Can you predict the future?

Donna ColesLeave a comment

Continuing on from last week’s challenge, Candra Mcrae set this #WOW2021 challenge to introduce another new feature of v2020.4 (so you’re going to need this version as a minimum to complete this challenge). Predictive modelling isn’t something I use at all, so I was quite reliant on the help documentation Candra referenced. I’m also not overly familiar with the various different models, and when they should be used, so this isn’t something I’m likely to use personally, without careful consideration and thought – it’s worth reiterating Candra’s warning :

Viz Responsibly: While predictive analysis in Tableau has never been easier, we encourage you to be intentional in how you use this powerful feature because not doing so (e.g., selecting a model type not appropriate for your data) could lead to inaccurate results.  

The focus of this blog will be

  • Building the main chart
  • Creating the tooltips
  • Determining the data for the title
  • Building the measure selector
  • Adding the measure selector interactivity

Building the main chart

The data provided has some records related to years before 1993, but the requirement is just to use data from 1993 onwards, so the first thing I did was to set a data source filter (right click on data source -> Edit Data Source Filters) to restrict the whole data source to Year 1 >=1993

Next I created the measures we need to display

Total Enrollment

[Total enrollment 2 All students]

simply references the existing measure.

% Black Students

SUM([Total enrollment 2 Black students]) / SUM([Total Enrollment])

% Non-Black Students

1- [% Black Students]

These 3 measures need to be displayed on a single chart where the actual measure displayed is determined by user selection. This selection will be driven by the use of a parameter, that will be set by a parameter action. For now, we just need to establish the parameter.

pSelect_Measure

This is simply a string parameter which will store the value of Total Enrollment by default.

Using this parameter, we can now decide which value we’re going to plot on the chart

Actual Value

CASE [pSelect_Measure]
WHEN ‘Total Enrollment’ THEN SUM([Total Enrollment])
WHEN ‘% Black Students’ THEN [% Black Students]
ELSE [% Non-Black Students]
END

Add this to a view by placing

  • Year 1 as continuous (green) pill on Columns
  • Actual Value on Rows

and you get the basic shape, although it’s not as ‘peaky’. This is resolved by editing the Actual Value axis (right click axis -> edit) and unchecking the Include zero checkbox.

Now change the text in the pSelect_Measure input parameter that’s displayed to % Black Students, and the chart will change. Verify it changes with the text % Non-Black Students too.

In reading through the Tableau KB documentation about Predictive Modeling Functions in Time Series Visualizations, I came to learn of the feature to Extend Date Range, something I’ve never come across before, and I’m not sure what version it first appeared in. Anyway, for this, you need to be working with a field which is a date datatype. The Year 1 field provided is an int.

I’m not entirely sure what I’ve done next is the optimum method, but it worked for me… some of it involved a bit trial and error when I came to defining and invoking the modelling feature later on. In writing this up, I’m essentially helping you to avoid the ‘back and forth’ steps I took in getting it all to work.

Anyway, I needed a date field to represent the year

Year

MAKEDATE([Year 1],1,1)

This resolves to a field containing 1st Jan YYYY for each Year in the data set.

Replace Year 1 on the chart with this field, and changing it to the ‘continuous’ instance of the date by choosing the second ‘Year’ option from the context menu

This changes the pill to green and then the option to Extend Date Range is visible on the menu. Set this to 5 years using the custom option

After doing this you’ll get a 5 nulls indicator displayed, which is basically saying there’s some years without any data. This what we expect for now.

Now onto the modelling part. We need a new calculated field to contain the predicted value using the Gaussian process regression.

Predicted Value

MODEL_QUANTILE(‘model=gp’, 0.5,[Actual Value],ATTR(DATETRUNC(‘year’,[Year])))

again, this took a bit of trial and error to get the date field in the required format.

Add this to the Rows and again edit the axis to ‘exclude zero’. You should now see the data extending for the predicted value beyond 2018. You can now hide the 5 nulls indicator (right click -> hide indicator)

You can now combine to be a dual axis chart (don’t forget to synchronise axis), and apply the relevant formatting to change the marks, apply relevant colours, hide axis & gridlines etc. Note, I set the area chart to 25% opacity, rather than the 75% stated in the requirement, as this gave me the colour most similar to the solution.

Creating the tooltips

Hovering over the chart, the tooltips display the Actual, Predicted and Residual (Actual-Predicted) value for each point. But depending on the measure selected, the format differs – Total Enrollment is in K and the others are in %.

We can’t use the number formatting feature of a field to resolve this, so we need to be a bit more creative. I confess I started creating individual fields for each measure (Actual, Predicted, Residual) based on the measure type selected (Total Enrollment, % Black Students, % Non-Black Students), but this meant I was creating loads of calculated fields, which just seemed a bit unnecessary.

So after a bit of a think, I realised there was a better way.

First up, let’s get our residual

Prediction Residual

[Actual Value]-[Predicted Value]

Now, if we put the values in a tabular form, you can see what the precision of the values are depending on the measure stated

We need to format appropriately. Both displays required the values to be formatted to 1 decimal place, and both have a suffix, either a K or a %.

To get the value in the required display format

Tooltip – Actual Value

IF [pSelect_Measure]=’Total Enrollment’ THEN [Actual Value]/1000
ELSE [Actual Value] * 100
END

Format this to 1 dp.

Create a similar field for the predicted value, which should also be formatted to 1 dp.

Tooltip – Predicted Value

IF [pSelect_Measure] = ‘Total Enrollment’ THEN [Predicted Value]/1000
ELSE [Predicted Value] * 100
END

And finally Tooltip – Residual

[Tooltip – Actual Value] – [Tooltip – Predicted Value]

This needs to be custom formatted to +#,##0.0;-#,##0.0 which ensures a + symbol is always displayed for positive values.

Pop these onto the tabular display we built earlier, and you can see the values are now displaying in the way we need

Finally we need to create a field to store the required suffix

Tooltip – Value Suffix

IF [pSelect_Measure] = ‘Total Enrollment’ THEN ‘K’
ELSE ‘%’
END

We can now add these 4 fields onto the Tooltip shelf of the ‘All’ marks card, and create the tooltip as required

Determining the data for the title

As we need to only use 2 sheets to build this solution, and 1 sheet will be required for the measure selection, we have to incorporate the summary data displayed at the top of the dashboard as part of the title of the chart viz.

In the title, we need to display the following :

  • The latest year in the provided data set (ie 2018)
  • The latest year including the extended date range (ie 2023 – 5 years later)
  • The actual value from 2018 based on the selected measure
  • The predicted value from 2023 based on the selected measure
  • An indicator to show whether the values were likely to increase or decrease

The requirement was to ensure there was no ‘hardcoding’. And as we’re working on getting information related to a specific row (ie year) in a set of data that consists of multiple rows (years), then we’re going to need to make use of table calculations for this.

Let’s start with the ‘easier’ bits first. We want the Year of the latest in the actual data set, and we want this value to be essentially stored against every row in the data

Latest Year

{FIXED: MAX([Year])}

This returns the value of 2018.

Latest Year + 5

DATE(DATEADD(‘year’,5,[Latest Year]))

This simply adds 5 years to the Latest Year, so returns 2023.

Now when I’m using table calculations, I often prefer to see what the data is doing in the table itself, so I can be sure I’m doing things correctly. With the ‘extended year’ stuff, it’s a bit fiddly creating the table from scratch, so I simply started by duplicating the chart sheet ‘as crosstab’ (right click on the sheet name tab, -> Duplicate as Crosstab). Rearrange the fields so Measure Names is on Columns and Year is on Rows and the ‘Tooltip’ named fields are visible. Add Latest Year and Latest Year+5 to Rows, and you can see how these fields show the same value against every row.

Now, remove these fields, as by adding them, we’ve lost the additional ‘extended dates’ rows (ie the ‘fake’ rows that don’t actually exist in the data). Ok, so now we want to get the Actual Value associated to 2018, but then perpetuate this across every row in the data.

Latest Year – Actual

WINDOW_MAX(IF MIN([Year]) = MIN([Latest Year]) THEN [Tooltip – Actual Value] END)

If the Year is the same as Latest Year, then display the value from the Tooltip – Actual Value field. The WINDOW_MAX table calc, then spreads this same value across all rows displayed. Format to 1dp and add this to the table.

We need to do something similar to get the Predicted Value for 2023

Latest Year +5 – Predicted

WINDOW_MAX(IF LAST()=0 THEN [Tooltip – Predicted Value] END)

If we’re at the last row in the data, then display the value from the Tooltip – Predicted Value field. Again the WINDOW_MAX spreads the value across all the rows. Set this to 1 dp and add to the table.

And now we just need to get the increase/decrease indicator

Increase | Decrease

IF ([Latest Year – Actual])-[Latest Year +5 – Predicted]>0 THEN ‘decrease’ ELSE ‘increase’ END

So now we know we’ve got the correct values we need, we can add these fields to the Detail shelf of the chart sheet, so we can reference them in the Title of the chart.

We also need the Latest Year and Latest Year +5 fields added to the Detail shelf, but when you add these, you’ll notice that you lose the ‘extended years’. You can fix this by wrapping the fields in an ATTR function. Double click on the field, which will allow you to ‘type in’ to the field.

You should now be able to create the text in the chart title

Building the measure selector

Phew! Are you still with me… there’s a fair bit going on already, and now we’ve got to build the chart that will drive the user selection.

On a separate sheet, add

  • Measure Names to Rows
  • Measure Values to Detail
  • Measure Names to Text
  • Measure Names to Filter, and restricted to the 3 original measures – Total Enrollment, % Black Students, %Non-Black Students

Uncheck Show Header on the pill on the Rows, then format

  • Set background colour of the pane to a navy blue
  • Set row & column borders to be white
  • Set the text label to be white text, centred, and increase the font
  • Turn off the tooltip

Adding the measure selector interactivity

Create the dashboard and add both the charts. To add the interactivity so that on click of a row in the Measure Selection sheet, it changes the measure being displayed, we need to add a dashboard action, that changes a parameter (Dashboard menu -> Actions -> Add Action -> Change Parameter). Set the action to run on Select when the Measure Select sheet is clicked. The target parameter is pSelect_Measure and the Measure Names field should be passed into this.

And with all that, you should hopefully now have a working solution. My published viz is here (note, my Measure Selection sheet is slightly different from what I’ve described above). The above is a bit simpler I think.

Happy vizzin’! Stay Safe!

Donna

Can you use map layers to show profit at state and city levels?

Donna ColesLeave a comment

It was Sean Miller’s turn to present this week’s #WOW2021 challenge, and one of the common features of Sean’s challenges is to utilise new features of Tableau. So for this challenge, you’re going to need v2020.4 (or higher if you’re reading this in a few months time 🙂 )

The core components of this challenge are relatively basic, and I am therefore making assumptions you know how to build a map and a bar chart. The focus for this will be on

  • Create the layered map
  • Sizing the circles (cities)
  • Colouring the circles (cities)
  • Removing interactivity on the states
  • Colouring the Profit on the Tooltip
  • Hover action from bar to cities

Create the layered map

Start by building out a basic filled map (double click State, then double click Profit and move Profit to the Colour shelf – if a map of the US doesn’t appear, you’ll need to edit locations via the Map -> Edit Locations option). Change the Profit colour legend to be red-black diverging, and set the centre point to 0. Remove all the map ‘features’, via the Map -> Map Layers option.

Then drag the City field onto the map, and when the Add a Marks Layer option appears, drop the field onto the ‘layer’ icon.

This will create a marks card for each field on the layers – one for City and one for State

Note if you get an indicator on the lower right of the map that suggests there’s multiple ‘unknown’ cities, click on the indicator to Edit Locations and make sure the State field is mapped to State.

Sizing the circles (cities)

The City layer needs to be sized based on Profit, so drag this field to the Size shelf of the City marks card. By default this will size from small to large with the smallest value being the lowest Profit value

But in the solution, you’ll see Profit is sized based on its ‘absolute’ value, ie -13,000 should be the same size as +13,000.

This was the last thing I actually resolved when building this viz, as it isn’t anything I’ve seen before. I knew it couldn’t be something too tricky, due to the nature of the current set of challenges, so I had a bit of google and discovered it’s just a case of setting the Sizes vary option to be From zero. You might need to the adjust the slider on the Size shelf itself to get the circles at an appropriate size

Colouring the circles (cities)

Create a calculated field

Profit is +ve?

SUM([Profit])>=0

which will return true or false and add this to the Colour shelf of the City marks card. Colour accordingly, and add a grey border to the circles (setting on the Colour shelf).

Removing interactivity on the states

As you move your mouse over the map, you’ll see the borders of the states are ‘selected’. To remove this, click the arrow next to the title of the State marks card, and choose Disable Selection.

As an additional step, remove the tooltips from showing on the State marks card.

Colouring the Profit on the Tooltip

The tooltip displays +ve profit in black and -ve profit in red. For this you’ll need two calculated fields, one to store the profit value only when it’s positive, and one to store the profit value only when it’s negative.

+ve Profit

IF [Profit +ve?] THEN SUM([Profit]) END

-ve Profit

IF NOT([Profit +ve?]) THEN SUM([Profit]) END

Add both these fields to the Tooltip shelf of the City marks card, then ensure they sit side by side in the tooltip text. Colour the text accordingly.

Hover action from bar to cities

When the 2 charts are on the dashboard, add a dashboard Highlight action to run on Hover from the Bar sheet to the Map sheet

Hopefully this has covered all the key points of this challenge. My published viz is here.

Happy vizzin’! Stay Safe!

Donna

Can you build a Control Chart?

Donna ColesLeave a comment

Lorna Brown returned this week to set another table calculation based challenge involving a line chart which ‘on click’ of a point, exposed the ability to ‘drill down’ to view a tabular view of the data ‘behind’ the point. This is classic Tableau in my opinion – show the summarised data (in the line chart) and with ‘drill down on demand’. Lorna added some additional features on the dashboard; hiding/showing filter controls to change how the data is displayed in the chart, and a back navigational button on the ‘detail’ list.

The areas I’m going to focus on in this blog are

  • Setting up the parameters
  • Defining the date to plot on the chart
  • Restricting the data to the relevant years
  • Defining the reference bands
  • Colouring the marks
  • Working out the date range for the tooltip
  • Building the table
  • Drill down from chart to table
  • Un-highlight selected marks
  • Hide/Show filter controls
  • Add navigation button

Setting up the parameters

This challenge requires 3 parameters.

Select a Date

a string parameter containing the 2 options available (Date Submitted & Date Selected) for selection, which when displayed on the dashboard will be set to a single value list control (ie radio buttons)

Latest X Years

an integer parameter, defaulted to 3, which allows a range of values from 1 to 5.

NOTE – Ensure the step size is set to 1, as this is what allows the Show buttons option to be enabled when customising the Slider parameter control type.

STD

another integer parameter, defaulted to 1, that allows a range of values from 1 to 3

Defining the date to plot on the chart

The Select a Date parameter is used to switch the view between different dates in the data set. This means you can’t plot your chart based on date field that already exists in the data set. We have to create a new field that determines which date field to select based on the parameter

Date to Plot

DATE(DATETRUNC(‘week’, IIF([Select a Date]=’Date Submitted’,[Date sent to company], [Date received])))

The nested IIF statement, is basically saying, if the parameter is ‘Date Submitted’ then use the Date sent to company field, else use the Date received field. This is all wrapped within a DATETRUNC statement to reset all the dates to the 1st day of the week (since the requirement is to report at a weekly level).

Note – there was some confusion which field the parameter option should map to. I have chosen the above, but you may see solutions with the opposite. Don’t get hung up on this, as the principal of how this all works is most important.

Restricting the data to the relevant years

The requirement is to show ‘x’ years worth of data, where 1 year’s worth of data is the data associated to the latest year in the data set (ie from 01 Jan to latest date, rather than 12 months worth of data). So to start with I calculated, rather than hardcoded, the maximum year in the data

Year of Latest Date

YEAR({MAX([Date to Plot])})

Then I could work out which dates I wanted via

Dates to Include

[Date to Plot]>= MAKEDATE([Year of Latest Date] – ([Latest X Years]-1),1,1)

In the MAKEDATE function, I’m building a date that is the 1st Jan of the relevant based on how many years we need to show.

So if Year of Latest Date is 2020 and Latest X Years =1 then Year of Latest Date – (Latest X Years -1) = 2020 – (1-1) = 2020 – 0 = 2020. So we’re looking for dates >= 01 Jan 2020.

So if Year of Latest Date is 2020 and Latest X Years =3 then Year of Latest Date – (Latest X Years -1) = 2020 – (3-1) = 2020 – 2 = 2018. So we’re looking for dates >= 01 Jan 2018.

This field is added to the Filter shelf and set to true.

So at this point, our basic chart can be built as

  • Year on Columns (where Year = YEAR([Date to Plot])), and allows the Year header to display at the top
  • Date to Plot on Columns, set to Week Number display via the pill dropdown option, and also set to be discrete (blue pill). This field is ultimately hidden on the display.
  • Number of Complaints on Rows (where Number of Complaints = COUNT([XXXX.csv], the auto generated field relating to the name of the datasource).

To get the line and the circles displayed, this needs to become a dual axis chart by duplicating the Number of Complaints measure on the Rows, synchronising the axis and setting one instance to be a line mark type, and the other a circle.

Defining the reference bands

The reference bands are based on the number of standard deviations away from the mean/ average value per year.

Avg Complaints Per Year

WINDOW_AVG([Number of Complaints])

Once we have the average, we need to define and upper and lower limit based on the standard deviations

Upper Limit

[Avg Complaints Per Year] + (WINDOW_STDEV([Number of Complaints]) * [STD])

Lower Limit

[Avg Complaints Per Year] + (WINDOW_STDEV([Number of Complaints]) * -1 * [STD])

Add both these fields to the Detail shelf of the chart viz (at the All marks card level) and set the table calculation of each field to Compute By Date to Plot

This ‘squashes’ everything up a bit, but we’ll deal with that later.

Add a Reference Band (right click on axis – > Add Reference Line) that ranges from the Lower Limit to Upper Limit.

If an Average Line also appears on the display, then remove it, by right clicking on the axis -> Remove Reference Line – > Average

Colouring the marks

I created a boolean field based on whether the Number of Complaints is within the Upper Limit and Lower Limit

Within STD Limits?

[Number of Complaints]<[Upper Limit] AND [Number of Complaints]>[Lower Limit]

Add this to the Colour shelf of the circle mark type, and set to Compute Using Date to Plot. The values will be True, False or Null. Right click on the Null option in the Colour Legend, and select Exclude. This will add the Within STD Limits? to the Filter shelf, and the chart will revert back to how it was. Adjust the colours accordingly.

The Tooltip doesn’t show true or false though, so I had another field to use on that

In or Out of Upper or Lower Limits?

If [Within STD Limits?] THEN ‘In’ ELSE ‘Out’ END

Working out the date range for the tooltip

The Tooltip shows the start and end of the dates within the week. I simply built 2 calculated fields to show this.

Date From

[Date to Plot]

This 2nd instance is required as Date to Plot is formatted to dd mmm yyyy format and also used in the Tooltip. Whereas Date From is displayed in a dd/mm/yyyy format.

Date To

DATE(DATEADD(‘day’, 6, [Date to Plot]))

Just add 6 days to the 1st day of the week.

Building the table

Create a new sheet and add all the relevant columns required to Rows in the required order. For the last column, Company response to consumer, add that to the Text shelf instead (to replace the usual ‘Abc’ text). The in the Columns shelf, double click and type in ‘Company response to consumer’ which creates a ‘fake’ column heading. Format all the text etc to make it all look the same.

Add the Dates to include = true filter.

Also add the WEEK(Date to Plot) field to the Rows shelf, as a blue discrete field (exactly the same format as on the line chart). But hide this field (uncheck Show Header). This is the key linking field from the chart to the detail.

Drill down from chart to table

Create one dashboard (Chart DB) that displays the chart viz. And another dashboard that displays the table detail (Table DB). On the Chart dashboard, add a Filter Dashboard Action (Dashboard menu -> Actions -> Add Action -> Filter), that starts from the Chart sheet, runs as a Menu option, and targets the Detail sheet on the Detail dashboard. Set the action to exclude all values when no selection has been made. Name the action Click to Show Details

On the line chart, if you now click a point on the chart, the tooltip will display, along with a link, which when clicked on, will then take you to the Detail dashboard and present you with the list of complaints. The number of rows displayed should match the number you clicked on

Un-highlight selected marks

What you might also notice, is when you click on a point on the chart, the other marks will all ‘fade’ out, leaving just the one you selected highlighted. It’s not always desirable for this to happen. To prevent this, create a new field called Dummy which just contains the text ‘Dummy’. Add this onto the Detail shelf of the All marks card on the chart viz.

Then on the chart dashboard, add another dashboard action, but this time choose a highlight action. Set the action to run on select and set the source & target sheets to be the same sheet on the same dashboard. But target highlighting to selected fields, and select the Dummy field only

Hide/Show filter controls

Check out this post by The Data School that explains very simply how to work with floating containers to show/hide buttons. When creating in Desktop, the ‘onclick’ interactivity won’t work, you’ll have to manually select to show and hide, but once published to Tableau Public, it’ll behave as desired.

You have options to customise what the button looks like when the container contents are hidden, and what it looks like when they’re shown, via the Button Appearance

Add Navigation Button

On the Detail dashboard, simply add a Navigation object to the dashboard

and edit the settings to navigate back to the chart dashboard, as well as customise the appearance

Hopefully I’ve covered all the key features of this challenge. My published viz is here.

Happy vizzin’! Stay safe!

Donna

Can you build a Customer Lifetime Value Matrix?

Donna Coles3 Comments

Ann Jackson challenged us this week to build this matrix depicting the average worth of customer cohorts during their lifetime.

This challenge involves a mix of LoDs (Level of Detail calculations) and table calculations.

First up , we need to define our customer cohorts (ie group the customers), which for this challenge is based on identifying the quarter they placed their first order in. This will involve an LoD calculation. For a good introduction to LoDs with some worked examples (including a similar cohort analysis example), check out this Tableau blog post.

ACQUISITION QUARTER

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

The 2nd part of the formula in the { … } returns the earliest Order Date associated to the Customer ID, which is then truncated to the 1st day of the quarter that date falls in ie 23 Feb 2019 is truncated to 01 Jan 2019.

For the ‘quarters since birth’ field, we need to calculate the difference in quarters, between the ACQUISITION QUARTER and the ‘quarter’ associated to the Order Date of each order in the dataset.

QUARTERS SINCE BIRTH

DATEDIFF(‘quarter’,[ACQUISITION QUARTER],DATETRUNC(‘quarter’,[Order Date]))

Drag this field into the ‘dimensions’ area of the left hand data pane (above the line if you’re using later versions of Tableau).

Lets sense check what this looks like, by adding

  • ACQUISITION QUARTER to Rows (Discrete, Exact Date)
  • ORDER DATE to Rows, set to Quarter (quarter year ie May 2015 format which will make a green pill), then set to discrete to change to blue
  • QUARTERS SINCE BIRTH to Rows

You can see that while the first row against each cohort starts with a different quarter, the QUARTERS SINCE BIRTH always starts at 0 and counts sequentially down the table.

Next we want to count the number of distinct customers in each cohort, and we’ll use another LOD for this.

CUSTOMERS

{FIXED [ACQUISITION QUARTER]: COUNTD([Customer ID])}

Once again move this field into the Dimensions section of the data pane.

Add this onto the Rows of the above data table, and you should get every row for the same cohort displaying the same number

Add Sales onto Text to get the value of sales made by the customer in each cohort in each quarter. The ‘customer lifetime value’ we need is defined as the total sales so far / number of customers in the cohort.

Remove the QUARTER(Order Date) field from the table, as we’re not going to need this for the display, and it’ll affect the next steps if it’s left.

To get the cumulative sales, we need a Running Total Quick Table Calculation. Click on the Sales pill on the Text shelf and select Quick Table Calculation -> Running Total. The click again and Compute By -> QUARTERS SINCE BIRTH. Add Sales back into the table, so you can see the quarterly Sales value and how it’s cumulating until it reaches the next cohort.

We’ve now got the building blocks we need for the CLTV value we need to plot

Avg Lifetime Value

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

Note – I purposefully haven’t called this field what you might expect, as I’m going to ‘fill in the gaps’ that Ann describes in the requirements, and I’ll use that name then.

Pop this field into the table above, again setting the table calculation to compute by QUARTERS SINCE BIRTH

You can now use the data table above to validate the calculation is what you expected.

Now let’s build the viz out.

On a new sheet

  • QUARTERS SINCE BIRTH to Columns
  • ACQUISITION QUARTER (exact date, discrete blue pill) to Rows
  • Avg Lifetime Value to Text, setting the table calculation to Compute By QUARTERS SINCE BIRTH

From this basic text table, you can see the ‘blank’ fields, Ann mentioned. In the data table view, it’s not so obvious. The blank is there because there are no sales in those quarters for those cohorts. To fix we need another table calculation

CUSTOMER LIFETIME VALUE (CLTV)

IF ISNULL([Avg Lifetime Value]) AND NOT ISNULL(LOOKUP([Avg Lifetime Value],-1)) AND NOT ISNULL(LOOKUP([Avg Lifetime Value],1))

THEN LOOKUP([Avg Lifetime Value],-1) ELSE [Avg Lifetime Value] END

This says, if the Avg Lifetime Value field is NULL but neither the previous or the subsequent values are NULL, then use the Avg Lifetime Value value from the previous column (LOOKUP).

Replace the Avg Lifetime Value with the CUSTOMER LIFETIME VALUE (CLTV) field (setting the Compute By again), and the empty spaces have disappeared.

If you hover over the cells in the lower right hand side of the view, you’ll see tooltips showing, indicating that a mark has been drawn on the viz with Null data. To fix this, add CUSTOMER LIFETIME VALUE (CLTV) to the Filter shelf and specify non-null values only to show via the Special tab.

Now if you hover over that area you don’t get any tooltips displaying, as there aren’t any marks there.

Now it’s just a case of formatting the viz a bit more

  • Add CUSTOMERS to Rows
  • Add CUSTOMER LIFETIME VALUE (CLTV) to the Colour shelf by holding down the Ctrl key then clicking on the field that’s already on the Text shelf, and dragging via the mouse onto the Colour shelf. Using Ctrl in this way has the effect of copying the field including the table calculation settings, so you don’t need to apply them again. This will change the colour of the Text.
  • Then change the mark type to Square, which will then fill out the background based on the colour.
  • Then edit the colour legend to the relevant palette (which you may need to install via Ann’s link).
  • Set the border of the mark via the Colour shelf to white
  • Remove the row & column dividers
  • Set the row Axis Ruler to a dark black/grey line
  • Format the 1st 2 columns so the font is the same and centred. Widen the columns if required.
  • Update the tooltip

And then you should be ready to add the viz to your dashboard. My published version is here.

This blog is a bit more detailed that my recent posts, but I’m also conscious I’ve skipped over some bits that if you’re brand new to Tableau, you may not be sure how to do. Feel free to add comments if you need help!

Happy vizzin’! Stay Safe!

Donna

Can you find the variance along a line?

Donna ColesLeave a comment

So after a couple of weeks off blogging due to Christmas, I’m back providing solutions for #WOW2021! New contributor Candra Mcrae started off the year with this gentle workout focussing on table calculations. For those who have followed my previous blogged solutions, you’ll probably recall that table calcs don’t phase me as much as they do some – I started using Tableau before LODs existed, so they were the only tool at my disposal in the past. That said, I’m still not such an expert that I get it right first time – there’s often plenty of trial and error as I choose which fields I want to compute by, although I’ve got much better at this since I read Andy Kriebel’s Table Calculations Overview a couple of years ago.

As with any challenge that involves table calcs, I tend to start by building out a tabular view of all the data I’m going to need to build the viz. This ensures I can validate the data much easier and set the table calculation settings to what I need.

So let’s start….

For the line chart itself, we’re simply going to be plotting Year against Food insecurity which has been formatted to 1 decimal place and displays the % as a suffix

We will also need to capture 2 other values which will represent the coloured circles. Parameters are needed to help identify what these circles will be.

pSelected Year is an integer parameter that can be built by right clicking on Year and selecting Create -> Parameter. This will populate the parameter with all the values of the Year field. Default the value to 2018 and adjust the formatting of the displayed value so that it is 0 decimal places and include thousand separators is unchecked.

With this parameter, we can capture the value associated which is represented by the pink dot in the viz

Selected Year %

IF [Year] = [pSelected Year] THEN [Food insecurity (includes low and very low food security) Percent of households] END

It’s a bit more work to identify the black dot, as this will vary based on another parameter

pComparison

I created this an integer parameter storing the values 0, 1 & 2 (defaulted to 2), but aliased for display as First Year, Most Recent Year, Previous Year. I’ll be using this is an case statement/if clause later and comparing integers to strings is much more efficient in Tableau.

I like to take things step by step, especially when there’s table calcs involved, to ensure all the values I’m referencing are correct, so rather than identifying the selected value in a single calculated field, I’m using multiple.

Firstly I want to identify the 1st year in the dataset (without hardcoding).

First Year

WINDOW_MIN(MIN([Year]))

This will store the value of the earliest year (1995) against every row in the data that is outputted in the view (a bit like you would get with {FIXED : MIN([Year])}, but this is a no LOD challenge).

From this I can work out

First Year %

IF MIN([Year]) = [First Year] THEN MIN([Food insecurity (includes low and very low food security) Percent of households]) END

Notice the MIN() functions used in this statement, as opposed to the Selected Year % above. This is because First Year is a table calc which is an aggregation, and subsequently other fields referenced in the calculation also need to be aggregated. In this case other aggregations such as AVG, MAX, ATTR would also suffice.

Similarly, I’m going to derive the Latest Year % with

Latest Year

WINDOW_MAX(MAX([Year]))

Latest Year %

IF MIN([Year]) = [Latest Year] THEN MIN([Food insecurity (includes low and very low food security) Percent of households]) END

Finally, I’m also going to work out the value for the previous year

Previous Year

[pSelected Year]-1

Previous Year %

IF [Year] = [Previous Year] THEN [Food insecurity (includes low and very low food security) Percent of households] END

With these 3 % fields, I can now create a fourth field which stores the value of the % I want to compare with

Selected Comparison %

CASE [pComparison]
WHEN 0 THEN [First Year %]
WHEN 1 THEN [Latest Year %]
WHEN 2 THEN SUM([Previous Year %])
END

This ultimately just stores the 2nd value required for the black dot. This could all have been written within this single calculation, but I find it easier to troubleshoot if things are broken down a bit.

Putting these onto a table we can see how the values in each row change, as the parameters are changed.

Note, for all the table calculation fields (all denoted by the triangle symbol on the pill), I have explicitly set them to Compute Using the specific dimension of Year rather than the default of table down. While this will give the same result, I prefer to be explicit to ensure the values don’t change if pills get subsequently moved around the canvas (in the case of the Selected Comparison % field, all Nested Calculations within the Edit Table Calculation dialog box need to be set).

This is enough information to build the main viz itself by

  • adding Year to Columns (green continuous pill)
  • adding Food insecurity to Rows
  • adding Selected Year % to Rows next to Food insecurity
  • then drag Selected Comparison % to the Selected Year % axis which will automatically change the display to have Measure Values on Rows instead. Set the table calculation setting to compute by Year

This chart can then be set to be dual-axis and the axes synchronised. The Food insecurities should remain as as line mark, and the Measure Values should be a circle. The colours, formatting and tooltip then need to be applied.

Now we need to go back to our table of data to build out other calculations. The requirement is a single viz, so we need to provide the % value of the selected year within the title of the chart, along with the difference from the comparison value. For this to work, we need to store the relevant values against every row in the data set.

We already have the Selected Year % value identified, but this is only captured against the row of the selected year in the data output. To get it to display against every row we need

Window Max – Selected Year %

WINDOW_MAX(MIN([Selected Year %]))

This is formatted to 0 dp with the % sign as suffix, as this is the field that will be displayed in the title. Added to our data table, with the table calc set to compute by Year, you can see the value replicated across every row

Similarly, we already have the Selected Comparison % captured, but in order to work out the difference, we also need to get this value against every row too

Window Max – Selected Comparison %

CASE [pComparison]
WHEN 0 THEN WINDOW_MAX([First Year %])
WHEN 1 THEN WINDOW_MAX([Latest Year %])
WHEN 2 THEN WINDOW_MAX(MAX([Previous Year %]))
END

Adding this in to the table (and remembering to set the table calc settings), you can also see the relevant value perpetuated against every row. Change the pComparison value and you’ll see the values change accordingly, but still the same on every row.

So for the difference…. at this point I chose to deviate from the solution published. We’re already dealing with a measure that is quantified as a % (as opposed to a measure that is quantified as £ say). So to get the % difference between two percentage measures, I simply chose to show the difference between the two values (ie selected – comparison). Some would refer to this a the point difference between the values. This makes most sense to me for this particular scenario. The alternative is to calculate the % difference in the more traditional way of (selected – comparison) / comparison as you may do if you were presenting the % difference between the value of sales in different years. But I personally found the result could be confusing to the reader .

Difference

[Win Max – Selected Year %]-[Win Max – Selected Comparison %]

BUT, the value displayed within the title is the absolute value (ie no -ve sign) and actually doesn’t display if the value is 0 (which you get if you select the year to be 2019 and comparison to most recent year). So I resolved this with

DISPLAY : Difference

IF [Difference] <> 0 THEN ABS([Difference]) END

which is formatted to 1 dp with % suffix

Finally, we have a symbol that is used to indicate if the change is +ve, -ve or remains the same.

Difference Indicator

IF [Difference] > 0 THEN ‘▲’
ELSEIF [Difference] < 0 THEN ‘▼’
ELSE ‘N/C’
END

Add this into the table too, once again remembering to set the table calc properties for all nested calculations.

So now you have these fields, you can add Window Max – Selected Year % and DISPLAY : Difference and Difference Indicator to the Detail shelf of the All marks card on the chart viz, and these will then be available in title dialog to add. Once again, ensure you set the table calcs to compute by Year.

You’ll also need to add some spacing in the title, to allow the parameter controls to be ‘floated’ into place on the dashboard. Getting the position right is really tricky. I positioned it very carefully on Desktop, but when I published, the controls were in completely different places. The easiest way I found to resolve this, was to use the Edit feature in Tableau public to edit the dashboard online and move the objects that way.

My published viz is here.

Happy vizzin’! Stay Safe!

Donna

Can you build a divergent histogram?

Donna ColesLeave a comment

It was Ann Jackson’s last #WOW2020 challenge of the year, to build this divergent histogram depicting the average life expectancy by gender, with each bar indicating the number of countries with that life expectancy.

Ann hinted that reshaping the data may help, so I chose to do this by pivoting the data. To do this, in the data pane, I selected both the Life Expectancy Male and Life Expectancy Female columns, then right-clicked and selected Pivot.

This results in the number of rows in the data set being doubled, with a field called Pivot Field Names containing the values Life Expectancy Male and Life Expectancy Female, and then a field called Pivot Field Values which contains the life expectancy value. This field I renamed to Life Expectancy Age and moved it to the top section of the fields pane (above the line), so it is treated like a dimension rather than a measure.

We need to count the countries, so I created

Country Count

COUNTD([Country/Region])

Let’s see what this all looks like

Filtering by Year (2012), adding Life Expectancy Age to Rows, Pivot Field Names to Columns and Country Count to Text we get

Filtering out the Null and we have the basic data we want to plot, which we can do by

  • Year on Filter set to 2012
  • Life Expectancy Age on Filter set to exclude Null
  • Pivot Field Names on Columns
  • Life Expectancy Age on Columns (continuous)
  • Country Count on Rows
  • Mark Type set to Bar
  • Size set to Fixed

If we move Pivot Field Names from Columns to Colour, we get an overlapping bar.

But we want the males to be going down the chart, so let’s change the Country Count field to be

Country Count

IF CONTAINS(ATTR([Pivot Field Names]),’Female’) THEN COUNTD([Country/Region]) ELSE COUNTD([Country/Region])*-1 END

which is basically inverting the values for the male, and is pretty much the crux of the challenge 🙂

We need to label the max bar for each gender, or in this case the max & min. So showing mark labels and setting to just label the max and min values, we have a couple of issues….

… the labels are positioned in the centre, and males is labelled -13, rather than 13. To fix the positioning, we need to turn stacked marks off via the Analysis menu (took a bit of while to figure this one out).

To resolve the negative labelling, I formatted the County Count field to custom format to #,##0;#,##0

The final requirement is to ensure the axis range is identical. To do this I created reference lines based on the maximum value

Max Count Ref Line

WINDOW_MAX([Country Count])

which I then copied

Max Count Ref Line (Copy)

WINDOW_MAX([Country Count]) *-1

Adding both of these to the Detail shelf, and setting the table calculation to compute by the fields below

And apart from some formatting and the tooltips, that should be complete. My published viz is here. When I looked at Ann’s version, she tackled it differently, which is the beauty of these challenges – there’s often more than one solution.

Happy vizzin’! Stay Safe!

Donna

Can you highlight profits with Measure Names?

Donna ColesLeave a comment

Luke Stanke provided his last challenge of #WOW2020 for the year with this viz to utilise Measure Names & Measure Values. This plots Cost against Sales per Sub-Category (the circles), which are then bounded by a lozenge. The Sales circle and the lozenge are coloured based on profitability, and the lozenges are labelled by Profit, although the position of the label is also based on profitability.

Obviously as the title of the challenge suggests, using Measure Names & Measure Values is key to this. As usual I’ll try to just pull out the main points I think you might find most tricky.

  • Building the core chart
  • Aligning the Sub-Categories against a horizontal line
  • Colouring the circles & lozenge
  • Positioning the labels
  • Formatting the labels
  • Creating a border around the white Cost circle

Building the core chart

You’ll need to create a calculated field to store the cost

Cost

SUM([Sales]) – SUM([Profit])

Once done, then add Sub-Category to Rows and sort by descending by Profit. Add Measure Values to Columns and filter just to Sales and Cost. Change the mark type to circle.

Add a 2nd instance of Measure Values to Columns and change the mark type to Line. Add Measure Names to Path.

Then make this chart dual axis and synchronise the axis. This will give the base chart.

Aligning the Sub-Categories against a horizontal line

Whenever you add a blue discrete pill to the rows or columns, you’ll get a ‘header’ pane, which means the label for the pill is by default positioned in the centre of a row, as demonstrated below if I add row dividers to the chart

The requirement is to show the row label aligned with a horizontal line. To achieve this, we need to add a MIN(0) field the Rows. Adding this green pill, creates an axis which has a 0 horizontal 0 line, which is positioned exactly in alignment with the row label

Removing the row dividers, and uncheck Show Header on the MIN(0) pill, and the right aligning the row label, we get the desired result.

Colouring the circles & lozenge

The circles are either coloured white for Cost or dark red or green for Sales depending on the profitability of the Sub-Category.

So we need a new calculated field

Is Profitable?

SUM([Profit])>0

This returns true or false.

On the circles Measure Names mark card, add Is Profitable to the Colour shelf. If it’s not already there, then add Measure Names to the Detail shelf, then select the detail icon and choose the colour icon to also add Measure Names to the Colour shelf

This has the effect of giving you 4 colour combinations

which can then be edited to set the 2 combinations relating to Cost to white, Sales & True to dark green and Sales & False to dark red (at this point, you probably want to be setting the background colour of the whole worksheet to light grey (right click on sheet, format, select the Shading icon)

For the lozenge, you just need to add the Is Profitable field the the Colour shelf of the line Measure Names card, and adjust the size of the to make it slightly bigger than the circles (you may need to move marks to back to get the lozenge to sit behind the circles).

Positioning the labels

So the tricky thing about the labels is where they are positioned – they are to the right for the profitable sub-categories, and to the left for the non-profitable sub-categories. When you add a label, you can’t provide that sort of conditional logic to control the positioning. However, we happen to have 2 sets of marks cards (as dual axis), and so can use different fields to apply the labelling.

Label Profit is +ve

IF [Is Profitable] THEN SUM(Profit) END

This will only return a value for profitable sub-categories, otherwise the field will store blank.

Label Profit is -ve

IF NOT([Is Profitable]) THEN SUM(Profit) END

Conversely, this will only return a value for the non-profitable sub-categories.

Add the Label Profit is +ve field to the Label shelf of the Circle marks card. Adjust the positioning to be middle right, and adjust the settings as below (tbh this was just a bit of trial and error to find this worked :-)). You may want to additionally add a few <spaces> to the start of the label text to add a bit of breathing room.

Then on the Line marks card, add the Label Profit is -ve field to the Label shelf, but this time choose to Label Line Ends, and select Label end of line. Note – you can try to apply the same settings as above, but I found for some reason it increased the size of the mark around the line end… weird…

Formatting the labels

Right click on the Label Profit is +ve/-ve fields created and custom format with

+”$”#,##0;-“$”#,##0

Creating a border around the white Cost circle

Finally, a sneaky requirement was to add a border around the white circle that represented Cost only. This implied that using the formatting options on the Colour shelf that allows a border to be added to the mark wasn’t going to work, as this will apply to all marks and not just to the Cost circle.

I handled this by creating a 2nd instance of the Cost field (right click Cost and select duplicate).

I then added this Cost (copy) field to the Measure Values section of the chart on the left hand side. This doesn’t appear to do much, since the mark is directly on top of an existing mark, but you will see the Colour legend associated to the Circle marks card change and add 2 new entries.

I changed the colours of the 2 new entries in the Colour legend to a dark grey, then added Measure Names to the Size shelf of the Circles marks card. I then adjusted the range and size of the circles so that the Cost measure is slightly smaller than the Cost (copy) measure. Ultimately all 3 circles are slightly different sizes, but the range is so narrow, it’s hard to tell.

And that’s it. A lovely looking viz with a fair bit going on under the bonnet, but nothing too complex! My published version is here.

Happy vizzin’! Stay Safe!

Donna

Can you toggle between AND|OR filtering logic?

Donna ColesLeave a comment

Week 49 of #WOW2020 was a guest challenge by Sam Epley to demonstrate the ability to toggle between filtering using AND logic and filtering using OR logic. Sam stated that it had been something he and colleagues had been pondering for a while but v2020.2 provided a possible solution.

So the first thing I did was a quick check on what functionality had been released in v2020.2 which included set controls. I also noticed when examining the published solution, that if I hovered over the Select Values filter controls, a small set icon would appear, which indicated these filters were indeed based on set controls

This blog will focus on

  • Select a Field for Slicer x parameter
  • Select Values for Slicer x filter
  • Building the Logic

Select a Field for Slicer x parameter

I had a slight ‘moment’ with this, until I realised I just had to copy the ╚ character and paste it into the parameter list I created

You’ll need to create 3 instances of this parameter (or create once, duplicate and rename).

Select Values for Slicer x filter

Note – what I describe here will need to be duplicated for each slicer.

There’s a chance I may well have been a bit long-winded in how I went about this, but it worked for me…

First up, I extracted the value selected in the Select a Field parameter, only returning a value if it contained the special ╚ character. So this would return the word ‘Region’ or ‘Segment’ but would return NULL if ‘Location’ or ‘Customer’ had been selected.

Selected Slicer 1

IF CONTAINS([Select a Field for Slicer 1:],’╚ ‘) THEN REPLACE([Select a Field for Slicer 1:],’╚ ‘,”)
ELSE NULL
END

I then needed to ‘map’ this value to the actual field in the data source, so I could get a handle on the actual values associated to the field

Selected Slicer 1 Values

IF [Selected Slicer 1] = ‘Region’ THEN [Region]
ELSEIF [Selected Slicer 1] = ‘State’ THEN [State]
ELSEIF [Selected Slicer 1] = ‘Category’ THEN [Category]
ELSEIF [Selected Slicer 1] = ‘Sub-Category’ THEN [Sub-Category]
ELSEIF [Selected Slicer 1] = ‘Segment’ THEN [Segment]
ELSEIF [Selected Slicer 1] = ‘Ship Mode’ THEN [Ship Mode]
ELSE ‘No Selection’
END

From this I could create a set to store the values, by right-clicking on the Selected Slicer 1 Values field and choosing Create -> Set, and selecting the Use all option.

The values in the set can then be accessible on the sheet foe selection by right-clicking on the set field and choosing Show Set

Building the Logic

To help with this, you can build out a basic view by Region that returns In or Out for each of the sets

As you can see when everything is set to ‘None Selected’, everything is In.

As we change the filter options, you can see the values change to be In and Out

For the AND logic to work, we’re looking for the rows of data where every In/Out column is In.

For the OR logic to work, we’re looking for the rows of data where In exists in at least one column.

However, when there is a No Selection option selected, all values are In the set, and when we’re using the OR logic, we don’t want these.

So we need to identify when No Selection is selected in the filter

Slicer 1 is Not Selected?

[Selected Slicer 1 Values] = ‘No Selection’

and we need this for the other slicers too.

Then we need to build a new calculated field that is going to handle all this logic, which is driven by a pLogic parameter which simply contains the values of AND and OR.

In Combined Set

IF [pLogic]=’AND’ THEN [Select Values for Slicer 1:] AND [Select Values for Slicer 2:] AND [Select Values for Slicer 3:]
ELSE //It’s OR
IF [Slicer 2 Is Not Selected?] AND [Slicer 3 Is Not Selected?] THEN [Select Values for Slicer 1:]
ELSEIF [Slicer 1 Is Not Selected?] AND [Slicer 3 Is Not Selected?] THEN [Select Values for Slicer 2:]
ELSEIF [Slicer 1 Is Not Selected?] AND [Slicer 2 Is Not Selected?] THEN [Select Values for Slicer 3:]
ELSEIF [Slicer 3 Is Not Selected?] THEN [Select Values for Slicer 1:] OR [Select Values for Slicer 2:]
ELSEIF [Slicer 2 Is Not Selected?] THEN [Select Values for Slicer 1:] OR [Select Values for Slicer 3:]
ELSEIF [Slicer 1 Is Not Selected?] THEN [Select Values for Slicer 2:] OR [Select Values for Slicer 3:]
ELSE [Select Values for Slicer 1:] OR [Select Values for Slicer 2:] OR [Select Values for Slicer 3:]
END
END

This field returns a true or false against each row in the data set, and can be used to build the bar chart display.

This covers the most ‘complex’ bit of this challenge – below shows what one of the charts looks like

If you’re not familiar with how to use measure swapping, which is another feature of this challenge, then check out a previous blog I wrote.

I also created a field to add to the Tooltip to show a $ symbol in the event the measure selected was Sum of Sales.

My published solution is here.

Happy vizzin’! Stay Safe!

Donna

Can you create a variable width bar chart?

Donna ColesLeave a comment

For #WOW2020 Week 48, Jami Delagrange set the challenge based on a problem she’d been puzzling over for a while.

The data set was one Jami created herself about turkeys creating a ‘conference’ (it is Thanksgiving week in the US).

The above is basically 2 bar charts plotting the time an event happened (on the x-axis) against the number of turkeys attending the event (on the y-axis). One bar chart is coloured based on the location, the other is a white bar which has a width based on the duration of the event.

There are a couple of calcs needed for the basic data

# Turkeys

COUNTD([User Name (Original Name)])

Duration (mins)

DATEDIFF(‘minute’,[Start time],[End time])

With these we can get a basic set of data

The Start Time and End Time fields can be custom formatted to hh:mm AMPM.

The first bar chart we need is very simple, plotting Start Time against # Turkeys, coloured by Location.

For the second chart, we need to make use of the Size shelf, and the feature that allows you to fix the size based on a field. If we add Duration (mins), we don’t get what we need

The bars are far too wide. As the Start Time being plotted is at the ‘minute’ level, each unit of the axis is 1 minute of a day. There are 24 * 60 = 1440 minutes in a day. So the size needs to be based as a proportion of the number of minutes in a day ie

Duration Proportion of Day

[Duration (mins)] / (24 * 60)

Adding into our data table, you can see what these values translate to

and if we drop this onto the Size shelf instead, we get the required display

Build these charts as a dual axis, and you’ve got the output. Tableau’s own help article, discusses the Size feature a bit more.

My published viz is here.

Happy vizzin’! Stay Safe!

Donna

Can you create a clustered histogram?

Donna ColesLeave a comment

Ann Jackson returned with this week’s #WOW2020 challenge, to create a ‘clustered histogram’, whereby the orders in 2020 were placed in ‘bins’ based on the sale value.

I found this challenge quite straightforward this week, as its very similar to a previous challenge I’ve already blogged about in week 23, which created a side by side bar chart by month.

So what are the main points for this blog

  • Creating the bins
  • Getting the bars side by side
  • Tooltips

Creating the bins

I first created an LoD to store the total value of the sale for an Order

Order Value

{FIXED [Order ID]: SUM([Sales])}

then I need to ‘bin’ this, but as the sales over $2000 needs to be lumped together, we can’t use the traditional binning functionality. We also don’t want to have a massive case statement to assign the values. Instead we can do a bit of maths…. we want to essentially round each order value to the nearest 100.

Round Up to 100

(CEILING([Order Value]/100) * 100)

So if the Order Value is 39 for example, when divided by 100 this will be 0.39. The CEILING function always rounds up to the nearest whole number, so in this case will return 1, which is then multipled by 100 to give us 100.

Doing this, every order value is then assigned a ‘bin’ of 100, 200, 300 etc

I then created

Sales Bin

IF [Round Up to 100] >2100 THEN 2100 ELSE [Round Up to 100] END

to apply the grouping of all the values which were greater than 2100 (since 1999 would be rounded up to 2000 and 2001 would be rounded up to 2100).

Getting the bars side by side

We have the Sales Bin measure and will also need to plot the count of orders

# Orders

COUNTD([Order ID])

Plotting this out and splitting by Segment we get

but we don’t want the segments stacked, we want them side by side.

The Sales Bin axis is ‘continuous’, which means a value can be plotted at any number along the line, it just happens to be at the ‘100’ marks as that’s where are bins are.

So we use a sort of ‘jittering’ to plot each bar at a slightly different value depending on the segment

SALE AMOUNT

CASE [Segment]
WHEN ‘Consumer’ THEN [Sales Bin] – 75
WHEN ‘Corporate’ THEN [Sales Bin]- 50
ELSE [Sales Bin]- 25
END

So for all the orders in the ‘100’ bin (ie the order value was between 1 and 100), all the Consumer orders will actually get plotted at 25, Corporate at 50 and Home Office at 75.

All this is explained in much more detail in the Week 23 blog post referenced at the top of this post.

Tooltips

The tooltip has different text depending on where you hover.

We need the lower value for the bin range (eg $0-100$), so I created

Round Up to 100 minus 100

[Round Up to 100]-100

And I then created 4 different ‘tooltip’ calculations which I could place on the tooltip to give me the display I needed:

TOOLTIP Upper

IF [SALE AMOUNT]<2000 THEN [Round Up to 100] END

TOOLTIP Lower

IF [SALE AMOUNT]<2000 THEN [Round Up to 100 minus 100] END

TOOLTIP between

IF [SALE AMOUNT]< 2000 THEN ‘ between’ END

TOOLTIP Symbol

IF [SALE AMOUNT]< 2000 THEN ‘ – ‘ ELSE ‘$2000+’ END

The final thing needed is to create a reference band to colour the section at the end, and fix the axis to start after 0 and end at 2099, so you don’t get 0 and 2100 displayed on the axis.

So a relatively short write up today – think this is the quickest blog I’ve written.

My published viz is here.

Happy vizzin’! Stay Safe!

Donna