Tag: Formatting

Can you build a colour-coded filter?

Donna ColesLeave a comment

Erica collaborated with Giulio D’Errico for this week’s #WOW2025 challenge, which contained lots of features, though the main challenge was to display filter on Region, which along with the Region name also displayed a KPI indicator that could change based on selection from other parameters.

Defining the parameters

We need 3 parameters for this challenge

pMeasure

strig parameter that lists the two options Profit and Sales; defaulted to Profit.

pProfitThreshold

integer parameter that lists the specified values, defaulted to 2,000

pSalesThreshold

integer parameter that lists the specified values, defaulted to 30,000

Building the core scatter plot

Add Sales to Columns and Profit to Rows and Sub-Category to Detail. Show the 3 parameters.

When pMeasure = Profit, we need to display horizontal reference lines against the Profit axis, and when Sales is selected we need to display vertical reference lines against the Sales axis. We need the following fields to return the user defined thresholds:

Ref – Profit Threshold

IF [pMeasure] = ‘Profit’ THEN [pProfitThreshold] END

Ref – Sales Threshold

IF [pMeasure] = ‘Sales’ THEN [pSalesThreshold] END

We also need to define the average per measure for each region:

Sales Avg Per Region

{FIXED [Region]: SUM([Sales])} / {FIXED: COUNTD([Sub-Category])}

Profit Avg Per Region

{FIXED [Region]: SUM([Profit])} / {FIXED: COUNTD([Sub-Category])}

and then we can create

Ref – Profit Avg

IF [pMeasure] = ‘Profit’ THEN [Profit Avg Per Region] END

Ref – Sales Avg

IF [pMeasure] = ‘Sales’ THEN [Sales Avg Per Region] END

Add the 4 Ref – XXX fields to the Detail shelf. Then add 2 reference lines to the Sales axis; one referencing Ref – Sales Avg (coloured as a purple dashed line at 100% opacity) and one referencing Ref – Sales Threshold (coloured as a black dashed line at 50% opacity). Display a custom label and then format the label to be aligned vertically and coloured based on the relevant line, and with a 0% shading. Set the pMeasure to Sales to make these display.

Then repeat, adding 2 reference lines to the Profit axis instead. Change pMeasure to Profit for these to appear.

Change the mark type to square. Each mark needs to be coloured whether it is above or below the average for the measure selected.

Colour

IF [pMeasure] = ‘Profit’ THEN
IIF(SUM([Profit]) >= SUM([Profit Avg Per Region]),1,0)
ELSE
IIF(SUM([Sales]) >= SUM([Sales Avg Per Region]),1,0)
END

Set this to be discrete and then add to the Colour shelf and adjust accordingly.

Creating the colour-coded filter

The viz needs to be filtered by Region, but the filter displayed needs to show the region name along with an indicator based on whether the average for the region is above the threshold or not. This took a bit of an effort, but I finally managed it.

We need a field to capture the ‘KPI indicator’ based on which measure was selected. It also needs to be computed per region. We need a FIXED LoD for this, and I made use of coloured unicode characters from this site.: https://unicode-explorer.com/ (large yellow circle and large green circle which you can just ‘copy and paste’ into the calculation)

Region Indicator

{FIXED [Region]: (

IF [pMeasure] = ‘Profit’ THEN
IIF(SUM([Profit Avg Per Region])>=[pProfitThreshold],’🟢’,’🟡’)
ELSE
IIF(SUM([Sales Avg Per Region])>=[pSalesThreshold],’🟢’,’🟡’)
END
)}

I then created

Filter – Region

[Region Indicator] + ‘ ‘ + [Region]

Add this to the Filter shelf, select all values and show the filter. If you change the pProfitMeasure to Profit and pProfitThreshold to 6,000, you’ll see the KPI indicators change.

Format the Tooltip

The tooltip requires several calculated fields to be created. Certain fields only need a value based on the pMeasure and others have conditional formatting (different colours) applied. For the tooltip, I created all these fields:

Tooltip – Measure Value

IIF([pMeasure]=’Profit’,[Profit],[Sales])

Tooltip -Threshold

IIF([pMeasure]=’Profit’, [pProfitThreshold], [pSalesThreshold])

Tooltip – Measure Value Above Avg

IF [Colour] = 1 THEN SUM([Tooltip – Measure Value]) END

Tooltip – Measure Value Below Avg

IF [Colour] = 0 THEN SUM([Tooltip – Measure Value]) END

Tooltip – Above text

IF [Colour] = 1 THEN ‘above’ END

Tooltip – Below text

IF [Colour] = 0 THEN ‘below’ END

Label – Region

IIF(COUNTD([Region])>1, ‘All regions’, MIN([Region]))

Add all these fields to the Tooltip. Update the Tooltip to reference the fields and the pMeasure parameter, colouring the text as required. Some of the fields will only display based on the filters selected, so they get places side by side with no spacing.

Finalise the viz by updating the title to reference the pMeasure and the Label – Region field. Remove all gridlines, axis rulers, zero lines etc. Colour the background of the sheet to pale blue.

Building the Overall Indicator

For the bonus challenge, we need to display an indicator that is green if all the regions are green and yellow if at least 1 region is yellow.

On a new sheet, add Region and Region Indicator to Rows and show the 3 parameters.

We’re going to create a field that will return 1 if the Region Indicator is yellow and 0 otherwise.

Region Indicator – Is Below

IIF( [Region Indicator] = ‘🟡’,1,0)

Set to be discrete and add to Rows. Change the pProfitThreshold to 6000 to see the flag change.

Create

Overall Region Indicator

IIF(WINDOW_MAX(MAX([Region Indicator – Is Below]))=1,’🟡’, ‘🟢’)

This says, if the maximum value of the rows ‘in the window’ is 1, then display a yellow indicator, else green. Add to Rows, and adjust the pProfitThreshold to see the behaviour.

Create a field

Filter – Index = 1

INDEX() = 1

Add to Filter shelf and set to True

Move Region to Detail. Remove Region Indicator and Region Indicator – Is Below. Adjust the table calculation setting of Overall Region Indicator to compute using Region only, and do the same for the tableau calculation on Filter – Index = 1 (re-edit the filter after changing, so only True is selected).

Note – originally I used a transparent shape mark type and displayed the indicator as a label, but after publishing to Tableau Public, the indicator became distorted, so I adjusted how this was built.

Set the mark type to circle and add Overall Region Indicator to Colour. Adjust to be green or yellow, depending on the colour of the KPI indicator (you’ll need to change the pProfitThreshold value to ensure you set the colour for both the yellow and green options).

Finally create

Tooltip – Overall Indicator

IIF([Overall Region Indicator]=’🟢’,’All regions meet the ‘ + [pMeasure] + ‘ target’, ‘At least one region does NOT meet the ‘ + [pMeasure] + ‘ target’)

Add this to Tooltip and then set the background colour to pale blue.

Building the dashboard & adding dynamic zone visibility

Using layout containers, add the viz to a dashboard. Use a horizontal layout container to arrange the parameters, the Region filter and the Overall Indicator sheet. I used a floating text object to display the ‘legend key’ again copying come unicode characters from the website referenced earlier.

Then create 2 new boolean calculated fields

Is Profit

[pMeasure] = ‘Profit’

Is Sales

[pMeasure] = ‘Sales’

Select the Sales Threshold parameter object, then update the visibility so it only displays if Is Sales is true (via the Layout > Control visibility using value option)

Repeat the same for the Profit Threshold object, but reference the Is Profit field instead.

Now as you switch the measure between Sales and Profit, the relevant threshold parameter will display. My published viz is here.

Happy vizzin!

Donna

Can you visualise yearly rank change in Sub-Category sales?

Donna ColesLeave a comment

Yusuke set this interesting challenge : to combine a ‘bump’/’slope’ chart visualising the change in rank whilst also visually displaying the Sales value for the relevant Sub-Category in the ranked position.

Defining the calculations

This challenge will involve table calculations, so I’m going to start by building out the various calculations that will be required and displaying in a tabular view.

Add Category to Filter and select Office Supplies. Then add Sub-Category and Order Date at the Year level as a discrete (blue) pill to Rows. Add Sales to Text.

Create a new field

Sales Rank

RANK(SUM([Sales]))

And add to the table, and verify the table calculation is set to compute by Sub-Category only.

We will need to ‘colour’ the viz based on the rank compared to the previous year. For this create

Is Min Year

{MIN(YEAR([Order Date]))} = YEAR([Order Date])

which will return true for the first year in the data (in this instance 2022) and then create

Colour

IF [Sales Rank] = LOOKUP([Sales Rank],-1) OR ATTR([Is Min Year]) THEN ‘Same as last year’
ELSE ‘Different from last year’
END

If the rank is the same as the previous one, or it’s the first year, then treat as the same, otherwise treat as different.

Add the Colour field to the table, and this time make sure the table calculation for Colour is computing by Year of Order Date only (while the nested calc for Sales Rank should still be computed by Sub-Category only)

The labels on the viz only want to show in certain scenarios – if it’s the first record (ie for 2022) or there has been a change in rank. We need

Label : Rank & Sub Cat

IF ATTR([Is Min Year]) OR [Colour] = ‘Different from last year’ THEN STR([Sales Rank]) + ‘ | ‘ + MIN([Sub-Category]) END

and

Label : Sales

IF ATTR([Is Min Year]) OR [Colour] = ‘Different from last year’ THEN SUM([Sales]) END

format this to $ with 0dp

Add these to the sheet, and double check the nested table calculations on each pill are computing as required (Sales Rank by Sub-Category only, Colour by Year Order Date only)

Now we have all this, we can start building

Creating the Viz

On a new sheet, add Category to Filter and select Office Supplies. The add Order Date to Columns, but set to be continuous (green) pill at the Year level. Add Sub-Category to Detail and add Sales Rank to Rows as a discrete (blue) pill. Verify the table calculation setting against the Sales Rank pill is by Sub-Category only.

Change the mark type to line and then add Order Date to Path. By default it should be at the Year level as a discrete pill.

This is the ‘bump’ chart.

Now add another instance of Order Date to Columns as a continuous pill at the Year level to essentially duplicate the display. On the 2nd marks card, change the mark type to Gantt

This gives us the ‘starting point’ for each ‘bar’. But we need to determine the size for each bar. First we’re going to ‘normalise’ the sales values for all the sales being displayed so we get a value between 0 and 1, where 0 is the smallest sale, and 1 is the largest.

Normalised Sales

((SUM([Sales]) – WINDOW_MIN(SUM([Sales]))) / (WINDOW_MAX(SUM([Sales])) – WINDOW_MIN(SUM([Sales]))))

To see what this is doing, format the field to 2dp, then add the field to the tabular view, and ensure the table calculation is computing by both Sub-Category and Year Order Date.

But the ‘axis’ we want to plot the bar length against is in years, so we need to adjust this size to be a proportion of a year (ie 365 days)

Gantt Size

//proportion of a year
[Normalised Sales] * 365

Add this to the Size shelf on the 2nd marks card on the viz. Adjust the table calc setting so it is computing by all the fields listed.

We now have the core concept so now we can start finalising the display.

Make the chart dual axis and synchronise the axis.

Set the view to fit height.

On the 1st marks card (that represents the line)

  • change the line style to dotted (via the Path shelf)
  • reduce the Size to suit
  • change the colour to pale grey
  • Add Label : Sales and Label : Rank & Sub Cat to the Label shelf.
    • Adjust the table calc settings of each so the nested table calcs in each have Sales Ranks by Sub Category only and Colour by both the Year Order Date fields only.
    • Adjust the layout of the text as required
    • Align the font to be top right
    • Change the font style (bold & black)
    • Ensure the Label is set to ‘allow labels to overlap marks’
  • Remove the Tooltip

On the 2nd marks card, the gantt bar

  • Add Colour to the Colour shelf and adjust the colours accordingly.
    • Verify the table calc settings are as expected
    • I chose to reduce the opacity slightly, so I could see the dotted line underneath (set to 70%)
  • Add Sales to Tooltip (format to $ with 0 dp) and the adjust Tooltip as required

Then we just ned to finalise the formatting/display

  • Set the font of the years and rank numbers to black & bold.
  • hide the Sales Rank label heading (right click > hide field labels for rows)
  • remove row & column dividers
  • Add black column gridlines (I set to the 2nd thickness level), and remove any row gridlines
  • Edit the top axis to have a fixed start (use default option) and end at 31/12/2025 so the 2026 label and line disappears.
  • Remove the title from the top axis.
  • Edit the bottom axis – remove the tile, and then set the tick marks to None, so the bottom axis now looks empty.

And that should be it. Now add the sheet to a dashboard and display the category filter as a single select, customising the remove the ‘all’ option.

My published viz is here

Happy vizzin’!

Donna

Can you build an alternative to a stacked bar chart?

Donna Coles2 Comments

For this week’s challenge we’re going to look at building an alternative to a stacked bar chart. This challenge was inspired by this post by the Flerlage twins, which in turn was inspired by other members of the #datafam community. Once again, we’re making use of some Premier League data. 

Building the viz

Add Team to filter and select Manchester United, Chelsea, Arsenal and Manchester City then add Season to filter and select the last five seasons. Then add Team and Season to Columns and Points to Rows.

Sort team by the field Points descending. Add Team to colour and adjust accordingly. Show Mark labels to display the number of points for each bar.

Add subtotals via the analysis menu >  totals > show all subtotals

Hide the subtotal bar by clicking on one of the total bars and selecting hide from the drop down which is defaulted to automatic


Right click on the Total label on the axes and select format – in the left hand side, delete the label field

Create a new calculated field

Total Points

WINDOW_SUM(SUM([Points]))

And add this to rows. Remove Team from the colour shelf on the marks card, and change the colour to pale grey. Increase the size of the bars to be as wide as possible. Uncheck show mark labels. Hide the total bar like we did above.

Make the chart dual axis and synchronise axis. If need be, right click the right axis and move marks to back. Adjust the table calculation on Total Points so it is computing by Season only. Hide the total bar again if it reappears.


We want to label the total bar with the Team and the Total Points so we need to create some more calculated fields

Label: Team 

IF [Season] = ‘2021-22’ THEN [Team] ELSE NULL END

We’re being sneaky here, and just labelling the central bar.

Label: Total Points

IF MIN([Season]) = ‘2021-22’ THEN [Total Points] END

Add Label: Team to the label shelf of the Total Points marks card and adjust so it is an attribute – this means it’s not referenced in any table calculations. Add Label: Total Points to the label shelf too.

Adjust the label as required and then change the alignment so the direction of the text is swapped. Format the font as desired.

Delete all the text from the Tooltip on the Total Points marks card and adjust the text on the Points marks card to match the required format. 

Hide the right hand axes (right click axis > uncheck show header). Hide the Team column heading. Remove all gridlines/ axis lines/ zero lines  and row and column dividers. 

Hide the Season label heading (right click on the label > hide field labels for columns).

Format the axes font to be smaller and rotate the axes labels on the Season axis. 

Name the sheet Bar chart or similar and add to dashboard.

My published viz is here

Happy vizzin’!

Donna

Can you rank by multiple measures?

Donna ColesLeave a comment

This week’s challenge once again has 3 levels, so I will blog the first solution and then build on/adapt from there. The main requirement is focused on working out how to rank the football teams based on multiple measures, and this is relevant for all the levels. Once again, we’re looking at data related to football teams in the English Premier league, and where they finished each season.

Defining the calculation to rank by

A team’s finishing position is based on a combination of points scored, goal difference and goals scored. But on rare occasions, a team is deducted points in the season, so we need to account for that.

Adjusted Points

SUM([Points]) – ZN(SUM([Points Deducted]))

Putting the key measures (Adjusted Points, Goal Difference and Goals For) per Team into a tabular view for just a single Season (eg 1993), we can see multiple instances of teams ending on the same number of Adjusted Points

Just ranking on this field, won’t necessarily order the teams in the correct order as Goal Difference and Goals For also need to be taken into consideration. We need to create a field we can rank based on a combination of all three fields, and to do this we’ll ‘weight’ each field by multiplying each field by a suitable proportion based on precedence. In the example shared via the community forum post the leading indicator was multiplied by 1000. In my case I’m going to divide.

Adj Points | GD | GF

[Adjusted Points] + (SUM([Goal Difference])/100) + (SUM([Goals For])/10000)

Add this field into the table, and sort by it descending. You can see that for the 3 teams with 52 points, two of those teams have the same Goal Difference too, but are being correctly ordered based on the Goals For due to the result of the Adj Points | GD | GF calculation

Now we’ve got the core calculation nailed, we can start to build the viz.

Building the Beginner viz

On a new sheet, add Team to Rows and Season End Year to Columns.

We want to be more specific about the season, so create

Label Season

STR([Season End Year]-1) + ‘-‘ + RIGHT(STR([Season End Year]),2)

and add to Columns too.

To make the ‘squares’ rather than use the square mark type which can sometimes be a bit fiddly to get the size just right, I’m going to use a fake axis and a bar mark type.

Double click into Columns and type MIN(1.0) and change the Mark type to Bar. Adjust the Size to be as big as possible. Edit the axis to be fixed from 0-1.

Hide the Season End Year (right click the pill and uncheck Show Header). Rotate the Label Season (right click on the actual label and Rotate Label). Then make each column narrower and widen each row until you’re happy with the sizing.

We want to the Teams to remain listed in alphabetical order, but we want to know the position the team finished in the season, so we create

Rank Desc

RANK([Adj Points | GD | GF], ‘desc’)

Initially add this to Label, and then adjust the table calculation so it is explicitly computing by Team only.

You can verify the number associated to the teams in the 1993 season with the sorted table you made earlier.

But we’re not going to show the rank for every square, so move the Rank Desc from Label to Tooltip.

We do want to label just the team in first though so create

Is First

IF [Rank Desc] = 1 THEN ‘1st’ END

and add this to the Label shelf instead. Again adjust the table calculation as described above.

For the Tooltip we need to create

Tooltip Rank Suffix

IF [Rank Desc] = 1 OR [Rank Desc] = 21 THEN ‘st’
ELSEIF [Rank Desc] = 2 OR [Rank Desc] = 22 THEN ‘nd’
ELSEIF [Rank Desc] = 3 OR [Rank Desc] = 23 THEN ‘rd’
ELSE ‘th’
END

add this to the Tooltip shelf, adjust the table calculation as required. Also create

Tooltip – Note

IF ZN([Points Deducted])<>0 THEN ‘Additionally the team was deducted ‘ + STR([Points Deducted]) + ‘ points due to ‘ + [Notes] END

And then add Adjusted Points and Tooltip – Note to the Tooltip too. Adjust the Tooltip as required.

Finally adjust the look and feel by

  • Set the background colour of the worksheet to pale lilac (#f1eff6)
  • Set the mark colour to purple (#938bc1) and add a pale grey border
  • Set row and column dividers to a thin solid pale grey line (#e6e6e6)
  • Adjust the font colour of the row and column labels to black
  • Hide the Team row heading label (right click > hide field labels for rows).
  • Hide the Label Season column heading label ((right click > hide field labels for columns).
  • Hide the MIN(1.0) axis (right click pill – uncheck show header)
  • Make the mark label bold and centred
  • Set the sheet to fit width
  • Add a title
  • Name the sheet Beginner or similar

Building the Intermediate Viz

The requirement here, is we now need to highlight the teams that won and those that were relegated each season.

For this, we’re going to create another ranking field, but this time ranking in the other direction

Rand Asc

RANK([Adj Points | GD | GF], ‘asc’)

and then determine the team’s position in the season, giving consideration to the fact that in the 1994-95 season, 4 teams were relegated instead of the usual 3.

Position

IF [Rank Desc] = 1 THEN ‘C’
ELSEIF (MIN([Season End Year]) =1995 AND [Rank Asc] <=4) OR ([Rank Asc] <=3) THEN ‘R’
END

Duplicate the Beginner viz. Remove Is First from Label and add Position instead. Add Position to Colour and adjust accordingly.

Building the Advanced Viz

Now we want to extend the colouring, so the teams who were not the champions and who weren’t relegated, are coloured based on their league position.

For this duplicate the Intermediate sheet. Add Rank Desc to the Detail shelf, verifying the table calc is computing by Team only. Change it to be a discrete (blue) pill, and the change the pill so it is also on the Colour shelf, by updating the ‘detail’ icon to the left of the pill.

This will result in 2 pills on the Colour shelf and the Colour Legend listing all the permutations.

Adjust the colour legend, so all the R’s are red, the C is yellow, and the Nulls are set from automatically assigning the BuPu 3 Color Seq Palette. Use ctrl (windows) to select multiple entries in the colour legend, rather than assigning one by one.

Note if you don’t already have the BuPu 3 Color Seq Palette in your preferences file, you will need to add and then reload Tableau. For further information see here.

And that should complete all levels of the challenge. My published viz is here.

Happy vizzin’!

Donna

Formatting & Intermediate Tableau Charts

Donna ColesLeave a comment

Erica set this challenge primarily aimed at building a beautifully presented dashboard, with the requirement to consider the use of layout containers and padding. She threw in creating some very specific chart types too. The easiest way to blog this, is by chart type.

Building the Histogram

Add Quantity to Columns as continuous dimension (green unaggregated pill) and add Order ID as a measure using the CNT aggregation to Rows. The easiest way to do this is right click and drag Order ID from the left hand date pane and drop onto rows. When you release the mouse, the option to select the aggregation should be available.

Change the mark type to bar and adjust the colour. Edit the title of the y-axis and remove the title from the x-axis. Update the Tooltip.

Double -click into Columns and manually type ‘Quantity in Order’ (including the quotes). Right click on the first text displayed and hide field labels for columns. Adjust the font of the Quantity in Order label that remains.

Remove row and column dividers and column gridlines. Remove Row axis rulers.

Note, when you add to the dashboard , you may find you want to adjust the Size of the bars.

Building the Peas in a Pod chart

On a new sheet, add Category to Filter and select Technology. Add Order Date to Filter and select Years then choose 2022,2023 and 2024.

Rename the Sub-Category field to Sub-Cat and add to Rows. Add Sales to Columns. Change the mark type to circle. Add Order Date to Colour. By default it should display YEAR(Order Date). Adjust colours to suit. Widen each row a bit.

Add another instance of Sales to Columns.

On the Sale (2) marks card change the mark type to line and move YEAR(Order Date) to Path. Increase the size and adjust the colour so it’s a grey lozenge.

Make the chart dual axis and synchronise the axis. Right click the top axis and move marks to back. Adjust the Tooltip. Edit the title of the x-axis.

Hide the top axis. Remove row and column dividers. Remove row gridlines. Remove axis rulers for both columns and rows.

Note, when you add to the dashboard , you may find you want to adjust the Size of the circles and the line. I found it was best adjusted on the web after I published to Tableau Public.

Building the +/- Bar Chart

On a new sheet add Order Date to Filter and select Years then choose 2022,2023 and 2024. Add Order Date to Columns and select to be at the continuous month level (green pill, May 2015 format). Add Sales to Rows and change the mark type to bar.

Add a quick table calculation of Difference to the Sales pill.

Adjust the size of the bars (select manual over fixed and adjust the slider).

Create a new calculated field

Diff is +ve

ZN(SUM([Sales])) – LOOKUP(ZN(SUM([Sales])), -1) > 0

and add to the Colour shelf. Adjust colours to suit. Hide the null indicator. Adjust the Tooltip. Adjust the title of the x-axis.

Remove all gridlines and axis rulers. Remove the columns zero line. Set the rows zero line to be a continuous unbroken line.

Note – once again the size may need further adjusting once on the dashboard and/or after publishing.

Building the slope chart

Add Category to filter and select Office Supplies. Add Region to filter and select West. Add Order Date to filter and select Years then choose 2021 and 2024 only.

Add Order Date to Columns and Sales to Rows. Add Sub-Cat to Detail.

Add Sales to Colour then add a quick table calculation of Percentage Difference. This only sets a value against the 2024 marks though, whereas we want a value for the whole line for each Sub-Cat.

Double-click into the Sales pill on Colour to edit it, and wrap the whole calculation in a WINDOW_MAX() function – the whole calculation should look like

WINDOW_MAX((ZN(SUM([Sales])) – LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN(SUM([Sales])), -1)))

Adjust the colour legend. I set the start & end colours to #ff00ff (hot pink) and #5d6068 (dark grey) and then applied an upper limit to the range and centred at 0 as below.

Hide the Order Date heading at the top of the chart. Adjust the Tooltip.

Remove column gridlines, zero lines and axis rulers.

Create new fields

2021 Sales

IF YEAR([Order Date]) = 2021 THEN [Sales] END

and

2024 Sales

IF YEAR([Order Date]) = 2024 THEN [Sales] END

then create

% Difference

(SUM([2024 Sales]) – SUM([2021 Sales]))/SUM([2021 Sales])

Edit the Sort of the Sub-Cat pill on the Detail shelf, so it is sorting by % Difference ascending. This will ensure the lines are displayed overlapping in the expected manner.

Building the Bar-in-Bar Chart

On a new sheet, add Category to filter and select Furniture. Add Region to filter and select West. Add Order Date to filter and select Years then choose 2023 and 2024 only.

Create a new field

2023 Sales

IF YEAR([Order Date]) = 2023 THEN [Sales] END

Add Sub Cat to Rows and 2023 Sales to Columns. Add a sort to the Sub-Cat pill to sort by 2024 Sales descending. Add 2024 Sales to Columns. Make the chart dual axis and synchronise the axis. Change the mark type on the All marks card to bar. Remove Measure Names from the Colour shelf on the All marks card. Set the colour of the 2023 Sales marks card to light grey. Increase the width of each row, then reduce the size of the bar on the 2024 Sales marks card.

Create a new field

Sales Decreased

SUM([2024 Sales]) < SUM([2023 Sales])

and add to the Colour shelf of the 2024 Sales marks card. Adjust colours to suit.

In the solution, the Tooltip shows an indicator – I’m not sure if this was necessary, but I added it just in case

2024 Sales > 2023 Sales

IF [Sales Decreased] THEN ‘●’ END

Add this to the Tooltip shelf of the All marks card, along with the 2023 Sales and 2024 Sales fields. Adjust the Tooltip accordingly.

Hide the top axis. Remove the title of the x-axis.

Remove row and column dividers. Remove row gridlines and row axis rulers and ticks. Remove all zero lines.

Building the side-by-side bar chart

On a new sheet, add Category to filter and select Technology. Add Region to filter and select West. Add Order Date to filter and select Years then choose 2023 and 2024 only.

Add Sub Cat to Rows and Sales to Columns. Apply a Sort to Sub-Cat based on 2024 Sales descending.

Create a new field

Year

YEAR([Order Date])

And add to Rows and Colour. Adjust colour to suit. Widen each row.

Create new field

Diff is Neg Indicator

IF NOT([Diff is +ve]) THEN ‘●’ ELSE ” END

Add to Rows before Year and then adjust the table calculation setting so it is just computing by Year only.

Adjust the alignment of the Sub-Cat column so it is aligned middle right. Narrow the width of the Diff is Neg Indicator column to try to remove all the column heading text. If some still shows, rename the field so it is padded with some spaces at the front. Adjust the Tooltip.

Remove the x-axis title. Remove Column dividers. Adjust the row dividers so they are at level 1 and are partitioning each Sub Cat only and not splitting the Year column.

Remove all gridlines

Building the dashboard

It’s always hard to walk through the steps for placing objects on a dashboard in the specified places. My general rules are

  1. Start with a floating vertical container that is positioned 0,0 and set to the dashboard height and width. I name this Base.
  2. Then add tiled objects such as a text object for the title, blank objects, other containers, charts etc.
  3. When you add a container, add a blank object initially to help get everything into place. Remove once you have at least 2 objects side by side / on top of each other depending on the direction you’re organising.
  4. The item hierarchy shouldn’t have any containers of type Tiled listed.
  5. Try to name your containers to help maintenance in the future

Below is a picture of the item hierarchy I ended up with using this approach

I created a floating vertical container called Base, positioned 0,0 and 1200 x 850. Background set to None, no border and inner and outer padding all 0.

I added a text object to contain the title. Background set to None and no border. Outer padding set to 10 all round, and inner padding 0.

I added a blank object, which I renamed Horizontal divider. Background set to light grey, no border. Outer padding set to left and right 10 and top and bottom 0. Inner padding all 0. Height set to 2.

I added another Vertical container, which I renamed Body. Background set to None, no border and all inner and outer padding set to 0.

I added 3 horizontal containers on top of each other, and set the property of the Body vertical container to distribute contents evenly so each horizontal container was the same height.

1st horizontal container

I named Row 1 – Level 1. I set the background to the pale green. No border. Outer padding set to left & right 10, top & bottom 5. Inner padding all 0.

Into this I added a text field to describe the levels. Background of this was white, no border and outer padding set to 0 (so the green background disappears). Inner padding was set to top: 20 and 10 for the rest.

Next the Histogram chart. Border set to green. Background white. Outer padding right:5, rest 2. Inner padding set to 10 all round. Width of chart fixed to 380 px.

Next the Level 1 text object. No border, no background. Outer padding 4 all round; inner padding 0. Formatted text object to rotate text. Width of object set to 40 px.

2nd horizontal container

I named Row 2- Level 2. I set the background to the pale blue. No border. Outer padding set to left & right 10, top & bottom 5. Inner padding all 0.

Into this I added a text field to describe the challenge. Background of this was white, no border and outer padding set to 0 (so the blue background disappears). Inner padding was set to 10 all round. Width of object set to 380px.

Next the Peas in a Pod chart. Border set to blue. Background white. Outer padding right:5, rest 2. Inner padding set to 10 all round.

Next the +/- bar chart. Border set to blue. Background white. Outer padding right and left 5, top & bottom 2. Inner padding set to 10 all round. Width of object set to 380px.

Next the Level 2 text object. No border, no background. Outer padding 4 all round; inner padding 0. Formatted text object to rotate text. Width of object set to 40 px.

3rd horizontal container

I named Row 3- Level 3. I set the background to the pale purple. No border. Outer padding set to left & right 10, top & bottom 5. Inner padding all 0.

I added the Slope chart. Border set to purple. Background white. Outer padding right:5, rest 2. Inner padding set to 10 all round. Width of object set to 380px.

Next the bar-in -bar chart. Border set to purple. Background white. Outer padding right & left 5, top & bottom 2. Inner padding set to 10 all round.

Next the side-by-side bar chart. Border set to purple. Background white. Outer padding right and left 5, top & bottom 2. Inner padding set to 10 all round. Width of object set to 380px.

Next the Level 3 text object. No border, no background. Outer padding 4 all round; inner padding 0. Formatted text object to rotate text. Width of object set to 40 px.

It was a bit of trial and error to get the spacing as required, and a few calculations to work out how wide I wanted each chart to be, based on the width of the dashboard and the other items in each row.

Anyway, my published viz is here.

Happy vizzin’!

Donna

Can you create these KPI Cards?

Donna ColesLeave a comment

Lorna set a table calculation filled challenge this week to recreate some KPI cards using an aggregated and amended version of the Superstore data set.

Lorna purposefully used a string Period field to define the timeframe to encourage the use of table calculations. No doubt, there are ways you could use string functions/regex to extract the relevant year and month number to come up with a different solution, but I’m going to head down the intended route.

And as with any table calculation based challenge, I find it best to always build out all the calculations I need into a tabular display to start with before building the viz. So let’s get started…

Building out the Calculations

We need to have a handle on more of the data than just that associated to the Period we’re interested in, so we can’t use a simple Quick Filter on the Period field to restrict the data, otherwise we can’t ‘access’ data for the previous period. So to manage which Period we want to focus on, I created a parameter

pSelectedPeriod

string field that uses a list where the values are added from the Period field. Default to P12 Y2022/23

On a new sheet, add Period to Rows and then add the Sales , Profit and Quantity measures to Text. Show the pSelectedPeriod parameter

What we’re going to do is get the values for each measure that is associated to the pSelectedPeriod, and display that value over every row.

Curr Sales

WINDOW_MAX(SUM(IF [Period] = [pSelectedPeriod] THEN [Sales] END))

If the Period matches that in the parameter, then get the Sales value and then use Window_MAX to ‘spread’ that value over every row

Add this to the table, and edit the table calculation to ensure the value is being computed by Period.

Repeat the same for Profit and Quantity

Curr Profit

WINDOW_MAX(SUM(IF [Period] = [pSelectedPeriod] THEN [Profit] END))

Curr Qty

WINDOW_MAX(SUM(IF [Period] = [pSelectedPeriod] THEN [Quantity] END))

If you change the parameter, you should see all the values in the last 3 columns changing to reflect the value from the first 3 columns of the relevant row.

Now we need to get the value from the previous period, ie the data from the previous row

Prev Sales

WINDOW_MAX(IF MIN([Period]) = [pSelectedPeriod] THEN LOOKUP(SUM([Sales]),-1) END)

The LOOKUP function is taking the Sales value from the previous 1 row (-1), and then WINDOW_MAX is once again ‘spreading’ this value across every row. We also need

Prev Profit

WINDOW_MAX(IF MIN([Period]) = [pSelectedPeriod] THEN LOOKUP(SUM([Profit]),-1) END)

Prev Qty

WINDOW_MAX(IF MIN([Period]) = [pSelectedPeriod] THEN LOOKUP(SUM([Quantity]),-1) END)

Add these to the table, and again adjust the table calc settings for every field to compute by Period.

Now we have the current and previous values for each measure, we can work out the % difference

Diff Sales %

([Curr Sales]-[Prev Sales])/[Prev Sales]

Format this to a % with 1 dp

Diff Profit %

([Curr Profit]-[Prev Profit])/[Prev Profit]

Diff Qty %

([Curr Qty]-[Prev Qty])/[Prev Qty]

Add all these onto the sheet, and remember the table calc settings (for these, there are nested table calcs, so make sure both are set properly).

We now need an arrow indicator to display up or down depending on the % value, and this needs to display as a different colour, so we need two fields per measure.

Diff Sales Indicator +ve

IF [Diff Sales %] >= 0 THEN ‘▲’ END

Diff Sales Indicator -ve

IF [Diff Sales %] < 0 THEN ‘▼’ END

I use this site to copy the text symbols from

Diff Profit Indicator +ve

IF [Diff Profit %] >= 0 THEN ‘▲’ END

Diff Profit Indicator -ve

IF [Diff Profit %] < 0 THEN ‘▼’ END

Diff Qty Indicator +ve

IF [Diff Qty %] >= 0 THEN ‘▲’ END

Diff Qty Indicator -ve

IF [Diff Qty %] < 0 THEN ‘▼’ END

Add these to the sheet if you wish too (apply the table calc settings), but you’ll only get a value for one or the other field depending on whether the difference was +ve or -ve. Below I’ve just added the two Sales indicators

The Tooltip displays the value of the two Periods being compared. One of these is in the parameter, but we need to capture the other

Previous Period

WINDOW_MAX(IF [pSelectedPeriod] = MIN([Period]) THEN LOOKUP(MIN([Period]),-1) END)

So now we have all the values we need for the KPIs captured against every row in the dataset. So now we want to just show a single row. It could be the first, it could be the last… based on Lorna’s hint, let’s filter to just show the row related to the pSelectedPeriod value.

Filter Selected Period

[pSelectedPeriod] = LOOKUP(MIN([Period]),0)

Using the offset of 0 with the LOOKUP, returns the value for the row you’re on, so adding this to the FIlter shelf and selecting True, filters the display to the row where the Period matches the parameter. NOTE if you adjust the table calc settings of this field after adding to the filter shelf, you’ll need to reselect the option to filter to True.

As this is a table calculation, the ‘filter’ is applied later in the order of operations, so information about the other rows in the table can be referenced. Filtering just by Period as a quick filter, is essentially a dimension filter and that happens earlier on in the process, meaning the data about the other rows would be inaccessible.

So we have all the fields, now build the cards.

Building the KPI Cards

On a new sheet, double click into Columns and type in MIN(0). Repeat this 2 more times. This gives us 3 axis to build each of the 3 cards.

On the All marks card, add Period to the Detail shelf. Show the pSelectedPeriod parameter. Add the Filter Selected Period to the Filter shelf and set to true (adjusting the table calc and resetting the filter value as required).

Change the mark type of the All marks card to shape and select a transparent shape (see here for more details).

On the first MIN(0) marks card, add Curr Sales, Prev Sales, Diff Sales %, Diff Sales Indicator +ve and Diff Sales Indicator -ve to the Label shelf. Remember to apply the table calc settings for all of the fields!

Adjust the text within the label, so it is formatted and positioned as required and then align middle centre.

On the middle MIN(0) marks card, do similar by adding the equivalent profit fields onto the Label shelf, and then repeat again for the bottom MIN(0) marks card, adding the quantity fields to the Label shelf.

On the All marks card, add Previous Period, Curr Sales, Curr Profit, Curr Qty Prev Sales, Prev Profit, Prov Qty, Diff Sales %, Diff Profit % and Diff Qty % to the Tooltip shelf (remember to set those table calc settings!) Then adjust the Tooltip to display the text as required. I used the ruler to shift the starting position, along with tabs (the tab keyboard button) to ‘try’ to get everything to align, and it works for most circumstances….

Finally format the KPI card

  • Set the worksheet background colour to light grey
  • Remove all gridlines, zero line, axis lines
  • Set the column divider to be a thick white line
  • Set the row divider to be a thick white line
  • Hide the axis

Add the sheet onto a dashboard, and you should be done.

My published viz is here.

Happy vizzin’!

Donna

Can you build a flexible tabular display?

Donna Coles1 Comment

The challenge this week was to provide a balance between the user requesting ‘all the numbers’ and the visualisation designer trying to encourage ‘best practice’. It was inspired by Sam Parsons‘ viz here, which he discusses in this video.

We’re going to use 2 sheets to build this, one to display the text and one to display the bars. But first we need to define some parameters for the user to drive the display

pDisplayType

string parameter containing a list of two options, and defaulted to ‘Bars’

pShowExpanded

boolean parameter, defaulted to False

The measures we’ll be displaying are the Sales in 1000s and the Profit Ratio percentage, so we’ll need

Sales (k)

[Sales]/1000

format to 1 dp

and

Profit Ratio %

(SUM([Profit])/SUM([Sales]))*100

custom format this to ▲ #,##0.0;▼ #,##0.0 (you can use this site to copy & paste the symbols required).

Let’s start to build out the tabular view.

Add Category and Sub-Category to Rows. Add Region and Order Date (as a blue, discrete pill, at the YEAR level) to Columns. Add Measure Names to Filter and filter to Sales (k) and Profit Ratio %. Add Measure Values to Text. Add Measure Names to Columns in front of the Order Date field. Swap the order of the pills in the Measure Values box.

Add subtotals (Analysis > Add all Subtotals) and set Row Totals to Left and Column Totals to Top. Add Column Grand Totals (Analysis > Show Column Grand Totals).

Set the totals, so they are displaying the average values (Analysis > Totals > Total All Using > Average)

Right click on the label ‘Grand Total’ in the table, and format. In the left hand pane, change the label from ‘Grand Total’ to ‘All Categories’

Right click on the ‘Total’ label in the columns and format and change the Totals label to ‘Avg per Yr’

Now we have the content of the table, we need to add the functionality to expand/collapse (hide or show) the Year columns based on the pShowExpanded parameter. For this we need

Show / Hide Columns

IF [pShowExpanded] THEN ‘Show’ ELSE ‘Hide’ END

Add pShowExpanded to the view, and add Show/Hide Columns to Rows.

You should see that the value for Show/Hide Columns is only visible against the Year columns and not against the sub totals. With pShowExpanded set to False the value displayed is ‘Hide’

Now right click on the text ‘Hide’ and select the Hide option from the context menu

The Year columns will disappear. Change pShowExpanded to True, and voila! the columns are displayed. Uncheck Show Header against the Show/Hide Columns so the text ‘Show’ is not visible when expanded.

The ‘hide’ option is a really sneaky trick, but so very effective at not displaying specific rows/columns without filtering them out. It isn’t obvious it’s been applied though (there’s no clues in the canvas), so as a top tip, I recommend always adding a note to the worksheet caption to remind your future self (or a colleague who needs to maintain the workbook), that this has been applied.

Formatting the table

Now the functionality has been nailed, we need to apply the formatting & styling to the table.

I set the fonts to Tableau Medium, colour #7d4e47 throughout. The ‘All Categories’ Grand Total label was 11pt, the Category labels 9pt and the Sub-Category labels 8pt. The Region labels were 11pt, the Measure Names labels were 9pt and the ‘Avg per Yr’ and YEAR(Order Date) labels all 8pt. The Text of the numbers in the main body of the table were all set to 8pt too.

The background of the workbook was set to cream: #fefaf1

and then the total and grand total rows were coloured #faf4e7 and #f5eddc accordingly

To colour the font, add Measure Values to Colour, and right click and select Use Separate Legends

This will then give you two colour legends.

Adjust the Sales (k) colour legend – select any diverging colour palette, set Stepped Colour to 2 steps, and check Include Totals, and then click on the colours at each end and set both to the same brown (#7d4e47).

Repeat similar steps for the Profit Ratio % colour legend, but this time also set the range to be centred at 0, and apply different colours to the ends (#ff6f6a and #7d4e47).

Remove the Category & Sub-Category and Region/Order Date labels from displaying (hide field labels for columns/rows), and adjust the width and height of the cells to give a bit more spacing. Hide the tooltips from showing.

Create the bar table

Duplicate the sheet. Add Measure Values to Columns, and the click on the Label shelf and uncheck Show Mark Labels. Click on the Colour shelf and change the opacity to 35%. Reduce the Size of the bar to suit, then hide the Measure Values axis (uncheck Show Header).

Click on the Tooltip shelf and enable tooltips, then adjust accordingly.

Re-apply any of the formatting to the text that may have been lost – for some reason I lost all the Tableau Medium style fonts…???

Swapping the sheets on the dashboard

Create a dashboard, add a vertical layout container and add both sheets.

We will make use of dynamic zone visibility to only display the sheet we want based on the value of pDisplayType. To do this, we need some more calculated fields

Show Bar

[pDisplayType] = ‘Bars’

Show Numbers

[pDisplayType] = ‘Numbers’

Go back to the dashboard. Add the pDisplayType parameter. Click on the Bar sheet so it is selected (grey box) and on the Layout tab, check the Control visibility using.. checkbox and choose the Show Bar field.

Repeat the same steps for the Text table, but select the Show Numbers field instead. Depending what value is displayed in the pDisplayType parameter, one of the sheets should disappear, and changing the pDisplayType value should swap between them.

Now, style the dashboard as required.

My published viz is here.

Happy vizzin’!

Donna

Antarctic Ice Extent

Donna Coles1 Comment

It was Luke’s turn to set the #WOW2023 challenge this week and he chose to focus on remaking a visualisation relating to the change in the Antarctic Sea Ice, inspired by charts created by Zach Labe.

The challenge involved the use of extensive calculations, which at times I found hard to validate due to the steps involved in reaching the final number, and only having visibility of the final number on hover on a point on the chart. If it didn’t match, it became a bit of a puzzle to figure out where in the process I’d gone wrong.

Getting the data for the faded yearly line charts was ok, but I ended up overthinking how the decade level darker line chart was calculating and couldn’t get matches. Anyway, after sleeping on it, I realised my error, and found I didn’t need half the calculations I’d been playing with.

So let’s step through this. As we’re working with moving averages, we’re looking at using table calculations, so the starting point is to build out the data and the calculations required into a tabular form first.

Setting up the calculations

I used the data stored in the Google sheet that was linked to in the challenge, which I saved down as a csv file. After connecting to the file, I had separate fields for Day, Month and Year which I changed to be discrete fields (right click on field and Convert to discrete).

We need to create two date fields from these fields. Firstly

Actual Date

MAKEDATE([Year],[Month],[Day])

basically combines the 3 separate fields into a proper date field. I formatted this to “14 March 2001” format.

Secondly, we’ll be plotting the data on an axis to span a single year. We can’t use the Actual Date field for that as it will generate an axis that runs from the earliest date to the latest. Instead we need a date field that is ‘normalised’ across a single year

Date Normalise

MAKEDATE({max([Year])}, [Month], [Day])

the {max([Year])} notation is a short cut for {FIXED: MAX([Year])} which is a level of detail (LoD) expression which returns the greatest value of the Year field in the data set. In this case it returns 2023. So the Date Normalise field only contains date for the year 2023. Ie if the Actual Date is 01 Jan 2018 or the Actual Date is 01 Jan 2020, the equivalent Date Normalise for both records will be 01 Jan 2023.

Let’s start to put some of this out into a table.

Put Year on Columns, and Date Normalise as a blue (discrete) exact date field on Rows. Add Area(10E6M2) to Text and change to be Average rather than Sum (in leap years, the 29 Feb seems to have been mapped to 01 March, so there are multiple entries for 01 March). This gives us the Area of the Ice for each date in each year.

We need to calculate the 7 day moving average of this value. The easiest was to do this is add a Moving Average Quick Table Calculation to the pill on the Text shelf.

Once done, edit the table calculation, and set so that is average across the previous 6 entries (including itself means 7 entries in total) and it computes down the table (or explicitly set to compute by Date Normalise).

It is best to create an explicit instance of this field, so if you click on the field and press ctrl while you drag and drop it into the data pane on the left hand side, you can then rename the field. I named mine

Moving Avg: Area

WINDOW_AVG(AVG([Area (10E6M2)]), -6, 0)

It should contain the above syntax as that’s what the table calculation automatically generates. If you’re struggling, just create manually and then add this into the table instead.

Add Area (10E6M2) back into the table too. You should have the below, and you should be able to validate the moving average is behaving as expected

Now we need to work out the data related to the ‘global’ average which is the average for all years across a single date.

Average for Date

{FIXED [Date Normalise]: AVG([Area (10E6M2)])}

for each Date Normalise value. return the average area.

Pop this into the table, and you should see that you have the same value for every year across each row.

We can then create a moving average off of this value, by repeating similar steps above. In this instance you should end up with

Moving Avg Date

WINDOW_AVG(SUM([Average For Date]), -6, 0)

Add into the table, and ensure the table calculation is computing by Date Normalise and again you should be able to validate the moving average is behaving as expected

Note – you can also filter out Years 1978 & 1979 as they’re not displayed in the charts

So now we have the moving average per date, and the global moving average, we can compute the delta

Ice Extent vs Normal

[Moving Avg: Area] -[Moving Avg Date]

Format this to 3 dp and add to the table. You should be able to do some spot check validation against the solution by hovering over some of the points on the faded lines and comparing to the equivalent date for the year in the table.

This is the data that will be used to plot the faded lines. For the bolder lines, we need

Decade

IF [Year] = {max([Year])} THEN STR([Year])
ELSE
STR((FLOOR([Year]/10))*10) + ‘s’
END

and we don’t need any further calculations. To verify, simply duplicate the above sheet, and then replace the Year field on Columns with the Decade field. You should have the same values in the 2023 section as on the previous sheet, and you should be able to reconcile some of the values for each decade against marks on the thicker lines.

Basically, the ‘global’ values to compare the decade averages against are based on the average across each individual year, and not some aggregation of aggregated data (this is where I was overthinking things too much).

Building the viz

On a new sheet add Date Normalise as a green continuous exact date field to Columns, and Ice Extent vs Normal to Rows. Add Year to Detail and Decade to Colour. Adjust colours to suit and reduce to 30% opacity. Reduce the size to as small as possible. Add Decade to Filter and exclude 1970s. Ensure both the table calculations referenced within the Ice Extent vs Normal field are computing by Date Normalise only.

Add Actual Date to the Tooltip and and adjust the tooltip to display the date and the Ice Extent vs Normal field in MSM.

Now add a second instance of Ice Extent vs Normal to Rows. On the 2nd marks card that is created, remove Year from Detail and Actual Date from Tooltip. Increase the opacity back up to 100% and increase the Size of the line. Sort the colour legend to be data source order descending to ensure the lines for the more recent decades sit ‘on top’ of the earlier ones.

Modify the format of the Date Normalise field to be dd mmmm (ie no year). Adjust the Tooltip as below

Make the chart dual axis and synchronise the axis. Remove the right hand axis.

Edit the axis titles, remove row and column dividers and add row & column gridlines.

Adding the labels

We want the final point for date 18 June 2023 to be labelled with the actual Area of ice on that date and the difference compared to the average of that date (not the moving average). I create multiple calculated fields for this label, using conditional logic to ensure the value only returns for the maximum date in the data

Max Date

{max([Actual Date])}

Label:Date

IF MIN([Decade])=’2023′ AND MIN([Actual Date])=MIN([Max Date]) THEN MIN([Max Date]) END

Label: Area

IF MIN([Decade])=’2023′ AND MIN([Actual Date])=MIN([Max Date]) THEN AVG([Area (10E6M2)]) END

Label:Ice Extent v Avg for Date

IF MIN([Decade])=’2023′ AND MIN([Actual Date])=MIN([Max Date]) THEN AVG([Area (10E6M2)]) – SUM([Average For Date]) END

Label:unit of measure

IF MIN([Decade])=’2023′ AND MIN([Actual Date])=MIN([Max Date]) THEN ‘MSM’ END

Label: unit of measure v avg

IF MIN([Decade])=’2023′ AND MIN([Actual Date])=MIN([Max Date]) THEN ‘MSM vs. avg’ END

All these fields were then added to the Text shelf of the 2nd marks card and arranged as below, formattign each field accordingly

And this sheet can then be added to the dashboard. The legend needs be adjusted to arrange the items in a single row.

My published viz is here.

Happy vizzin’!

Donna

Can you build a heat map with bathymetry lines?

Donna Coles2 Comments

What??? That was the first thought that went through my mind when Luke set this challenge. Bathy..huh… ??? What’s that all about. Well read the challenge to find out more 🙂

The what?? was quickly followed by errr…..? The indicator on the challenge overview page said ‘hard’ and this was a Luke challenge after all, so certainly not for the faint hearted! I wasn’t sure how this was going to go, so just started with a basic tabular view and went from there. (hint – it might be worth reading the blog to the end before you start building.. it could save some time 😉 )

I quickly built a basic heat map. I added Order Date at the discrete (blue) month level to Columns and Sub-Category to Rows. I added Sales to Colour and changed the mark type to square then added Sales to Label.

We ultimately need the cumulative Percent of Total Sales per Sub-Category, to display as the label. Click on the Sales pill on the Label shelf and Add Table Calculation. From the dialog window, choose Running Total and verify the calculation is computing Table (across) (or amend and select Specific Dimensions and ensure Month Order Date is selected). Then check Add secondary calculation and in the secondary calc dialog, select Percent of Total, again ensuring Table (across).

If applied correctly, the values in the December column should all be 100%.

I wanted to ensure this calculation was stored so I could reuse, so while pressing shift key, I dragged the Sales pill from the label shelf into the data pane, and renamed the calculation

Cumulative % of Total

RUNNING_SUM(SUM([Sales])) / TOTAL(SUM([Sales]))

Format the field to be % with 0 dp

Editing the field should show the details above. Of course, you can just create the calculated field manually and type in the syntax. If you do that, then replace the Sales field on the label field, with this one. Centre align the text.

With this field, we can create the calculation Luke provides for the colouring

Colour

ROUND([Cumulative % of Total]*50, -1)

Replace the Sales field on the Colour shelf with this field, and adjust the colour to use the Blue sequential colour palette. You should now have the basic structure and colouring of the heat map.

Adjust the font style and alignment of the Sub-Category and Order Date Month label headings, and change the month labels to be abbreviated. Hide the Order Date column label and the Sub-Category title (right click hide field labels for columns/rows). Remove all gridlines, row/column dividers etc.

Looking good.. but what next.. how to get those divider lines…. I wasn’t too sure at this point, but I knew I had to identify the cells when a ‘change in colour’ happened both horizontally and vertically.

So the first thing I did was to duplicate the above sheet into a basic crosstab (right click sheet > duplicate as crosstab), and I removed Cumulative % of Total from the display, so I just had the values used for the colour.

I decided I wanted to flag each cell with a 1 or 0 depending on whether the next cell was different or not. I started horizontally. So for each row, and starting with January, I wanted to compare the colour value for Jan with the colour value for Feb. If they were the same, I wanted to record 0 against Jan. If they were different I wanted to record 1 against Jan. I used the following calculation

Horizontal – Next Value Diff

IF ([Colour] = LOOKUP([Colour],1) OR LAST()=0) THEN 0 ELSE 0.99 END

If the Colour value for the current cell (eg Jan) matches the Colour value of the next cell (eg Feb) OR, the current cell is the last month (ie Dec), then return 0 otherwise there is a mismatch so return 0.99. Originally I used 1, but later found I had to adjust the calc to make the line show as I wanted.

Add this field to the tabular display and verify the table calculations used within the field are running Table (across).

You can see that for Accessories, the Colour value for Feb is not equal to the Colour value for Mar, and so the Horizontal – Next Value Diff value for Feb is 0.99. Whereas as the Colour value for Apr matches May, the Horizontal – Next Value Diff for Apr is 0.

I created a similar calculation to test the vertical settings

Vertical – Next Value Diff

IF ([Colour] = LOOKUP([Colour],1) OR LAST()=0) THEN 0 ELSE 1 END

This is essentially exactly the same calculation, but when added to the tabular view, the table calculation for the Vertical – Next Value Diff calc should be set to Table (down) instead

You can see that in Feb, the Colour value for Paper is equal to the Colour value for Phones, so the Vertical – Next Value Diff calc for Paper is 0. But in Mar, the Colour value for Storage is different from the Colour value for Supplies, and so the Vertical – Next Value Diff calc for Storage is 1.

Using these markers, I now want to ‘plot’ them on a viz.

So back to the heat map sheet we built above, we need some axis.

Double click into the Columns shelf and type MIN(1.0) and change the mark type to bar.

You can see a hole has appeared. To fix this, on the Analysis menu, select Infer Properties from Missing Values. The cell will populate.

Confession – I didn’t get this bit initially. I had the chart built with the lines in the correct places, but with a hole. I’d tried all sorts of combinations of ZN, and LOOKUP(expression,0) in calculations to try to make the number appear, but couldn’t get anything to work. I showed my colleague Sam Parsons, who mentioned the above setting. I have honestly never ever used it and was completely unaware of it’s existence. But it solved the problem, so massive hi-five to Sam 🙂

Double click into the Rows shelf and again type MIN(1.0). Having an axis on both rows and columns, means we can now fix the Size of the bar. Click on the Size shelf and select Fixed, width in axis units to 1 and alignment right.

Now we have both an x and y axis, we can add additional detail to get the white bar dividers displaying.

First, adjust the label so it is aligned middle centre.

Add Horizontal – Next Value Diff to Columns. Ensure the table calc settings are Table (across).

Remove both the Colour and the Cumulative % of Total fields from the marks card, and then add Horizontal – Next Value Diff as a discrete (blue) pill to Colour. Remove all gridlines, zero lines, axis ticks and row/column dividers. Change the Size to be Manual and reduce size. Make the chart dual axis and synchronise the axis.

You should have something similar to the above, assuming the colours for the Horizontal – Next Value Diff are distinguishable enough.

I adjusted the colours to set 0 to be a transparent colour (refer to this blog to learn how to add a transparent colour hex code into your custom colour palette, and then 0.99 to be white. Edit the x-axis and fix to be from 0 to 1. The white dividers between the relevant months should now be noticeable.

To set the dividers between the Sub-Category, I decided I just needed to use a constant 0 reference line. For this I needed

Vertical Ref Line

IF [Vertical – Next Value Diff] = 1 THEN 0 END

Add this to the Detail shelf on the All marks card, and verify the table calc setting for the Vertical – Next Value Diff calc is set to Table (down).

Right click on the y-axis and Add Reference Line. Set it per cell using the Vertical Ref Line field, with no label or tooltip displaying. Set the Line properties to be white, 100% opacity and a thick width.

Then edit the y-axis to also be fixed between 0 and 1. The lines separating the Sub-Category should now be prominent.

Then it’s just a case of tidying up – hide the axis, stop tooltips from displaying, and then add to the dashboard, setting as fit to entire view.

If need be you may want to tweak either the size of the bar on the Horizontal – Next Value Diff marks card, or change the thickness of the reference line to get similar sizes.

I have no idea when I’d ever need to use this type of display, but I enjoyed the puzzle and discovered something new which is what I love about using Tableau!

This version of my published viz is here.

BUT….

after writing all this up, and stepping away for a bit, I suddenly realised I’d over-complicated things! I didn’t need a dual axis after all. I could just use a reference line to show the dividers between the months, exactly like I did for the 0 constant reference line – doh! So I made some adjustments..

Amend

Horizontal – Next Value Diff

IF ([Colour] = LOOKUP([Colour],1) OR LAST()=0) THEN 0 ELSE 1 END

Then create

Horizontal Ref Line

IF [Horizontal – Next Value Diff] = 1 THEN 1 END

Then remove the Horizontal – Next Value Diff field from the Rows shelf, so there is no longer a dual axis. Add Horizontal Ref Line to Detail , and display the x-axis MIN(1.0) axis, and add a reference line per cell which references Horizontal Ref Line. Apply the same settings to the reference line as detailed above.

And this resulted in a much simpler looking viz

I know I could reduce the number of calcs used, but I like to have ‘building blocks’ to follow my thought process. I have published the simpler version within the same workbook on a separate tab, here.

Happy vizzin’!

Donna

Can you build a comet chart as an alternative to a side by side bar chart?

Donna Coles1 Comment

In the final week of ‘alternative charts month’, Luke set this challenge as different way of presenting data that you might typically see in a side-by-side bar chart.

Luke had indicated on the #WOW splash screen, that this challenge was ‘easy’, but that’s always dependent on your level of Tableau. He also added a note in the requirements that if you wanted to be ‘advanced’ to solve it with Table Calcs only.

I figured I’d just start and see what I ended up with (sometimes, my natural brain thinking takes me down a table calc route..)

In a change to my usual starting point, I started trying to remember what I needed to do to get the comet… I felt pretty sure that path would be involved somewhere.

So, I added Order Date to the Filter shelf and filtered to years 2021 and 2022 only.

Then I added Sub-Category to Rows, Sales to Columns and Order Date (which defaulted to YEAR(Order Date)) to Detail. I changed the mark type to circle initially.

Ok – I had what I was expecting – 2 circles per row, one for each year.

So then I change the mark type to line and moved YEAR([Order Date]) from Detail to Path. This meant my lines were joined.

I then added Order Date to Size, and reset Order Date to be at the YEAR level. Hey presto! My comet shapes appeared.

I now wanted to show a white circle mark just for the 2022 sales, so I created

Max Year Sales LOD

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

this looks a bit long-winded ( I do usually break this up)… so let’s review what’s going on…

{FIXED:MAX(YEAR([Order Date]))} returns the latest year in the data set (ie 2022) and spreads that across every row of data. So the formula is comparing each row, and if the Order Date year matches 2022, the value of the Sales is returned. This is then all aggregated and totalled for each Sub-Category.

Add this field to Columns, make dual axis and synchronise axis.

Remove Measure Names from the All marks card and change the mark type of the Max Years Sales LOD card to circle. Colour white.

Remove the YEAR([Order Date]) pill from the Size shelf of the Max Years Sales LOD card, so the size of the comet (the Sales card) and the circle can be adjusted independently. Adjust the sizes enough so the comet is visible around the circle.

Sort the Sub-Category field by Max Year Sales LOD descending

Next we need to colour the comets based on whether Sales increased or decreased.

Prev Year Sales LOD

{FIXED [Sub-Category]: SUM( IF YEAR([Order Date]) = {FIXED:MAX(YEAR([Order Date]))} -1 THEN [Sales] END)}

is doing similar to the above calculation, but {FIXED:MAX(YEAR([Order Date]))} -1 returns 2021 instead.

and with this we can created

Sales Increased? LOD

[Max Year Sales LOD] > [Prev Year Sales LOD]

Add this to the Colour shelf of the Sales marks card, and adjust accordingly.

To label the comets, check the show mark labels checkbox on the Label shelf dialog, and set to line ends and label end of line. You may need to check the allow labels to overlap option too if you’re not seeing all the labels.

The dashboard shows a circular size legend which is related to the circle mark, so I created

Order Date (Years)

YEAR([Order Date])

and added this to the Size shelf of the Max Year Sales LOD marks card.

Add Sales to the Tooltip shelf of the Max Year Sales LOD marks card too and adjust the tooltips.

Add row dividers, and remove all column dividers, gridlines and axis. Adjust the formatting of the Sub-Category row labels and hide the column title. Set the background of the worksheet to a grey colour.

And so that ended up being the LOD version of the chart, which is accessible from here.

But I had time, so I figured I’d see if I could crack the Table calcs version…

Building the Table Calculation Solution

This starts by repeating the intial steps above to get a basic single axis comet chart for Sales, split by Year.

We now need to get the sales for 2022 only. For starters, let’s identify the latest year

Latest Year

WINDOW_MAX(MAX(YEAR([Order Date])))

and let’s build up a table, so we can start to sense check what’s going on, as table calcs can be pesky!

Our Latest Year table calc is returning 2022 for every row in our table. To get the sales just for 2022

Window Max Year Sales

WINDOW_MAX(IF MIN(YEAR([Order Date]))=[Latest Year] THEN SUM([Sales]) END)

if the order date year is 2022, then return Sales (otherwise null) and spread the maximum value across the rows. When we add this into the table, we need to set the table calculation to compute using Year or Order Date, so that it is calculating the WINDOW_MAX for each Sub-Category

Add this field to Columns on the comet chart, and adjust the table calculation so Window Max Year Sales is computing by Year of Order Date only, and Latest Year by both fields (see the Nested Calculations dropdown)

Make the chart dual axis, and synchronise the axis. Make the adjustments to the mark types and sizes as described above.

We can’t sort the Sub-Category field in the way we did above, as table calculation fields aren’t accessible in the sort dialog. Instead add Window Max Year Sales to Rows and change it to be discrete (blue pill) and move it to be in front of Sub_Category. Adjust the table calc settings to match that described above. This should make the chart sort ascending.

To reverse, double click into the blue Window Max Year Sales pill on Rows and add * -1 to the end

Annoyingly this will revert it back to a measure, so reapply the steps above, and you should end up with a correctly sorted display. Hide the Window Max Year Sales blue pill.

Now to colour the comet.

Back to the tabular view. Add a Difference quick table calculation to the Sales pill and edit the table calculation to compute using Year(Order Date) only.

Drag the Sales pill with the difference table calc from the Measure Values section and drop into the left hand data pane. This will create a dedicated instance of the calculation. Rename it to

Sales Diff – TC

ZN(SUM([Sales])) – LOOKUP(ZN(SUM([Sales])), -1)

If you examine it, it should contain the above calculation.

With this we can then work out if sales have increased or not

Sales Increased? TC

WINDOW_MAX(IF [Sales Diff – TC] > 0 THEN 1 ELSE 0 END)

Adding this into the table, and setting the nested table calcs to both compute by Year Order Date, you can see that the values for each Sub-Category are either 1 or 0.

Add this to the Colour shelf of the Sales marks card. Make sure the field is discrete and the table calcs are set to compute by Year Order Date. Adjust the colours.

Finally make adjustments for the tooltip and adjust the formatting to clean up the chart. My table calc version of the viz is here.

Happy vizzin’!

Donna