Tag: Baseline Time Axis

Can you visualise when it gets busy?

Donna ColesLeave a comment

It was Yoshi’s first challenge as a #WOW coach this week, and it provided us with an opportunity to develop our data densification and date calculation skills. I will admit, this certainly was a challenge that made me have to think a lot and reference other content where I’d done similar things before.

Modelling the data

Yoshi provided us with an adapted data set of patient waiting times, based on the Healthcare dataset from the Real World Fake Data (#RWFD) site. This provides 1 row per patient with a wait start time and end time. The requirement was to understand how many patients were waiting in each 30 min time slot in a 24hr period, so if a patient waited over 30 mins, or the start & wait time spanned a 30 min time slot, then the data needed to be densified appropriately. Yoshi therefore provided an additional data set to use for this densification, and I have to be honest, it wasn’t quite what I was expecting.

So the first challenge was to understand how to relate the two sets of data together, and this took some testing before I got it right. Yoshi provided hints about using a DATEDIFF calculation to find the time difference between the wait start time (rounded to 30 mins) and the wait end time (rounded to 30 mins).

To work out the calculation required, I actually first connected to the Hospital ER data set and then spent time working out the various parts of the formula required. I wanted to work out what the waiting start time was ’rounded down’ to the previous 30 min time slot, and what the waiting end time was ’rounded up’ to the next 30 min time slot.

Date Start Round

//Rounding down the wait start time

IIF(DATEPART(‘minute’, [Date Start])<30 , DATETRUNC(‘hour’, [Date Start]) , DATEADD(‘minute’, 30, DATETRUNC(‘hour’, [Date Start])))

If the minute of Date Start is between 0 and 29, then return the date at the hour mark, otherwise (if the minute of Date Start is between 30-59) then find the date at the hour mark, and add on 30 minutes. So if Date Start is 23/04/2019 13:23:00 then return 23/04/2019 13:00:00, but if Date Start is 23/04/2019 13:47, then return 23/04/2019 13:30:00.

Date End Round

//Rounding up the wait end time

IIF(DATEPART(‘minute’, [Date End])<30 , DATEADD(‘minute’, 30, DATETRUNC(‘hour’, [Date End])) , DATEADD(‘hour’, 1,DATETRUNC(‘hour’, [Date End])))

If the minute of Date End is between 0 and 29, then find the date at the hour mark, and add on 30 minutes, otherwise (if the minute of Date End is between 30-59) then find the date at the hour mark, and add on 1 hour. So if Date End is 23/04/2019 13:23:00 then return 23/04/2019 13:30:00, but if Date End is 23/04/2019 13:47, then return 23/04/2019 14:00:00.

With this, I was then able to relate the Hospital ER data set to the dummy by 30 min data set by adding a relationship calculation to the Hospital ER data set of

DATEDIFF(‘minute’,
IIF(DATEPART(‘minute’, [Date Start])<30 , DATETRUNC(‘hour’, [Date Start]) , DATEADD(‘minute’, 30, DATETRUNC(‘hour’, [Date Start]))),
IIF( DATEPART(‘minute’, [Date End])<30 , DATEADD(‘minute’, 30, DATETRUNC(‘hour’, [Date End])) , DATEADD(‘hour’, 1,DATETRUNC(‘hour’, [Date End])))
)

(which returns the difference in minutes between the ’rounded down’ Date Start and the ’rounded up’ Date End – you can’t reference calculated fields in the relationship, so you have to recreate)

and set this to be >= to Range End from the dummy by 30 min data set

Let’s see what this has done to the data.

On a sheet add Patient ID. Date Start (as discrete exact date – blue pill), Date End (as discrete exact date – blue pill), Date Start Round (as discrete exact date – blue pill), Date End Round (as discrete exact date – blue pill) and Index (as discrete dimension – blue pill) to Rows and add Patient Waittime to Text.

Looking at the data for Patient Id 899-89-7946 : they started waiting at 06:18 and ended at 07:17. This meant they spanned three 30 min time slots: 06:00-06:30, 06:30-07:00 and 07:00-07:30, and consequently there are 3 rows displayed, and the ’rounded’ start & end dates go from 06:00 to 07:30.

Identifying the axis to plot the 24hr time period against

Having ‘densified’ the data, we now need to get a date against each row related to the 30 min time slot it represents. ie, for the example above we need to capture a date with the 06:00 time slot, the 06:30 time slot and the 07:00 time slot.

But, as the chart we want to display is depicted over a 24hr timeframe, we need to align all the dates to the exact same day, while retaining the time period associated to each record. This technique to shift the dates to the same date, is described in this Tableau Knowledge Base article.

Date Start Baseline

DATEADD(‘day’, DATEDIFF(‘day’, [Date Start Round], #1990-01-01#), [Date Start Round])

Add this into the table, you can see what this data all looks like – note, Iā€™m just choosing a arbitrary date of 01 Jan 1990, so my baseline dates are all on this date, but the time portions reflect that of the Date Start Round field.

With the day all aligned, we now want to get a time reflective of each 30 min timeslot. The logic took a bit of trial and error to get right, and there may well be a much better method than what I came up with. It became tricky as I had to handle what happens if the time is after 11pm (23:00) as I needed to make sure I didn’t end up returning dates on a different day (ie 2nd Jan 1990). I’ll describe my logic first.

If the Index is 0 then we just want the date time we’ve already adjusted – ie Date Start Baseline

If the Index is 1, then we need to shift the Date Start Baseline by 30 minutes. However, if Date Start Baseline is already 23:30, then we need to hardcode the date to be 01 Jan 1990 00:00:00, as ‘adding’ 30 mins will result in 02 Jan 1990 00:00:00.

If the Index is 2, then we need to shift the Date Start Baseline by 1 hour. However, if Date Start Baseline is already 23:30, then we need to hardcode the date to be 01 Jan 1990 00:30:00, as ‘adding’ 1 hour will result in 02 Jan 1990 00:30:00. Similarly, if Date Start Baseline is already 23:00, then we need to hardcode the date to 01 Jan 1990 00:00:00, otherwise we’ll end up with 02 Jan 1990 00:00:00.

As the relationship didn’t result in any instances of Index > 2, I stopped my logic there. This is all encapsulated within the calculation

Date to Plot

CASE [Index]
WHEN 0 THEN [Date Start Baseline ]
WHEN 1 THEN IIF(DATEPART(‘hour’, [Date Start Baseline ])=23 AND DATEPART(‘minute’, [Date Start Baseline ])=30, #1990-01-01 00:00:00#, DATEADD(‘minute’, 30, [Date Start Baseline ]))
WHEN 2 THEN IIF(DATEPART(‘hour’, [Date Start Baseline ])=23 AND DATEPART(‘minute’, [Date Start Baseline ])=30, #1990-01-01 00:30:00#, IIF(DATEPART(‘hour’, [Date Start Baseline ])=23 AND DATEPART(‘minute’, [Date Start Baseline ])=0, #1990-01-01 00:00:00#, DATEADD(‘hour’, 1, [Date Start Baseline ])))
END

Phew!

Add this to the table as a discrete exact date (blue pill), and you can see what’s happening.

For Patient Id 899-89-7946, we have the 3 timeslots we wanted: 06:00 (representing 06:00-06:30), 06:30 (representing 06:30 -07:00) and 07:00 (representing 07:00-07:30).

On a new sheet, add Date To Plot as a discrete exact date (blue pill) and then create

Count Patients

COUNT([Patient Id])

and add to Text and you have a list of the 48 30 min time slots that exist within a 24hr period, with the patient counts.

We need to be able to filter this based on a quarter, so create

Date Start Quarter

DATE(DATETRUNC(‘quarter’,[Date Start]))

and custom format this to yyyy-“Q”q

Add to the Filter shelf selecting individual dates and filter to 2019-Q2, and the results will adjust, and you should be able to reconcile against the solution.

Building the 30 minute time slot bar chart

On a new sheet, add Date Start Quarter to the Filter shelf and set to 2019-Q2. Then add Date to Plot as a continuous exact date (green pill) to Columns and Count Patients to Rows. Change the mark type to a bar and show mark labels.

Custom format Date To Plot to hh:nn.

We need to identify a selected bar in a different colour. We’ll use a parameter to capture the selected bar.

pSelectTimePeriod

date parameter defaulted to 01 Jan 1990 14:00 with a display format of hh:nn

Then create a calculated field

Is Selected Time Period

[Date to Plot] = [pSelectTimePeriod]

and add to the Colour shelf, adjusting colours to suit and setting the font on the labels to match mark colour.

A ‘bonus’ requirement is to make each bar ’30 mins’ wide. For this we need

Size – 30 Mins

//number of seconds in 30 mins divided by number of seconds in a day (24hrs)
(30 * 60) / 86400

Add this to the Size shelf, change it to be a dimension (so no longer aggregated to SUM), then click on the Size shelf, and set it to be fixed width, left aligned.

Via the Colour shelf, change the border of the mark to white.

Next, we need to get the tooltip to reflect the ‘current’ time slot, as well as the next time slot. For this I created

Next Time Period

IIF(LAST()=0, #1990-01-01 00:00:00#, LOOKUP(MIN([Date to Plot]),1))

This is a table calculation. If it’s the last record (ie 01 Jan 1990 23:30), then set the time slot to be 1st Jan 1990 00:00, otherwise return the next time slot (lookup the next (+1) record). Custom format this to hh:nn and add to the Tooltip shelf. Set the table calculation to compute by Date to Plot and Is Selected Time Period.

Update the Tooltip as required.

The final part of this bar chart is another ‘bonus’ requirement – to add 2 hr time interval ‘bandings’. For this I created another calculated field

2hr Time Period to Plot

IIF(DATEPART(‘hour’, MIN([Date to Plot]))%4 = 0 AND DATEPART(‘minute’, MIN([Date to Plot]))=0,WINDOW_MAX([Count Patients])*1.1,NULL)

If the hour part of the date is divisible by 4 (ie 0, 4, 8,12,16, 20), and we’re at the hour mark (minute of date is 0), then get the value of highest patient count displayed in the chart, and uplift it by 10%, otherwise return nothing.

Add this to Rows. On the 2nd marks card created, remove Is Selected Time Period from Colour and Next Time Period from Tooltip. Adjust the table calculation of the 2hr Time Period to Plot to compute by Date to Plot only. Remove labels from displaying.

The bars are currently showing all at the same height (as required) but at a width of 30 minutes. We want this to be 2 hrs instead, so create

Size – 2 Hrs

//number of seconds in 2hrs / number of seconds in a day (24 hrs)
(60*60*2)/86400

Add this to the Size shelf instead (fixing it to left again). Adjust the colour to a pale grey, and delete all the text from the Tooltip.

Hide the nulls indicator. Make the chart dual axis and synchronise the axis. Right click on the 2hr Time Period Plot axis (the right hand axis) and move marks to back.

Tidy up the chart by removing row & column dividers, hiding the right hand axis, removing the titles from the other axes. Update the title accordingly.

Building the Patient Detail bar chart

On a new sheet, add Is Selected Time Period to Filter and set to True. Then go back to the other bar chart, and set the Date Start Quarter Filter to Apply to Worksheets -> Selected Worksheets and select the new one being built.

Add Department Referral, Patient Id, Date Start (as discrete exact date – blue pill) and Date End (as discrete exact date – blue pill) to Rows and Patient Waittime to Columns. Manually drag the None Department Referral option to the top of the chart. Add Patient Waittime to Colour and adjust to use the grey colour palette. Remove column dividers.

The title of this chart needs to display the number of patients in the selection, as well as the timeframe of the 30 min period. We already have the start of this captured in the parameter pSelectTimePeriod. We can use parameters to capture the other values too.

pNumberPatients

integer parameter defaulted to 0

pNextTimePeriod

date parameter, defaulted to 01 Jan 1990 14:30:00, with a custom display format of hh:nn

Update the title of the Patient Detail bar chart to reference these parameters (don’t worry about the count not being right at this point).

Capturing the selections

Add the sheets onto a dashboard. Then create the following dashboard parameter actions.

Set Selected Start

On select of the 30 Min bar chart, set the pSelectTimePeriod parameter passing in the value from the Date To Plot field.

Set Selected End

On select of the 30 Min bar chart, set the pNextTimePeriod parameter passing in the value from the Next Time Period field.

Set Count of Patients

On select of the 30 Min bar chart, set the pNumberPatients parameter passing in the value from the Count Patients field, aggregated at the SUM level.

With these all set, you should find you can click on a bar in the 30 minute chart and filter the list of patients below, with the title reflecting the bar chosen.

The basic viz itself wasn’t overly tricky, once you get over the hurdle of the calculations needed for the relationship and identifying the relevant 30 min time periods.

My published viz is here. Enjoy!

Happy vizzin’!

Donna

How many patients were admitted every 15 minutes?

Donna ColesLeave a comment

This week, Luke set the challenge which is focussed on manipulating time. Medical admissions over many days is represented in a bar chart which spans a 24 hour period. All admissions needs to be ‘bucketed’ into 15 minute intervals over the 24 hours ie admissions between midnight and 12:14am is counted within the same 12:00am ‘bucket’.

The data for this challenge is embedded within a workbook which you need to download via the challenge page. I did as Luke instructed; downloaded the workbook, deleted all the existing sheets, then re-saved as my own file.

The first step that is required is to ‘baseline’ / normalise the admission dates so they all look to be on the same day.

There’s different ways to do this; on this occasion I used the 3rd method from this Tableau KB, although I simply hardcoded a date of 1 Jan 2023 rather than use TODAY(). It doesn’t matter what this particular date is, its just an arbitrary date.

Baseline Admission Date

DATEADD(‘day’, DATEDIFF(‘day’, [Admission Date], #2023-01-01#), [Admission Date])

Once we’ve got this, we then need to manipulate this date again to ‘group’ into the 15 min interval. This isn’t something I know ‘just to do’, but I know I’ve done it before. So a quick google was needed and I used this blog for the required calculation.

Baseline Admission Date 15 mins

DATETIME((INT(FLOAT([Baseline Admission Date])*96))/96)

Pop these fields out into a table to see how these calculated fields are working

The Baseline Admission Date 15 mins is what we’ll use for the x-axis. The next step is work out the value being plotted Stays per Day.

Now when the challenge was first placed, a couple of the requirements were missing, so there was a bit of head-scratching trying to figure out what numbers were being used to get the values presented.

The following fields need to be added to the Filter shelf:

  • Stay Type = Outpatient
  • Admission Date starting from 30 Aug 2017 00:00

When on the filters shelf, both these should then be added to Context, as the data needs to be filtered before the LOD calc we need to use gets calculated (defined below).

Count Days with 15 min interval

{FIXED [Baseline Admission Date 15 mins]: COUNTD(DATETRUNC(‘day’, [Admission Date]))}

This is counting the distinct days when there was admission within each 15 minute period ie if there were 2 admissions on the same day within the same 15 minute window, the day would only count as 1.

From this we can then compute

Stays per Day

SUM([Number of Records])/SUM([Count Days with 15 min interval])

Now we’ve got the data we need, so we can build the viz.

Add Baseline Admission Date 15 mins to Columns as a green continuous pill, and Stays per Day to Rows. Don’t forget to add Admissions Date and Stay Type to the Filter shelf as mentioned above.

The bars look ‘blocky’. You can manually adjust the size, but you might notice that the widths between isn’t exact – the whitespace looks larger between some bars than others. To resolve this, I created a field to control the size, which is based on the number of 15 minute intervals there are in a 24 hour period – 96.

Size

1/96

Add this to the Size shelf, change the aggregation to MIN, and adjust the size to be Fixed and aligned Centre.

Add another instance of Stays per Day to the Rows shelf. Then make it dual axis and synchronise axis. Change the mark type of the 2nd Stays per Day instance to Gantt and change the colour. Then change the colour of the 1st Stays per Day.

Show mark labels, and set the Label just to show the max value.

Right click on the time axis, and format, and custom format to h:nn am/pm

And essentially, that’s it. There’s formatting to do to remove the secondary axis, column & row banding etc and add tooltips, but the core of the viz is complete.

My published instance is here. Note the time formatting seems to be an issue on Tableau Public. Someone did comment that this was an issue with the MAKETIME function, but I didn’t actually use this function.

Happy vizzin’! Stay Safe!

Donna

When do extracts run during the day?

Donna Coles5 Comments

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

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

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

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

Understanding the data

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

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

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

Extract Status

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

Building the required calculated fields

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

Now

Datetime parameter defaulted to 05 Feb 2021 16:30

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

Waiting Time

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

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

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

Processing Time

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

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

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

Created At Baseline

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

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

Started At Baseline

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

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

Building the Gantt chart

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

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

This will automatically create a Gantt chart view

Next

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

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

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

Over Wait Time Threshold

[Waiting Time] > 0.007

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

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

Formatting the Tooltips

The tooltip for the Waiting Time bar displays

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

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

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

Formatting the axes

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

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

Adding the reference band

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

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

Filtering the dates displayed

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

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

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

pDaysToShow

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

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

Filter : Days to Show

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

Add this to the Filter shelf and set to True.

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

Building the Key legend

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

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

Create a new field

Key Label

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

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

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

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

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

The information can all now be added to a dashboard.

Using your own data

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

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

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

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

My published version is here.

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

Happy vizzin’! Stay safe!

Donna