Visualise Our Survey Data

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

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

Donut Chart

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

# of Respondents


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

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

Total # of Respondents


and then

Track – % of Total

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

along with the ‘inverse’ of

Non Track – % of Total

1-[Track – % of Total]

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

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

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

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

Participation Bar Chart

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

Diverging Bar Chart

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

# of Respondents – Diverging +ve

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

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

Similarly I then have

# of Respondents – Diverging -ve

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

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

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

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

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

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

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

Vertical bar chart

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

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


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

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

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

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

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

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

The dashboard

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

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

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

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

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

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

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

Happy vizzin’! Stay Safe!


COVID-19 New Case Trends Cartogram

The challenge this time was set by Luke using the data being collated via Tableau’s Covid-19 data hub.

This viz is essentially equivalent to a small multiple display where the charts for a specific dimension (in this case State) get displayed across numerous rows and columns. The difference here, is the row and column for the State to be displayed in, is specifically defined, rather than just sequentially based on how the data is being sorted.

Luke very kindly provided the logic to determine the rows and columns.

Building out the data

Once again, I’m going to start by putting all my data into a table so I can check my calculations, especially since this challenge does involve table calculations (there’s a hint on the Latest Challenges page)

Data Source Filter

Although not explicitly mentioned in the requirements, the information we need to present is based on the dates from 1st March 2020 to 31st July 2020. I messaged Luke to check this, before I then realised it was stated in the title of the viz – doh!

To make things easier, I therefore added a data source filter to remove all the other dates, setting the Report Date to range from 01 March 2020 to 31 July 2020 (right click on the data source -> add data source filter

I then added the basic fields I needed to a table

  • Province State Name to Rows
  • Report Date discrete, exact date (blue pill) to Rows, custom formatted to mmmm, dd
  • People Positive New Cases to Text

I excluded the fields where Province State Name = Null

We need to calculate the 7 day rolling average per Province State Name which we can do by using the UI to create a Moving Average quick table calculation against the People Positive New Cases pill, and then editing to compute over the previous 6 records (+ the current record makes 7 days). But I want to be able to reference this field, so I’m going to ‘bake it’ into the data model by creating a specific calculated field

7 day moving Avg

WINDOW_AVG(SUM([People Positive New Cases Count]), -6, 0)

Add this into the table, and edit the table calculation to compute by Report Date only and set the Null if not enough values checkbox

Do a basic sense check that the averages are correct by summing up 7 sequential rows and working out the average by dividing by 7.

So it looks like we’ve got the core measure to be plotted, but we’re going to need some additional fields in the presentation.

Again it’s not explicitly stated in the requirements, but it is in the title, that the values plotted need to be normalised. This is to ensure the data for each state is visible; if a state with a relatively low number of cases is positioned in the same row as one with a very high number of cases, it will be hard to see the data for the state with the low cases, because while the axis can be set to be independent, this will only work against a row and not an individual instance of a chart.

To normalise, we need to understand the maximum 7 day rolling average for each state.

Max Avg Per State

WINDOW_MAX([7 day moving Avg])

Add this to the table, setting both the nested table calcs to compute by Report Date.

In normalising, what we’re essentially going to do is determine the 7 day moving Avg as a proportion of the Max Avg Per State, so every value to plot will be on a range of 0-1.

Normalised Value

[7 day moving Avg]/[Max Avg Per State]

Format this to 2 dp, and add to chart, remembering to check the table calcs continue to compute by Report Date only.

Above you can see the max value for Alabama occurred on 19th July, so the Normalised Value to plot is 1

In the chart displayed, each State is titled by the name of the State. In a small multiple grid of rows & columns, we can’t use a dimension field for this, as it won’t appear where we want it. Instead we’re going to achieve this using dual axis, and plotting a mark at the centre point. For this we need to determine the centre date

Centre Date


FLOOR(DATEDIFF(‘day’,{FIXED:MIN([Report Date])},{FIXED:MAX([Report Date])})/2)
{FIXED:MIN([Report Date])}

This looks a bit complex, so I’ll break it down. What we’re doing is finding the number of days between the minimum date in the data set and the maximum date in the data set. This is

DATEDIFF(‘day’,{FIXED:MIN([Report Date])},{FIXED:MAX([Report Date])})

We’re then halving this value ( /2) and rounding it down to the nearest whole number (FLOOR).

We then add this number of whole days to the minimum date in the data set (DATEADD), to get our central date – 16 May 2020.

Now we need a point where we can plot a mark against that date. It needs to be above the maximum value in the main chart (which is 1). After a bit of trial and error, I decided 1.75 worked

Plot State

IF [Report Date] = [Centre Date] THEN 1.75 END

Finally we need to create our Rows and Columns fields which provides the co-ordinates to plot each state. The calculations for these were just lifted straight out of the requirements – thanks Luke!

Building the Viz

Start by adding the Rows and Columns fields to their respective shelf. Set them to be discrete dimensions (blue pills). You should immediately see a ‘map’ type layout of the US States.

Exclude the Null Rows value.

Now add Report Date as a continuous exact date (green pill) to Columns and Normalised Value to Rows, remembering to set the table calc to compute by Report Date only for all nested calculations. Change the mark type to Area.

Add 7 day moving Avg to Label and set the label to display the max value only and adjust the font size – I ended up at 7pt. Then add Province State Name & People Positive New Cases Count to Tooltip. Format the tooltip to match.

Remove all column/row lines and grid lines, zero lines etc.

There is a requirement to ‘add a line underneath each of the area trends’.

For this I added a 0 constant reference line formatted to be a solid black line.

But you’ll notice that for the charts that sit directly side by side, the line seems to be continuous, but I want to break it up. I re-added the column divider line to be a thick white line to get the desired effect.

Right, now lets get the State label added.

Add Plot State to Rows before Normalised Value and change the aggregation from SUM to MIN.

Change the Mark Type to Text and move the Province State Name field from Tooltip to the Text shelf. Adjust the text label to remove any other fields that are displaying, and resize the font – again I used 7. Clear the Tooltip for the this mark, so nothing displays on hover.

Make the chart dual axis and synchronise axis. Remove the Measure Names pill from the Colour shelf on both marks cards which will have automatically been added.

And now all you need to do is remove all the headers (uncheck Show Header) against Rows, Columns, Report Date & Plot Value, then right click on the >8k nulls label at the bottom right and select Hide Indicator.

You’re all done – you just need to add to a dashboard now. My published version is here.

I really enjoyed this challenge – a nice mix of calculations & format complexity but not overly cumbersome, which meant this blog didn’t take so many hours to write this week 🙂

Happy vizzin’! Stay Safe!