Category: #WOW2021

Can you use set actions to create a reference date range?

Donna ColesLeave a comment

There’s a lot packed into the challenge this week, which was “set” by Lorna Brown and Erica Hughes to test our Set Action skills. How detailed this blog will go, I have yet to decide… I’ve got a couple of hours to get this nailed, so it could get quite brief as we get towards the end 🙂

I’ve got 6 sheets/charts making up this dashboard, so my intention is to summarise each one, and I’ll define the various calculations that are going to be needed as we go.

  • The overall summary table
  • The selected months summary table
  • The trend line
  • The donut chart
  • The top 3 states table
  • The map
  • Adding the interactivity

The overall summary table

This challenge is focused on understanding the Sales per month. Whilst its possible to use the built in aggregation features of a date field, I often prefer to create explicit date fields at the level I require, so it’s easier to reference. Therefore, the first field I created for this challenge was

Order Date To Plot

DATETRUNC(‘month’, [Order Date])

This essentially ‘groups’ every order placed in a month to be tagged with the 1st of the month. I custom formatted this field to MMM yyyy (ie Oct 21).

For the overall summary table, I need to capture the total sales of the whole data set, and I use a Fixed LoD calculation for this.

Total Sales

{FIXED: SUM([Sales])}

This field is formatted to $0.00M

NOTE – I actually named this field <space>Total Sales<space> as I want to display the name of the field (the measure name) in the summary table, but the ‘selected months’ summary table also has a Total Sales measure which is a different calculation (see later). Adding the <spaces> is a sneaky way to get two fields with what appears to be the same name. As this field when displayed will be centred, the <spaces> aren’t noticeable.

We also need to get the monthly average sales for the whole data set

Average Sales by Month

AVG({FIXED [Order Date To Plot]: SUM([Sales])})

Format this to to $0.0K

We can now build the summary table by adding Measure Names to the Filter shelf and selecting these 2 fields. The placing Measure Names on Rows and Measure Names and Measure Values on Text. Reorder the measures as required, hide Measure Names on Rows and format the Text as required.

Change the title of the sheet to Superstore Sales, ensure the tooltip doesn’t display and remove all gridlines / row banding etc.

The selected months summary table

The core requirement of this challenge is to make use of set actions, so we’re obviously going to need a set which will contain the dates (months) the user will select on the chart. This set will be based off of Order Date To Plot. Right click on the field > Create > Set. Name it Order Dates To Plot Set and by default, select all the months between Oct 20 to Jun 21 inclusive.

Later I’ll describe how the values of this set will get updated, but for now, we need to get some information relating to the sales in these selected months.

Firstly, we want the total sales for the months in this set.

Total Sales

IF [Order Date To Plot Set] THEN [Sales] END

The default format for this field is set to $ with 0 dp.

Note – this is the other ‘total sales’ field mentioned earlier. This field name has no leading/trailing spaces.

To get the average, I needed a field just to store each member of the set (ie each selected month)

Selected Dates

IF [Order Date To Plot Set] THEN [Order Date To Plot] END

and with this I can then work out

Average Sales

AVG({FIXED [Selected Dates]: SUM([Total Sales])})

The final measure required for this section is the change within the date range, which is basically comparing the value of sales at the first month in the selection with the sales in the final month selected. We need a few fields to get to this.

Firstly, we want to identify the first and last months

Min Selected Date

{FIXED:MIN(IF [Order Date To Plot Set] THEN [Order Date To Plot] END)}

If the date is in the set, then return the date and then take the minimum of all the dates, and store against all the rows in the data. Similarly we have

Max Selected Date

{FIXED:MAX(IF [Order Date To Plot Set] THEN [Order Date To Plot] END)}

Putting this info into a table, you can see how the calculations are working. The values for the Min & Max dates are the same across every row.

Next we need to get the Sales at the min & max points, and spread that value across all rows

Sales at Min Date

{FIXED: SUM(IF [Order Date To Plot]=[Min Selected Date] THEN [Sales] END)}

Sales at Max Date

{FIXED: SUM(IF [Order Date To Plot]=[Max Selected Date] THEN [Sales] END)}

Now we can work out the difference

Change within Date Range

([Sales at Max Date]-[Sales at Min Date])/[Sales at Min Date]

format this to a percentage set to 1 dp

Finally, we need to know the number of months in the set, which is displayed in the title of the monthly summary sheet.

Months in Set

{FIXED: COUNTD(IF [Order Date To Plot Set] THEN [Order Date To Plot] END)}

If the date is within the set, then capture the date, and the count the distinct set of dates captured.

Make this a discrete field (move from the measures section at the bottom of the data pane to the dimensions section at the top (above the line), and add to the tabular view

Now we can build the summary sheet.

Add Measure Names to the Filter shelf and this time filter by Total Sales, Average Sales and Change within Date Range. Add Measure Names to Rows and Measure Values to Text. Reorder the measures.

Format the Total Sales to be in $K, by selecting the Format option from the context menu of the Total Sales pill on the Measure Values shelf (hover on the pill and click the carrot/down arrow that appears – by formatting this way, we’re changing the display of this field for this sheet only).

Add Min Selected Date and Max Selected Date to the Detail shelf and set to be Exact Date. Format both these fields via the pill context menu to be the ‘March 2001’ format

Also add Months in Set to the Detail shelf.

Adjust the title of the sheet as below

Finally you need to set the background of the worksheet to the relevant purple (I used #8074a8), adjust the colours of all the fonts to white and adjust the size/style of the fonts in the table. Remove all gridlines/row banding etc, and you should have something like below

The Trend Line

By this point we’ve built all the calculated fields we need for this chart. This is a dual axis line chart, as we want the colour of the line for the selected dates to be different from the non selected ones, and we want to display a label for the highest sales in the selected timeframe.

  • Add Order Date to Plot to Columns, and set as a Continuous (green) pill set to Exact Date
  • Add Sales to Rows
  • Add Total Sales to Rows
  • Make the chart dual axis, and synchronise axis.
  • Adjust the colours of the Measure Names colour legend
  • On the Label shelf of the Total Sales marks card, set to label the maximum value only
  • On the All Marks Card, add Min Selected Date and Max Selected Date to the Detail shelf and set to Exact Date.
  • Right click on the Order Date To Plot axis and Add Reference Line
  • Create a reference band that starts at the constant Min Selected Date, ends at the constant Max Selected Date, is bounded by dotted lines and shaded between
  • Hide the Sales and Total Sales axis, format tooltips and adjust the row & column dividers.
  • Change the title and you should get to

The donut chart

Donut charts are 2 different sized pie charts on top of each other, created using a dual axis chart. On the Rows shelf type in MIN(0). Then type the same next to it. This gives you two axis and two marks cards.

We only care about information related to the selected dates for this chart, so we can add Order Date To Plot Set to the Filter shelf, which by default will just restrict the information to the data ‘in’ the set.

Change the mark type of the 1st MIN(0) marks card to Circle and add Sales to the Tooltip shelf. Adjust the size of the charts/mark.

Change the mark type of the 2nd MIN(0) marks card to Pie and add Sales to the Angle shelf. Add State to the Detail shelf. Sort the State field by Sales descending.

Note – the circles might look the same at this point, but if you hover over the bottom one, you should see that it’s segmented by State.

We need some new fields now to help us identify the top ranking states.

Sales Rank

RANK(SUM([Sales]))

This is a table calculation, so it’s best to see how this field will work in a table view – build one out as below, and set the table calculation on the Sales Rank field as shown

We’re now going to ‘group’ the ranks into the top 3 and everything else

Sales Rank Group

IF [Sales Rank]<=3 THEN [Sales Rank] ELSE 10 END

We can now use this Sales Rank Group field to colour the pie chart. On the 2nd MIN(0) marks card, add Sales Rank Group to the Colour shelf. Adjust the table calculation to compute using State as above, then change the field to be Discrete (blue). Adjust the colours to suit.

Now make the chart dual axis, and synchronise the axis. Adjust the size of the 1st MIN(0) circle to be smaller than the pie. If it’s not showing, right click on the right hand axis and move marks to back. Colour the circle white. Adjust tooltips to suit and hide axis, column/row dividers etc. Update the title. You should have

The top 3 states table

  • Add Order Date To Plot Set to Filter
  • Add State to Rows and Sales to Text and sort descending.
  • Add Sales Rank to Filter and set to At Most is 3. This will just show the top 3 states.
  • Add State to Text
  • Add a Percent of Total Quick Table Calculation to the existing Sales field that’s on the Text shelf (via the context menu of the pill)
  • Add another instance of Sales back onto the Text shelf
  • Adjust / format the font size and layout of the fields on the Text shelf
  • Add Sales Rank to the Size shelf and set to be discrete (blue) and set the mark type to be Text. Adjust the size of the marks – it’s likely it’ll need to be reversed and the range adjusted.
  • Hide the State field on Rows, adjust the font colours, remove row banding and row/column dividers. You should end up with…

The map

  • Add Order Date To Plot Set to the Filter shelf
  • Add State to Detail – this should create a map (edit locations to be US if need be – Map -> Edit Locations menu)
  • Add Sales to the Colour shelf
  • Edit the colour range to a suitable purple range ( I set the darkest colour of the range to #6c638f)
  • Adjust the map layers (Map -> Map Layers) so only the option highlighted below is selected.

Adding the interactivity

Add all the sheets to the dashboard, using vertical and horizontal containers to arrange the relevant layout. Then add a Set Action (Dashboard > Actions > Add Action > Change Set Values). The Set Action should be configured as below :

And fingers crossed, you should now be able to select marks on the trend line and see all the other charts, except the initial summary table, all update. My published viz is here.

Happy vizzin’! Stay Safe!

Donna

How much do top sub-categories contribute to sales?

Donna ColesLeave a comment

A colourful #WorkoutWednesday challenge this week, courtesy of Ann Jackson incorporating pie charts, top N functionality and interactivity and a highlight table. Pie charts can cause much debate amongst the data viz community and if this one was just representing the multitude of sub-categories, it certainly wouldn’t be ideal. But when the core aim is to simply present 2 key measures (those in the top N against the rest), the pie is a familiar and effective visual. In this instance, the outer ring segmenting all the sub-categories provides additional context without detracting from the main purpose of the viz.

So lets build…

  • Creating the core calculations
  • Building the Pie Chart
  • Building the Highlight Table
  • Adding the Interactivity

Creating the core calculations

First up, we’re going to need a parameter to define the ‘Top N’. Create an integer parameter with a range from 1 to 17, that steps every 1 interval, and is defaulted to 5.

pTopN

Next we’re going to use a Set to capture the Sub-Categories that are in the Top N Sales. Right click on Sub-Category -> Create ->Set. Use the Top tab to define a set captures the Sum of Sales that is based on the pTopN parameter.

Now, we want to create a grouping of those in and out of the set, which will be used as part of the highlight table

Sub-Cat Group

IF [Sub-Category Set] THEN ‘IN TOP ‘ + STR([pTopN])
ELSE ‘ALL ELSE’
END

Pop all these fields out into a table so you can see what’s going on as you change the pTopN parameter. Sort the Sub-Category by Sales descending.

Now we need to identify the % value of Sales for the Sub-Categories that are in the Top N (this is the label on the darker segment of the central pie chart), so for that we need

Total Sales

{FIXED:SUM([Sales])}

Top N Sales (in hindsight, this should have been named Sales per Group or similar)

{FIXED [Sub-Category Set] : SUM([Sales])}

Top N Sales %

IF ATTR([Sub-Category Set]) THEN
SUM([Top N Sales])/SUM([Total Sales])
END

Format this to percentage with 0 dp.

Adding to the table, we can see the values

The final field we need in order to build the pie, is an additional one to store the label text

Label:SubText

IF [Sub-Category Set] THEN ‘TOP ‘ + STR([pTopN]) END

Building the Pie Chart

To achieve this we’re going to build a dual axis pie chart, where one pie is used to define the In/Out of Top N segmentation in the centre, and the other pie is used to create the outer ring.

Create an axis by typing in MIN(0) onto the Rows shelf, and then adding another instance of MIN(0) next to it. This will generate 2 marks cards, which is where the fields to build the pie charts will be placed.

In the first MIN(0) marks card, change the mark type to Pie, then add Top N Sales to the Angle shelf and Sub-Category Set to the Colour shelf. Adjust colours to suit. Then add Top N Sales % and Label:SubText to the Label shelf. Adjust size of the view and the chart to suit. Also remove all text from the Tooltip.

Positioning the text is a bit fiddly. If you click on the text so the cursor changes to a cross symbol, you can then drag it to a better location. However, when you change the Top N parameter, the text will move. You can go through each parameter value and reposition the text each time (which I did.. it wasn’t too onerous for 17 values), however I found when published to Tableau Public, the positioning wasn’t honoured. Ann’s solution was the same, so I didn’t get too hung up on this, although if anyone resolved it, I’d love to know!

Now on the 2nd MIN(0) marks card, again change the mark type to Pie, and this time add Sales to the Angle shelf and Sub-Category to Colour. Sort the Sub-Category field by Sales descending. Additionally add Sub-Category Set to the Detail shelf (this will be needed later on to make the interactivity work). Edit the colour palette to use the Hue Circle options. Adjust the size of the pie chart. Adjust the tooltip too.

Now make the chart dual axis and synchronize the axis. If the colourful chart is displayed ‘on top’, then right click on the right hand axis and select move marks to back. Adjust the sizes of both pies, so the colour wheel is slightly larger than the other one.

Now hide the axis, and remove all borders and gridlines.

Building the Highlight Table

I’ve built the highlight table as a bar chart. Start off by adding Sub-Category Set, Sub-Cat Group and Sub-Category to Rows. Sort Sub-Category by Sales descending. Then type in MIN(1) into the Columns shelf.

Now add subtotals via the Analysis > Totals > Add all Subtotals menu. This adds 2 additional rows to each section

But we don’t want the ‘grand total’, so click on the Sub-Category Set context menu, and uncheck Subtotals

To position the totals at the top, go to Analysis > Totals > Column Totals To Top

Then add Sub-Category to the Colour shelf, and adjust the colour of the Total bar to white

We now need to get some text onto those bars, but we need some additional calculations to help up with this. Firstly, we want to get the rank of the Sub-Category. We’ll use a table calculation for this

Sales Rank

RANK(SUM([Sales]))

We also need a way to identify the Total rows differently from the main Sub-Categories. I referred to this Tableau KB for help here, and subsequently created

Size

SIZE()

To see what this is doing, add Size to the Label shelf, and adjust the table calculation setting to compute by all fields except the Sub-Category Set. The size of the total rows is 1.

Based on this logic, we can then create

LABEL:Bar

IF [SIZE]=1 THEN ‘SUBTOTAL FOR GROUP’
ELSE ‘#’+STR([Sales Rank]) + ‘ ‘ + ATTR([Sub-Category])
END

Add this to the Label shelf instead of the Size field and adjust the table calc settings as above. Align left. Then add Sales to the Label shelf too and adjust so its on the same row. Adjust the tooltip too.

Now hide the Sub-Category Set and the Sub-Category fields. Right click on the ‘IN TOP x’ text and Rotate Label, then click on Sub-Cat Group text and Hide Field Labels for Rows. Format the header text to suit.

Hide the MIN(1) axis, and set columns and gridlines to None. Adjust the Row dividers to be darker

Adding the Interactivity

Add the 2 sheets onto a dashboard, and add a Highlight Dashboard Action, that on Hover of either of the charts, it highlights the other chart based on the Sub-Category Set only.

I think that’s covered everything. My published viz is here.

Happy vizzin’! Stay Safe!

Donna

Can you make stacked bar charts easier to compare?

Donna ColesLeave a comment

Candra McRae was back to set the challenge this week. I found it relatively straightforward, so if you’re relatively new to Tableau, this is quite a good challenge to start with. In this we’ll cover

  • Grouping the states
  • Applying the sort
  • Adding the total value
  • Adding the interactivity

Grouping the states

The states need to be grouped based on the initial letter. Candra stated she wasn’t expecting a large IF STARTWITH… type formula. I did it by making use of the fact characters can be converted into ASCII which provides a numerical representation of a letter, which we can then utilise. So we need

State Initial ASCII

ASCII(UPPER(LEFT([State],1)))

This takes the 1st letter of the State, ensures it is uppercase, and converts to ASCII. You can see below what this looks does

With this knowledge, we can then create

State Group

IF [State Initial ASCII] <=77 THEN ‘A-M’
ELSE ‘N-Z’ END

and you can now easily create the stacked bar chart (note, I’ve already removed the various gridlines etc)

Applying the Sort

The ultimate intention is to capture the Category a user clicks on into a parameter, so we need to define that parameter

pSelectedCategory

A string parameter defaulted to Office Supplies

Show this parameter on your sheet, so you can manually test how the sort will work by manually changing the values.

Create a new calculated field to define the sort

Sort

IF [pSelectedCategory]=[Category] THEN 0 ELSE 1 END

Then edit the sort property of the Category field that’s on the Colour shelf as below

Now change the value in the parameter box to Technology and see how the chart changes.

Adding the total value

Create a new field to store the total values

Total Sales by State Group

{FIXED [State Group]: SUM([Sales])}

Add this onto the Detail shelf, then add a reference line per line as below

Adding the interactivity

Once you’ve added the chart onto a dashboard, you need to add a parameter action which will set the pSelectedCategory parameter with the value from the Category field on select.

To prevent the selected category from ‘remaining selected’ on click, I applied the ‘true=false’ trick I use a lot.

Create a field True = true and a field False = false, then add both to the Detail shelf of the chart viz. On the dashboard add a new filter action which on select passes selected fields only setting true = false. As this condition can never be true, the filter doesn’t apply and this clears the highlight action.

And that is it for this week – short & sweet, but covers a handful of bite-size concepts. My published viz is here.

Happy vizzin’! Stay Safe!

Donna

Can you quickly tell the net change between two dates?

Donna ColesLeave a comment

Continuing with ‘Community Challenge’ month, it was the turn of Will Perkins to set the challenge for this week; a challenge inspired by Google’s stock tracker.

By interacting with the published solution and reading the requirements, I deduced that I was likely to need 3 sheets – 1 for the Region headings & KPIs, 1 for the trend line chart, and 1 to drive the timeframe selections. The trend line chart looked like it was going to involve a dual axis combining a line and and area chart, along with ‘filled’ reference bands, although exactly how it would work I wasn’t entirely sure initially. Finally, there was going to be some ‘parameter actions’ action along with the ‘true = false’ trick to ensure selected marks didn’t remain highlighted.

But before we can tackle the actual chart build, we need to nail some of the calculations involved.

Identifying the date range to highlight

The intention of the chart is that on initial load, it has highlighted the timeframe for the last 14 days up to ‘today’. As this chart is being built with a static data set, which only has data up to the end of 2021, I chose to ‘hardcode’ my ‘today’ value into a parameter. This is so that in a year’s time when I might look at this again, I won’t be presented with a broken looking viz.

pToday

Date parameter defaulted to 20 Sept 2021

The user also has the ability to highlight/select dates on the chart itself, which will define a start and end date range. So we also need some additional parameters to capture this information.

pStartRange

Date parameter defaulted to 01 Jan 1900

Similarly you’ll need a pEndRange parameter too, also defaulted to 01 Jan 1900.

Later on we’ll define parameter actions which will ‘set’ these values based on user interaction.

With these fields, we can then define calculated fields to store the start and end dates depending on whether we’re using the defaults due to initial load (ie 14 days to today), or a user selected range.

Selected Range Start Date

IF [pStartRange] = #1900-01-01# THEN DATE(DATEADD(‘day’,-14,[pToday]))
ELSE DATE([pStartRange])
END

Selected Range End Date

IF [pEndRange] = #1900-01-01# THEN DATE([pToday])
ELSE DATE([pEndRange])
END

We’re going to be plotting Order Date on our axis at the day level, and so to simplify things IMO, I created

Order Date Day

DATE(DATETRUNC(‘day’,[Order Date]))

which I then reference in the following calculated field, which is just to capture all the days within the range selected

Selected Dates To Plot

IF [Order Date Day]>= [Selected Range Start Date] AND [Order Date Day]<=[Selected Range End Date] THEN [Order Date Day] END

We can now start to build out the basic chart

Plotting Order Date Day and Selected Dates to Plot side by side you can see the date axis differ, with only the dates from 06 Sep – 20 Sep 21 displaying on the right hand side. The marks type for Selected Date to Plot is set to Area, and to get the marks to join up, you need to turn Stack Marks Off (Analysis -> Stack Marks -> Off menu).

Defining the Timeframe to Display

We’re going to use another parameter to store the timeframe value

pTimeframe

String parameter defaulted to 6 MONTHS (note the case – it’s simpler to match it to the display format that’s going to be used)

We then need a calculated field to tell us what to do with this value

Timeframe to Display

CASE [pTimeframe]
WHEN ‘1 MONTH’ THEN [Order Date Day]>=DATEADD(‘month’,-1,[pToday]) AND [Order Date Day]<= [pToday]

WHEN ‘6 MONTHS’ THEN [Order Date Day]>=DATEADD(‘month’, -6, [pToday]) AND [Order Date Day]<= [pToday]

WHEN ‘YTD’ THEN [Order Date Day]>=DATETRUNC(‘year’,[pToday]) AND [Order Date Day]<= [pToday] WHEN ‘1 YEAR’ THEN [Order Date Day]>= DATEADD(‘year’,-1,[pToday]) AND [Order Date Day]<= [pToday]
ELSE [Order Date Day] <= [pToday]
END

This field will return true for all the dates that fall within each statement and false otherwise.

Add this field to the Filter shelf and select True.

You can test how the left hand side of the chart is affected by manually typing the different values into the parameter

Colouring the chart

The line and area charts are coloured based on whether dates fall in the selected range and whether the difference between the sales values at the start and end of the selected range is positive or not. We need several more calculated fields to work this out.

We firstly need to capture the min and max dates of the selected area for each region. Now, you initially might think that the Selected Range Start Date and Selected Range End Date fields already have these values. However there isn’t always a sale in every region for these dates. You could argue, that in that case, the sales value for that date should be 0 (ie there were no sales on that day), but to match the solution (and it was easier), we just get the min and max dates within the selected range that have a sales value for each region.

Min Selected Date Per Region

{FIXED [Region]: MIN([Selected Dates to Plot])}

Max Selected Date Per Region

{FIXED [Region]: MAX([Selected Dates to Plot])}

Pop these out into a quick view, and you can see how the dates differ per region compared to the default start & end date values

Now we want to work out the sales value on these dates

Min Date Sales

{FIXED [Region]: SUM(IF [Order Date Day]=[Min Selected Date Per Region] THEN [Sales] END)}

Max Date Sales

{FIXED [Region]: SUM(IF [Order Date Day]=[Max Selected Date Per Region] THEN [Sales] END)}

and then we can work out the difference and the % difference

Range Sales Diff

SUM([Max Date Sales])-SUM([Min Date Sales])

custom formatted to +”$”#,##0.00;-“$”#,##0.00 to show a ‘+’ prefix for positive values

Range Sales % Diff

[Range Sales Diff]/SUM([Min Date Sales])

custom formatted to â–²0.0%;â–¼0.0%

Now we can compute a field to use to colour the line/area chart

Colour – Trend

IF MIN([Order Date Day]) >= [Selected Range Start Date] AND MIN([Order Date Day])<= [Selected Range End Date] THEN

IF [Range Sales Diff]>= 0 THEN 1 ELSE -1 END
ELSE 0
END

If we’re within the selected date range, then test to see if the value is positive (set to 1) or negative (set to -1), otherwise we’re outside the selected date range, so set to 0

Go back to the trend chart and add this field to the Colour shelf of the All Marks card (so it gets added to both sets of marks). Change it to be a discrete (blue) pill and the adjust the colours accordingly. At this point you may want to change the background colour if you’re using a white line. I’m just setting it to a light grey at this point, but eventually it’ll get set to black.

Adding the highlight band

This took a lot of thinking. I knew I’d need a reference band, but it took some time to figure out how to get the backgrounds coloured differently, since you only have the option to fill between the band with one colour.

The trick is to make use of the two date axes we have and to apply a band per pane.

But we need some more fields to make this happen.

Ref Line Start Date -ve

IF [Range Sales Diff]<0 THEN [Selected Range Start Date] END

Ref Line End Date -ve

IF [Range Sales Diff]<0 THEN [Selected Range End Date] END

Add these fields to the Detail shelf of the Order Date Day card and set to be continuous (green). Then add a reference band to this axis, applying the settings as below (note, the Line is a white dotted line, so isn’t showing up in the field setting, though you can see it on the viz).

Because the reference band has been set at the pane level, and the reference line dates are only relevant if the difference is negative, then the band is just showing on one row.

We then do something very similar, but this time we get some dates only if the difference is positive.

Ref Line Start Date +ve

IF [Range Sales Diff]>=0 THEN [Selected Range Start Date] END

Ref Line End Date +ve

IF [Range Sales Diff]>=0 THEN [Selected Range End Date] END

Add these as continuous pills on the Detail shelf of the Selected Dates to Plot card, and add another reference band to this axis instead.

Now you can set the chart to be a dual axis, synchronising the axes, and removing the Measure Names field from the All Marks card which will have automatically been added

This is the core viz, that will need further formatting before its ready to put on the dashboard – remove gridlines, borders etc, set background, remove headers. NOTE– You’ll need to manually re-sort the Regions before the field is hidden.

The KPI table

We need to build a ‘fake table’ for this, by putting Region on Rows and typing MIN(0) on Columns, then adding the Range Sales Diff and Range Sales % Diff fields to the Text shelf. We need an additional field to colour the text though.

Colour – KPI

[Range Sales Diff]>=0

Finally, I capitalised the Region values by using Aliases. This is a quick method when there aren’t many values, but otherwise I would usually create a field with UPPER([Region}).

Once again don’t forget to sort the Regions, and the apply relevant formatting.

The Timeframe Selector

Will states you can use a separate data source for this, so create the list in Excel

and then copy and paste (via the Data > Paste) menu into your workbook

On a new sheet add Date Range to Columns and Date Range to Text. The size and colour of the text differs based on which one has been selected. So create a field

Timeframe Selected

[Date Range] = [pTimeframe]

and add this field to both the Size and Colour shelves. You’ll need to adjust the settings, and hide headers, remove gridlines etc. Try to avoid touching the Text formatting directly, as you might find the Size then doesn’t adjust.

Adding the interactivity

You’ll need to use layout containers to organise all the objects on the dashboard. Then you can add the various dashboard parameter actions needed

Selecting the timeframe

Add a parameter action that on select passes the Date Range field into the pTimeframe parameter

Selecting the date range to highlight

You’ll need 2 parameter actions for this, one that passes the minimum Order Date Day selected into the pStartRange parameter, and the other that passes the maximum Order Date Day selected into the pEndRange parameter.

Deselecting the highlighted marks

By default when you click on a mark/select marks in Tableau, they are highlighted/selected and the other marks are faded, until you ‘click’ again. To stop this from happening I use a ‘true = false’ trick, that has become very common in #WOW challenges, and I’ve blogged many times before.

Create calculated fields

True

True

and

False

False

and add these to the Detail shelf of the All Marks card on the trend line chart.

Then on the dashboard add a Filter action that on select targets the sheet directly, mapping the true field to the false field. As this can never be ‘true’ the filter doesn’t apply, and the marks become unselected.

Repeat the same on the Timeframe Selector sheet.

Hopefully that’s covered all the core points. My published viz is here.

Happy vizzin’! Stay Safe!

Donna

When do extracts run during the day?

Donna Coles5 Comments

When I was approached by the #WOW crew to provide a guest challenge, I was a little unsure as to what I could do. I primarily work as a Tableau Server admin, so rarely have a need to build dashboards (which is why I like to do the weekly #WOW challenges, to keep up my Desktop skills). Then the next day I was looking at a dashboard I’d built to monitor extracts on our Tableau Servers, and I thought it would be an ideal candidate for a challenge. I also thought it would provide any users of Tableau Server with the opportunity to implement this dashboard in their own organisation if they wished, by sharing with their Server Admins.

As a Tableau Server Admin, you get access to a set of ‘out of the box’ Admin views, one of which is called ‘Background Tasks for Extracts’ which gives you a view of when extracted data sources and workbooks run on the server. However while the provided view is fine if you want to quickly see what’s going on now, it’s not ideal if you want to see how things ran over a longer timeframe – it involves a lot of horizontal scrolling.

Many server admins will have ‘opened’ up access to the Tableau repository, the PostgreSQL database which stores a rich array of data, all about your Tableau Server [see here for further info], and enables admins to extend their analysis beyond the provided Admin views. This site even provides a set of pre-curated data sources to help you get started! These aren’t formally supported by Tableau, but is the brain-child of Matt Coles, a server admin at Tableau (no relation to me though!).

My dashboard doesn’t actually use one of these data sources though. For the challenge, I’ve just created some sample, anonymised data in the required structure. I’ll explain later at the end of the post how to go about converting this to use ‘real’ server data, if you do want to plug it into your own server environment.

Understanding the data

When using Tableau Server, published data sources and workbooks can connect to their underlying data source (eg a SQL Server database, an Excel file etc) directly (ie via a live connection) or via an extract. An extract means that a copy of the data is pulled from the underlying data source and stored on Tableau Server utilising Tableau’s ‘in memory’ engine when the data is then queried. An extract gets added to a schedule which dictates the time when the extract will get refreshed; this may be weekly, daily, hourly etc. Every time the extract runs, a background task is created which provides all the relevant details about that task. The data for this challenge provides 1 row for each extract task that was created between Monday 11th Jan 2021 and Friday 5th Feb 2021. The key fields of note are:

  • Id – uniquely identifies a task
  • Created At – when the task was created
  • Started At – when the task actually started running (if too many tasks are set to run at the same time, they will queue until the server resources are available to execute them).
  • Completed At – when the task finished, will be NULL if task hasn’t finished.
  • Finish Code – indicates the completion status of the job (0=success, 1=failed, 2= cancelled)
  • Progress – supposed to define the % complete, but has been observed to only ever contain 0 or 100, where 100 is complete.
  • Title – the name of the extract
  • Site – the name of the site on the server the extract is associated to

Based on the Finish Code and Progress, I have derived a calculated field to determine the state of the extract (to be honest, I think this is a definition I have inherited from closer analysis of the Background Tasks for Extracts Server Admin view, so am trusting Tableau with the logic).

Extract Status

IF [Finish Code] = 1 AND [Progress] <> 100 THEN ‘In Progress’
ELSEIF [Finish Code] = 0 AND NOT [Progress] = 1 THEN ‘Success’
ELSE ‘Failed’
END

Building the required calculated fields

The intention when being used ‘in real life’, is to have visibility of what’s going on ‘Now’ as well as how extracts over the previous few days have performed. As we’re working with static data, we need to hardcode what ‘Now’ is. I’ll use a parameter for this, so that in the event you do choose to plug this into your own server, you only have to replace any reference to the Now parameter with the function NOW().

Now

Datetime parameter defaulted to 05 Feb 2021 16:30

The chart we are going to build is a Gantt chart, with 1 bar related to the waiting time of the task, and 1 bar related to the running time of the task. We only have the dates, so need to work out the duration of both of these. These need to be calculated as a proportion of 1 day, since that is what the timeframe is displayed over.

Waiting Time

(DATEDIFF(‘second’, [Created At], IF ISNULL(Started At]) THEN [Now] ELSE [Started At ] END))/86400

Find the difference in seconds, between the create time and start time (or Now, if the task hasn’t yet started), and divide by 86400 which is the number of seconds in a day.

We repeat this for the processing/running time, but this time comparing the start time with the completed time.

Processing Time

(DATEDIFF(‘second’, [Started At], IF ISNULL([Completed At]) THEN [Now] ELSE [Completed At] END))/86400

As mentioned the timeframe we’re displaying over is a 24 hr period, and we want to display the different days over multiple rows, rather than on a single continuous time axis spanning several days.

To achieve this, we need to ‘baseline’ or ‘normalise’ the Created At field to be the exact same day for all the rows of data, but the time of day needs to reflect the actual Created At time . This technique to shift the dates to the same date, is described in this Tableau Knowledge Base article.

Created At Baseline

DATEADD(‘day’, DATEDIFF(‘day’, [Created At], #2021-01-01#), [Created At])

And again, we’re going to need to do a similar thing with the Started At field

Started At Baseline

DATEADD(‘day’, DATEDIFF(‘day’, [Started At], #2021-01-01#), [Started At])

Putting this out into a table, you can see what this data all looks like (note, I’m just choosing a arbitrary date of 01 Jan 2021, so my baseline dates are all on this date:

Building the Gantt chart

We’re going to build a dual axis Gantt chart for this.

  • Add Site to Rows
  • Add Title to Rows
  • Add Created At to Rows. Set it to the day/month/year level and set to be discrete (ie blue pill). Format the field to custom format of ddd dd mmm yyyy so it displays like Mon 11 Jan 2021 etc
  • Add Created At Baseline to Columns, set to exact date
  • Add Waiting Time (Avg) to Size and adjust to be thin

This will automatically create a Gantt chart view

Next

  • Add Started At Baseline to Columns, set to exact date, and move so the pill is now placed to the right of the Created At Baseline pill
  • On the Started At Baseline marks card, remove Waiting Time and add Processing Time (Avg) to the Size shelf instead. Adjust so the size is thicker.
  • Set the chart to be dual axis and synchronise the axes

The thicker bars based on the Started At / Processing Time need to be coloured based on Extract Status. Add this field to the Colour shelf of the Started At Baseline marks card and adjust accordingly.

The thinner bars based on the Created At / Waiting Time need to be coloured based on how long the wait time is (over 10 mins or not).

Over Wait Time Threshold

[Waiting Time] > 0.007

0.007 represents 10 mins as a proportion of the number of minutes in a day (10 / (60*24) ).

Add this field to the Colour shelf of the Created At Baseline marks card and adjust accordingly (I chose to set to the same red/grey values used to colour the other bars, but set the transparency of these bars to 50%).

Formatting the Tooltips

The tooltip for the Waiting Time bar displays

The Created At Baseline and Started At Baseline should both be added to the Tooltip shelf and then custom formatted to h:mm am/pm

The Waiting Time needs to be custom formatted to hh:mm:ss

The tooltip for the Processing Time bar is similar but there are small differences in the display,

Formatting the axes

The dates on the axes are displayed as time in am/pm format.

To set this, the Created At Baseline / Started At Baseline pills on the Columns shelf need to be formatted to h:mm am/pm

Adding the reference band

The reference band is used to highlight core working hours between 8am and 5pm. Right click on the Created At Baseline axis and Add Reference Line. Create a reference band using constants, and set the fill colour accordingly.

Apply further formatting to suit – adjust sizes of fonts, add vertical gridlines, hide column/axes titles.

Filtering the dates displayed

As discussed above, when using this chart in my day to day job, I’d be looking at the data ‘Now’. As a consequence I can simply use a relative date quick filter on the Started At field, which I default to Last 7 days.

However, as this challenge is based on static data, we need to craft this functionality slightly differently.

We’re only going to show up to 10 days worth of data, and will drive this using a parameter.

pDaysToShow

An integer parameter, ranging from 1 to 10, defaulted to 7, and formatted to display with a suffix of ‘ days’.

We then need a calculated field to use to filter the dates

Filter : Days to Show

DATETRUNC(‘day’,[Created At]) >= DATEADD(‘day’,([pDaysToShow]-1)*-1,DATETRUNC(‘day’,[Now]))

Add this to the Filter shelf and set to True.

Additionally, the chart can be filtered by Site, so add this to the Filter shelf too.

Building the Key legend

Some people may build this by adding a separate data source, but I’m just going to work with the data we have. This technique is reliant on knowing your data well and whether it will always exist.

On a new sheet, add Site to the Filter shelf and filter to sites 7 and 9.

Create a new field

Key Label

If [Site] = ‘Site 9’ THEN ‘Waiting’ ELSE ‘Processing’ END

and add this to the Columns shelf and sort the field descending, so Waiting is listed before Processing.

Alongside this field, type directly into the Columns shelf MIN(1).

Edit the axes to be fixed to from 0 to 1. Then add the Site field to the Colour shelf and also to the Size shelf and adjust accordingly (you may need to reverse the sizes). I lightened the colour by changing the opacity to 50%.

Now hide the axes, remove row & column borders, hide the column title and turn off tooltips.

The information can all now be added to a dashboard.

Using your own data

To use this chart with your own Tableau Server instance, you need to create a data source against the Tableau postgres repository that connects to the _background_tasks (bgt) table with an inner join to the _sites (s) table on bgt.site_id = s.Id. Rename the name field from the _sites table to Site. If you don’t use multiple sites on your Tableau Server instance, then the join is not required. The sole purpose of the join is to get the actual name of the site to use in the display/filter.

You should then be able to repoint the data source from the Excel sheet to the postgres connection. You may find you need to readjust some of the colours though.

When I run this, I’m using a live connection so I can see what is happening at the point of viewing, rather than using a scheduled extract. To help with this, I add a data source filter to limit the days of data to return from the query (eg Created at <=10 days), which significantly reduces the data volume returned with a live connection.

Hopefully you enjoyed this ‘real world’ challenge, and your server admins are singing your praises over the brilliance of this dashboard 🙂

My published version is here.

If you’ve got any feedback or suggestions on improvements to enhance the viz even further, please do let me know.

Happy vizzin’! Stay safe!

Donna

How much change has occurred?

Donna Coles2 Comments

A relatively straightforward challenge was set by Luke this week, to visualise the difference in Sales between 2020 and 2021 in a slightly different format than what you might usually think of.

Start by filtering the data to just the years 2020 and 2021 (add Order Date to the Filter shelf and select specific years, or add a data source filter to limit the whole data set).

Add Sub-Category to Rows, and Sales to Columns, then add Order Date to Colour which by default will display as YEAR(Order Date). Colour the years appropriately.

Now unstack the marks (Analysis menu -> Stack Marks -> Off), and re-order the colour legend, so 2021 is listed first (this makes the 2021 bars sit ‘on top’ of 2020).

Adjust the size to make the bars thinner.

Now add another instance of Sales to the Columns shelf, and make the chart dual axis (synchronising the axis). Reset the mark type of the original SUM(Sales) marks card back to bar.

We need the circle mark for the 2021 Sales to be blue. To do this, duplicate the Order Date field, then add Order Date (copy) to the Colour shelf of the SUM(Sales)(2) marks card. This will show another colour legend, and you can set the colours accordingly. Add a white border around the circle marks.

To work out the % difference to display on the label, we need the following fields

2021 Sales

{FIXED [Sub-Category]: SUM(IF YEAR([Order Date])=2021 Then [Sales] END)}

This returns the value of the 2021 sales for each Sub-Category against all the years in the data set. Similarly we need

2020 Sales

{FIXED [Sub-Category]: SUM(IF YEAR([Order Date])=2020 Then [Sales] END)}

which means we can then create

% Difference

(SUM([2021 Sales])-SUM([2020 Sales]))/SUM([2020 Sales])

format this using custom formatting to display as +0%;-0%

Now we can add % Difference to the Label field of the Sum(Sales)(2) marks card.

You’ll notice you’ll have duplicate labels displayed. To resolve this, you need to adjust the label settings as below

To sort the rows, you need to sort the Sub-Category field by 2021 Sales descending

And finally to show the value of the 2021 Sales, add this field to the Rows shelf, and change to be discrete (blue pill).

All that’s left to do now is adjust the wording of the tooltips as you see fit, and format to remove gridlines, headers etc.

My published version is here.

Happy vizzin’! Stay Safe!

Donna

How many consecutive starts?

Donna Coles1 Comment

Another table calculation related challenge this week, set by Luke, visualising cumulative starts for NFL Quarterbacks per team from 2006.

Luke provided the data within a Tableau workbook template on Tableau Public, so I started by downloading the workbook and understanding the data structure.

The challenge talks about teams playing over 17 weeks, but the data showed some data associated to weeks 28-32. So I excluded these weeks by filtering them out.

I then started to build out the data in tabular form, so I could start to build up what was required. I added Team, Season, Week and Player ID to Rows, and just to reduce the amount of data I was working with while I built up the calcs, chose to filter to the Teams ARZ, ATL & BLT.

What we’re looking to do is examine each Player ID and work out whether it is the first record for the Team or whether it differs from the previous row’s data. If so then we’re at the ‘start’ of a run, so we record a ‘counter’ value of 1. If not, the values match, so we need to increment the counter.

We’ll do this in stages.

Firstly, let’s get the previous Player ID value.

Prev Player ID

LOOKUP(MIN([Player Id]),-1)

This ‘looks up’ the record in the previous (-1) row. Change this field to be discrete and add to the Rows. Set the table calculation to compute by all fields except Team.

Each Prev Player ID value matches the Player ID from the row before, unless its the first row for a new Team in which case the value is Null.

Then we can create a field to check if the values match

Match Prev Player ID

MIN([Player Id])=[Prev Player ID]

Add this to the view and set the table calc as above, and the data shows True, False or NULL

Now we can work out the consecutive streak values

Consecutive Streak

IF (NOT([Match Prev Player ID])) OR ISNULL([Match Prev Player ID]) THEN 1
ELSE 1+PREVIOUS_VALUE(-1)
END

If we don’t match the previous value or we’re at the start of a new team (as value is NULL), then start the streak by setting the counter to 1, otherwise increment the counter. Add this to the view and set the table calc for both the nested calculations as per the settings described above.

Next we need to identify the last value of the Consecutive Streak for each Team.

Current Streak

WINDOW_MAX(IF LAST()=0 THEN [Consecutive Streak] END)

The inner IF statement, will return the value of Consecutive Streak stored against the last row for the Team. All other rows will be Null/blank. The WINDOW_MAX() statement then ‘spreads’ this value across all the rows for the Team.

Add this onto the view, and set the table calc for all the nested calcs.

Finally, we need one more bit of data. The chart essentially plots values from 2006 week 1 through to 2020 week 17. We need to ‘index’ these values, so we have a continuous week number value from the 1st week. We can use the Index table calculation for this

Index

INDEX()

Add this field to the view, set it to be discrete (blue pill) and position after the Week field on the Rows. Set the table calc as usual, so the Index restarts at each Team.

Now we’ve got all the data points we need, we can build the viz. I did this by duplicating the tabular view and then

  • Remove Prev Player ID and Match Prev Player ID
  • Move Season, Week and Player ID from Rows to Detail
  • Move Current Streak from Text to Rows and change to be discrete (blue)
  • Move Index from Rows to Columns and change to be continuous (green)
  • Move Consecutive Streak from Text to Rows
  • Change mark type to bar, and set to fit width to expand the view.
  • Change Size to be Fixed, width size 1 and aligned right
  • Set the border on the Colour shelf to be None.
  • Remove the Team filter and adjust the row height

All that’s left now is to set the tooltip (add Player to the Tooltip shelf to help this), and then apply the formatting. You can use workbook formatting (Format -> Workbook menu) to set all the font to Times New Roman.

Hopefully this is enough to get you to the end 🙂 My published viz is here.

Happy vizzin’! Stay Safe!

Donna

Can you create a moving average chart with a focus on selected subcategories?

Donna ColesLeave a comment

Following the #WOW survey where practice in table calculations was the most requested feature, Lorna continues with the theme in this challenge, where the focus is on the moving average table calculation, plus a couple of extra features thrown in.

Moving average

This is based on the values of data points before and after the ‘current’ point, as defied by the parameters which will need to be created.

pPrior

Integer parameter ranging from 1 to 6 and defaulted to 3. You need to explicitly set the Step size to 1 to ensure the step control slider appears when you add the parameter to the dashboard. This will be used to define the number of data points prior to the current to use in the calculation.

Create an identical parameter pPost to define the number of data points to use after the current one.

With these parameters, we can now create the core calculation

Moving Avg

WINDOW_AVG(SUM([Sales]), (-1*[pPrior])+1, [pPost])

As the requirement states that the ‘prior’ parameter needs to include the ‘current’ value, then we need to adjust the calculation – ie if the parameter is 3, we actually only want to include 2 prior data points, as the 3rd will be the current point itself. This is what the +1 is doing in the 2nd argument of the function.

Lorna has stated that 3 Sub-Categories are grouped to form a Misc category, so we need to create a group off of Sub-Category (right click Sub-Category -> Create -> Group).

Multi-select the 4 options that need to be grouped (hold down Ctrl as you select), and then group, and rename the group Misc.

Now we can check what the calculation is doing. If you add the fields onto the view as below, and set the Moving Avg table calculation to compute using Month of Order Date only (see further below), you should be able to see that each month’s moving avg value is calculated based on the sales value of the set of previous & post months as defined by your parameters. In the image below the Moving Avg for Accessories in June 2018, is the average of the Sales values from April 2018 – Sept 2018.

With this you can start the beginnings of the viz – don’t forget to set the table calc as above.

Colouring the lines

This will be managed by using a set.

Right click on the Sub-Category (group) field -> Create -> Set. Initially select all values. Add this field to the Colour shelf. Additionally, click the Detail symbol (…) to the left of the Sub-Category (group), and select the Colour symbol, so this field is also added to the Colour shelf.

The resulting colour legend will look something like this
Edit the colour legend, then choose Hue Circle and select Assign Palette to randomly assign colours to all the options

To show the set values, click on the context menu of the Sub-Category (Group) field on the Colour shelf, and Show Set.

This will add the list of options for selection

Uncheck All so none are selected, which will change the colour legend to read ‘Out, xxx’. Edit the colour legend again, and control-click to multi select all options, then set to a single grey

Now if you select a few options, the ones selected will be coloured, while the others remain grey

Additionally add the set field onto the Size shelf and make the In option bigger than the Out.

Shading the background

For this we need to create an unstacked area chart with one measure representing the maximum moving average value for the month, and the other representing the minimum moving average value for the month. We’ll need new calculated fields for this:

Window Max Avg

WINDOW_MAX([Moving Avg])

Window Min Avg

WINDOW_MIN([Moving Avg])

If you’ve still got your data sheet available, then move Sub-Category (Group) onto Rows, then add the two newly created fields.

In this case there are ‘nested’ table calcs. You need to ensure the setting related to the Moving Avg is computing by Month Order Date only, but the setting related to the Window Max Avg (or Window Min Avg) is computing by Sub-Category (Group).

If set properly, you should see that for each month the max / min values are displayed against every row.

Back to your chart viz sheet, and add Window Max Avg to Rows. Set the table calc settings as described above, then remove the Sub-Category (group) Set field from the Colour shelf of this measure, and change the Sub-Category (group) to be on the Detail rather than Colour shelf.

Change the mark type to Area, set the Opacity of the colour to 100% and set stack Marks to be Off (Analysis Menu -> Stack Marks -> Off).

Now drag Window Min Avg onto the Window Max Avg axis and drop it when the ‘2 columns’ image appears.

This will change the view so Measure Values is now on the Rows shelf and Window Min Avg is now displayed in the Measure Values section on the left hand side.

Adjust the table calc setting of Window Min Avg to be similar to how we set the Max field. And now drag the fields so Window Min Avg is listed before Window Max Avg. Measure Names will now be on the Colour shelf of this marks card, so adjust so Window Min Avg is white and Window Max Avg is pale grey.

Now make the chart dual axes, synchronise the axes, and set the Measure Values axis to the ‘back’.

Everything else is now just formatting and adding onto a dashboard. My published viz is here.

Happy vizzin’! Stay Safe!

Donna

Visualise Our Survey Data

Donna Coles1 Comment

This week, Ann Jackson set a table calculations based challenge, using the responses from a recent survey on #WorkoutWednesday, as the most requested topic was for table calcs!

There’s a lot of visuals going on in this challenge, and I’m shortly off on my holibobs (so will be playing catch up in a couple of weeks), so I’m going to try to pare down this write up and attempt just to focus on key points for each chart.

Donut Chart

By default when you connect, Respondent is likely to be listed in the ‘Measures’ part of the data pane – towards the bottom. This needs to be dragged into the top half to turn it into a dimension. You can then create

# of Respondents

COUNTD([Respondent])

which is the key field measures are based on throughout this dashboard.

When building donuts, we need to get a handle on the % of total respondents for each track, along with the inverse – the % of total non-respondents for each track. To do this I created fields

Total # of Respondents

TOTAL(COUNTD([Respondent]))

and then

Track – % of Total

[# of Respondents]/([Total # of Respondents])

along with the ‘inverse’ of

Non Track – % of Total

1-[Track – % of Total]

To then build the donut, we ultimately need to create a dual axis chart, with Which track do you participate in? on Columns and a MIN(1) field on Rows. Manually reorder the entries so the tracks are listed in the relevant order.

On the first MIN(1) axis/marks card, build a pie chart. Add Measure Names to the filter shelf and filter to the Track & Non Track % of Total fields. Set Mark Type to Pie Chart and add Measure Values to the angle shelf. Add both Which track do you participate in? and Measure Names to the Colour shelf. Set a white border on the Colour shelf. Reorder the entries in the colour legend, and set the colours appropriately.

The create another MIN(1) field next to the existing one on the Rows shelf

Set this marks type to circle, and remove all the fields from the colour & detail shelves. Set the colour to white. Add Which track do you participate in? and Track – % of Total to the Label shelf and format. Reduce the Size. Make dual axis, and synchronise. Further adjust sizes to suit.

Participation Bar Chart

Plot How often do you participate? against # of Respondents, and then add a Quick Table Calculation to the measure using Percent of Total. Manually re-sort the order of the entries, show mark labels and Colour the bars light grey. Apply relevant formatting.

Diverging Bar Chart

In this bar chart, the percentage of ‘agree’ responses are plotted to the right on the +ve scale and the percentage of the ‘disagree’ responses are plotted to the left on the -ve scale. The percentage of the ‘inbetweeners’ (neither agree nor disagree) is halved, and displayed on both sides. To address this, I created the following:

# of Respondents – Diverging +ve

CASE ATTR([Answer])
WHEN ‘Agree’ THEN [# of Respondents]
WHEN ‘Strongly Agree’ THEN [# of Respondents]
WHEN ‘Neither Agree nor Disagree’ THEN [# of Respondents]/2
END / [Total # of Respondents]

This is the % of total respondents for the ‘agree’ responses and half of the ‘inbetweeners’.

Similarly I then have

# of Respondents – Diverging -ve

(CASE ATTR([Answer])
WHEN ‘Disagree’ THEN -1*[# of Respondents]
WHEN ‘Strongly Disagree’ THEN -1 *[# of Respondents]
WHEN ‘Neither Agree nor Disagree’ THEN ([# of Respondents]/2) * -1
END) / [Total # of Respondents]

which is doing similar for the ‘disagree’ responses, except all results are multiple by -1 to make it negative.

The Question field is added to the Filter shelf and the relevant 5 questions are selected. Answer is also on the Filter shelf with the N/A answer excluded.

Add Question to Rows (and manually sort the entries), then add # of Respondents – Diverging -ve and # of Respondents – Diverging +ve to Columns and add Answer to the Colour shelf. Manually resort the entries in the colour legend and adjust the colours accordingly.

Make the chart dual axis, and synchronise the axis. Change the mark type back to bar and remove Measure Names from the colour shelf if it was added. Edit the bottom axis to fix the range from -0.99 to 0.99 and amend the title. Format the axis to display as percentage to 0 dp. Hide the top axis.

Additionally format both the measures to be percentage 0dp, but for the # of Respondents – Diverging -ve custom format, so the negative value is displayed as positive on the tooltip.

Adjust formatting to set row banding, remove gridlines etc and set tooltips.

Vertical bar chart

The best way to start building this chart is to duplicate the diverging one. Then remove both measures from the Columns shelf and add Answer to Columns. Manually re-sort the answers. Add # of Respondents to Rows and add a Quick Table Calculation of percent of total.

Show the marks label, and align bottom centre, and match mark colour. Hide the axis from displaying, and also hide the Question field (uncheck show header). Update the Tooltip.

Heatmap

Right click on the Question field > Aliases and set the alias for the relevant questions

Also add Question to Filter and select relevant values. Add Answer to Filter too and exclude NULL.

Add Question to Columns and add Answer to the Text shelf. Add # of Respondents to the Text shelf, and set to Percent of Total quick table calculation. Edit the table calculation to compute using the Answer field only

We need to get each of these columns ‘sorted’ from high to low – we want to rank them. To do this, add # of Respondents to Rows, then change it to be a blue discrete pill. Add a Rank quick table calculation and once again set to compute by Answer only. Also set the rank to be Unique

Now change the mark type to square, and then add the # of Respondents percent of total field onto Colour as well as Text (the easiest way to do this to retain all the table calc settings, is to hold down Ctrl then click and drag the pill from the Text shelf onto Colour. This should duplicate the pill.

Format the % of total displayed to be 0dp, and adjust the label. Change the Colour to use the purple range and set a white border too. Hide the ‘rank’ field from displaying and hide field labels for columns too.

The dashboard

I used a vertical container then added the objects as required, using nested horizontal containers to organise the side by side charts.

To make the diverging bar and vertical bar charts look like they are one chart, adjust the padding of diverging bar chart object to have 0 to the right, and similarly, adjust the padding of the vertical bar to have 0 padding to the left.

I found it a bit fiddly to get the charts to line up exactly. Both charts were set to fit entire view. The diverging bar chart displays it’s title. I also displayed a title on the vertical bar chart, but made the text white so it’s invisible.

Dashboard filter actions are set against the donut and the participation bar charts.

The filter uses selected fields, which for the donut chart references the Which track do you partcipate in? field. A similar dashboard action needs creating for the participation chart as the source and references the How often do you partcipate? field.

A highlight dashboard action is required for the diverging and vertical bar charts. They only impact each other and should be set up as below on hover.

Hopefully I’ve covered everything… my published version is here.

Happy vizzin’! Stay Safe!

Donna

Can you find the needle in the haystack?

Donna ColesLeave a comment

It was Candra’s turn to ‘set’ the #WOW2021 challenge this week providing a hint in the challenge description that the solution would involve sets.

As with many challenges, I built the data out in tabular format to start with to verify I had all the components and calculations correct. The areas of focus are

  • Identify number of distinct customers per product
  • Identify overall average number of distinct customers per product
  • Identify if product above or below average distinct customers
  • Identify Top 50 products by Sales
  • Identify Unprofitable Products
  • Identify products that are both in the top 50 AND unprofitable
  • Building the viz

Identify number of distinct customers per product

To start off, add Product Name, Sub-Category, Category to the Rows shelf to begin building out a table. Add Sales (formatted to $k 0dp) and Profit (formatted to $k 0dp with negative values as () ) to Text and sort by Sales descending.

To identify the distinct customers per product, we can create

Customer Count per Product

{FIXED [Product Name] : COUNTD([Customer ID])}

Add this to the view.

Identify overall average number of distinct customers per product

What we’re looking for here is the average of all the values we’ve got listed in the Customer Count per Product column. Ie we want to sum up those values displayed and divide by the number of rows.

The number of rows is equivalent to the number of products, which we can get from

Count Products

{FIXED : COUNTD([Product Name])}

And so to get the overall average we calculate

Avg Overall Customer Count

{FIXED: SUM([Customer Count Per Product])} / [Count Products]

Add these fields to the view as well, so you can see how the values work per row. The last two calculations give you the same value across all rows.

Identify if product above or below average distinct customers

Given the above display, this is just a case of comparing values in 2 columns

Higher than Avg Customer Count

AVG([Customer Count Per Product]) > SUM([Avg Overall Customer Count])

this returns true or false – add this to the view too.

Identify Top 50 products by Sales

We can create a set for this. Right click on Product Name > Create > Set. Name the set something suitable eg Top 50 Products, and on the Top tab, state the number (50) and the field (Sales) and the aggregation (Sum)

Add this to the view, and if you’ve sorted by the sales, you should find the top 50 rows are all In the set, and the rest are Out.

Identify Unprofitable Products

We can use another set for this. Again create a set off of Product Name, call it Unprofitable Products, and on the Condition tab, set the condition so that the Sum of Profit is less than 0

Add this onto the view too.

Identify products that are both in the top 50 AND unprofitable

For this, we’re explicitly looking for the rows that are both In the Top 50 Products set and In the Unprofitable Products set.

We can use the Combined Set functionality to do this.

In the left hand data pane, select both the Top 50 Products and the Unprofitable Products sets (hold down ctrl to multi select), then right click and Create Combined Set. I called the set Products to Include, and select to combine the sets by including Shared members in both sets

If you then add this field to the Filter shelf, you will be left with just the 13 Products that match

This is the single filter field you can use as per Candra’s requirements.

Building the viz

To get the text to display to the left of the bar, you actually need to create a ‘fake’ bar chart.

  • Add Products to Include to Filter
  • Add Product Name to Rows
  • On the Columns shelf, double click and type in MIN(1)
  • Add Sales to Columns to the right of MIN(1)
  • Sort by Sales descending

Against the MIN(1) marks card

  • Change the Size to small
  • Set the Opacity of the Colour to 0% and the border to None
  • Add Product Name, Sub-Category and Category to the Label shelf and adjust accordingly, aligning left
  • Increase the height of each row to make the text visible

On the Sales marks card

  • Add Higher than Avg Customer to the Colour shelf and adjust
  • Show mark labels
  • Create a new field Profit Ratio : SUM([Profit])/SUM([Sales]) Format to % with 0dp and add to Tooltip
  • Add Profit, and Customer Count by Product to Tooltip and adjust accordingly

Finally, uncheck Show Header against Product Name and MIN(1) and Sales and format the borders/gridlines etc. Add the title, then add to the dashboard.

All done (I hope…)! My published version is here.

Happy vizzin’! Stay Safe!

Donna