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

DATE(DATETRUNC(‘day’,[Date]))

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

DATENAME(‘month’,[Date])

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

Day of Date

DATENAME(‘weekday’,[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]
AND
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”

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
END

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’!

Donna

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 ðŸ™‚

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.

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.

Difference

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

Now create a new calculated field

Tooltip:Difference

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

Size

[Tooltip:Difference]*-1

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’!

Donna

Can you use Tableau to estimate Aaron Judge’s home run trajectories?

It’s community month still for #WOW2022, and this week saw Samuel Epley set this challenge to visualise the home run trajectories of Aaron Judge.

I had a little mini-break to Rome this week, so was hoping I was going to be able to get this week’s challenge done and dusted on the Tuesday evening if it landed early enough, as I wasn’t going to be around.

It did land on the Tuesday for me, but wow! it was not going to be easy! I managed to build the KPIs & the scatter plots on the Tuesday evening, and knowing I didn’t have much time, just chose to use the Home Runs stats data set only. I knew these charts weren’t going to need any data densification, so found this approach simpler.

I’m afraid I’m still constrained by time at the moment, so this post isn’t going to be the detailed walkthrough you might usually expect – sorry! I’m just going to try to pull out key points from each chart.

KPIs

I built this on a single sheet, using Measure Names and Measure Values.

I used aliases on the Measure Names (right click -> Aliases) to change the label you can see displayed ie the Distance pill is aliased to ‘Average Distance’

I also custom formatted the various numbers and applied suffixes to display the unit of measure

Note – to To get the degree symbol, I typed Alt+ 0176

Scatter Plots

I built the Exit Velocity by Distance scatter plot first, and completed all the formatting & tooltips. Then I duplicated the sheet to form the basis of the other scatter plots, and just swapped the relevant pills as needed.

For the ball shape, I loaded the provided images as custom shapes into my shapes repository. I then just created the following calculated field to use as a discrete dimension I could add to the Shape shelf

Ball Shape

[HR Number]%9

It’s not as completely randomised as perhaps it should be, but it looks random enough on the display.

The Pitcher in the data is in the format <Surname>, <Forename>, but on the tooltip it needs to display as <Forename> <Surname>, so I just used a transformation on the Pitcher field to split the field based on the comma (right click Pitcher -> Transform -> Split). This automatically created 2 fields I could use on the Tooltip.

I also noticed a very subtle wording change in the tooltip based on whether the match was Home or Away. If Home, the tooltip read ‘New York Yankees vs. <Opposition>’ otherwise it read ‘New York Yankees at <Opposition>’. I used a calculated field for this logic

TOOLTIP: vs or at

IIF([Location]=’Home’,’vs.’, ‘at’)

The Trajectory Plot

OK, so this was the hardest part of this challenge, and mainly due to getting your head round the physics involved, as so many of the calculations are dependent on each other.

I’m generally pretty confident with my maths, but this was complex, especially with the force calculations for the y-axis. Samuel stated that both gravity and drag impacted the Y-axis calcs, but it wasn’t clear to me how both these forces should be applied (a bit of trial and error and I ended up adding them within the formula).

By the time I came to tackle this challenge, Samuel had already posted a video walkthrough, which can be viewed here and is another reason why I’m not going down to the nth degree in this post.

My suggestion is to watch Samuel’s video and/or feel free to download my workbook. I built my workbook independent of Samuel’s video, so there may be steps/calculations that differ.

However, I have tried to number my calculations in the order in which I created them, so you can hopefully follow the thought process. I have also left a CHK:Data sheet in the workbook, which I used to sense check what I was doing.

All the table calculations in the CHK:Data sheet are just set to the default ‘table down’ as I have filtered the sheet to a specific Home Run (HR Number = 1) only (ie I didn’t change any of the table calc settings as I added the pills to the sheet).

However, when you build the main trajectory chart, you have multiple HR Numbers in the view, so all the table calculations must be set so that calculations are only working for each HR Number. This means that any table calc (and any nested calculations) need to have all the fields except HR Number checked

When using the Pages shelf, which isn’t something I’ve ever really had to do before, you need to Show History and adjust the various settings to get the trail lines to show

To rotate the ball (the bonus option), you need another field to use on the Shape shelf. I had lost the will to live a bit by this point, so used the formula from my friend Rosario Gauna’s solution.

Rotation Shape

STR(IIF([14-Start Position Y m] <= 0, 0,
(MIN([Time Interval]) * 1000 / 25) % 9))

Note – when you add this to the Shape shelf, and select your baseball palette, just then use the Assign Palette button to automatically assign a ball to a number – this will get them into the correct order, without you having to do it one by one.

Finally, when adding the reference average lines, be sure to set the scope to per pane rather than table, otherwise you’ll end up with the wrong figures.

I think I’ve pretty much covered all the ‘little’ points that I came across that may trip you up, aside from all the tricky calcs of course!

My published workbook is here. I hope what I’ve written is enough for you to build it yourself. I think I’d still be here next year if I tried to do anything more fully! I’m off for a lie down now!

Happy vizzin’!

Donna

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

pToday

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

Weekday

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.

pDatePart

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

pStartDate

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

pEndDate

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.

Create

Order Date Year

YEAR([Order Date])

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 â†‘ 0.0%; â†“ 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.

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.

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

pDateHovered

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.

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’!

Donna

Can you create a Waterfall Pipeline using Salesforce data?

For the Salesforce Dreamforce (#DF22) conference, Lorna set this challenge based on using Salesforce data. You could access the data either by creating a Salesforce Developer account, or using the provided csv data set. I chose to use the latter.

I’m not overly familiar with the SF data, so it’s possible in the course of this blog, I may have missed a field in the data set that could have been used instead of whatever technique I describe. Feel free to let me know in the comments if this is the case.

Setting up the calcs

After connecting to the data, the first step was to amend the amount based on whether the Stage Name was ‘Closed Lost’ or not.

Revised Amount

IF [Stage Name]=’Closed Lost’ THEN -1 * [Amount] ELSE [Amount] END

I set the format of this field to be Currency Â£M with 1 dp.

Now the crux of the waterfall, is that the position of the mark that needs to be plotted it is based on the cumulative sum of the values displayed. So for this we need to create a table calculation

Revised Amount (Running Total)

RUNNING_SUM(SUM([Revised Amount]))

I set the format of this field to be Currency Â£M with 0 dp.

Let’s put the data into a table to see what’s going on.

The table calculation of the 2nd column (Revised Amount (Running Total)) is computing ‘down’ the table, so for this to work as we require the order of the rows is important. I just manually sorted by dragging each Stage Name into the relevant position. and then added a grand total.

With these 2 fields, we can now build the basic waterfall.

Build the Waterfall

I chose to duplicate the above sheet and then move the pills around as follows

• Move Stage Name to Columns
• Move Revised Amount (Running Total) to Rows. Amend the table calculation setting so it is set to explicitly compute using Stage Name.
• Change the Mark Type to Gantt Bar
• Move Revised Amount from Text to Size, then double click on the Revised Amount pill so it becomes editable and add * -1 to the end, to invert the value

• Add Stage Name to Colour and manually adjust each colour accordingly

• Edit Revised Amount (Running Total) axis (right click axis -> Edit Axis) and amend axis title
• Format the Grand Total label (right click label -> Format) and amend says ‘Total’ and is bold.
• Hide the Stage Name label at the top of the chart (right click label -> hide field names for columns)

Labelling the bars

Labelling the bars isn’t quite as simple as you think it might be, as the position of the label differs depending on whether the Revised Amount is positive (at the top) or negative (at the bottom).

So we need to create some new calculations

LABEL – +ve Revised Amount

IF SUM([Revised Amount])>=0 THEN SUM([Revised Amount])

formatted to Â£M to 1 decimal place

LABEL – -ve Revised Amount

IF SUM([Revised Amount])<0 THEN SUM([Revised Amount]) END

formatted to Â£M to 1 dp

Adding these to the tabular layout we had earlier you can see how these fields are behaving.

Switch back to the waterfall chart, and add LABEL – +ve Revised Amount to the Label shelf. Format the label to be smaller font (I chose 8pt), bold and explicitly aligned to the top.

Now duplicate the Revised Amount (Running Total) pill that is on Rows to add another instance directly next to it. I do this by holding down ctrl as I then click and drag the pill. This will create another axis.

On the second marks card, remove LABEL – +ve Revised Amount from Label and add LABEL – -ve Revised Amount instead. Adjust the label so it is aligned at the bottom instead.

Now make the chart dual axis and synchronise the axis.

Then hide the right hand axis, and remove all divider and gridlines.

Then add to the dashboard. I used a vertical layout container so I could add blanks of 1 pixel in height with a black background colour to present the this black lines separating the header and footer text.

My published viz is here.

Happy vizzin’!

Donna

Let’s build a Marrimeko Chart!

Sean set the challenge this week and went retro, revisiting a challenge from 2017 originally set by Emma Whyte to build a Marrimeko chart.

Hang onâ€¦ a what chart? Marrimeko…???

A good place to understand what a Marrimeko chart actually is, is this blog post by Tableau Visionary & Ambassador, Jonathan Drummey. This leads onto this post which explains the steps to help build.

The main concept of this type of chart is to show part-to-whole relationships across two variables at once.Â  In the above for each job title, we have a split vertically based on proportion by gender; but the proportion of people with each job title is also being represented horizontally by the width of the â€˜barsâ€™. Both the x and the y axis are up to 100%.

I was actually around when the original challenge was set, so have my solution from then already on my Tableau Public profile. But it was a long time ago, with an older version of Tableau, so I built this from scratch (using the referenced blogs as a quick refresher). This blog will take you through the steps I took (which actually didn’t end up that different from the last time).

The data

The data set isn’t very large and contains all the information we will need, but not quite in the structure we might expect

The Sub-Type = Total field contains the % we will need to define how the width of the bars should be split (all the Total values add up to 100% or just about).

Whereas the combination of the Sub Types of Male and Female define how the height of the bars should be split (Male + Female = 100% for each Job Type).

So when we come to build, we ideally want to ‘filter out’ the Sub-Type = Total field, but we still need to retain this information to build the viz.

We need to get the % values associated to each Total row to be reflected against the Male/Female rows.

We’ll create an LoD for this

Job Type Percentage

{FIXED [Job Type]: SUM(IF [Sub-Type] = ‘Total’ THEN [Percentage]END )}

format this to % with 0 dp

If we pop the data out into a tabular format as below and add Sub-Type to the Filter shelf so it excludes the Total row, we can see we have essentially transposed the values which were stored against the Total row into it’s own column.

Building the Marrimeko chart

As stated above, the Job Type Percentage field defines the width of the bars we’ll be displaying. But before we can get to the point, we also need to define where on the x-axis each mark should be positioned. This too is based on Job Type Percentage, but is the cumulative value.

On a new sheet add Job Type to Rows, Filter by Sub-Type to exclude Total, and add Job Type Percentage to text. Sort by Job Type Percentage descending.

Now add a Running Total Quick Table Calculation to the Job Type Percentage field, and the cumulative values will display.

It is these values we need to plot on the x-axis.

Duplicate the sheet.

Edit the table calculation against the Job Type Percentage field, so that it is explicitly set to compute using Job Type. This is important to ensure the calculation is retained regardless as to where we put the pill on the canvas.

Now move this pill from the Text shelf to Columns, and change the mark type to Gantt Bar. Move Job Type from Rows to the Detail shelf. Reapply the Sort to the Job Type field so its sorting by Job Type Percentage descending

The markers for the Gantt should all be positioned at the correct position.

Now add Percentage to Rows and change the mark type to Bar.

Add Job Type Percentage to the Size shelf, then click on the shelf, and change to be Fixed and aligned Right.

Now add Sub-Type to Colour. Manually drag the values in the COlour legend to re-order (so Male is listed first) and adjust colours to suit.

Ta dah! The crux of the chart.

Now to add the ‘bells and whistles’.

The chart is labelled, but only for the Entry Job Type. We need a calculated field to manage this.

Label: Sub Type

IF [Job Type] = ‘Entry’ THEN [Sub-Type] END

Add this to the Label shelf, and set to Show Mark labels. If it doesn’t show (like it didn’t on mine), change the field to be an attribute (I need to do a dig on why this is required… I think it’s something to do with the table calcs….).

I tried to use the alignment setting of the label to set to ‘top left’, but while they would left align they wouldn’t move to the top. I manually moved them instead – simply click on the label and when the cursor changes to a cross, drag the label to the desired position.

Do this for both labels, and adjust the formatting of the label too (I set it to 12pt bold).

Remove all gridlines, axis lines, zero lines etc.

Add a 50% reference line on the y-axis. Right click on the Percentage axis ->Add Reference Line. Add a table level constant of 0.5 which is a thin dotted grey line that is almost invisible (due to the colour selected).

Hide the axes (uncheck show header).

The tooltip has a minor nuance in that it refers to ‘Women’ & Men rather than Male & Female, so we need a field for this

Gender

CASE [Sub-Type]
WHEN ‘Female’ THEN ‘Women’
WHEN ‘Male’ THEN ‘Men’
END

Labelling the axes

The final viz has labels on both the x and y axis, but these are all managed by text/image objects positioned ‘cleverly’ on the dashboard. It’s a bit of trial and error to get everything aligned as required.

To label the Job Type, I used a horizontal container, positioned beneath the chart, with several text objects, some of which had the text rotated.

The Equal Proportions and Less Equality text are both floating text objects. I saved an ‘arrow’ image from the internet and added a floating image object too.

My published viz is here.

Happy vizzin’!

Donna

Can you compare the latest month to the prior X months average?

Lorna set this challenge this week to test some LoD fundamentals. My solution has a mix of LoDs and table calculations – there weren’t any ‘no table calcs’ allowed instructions, so I assumed they weren’t off limits and in my opinion, were the quickest method to achieve the line graph.

Note – For this challenge, I downloaded and installed the latest version of Tableau Desktop v2022.2.0 since Lorna was using the version of Superstore that came with it. The Region field in that dataset was set to a geographic role type. I built everything I describe below using the field fine, but when I extracted the data source at the end and ‘hid all unused fields’ before publishing, the Region field reported an error (pill went red, viz wouldn’t display). To resolve, I ended up removing the geographic role from the field and setting it just to be a string datatype. At this point I’m not sure if this an ‘unexpected feature’ in the new release…

Ok, let’s get on with the build.

Building the basic viz

I started by building out the basic bar & line chart. Add Region to Rows, Order Date as continuous month (green pill) to Columns and Sales to Rows. Change the mark type to bar, change the Size to manual and adjust.

Drag another copy of the Sales pill to Rows, so its next the other one. Click on the context menu of that 2nd pill, and select Quick Table Calculation -> Moving Average

Change the mark type of the 2nd Sales pill to line.

Now click on the context menu of the 2nd Sales pill again and Edit Table Calculation. Select the arrow next to the ‘Average, prev 2, next 0’ statement, and in the resulting dialog box, change the Previous values to 3 and uncheck the Current Value box

At this point you can verify whether the values match Lorna’s solution when set to 3 previous months.

But, we need to be able to alter the number of months the moving average is computing over. For that we need a parameter

pPriorMonths

integer parameter, default to 3, that ranges from 2 to 12 in steps of 1.

Then click on the 2nd Sales pill and hold down Shift, and drag the pill into the field pane on the left hand side. This will create a new field for you, based on the moving average table calculation. Edit the field. Rename it and amend it so it references the pPriorMonths parameter as below

Moving Avg Sales

WINDOW_AVG(SUM([Sales]), -1*[pPriorMonths], -1)

Adjust the tooltip for both the line and the bar (they do differ). Ignore the additional statement on the final bar for now.

Colour the line black and adjust size. Then make the chart dual axis and synchronise axis. Hide the right hand axis. Remove Measure Names from the Colour shelf of both marks cards.

Colouring the last bar

In order to colour the last bar in each row, we need 3 pieces of information – the value of Sales for the last month, the moving average value for the last month, and an indicator of whether one is bigger than the other. This is where the LoDs come in.

First up, lets work out the latest month in the dataset.

Latest Month

DATE(DATETRUNC(‘month’,{FIXED : MAX([Order Date])}))

finds the latest date in the whole dataset and truncates to the 1st of the month. Note, this works as there’s sales in the last month for all Regions, if there hadn’t been, the calculation would have needed to be amended to be FIXED by Region.

From this, we can get the Sales for that month for each Region

Latest Sales Per Region

{FIXED [Region] :SUM( IF DATETRUNC(‘month’, [Order Date]) = [Latest Month] THEN [Sales] END)}

To work out the value of the moving average sales in that last month, we want to sum the Sales for the relevant number of months prior to the last month, and divide by the number of months, so we have an average.

First let’s work out the month we’re going to be averaging from

Prior n Month

This subtracts the relevant number of months from our Latest Month, so if the Latest Month is 01 Dec 2022 and we want to go back 3 months, we get 01 Sept 2022.

Avg Sales Last n Months

{FIXED [Region]:SUM( IF DATETRUNC(‘month’, [Order Date]) >= [Prior n Month] AND
[Order Date] < [Latest Month] THEN [Sales] END)} / [pPriorMonths]

So assuming we’re looking at prior 3 months, for each Region, if the Order Date is greater than or equal to 01 Sept 2022 and the Order Date is less than 1st Dec 2022, get me the Sales value, then Sum it all up and divide by 3.

And now we determine whether the sales is above or below the average

Latest Sales Above Avg

SUM([Latest Sales Per Region]) > SUM([Avg Sales Last n Months])

If you want to sense check the figures, and play with the previous months, then pop the data into a table as below

So now we’re happy with the core calculations, we just need a couple more to finalise the visualisation.

If we just dropped the Latest Sales Above Avg pill onto the Colour shelf of the bar chart, all the bars for every month would be coloured, since the calculation is FIXED at the Region level, and so the value is the same for all rows associated to the the Region. We don’t want that, so we need

Colour Bar

IF DATETRUNC(‘month’, MIN([Order Date])) = MIN([Latest Month]) THEN
[Latest Sales Above Avg]
END

If it’s latest month, then determine if we’re above or below. Note the MIN() is required as the Latest Sales Above Avg is an aggregated field so the other values need to be aggregated. MAX() or ATTR() would have worked just as well.

Add this field to the Colour shelf of the bar marks card and adjust accordingly.

Sorting the Tooltip for the last bar

The final bar has an additional piece of text on the tooltip indicating whether it was above or below the average. This is managed within it’s own calculated field.

Tooltip: above|below

IF DATETRUNC(‘month’ ,MIN([Order Date])) = MIN([Latest Month]) THEN
IF [Latest Sales Above Avg] THEN ‘The latest month was above the prior ‘ + STR([pPriorMonths]) + ‘ month sales average’
ELSE ‘The latest month was below the prior ‘ + STR([pPriorMonths]) + ‘ month sales average’
END
END

If it’s the latest month, then if the sales is above average, then output the string “The latest month was above the prior x month sales average” otherwise output the string “The latest month was below the prior x month sales average”.

Add this field onto the Tooltip shelf of the bar marks card, and amend the tooltip text to reference the field.

Finalise the chart by removing column banding, hiding field labels for rows, and hiding the ‘4 nulls’ indicator displayed bottom right.

Creating an Info icon

On a new sheet, double click into the space within the marks card that is beneath the Detail, Tooltip, Shape shelves, and type in any random string (”, or ‘info’ or ‘dummy’). Change the mark type to shape and select an appropriate shape. I happened to have some ? custom shapes, so used that rather than create a new one. For information on how to create custom shapes, see here. Amend the tooltip to the relevant text. When adding to the dashboard, this sheet was just ‘floated’ into the position I was afte. I removed the title and fit to entire view.

My published viz is here.

Happy vizzin’!

Donna

What is the time-weighted return?

Indeed! What is the time-weighted return? It’s not a term I’d ever heard of until Luke set the challenge this week. Fortunately he provided a link to a web page to explain all about it – phew!

I actually approached the challenge initially by downloading the data source, copying the contents of sheet2 into a separate excel workbook, and then stepping through the example in the web page, creating new columns in the excel sheet for each part of the calculation. I didn’t apply any rounding and ended up with slightly different figures from Luke. However after a chat with my fellow WOW participant Rosario Gauna, we were both aligned, so I was happy. I’d understood the actual calculation itself, so I then cracked open Desktop to begin building. This bit didn’t really take that long – most of the time on this challenge was actually spent in Excel, but shhhhh! don’t tell anyone ðŸ™‚

Building the calculations

I’m going to build lots of calculated fields for this, primarily because I like to do things step by step and validate I’m getting what I expect at each stage. You could probably actually do this in just 2 or 3 calcs. As I built each calc, I added it into a tabular view of the data. I’m not going to screen shot what the table looks like after every calc though.

To start though, I chose to rename some of the existing fields as follows :

• Flows -> A: Flows
• Start Value -> B: Start Value
• End Value -> C: End Value

I did this because I have several new fields to make, and coming up with a meaningful name for each was going to be tricky – you’ll understand as you read on ðŸ™‚

First up, create a sheet and add Reporting Date as an exact date discrete blue pill to Rows then add the above 3 fields.

Now onto the calculations, and this all just comes from the website. Each calc builds on from the previous. I chose to add inline comments as a reminder. After I built each calc I added to the table.

D: A+B

//sum flow + start point
SUM([A: Flows]) + SUM([B: Start Value])

E: C-D

//difference between end value and (flow+start)
SUM([C: End Value]) – [D: A+B]

F: E/D

//Difference as a proportion of (start + flow)
[E: C-D]/ [D: A+B]

I formatted this one to 3 decimal places so the figure showed on screen, but ‘under the bonnet’ Tableau will use the full number in the onward calcs.

At this point now, F: E/D stores what the article describes as the ‘HP’ value for each date. Now we need to start building the calcs we need to compute the TWR value.

G: 1+F

1+[F: E/D]

formatted to 3 dp

H: Multiply G values

//Running Product of column G
[G: 1+F]*PREVIOUS_VALUE(1)

This is the core calculation for this challenge. This takes the value of the G: 1+F field in each row, and multiples it by the value of the G: 1+F field in the previous row. As each row ends being the product of itself and the former row, this ultimately builds up a ‘running product’ value.

Add these fields onto the table and explicitly set the table calculation of the H: Multiply G values field to compute using Reporting Date.

And finally we can create

I: TWR

//subtract 1 from the running product -1
[H: Multiply G values]-1

format this to be a % with 0dp. Add to the table and again ensure the table calc (and all nested calcs) are set to compute using Reporting Date

The final calculation needed is one to use to display the TWR value for the last date.

J: Latest TWR Value

//get the last value computed
WINDOW_MAX([I:TWR])

format this to be a % with 0dp.

This function looks for the highest number across the whole table, and spreads that value across every row. Since the I: TWR field is cumulative, the highest number will be the last value. Add this onto the table and verify all the tab calcs are set to compute by Reporting Date.

And now you can build the viz

Building the Viz

Add Reporting Date as an exact date continuous green pill to Columns and I:TWR to Rows, setting the compute by of the table calculation as described above. Add J:Latest TWR Value to Detail, and again set the compute by.

Then update the title of the sheet to reference the J:Latest TWR Value field, amend the tooltips and update the label on the y-axis. Add to a dashboard and you’re done ðŸ™‚

My published viz is here.

Happy vizzin’!

Donna

Can you maintain rank?

Building the WAR chart

Build a basic bar chart by adding Name to Rows and WAR to Columns

Whilst we could sort the data at this point, we’re going to leave for now, as we want the ‘ranking’ field to define the sort.

Now rather than use the RANK table calculation option, the essence of this being included in a tipping session, is to utilise the INDEX table calculation instead. By using INDEX, we don’t need to create as many fields as if we used RANK. We can reuse the INDEX across all the charts.

So, let’s create that field

Index

INDEX()

Format this to be a number with 0dp and prefixed by #

The INDEX() table calculation simply lists your rows or columns of data from 1 to however many entries there are. The Index can span the whole width/length of the table or can restart at intervals depending how you choose to partition/structure your table. You can also control how the data will be indexed based on how you want it sorted.

Add Index to Rows, then change it to be discrete (blue), and position in front of Name. Edit the table calculation setting so that it is computing using Name and is sorted by WAR descending.

We need to capture the name of a player, so we’ll create a parameter for this

pSelectedPlayer

String parameter defaulted to Ken Giffey Jr.

Show this parameter on the view.

We also need a field to identify whether the row matches the parameter

Is Selected Player?

[pSelectedPlayer] = [Name]

Then re-edit the table calculation so it is computing by Is Selected Player as well.

Note – to prevent having to change the table calculation, the alternative is to make the Is Selected Player field on the Colour shelf an Attribute (just choose this setting from the context menu when you click on the pill).

Additionally add Is Selected Player to Rows before Index., the manually sort that field so True is listed first. This will move the relevant record to the top of the scree, but the rank number will be preserved.

Hide the Is Selected Player field (uncheck Show Header), and Hide Field Labels for Rows.

Adjust row dividers, so the divider is at level 1, and the header is dotted line, while the pane is solid.

Remove column gridlines, but add a column axis ruler.

Show mark labels, set them to be left aligned and the colour to match mark colour. You may need to widen the rows to see the labels display.

Add a border to the bars via the Colour shelf.

Format the Index and Name fields displayed – I used Tableau Regular font size 12, and right aligned the Index field.

The final step, which we’ll do now, is to add a couple of fields to stop the rows from being highlighted when selected on the dashboard.

Create a field True = TRUE and False = FALSE, and add these fields to the Detail shelf.

Building the Batting Average chart

Create a basic bar chart with Name on Rows and BA on Columns. Add Is Selected Player to Colour and apply border.

Format the BA field using custom formatting of .000;-.000 then show mark labels, and left align and format as you did above.

Add Index to Rows, make discrete and move to be in front on Name. Adjust the table calculation to compute by Name and Is Selected Player and this time adjust the sort to use BA descending.

We now need to filter the data to only show the 5 rows above & below the selected index. First we need to identify them.

Selected Player Index

WINDOW_MAX(IF ATTR([Is Selected Player]) THEN INDEX() END)

The inner IF statement returns the index associated to the selected player. The WINDOW_MAX function then ‘spreads’ this value across all the rows in the data. So we can then identify the records we need

Records to Keep

[Index] >= [Selected Player Index]-5 AND
[Index] <= [Selected Player Index]+ 5

Add this to the Filter shelf and initially select all the values.

Now edit the table calculation settings of this field. It will now contain nested calculations – one related to Index and one related to Selected Player Index. Ensure both are computing by Name and Is Selected Player and both are sorting by BA descending.

Then edit the filter and just select True.

Then follow the same steps to create similar charts of the OPS and HR measures, remembering to apply the sorts on the table calculations to the relevant field.

Once you have all four sheets built, add to a dashboard. Then create a parameter action to select the player from the WAR sheet.

Select Player

On select of the WAR sheet, set the pSelectedPlayer parameter passing in the Name field.

Then create a filter action to prevent the selection on the WAR sheet from remaining highlighted.

Unhighlight WAR

On selection of the WAR sheet on the dashboard, target the WAR sheet directly and use Selected fields, setting True = False.

Fingers crossed and you should now have a working dashboard. My published version is here.

Happy vizzin’!

Donna

Let’s build a trellis chart!

It only seems like yesterday I was writing a solution guide, and I’m back at it again. This week Sean asked us to recreate this challenge to build a small multiple / trellis chart using table calculations only.

A note on the data

After downloading and connecting to the provided data source, I found the dates weren’t coming through as intended – they’d been transposed from dd/mm/yyyy to a mm/dd/yyyy so consequently the only dates I was getting were for the first 12 days in January for every year. Rather than trying to solve this at source, I just created a new field which transposed the Date field back so it behaved as I expected

Date Corrected

(MAKEDATE(YEAR([Date]), DAY([Date]),MONTH([Date])))

You may not need to do this if the data pulls in correctly.

Filters

There are two filters that should be applied, which can either be added as data source filters (right click data source > Edit data source filters) or can be applied to the Filter shelf on any sheets created. Ultimately, this challenge only requires 1 sheet, but when building and verifying logic, I tend to have additional ‘check sheets’. I therefore added the filters below to the Filter shelf of the first sheet I started working with, but set them to apply to all worksheets using the data source (right click pill once it’s on the filter shelf -> Apply to Worksheets -> All using this data source).

Gender : All

Date Corrected : starting date = 01 Jan 2012

Setting up the data

As is good practice when working with table calculations, I start by building out the calculations I need and validating them in a tabular format before I build any vizzes. So let’s do that.

All the countries are displayed in capital letters, so we need

Country UPPER

UPPER([Country Name])

Additionally, for the purpose of validation and performance only, add this field to the Filter shelf too and just filter to Australia and Austria.

If you haven’t already added them as data source filters, apply the filters mentioned in the section above to this sheet too and set to apply to all worksheets using the data source.

Add Date Corrected to Rows as a discrete (blue pill) exact date. Format the date so it displays in month year format.

Add Unemployment Rate to Text. Format this number to 1 decimal place and add a % as a suffix.

Now for the table calcs

Median

WINDOW_MEDIAN(SUM([Unemployment Rate]))

Format this to display as a % using the same option as above. Add this to the table and set to compute using Date Corrected

You should find that your median value only differs by country.

Now we work out

Variance

SUM([Unemployment Rate]) – [Median]

Format this to display as a % and add to the table, setting the table calc to compute by Date Corrected again. This is the measure that will be used to plot the trend line against.

We also need to display the range of Unemployment rates for each country – ie we need to work out the minimum and maximum values.

Max Unemployment Rate

WINDOW_MAX(MAX([Unemployment Rate]))

Min Unemployment Rate

WINDOW_MIN(MIN([Unemployment Rate]))

Format both of these to display as 5 with 1dp, and add to the table, verifying the calculations are computing by Date Corrected once again. Verify you get the same values for all the rows associated to a single country.

Now we know the calculations are as expected, we can start to build out the viz.

Building the core chart

To start with we’ll just focus on getting the line chart with the associated text displayed for the two countries Australia & Austria. So on a new sheet add Country (UPPER) to Filter and filter to these selections. The other filters should automatically add.

Add Country UPPER and Date Corrected (green continuous exact date) to Columns and Variance to Rows. Set Variance to Compute By Date Corrected.

Add Unemployment Rate to the Tooltip and adjust the text to match.

To add the country title and other displayed text, we’re going to use a ‘fake axis’ and plot a mark at a central date. On hovering over the solution, October 2016 seems to be the appropriate date selected. So we need

Title Position to Plot

IF [Date Corrected] = #2016-10-01# THEN 1 END

Add this to Rows in front of the existing pill. Change the mark type of this measure only to a circle and re-Size to make it as small as possible and adjust the Colour Opacity to 0%. This will make the mark ‘disappear’.

Add Country UPPER, Median, Max Unemployment Rate and Min Unemployment Rate to the Label shelf of this marks card. Ensure all the table calculation fields are set to compute by Date Corrected. Adjust the text as required, and align centre. Ensure the Tooltip is blank for this marks card.

Change the colour of the variance line to grey, then remove all gridlines, row dividers and axis. Set the Column Dividers to be a thick white line (this will help provide a separator between the small multiples later).

Creating the trellis

There are multiple blog posts about creating trellis charts. My go to post has always been this one by Chris Love. It’s a more complex solution that dynamically flexes the number of rows and columns based on the number of members in the dimension you’re visualising. There have also been other Workout Wednesday challenges involving trellis charts, which I’ve blogged about too (see here).

Ultimately we’re aiming to determine a ‘grid position’ for each member of our dimension. In this case the dimension is Country UPPER and its a static list of 36 values, which we can display in a 6 x 6 grid. So Australia needs to be in row 1 column 1, Austria in row 1 column 2….. Costa Rica in row 2 column 1… USA in row 6 column.

As our members are static, the calculations we can use for this can be a bit simpler than those in Chris’ blog.

Firstly, let’s get our data in a tabular layout so we can ‘see’ the values as we go.

Duplicate the data sheet we built up, then move Measure Name and Date Corrected from Rows/Columns to the Detail shelf. Remove the Country UPPER field from the Filter shelf. You should have something like below, showing 1 row per country

Double click into the Rows shelf and type in INDEX(), then change the resulting pill to discrete (blue). You will see that index numbers every row. It’s a table calculation and although working as expected, let’s explicitly set it to compute using County UPPER.

Let’s now create our grid position values.

Cols

FLOAT(INT((INDEX()-1)%6))

This takes the Index value and subtracts 1, and returns the remainder when divided by 6 (%6=modulus of 6 – ie 6%6=0, 7%6=1). 6 is the number of columns we want.

Rows

FLOAT(INT((INDEX()-1)/6))

This takes the Index value and subtracts 1, and returns the integer part of the value when divided by 6. Again we’re using 6 as this is the number of rows we want to display.

Add these to the table, set to be discrete (blue) and compute using Country UPPER.

You can see that the first 6 countries are all in the same row (row 0) but different columns (0-5).

Now that’s understood, we can create the small multiples on the viz.

Duplicate the sheet we created further above which displays the trend graph for Australia & Austria. As we’re now going to make the changes to create the charts for every country, if things go a bit screwy, you can always get back to this one to try again :-).

Add Cols to Columns. Set to discrete and compute using Country UPPER. Add Rows to Rows and do the same thing. Move Country UPPER from Columns to the Detail shelf on the All marks card. Then remove Country UPPER from the Filter shelf.

Hopefully everything worked as expected and you have

Final step is to uncheck Show Header against the Cols and Rows pills so they don’t display and you can add to a dashboard.

My published viz is here.

Happy vizzin’!

Donna