The focus of this week’s challenge, set by Yoshi, is showing “detail on demand” using filters.
Defining the calculations
We’re only concerned about current year and previous year, so to simplify this, after connecting to the data source, I added a data source filter based on Order Date to restrict the information to years 2023 and 2024 only.
I then created the following fields
Latest Year
{MAX(YEAR([Order Date]))}
Previous Year
[Latest Year]-1
Sales – LY
IF YEAR([Order Date]) = [Latest Year] THEN [Sales] END
Format to $ with 0 dp
Sales – PY
IF YEAR([Order Date]) = [Previous Year] THEN [Sales] END
On a new sheet, add Category to Columns, and Sales – LY and YoY to Text and format/layout the text as required. Adjust the formatting of the column headings too.
Add YoY to Colour too to match the solution (though personally, if I was doing this for a business need, I wouldn’t do this as some of the text becomes quite washed out).
Building the Sub Category Viz
On a a new sheet, add Category and Sales – LY to Columns and Sub-Category to Rows.
However, you can see that as not every Sub-Category exists in every Category, we get ‘blanks’ in the display, so we need use something on rows that exists for every Category. We can do this using the rank of Sales.
Add Sales -LY to Rows and change it to be a discrete (blue) dimension.
Move Sub-Category to Detail and then apply a quick table calculation of rank to the Sales – LY pill on Rows. Adjust the table calculation so it is computing explicitly by Sub-Category only.
Make the rows a little wider, move Sub-Category to Label. Add Sales -LY to label too. Adjust the format/layout of label and align middle right. Add YoY to Colour.
Add Sales – PY to Detail then add a Reference Line to the Sales – LY axis, that refers to Sales – PY per cell.
Add Latest Year to Tooltip and adjust the text to suit. Finally, hide the Sales – LY Rank row header and the Category column header (uncheck show header) and remove the title of the Sales – LY axis. Add a title.
Building the Product Viz
On a new sheet, add Product Name to Rows and Sales – LY to Columns. Sort by Sales – LY descending. Hide the null value indicator.
Add YoY to Colour. Add Sales – LY to Label. Add Sales – PY to Detail and add a Reference Line as described above. Add Sub-Category to Detail and add Latest Year to Tooltip and adjust. Remove the title of the Sales – LY axis, and hide the Product Name column header title (right click and hide field labels for rows). Add a title.
Filtering the display
The requirement is to be able to ‘filter’ the display based on the Category selected, and in the event not all categories are selected, the Product bar chart should show. Due to this second requirement, just using a standard ‘quick filter’ is a bit tricky – we need a way to understand what has been selected in the filter.
We can use a Set for this. Create a set of categories – right click Category > create > set
Category Set
Select all values
Add this to the Filter shelf of the KPI sheet, and then apply this same filter to the bar chart sheets too (right click the pill on the Filter shelf > apply to worksheets > selected worksheets > select the relevant sheets).
If you select show set, this then displays the input control to select which options are in or out of the set.
To manage the visibility of the Product bar chart, we need to know how many Categories there are in total, and how many Categories are selected. So create
Count Categories
{FIXED: COUNTD([Category])}
And
Count Set Members
{ COUNTD(IIF([Category Set], [Category],NULL))}
Both of these are LODs as we need to reference them in another boolean calculation that will be used to drive dynamic zone visibility.
Not all Categories Selected
{(SUM([Count Categories]) <> SUM([Count Set Members]))}
Building the dashboard
Create a dashboard. The three sheets should be arranged within layout containers. I used a horizontal container, where the left hand column contained a vertical container which in turn contained the KPI sheet on the top and the Sub Category bar chart underneath. The right hand column of the horizontal container contained the Product bar chart.
To control the visibility of the Product bar chart, select the object, then from the Layout tab on the left hand side, select the control visibility using value checkbox, and choose the Not All Categories Selected field.
The bar will initially disappear, but if you then deselect a value in the Category ‘filter’ control, it will reappear.
Added extra
We can also set the Product bar chart to filter if a Sub-Category bar is clicked on. Create a dashboard filter action
Filter Products
On select of the Sub-Category bar chart sheet, target the Product Bar Chart sheet passing all values. Show all values when the selection is cleared.
Erica set the challenge this week, and I’m not gonna lie, I found this tough. On the face of it, it looks like something I felt I should be ok at, but nuances cropped up as I was building that meant I often had to change tact and try something different.
My intention was to build each version – Beginner, Intermediate then Advanced, adding to my solution each time, but decisions I made early on, then caused me grief later. For example, I did choose to utilise a lot of table calcs, but that meant when it came to applying the sorting mechanism that I wanted, I couldn’t reference the sort field I needed, as it contained a table calc. So I had to unpick the logic and build with LODs instead which took me a while to get right. Getting single lines to display in each ‘state’ cell also proved tricky at times, and that was even before I’d got to the requirement to pad out the ‘missing values’ with 0s. I also seemed to find that some things only seemed to work if I added pills and applied settings in a particular order. All in all, quite a challenge, and while I did get there in the end, I did have to peek at the solution at times to figure out if I was going nuts, but I found trying to scale back Erica’s solution to the beginner/intermediate version also suffered the same issues I was experiencing. I built with Desktop v2024.1 and there were times I was wondering if something had “broken” in that version, although having finally reached the end, I’ve yet to test that theory.
So, I’m blogging this guide with several caveats – Going from beginner to intermediate is ‘okay’, but when it gets to advanced I had to start again(ish). Some of the calcs I provide will just be ‘as is’. I will do my best to explain what’s going on, but there are times that I just don’t get it, and it’s just been trial and error that got me the results I needed – sorry!
So with all that in mind, let’s get building.
Initial steps
After connecting to the provided hyper file, I found I did have to create the modified sales value
Sales Modified
IF SUM([Sales])<5000 THEN SUM([Sales])*10 ELSE SUM([Sales]) END
I also decided to add a data source filter (right click data source > edit data source filters) to restrict the data to just Country/Region = USA.
The later versions of superstore have Canadian Provinces included too, and I don’t think these were listed in Erica’s solution. It just felt easier all round to exclude these records from source.
Beginner challenge
When building a trellis chart, we need to determine which Row and which Column our specific dimensions (in this case State/Province) will sit in. As the requirements already stated it was to be a 3 column grid, our calculations for this didn’t need to be so complex.
Cols
(INDEX()-1)%3
INDEX() returns an incremental number starting at 1 for whatever dimension or set of dimensions we’re counting over. In this case we’re counting the number of State/Provinces. %3 returns the remainder when the index is divided by 3, so we get values of 0, 1 and 2.
Change this field to be discrete (right click -> convert to discrete)
Add State/Province to Rows then add Cols to Rows. Edit the table calculation so the field is computing by State/Province. You can see that the first 3 rows will be positioned in columns 0, 1,2 respectively and so on.
Create a new field
Rows
INT((INDEX()-1) / 3)
This takes the index value (minus 1), divides by 3 and ’rounds’ to a whole number. Make this discrete too and add to Rows, setting the table calculation as described above. Now we can see the first 3 rows will all actually be in the same row (row 0), then next 3 rows in row 1 and so on.
Shift the pills around so Cols is on Columns, Rows is on Rows and State/Province is on Detail. Add Sales Modified to Rows.
Create a new field
Quarter Date
DATE(DATETRUNC(‘quarter’, [Order Date]))
and add this to Columns setting it as a continuous exact date (green pill). We’ve got a bit of unexpected ‘spaghetti’ going on…
To fix it, do the following ..
Add Quarter Date to Detail as a discrete exact date (blue pill). Change Quarter Date on Columns to be a continuous attribute (green pill – first change to attribute, then change to continuous). Edit the table calculation settings for both the Rows and the Cols fields to be computing by both State/Province and Quarter Date at the level of State/Province.
I had to reference previous challenges and blog posts I’d written to manage this… maybe there is something simpler, as this is pretty taxing for the ‘beginner’ part of the challenge.
Add another instance of ATTR(Quarter Date) to Columns and make dual axis and synchronise the axis. This will create an axis at the top of the chart as well as the bottom.
Format the ATTR(Quarter Date) pill so the axis format is custom formatted to “Q”q “‘”yy
Edit all the axis (top/bottom and left) and update the title. Adjust the Tooltip. Hide the Cols and Rows fields (right click and uncheck show header). Change the Colour of the line to grey.
This should be the Beginner solution. I have published this here.
Intermediate challenge
For this part of the challenge, we need to set up lots of new calculations, so let’s do this first. As usual, I’ll manage this in a tabular format. So on a new sheet, add State/Province and Quarter Date as a discrete exact date (blue pill) to Rows. Add Sales Modified to Text.
We need to get the threshold value for each State/Province, which is the average of the numbers all listed above, multiplied by 2. We’ll use LODs for this
working inside out… get the value of the Sales Modified value for each State/Province and Quarter Date (which is the same as the values you see listed above) and then average this at the State/Province level and multiple the final result by 2. Add this to the table. This is the field we’ll be using for the horizontal reference line.
Next we need to identify the rows where the Sales Modified value exceeds the threshold, and then return the Sales Modified values for only these rows. I’ll do this in 2 stages
Is Above Threshold?
INT([Sales Modified] > SUM([Threshold]))
This returns a 1 or 0 depending on whether the statement is True or False. Using actual numeric values rather than boolean helps later on. Set this field to be discrete and add to the table.
Above Threshold Sales
IF [Is Above Threshold?]=1 THEN [Sales Modified] END
Add this to the table too. For the rows where we have 1’s, a value is displayed. This is the field we’ll be using for the red circles.
Finally we need to determine some fields to help us define a reference band. These need to be dates as they’ll be applied to the date axis, but the band doesn’t stretch to the previous/next quarter, and is only present if the last value is over the threshold.
Again using LODs let’s get the final date in the quarter
Max Quarter Per State
{FIXED [State/Province]: MAX([Quarter Date])}
Add this to the table as a discrete exact date (blue pill).
Now we need to know if the value associated with the final quarter is above the threshold or not
Final Quarter Above Threshold
INT(MIN([Quarter Date]) = MIN([Max Quarter per State]) AND [Is Above Threshold?]=1)
Again this will return a 1 or 0. Change to discrete and pop that into the table too. We can see Colorado is the first state listed where this is true.
Now we want to ‘spread’ that value across every row associated to the state
For each State/Province and Quarter Date, get the Final Quarter Above Threshold value and then get the maximum value of this for each State/Province. This is where having the values as 1’s and 0s helps.
Make discrete and add this to the table. Every row for Colorado has this set to 1
Now we can work out some dates
Ref Band Min
DATE(IF [State Has Final Quarter Above Threshold] = 1 THEN DATEADD(‘month’, -1, [Max Quarter per State]) END)
If the State/Province is over the threshold for it’s final quarter, then get a date and set it to be 1 month less than the final quarter for that state.
Similarly
Ref Band Max
DATE(IF [State Has Final Quarter Above Threshold] = 1 THEN DATEADD(‘month’, 1, [Max Quarter per State]) END)
If the State/Province is over the threshold for it’s final quarter, then get a date and set it to be 1 month more than the final quarter for that state.
Add both of these of the table as discrete exact dates (blue pills).
Now we have all the building blocks to build the next bit of the challenge.
Start by duplicating the Beginner viz.
Add Above Threshold Sales to Rows and make Dual axis and synchronise the axis.. Remove the 2nd instance of the Quarter Date from Columns, so we now have marks cards relating to the 2 measures rather than the 2 dates. Remove Measure Names from the All marks card.
Change the mark type on the Above Threshold Sales marks card to circle and change the colour to red. Adjust size to suit.
Add Threshold to the Detail shelf of the All marks card. Right click on the Sales axis and add reference line. Set it to be per pane and use the Threshold field. Display the value as the Label. Don’t show a Tooltip. Display a dotted black line.
Update the Tooltip to now have a reference to the threshold value too.
Add Ref Band Max and Ref Band Min to the Detail shelf of the All marks card. Set them both to be continuous attributes (green pills).
Right click on the Quarter Date axis and add reference line. Set it to be a bandper pane that goes from the Ref Band Min to the Ref Band Max. Don’t display labels or tooltips or a line. Fill with a pale shade ot red/pink.
Add back in the additional Quarter Date dual axis as described in the Beginner section to get the dates listed the top again.
Hide the right hand axis (uncheck show header) and hide the NULL indicator.
This completes the Intermediate challenge. My version is published here.
Advanced challenge
Go back to the tabular sheet we were using to check the calculations needed for the Intermediate challenge, as we’ll build on this.
Firstly the sorting. We need to sort the states based on whether the final quarter was above the threshold or not, and then by the number of times the state was above the threshold. Let’s get the count to start with
For each State/Province and Quarter Date, get the Is Above Threshold? value and then sum these up for each State/Province. This is where again having the values as 1’s and 0s helps.
Make this discrete and add to the table
then create a field we’ll use for the sort. This is going to be a numeric field
Sort
IF [State Has Final Quarter Above Threshold] = 1 THEN 100000 + [Above Threshold Count Per State] ELSE [Above Threshold Count Per State] END
We’re just using a very large arbitrary number to force those states where the final quarter is over the threshold to be higher in the list. Make this discrete and add to the table too.
We can now apply a Sort to the State/Province field to sort by the Sort field descending
This results in Colorado moving to the top of the list followed by Minnesota, which also has 3 quarters above the threshold, including the last quarter, but alphabetically falls after Colorado so is listed 2nd.
To filter the data, I created another field, just for ease
Filter
IIF([State Has Final Quarter Above Threshold]=1,’Urgent’,’Non-Urgent’)
Add this to the Filter shelf and set to Urgent. The states should now be restricted to just those where the final quarter is above threshold.
For the final requirement of this challenge, we’ll build out another table on another sheet to demonstrate, as we need to work with a different instance of the quarter date.
On a new sheet, add State/Province to Rows and add Order Date at the Quarter (month year) level as a discrete field (blue pill) to Rows. Add Sales Modified to Text
For Alabama, we don’t have a 2020 Q3 or a 2023 Q3. Click on the Quarter(Order Date) pill and select Show Missing Values. These quarters appear but with no Sales Modified value.
We’ve had to use this different way to define the date quarter as if we tried to ‘show missing values’ against the Quarter Date field that is set to ‘exact date’, we send up getting every day that is missing, not just the dates relating to the quarter. Also, the ATTR(Quarter Date) field we’ve used on the previous vizzes, doesn’t allow the Show Missing Dates option.
Anyway, we need to get 0s in to these dates.
Show Modified with 0
ZN(LOOKUP([Sales Modified],0))
Apart from the Rows/Cols calcs needed for the trellis, this is the only table calculation I ended up using. It’s basically looking up it’s own row (LOOKUP([field],0)) and if it can’t find a value (as it’s missing) it’s returning 0 (the ZN() function). Add that to the table.
Ok so now we have the components needed, let’s build the viz. We can use the Intermediate version as a starting point, but will need to reapply some of the features.
Duplicate the Intermediate viz.
Remove the 2nd instance of the ATTR(Quarter Date) pill on Columns. Drag the Sales Modified with 0 pill and drop it directly over the Sales Modified pill so it replaces it. Hopefully the chart should still look the same.
Right click the Order Date field from the left hand data pane, and drag it directly onto the ATTR(Quarter Date) pill. Release the mouse and select the continuous quarter/year option from the dialog that displays.
Things will start to look a bit odd… you’ve lost your lines… Remove the Quarter Date pill from the Detail shelf on the All marks card. Fix the Rows and Cols table calc fields by just updating them to compute by State/Province only. Adjust the table calc of the Sales Modified With 0 field to compute by Quarter of Order Date and State/Province in that order.
Things still look crazy…. but just one more step… Show Missing Values on the Quarter(Order Date) pill.
Now every cell should be associated to a single State/Province with no broken lines, and Wyoming, right at the bottom, should show more than a single dot. This was A LOT of trial and error to fathom all this out.
Add back in the reference band following the instructions above (you should find the reference line for the threshold value will just then appear) and re-update the Tooltip.
Add another instance of Order Date at the quarter/year continuous level (green pill) to Columns and make dual axis and synchronise the axis. Edit the axis titles, and format them to the “Q”q “‘”yy custom format.
Apply the sort to the State/Province field on the Detail shelf so it is sorting by Sort Descending. Add Filter to the Filter shelf and select Urgent.
Add this to a dashboard, and that should be the completed Advanced challenge which I’ve published here.
There really was some black magic going on here at times. Tough one this week!
It was Lorna’s turn to set the challenge this week, and she opted for a gentle workout to soothe us into the Christmas holidays – I certainly appreciated it as I am super busy and super stressed trying to finalise Christmas presents and preparations!
I built this with 4 calculations in total. Let’s start by just building out a tabular view of the data, so we know what we’re looking for.
Add Customer ID and Order Date set to be a discrete (blue) exact date onto Rows.
To start with, we want to capture, against each customer, the earliest order date
First Purchase Date
{FIXED [Customer ID]: MIN([Order Date])}
Add this into Rows as a discrete exact date.
You can see that for every row associated to the same customer, the First Purchase Date matches the first row.
Now I want to identify the row that represents the first order for each customer
Order is first order
[Order Date] = [First Purchase Date]
Add this onto Rows and we can see the first row against each customer is True, while the rest are False.
I can now use this to identify the date of the 2nd order for each customer – it is the earliest date where the order isn’t the first order
Second Purchase Date
{FIXED [Customer ID]: MIN(IF NOT [Order is first order] THEN [Order Date] END)}
Add this onto Rows as a discrete exact date, and you can see that every row associated to the customer, now has the date that matches the 2nd Order Date listed.
Remove Order Date and Order is first order from the Rows and we have 1 row per customer with the 2 dates we’re interested in.
Add this onto Text, so you can validate the result.
Now we can build out the matrix.
On a new sheet add First Purchase Date to Rows and set to be at the discrete (blue) quarter level. Then add Quarters Since First Purchase to Columns and change to be a discrete Dimension.
This already gives us the shape we’re after. Right click on the Null value in the column heading and select Edit Alias and update the word to Lapsed.
Add Customer ID to the Colour shelf, and the click on the pill and change to be a Measure of Count Distinct. This will change how the viz is displayed. Adjust the colours accordingly.
Finally update the Tooltip to match the required text, and adjust the formatting so there are pale grey thin dotted row and column dividers on the pane only, but not the header.
Add the viz to a dashboard, and you’re done! My published viz is here.
For this week’s #WOW2023 challenge , guest poster, Venkatesh Iyer asked us to create an UpSet Plot, with the added requirement of using just 1 calculated field.
To start with, I had to read up on what an UpSet plot was and looked through the blog post by Chris Love that was referenced in the challenge introduction. While this post gave me more clarity, it introduced more calculations than I was hoping for, so I started looking a bit wider for a bit of help. This YouTube video set me on my way.
Setting up the data
The requirements stated to limit to the Category of Furniture only, so after connecting to the Superstore data source, I added a Data Source Filter (right click data source > Edit Data Source filters) to restrict the information throughout the workbook just to the Furniture Category.
Doing this means I don’t have to keep adding the Category to the Filter shelf, and any FIXED LoDs I create will only be based on the subset of data that has been ‘pre-filtered’.
There are multiple charts in this challenge, and I used 5 sheets in total. Let’s start with the easy ones.
Building the Customer List
On a new sheet add Customer Name to Rows and Sales to Text. Format Sales to $ with 0dp, and widen each row. Remove row/column dividers and remove the ‘Customer Name’ column heading (right click and hide field labels for rows). Uncheck the Show Tooltips option from the Tooltip shelf. Name the sheet Customer List.
Building the Sub-Category Bar Chart
Add Sub-Category to Rows. Add Customer ID to Columns, then use the context menu to change the field to use the Count (Distinct) measure.
Note– I would typically create a specific calculated field containing the function COUNTD([Customer ID]), but as we only want a single calculated field in the solution, then this is the method to adopt.
Sort the resulting bar chart descending, and add Sub-Category to the Colour shelf and adjust to suit.
Widen each row and then click on the Label shelf and check the Show mark labels tick box. Align the labels middle left and format the font. Hide the axis and the Sub-Category column heading (uncheck show header). Remove all gridlines, row/column dividers/ zero lines etc. Uncheck the Show Tooltips option from the Tooltip shelf. Name the sheet Sub Cat Bar.
Identifying the groupings
Ok, now we’re at the point we need to identify the different ‘cohorts’ of customers based on what Sub-Categories they have purchased. Let’s build out a tabular ‘check’ sheet so we can see what we’re up to…
On a new sheet add Customer Name and Sub-Category to Rows. This simple table shows us that Aaron Bergman has at some point only ever bought Bookcases & Chairs, while Aaron Hawkins has purchased Chairs & Furnishings. These 2 customers are in different cohorts as they haven’t bought exactly the same combination of Sub-Categories. There are 15 different combinations in total.
Based on what I observed in the video, I can create a FIXED LoD calculated field to identify if a customer has bought Bookcases.
Pop this into the view, and we can see that there is a 1 reported against all the rows associated to each customer who bought Bookcases. So Aaron Bergman has a 1 against both rows, and Aaron Hawkins has 0 against both rows.
Creating similar calculated fields, specific for each of the 4 different Sub-Category values, and putting them into the table, we can see we have various combinations of 1s and 0s for each customer. Adam Shillingburg has bought all 4 types, so has 1’s across the board, while Adrian Sharni has only bought Furnishings, so has 3 0’s and a single 1.
Based on our understanding of what these fields are doing, we can combine what each one is doing into a single calculated string field.
Note the order is based on the order of Sub-Category bar chart. Add this into the view on Rows (rejig the order of the measure values to match).
So with this one calculated field Combo, we now have a dimension we can use to count the customers against. The calculated fields I used to demonstrate the concept are now superfluous and can be deleted if you wish, if you remove the check sheet too. I chose to keep mine in for reference.
Building the Combo bar chart
ON a new sheet, add Combo to Columns and then add Customer ID to Rows, but as before, set it to use the COUNTD aggregation.
Sort the bar to be ascending. Check the Show mark labels option on the Label shelf and adjust the alignment to be bottom middle, and the font to be bold. Change the colour of the bar to suit. Hide the axis and the Combo heading (uncheck show header). Remove all gridlines, row/column dividers/ zero lines etc. Uncheck the Show Tooltips option from the Tooltip shelf. Name the sheet Combo Bar.
Building the dot plot
Add Sub-Category to Rows and Combo to Columns. Manually sort the Sub-Category rows so they are listed with Furnishings at the top then Chairs > Tables and Bookcases at the bottom. Sort the Combo field by COUNTD of Customer ID ascending
Double click into Columns and manually type MIN(0) to generate an axis. Change the mark type to circle. Add Sub-Category to Colour. Widen each row.
Add Sub-Category to Label. Then double click into the pill on the label shelf and manually change to add the LEFT function around the pill, so the pill becomes LEFT([Sub-Category],1) to get the initial. Again, this is typically something I would explicitly store in its own calculated field. Manually re-sort the rows again, as this seems to break the sorting.
Align the label middle centre and bold the font. Then add another instance of MIN(0) on Columns to create a 2nd marks card. Change this mark type to Line. Remove the field from the Text shelf, and move the Sub-Category pill from Colour to Path.
Make the chart dual axis and synchronise axis. Right click on the MIN(0) axis title at the top of the chart and move marks to back.
Then hide the axis and the Combo column and Sub-category row (uncheck show header). Remove all gridlines, row/column dividers/ zero lines etc. Adjust the size of the marks as required. Uncheck the Show Tooltips option from the Tooltip shelf. Name the sheet Dot Plot. .
Building the Legend
The quickest way to do this is to duplicate the dot plot sheet. Then remove Combo from the Columns. mark type of the line from line to circle and decrease the size to the smallest possible. Move Sub-Category from Detail to Label and align middle right.
Edit the label and add some spaces in front of the text to push the labels further to the right. Format the font to suit.
Adjust the MIN(0) fields to both be MIN(0.2) instead (just double click into the fields to edit). Then edit the axis to be fixed from 0 to 1. This forces the display to the left.
Hide the axis, and name the sheet Legend.
Adding the interactivity
Arrange the items on a dashboard, using layout containers and padding to organise the 4 main charts and ensure the dot plot aligns both vertically and horizontally with the other two bar charts.. The legend chart is a floating object.
Add a filter dashboard action to filter the customer list
Filter Customers
On select of the Combo BarorSub Cat Bar sheets, target the Customer List sheet, showing all values when the selections are cleared.
Donna + DataViz 