a boolean True / False value, and we can then determine which combination of player/game each mark represents, which we need in order to colour the marks.

Colour : Scatter

IF [Is Selected Game] AND [Is Selected Player] THEN ‘Both’
ELSEIF [Is Selected Player] THEN ‘Player’
ELSEIF [Is Selected Game] THEN ‘Game’
ELSE ‘Neither’

Add this field to the Colour shelf, change the mark type to Circle and adjust the colours accordingly. Manually sort the order of the values so that the light grey ‘Neither’ option is listed last (which means it will always be underneath any other mark).

While not stated in the requirements, the marks for the selected player are larger than the others, so add Is Selected Player to the Size shelf, and adjust the size range to suit.

Adjust the tooltip and name the sheet Scatter.

Building the Bar Chart

On a new sheet, add Player Image and Game ID to Rows and All Run Metres and Passes to Columns. Add Is Selected Player to Filter and set to True. Edit the title of the sheet so it references the pSelectedPlayer parameter, and align centre.

Set the Player Image field to use the actual image of the player stored at the URL, by clicking on the ABC datatype icon to the left of the field in the data pane, and selecting Image Role -> URL

This will change the data type icon and update the view to show the actual player image

Add Is Selected Game to the Colour shelf on the All marks card and adjust accordingly. Remove the tooltips.

We need to show the axis titles at the top, rather than bottom. To do this add another instance of the All Run Metres field to the right of the existing one. Make dual axis and synchronise axis. Repeat with the Passes field by adding another instance to the right of the existing one and making dual axis again.

Set the mark type on the All marks card back to bar.

Right click on the All Run Metres bottom axis, remove the title, and set the tick marks to none. Repeat for the Passes bottom axis.

Right click on the All Run Metres top axis, and just set the tick marks to none. Again repeat for the Passes top axis.

Adjust the font of both axis title (right click axis -> format; I just set to Tableau Book) and then manually decrease the height of the axis.

Finally click the Label button on the All marks card and check the Show mark labels check box.

Then remove all column and row divider lines.

Adding the interactivity

On a dashboard, add a Vertical Layout Container, then add the bar chart into it and then add the scatter plot underneath. Remove the container that contains all the legends and parameters – we don’t need to show any of these. Hide the title of the scatter plot.

Add a dashboard parameter action to set the pSelectedPlayer parameter on select of a circle on the scatter plot. Set this parameter to pass the Name field into the parameter, and when clearing the selection, set the value to empty string/ nothing / ”

Select Player

Add another similar dashboard parameter action, which sets the pSelectedGame parameter in a similar way. This time, the Game ID field is passed into the parameter which is then set to 0 when the selection is cleared.

Select Game

If you test clicking on the scatter plot, you should now see the bar chart data disappear (although some white space about the height of the title remains) and you’ll also see that the circle selected is ‘highlighted’ compared to all the others.

To resolve the highlighting issue, navigate back to the scatter plot sheet, create a new calculated field called Dummy which just contains the string ‘Dummy’. Add this field to the Detail shelf.

Navigate back to the dashboard, and add a dashboard highlight action, that on select on the scatter plot, targets the same sheet on the same dashboard, but only focus on the Dummy field.

Deselect Marks

To make the whole section where the player bar chart is displayed, completely collapse, we need another parameter


boolean parameter defaulted to false

We also need a boolean calculated field called

Show Player Detail


Add this to the Detail shelf of the Scatter plot sheet.

Then navigate back to the dashboard, and create another parameter dashboard action, which on select of a circle on the scatter plot, sets the pShowPlayerDetail parameter based on the value from the Show Player Detail field. When the selection is cleared, reset the parameter to False.

Show Player Detail

Finally select the bar chart on the dashboard, then click the Layout tab on the left and side, and tick the Control visibility using value check box, and in the drop down select Parameters – > pShowPlayerDetail.

Now when a circle is clicked on, the bar chart section will completely disappear and the scatter plot will fill up the whole space.

Finalise the dashboard by adding the title and legend. My published viz is here.

Happy vizzin’!


Let’s show quarterly sales with end-user flexibility

It was Sean’s final #WOW2022 challenge of the year, and he set this task to provide alternative options for visualising time series across dimensions with high cardinality.

I had a play with Sean’s viz before I started tackling the challenge and noticed the following behaviour in addition to the requirements listed, which may or may not have been intentional.

When the cardinality of the dimension to display was more than 1 higher than the top n parameter, and the Show Others? option was set to Group By, the sales value to display was based on average sales rather than sum (as indicated in the requirements), and all the values not in the top n, were grouped under an ‘Other (Avg)’ label. But if the cardinality of the dimension was only 1 more than the top n parameter (so there was essentially only 1 value within ‘other’), then this value would display as itself (ie not labelled ‘other’) and the sales values would be summed rather than averaged (eg if the lines were to be split based on Ship Mode, and the top n was set to top 3 and Show Others? set to Group By, all four ship modes would display with the sum of sales rather than average).

This observation meant some of the calculations were slightly more complex than what I thought they would need to be initially, as I had to build in logic based on the number of values within a dimension.

So with that understood, let’s build the calcs…

Defining the calculations

Firstly we need some parameters


String parameter defaulted to Subcategory, with the list of possible dimensions to split the chart by


integer parameter defaulted to 5


I used an integer parameter with values 0 and 1 which I ‘aliased’ to the relevant values, defaulted to ‘Group Others’

I then needed to determine which dimension to be used based on the selected from the pDimensionToDisplay parameter.


CASE [pDimensionToDisplay]
WHEN ‘Subcategory’ THEN [Sub-Category]
WHEN ‘State’ THEN [State/Province]
WHEN ‘Ship Mode’ THEN [Ship Mode]
WHEN ‘Segment’ THEN [Segment]
ELSE ‘All’

From this, I could then use a Set to determine which of the values would be in the top n. Create a set of off DimensionSelected (right click -> Create -> Set)

Dimension Selected Set

select the Top tab, and create set based on the pTop parameter of Sales

To check this is working as expected, on a sheet, add DimensionSelected and Dimension Selected Set to Rows, add Sales to Text and sort by Sales descending. Show the pTop and pDimensionToDisplay parameters. Change the parameters and observe the results behave as expected.

Now we need to determine how many values are not in the set, ie, how many of the DimensionSelected values display as ‘Out’ in the above image.

Count Non-Set Items

{FIXED:COUNTD(IF NOT([Dimension Selected Set]) THEN [DimensionSelected] END)}

If the entry is not in the set, then return the entry, and the count the number of distinct entries we have. Using the FIXED level of detail calculation, wraps the value across every row.

Now this is understood, we need to work out whether we want to group the ‘non-set’ values under ‘other’

Dimension To Display

IF [pShowOthers]=0 AND NOT([Dimension Selected Set]) AND [Count Non-Set Items]>1 THEN ‘Other (Avg)’
ELSE [DimensionSelected]

If we’re opting to ‘group’ the values, and the entry isn’t in the set, and we’ve got more than 1 entry that isn’t in the set, then we can label as ‘Other (Avg)’, otherwise, we just want the dimension value.

We use similar logic to determine whether to display the SUM or AVG Sales.

Sales to Display

IF [pShowOthers]=0 AND SUM([Count Non-Set Items]) > 1 THEN AVG([Sales])

Format this to $ with 0 dp.

We can then remove the DimensionSelected from our view, and test the behaviour, switching the pShowOthers parameter

Building the core viz

On a new sheet, add Order Date set to continuous quarters (green pill) to Columns and Sales To Display to Rows and Dimension To Display to Label. Show all the parameters.

Create a new field


[pShowOthers]=0 AND NOT([Dimension Selected Set]) AND [Count Non-Set Items]>1

add add to Colour shelf, and set colours accordingly.

Add Sub-Category, Ship Mode, State and Segment to the Filter shelf. Show them all, and then test the behaviour is as expected.

Extending the date axis

The date axis in the solution goes beyond 2022 Q4, and means the labels have a bit more ‘breathing space’. To extend the axis, click on Order Date pill in the Columns, and select Extend Date Range -> 6 months.

Then double click into the space next to the Sales To Display pill on the Rows and type MIN(0). This will create a second axis, and the date axis should now display to 2023 Q2

On the MIN(0) marks card, remove the Colour and Dimension To Display pills. Reduce the Size to as small as possible, and set the Colour opacity to 0%. Set the chart to dual axis, and synchronise the axis.

Finally tidy up the chart – hide the right hand axis, remove the title from the date axis, change the title on the left axis. Remove all row and column divider lines, but ensure the axis rulers are displayed. Title the viz referencing the pDimensionToDisplay parameter.

Building the dashboard

When putting the dashboard together, you need to ensure that the filters, parameters and main viz are all contained within a vertical container to ensure the viz ‘fills up’ the space when the controls section is collapsed.

The controls section itself is also a vertical container, which consists of 2 horizontal containers, one of which contains all the filters, and one which contains the parameters.

The layout tab shows how I managed this (I also like to rename the objects to keep better control).

The green Base vertical container is the first container. This essentially contains 3 ‘rows’ – the title, the User Controls vertical container and the viz itself.

The User Controls vertical container, then contains 2 rows itself – the Filters horizontal container (which has a pale grey background colour) and the Fine Tune horizontal container (which has a slightly darker grey background colour).

The User Controls vertical container is then set to ‘hide/show’ by selecting the whole container (click on the object in the item hierarchy on the left), and then selecting Add Show/Hide Button from the context menu. Adjust the button settings as required (I just altered the text displayed on hover) and then position accordingly.

It can take a bit of trial and error to get this right, and to get your containers working as expected. My published viz is here.

Happy vizzin’!


Can you label & sort small multiples?

A colourful #WOW2022 challenge this week set by Kyle Yetter and using his favourite data – Baseball. Let’s jump straight in.

Building the required calculations

First up we need to calculate the core measure the viz is based on – % of wins

Win %


I formatted this to 3 decimal places, then applied a custom number format to remove the leading 0 (custom number format looks like ,##.000;-#,##.000).

We also need to know the number of losses as this is part of the tooltip.


SUM([Games]) – SUM([Wins])

Let’s pop all these out into a table (I formatted all the whole numbers to display without any decimal places).

The viz however isn’t plotting the actual Win%, it’s plotting the difference from 50% (or 0.5), so values less than 50% are negative and those above are positive.

Plot Postion

[Win %] – 0.5

And we also need to know whether the Win% is above 50% or not

Above 50%

[Win %]>0.5

Pop these out onto the table too

The viz also displays the overall Win% for each team, and also uses this to sort the data. As it is used for sorting, we need to use an LoD calculation (rather than a table calculation).

Overall Win% LOD

{FIXED [Team]:SUM([Wins])} / {FIXED [Team]: SUM([Games])}

for each team, get the total wins, and divide by the total games for the team. Format this to 3 dp with no leading 0 as before.

pop this into the view (you’ll see it’s the same value for each row for a single team), and then apply a Sort on the Team field to sort descending by the Overeall Win% LOD.

Now we have the data sorted, we can create the fields needed to build the trellis chart.

I have already blogged challenges relating to trellis charts / small multiples (see here) which in turn reference other blogs in the community, so I’m not going to go into all the details. We just need to build two calculated fields to identify which row and which column each Team will sit in. The table is fixed at 6 columns wide as the data wea re using is static. Some solutions work with a more dynamic layout depending on how many entities you need to display. We’re keeping things simpler.





Add both these fields to the table as discrete dimensions (blue pills), and as they are both table calculations, set them both to Compute Using – > Team.

Building the Core Viz

On a new sheet, add Cols to Columns as discrete dimension, Rows to Rows as discrete dimension and Team to Detail. Set both Rows and Cols to Compute Using Team.

Add Year as continuous (green) pill to Columns and Plot Position to Rows and change the mark type to Bar and reduce the size. Sort the Team field based on Overall Win% LOD descending.

Add Wins, Losses, and Win% to the Tooltip shelf and adjust the tooltip to display as required. Add Above 50% to the Colour shelf (you may need to readjust the size). Leave the colours as they are for now – we’ll deal with this later.

Adding the labels

Create a new calculated field

Dummy Plot

FLOAT(IF [Year]=2000 OR Year = 2020 THEN 0.35 END)

This is basically going to position a mark at height 0.35 but only if the year is either 2000 or 2020. These values were all just based on a bit of trial and error as to what worked to get the desired result.

Also create a field


IF [Year]=2000 THEN [Team] END



IF [Year]=2020 THEN [Overall Win % LOD] END

format this to 3dp and exclude the leading 0.

Add Dummy Plot onto Rows and change the mark type of this measure to circle. Amend the Tooltip of this marks card so it’s empty.

Add LABEL:TEAM and LABEL:Win% to the Label shelf, and adjust the label so both fields sit side by side (only 1 value will only ever actually display). Adjust the table calculation of both the Rows and Cols pills so they now compute using both the Team and the LABEL:Team fields.

Adjust the alignment of the labels so they are positioned bottom centre. Set the font colour to match mark colour and bold.

Then reduce the size of the circle mark to as small as possible, reduce the opacity of the mark colour to 0.

Now make the chart dual axis and synchronise the axis. Remove the Measure Names field that has automatically been added to the All marks card.

Hide all the headers and axis (uncheck Show Header), remove all grid lines, zero line, axis rulers.

Hide the null indicator (bottom right).

Colouring by Team

Copy the colour palette text Kyle provided into your preferences.tps file (usually located in the My Tableau Repository directory). For more information on working with custom colour palettes see thisΒ Tableau help article.

You’ll need to save your workbook and re-open for the new palette to be available for use.

In order to prevent having to manually set all the colours (and believe me you don’t want to do this!), perform the following steps in order

  • Add Team to also be on the Colour shelf. Click on the 3 dots (…) that are to the left of the Team pill on the All marks card, and change it to Colour. This means there are now 2 fields on colour. Move the Team field so it is listed above the Above 50% pill. This means your colour legend should be listed as <Team>, <True|False>
  • Adjust the Sort of the Above 50% pill, so it is manually sorted to list True before False.

  • Now change the Sort on the Team field so it is sorted alphabetically ascending instead. This will cause the viz to change its sort order, but don’t worry for now. It also changes the list on the colour legend, so ARI, True is listed first then ARI, False etc.

  • Now edit the Colour Legend and select the new MLB Team Colours palette we added. Click the Assign Palette button to automatically assign the colours. As we’ve made sure the entries listed are in the right order, they should get the correct colours.

  • Change the Sort on the Team field back to be based on Overall Win% LOD descending

And that should be it. You can now add the viz to a dashboard and publish. My published version is here.

Happy vizzin’!


Let’s Build a Map!

Inspired by a viz from Klaus Schulte, Sean Miller set this week’s challenge to recreate a hex map with state shapes using Superstore.

Building the data model

I referenced this Tableau blog post and downloaded the HexmapPlots excel file included. I then used a relationship to ‘join’ the Sample – Superstore excel file I was using with the HexmapPlots file, joining on State/Province = State

Since I had the other blog post already open, I then followed the steps included to start building the map.

Building the hex map

Add Column to Columns and aggregate to AVG, and add Row to Rows and also aggregate to AVG. Add State/Province to Detail. Edit the Rows axis and set to be Reversed.

Note – it’s possible you may have extra States showing. As I’m writing I’ve realised I’m rebuilding against an extracted data source that has a filter I originally applied as a global filter, which has now been included in the extract. So you may need to add State/Province to the Filter shelf, and set to exclude NULL and District of Colombia. This filter will need to be applied to all sheets you build.

Change the mark type to Shape and select a Hex shape. I already have a palette full of Hex shapes, but the blog post provides a shape to use and add as a custom shape if you haven’t got one. Increase the size of the marks.

Create a new field

Profit Ratio

SUM([Profit]) / Sum([Sales])

format this to % with 1 dp, and then add to the Colour shelf.

Now add a second instance of Row to the Rows shelf and set to AVG again. Set the mark type of this marks card to Map. Remove the Profit Ratio field from Colour on this card too. Assuming your Map location is set to USA, you should have State outlines depicted (Map -> Edit Location).

Set the chart to be dual axis and synchronise the axis. The State shape axis should now be inverted. Independently adjust the sizes of the Hex shape and the state shapes, so the states sit inside the hexagons.

Right click on the right axis and move marks to back. The adjust the Colour of the hex shapes so its around 85% transparent.

Now adjust the Tooltip on the hex marks to match the requirement.

To fill-out the available area, I also chose to fix both the axis (right click axis -> edit axis). The Column x-axis I set to range from 1 – 12, and the Row y-axis, I set to range from -0.9 – 9. Then hide all axis, and remove all row/column gridlines, divider lines, axis lines and zero lines. Hide the 10 unknown indicator, and set the background colour of the whole worksheet to a pale grey.

Building the Line Chart

This is super simple, Tableau 101 πŸ™‚

Add Order Date to Columns and set to be a continuous month (green pill showing month-year). Add Sales to Rows. Change the colour of the line to grey. Hide the x-axis and remove all gridlines, dividers, axis lines and zero lines. Set the background colour of the worksheet to None (ie transparent). Update the tooltip.

Building the Scatter Plot

Add Sales to Columns, and Profit to Rows. Add State/Province to Detail shelf and add Profit Ratio to Colour. Change the mark type to circle. Remove all gridlines, row column dividers and axis rulers. Only the zero lines should remain. Adjust the tooltip and set the worksheet background to None (transparent).

Putting it all together

Create a dashboard and set the size as stated. Set the background of the dashboard to pale grey (Dashboard – > Format).

Add the Hex map, and hide the title. Click on the Profit Ratio legend object and set to be floating. Then remove the right hand vertical container. Move the Profit Ratio legend to a suitable location.

Then add a text box as a floating object and use it to create the title. Add both the trend line and the scatter plot charts as floating objects without titles. Just position them as required. You can always use gridlines (Dashboard -> Show Grid) to help you line things up.

Finally add the interactivity.

Add a highlight dashboard action which highlights the hex map and the scatter plot when either of the other is selected ‘on hover’, and just targets the State/Province field.

Then add a Filter action which on hover of the Trend chart, targets the remaining charts.

And hopefully that’s it. My published viz is here.

Happy vizzin’!


Average Patient Wait Times

Erica set this challenge this week to provide the ability to compare the average wait time for patients in a specific hour on a specific day, against the ‘overall average’ for the same day of week, month and hour.

As with most challenges, I’m going to first work out what calculations I need in a tabular form, and then I’ll build the charts.

  • Building the Calculations
  • Building the Wait Time Chart
  • Building the Patient Count Chart

Building the Calculations

The Date field contains the datetime the patient entered the hospital. The first step is to create a field that stores the day only

Date Day


From this I could then create a parameter

Choose an Event Date

date parameter, defaulted to 6th April 2020, and custom formatted to display in dddd, d mmmm yyyy format. Values displayed were a list added from the Date Day field.

I also created several other fields based off of the Date field:

Month of Date


if the Date is Friday, 10 April 2020 16:53, then this field returns April.

Day of Date


if the Date is Friday, 10 April 2020 16:53, then this field returns Friday.

Hour of Date

DATEPART(‘hour’, [Date])

if the Date is Friday, 10 April 2020 16:53, then this field returns 16. I added this to the Dimensions (top half) of the data pane.

The viz being displayed only cares about data related to the day of the week and the month of the date the user selects, so we can also create

Records to Keep

DATENAME(‘weekday’, [Choose an Event Date]) = [Day Of Date]
DATENAME(‘month’, [Choose an Event Date]) = [Month of Date]

On a new sheet, add this to the Filter shelf and set to True. When Choose an Event Date is Monday, 06 April 2020, then Records To Keep will ensure only rows in the data set relating to Mondays in April will display. Let’s build this.

Add Hour of Date, Day of Date, Month of Date to Rows, and then also add Date as a discrete exact date (blue pill).

You can see that all the records are for Monday and April. And by grouping by Hour of Date, you can see how many ‘admissions’ were made during each hour time frame. Remove Day of Date and Month of Date – these are superfluous and I just added so we could verify the data is as expected.

The measures we need are patient count and patient wait time. I created

Count Patients

COUNTD([Patient Id])

(although the count of the dataset will yield the same results).

Add Count Patients and Patient Waittime into the table.

This is the information that’s going to help us get the overall average for each hour (the line chart data). But we also need to get a handle on the data associated to the date the user wants to compare against (the bar chart).

Count Patients Selected Date

COUNTD(IF [Choose an Event Date] = [Date Day] THEN [Patient Id] END)

Wait Time Selected Date

IF [Choose an Event Date] = [Date Day] THEN [Patient Waittime] END

Add these onto the table – you’ll need to scroll down a bit to see values in these columns

So what we’re aiming for, using the hour from 23:00-00:00 as an example: There are 3 admissions on Mondays in April during this hour, 2 of which happen to be on the date we’re interested in. So the overall average is the sum of the Patient Waittime of these 3 records, divided by 3 (650.5 / 3 = 216.83), and the average for the actual date (6th April) in that hour is the sum of the Wait Time Selected Date for the 2 records, divided by 2 (509.5 / 2 = 254.75).

If we remove Date from the table now, we can see the values we need, that I’ve referenced above.

So let’s now create the average fields we need

Avg Wait Time

SUM([Patient Waittime])/[Count Patients]

Avg Wait Time Selected Date

SUM([Wait Time Selected Date]) / [Count Patients Selected Date]

Pop these into the table

This gives is the key measures we need to plot, but we need some additional fields to display on the tooltips.

Avg Wait Time hh:mm

[Avg Wait Time]/24/60

This converts the value we have in minutes as a proportion of the number of minutes in a day. Apply a custom number format of h”h” mm”mins”

Do the same for

Avg Wait Time Selected Date hh:mm

[Avg Wait Time Selected Date]/24/60

Apply a custom number format of h”h” mm”mins”

Add these to the table.

For the tooltip we need to show the hour start/end.

TOOLTIP: Hour of Date From

[Hour of Date]

custom format this to 00.00

TOOLTIP: Hour of Date To

IF [Hour of Date] =23 THEN 0
ELSE [Hour of Date]+1

custom format this to 00.00.

We can now start building the charts.

Building the Wait Time Chart

On a new sheet, add Records To Keep = True to the Filter shelf. Add Hour of Date as continuous dimension (green pill) to Columns and Avg Wait Time Selected Date to Rows. Change the mark type to Bar.

Then add Avg Wait Time to Rows and change the mark type of this measure to Line. Make the chart dual axis, synchronise the axis and adjust the colours of the Measure Names legend.

Add TOOLTIP: Hour of Date From and TOOLTIP: Hour of Date To and Avg Wait Time Selected Date hh:mm to the Tooltip of the bar marks card, and adjust the tooltip accordingly.

Add TOOLTIP: Hour of Date From, TOOLTIP: Hour of Date To, Avg Wait Time hh:mm, Day of Date and Month of Date to the Tooltip of the line marks card, and adjust the tooltip accordingly.

Remove the right hand axis. Remove the title of the bottom axis. Change the title on the left hand axis. Hide the ‘9 nulls’ indicator (right click). Remove column gridlines, all zero lines, all row and column dividers. Keep axis rulers. Format the numbers on the x-axis.

Change the data type of the Hour Of Date field to be a decimal, then edit the x-axis and fix to display from -0.9 to 23.9.

Amend the title of the chart to match the title on the dashboard.

Building the Patient Count Chart

On a new sheet, add Records To Keep = True to Filter, Hour of Date as continuous dimension to Columns and Count Patients Selected Date to Rows. Change Mark Type to Bar.

Right click on the y-axis and edit the axis. Remove the title, and check the Reversed checkbox to invert the axis. Edit the x-axis and fix from -0.9 to 23.9 as before.

Add TOOLTIP: Hour of Date From and TOOLTIP: Hour of Date To to the Tooltip and adjust accordingly.

Set the colour of the bars to match the same blue of the bars in the wait time chart, then adjust the opacity to 50%.

Remove all gridlines and zero lines. Retain axis ruler for both row and columns. Retain row divider against the pane only. Hide the x-axis.

We need to display a label for ‘Number of Patients’ on this chart. We will position it based on the highest value being displayed (rather than hardcoding a constant value). For this we create a new calculated field

Ref Line

WINDOW_MAX([Count Patients Selected Date]) +1

This returns the maximum value of the Count Patients Selected Date field, adds 1 and then ‘spreads’ that value across all the ‘rows’ of data.

Add this field to the Detail shelf. Then right click on the y-axis and Add Reference Line.

Add the reference line based on the average of the Ref Line field, and label it Number of Patients. Don’t show the tooltip, or display any line.

Once added, then format the reference line, and adjust the size and position of the text.

The sheets can now be added to a dashboard, placing them above each other. They should both be set to Fit Entire View. The width of the y-axis on the Patient Count chart will need to be manually adjusted so it is in line with the Wait time chart, and ensures the Hour columns are aligned..

My published viz is here.

Happy vizzin’!


What is the lifetime value of customers?

Luke Stanke set the #WOW2022 challenge this week (see here) asking us to visualise the lifetime value of customers. I have to admit, I did struggle to really understand what was being requested, and to get the numbers to match Luke’s. I ended up referring to my own blog post on a similar challenge Ann Jackson set in week 2 of 2021 to get the calculations I needed πŸ™‚

We first need to define the quarter that each customer made their first purchase.

Customer First Order Quarter

DATE(DATETRUNC(‘quarter’, {FIXED [Customer ID]:MIN([Order Date])}))

The {FIXED LOD} calculation returns the minimum order date per customer, then truncates this to the first day of the quarter that date falls in.

We then need to determine the difference in quarters between the Customer First Order Quarter and the quarter associated to the Order Date of each order in the data set. The requirement indicated we needed to add 1 to the result. I split this into multiple calculated fields. Firstly,

Order Date Quarter

DATE(DATETRUNC(‘quarter’, [Order Date]))

gets me the quarter of each Order Date, then

Quarters Since First Purchase

DATEDIFF(‘quarter’, [Customer First Order Quarter], [Order Date Quarter])+1

Let’s pop these fields out into a table to check things are behaving as expected.

Add Customer First Order Quarter and Order Date Quarter to Rows as discrete exact dates (blue pills), and add Quarters Since First Purchase to the Text field, but set it to be a dimension, so it is disaggregated.

Now we need to count the number of distinct customers that made a purchase in each Customer First Order Quarter (the cohort)

Count Customers Per Cohort

{FIXED [Customer First Order Quarter]: COUNTD([Customer ID])}

Add this to to the sheet, along with Sales (I moved Quarters Since First Purchase to rows too)

As expected, we can see the same customer count for each Customer First Order Quarter cohort.

We’ve now got all the building blocks to move on the the next requirement, but I’m going to rearrange the table a bit, to start to reflect the data actually needed for the output.

The x-axis of the chart is going to be based on the Quarters Since First Purchase field, so move that to be the first column of data (1st entry in Rows). Then remove Customer First Order Quarter and Order Date Quarter, as we don’t need this level of information in the final viz.

We now have the sum of all the customers and the total sales, so we can now create the division reqiurement

Sales / Customer

SUM([Sales])/SUM([Count Customers Per Cohort])

I set this to currency $ with 0 dp.

Add this to the table

Then to make this a running total, create a Running Total quick table calculation off of this field (right click on field -> Quick Table Calculation -> Running Total). Add back in Sales/ Customer.

We’ve now got all the components needed to build the viz, but do require an additional calculation to be displayed in the tooltip, which is the difference between each row.

Right click the existing running total Sales / Customer pill (the one with the triangle) and choose to Edit Table Calculation. Tick the Add secondary calculation checkbox and choose the Difference From table calc to run down the table, making the calc relative to the previous row.

Re-add a Running Total quick table calc to the other Sales / Customer pill, and then add Sales / Customer back into the view (it’s annoying that Tableau won’t let you add multiple pills of the same measure name unless they have a different calculation against them).

The snag with this is that we need a value to display for the first row. We need to create a new field that can reference the data in the second table calculation. Click and drag the ‘difference’ table calculation field into the Data pane, and name the field Difference. It should look like below, but you shouldn’t have needed to type any of that.


ZN(RUNNING_SUM([Sales / Customers])) – LOOKUP(ZN(RUNNING_SUM([Sales / Customers])), -1)

Now create a new calculated field


IF FIRST()=0 THEN [Sales / Customers] ELSE [Difference] END

Set this to a customer number format of 1dp, prefixed by + (the data is always cumulative so is never going to have a -ve value, so this works).

Now we can build the viz.

On a new sheet add Quarters Since First Purchase to Columns as a continuous dimension (green pill), then add Sales / Customers to Rows and set to be a Running Total quick table calc. Change the mark type to be a Gantt Bar.

Create a new field



and add this to the Size shelf.

Add a second instance of the Sales / Customer running total calc (press ctrl and click and drag the existing pill in rows to create another next to it). Change the mark type of this to be bar. Remove the Size pill, and then click the Size shelf button, and set the Size to be fixed, aligned left.

Now make the charts dual axis and synchronise the axis. Set the colour of each mark type to the relevant palette, and set the mark borders to None. Hide the right hand axis.

On the All marks card, add the Tooltip:Difference and Count Customers Per Cohort fields to the Tooltip shelf, and amend the tooltip to match.

On the bar marks card, click the Label shelf button and tick the show mark labels checkbox. Align these left.

Remove the left hand axis, remove all gridlines and row/column dividers. Add column axis ruler and dark tick marks

Edit the x-axis and rename the title to Quarter of Order.

If you find you have the x-axis going from 0 to 18, then change the datatype of Quarters Since First Purchase from a whole number to decimal. (right click pill in data pane -> change data type -> Number(decimal).

Add the chart to a dashboard and boom! you’re done. My public viz is here. (note, I did notice some vertical dividers displaying in the area chart on public, but think this is a Tableau Public ‘bug’ as I’ve switched off all the lines I can think of, and Desktop displays fine….

Happy vizzin’!


Can you do YoY comparisons?

Community month for the #WOW2022 team continued this week, with Liam Huffman setting this challenge to build a year on year comparison chart with twist.

Typically when building YoY charts you compare the month from last year with the same month this year, or a week from last year with this year (eg week 3 2021 v week 3 2022), or a specific date last year with this year (eg 4th March 2021 with 4th March 2022). However for this challenge, the focus was on being able to compare based on equivalent weekdays (at least that’s what I understood from reading). By this I mean if 10 October 2022 is a Monday, that needs to be compared with the equivalent weekday in the previous year. 10 October 2021 was a Sunday, so we actually need to compare Monday 10 Oct 2022 with Monday 11 Oct 2021.

So that’s the direction I took with this challenge, but in doing so couldn’t get the numbers to exactly match with Liam’s solution. Unfortunately I just couldn’t get my head around Liam’s approach even after looking at it. So I’m blogging this based on my interpretation of the requirement, which may be flawed.

Baselining the Dates

When building YoY charts like this which need to be flexible based on the date part selected, you need to ‘baseline’ the dates; well that’s the term I use, others might refer to it as normalising. Ultimately you need to get the dates that span multiple years to all align to the same year, so you have a single x-axis that just spans a single year.

Now based on what I talked about above, its not just a case of changing the year of every Order Date to match for all the records, we need to find the equivalent weekday from the previous year and align that.

To do this I first created a parameter


date parameter defaulted to 10 Oct 2022

This is simply to provide a constant end point. In a business scenario when the data changes, any reference to pToday would just use TODAY().

I then created

1st Day of Current Year

DATE(DATETRUNC(‘year’, [pToday]))

This returns 1st Jan 2002

I then worked out what weekday this was

Weekday of 1st Day Current Year

//determine the day of the week this year started on, based on todays date
DATENAME(‘weekday’, [1st Day of Current Year])

This returns Saturday

I then wanted to find the date of the first Saturday in every year, but firstly I needed to determine the weekday for each Order Date


DATENAME(‘weekday’, [Order Date])

Date of 1st weekday Per Year

//get the first date in each year which falls on the same weekday as the first day of the current year
//ie 1st Jan 2022 is a Saturday. This is day 0 for 2022. We need to find day 0 for every other
//year, which is the date of the first Saturday in that year

{FIXED YEAR([Order Date]): MIN(IF [Weekday]=[Weekday of 1st Day Current Year] THEN [Order Date] END)}

For each year, get all the dates which fall on a Saturday, then returned the earliest one of those.

Popping all this information out in a table, we can see that in 2019, the first Saturday in the year was 5th Jan 2019, so all the records for dates in 2019 are stamped with 5th Jan 2019.

This Date of 1st weekday Per Year is essentially day 0 for each year. We now need to record a number against each day in the year

Days From Date

//get the number of days from the date of 1st weekday per year
DATEDIFF(‘day’, [Date of 1st weekday Per Year], [Order Date])

And with this number, we can now record the equivalent date in 2022 against each date

Baseline Date

//need to normalise all dates for every year to the current year,
//ie Day 0 for every year = 01 Jan 2022

DATE(DATEADD(‘day’, [Days from Date], [1st Day of Current Year]))

Add these fields into the tabular view and you should hopefully see how this is working

Building the Line Chart

We need some additional parameters to help build the chart.


string parameter defaulted to Month, with 3 values listed as shown below


date parameter set to use the 1st Day of Current Year when the workbook opened, meaning current value was 1st Jan 2022


date parameter set to use the pToday parameter when the workbook opened, meaning current value was 10th Oct 2022

With the pDatePart field, we need to define the actual date we’re going to plot

Date to Plot

DATE(DATETRUNC([pDatePart], [Baseline Date]))

when this is set to week, the date for all days in the same week are set to the first date in the week. Similarly, when this is set to month, all dates in the same month are set to 1st of the month.

The data also needs to be filtered based on the start & end dates selected, so we need

Dates to Include

[Baseline Date]>= [pStartDate] AND [Baseline Date]<=[pEndDate]

Add this to the Filter shelf of the tabular view and set to True. Also add Date to Plot into the table, and show all the parameters. Change the dates and the date part field and familiarise yourself with how the parameters impact the data.

Now we’re happy we’ve got all the key fields we need, then create a new sheet, show the parameters and add Dates to Include = True to Filter.

Then add Date To Plot as an exact date continuous (green) pill to Columns and Sales to Rows.


Order Date Year

YEAR([Order Date])

and add to Colour. Adjust the colours accordingly.

Format the Sales axis, so the numbers are displayed as numbers rolled up to thousands (k) with 0 dp. Edit the Date to Plot axis to remove the title, then format the dates displayed to be in dd mmm custom format. Remove all gridlines and zero lines. Only axis lines should be displayed. Update the title.

Add Date to Plot to Tooltip as a discrete attribute (blue pill). Add MIN(Order Date) to Tooltip too. Adjust tooltip wording to suit.

Building the bar chart

Add Order Date Year to Rows and Sales to Columns. Sort Order Date Year descending.

Add Order Date Year to Colour. Adjust the opacity to about 50% and add a border. Widen the rows. Add Sales to Label and format label to be $K to 1 dp.

Add another instance of Sales to Detail, then adjust to use a Quick Table Calculation of Percent Difference. Move this pill from Detail to Label. Adjust the table calculation so it is relative to Next rather than previous

Format the Sales field that has the table calc, so it is custom formatted to ↑ 0.0%; ↓ 0.0%

Modify the label so the % change is in ( ) .

Add MIN(0) to Columns (type directly in to the columns shelf). Remove the two Sales fields from the marks card, and add another instance of Order Date Year to the Label shelf. Adjust the Label so the font is larger, matches mark colour and is rotated.

Make the chart dual axis, and synchronise axis.

Turn off all tooltips, hide the Order Date Year column and both axis. Remove all gridlines and row & column borders. Add a title. Remove any instance of Measure Names that may have been added to the Colour shelf of either marks card.

And add Dates To Include = true to the Filter shelf.

Adding the interactivity

Add the sheets onto a dashboard and adjust the layout to match. I floated the parameters and positioned with some floating text boxes too, to get the desired display.

Add a highlight dashboard action

Highlight Trend

which on hover of the bar, highlights the trend line via the Order Date Year field only.

To manage the filtering of the bars, I decided to use a parameter action, by passing the date related to the point selected. For this I created


I used a string parameter, so I had a value I could use to reset to

I then needed to create an additional field

Date to Plot String

STR([Date to Plot])

and I added this to the Detail shelf on the trend sheet. This needs to be set to be an attribute, so the lines remained joined up.

Additionally I needed

Filter On Hover

[pDateHovered] = ” OR [Date to Plot] = DATE([pDateHovered])

which I added to the Filter shelf of the Bar chart sheet and set to true.

Then back to the dashboard, create a parameter action

Filter Bars

which on hover on the trend chart, updates the pDateHovered parameter passing through the Date To Plot String field, and resets back to <empty string> when released.

And that should be it… my published viz is here.

Happy vizzin’!