Ok, so this chart may not be totally useless, but I’m going to keep it in this category because it’s something I would never build at work. I love a good bump chart, but there is one major limitation with traditional bump charts. They are great for displaying changes in rankings over time, but what about the data driving those rankings. How much has a value changed from one period to the next? How much separation is there between #1 and #2? Surely a bar chart would be better for that type of insight right? Well, in this tutorial, we’re going to walk through building a chart that combines all of the benefits of bump charts and bar charts, and with some fancy curves to boot.
I’m sure I’m not the first to build this type of chart, but the first time I used it was in my 2020 Iron Viz Submission to show Happiness Scores by continent over time.
Looking back, there are definitely some things I would change about this, and I’m going to address those in the chart we are about to build. The data we’re going to be using for today’s walkthrough is on Browser Usage Share over the last 13 Years (showing usage at every 3-year increment). You can find the sample data here, and the sample workbook here. This is what we’re going to build.
Building Your Data Source
Our data source (on the Data tab in the sample file) contains 5 fields. We have a Time Period (our Year field), a Dimension (the Browser), and a Value (the Share of Usage). There are also 2 calculated fields in this file. These could be calculated in Tableau using table calcs, but because of the complexity of some of the other calculations, we’re going to make it as easy as possible and calculate these in the data source. The [Period] field, is just a sequential number, starting at 1, and it is related to the Date field. The [Rank] field is the ranking of each Browser within that period.
On the next tab (Densification), we have our densification data. There is a [Type] field, which will allow us to apply different calculations, in the same view, for our bars and our lines. There is a [Points] field, which will be used to calculate the coordinates for all points for both the bars and the lines. And there is a [T] field, which will be used to draw the sigmoid curves connecting each of the bars.
If you are building this with a different set of data, just replace the Date, Browser, and Share fields in the Data Tab. The calculated fields should update automatically.
Now for joining this data. We are going to do two joins in the Physical layer in Tableau. We are going to do a self-join on the Data to bring in the rank for the next period, and we’re going to join to our densification data using a join calc.
To get started, connect to the Sample Data in Tableau and bring out the Data table. Then double-click to go into the physical layer. Now drag out the Data table again. First, join on [Browser]. Then create a join calculation on the left side of the join, [Period]+1 and join that to [Period]. Then set it as a Left Join. It should look something like this.
Now bring out your Densification table, and join that to the Data Table using a join calculation, with a value of 1 on both sides of the join. Like this.
Now go to a new worksheet and rename the following fields.
Period(Data1) change to Next Period
Rank(Data1) change to Next Rank
I would also recommend hiding all other fields from the Data1 table to avoid confusion later on.
Building the Bars
To build the bars in this chart, we are going to use polygons. So we’ll need to calculate the coordinates for all 4 corners for each of our bars. Let’s start with Y, since that one is a little easier.
The first thing we are going to do is create a parameter to set the thickness of our bars. Create a parameter called [Bar Width], set the Data Type to Float, and set the Current Value to .75.
We are going to use the [Rank] field as the base for these calculations. Then, we are going to add half of the [Bar Width] value to get the upper Y values, and subtract half of the [Bar Width] value to get the lower Y values. Let’s create a calculated field for each of those, called [Y_Top] and [Y_Bottom].
Y_Top = [Rank]+([Bar Width]/2)
Y_Bottom = [Rank]-([Bar Width]/2)
If you look back at our Densification table, we have 4 points for the Bars, one for each corner. The order of these doesn’t really matter (but it is important that you are consistent when calculating the X and Y coordinates). In this example, we are going to start with point 1 at the bottom left, point 2 at the top left, point 3 at the top right, and point 4 at the bottom right. Like this.
Now we’ll create a calculated field that will calculate the Y value for all 4 points. Call this [Bar_Y]
CASE [Points] WHEN 1 THEN [Y_Bottom] WHEN 2 THEN [Y_Top] WHEN 3 THEN [Y_Top] WHEN 4 THEN [Y_Bottom] END
Now we need to do something similar for our X values, but instead of calculating the top and bottom positions, we need to calculate the left and right positions. Here, we will use the [Period] field as the base (which will also be the left side value), and then add the length of the bar to get the right side values.
If you remember from earlier, our [Period] field is a sequential number, starting at 1, and in this example, ending at 6. So each period has a width of 1. But we don’t want our bars going all the way up to the next period, so we’ll use a parameter to add a little spacing. Create a parameter called [Period Spacing], set the Data Type to Float, and set the Current Value to .3.
With this parameter and it’s current value, our largest bar in the chart should have a length of .7 (or 1 minus .3). All of our others bars should be sized relative to that. But first, we need to find our max value, which we will do with a Level of Detailed Calculation called [Max Value].
Max Value = {MAX([Share])}
And now we’ll calculate the length of all of our bars by dividing their [Share] by that [Max Value], and then multiplying that by our maximum bar length of .7 (or 1 minus .3). Call this calculation [Bar Length]
Bar Length = ([Share]/[Max Value])*(1-[Period Spacing])
Now, similar to what we did for our Y values, we are going to create calculated fields for [X_Left] and [X_Right]. [X_Left] will just be the [Period] value, and [X_Right] is just the [Period Value] plus the length of the bar, or [Bar Length]
X_Left = [Period]
X_Right = [Period]+[Bar Length]
Let’s take another quick look at the order of points.
So Point 1 should be the left value, Point 2 should also be the left value, Point 3 should be the right value, and Point 4 should also be the right value. So let’s use this to calculate [Bar_X].
CASE [Points] WHEN 1 THEN [X_Left] WHEN 2 THEN [X_Left] WHEN 3 THEN [X_Right] WHEN 4 THEN [X_Right] END
Now let’s test out our calculations so far.
Drag [Type] to the filter shelf and filter on “Bar”
Right click on [Bar_X], drag it to Columns, and when prompted, choose [Bar_X] without aggregation
Right click on [Bar_Y], drag it to Rows, and when prompted, choose [Bar_Y] without aggregation
Right click on the Y axis, select Edit Axis, and then click on the “Reversed” checkbox
Change the Mark Type to “Polygon”
Right click on [Points], drag it to Path, and when prompted, choose [Points] without aggregation
Drag [Browser] onto Color
Right click on [Period], drag it to Detail, and when prompted, choose [Period] without aggregation
When finished, your view should look something like this.
Now onto our lines!
Building the Lines
Before starting this section, I would recommend taking a look at Part 3 of the Fun With Curves Blog Series on Sigmoid Curves.
The first calculation we need is [Sigmoid]. As I mention in the post above, this is a mathematical function that will appropriately space our Y values.
Sigmoid = 1/(1+EXP(1)^-[T])
Now, let’s calculate the Y coordinates, which in this case, we’ll call [Curve]. To calculate this, we’re going to use the [Rank] field as our base. Then we’re going to calculate the total vertical distance between the rankings for one period and the next and multiply that by our Sigmoid function to get the appropriate spacing. In the calculation below, the first part is optional, and is included only for labelling purposes. For the final period, there is no [Next Period], so this value would end up being blank, resulting in no label in our final step. So feel free to skip it and just use the portion between ELSE and END if you’re not going to label the bars.
Curve = IF [Period]={MAX([Period])} THEN [Rank] ELSE [Rank] + (([Next Rank] – [Rank]) * [Sigmoid]) END
The [Curve] calculation will give us our Y coordinates for all of points, so we just need X. Before we do that, let’s create one more Parameter that will be used to add a little spacing between the end of the bar and the start of our line (so the line doesn’t run through the label, making it difficult to read). Call this Parameter [Label Spacing], set the Data Type to Float, and set the Current Value to .15.
Now, we’re going to create a calculation for the start of our lines, which will be the end of the bar + spacing. I’ve also added a little additional logic so that the amount of spacing will depend on whether the label is 1 or 2 characters. If the [Share] value is less than 10 (1 character), we’ll multiply the [Label Spacing] by .7, and if it’s over 10, we’ll use the [Label Spacing] as is. Call this calculation [Line_Start].
Line Start = [X_Right]+IF [Share]<10 THEN [Label Spacing]*.75 ELSE [Label Spacing] END
Our next calculation will calculate the horizontal spacing for our points. In our Densification table, we have 25 Points for the lines, and this calculation will be used to evenly space those points between the end of one bar (plus the spacing for the label), and the start of the next bar. Call this calculation [Point Spacing].
Point Spacing = if [Period]={MAX([Period])} THEN 0 ELSE ([Next Period]-[Line_Start])/({COUNTD([Points])-1}) END
Similar to the [Curve] calculation, the first part of this IF statement is only for labelling purposes. If you’re not going to label your bars, you can just use the portion of the calculation between ELSE and END.
And now, with our [Line_Start] and our [Point_Spacing] fields, we can calculate the X coordinates for all 25 of points of each line. Call this [Line_X].
Drag [Type] to the filter shelf and filter on “Line”
Right click on [Line_X], drag it to Columns, and when prompted, choose [Line_X] without aggregation
Right click on [Curve], drag it to Rows, and when prompted, choose [Curve] without aggregation
Right click on the Y axis, select Edit Axis, and then click on the “Reversed” checkbox
Change the Mark Type to “Line”
Right click on [Points], drag it to Path, and when prompted, choose [Points] without aggregation
Drag [Browser] onto Color
Right click on [Period], drag it to Detail, and when prompted, choose [Period] without aggregation
When finished, it should look something like this.
Now all that is left to do is to combine our “Bar” calculations and our “Line” calculations in the same view.
Building the Final Chart
Because of the way we set up our Densification table, we have separate points for each of our bars and each of our lines. With just a few more simple calculations, we can bring everything together in the same view.
We want to leverage two different Mark Types in this view, Bar and Line, so we’ll need to create a Dual Axis chart. In order to do this, we’ll need a shared Field on either the Column or Row shelf, and then we’ll need separate fields for our Lines and Bars on the other Shelf. In this example, we’ll create a shared field for Rows, called [Final_Y].
Final_Y = IF [Type]=”Bar” THEN [Bar_Y] ELSE [Curve] END
This calculation is pretty straight forward. For our “Bar” records (where the [Type] field in our densification table = Bar), use the Y coordinates for our Bars, [Bar_Y]. Otherwise, use the Y coordinates for our Lines, [Curve]. We’re going to do something similar for X, but we need separate calculated fields for the Bars and Lines to create our Dual Axis. We’ll call these [Final_X_Bar] and [Final_X_Line].
Final_X_Bar = IF [Type]=”Bar” THEN [Bar_X] END
Final_X_Line = IF [Type]=”Line” THEN [Line_X] END
And now we’ll use those 3 calculated fields to build our final view. Let’s start with the Bars.
Right click on [Final_X_Bar], drag it to Columns, and when prompted, choose [Final_X_Bar] without aggregation
Right click on [Final_Y], drag it to Rows, and when prompted, choose [Final_Y] without aggregation
Right click on the Y axis, select Edit Axis, and then click on the “Reversed” checkbox
Change the Mark Type to “Polygon”
Right click on [Points], drag it to Path, and when prompted, choose [Points] without aggregation
Drag [Browser] onto Color
Right click on [Period], drag it to Detail, and when prompted, choose [Period] without aggregation
Drag [Type] to Detail
And now we’ll add our lines.
Right click on [Final_X_Line], drag it on to Columns to the right of [Final_X_Bar], and when prompted, choose [Final_X_Line] without aggregation
Right click on [Final_X_Line] on the Column Shelf and select “Dual Axis”
Right click on either X axis and choose “Synchronize Axis”
On the Marks Card, select the [Final_X_Line] Card
Change the Mark Type to “Line”
Remove “Measure Names” from Color
On the Marks Card, select the [Final_X_Bar] Card and remove “Measure Names” from Color
Right click on the Null indicator in the bottom right corner and select “Hide Indicator”
When finished, the chart should look something like this.
Now for the final touches
Formatting the Chart
All of these steps are optional and you can format the chart however you’d like. But these are some of the adjustments that I made in my version.
First, I added labels. You can’t label Polygons, so our label has to go on the [Final_X_Line] Card. So click on that Card and then drag the [Share] field out onto Label. Then click on Label and under “Marks to Label”, choose “Line Ends”, and under “Options”, choose “Label start of line”. Then, because the values in my [Share] field are whole numbers instead of percentages (ex. 20% is listed as 20 instead of .2), I added a “%” to the end of the label (by clicking on the Text option) and formatted the field as a Whole Number. The Label options should look like this
Those Label options, along with the [Label Spacing] parameter we created earlier, should give you a pretty nice looking label between the end of your Bar and the start of the Line.
Next, I cleaned up and formatted the Axis. For the X axis, I set the Range from .9 to 7 and then hid both of the X axes. For the Y axis, I set the range from .5 to 9.5, and set the Major Tick Interval to 1. I then removed the Axis title and made the text a bit larger. I also used a Custom Number Format, so I could put a “#” sign before the Ranking number. Like this.
Then I added some Grid Lines on Columns to give the Bars a uniform starting point, and some really faint Grid Lines on Rows to make it easier to track the position for any given Bar.
Finally, on the dashboard, I added a Title, I added a second simple worksheet to display the Years, and added a Color Legend that can also be used to Highlight any of the Browsers. And here’s the final version.
As always, thank you so much for reading, and if you happen to use this tutorial or the template, please reach out and share what you built with us. We would love to see it! Until next time.
Chord charts are a great way to display and quantify relationships between entities, but there are some limitations. Recently, I built a series of chord charts to show which actors have appeared together in films, and how often. Once I had built out the charts and attempted to add some color, I hit a wall. In some cases, when building these relationship diagrams, there is a logical directional flow to the relationship. Something moves from Entity 1 (Source) to Entity 2 (Target). In those cases, its easy to assign meaningful color to the chart. You can assign it by Source if you want to highlight where things are flowing from, or you can assign it by Target if you want to highlight where things are flowing to. But what if there is no flow to the relationship? How do you assign color then?
This roadblock got me thinking. If I want to add a unique color to each Entity in my chart, and there is no directional flow, then the color of the chord should be a blend of the two colors. Or better yet, it should transition from one color to the other. I have seen others do some really cool stuff with applying gradients to “solid” chart types. Ken Flerlage has an excellent post here about applying gradients to Bar Charts and Area Charts. There’s another great post from Ivett Kovacs about gradients here. Some different techniques, but the foundation is the same. If you want to apply a gradient to an otherwise “solid” chart, use a series of thin lines to “color in” the chart. So that’s exactly what I did.
I’ll warn you ahead of time, this is going to be a long post. But if you’re just interested in building one of these charts, and not so much in how to build it, I’ve got some templates for you to use. Just follow the instructions in the “Setting Up Your Data Source” section below, and swap out the data source in the template workbook. You can download the template here, and download the sample data here.
Setting Up Your Data Source
In the sample data (link above), there are two tabs; one for our data, and one for densification. You do not need to make any changes to the Densification table, but I’ll talk through the setup as we build each section of this chart. In the Base Data, you should only update the first four columns and not the calculated fields (red text) in the next five columns. Here is a quick explanation of each field and what it represents
From_ID: Unique Identifier for each Source entity. This should be a sequential number starting at 1 and ending at however many entities you have in your data. For each entity, you may have multiple rows, one for each relationship, but they should all have the same From_ID.
From_Name: A display name for each Source entity.
To_Name: A display name for the other entity in the relationship, or the “Target”.
Value: The measure being displayed. In the sample data, this value field represents the number of movies that the two actors appeared in together.
The following fields should not be changed. They will update automatically based on the first 4 columns, but here is a quick description of what they are calculating.
To_ID: This is a lookup to get the correct ID for the Target entity
From_Total: This is a sum of the Value field for each Source entity
From_Run_Total: This is a running total of the Value field
Unique_Relationship: This is a unique identifier for the relationship
Unique_Relationship_Order: This is used to identify the first and second occurrence of each Unique_Relationship (there will be two rows for each relationship, one where Actor 1 is the Source and Actor 2 is the Target, and one where Actor 2 is the Source and Actor 1 is the Target).
If you are building these charts from scratch, I would still recommend downloading the template to see how the Data Source is set up. In order to get the running total value for the Target, or the end of each chord, we are doing a self-join in the Physical Layer on the Base Data table (see below for join conditions). Then, we are re-naming the [From_Run_Total] field from the right side of that join (Base Data1) to [To_Run_Total]. And then we are joining to the Densification table using a join calculation (value of 1 on each side). Here is what the joins look like in our data source.
Building the Chart
This chart is actually comprised of 4 different sections, identified by the ShapeType field in the densification table. There are the outer polygons (Outer_Poly), the gradient chords (Inner_Lines), the borders for the gradient chords (Inner_Lines_Border), and a small rounded polygon on the end of each chord (Lines_End_Poly) to fill the gap between the chords and the outer polygons.
Before we start working on any of these individual sections, there are a number of calculations that we are going to need for all of them.
First, let’s create a parameter that will let us control the spacing between each of the outer polygons. Call this parameter [Section Spacing], set the data type to Float, and set the value very low, around .01. Once you have the chart built you can set this higher for more spacing, or lower for less spacing.
Now let’s use that parameter, along with the Max of our running total field from the data source (which represents the grand total of the [Value] field), to calculate the width of our spacing between polygons. We’ll call this [Section Spacing Width]
Section Spacing Width = [Section Spacing]*{MAX([From Run Total])}
Now we need an adjusted grand total that accounts for all of the spaces as well. We’ll call this [Max Run Total – Adj] and it will be our grand total plus the number of spaces * the width of the spaces.
Max Run Total – Adj = {MAX([From Run Total])+MAX([From ID]*[Section Spacing Width])}
Next, we want to calculate the position around the circle where each of our sections, or entities, will start. We’ll do this by subtracting the [Value] field from the running total, adding the spacing for all previous sections, and then dividing that by our adjusted grand total. Call this field [Section_Start].
Section_Start = { FIXED [From ID] : MIN(([From Run Total]-[Value]) + (([From ID]-1)*[Section Spacing Width]))} / [Max Run Total – Adj]
Now we need to do the same thing to calculate the position around the circle where each section ends. The only difference between this and the previous calc, is that we are going to use the running total without subtracting the value. Call this field [Section_End]
Section_End = { FIXED [From ID] : MAX([From Run Total] + (([From ID]-1)*[Section Spacing Width]))} / [Max Run Total – Adj]
Now for an easy one. Let’s calculate the width of each section by subtracting the [Section_Start] from the [Section_End]. Call this [Section_Width].
Section_Width = [Section_End]-[Section_Start]
Next, we need to do the same thing to get the start and end positions for each of the “sub-sections”, or each of the entity’s relationships. The calculations are almost identical, the only difference is that we are fixing the Level of Detail calculations on [From_ID] and [To_ID], instead of just [From_ID]. Call these calculations [From_SubSection_Start] and [From_SubSection_End].
From_SubSection_Start = { FIXED [From ID],[To ID] : MIN(([From Run Total]-[Value]) + (([From ID]-1)*[Section Spacing Width]))} / [Max Run Total – Adj]
From_SubSection_End = { FIXED [From ID],[To ID] : MAX([From Run Total] + (([From ID]-1)*[Section Spacing Width]))} / [Max Run Total – Adj]
And just like before, we’ll create a simple calculation to get the “width” of these sub-sections. Call this calculation [SubSection_Width].
Next, we need to do the same thing, but need to calculate the start and end position for the other end, or Target, of each relationship. The calculations are the same as above except we’ll use the [To_Run_Total] instead of [From_Run_Total] and [To_ID] instead of [From_ID]. Call these calculations [To_SubSection_Start] and [To_SubSection_End].
To_SubSection_Start = { FIXED [From ID],[To ID] : MIN(([To Run Total]-[Value]) + (([To ID]-1)*[Section Spacing Width]))} / [Max Run Total – Adj]
To_SubSection_End = { FIXED [From ID],[To ID] : MAX([To Run Total] + (([To ID]-1)*[Section Spacing Width]))} / [Max Run Total – Adj]
And finally, we need a simple calculation to get the total number of points for each of our shape types. Call this calculation [Max Point].
Max Point = { FIXED [Shape Type] : MAX([Points])}
Densification
Before we move on, let’s take a look at our densification table. In this table, we have 5 fields.
Shapetype: Corresponds to the 4 sections of the chart mentioned previously.
Points: Used, along with Side, to calculate the positions of every point, in every element of the chart.
Order: Used on Path to tell Tableau how to connect our Points.
Side: Used to differentiate between the interior and exterior “lines” for the outer polygons and chords.
Line ID: Used on Detail to segment our lines appropriately. For all sections of the chart, other than the Inner_Lines, this value will be 1, since we want one continuous line for the polygons, and for the borders of the chords. For the Inner_Lines, we have values from 1 to 1,000, so we can “color” our chart with up to 1,000 lines per chord.
These fields are used in slightly different ways in each section of the chart, so we’ll talk about them more as we start building.
Building the Outer Polygons
First, let’s take another quick look at our densification table. For the outer polygons, identified by Shapetype=Outer_Poly, we have a total of 50 records. The Order field, used to tell Tableau how to connect the points, is a sequential number from 1 to 50. Then there is the Points field, which is used to calculate the position of each point around the circle. This number goes from 1 to 25, where the Side field = Min, and repeats where the Side field = Max. This will allow us to draw two parallel lines (Min and Max), and then connect them together to create those outer polygons. And lastly, the Line ID has a value of 1 because we are “drawing” one continuous “line” for each of these polygons. Clear as mud right? Here’s a quick illustration to help visualize how these fields function together.
Before we start building the calculations for this section, we need a few parameters. The first is just going to be a generic value for the radius of our circle. The second, is going to determine the thickness of these outer polygons.
Create a parameter called [Radius], set the data type to Float and set the Current value to 10. This can be any number you want, it doesn’t really matter, we just need something to determine the size of our chart.
Next, create a parameter called [Outer_Poly_Thickness], set the data type to Float, and set the Current value to 1. You can play around with this number to get the thickness you want, but I would recommend setting it somewhere around 10% of the [Radius] value.
Now for our calculations. Going back to Part 1 of the Fun with Curves in Tableau Series, we know that we need 2 inputs to plot points around a circle. We need the distance of each point from the center of the circle, and we need the position of each point around the circle. Let’s start with the distance.
In the image from earlier in this section, we see that we need two lines that follow the same path, one for Min and one for Max. So these two lines will have different distances from the center. For the Min line, we’ll just use the [Radius] of our circle. For the Max line, we’ll use the [Radius] plus our [Outer_Poly_Thickness] parameter. Create a calculated field called [Outer_Poly_Distance].
Outer_Poly_Distance = IF [Side]=”Min” THEN [Radius] ELSE [Radius]+[Outer_Poly_Thickness] END
Next, we need to calculate the position of the points. Each of our polygons are going to be different lengths, depending on the [Value] field in our data source, so we need to calculate the spacing of each point (to evenly space 25 points along the two “lines”). Earlier, we calculated the start and end position for each of these polygons, and we’ll use those to calculate the spacing, or “width”, that each point covers. Create a calculated field called [Outer_Poly_Point_Width].
And now we’ll use that, along with the start position of each polygon, to calculate the position for each of our 25 points. Point 1 will be positioned at the start of the polygon, Point 25 will be positioned at the end of the polygon, and the remaining 23 points will be equally spaced between those two points. Call this calculation [Outer_Poly_Point_Position].
Let’s pause here and build a quick view to test out our calculations so far.
Drag [ShapeType] to filter and filter on “Outer_Poly”
Change Mark Type to Polygon
Right click on [Outer_Poly_X], drag it to Columns, and when prompted, choose Outer_Poly_X without aggregation
Right click on [Outer_Poly_Y], drag it to Rows, and when prompted, choose Outer_Poly_Y without aggregation
Drag [From_ID] to color
Right click on [Order], convert to Dimension, and then drag to Path
If all of the calculations are correct, your sheet should look something like this.
One section down, three more to go.
Building the Line End Polygons
Next, let’s build the small rounded polygons that are going to go at the end of each of our chords. The calculations will be pretty similar to the previous section. For those outer polygons, we essentially drew two curved lines that followed the same path, and then connected those lines by using the Polygon Mark Type, and using our [Order] field on Path. Now, we basically want to do the same thing, but with a few minor differences. Instead of drawing two lines, we only need to draw one. Tableau will automatically connect our first and last point, resulting in a small semi-circle. The other difference is that instead of drawing one polygon for each entity (actor in this example), we need to draw a polygon for each relationship.
To draw these polygons, we need the same exact 5 calculations as we did in the previous section, but with some modifications. To start, let’s build a parameter to control the spacing between these polygons, and the outer polygons. Call this parameter [Poly_Spacing], set the data type to Float, and set the current value to .25. Again, once the view is built, you can play with this parameter to get the spacing you want.
Next, we need to calculate the distance of our points from the center of the circle. We’ll do this by subtracting our [Poly_Spacing] value from the [Radius] . Call this calculation [Line_End_Poly_Distance].
Line_End_Poly_Distance = [Radius]-[Poly_Spacing]
Next, we need to calculate the position for our 25 points, just like we did in the previous section. The only difference here, is that instead of using the section start and section end, we’re going to use the sub-section start, and sub-section end, since we need to create these for each relationship. Call this calculation [Line_End_Poly_Point_Width].
Using that, along with our Subsection start calculation, we can calculate the position of all 25 points for these polygons. Call this calculation [Line_End_Poly_Point_Position].
Drag [ShapeType] to filter and filter on “Line_End_Poly”
Change Mark Type to Polygon
Right click on [Outer_Poly_X], drag it to Columns, and when prompted, choose Outer_Poly_X without aggregation
Right click on [Outer_Poly_Y], drag it to Rows, and when prompted, choose Outer_Poly_Y without aggregation
Drag [From_ID] to Color
Drag [To_ID] to Detail
Right click on [Order], convert to Dimension, and then drag to Path
When complete, the line end polygons should look something like this.
We’re halfway there! Kind of…
Building the Inner Line Borders
Now we need to build our chords. Let’s start with the borders since those are a little more straightforward, and will make the last section a little easier to understand.
Going back to Part 2 of the Fun with Curves in Tableau Series, we know that we need 3 sets of coordinates to create our bezier curves; one for the start of the line, one for the end of the line, and one for the mid-point. Since we’re building a chord chart, we’ll use 0,0, for the mid-point. So that just leaves the start and end of our lines.
As always, we’ll need 2 inputs to calculate these coordinates, the distance and position. The distance is easy. It’s going to be the same distance as our Line End Polygons, [Radius] – [Poly_Spacing]. The position is a little more tricky.
For each of our chords, we’re going to need 2 lines, one for the interior, or Min, line, and one for the exterior, or Max line.
Notice the Min Line, starts at the end of the first chord, or sub-section, and then ends at the start of the target sub-section. The opposite is true for the Max Line. This line starts at the start of the first chord, and ends at the end of the target sub-section. We have already calculated the position for all of those sections, so we can plug those into our X and Y calcs to get our coordinates. We’ll need two sets of calculations, one for the starting coordinates and one for the ending coordinates.
Chord_X_Start = if [Side]=”Min” then ([Radius]-[Poly_Spacing])* SIN(2*PI() * [From_SubSection_End]) else ([Radius]-[Poly_Spacing])* SIN(2*PI() * [From_SubSection_Start]) END
Chord_Y_Start = if [Side]=”Min” then ([Radius]-[Poly_Spacing])* COS(2*PI() * [From_SubSection_End]) else ([Radius]-[Poly_Spacing])* COS(2*PI() * [From_SubSection_Start]) END
Chord_X_End = if [Side]=”Min” then ([Radius]-[Poly_Spacing])* SIN(2*PI() * [To_SubSection_Start]) else ([Radius]-[Poly_Spacing])* SIN(2*PI() * [To_SubSection_End]) END
Chord_Y_End = if [Side]=”Min” then ([Radius]-[Poly_Spacing])* COS(2*PI() * [To_SubSection_Start]) else ([Radius]-[Poly_Spacing])* COS(2*PI() * [To_SubSection_End]) END
These calculations may look complicated, but they’re all relatively simple. They are all using the same distance input (Radius – Poly Spacing), and then depending on if it’s the Min line or the Max line, and if it’s the Start or End of the line, we’re plugging in the appropriate position field (From_SubSection_Start, From_SubSection_End, To_SubSection_Start, and To_SubSection_End).
Those four calculations will give us the 2 remaining sets of coordinates needed to draw our bezier curves. So let’s plug all of our coordinates into the bezier X and Y calculations. But first, we need to calculate [T] to evenly space points along our lines.
Let’s pause again and make sure our calculations are working as expected.
Drag [ShapeType] to filter and filter on “Inner_Lines_Border”
Change Mark Type to Line
Right click on [Chord_X], drag it to Columns, and when prompted, choose Chord_X without aggregation
Right click on [Chord_Y], drag it to Rows, and when prompted, choose Chord_Y without aggregation
Drag [From_ID] to Color
Drag [To_ID] to Detail
Drag [Line_ID] to Detail
Right click on [Order], convert to Dimension, and then drag to Path
When finished, your bezier curves should look something like this. There should be two lines for each relationship in your data source.
We’re getting close!
Building the Inner Lines
Here’s the fun part. This is the section that really makes this chart unique. And guess what…we already built all of the calculations needed to make this work. Go the sheet you built in the previous section, and change the filter from Inner_Lines_Border to Inner_Lines…I’ll wait. Pretty cool right? These may look like polygons, but really, each of these chords is being “colored” with 1,000 individual lines. Go ahead and click on a few to see for yourself.
This is what one of those chords would look like if we had used 50 lines instead of 1,000.
So how does this work? How were we we able to use the same exact calculations for the chord borders, and these inner lines, when all we did was change a filter? It’s all in how we set up the densification table.
For both of these sections, we used the [Points] field in the same way…to calculate the position of each point along the chords. The [Line ID] and [Order] fields are where the magic happens.
Let’s look at the chord borders first.
For these lines, the [Order] field has sequential numbers from 1 to 50, and the [Line ID] field has just two values, 1 and 2. With [Line ID] on Detail, and [Order] on Path, this will draw 2 curved lines.
Now look at the inner lines.
For these lines, the [Order] field has just two values, 1 and 2, and the [Line ID] field has sequential numbers from 1 to 50 (up to 1,000 in the final chart). In this case, when you put [Line ID] on Detail, and [Order] on Path, it will draw 50 straight lines.
So using the same exact calculations, but getting a little creative with our data source, we can draw both the borders of our chords, and “color” them in. Now let’s bring all of these sections together. We’re almost done!
Building the Final Chart
So now we have all of the calculations done for the four sections of our chart. Time to bring them together. In this viz we have two different Mark Types that we are trying to use together; Polygons and Lines. So we’ll need to use a dual axis and create a calculation for each mark type. All of our sections will share the same Y calculation, but we’ll have an X calculation for the polygon sections, and an X calculation for the line sections. These will all be simple case statements to use the appropriate X and Y values for each section of the chart.
Final_X_Line
CASE [Shape Type] WHEN “Inner_Lines” then [Chord_X] WHEN “Inner_Lines_Border” then [Chord_X] END
Final_X_Poly
CASE [Shape Type] WHEN “Outer_Poly” then [Outer_Poly_X] WHEN “Line_End_Poly” then [Line_End_Poly_X] END
Final_Y
CASE [Shape Type] WHEN “Inner_Lines” then [Chord_Y] WHEN “Inner_Lines_Border” then [Chord_Y] WHEN “Outer_Poly” then [Outer_Poly_Y] WHEN “Line_End_Poly” then [Line_End_Poly_Y] END
Now to build our view. Let’s start with the Line sections.
Change Mark Type to Line
Right click on [Final_X_Line], drag it to Columns, and when prompted, choose Final_X_Line without aggregation
Right click on [Final_Y], drag it to Rows, and when prompted, choose Final_Y without aggregation
Drag [From ID] to Detail
Drag [To ID] to Detail
Drag [ShapeType] to Detail
Drag [Line ID] to Detail
Drag [Order] to Path
When this part is complete, your chart should look something like this.
Now, let’s add our polygons.
Right click on [Final_X_Poly], drag it to the right of [Final_X_Line] on the Columns shelf, and when prompted, choose Final_X_Poly without aggregation
Right click on the green [Final_X_Poly] pill on the columns shelf and select “Dual Axis”
Right click on one of the axes, and select “Synchronize Axis”
On the Marks Card, go to the Final_X_Poly card and change the Mark Type to Polygon
Remove the [To ID] field from Detail
Remove the [Line ID] field from Detail
Drag the [Unique Relationship] field to Detail
At this point, your chart should look something like this.
Woohoo, our chart is built! Before we start fine tuning our design, there are a few filters we should add. All of these will improve performance, but one of them is needed to accurately color our chart.
First, for our outer polygons, there are actually a number of identical polygons stacked on top of each other. The way our data is set up, it’s creating one of these polygons for each relationship for each entity. But we only want one polygon per entity. This will not only improve performance, but also fix some weird display issues that happen on Tableau Public. It doesn’t happen all the time, but sometimes when you have multiple identical polygons stacked on top of each other, the curved lines end up looking jagged and weird. So this will help fix that. This filter will include anything that’s not an outer polygon, and only the first outer polygon for each entity. We’ll call this [Single_Outer_Poly_Filter].
Single_Outer_Poly_Filter = [Shape Type]!=”Outer_Poly” or ([Shape Type]=”Outer_Poly” and [To ID]={ FIXED [From ID] : MIN([To ID])})
Drag that to the Filter shelf and filter on True.
This next calculation helps with performance and also ensures an accurate color calculation in the next section. For each of our chords, as it stands now, there are actually two sets of lines stacked on top of each other. That’s because each unique relationship has two rows in our source data (one where Actor 1 is the Source and Actor 2 is the Target, and one where Actor 2 is the Source and Actor 1 is the Target). This calculation will keep all of the records for our two polygon sections, but for the line sections, it will only keep the first occurence of each relationship. Call it [Unique_Relationship_Filter].
Unique_Relationship_Filter = CONTAINS([Shape Type],”Poly”) or (not CONTAINS([Shape Type],”Poly”) and [Unique Relationship Order]=1)
Drag that to the Filter shelf and filter on True.
This last one is purely for performance and is completely optional. Depending on how large this chart will be in your viz, you may not need 1,000 lines to color the chords. You can use a parameter and a calculated field to set the number of lines you want to use. First, create a parameter called [Gradient Lines], set the data type to float, set the max value to 1,000, and set the current value to about 750. Then create a simple calc called [Gradient_Line_Filter].
Gradient_Line_Filter = [Points]<=[Gradient Lines]
Drag this field to the Filter shelf and filter on True. Then, right click on the blue pill, and select “Add to Context”.
Alright, now we’re ready to move on to Design!
Adding Color to the Chart
This part took me a little while to figure out. The goal is to be able to have a unique color for each of the entities, and then have the chord between them, transition from one of the entity’s colors, to the other. We have our [From_ID] field that is a sequential number for each entity, so that’s what we’ll use to color our polygons.
To color our chord borders, we’re going to use a parameter with a value lower than our first [From ID], and I’ll talk more about this a little later on. For now, create a parameter called [Color Range Start], set the data type to Float, and set the current value to .5.
To color the gradient lines, we’ll need a few calculations. The first thing we need to do, is calculate an evenly spaced value between the ID for the Source entity and the ID for the Target entity. For example, if we have a chord going from entity 1 to entity 2, we’ll take the difference of those and divide by the number of lines (1,000). Same thing if we have a chord going from entity 1 to entity 5. Let’s create that calc and call it [Color Step Size].
Color Step Size = ABS([From ID]-[To ID])/([Max Point]-1)
Now we’ll multiply that value by the [Points] field and add it to our [From ID] field to get evenly spaced values between the Source ID and the Target ID. Call this [Step Color Num].
Step Color Num = [From ID] + (([Points]-1)*[Color Step Size])
And finally, a simple case statement to use the [Step Color Num] value for the inner lines, the [From ID] value for the polygons, and the [Color Range Start] value for the chord borders. Call this [Color].
Color
CASE [Shape Type] WHEN “Inner_Lines” then [Step Color Num] WHEN “Line_End_Poly” then [From ID] WHEN “Outer_Poly” then [From ID] WHEN “Inner_Lines_Border” then [Color Range Start] END
Now, on the Marks Card, click on the “All” card. Then, right click on the [Color] field, drag that onto Color, and when prompted, choose Color without aggregation. Notice something kind of funky happens when you add the Color.
Our Border Lines are rising to the top and crossing over all of the Inner Lines, which is not what we want. The reason this is happening, is that Tableau uses the order of fields on the Marks Card to sort. When we added [Color], it moved to the top of the list of fields on the Marks Card, so that is the first thing being used to sort. Since our Borders all currently have the lowest value of any mark (based on the parameter we created earlier, it should be .5), those lines are coming to the top.
To fix that, on the Marks Card, on the Final_X_Line card, drag the [Color] field all the way to the bottom. The order of the fields on the Marks Card should be as follows to get the correct sorting.
From ID
To ID
Shape Type
Line ID
Order
Color
Now your chart should look something like this.
It’s starting to come together!
Picking the Right Colors
You could use any of the sequential palettes available in Tableau, and they would work just fine. But for my viz, I wanted unique colors for each of the entities, not different shades of the same color. The palette I ended up using is called “Inferno”. You can create any custom palette you would like, but you want to make sure you pick a palette that is “Perceptually Uniform”. There is a lot behind these palettes, but basically it means that each color in your palette is close to the next color, and the amount of change between one color and the next is consistent throughout the entire palette. Here are some great examples of Perceptually Uniform color scales.
Just a few things to keep in mind when building your palette. I would recommend ordering them in your preferences file from lightest to darkest. Also, on the very last line, add a line for White (#FFFFFF). This is what is used for the chord border color. Also, make sure that you set the type=sequential.
Once you add your custom palette, go ahead and select it from the Color options on the Mark Card. If you ordered your colors from lightest to darkest, you’ll also want to check the “Reversed” box in the color options.
Now that I’ve added my custom palette and applied it to the viz, this is what it looks like.
Just one more note about color. We had built that [Color Range Start] parameter to select a value that can be used for coloring the borders. In my sample data, I only had 5 entities, so the largest value in my [Color] field is 5. When I use .5 in that parameter, it gives me the full color range from my palette, and a white border. If you have more or less entities, that might not be the case. You may need to play with this parameter a bit to make it work correctly for your chart.
If you pick a number that is too low, you will not get the full color range from your palette. This is what it looks like if I set that value to -1.
On the other side of that, if you pick a number that is too high, the white from your palette will start to “bleed” into your chart. This is what it looks like if I set the value to .9
It’s not an exact science, so just play with that parameter until you have a white border and your full color range. If you don’t have the full color range, increase that parameter value. If the white is bleeding into your chart, decrease that value.
Finishing Touches
We are almost done! Just a few more steps that I would recommend. First, hide the null indicator in the bottom right of the chart, by right clicking on it and selecting “Hide Indicator”. Then clean up your borders and grid lines, and last, but definitely not least, I would recommend creating a calculated field to determine the thickness of your lines. For the gradient lines, we want a very thin line, but for the borders, it should be a little bit thicker. Create a calculated field called [Line Size].
Line Size = If [Shape Type]=”Inner_Lines_Border” then 2 else 1 END
Then, on the Marks Card, on the Final_X_Line card, right click on the [Line Size] field, drag it to Size, and then select Line Size without aggregation when prompted. Then, similar to what we did with Color, drag that field all the way down to the bottom on the Marks card, below [Color]. Again, you can play with these to get the look that you want. Double click on the Size Legend, and play with the slider. This is how mine are set.
And that’s it! We are done! If you have made it this far, please let me know on Twitter or LinkedIn. This was the most complicated topic we’ve covered so far, and I would love to get your thoughts, and to see what you came up with. As always, thanks for reading, and check back soon for more fun Tableau tutorials!
Recently, after using the post above to create a chord chart, someone had reached out asking if there was a way to tone down the curves, or, as they had put it, to make them less “bouncy”. I started racking my brain trying to figure out how to make that happen. In my mind, the only possible way to change the intensity of the curves would be to calculate a new mid-point for each of the lines, rather than using the center of the circle (0,0).
For the next hour or so, I wrote and tested 9 new calculated fields that would allow users to “smooth” their curves. Excitedly, I jumped back on Twitter to share my update, and found that this person had not only figured out how to do it, but the method they came up with was exponentially easier than what I had done. So a huge thank you to Anne-Sophie Pereira De Sá for figuring out Method 1 below for controlling your curves.
Luckily, the work I had done wasn’t a complete waste, as it led me to two more methods for controlling the intensity of the curves. So in this post we’re going to cover a total of three different approaches depending on what you want your curves to look like.
Also, just want to mention that these methods are really only relevant if you’re building a radial chart, like a chord chart. If you are using the Part 2 tutorial to build something like an Arc Chart, there are much easier methods for controlling the intensity of the curves (you can modify the intensity of the curves by adjusting the Y_Mid value).
If you want to follow along, I have updated the sample workbook for Part 2 of the series, which can be downloaded here.
The Methods
Method 1: Smoothing
The first method will allow you to tone down the intensity of your curves and it is incredibly easy to implement. It uses a parameter to set the intensity of your curve using values from 0 (standard curve) to 1 (no curve). The height of each curve is changed proportionally (ex. .1 will reduce the height of each arc by roughly 10% from it’s standard curve height).
Method 2: Consistent Scaling
This method allows you to create consistent curves (same arch height) for all of your lines, regardless of where the start and end points are on the circle. It’s much more complicated, but the result is pretty nice. Again, this method uses a parameter with values between 0 and 1 to set the intensity of the curve.
Method 3: Dependent Scaling
The final method allows you to set the arch height based on the length of the line, so shorter lines on the outside of the circle will have a lower arch, and longer lines going through the middle of the circle will have a higher arch. The approach is similar to Method 2, sharing many of the same calculations, and also uses a parameter with values between 0 and 1 to set the intensity of the curves.
Method 1 walk-thru
As I mentioned above, this method is incredibly easy to implement (thank you again Anne). All you need is a new parameter and a few small tweaks to the Bezier X and Y calculations. First, let’s create our parameter.
Create a new Parameter called “Smooth Factor”
Set Data type to “Float”
Set Current Value to 0
Set Allowable values to “Range”
Set Minimum to 0
Set Maximum to 1
Set Step size to .1
Next, let’s modify our calculations. The only difference between the calculations discussed in the original post, and the calculations used here, are the exponents. The original calculation has an exponent of 2 in two sections of the calculation. Anne had figured out that if you replaced this exponent with a 1, you got a straight line, and if you replaced it with anything between a 1 and a 2, it would tone down the curve of the line proportionally.
The only difference between the two calculations is that we replaced ^2 with ^(2-[Smooth Factor]) in both places. That’s it. So here are our new calculated fields for X and Y and we’ll keep the naming consistent with the original post
Now, assuming you had already created your chord chart using the original post, just replace [Circle_Bezier_X] with [Circle_Bezier_X_Smooth] and replace [Circle_Bezier_Y] with [Circle_Bezier_Y_Smooth] and you’re done. Just play around with the parameter to get the intensity of curve that you’re looking for.
Method 2 walk-thru
As I mentioned earlier, this method is a lot more complicated. It involves calculating a new mid-point for each of our lines using some geometry and trigonometry. But similar to the first method, we’ll need a parameter to set the intensity of our curves, so let’s start there.
Create a new parameter called “Consistent Factor”
Set Data type to “Float”
Set Current Value to .5
Set Allowable values to “Range”
Set Minimum to 0
Set Maximum to 1
Set Step size to .1
Now here comes the tricky part. And I am sure there is a much easier solution out there, but this is what I was able to come up with.
As we discussed in the original post, to draw these curved lines, we’re essentially creating a bunch of triangles and letting the Bezier X and Y calcs do the rest of the work.
Each of these triangles have a different height depending on where, around the circle, the starting point and the ending points appear. We’re using 0,0 as the mid point, so all of the triangles meet in the middle. But if we want to apply a consistent curve to all of our lines, then we need to calculate a new mid point for each of our triangles. And the result of that new mid-point calculation, should be a bunch of triangles with the same height.
You may remember from earlier posts, that in order to plot points around a circle, we need two inputs; the distance from the center of the circle, and the position around the circle. The position around the circle is much easier to calculate so let’s start there. Let’s create a calculated field called “Scale_Mid_Point_Position”
Mid_Point_Position = IF([Position_End]-[Position_Start])*360 >180 THEN (([Position_End]-[Position_Start])/2)+[Position_Start]+.5 ELSE (([Position_End]-[Position_Start])/2)+[Position_Start] END
Now this calculation looks a little complicated, but basically what it’s doing is calculating the center of the vertex angle for each of our triangles by comparing the start and end position of each of our lines. Because this is a circle, this comparison might result in an angle that is greater than 180 degrees, which isn’t possible for a triangle. Really, it just means that our starting point and ending point are farther apart (more than .5) so the direction of the triangle flips. So when that happens, we want the inverse of that position, which we can get by adding “.5” to the calculation. Otherwise, we are just subtracting the start position from the end position, splitting the difference, and then adding it back to the start position to get the position that is directly in the middle of the start and end.
Ok, if I haven’t lost you yet, let’s keep moving. Now we need to calculate the distance from the center for each of our new mid-points. To get there, first we need to calculate the height of our existing triangles (that meet in the middle at 0,0). Unfortunately, it’s not that easy. There are a lot of different ways to calculate different parts of a triangle, so we have to work with what we got. We know the length of two sides of the triangle (equal to the radius of the circle), and we know the vertex angle. That alone isn’t enough to calculate the height (as far as I know), but it gets us a step closer. With that information, we can calculate the length of the base.
First, a quick look at the parts of one of our triangles.
Now let’s calculate the Vertex angle, which is going to be very similar to the position calculation we just did.
Vertex_Angle = IF ([Position_End]-[Position_Start])*360 >180 then 360-(([Position_End]-[Position_Start])*360) else ([Position_End]-[Position_Start])*360 END
Now, with that, along with our circle’s radius, we can calculate the length of the base.
And now that we know the length of all three sides of the triangle, we can calculate the height.
Tri_Height = SQRT([Radius]^2 – ([Tri_Base]^2/4))
Now we have everything we need to calculate the distance from the center for each of our mid-points. We want all of our triangles to be the same height. So first, we need a consistent height to use. We can get there by multiplying our radius by our newly created Scale Factor. Our radius = 20, so if our scale factor equals 0, the height will be 0. So no curve. If our scale factor is .5, our height will be 10. Easy.
Now, if we take the height of our existing triangle, and then subtract our new standardized height, that will give us the distance from center, our second and final input.
You can see in the gif above that all triangles now have the same height.
Now we have the position and distance from center for our mid points. All that we have to do is calculate the coordinates for the mid-points using those inputs, and then plug those coordinates into our Bezier X and Y formulas.
Now, again assuming you had already built your chord chart using the original post, just replace [Circle_Bezier_X] with [Circle_Bezier_X_Con] and replace [Circle_Bezier_Y] with [Circle_Bezier_Y_Con] and play with the parameter to get the curve intensity you want.
Method 3 walk-thru
This method is going to be extremely similar to Method 2. The only thing we are changing is the value that we subtract from our triangle height to get the distance from the center for our new mid-points. If you went through Method 2 above, you are already 90% there. Like the other two methods, we need a parameter to control the intensity of our curves. Let’s create one called “Dependent” Factor”
Create a new parameter called “Dependent Factor”
Set Data type to “Float”
Set Current Value to .5
Set Allowable values to “Range”
Set Minimum to 0
Set Maximum to 1
Set Step size to .1
Now we need most of the same calculations that we used in Method 2. I’ll list them here again in case you haven’t already tried that method, but for explanations of these calcs, please read through method 2 in the previous section.
Mid_Point_Position = IF([Position_End]-[Position_Start])*360 >180 THEN (([Position_End]-[Position_Start])/2)+[Position_Start]+.5 ELSE (([Position_End]-[Position_Start])/2)+[Position_Start] END
Vertex_Angle = IF ([Position_End]-[Position_Start])*360 >180 then 360-(([Position_End]-[Position_Start])*360) else ([Position_End]-[Position_Start])*360 END
Here is the main difference between this method and the previous one. Instead of multiplying our factor (parameter) by a consistent value, like radius, we are going to multiply it by the base length of our triangles. This will result in a higher value for longer lines, and a lower value for shorter lines. Just want to point out that I divided the base length by 2 in these calculations, to tone down the curves a little more, but that is optional. Here is the calculation for the distance from center for each of our new mid-points.
Again, assuming you had already built your chord chart using the original post, or one of the previous methods, you just need to replace [Circle_Bezier_X] with [Circle_Bezier_X_Dep] and replace [Circle_Bezier_Y] with [Circle_Bezier_Y_Dep] and play with the parameter to get the curve intensity you are looking for.
That’s it. Three methods for controlling the intensity of your bezier curves. These certainly aren’t necessary when building out your chord charts, but they do produce some nice effects if you are looking to really customize your visualization. Personally, I lean towards Method 1 because of it’s simplicity. If you are looking for something more complicated, Method 2 and Method 3 are both nice, but if I had to pick, I would go with #3.
As always, thank you so much for reading and keep an eye on our blog for more fun Tableau tutorials.
Welcome to another installment of “It Depends”. In this post we’re going to look at two different ways to use BAN’s to swap KPI’s in your dashboard. If you’re not familiar with the term “BANs”, we’re talking about the large summarized numbers, or Big Ass Numbers, that are common in business dashboards.
When I build a KPI dashboard, I like to give my users the ability to dig into each and every one of their key metrics, and the techniques we cover in this post are a great way to provide that kind of flexibility. Here is a really simple example of what we’re talking about.
In the dashboard above, we have 4 BAN’s across the top; Sales, Quantity, Profit, and Profit Ratio. Below that, we have a bar chart by Sub-Category, and a Line Chart showing the current vs previous year. When a user clicks on any of the BANs in the upper section, the bar chart and the line chart will both update to display that metric. A couple of other things that change along with the metric are the dashboard title, the chart titles, and the formatting on all of the labels.
We’re going to cover two different methods for building this type of flexible KPI dashboard. A lot of what we cover is going to be the same, regardless of which method you choose, but there are some pretty big differences in how both the BANs and the Dashboard Actions are constructed in each method.
For this exercise we’re going to use Superstore Data, so you can connect to that source directly in Tableau Desktop. If you would like to follow along in the Sample Workbook, you can download that here.
The Two Methods
Measure Names/Values – In the first method we’re going to use Measure Names and Measure Values to build our BANs. When a user clicks on one of the Measure Values, we will have a dashboard action that passes the Measure Name to a parameter.
Individual BANs – In the second method, we’re going to use separate worksheets for each of one of our BANs. When a user clicks on one of the BANs, we’ll pass a static value that identifies that metric (similar to the Measure Name) to a parameter. With this method, we’ll need a separate dashboard action for each of our BANs.
Method Comparison
So at this point you may be wondering, why would you waste time building out separate worksheets and separate dashboard actions when it can all be done with a single sheet and a single action. Fair question. As you’re probably aware, Measure Names and Measure Values cannot be used in calculated fields, so with the Measure Names/Values method, you are going to be pretty limited in what you can do with your BANs. Let’s take another look at the BANs in the example dashboard from earlier.
Numbers alone aren’t always very helpful. It’s important to have context, something to compare those numbers to. Anytime I put a BAN on a dashboard, I like to add some kind of indicator, like percent to a goal, or growth versus previous periods. Another thing I like to do is to use color to make it very obvious which metric is selected and being displayed in the rest of the dashboard. Neither of these are possible with the first method as they both require calculated fields that reference either the selected measure or the value of that measure.
Unlike some of our other “It Depends” posts, the decision here is pretty easy.
Method 2 does take a little more time to set up, but in my opinion, it’s usually the way to go. Beyond the two decision points above, the second method also provides a lot more flexibility when it comes to formatting. But if you’re looking for something quick and these other considerations aren’t all that important to you or your users, by all means, go with the first one.
Methods in Practice
This section is going to focus only on building the BANs and setting up the dashboard actions. We’ll walk through how to do that with both of the methods first, and then we’ll move onto setting up the rest of the dashboard, since those steps will be the same for both methods.
Before we get started, let’s build out just a couple of quick calculations that we’ll be using in one or both methods.
First, let’s calculate the most recent date in our data source. Often, in real world scenarios, you’ll be able to use TODAY() instead of the most recent date, but since this Superstore Data only goes through the end of 2021, we’re going to calculate the latest date.
Max Date: {FIXED : MAX([Order Date])}
Now, let’s calculate the Relative Year for each date in our data source. So everything in the most recent year will have a value of 0, everything in the previous year will have a value of -1, and so on.
And lastly, we’re working with full years of data here, but that’s usually not the case. In my BANs, I want to be able to show a Growth Indicator, but in a real world scenario, that growth should be based on the value of that metric at the same point in time during the previous year. So let’s build a Year to Date Filter.
Year to Date Filter: DATEDIFF(“day”,DATETRUNC(“year”,[Order Date]),[Order Date])<=DATEDIFF(“day”,DATETRUNC(“year”,[Max Date]),[Max Date])
And that calculation is basically just calculating the day of the year for each order (by comparing the order date to the start of that year), and then comparing it to the day of the year for the most recent order. Again, in a real world scenario, you would probably use TODAY() instead of the [Max Date] calculation.
And finally, we just need one parameter that will store our selection when we click on any of the BANs. For this, just create a parameter, call it “Selected Metric”, set the Data Type to “String”, and set the Current Value to “Sales”.
Ok, that’s enough for now, let’s start building.
Measure Names/Values
Follow the steps below to build your BANs using the Measure Names/Values method. I’m going to provide the steps on how the ones in the sample dashboard were built, but feel free to format however you would like.
Building
Right click on [Relative Year] and select “Convert to Dimension”
Drag [Relative Year] to filter shelf and filter on 0 (for current year)
Drag Measure Names to Columns
Drag Measure Values to Text
Drag Measure Names to Filter and select Sales, Quantity, Profit, and Profit Ratio
Right click on Measure Names on the Column Shelf and de-select “Show Header”
Drag Measure Names to “Text” on the Marks Card
Formatting
Change Fit to “Entire View”
Click on “Text” on the Marks Card and change the Horizontal Alignment to Center
Click on “Text” on the Marks Card, click on the ellipses next to “Text” and format
Position Measure Names above Measure Values and set font size to 12
Change font size of Measure Values to 28
Set desired color
On the Measure Values Shelf, below the Marks Card, right click on each Measure and format appropriately (Currency, Percentage, etc.)
Go to Format > Borders and add Column Dividers (increase Level to get dividers between BANs)
Click on Tooltip on the Marks Card and de-select all checkboxes to “turn off”
When you’re done building and formatting your BANs, your worksheet should look something like this
Now we just need to add this to our dashboard, and then add a a Parameter Action that will pass the Measure Name from our BANs worksheet to our [Selected Metric] parameter.
Go to Dashboard > Actions and click “Add Action”
Select “Change Parameter” when prompted
Give your Parameter Action a descriptive Name
Under Source Sheets, select the BANs worksheet that you created in the previous steps
Under Target Parameter, select the “Selected Metric” parameter we created earlier
Under Source Field, select “Measure Names”
Under Aggregation, select “None”
Under Run Action on, choose “Select”
Under Clearing the Selection Will, select “Keep Current Value”
The Parameter Action should look something like this.
One last formatting recommendation that I would make is to use one of the methods described in this post, to remove the blue box highlight when you click on one of the BANs. Use either the Filter Technique, or the Transparent Technique.
So that’s it for this method…for now. We’re going to switch over to setting up the BANs and dashboard actions for Method 2 first, and then we’ll regroup and walk through the rest of the dashboard setup. If you plan on using Method 1, please skip ahead to the “Setting up the Dashboard” section below.
Individual BANs
Follow the steps below to build your BANs using the Individual BANs method. I’m going to walk through how to build one of the BANs, and then you’ll need to repeat that process for each one in your dashboard. A couple of other things we’ll do in these BANs include adding growth indicators vs the previous year, and adding color to show when that BAN’s measure is selected/not selected. And as I mentioned in the previous example, I’m going to cover how I formatted these BANs in the sample workbook, but feel free to format however you see fit.
Let’s start by building our “Sales” BAN.
Building
Right click on [Relative Year] and select “Convert to Dimension”
Drag [Relative Year] to filter shelf and filter on 0 and -1 (for current and prior year)
Drag [Year to Date Filter] to filter shelf and filter on True
Drag Relative Year to Columns and make sure that 0 is to the right of -1
Drag your Measure (Sales) to Text on the Marks Card
Add Growth Indicator
Drag your Measure (Sales) to Detail
Right click and select “Add Table Calculation”
Under Calculation Type, select “Percent Difference From”
Next to “Relative To”, make sure that “Previous” is selected
Drag the Measure with the Table Calculation from Detail onto Text on the Marks Card
Right click on the “-1” in the Header and select “Hide”
Right click on Relative Year on the Column Shelf and de-select “Show Header”
Formatting
Change Fit to “Entire View”
Click on “Text” on the Marks Card and change the Horizontal Alignment to Center
Click on “Text” on the Marks Card, click on the ellipses next to “Text” and format
Insert a line above your Measure and add a label for it (ex. “Sales”). Set font size to 12.
Change font size of the measure (ex. SUM(Sales)) to 28
Change font size of growth indicator (ex. % Difference in SUM(Sales)) to 14
Right click on your measure on the Marks Card and format appropriately (for Sales, set to Currency)
Right click on your growth measure on the Marks Card, select Format, select Custom, and then paste in the string below
▲ 0.0%;▼ 0.0%; 0%
When the growth is positive, this will display an upward facing triangle, along with a percentage set to 1 decimal point
When the growth is negative, this will display a downward facing triangle, along with a percentage set to 1 decimal point
When there is 0 growth, this will display 0% with no indicator
Click on Tooltip on the Marks Card and de-select all checkboxes to “turn off”
When you’re done building and formatting your Sales BAN, it should look something like this.
There are a couple more additional steps before we move on to the dashboard actions. First, we need a field that we can pass from this BAN to our parameter. For this, just create a calculated field called “Par Value – Sales”, and in the calculation, just type the word “Sales” (with quotes).
Par Value Sales: “Sales”
And then drag the [Par Value – Sales] field to Detail on your Sales BAN worksheet.
Just a quick note here. If I was building this for a client, I would probably use a numeric parameter, and pass a number from this BAN instead of a text value. It’s a little cleaner and better for performance, but for simplicity and continuity, we’ll use the same parameter we used in Method 1. Ok, back to it.
Now, we need one more calculated field to test if this measure is the currently selected one. This is just a simple boolean calc, and we’ll call it “Metric Selected – Sales”.
Now drag that field to Color on your Sales BAN worksheet. Set the [Selected Metric] Parameter to “Sales” (so the result of the calculation is True) and assign a Color. Now, set the [Selected Metric] Parameter to anything else (so the result of the calculation is False) and assign a color.
Now our Sales BAN is built, we just need to add it to our dashboard and then add a Parameter Action that will pass our [Par Value – Sales] field to our [Selected Metric] parameter when a user clicks on the Sales Ban.
Go to Dashboard > Actions and click “Add Action”
Select “Change Parameter” when prompted
Give your Parameter Action a descriptive Name
Under Source Sheets, select the Sales BAN worksheet that you created in the previous steps
Under Target Parameter, select the “Selected Metric” parameter we created earlier
Under Source Field, select [Par Value – Sales]
Under Aggregation, select “None”
Under Run Action on, choose “Select”
Under Clearing the Selection Will, select “Keep Current Value”
The Parameter Action should look something like this.
And just like with Method 1, I would recommend using one of the methods described in this post, to remove the blue box highlight when you click on the Sales BAN. You could use either the Transparent or the Filter Technique, but with this method, I would really recommend using the Filter Technique.
Now, repeat every step from the “Individual BANs” header above to this step, for each of your BANs. I warned you it would take a little longer to set up, but it’s totally worth it. And once you’re comfortable with this technique, it moves very quickly. To save some time, you can probably duplicate your Sales BAN worksheet and swap out some of the metrics and calculations, but be careful you don’t miss anything.
Setting up the Dashboard
Now our BANs are built and our dashboard actions are in place. Either method you chose has brought you here. We just have a few steps left to finish building our flexible KPI dashboard. Here’s what we’re going to do next.
Adjust our other worksheets to use the selected metric
Dynamically format the measures in our labels and tooltips
Update our Headers and Titles to display the selected metric
Show Selected Metric in Worksheets
The first thing we need to do here is to create a calculated field that will return the correct measure based on what is in the parameter. So users will click on a BAN, let’s say “Sales”. The word “Sales” will then get passed to our [Selected Metric] parameter. Then our calculation will test that parameter, and when that parameter’s value is “Sales”, we want it to return the value of the [Sales] Measure. Same thing for Quantity, Profit, etc. So let’s create a CASE statement with a test for each of our BAN measures, and call it “Metric Calc”.
Metric Calc
CASE [Selected Metric] WHEN “Sales” then SUM([Sales]) WHEN “Quantity” then SUM([Quantity]) WHEN “Profit” then SUM([Profit]) WHEN “Profit Ratio” then [Profit Ratio] END
Now, we just need to use this measure in all of our worksheets, instead of a static measure. In our Bar Chart, we’re going to drag this measure to Columns. In our Line Chart, we’re going to drag this measure to Rows.
Now, whenever you click on a BAN in the dashboard, these charts will reflect the measure that you clicked on. Pretty cool right? But there is a glaring problem that needs to be addressed.
Dynamic Formatting on Labels/Tooltips
In our example, we have 4 possible measures that could be viewed in the bar chart and line chart; Sales, Quantity, Profit, and Profit Ratio. So 4 possible measures, with 3 different number formats.
Sales = Currency
Quantity = Whole Number
Profit = Currency
Profit Ratio = Percentage
At the time of writing this post, Tableau only allows you to assign one number format per measure. But luckily, as with all things Tableau, there is a pretty easy way to do what we want. We’re going to create one calculated field for each potential number format; Currency, Whole Number, and Percentage.
Metric Label – Currency: IF [Selected Metric]=”Sales” or [Selected Metric]=”Profit” then [Metric Calc] END
Metric Label – Whole: IF [Selected Metric]=”Quantity” then [Metric Calc] END
Metric Label – Percentage: IF [Selected Metric]=”Profit Ratio” then [Metric Calc] END
Here’s how these calculations are going to work together. When a user selects;
Sales
[Metric Label – Currency] = [Metric Calc]
[Metric Label – Whole] = Null
[Metric Label – Percentage] = Null
Quantity
[Metric Label – Currency] = Null
[Metric Label – Whole] = [Metric Calc]
[Metric Label – Percentage] = Null
Profit
[Metric Label – Currency] = [Metric Calc]
[Metric Label – Whole] = Null
[Metric Label – Percentage] = Null
Profit Ratio
[Metric Label – Currency] = Null
[Metric Label – Whole] = Null
[Metric Label – Percentage] = [Metric Calc]
No matter what BAN is selected, ONE of these calculations will return the appropriate value and TWO of these calculations will be Null. In Tableau, Null values do not occupy any space in labels and tooltips. So all we need to do is line up these three calculations together. The two Null values will collapse, leaving just the populated value. Here’s how you do that.
Go to the Metric by Sub-Category sheet (or one of the dynamic charts in your workbook)
If [Metric Calc] is on Label, remove it
Format your measures
Right click on [Metric Label – Currency] in the Data Pane, select “Default Properties” and then “Number Format”
Set the appropriate format
Repeat for [Metric Label – Whole] and [Metric Label – Percentage]
Drag all 3 [Metric Label] fields to “Label” on the Marks Card
Click on “Label” on the Marks Card and click on the ellipses next to “Text”
Align all of the [Metric Label] fields on the first row in the Edit Label box, with no spaces between fields (see image below)
Your Label should look like this.
If all of your calculations and default formatting are correct, your Chart labels should now be dynamic. When you click on Sales or Profit, your labels should show as currency. When you click on Quantity, your labels should show as whole numbers. And when you click on Profit Ratio, your labels should show as Percentages. You can repeat this same process for all of your Labels and Tooltips.
Displaying Parameter in Titles
Saving the easiest for last! One last thing you’ll want to do is to update your chart titles so that they describe what is in the view. The view is going to be changing, so the titles need to change as well. Luckily this is incredibly easy.
Double click on any chart title, or text box being used as a title
Place your cursor where you would like the [Selected Metric] value to appear
In the top right corner, select “Insert”
Choose the [Selected Metric] parameter
It should look like this. Just repeat for all of your other titles (can work in Tooltips as well).
Finally, I think we’re done! Those are two different methods for building really flexible KPI dashboards. This is a model that I use all the time, and users always love it! It’s incredibly powerful and just a really great way to let your users explore their data without having to build different dashboards for each of your key indicators.
As always, thank you so much for reading, and see you next time!
When it comes to visualizing data, it’s no secret that the Mercator Projection has it’s issues. Certain countries, especially in the northern hemisphere, appear much larger than they are in reality, and it gets worse the farther you move from the equator. Just look at the difference in the size of Greenland between the Mercator and Robinson projections.
That’s because this particular projection was created for one specific purpose…to aid in navigation. And it is perfect for that purpose, but not so perfect for visualizing data. But when it comes to Tableau, it’s our only option…or is it?
I’d like to use this opportunity to point out that I did not come up with the methods discussed in this blog post, but they are techniques that I use pretty frequently, and something I get asked about a lot. So the goal of this post is to combine information from various sources, provide you with all of the files needed to create your own Robinson projections, and walk through, step by step, multiple approaches to building these maps in Tableau. For more information, I recommend checking out this blog post by Ben Jones, and this post by John Emery
I also want to use this opportunity to point out that these methods are more complicated, and less performant than using Tableau’s standard mapping, so I would recommend using them with caution.
Different Methods
This post is going to cover three different methods for creating Robinsons Projection Maps in Tableau.
This is by far the easiest method, but it’s a little less flexible than using a CSV File. To get started, download the following 4 files from the link above, and place them all in the same folder somewhere on your machine.
Country_ShapeFile_Robinson.DBF
Country_ShapeFile_Robinson.PRJ
Country_ShapeFile_Robinson.shp
Country_ShapeFile_Robinson.SHX
Now, let’s set up our data source
Building the Data Source
One quick note before we start. This particular Shapefile only has the 2-digit codes for countries. In order to relate this file to your data, you’ll need to make sure that your source also contains these codes (would not recommend trying to relate on country name). You can find a full list of codes here. Now let’s build our data source.
Connect to your Data
If you do not have data and just want to practice these techniques, you can use the Happiness_Scores.xlsx (from the World Happiness 2022 Report) file in the Google Drive
Connect to the Shapefile
From the Data Source Page, click “Add” at the top left to add a new connection
In the “To a File” options, select “Spatial File”
Navigate to the location where you stored the files above and select the Country_ShapeFile_Robinson.shp file
Drag the Shapefile into the data source pane
Update the relationship
Click on the noodle connecting the two sources
In the lower section, select the country code in each file (called ISO in Shapefile)
When complete, your data source should look like this
Building the View
Create a new worksheet
Double-click on the [Geometry] field
Add the 2-digit country code from your primary source onto Detail (in the sample data it’s called ISO A2)
Add whatever measure you would like to Color and assign your desired palette (in this example, I placed [Happiness Score] on color and assigned a Viridis palette)
When complete, your worksheet should look something like this.
Well, that’s a bit strange, isn’t it? Here’s an extremely quick explanation of what’s happening. Tableau only supports the Mercator projection. Also, in Tableau, you are limited to just the Map Mark Type for Shapefiles. When you add the [Geometry] field, Tableau generates the latitude and longitude for each polygon in your Shapefile and plots them on the map. But the coordinates that make up those polygons in our Shapefile are for a Robinson projection. So here you can see our Robinson Projection overlaid on Tableau’s Mercator Projection. There’s some magic happening behind the scenes in those Shapefiles that allows this to happen. You can read more about it in John’s post.
So now, let’s get rid of the Mercator Projection (or at least hide it).
First, you’ll want to remove the “Search” Map option. This search is based on the positions of countries in the Mercator Projection, not our new Robinson Projection, so if you try to search for a country, it’s going to bring you to the wrong place. I’d recommend disabling all of the Map options, but that’s up to you
In the upper toolbar, click on Map
Click on Map Options
Deselect the box labelled “Show Map Search”
Optional – Deselect all other options
Next we’ll want to remove all of the Map Layers
In the upper toolbar, click on Map
Click on Map Layers
Deselect all of the boxes in the Map Layers Window along the left side of the screen
The Map Layers Window should look like this
Finally, let’s remove our Worksheet Shading, just in case we want to put this on a colored dashboard, or lay it over a background image
In the upper toolbar, click on Format
Click on Shading
Change the first option (on the Sheet tab, under Default, labelled “Worksheet”) from White to None
And that should do it! At this point, your map should look something like this.
Wait a second…something’s still not right. Antarctica has no permanent population, so how did they respond to the World Happiness Survey? Well, they didn’t. In fact, there are several countries that are showing up on this map as yellow that aren’t in the World Happiness data. But because we’re using relationships, and because these countries exist in the Shapefile, they are being shown on the map and colored the same as the lowest value in the data source. This can be misleading, so let’s get rid of those.
Drag the measure (the same one currently on color) to the filter shelf
Click on “Special”
Select “Non-Null” Values
Ok, now we should be all set.
I got to be honest, something about this still bothers me. I would love to be able to see the countries that are missing data, but unfortunately, Tableau does not have the capability to ignore null values in the color application. But we have a few options.
Option 1: Create bins off of your measure and assign a color to each bin. Null will have its own bin
Option 2: The same as option 1 but use a calculated field to set thresholds instead of creating bins
Option 3: Duplicate our map worksheet, remove the measure from filter, remove the measure from color, set the color to how we want the “missing” values to appear, and then stack these two maps on top of each other on our dashboard (set to floating and set x, y, w, and h the same). The result would look something like this
Much better! Any of these methods will work, but I’m partial to the 3rd. This is mainly because you can use a diverging or continuous color palette instead of having to figure out which colors to assign to each “bin” with Option 1 and 2.
Now let’s move on to the CSV File method
Method 2: Using a CSV File (for countries)
This method has a lot of similarities to the first one, so I won’t go in to too much detail. The two main differences are; we are going to use a csv instead of a shapefile, and we are going to use the polygon mark type instead of a map.
To get started, download the CountryShapes.csv file from the Google Drive.
Building the Data Source
We’re going to set up our data source the same way as we did in Method 1. Connect to your data, add a connection for the csv file, and set up the relationship. This csv file, unlike the shapefile, has a ton of different country identifier fields. Do some prep work in advance to figure out which of these identifiers match what is in your data source, and set up the relationship using that field. When you’re finished, it should look something like this.
Building the View
Building the view is a little more complicated with this method. This csv file has the coordinates to “draw” all of the countries. But take a look at what happens if we try to use those coordinates as-is.
Now that looks an awful lot like a Mercator Projection. Well, that’s because it is. This file has the all of the coordinates to “draw” each country, but they are based on the Mercator Projection. So we need to re-calculate those latitude and longitude values. We’re going to build two new calculated fields, R_Lat (for our new latitude), and R_Lon (for our new longitude).
R_Lat
IF [Latitude]=0 THEN [Latitude]
ELSEIF ABS([Latitude])<5 THEN 0.5072 * (0+(0.0620-0) * ((ABS([Latitude])-0)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<10 THEN 0.5072 * (0.0620+(0.1240-0.0620) * ((ABS([Latitude])-5)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<15 THEN 0.5072 * (0.1240+(0.1860-0.1240) * ((ABS([Latitude])-10)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<20 THEN 0.5072 * (0.1860+(0.2480-0.1860) * ((ABS([Latitude])-15)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<25 THEN 0.5072 * (0.2480+(0.3100-0.2480) * ((ABS([Latitude])-20)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<30 THEN 0.5072 * (0.3100+(0.3720-0.3100) * ((ABS([Latitude])-25)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<35 THEN 0.5072 * (0.3720+(0.4340-0.3720) * ((ABS([Latitude])-30)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<40 THEN 0.5072 * (0.4340+(0.4958-0.4340) * ((ABS([Latitude])-35)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<45 THEN 0.5072 * (0.4958+(0.5571-0.4958) * ((ABS([Latitude])-40)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<50 THEN 0.5072 * (0.5571+(0.6176-0.5571) * ((ABS([Latitude])-45)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<55 THEN 0.5072 * (0.6176+(0.6769-0.6176) * ((ABS([Latitude])-50)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<60 THEN 0.5072 * (0.6769+(0.7346-0.6769) * ((ABS([Latitude])-55)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<65 THEN 0.5072 * (0.7346+(0.7903-0.7346) * ((ABS([Latitude])-60)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<70 THEN 0.5072 * (0.7903+(0.8435-0.7903) * ((ABS([Latitude])-65)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<75 THEN 0.5072 * (0.8435+(0.8936-0.8435) * ((ABS([Latitude])-70)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<80 THEN 0.5072 * (0.8936+(0.9394-0.8936) * ((ABS([Latitude])-75)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<85 THEN 0.5072 * (0.9394+(0.9761-0.9394) * ((ABS([Latitude])-80)/5)) * SIGN([Latitude])
ELSEIF ABS([Latitude])<90 THEN 0.5072 * (0.9761+(1-0.9761) * ((ABS([Latitude])-85)/5)) * SIGN([Latitude])
END
R_Lon
IF [Latitude]=0 THEN [Longitude]
ELSEIF ABS([Latitude])<5 THEN [Longitude] * (1-((ABS([Latitude])-0)/5) * (1-0.9986))
ELSEIF ABS([Latitude])<10 THEN [Longitude] * (0.9986-((ABS([Latitude])-5)/5) * (0.9986-0.9954))
ELSEIF ABS([Latitude])<15 THEN [Longitude] * (0.9954-((ABS([Latitude])-10)/5) * (0.9954-0.9900))
ELSEIF ABS([Latitude])<20 THEN [Longitude] * (0.9900-((ABS([Latitude])-15)/5) * (0.9900-0.9822))
ELSEIF ABS([Latitude])<25 THEN [Longitude] * (0.9822-((ABS([Latitude])-20)/5) * (0.9822-0.9730))
ELSEIF ABS([Latitude])<30 THEN [Longitude] * (0.9730-((ABS([Latitude])-25)/5) * (0.9730-0.9600))
ELSEIF ABS([Latitude])<35 THEN [Longitude] * (0.9600-((ABS([Latitude])-30)/5) * (0.9600-0.9427))
ELSEIF ABS([Latitude])<40 THEN [Longitude] * (0.9427-((ABS([Latitude])-35)/5) * (0.9427-0.9216))
ELSEIF ABS([Latitude])<45 THEN [Longitude] * (0.9216-((ABS([Latitude])-40)/5) * (0.9216-0.8962))
ELSEIF ABS([Latitude])<50 THEN [Longitude] * (0.8962-((ABS([Latitude])-45)/5) * (0.8962-0.8679))
ELSEIF ABS([Latitude])<55 THEN [Longitude] * (0.8679-((ABS([Latitude])-50)/5) * (0.8679-0.8350))
ELSEIF ABS([Latitude])<60 THEN [Longitude] * (0.8350-((ABS([Latitude])-55)/5) * (0.8350-0.7986))
ELSEIF ABS([Latitude])<65 THEN [Longitude] * (0.7986-((ABS([Latitude])-60)/5) * (0.7986-0.7597))
ELSEIF ABS([Latitude])<70 THEN [Longitude] * (0.7597-((ABS([Latitude])-65)/5) * (0.7597-0.7186))
ELSEIF ABS([Latitude])<75 THEN [Longitude] * (0.7186-((ABS([Latitude])-70)/5) * (0.7186-0.6732))
ELSEIF ABS([Latitude])<80 THEN [Longitude] * (0.6732-((ABS([Latitude])-75)/5) * (0.6732-0.6213))
ELSEIF ABS([Latitude])<85 THEN [Longitude] * (0.6213-((ABS([Latitude])-80)/5) * (0.6213-0.5722))
ELSEIF ABS([Latitude])<90 THEN [Longitude] * (0.5722-((ABS([Latitude])-85)/5) * (0.5722-0.5322))
END
Ok, so those calculations are a bit intense. But luckily, they are the only calculations that we need. Now let’s build our Robinson Projection.
Right click on [R_Lon] and drag it to Columns. When prompted, choose R_Lon without any aggregation
Right click on [R_Lat] and drag it to Rows. When prompted, choose R_Lat without any aggregation
Right click on your measure (Happiness score in the sample data) and drag it to Color. Choose MIN() when prompted
Drag [Polygon ID] to Detail
Drag [Sub Polygon ID] to Detail
Right click on [Point ID] and drag it to Path. When prompted, choose Point ID without any aggregation
Right click on the R_Lat axis, select “Edit Axis” and fix the axis to Start at -.6 and end at .6
Right click on the R_Lon axis, select “Edit Axis” and fix the axis to Start at -200 and end at 200
It should look something like this
You’ve probably noticed that we have the same problem as we did before. Countries that were not in our data are showing up in the map and colored the same as the lowest value. To remove these, you can follow the same process as we did with the first method (drag measure to filter, click on All Values, click on “Special”, select “Non-null values”). Another way to remove these is to just drag the country field from your data file to Detail.
Now, if we want to view those missing countries, but not apply the color, we could do exactly what we did with the first method (duplicate and stack). Or…we can do something a bit different. Let’s add a background image.
From the Google Drive, download the MapBackground.png file.
Now, let’s add that as a background image to our map
In the upper toolbar, click on Map
Click on Background Images
Select your data source (the same one used for the map)
Click “Add Image”
Give your background image a name like “Background Map”
Click “Browse” and select the MapBackground.png file
Under “X Field” make sure R_Lon is selected and set the Left = -180 and the Right = 180
Under the “Y Field” make sure R_Lat is selected and set the Left = -.51 and the Right = .52
Adjust the “Washout” for a lighter or darker map
The Background Image options should look like this when you’re done
You can also find or create your own background images, you’ll just have to play with them a bit to get them lined up correctly.
At this point, you’re pretty much done, but I would recommend a few finishing touches (similar to the first method)
Disable all Map Options
Hide the X and Y axes
Remove all Lines (Grid lines/Zero lines/etc.)
Remove Worksheet Shading (change White to Null)
And now, you’re map should look something like this
Alright, we’re going to cover one more method, but I promise, this one will be short. These two methods are great if you’re mapping countries, but what if you want to map cities?
Method 3: Using a CSV File (for cities)
In order to plot cities on a Robinson Projection, your data source will need to have the Latitude and Longitude values. Don’t have those? Well, let Tableau do the work for you. Here’s a link to a quick tutorial on how to export the generated Latitude and Longitude values from Tableau
Once you have those in your data source, connect to your file, and let’s start building. If you don’t have a file, you can download the “OlympicGames_Cities.xlsx” file from our Google Drive.
Building the View
So we have the coordinates for each of our cities, but similar to the last example, if we try to plot these in Tableau, it’s going to plot them using the Mercator Projection. So we are going to use the same R_Lon and R_Lat calculations that we used in the previous method. They’re crazy long, so I’m not going to paste them here, but you can find them earlier in the post.
Once you have those calculations built, the process is nearly identical to Method 2.
Right click on [R_Lon] and drag it to Columns. When prompted, choose R_Lon without any aggregation
Right click on [R_Lat] and drag it to Rows. When prompted, choose R_Lat without any aggregation
Drag City to Detail
Right click on the R_Lat axis, select “Edit Axis” and fix the axis to Start at -.6 and end at .6
Right click on the R_Lon axis, select “Edit Axis” and fix the axis to Start at -200 and end at 200
Make any other design adjustments desired (change mark type to circle, add field to color, adjust size, adjust transparency, etc)
Now, just follow the same exact process from Method 2 to add your Background Map and to put on the finishing touches. And you should have something like this
Well that’s it for this post. I hope you enjoyed it, and as always, please reach out with any questions or to share your creations. We would love to see them. Thanks for reading, and see you next time!
Welcome to our 2nd installment of Totally Useless Charts & How to Build Them, where we do…exactly what the name implies. Look at some totally useless charts and walk through, step by step, how to build them. If you missed the first installment, the goal of this series isn’t necessarily to teach you how to build these specific useless charts, but more to talk through the techniques, the approach, and the thought process behind each chart, so you can apply those concepts to your own custom charts.
In this installment we’re going to learn how to build “hand-drawn” bar charts in Tableau. These of course aren’t actually hand-drawn, but using some interesting techniques, and a lot of random numbers, we can kind of make them look that way. If you would like to follow along, you can download the workbook here, and the data here.
“Hand-Drawn” Bar Charts
First, let’s look at an example of what we’re talking about. Here is a viz that I published recently about relationships on the show “The Office”. You can check out the interactive viz here.
My goal was to make the entire viz look like an office desk belonging to everyone’s favorite receptionist/office manager, Pam Halpert. To do that, I had to make all of the visualizations appear to be “hand-drawn”, including the bar charts. Let’s zoom in one those.
Here we have two different bar charts, one for the Longest Relationship, and one for the Most Time in Relationships (by number of episodes). Today we’re going to be using different data, but the goal is still the same…build some bar charts that look “hand-drawn”.
Building Your Data Source
Let’s start with our data. For this example we’re going to look at the top 10 highest grossing films of all time. If you downloaded the sample data, you can find these in the “Data” tab.
Next, we need to do some densification. The first thing we need to do is to create a record for every line needed in each bar. If you look at one of these bars closely, you’ll see that it’s actually made up of a bunch of lines…one outer line (orange), and a number of cross lines (blue).
So we are going to densify our data with our first densification table, called “Lines” in the sample data. In this table, we have 1 record for our Outer Line, and 50 records for our Cross Lines
Then we’re going to join our “Data” table, and our “Lines” table using a join calculation with a value of 1 on each side.
But we’re not quite done yet. Now we have a record for each of our lines, but each of those lines is made up of multiple points. Our “Outer Line’ is made up of 4 points, and each of our “Cross Lines” is made up of two points
So we’re going to use one more densification table to create additional records for each of these points, for each of the lines. This table is called “Points” in the sample data
And we’re going to join this to our “Lines” table on the [LineType] Field.
Now for each of our 10 films, we have 4 records for our “Outer Line” and 100 records for our “Cross Lines”, 2 for each of the 50 lines in the “Lines” table.
Drawing the “Outer Lines”
Now we have our data, let’s start building our Totally Useless Chart. We’re going to start with the Outer Lines. To do this, and to make it a bit dynamic so you can play around with how the chart looks, we’re going to build 4 Parameters. Each of these is going to have a Data Type of “Float” and the Default values are below
Bar_Width = .6 (used for the height of each bar and the spacing between the bars)
Scale_Bar_Outer_Height = .03 (used along with a random number to jitter points vertically)
Scale_Bar_Outer_Length = .1 (used along with a random number to jitter points horizontally)
Cross Lines = 50 (used to limit the number of cross-lines in each bar. This is optional)
Next, we’ll start building our calculations. The first, and arguably most important of these calcs is going to be our [Jitter] calculation. We want a random number between -1 and 1. The Random() function will give us a random number between 0 and 1, so we can modify that by multiplying the random number by 2 and then subtracting 1 (so a random number of .6 would become .2, and a random number .4 would become -.2)
Jitter = Random()*2-1
Next, we need to calculate the length of each of our “bars”. We’re going to do this by comparing each value to the maximum value and then multiplying it by the Max Length, which in our case will be the number of “Cross” lines we have. So the highest grossing film, Avatar, will have a length of 50, since we have 50 “Cross” lines. ((2.847B/2.847B)*50). Number 10 on the list will have a length of around 26.6 ((1.515B/2.847B(*50). So first, let’s calculate our Max length.
Max Length = {MAX([Line ID)}
Next, we’ll want to divide the Box Office Gross for each movie by the value for the highest grossing movie. The result of this will be a percentage which we’ll then multiply by our [Max Length] field to get our [Outer Bar Length]
Outer Bar Length = ([Box Office Gross]/{MAX([Box Office Gross])})*[Max Length]
Now, we need to calculate the X and Y coordinates for the 4 points of each “Outer Line”. So under normal circumstances, point 1 and point 4 would start at 0, and point 2 and point 3 would just be the [Outer Bar Length]. So if you connected those points, it would start at 0 for point 1, go to the end of the line for point 2, stay at the end of the line for point 3, and then return to 0 for point 4. But we want this to look “hand-drawn”, and if I was drawing bar charts by hand, there is no way they would align that neatly. That’s where our [Jitter] and “Scale” parameters come in.
Outer_Bar_X
CASE [Points]
WHEN 1 then 0+([Jitter]*[Scale_Bar_Outer_Length])
WHEN 2 then [Outer Bar Length]+([Jitter]*[Scale_Bar_Outer_Length])
WHEN 3 then [Outer Bar Length]+([Jitter]*[Scale_Bar_Outer_Length])
WHEN 4 then 0+([Jitter]*[Scale_Bar_Outer_Length])
END
We just want to move these points slightly to get that “hand-drawn” effect, which is why we are using the “Scale” parameters. For that first point, if we just did 0+[Jitter], that value could fall anywhere between -1 and 1, which is a pretty significant shift. But using the [Scale_Bar_Outer_Length] parameter, we can increase that value to get more jitter, or decrease the value to get less jitter. Using a value of .1 in the parameter, means that the value for that first point would now fall somewhere between -.1 and .1.
Next, we need to calculate our Y coordinates for those same 4 points. Again, under normal circumstances, for point 1 we would add half of the bar width to our starting point (the middle of the bar), same for point 2, and then for points 3 and 4, we would subtract half of the bar width from the starting point. So, along with the X coordinates, it would look something like this.
This is where the [Bar_Width] parameter comes into play. We need to know how thick these bars should be. We’re using the [Rank] field as our starting point, so the first bar will start 0,1, the second bar will start at 0,2, and so on. But we don’t want the bars to overlap, or be right up against each other, so we can control that with the parameter. A larger value in this parameter will result in wider bars and less spacing, a smaller value will result in skinnier bars, and more spacing. A value of 1 will result in no spacing between the bars.
Also, similar to the calculation for the X coordinates, we are using that [Jitter] field along with a “Scale” parameter to control how much jitter there will be. So a larger number in the [Scale_Bar_Outer_Height] parameter will result in more vertical jitter, and a lower number will result in less. Here is the calculation for the Y coordinates.
Outer_Bar_Y
CASE [Points]
WHEN 1 then [Rank]+([Bar_Width]/2)+([Jitter]*[Scale_Bar_Outer_Height])
WHEN 2 then [Rank]+([Bar_Width]/2)+(([Jitter]*[Scale_Bar_Outer_Height])/2)
WHEN 3 then [Rank]-([Bar_Width]/2)+(([Jitter]*[Scale_Bar_Outer_Height])/2)
WHEN 4 then [Rank]-([Bar_Width]/2)+([Jitter]*[Scale_Bar_Outer_Height])
END
Now we have all of the calculations needed to draw our “Outer” lines. So let’s do that
Drag [Line Type] to the filter shelf and filter on “Outer Lines”
Right click on [Outer_Bar_X], drag it to Columns, and when prompted, choose [Outer_Bar_X] without aggregation
Right click on [Outer_Bar_Y], drag it to Rows, and when prompted, choose [Outer_Bar_Y] without aggregation
Change the Mark Type to “Line”
Right click on [Rank], select “Convert to Dimension” and then drag [Rank] to Detail
Right click on [Points], drag it to Path, and when prompted, choose [Points] without aggregation
Right click on the Y-axis, select “Edit Axis”, and check the box labelled “Reverse” under Scale
When that’s finished, you should have something that looks like this. There are 10 “bars”, with all 4 points in each bar slightly jittered to give it that “hand-drawn” look.
Next, we need to add the “Cross Lines”
Drawing the “Cross Lines”
To help understand the approach we’re going to take, think about taking each of these bars and breaking them into individual segments. So, for example, our first bar has a length of 50 (think back to the Max Length calculation). So we want to break that into 50 individual segments and draw a diagonal line from the top left of the segment to the bottom right of the segment.
The image above is roughly what it would look like if we draw perfect lines across those segments. But we don’t want perfect lines. We want “hand-drawn” lines. So we’re going to leverage our [Jitter] field and our “Scale” parameters once again.
So let’s build our X and Y calculations. Remember when we built our data source, for our “Cross Lines”, we needed two points, 1 and 2. So Point 1 is going to start the line at the top left of our segment, and Point 2 is going to end the line at the bottom right of our segment. Here is the calculation
Cross_X = if [Points]=1 then [Line ID]-1 + ([Jitter]*[Scale_Bar_Outer_Length]*2) else [Line ID]+([Jitter]*[Scale_Bar_Outer_Length]*2) END
Here we are calculating the position for both points on the X axis. For the first point, when [Points]=1, we want to use our [Line ID] value and subtract 1, so we’re starting at the beginning of our segment (ex. line 1 will start at 0, line 2 will start at 1, line 3 will start at 2, and so on). When [Points]=2, we are going to use just the [Line ID] value (ex. line 1 will end at 1, line 2 will end at 2, line 3 will end at 3). And then we’re just using our [Jitter] field and our “Scale” parameter to jitter these points a little bit, similar to what we did with the “Outer” lines. You may notice that there is a “*2” in these calculations. I added these so I could re-use my same parameters, but could add a little extra jitter to the Cross Lines. I figured if these were actually being done by hand there would be a lot more variation in these lines, compared to the “Outer” lines.
Now let’s calculate our Y coordinates. Similar to how we calculated the Y coordinates for the “Outer” lines, we want one of our points to be half the width of the bar above our starting point, and the other one, half the width of the bar below the starting point. And then we want to jitter them. Here’s the calculation for the Y coordinates.
Cross_Y = if [Points]=1 then [Rank]-([Bar_Width]/2)+([Jitter]*[Scale_Bar_Outer_Height]*2) else [Rank]+([Bar_Width]/2)+([Jitter]*[Scale_Bar_Outer_Height]*2) END
Now this is a little bit confusing because we reversed our axis in an earlier step. So, for Point 1, instead of adding half of the width of the bar to our starting point, the [Rank] field, we need to subtract it from the starting point, to get it to appear above the bar (because the axis is reversed). So when [Points]=1 we’ll subtract half of the width of the bar ([Bar_Width]/2) from the starting point, [Rank]. When [Points]=2, we’ll add half of the width of the bar to the starting point. And then once again we’re using the [Jitter] field, the “Scale” parameter, and then multiplying by 2 to get a little extra jitter. If you wanted to reverse the direction of these lines, so they go from top right to bottom left, just change the calc so when [Points]=1 you add, and when [Points]=2 you subtract.
Now let’s build it.
Drag [Line Type] to the filter shelf and filter on “Cross Lines”
Right click on [Cross_X], drag it to Columns, and when prompted, choose [Cross_X] without aggregation
Right click on [Cross_Y], drag it to Rows, and when prompted, choose [Cross_Y] without aggregation
Change the Mark Type to “Line”
Right click on [Line ID], select “Convert to Dimension” and then drag [Line ID] to Detail
Right click on [Rank], select “Convert to Dimension” and then drag [Rank] to Detail
Right click on [Points], drag it to Path, and when prompted, choose [Points] without aggregation
Right click on the Y-axis, select “Edit Axis”, and check the box labelled “Reverse” under Scale
Once complete, you should have 50 “Cross Lines” for each of your “bars”.
Now we just need to bring it all together
Combining the Lines
We have our “Outer” lines, and we have our “Cross” lines, and because we have separate data points for each of these (because of the way we structured our data) we can bring them together in the same view pretty easily. We just need to 2 more “Final” calculations for the X and Y coordinates.
Final_X = if [Line Type]=’Outer Lines’ then [Outer_Bar_X] else [Cross_X] END
Final_Y = if [Line Type]=’Outer Lines’ then [Outer_Bar_Y] else [Cross_Y] END
These are pretty straightforward, but basically, if the [Line Type]=”Outer Lines” use the X and Y values from the “Outer_Bar” fields. Otherwise, use the X and Y values from the “Cross” fields. Now let’s build our “bars” with these “Final” calcs.
Right click on [Final_X], drag it to Columns, and when prompted, choose [Final_X] without aggregation
Right click on [Final_Y], drag it to Rows, and when prompted, choose [Final_Y] without aggregation
Change the Mark Type to “Line”
Right click on [Line ID], select “Convert to Dimension” and then drag [Line ID] to Detail
Right click on [Rank], select “Convert to Dimension” and then drag [Rank] to Detail
Drag [Line Type] to Detail
Right click on [Points], drag it to Path, and when prompted, choose [Points] without aggregation
Right click on the Y-axis, select “Edit Axis”, and check the box labelled “Reverse” under Scale
And now you should have everything together on the same view!
Wait…that doesn’t look right. We don’t want all of those extra “Cross” lines on our shorter bars. Luckily, we can filter those out pretty easily with a calculated field. This is just a boolean calc that checks to see if the Line ID is less than the length of the bar. Remember from earlier that the Line ID corresponds to the right side, or the end of each of these “Cross” lines. So we only want to keep the lines where that value is less than the length of the bar.
Extra Lines Filter = [Line ID]<=[Outer Bar Length]
Now just drag the [Extra Lines Filter] field onto the Filter shelf, and filter on TRUE and voila!
There is one more step that’s completely optional. The way we set up this data source, we can have up to 50 “Cross” lines for the largest bar. But maybe you want less than that. I like to make my visualizations as dynamic as possible so I can play around with how it looks. Earlier we created a parameter called [Cross Lines]. We can use that parameter to determine how many lines we want to use. We’re just going to create one additional calculated field.
Max Cross Lines Filter = [Line ID]<=[Cross Lines]
Just drag that field onto the Filter shelf, filter on TRUE, and then right click on the pill and choose “Add to Context”. Now you can adjust the number of lines, and in doing so, the spacing between the lines. Here’s what it looks like with 30 lines instead of 50.
If you want to use more than 50 lines, just add some additional rows to the “Lines” densification table.
Final Touches
So now our view is built, but there is one critical piece of information missing from our chart…Row Labels. There are a few different ways you can add these, but I’m going to cover two quick options; Shapes and Labels.
For the viz that I shared earlier, I was publishing it to Tableau Public, which has pretty limited options when it comes to fonts, and I really wanted a “hand-drawn” font. What I ended up doing was creating custom shapes for each of my labels in PowerPoint. If you decide to go this route, one thing you want to make sure that you do is to set the size of each of the text boxes equal. If the text boxes are different sizes when you save them as images, it will look like the text is a different size for each value because Tableau will attempt to “normalize” them.
So first, create your shapes in PowerPoint. Here, I inserted 10 text boxes, typed my movie names, set the alignment to “Right”, and then set the Font to “Caveat”. Then I clicked and dragged to highlight all 10 text boxes, and in the top right corner of PowerPoint, in the Shape Format options, I set the “Width” so that they would all be the same size. regardless of how long the text in each box actually is.
Then I right-clicked on each image, saved them to a folder in my “Shapes” repository. Finally, I created a new sheet using the “Shapes” mark type, positioned them by Rank, and then fixed and reversed the axis so they would align with the bars. Then you can throw these two sheets in a container on your final dashboard and have something like this.
So that’s an option if your data is static and you don’t have a lot of values. This would be nearly impossible to maintain if new values were constantly being introduced, and would be way too much work if there were a lot of values in your data source. In those cases, you may need to go with more traditional labels and be limited to the available fonts. But even that can be a little bit tricky because these are lines, not bars.
For this we need one more calculated field. We only want to label 1 point for each of our bars but we can’t filter out any points. We also can’t use a calculation that results in some null values and some populated values for a given Line Type (because it can inadvertently remove sections of the lines we worked so hard to draw).
Label Name = if [Line Type]=’Outer Lines’ then [Name] END
Now just drag the [Label Name] field to Label and then set the Label options as follows.
You can choose whichever font type and size you prefer, but make sure to set the Alignment to “Top Left’, select “Line Ends” under Marks to Label, and under Options, de-select “Label end of line”. Between the calculated field and these options, only 1 Label will appear per bar, and it will appear to the top left of Point 1 of the “Outer” line (which is the bottom left point in each bar). It should end up looking something like this.
Those are a couple of ways to add Row Labels to your “hand-drawn” bar charts. If you want to add value labels as well you can follow a pretty similar process, but it’s a little trickier. This post is already long enough so I’m not going to go into that, but if you make it this far and want to add value labels, please reach out and I’d be happy to help you. Or you can take the easy way out and do what I did, and just create an image of a “hand-drawn” axis and add it to your dashboard.
Thank you so much for reading, and keep an eye on the blog for more ‘Totally Useless Charts & How to Build Them’!
Welcome to installment #3 of the “It Depends” blog series. If you’re not familiar with the series, each installment will cover a question where there is no clear definitive answer. We’ll talk through all of the different scenarios and approaches and give our recommendations on what approach to use and when. The question we are tackling this week is “How can dashboard actions be used to filter on multiple selections in a sheet?”. Pretty easy right. Just use a filter action…or a set action…or set controls…or a parameter action. There are clearly a lot of different ways to accomplish this, but which one should you use? And I think you know the answer…It depends!
But before we start…why does this even matter? Why not just throw a quick filter on the dashboard and call it a day? For me, it’s about user experience. When a user sees a mark on a dashboard and they want to dig into it, would they rather a) mouse over to a container filled with filters, find the right filter, click on a drop down, search for the value they want to filter, click that value, and then hit apply, or b) just click on the mark? Don’t get me wrong, quick filters are important, and often times essential, but they can also be a bit clunky, and can hinder performance. So whenever possible, I opt for dashboard actions.
Using dashboard actions to filter from a single mark is pretty easy, but the process gets a little more complicated when you want to be able to select multiple marks. And as developers, we have a choice on whether or not we want to push that burden onto our users. We have to decide what’s more important, ease of use for our users, or ease of setup for ourselves. We’ll also need to take performance into consideration. Those will be the two biggest decision points for which of these 4 methods to implement. And those methods are, as I mentioned above; Filter Actions, Set Actions, Set Controls, and Parameter Actions. There are no restrictions on these four approaches. Any one of them could be used in any scenario, but it’s up to you to weigh the importance of those two decision points and to determine which method gives you the right balance for your specific situation.
The Four Methods
Filter Action – This is the easiest of the four methods to implement and provides users with a better user experience than quick filters, both in terms of interacting with the dashboard and performance. But there are some downsides. First off, in order to select multiple marks, users would either need to hold down the CTRL key while making their selections, or those marks would need to be adjacent so that users could click and drag to select multiple marks together. Not ideal. Also, with Filter Actions, the selections cannot be used in other elements of your dashboard (calculated fields, text boxes, etc), like they can with other methods. And finally, and this one might be a bit personal, you can’t leverage any of the highlighting tips that I discussed in Installment 1 of this series, Techniques for Disabling the Default Highlighting in Tableau. Technically, you could use the “Highlight Technique” but because you can’t use the selected values in a calculated field, there would be no clear way to identify which mark(s) have been selected. Full disclosure, I never use this technique, but I’m going to include it because technically it will accomplish what we’re trying to do.
Set Action – This method provides a somewhat similar user experience to Filter Actions. Better performance than quick filters, but users still need to hold CTRL, or click and drag to select multiple marks. The main benefit of this approach over Filter Actions is it’s flexibility. You can use it to filter some views, and in other views you can compare the selected values to the rest of the population. This type of analysis isn’t possible with Filter Actions. With Filter Actions, your only option is to filter out all of the data that is not selected. With Set Actions, you can segment your data into selected and not selected. You can also use those segments in calculated fields, which is another huge benefit over Filter Actions.
Set Controls – This method provides all of the same benefits of Set Actions, but with one major improvement. Users do not need to hold CTRL or click and drag. They can click on individual marks and add them one by one to their set. This method is a little more difficult to set up than the previous two, but in my opinion, it is 100% worth it. It’s possible that it may be marginally worse for performance, but I have never had performance issues using this method. The only things that I don’t like about this approach are that you can’t easily toggle marks in and out of the set (you can do this with a second set action in the ‘menu’, but it’s a bit clunky), and you can’t leverage the Filter Technique for disabling the default highlighting (we’ll talk more about this later).
Parameter Action – In my opinion this approach provides the best user experience. Users can select marks one by one to add to their “filter”, and can also, depending on how you set it up, toggle marks in and out of that filter. The main downside here is performance. This technique relies on some somewhat complicated string calculations that can really hurt performance when you’re dealing with large or complex data sets. It’s also the most difficult to implement. But when performance isn’t a concern, I love this approach.
Method Comparison
So which method should you use? Let’s take a look at those two decision points that I mentioned earlier, User Experience and Performance.
If neither of these decision points are important then you can ignore this entire post and just use a quick filter. But that’s usually not the case. If Performance is important, but you’re less concerned with User Experience, you can use either a Set Action or a Filter Action (but I would recommend Set Actions over Filter Actions). If User Experience is important and Performance is not a concern, you can use a Parameter Action. And if both User Experience and Performance are important, then Set Controls are the way to go. But as I mentioned earlier, you are not limited to any of these methods in any scenario, and there could be other elements that influence your decision. So let’s do a closer comparison of these methods.
*Flexibility refers to how the selected values can be used in other elements of the dashboard **Default Highlight Techniques refer to which technique (from Installment 1) can be used with each method
So now we have a pretty good idea which method we should use in any given scenario. Now let’s look at how to implement each of these.
Methods in Practice
For each of these methods we are going to use this ridiculously simple Sales dashboard. We’re going to use the bar chart on the left (Sales by Subcat) to filter the line chart on the right (Sales by Quarter). If you’d like to follow along, you can download the sample workbook here.
Filter Action
As I mentioned earlier, this is the easiest method to set up, but will require your users to either hold CTRL or click and drag to select multiple marks.
Go to Dashboard > Actions and click “Add Action”
Select “Filter” from the list of options
Give your Action a descriptive Name (ex. SubCat Filter)
Under “Source Sheets” select the worksheet that will drive the action. In this example it is our bar chart “Sales by SubCat”.
Under “Target Sheets” select the worksheet that will be affected by the action. In this example it is our line chart “Sales by Quarter”
Under “Run action on” choose “Select”
Under “Clearing the selection will” choose “Show All Values”
Click “OK”
When you’re finished, your “Add Filter Action” box should look like this
And now when we click on a mark, hold CTRL and click multiple marks, or click and drag to select multiple marks our line chart will be filtered to just the selected value(s). And when we click in the white space on that sheet, or the selected value (when only one mark is selected) the filter will clear and the line chart will revert to show all values
Set Action
Filtering with Set Actions is slightly more involved but still pretty straightforward. For this method we need to create our set, we need to add that set to the Filter Shelf on our line chart, and then we need to add the dashboard action to update that set.
Go to the sheet that will be affected by the action. In this case it is our line chart (Sales by Quarter).
Create a set for the field that will be used for the filter. In this case it is our [Sub-Category] field.
Right click on [Sub-Category]
Click on “Create”
Click on “Set”
Click the “All” option to select all values
Click “OK”
Add the filter to the sheet that will be affected by the action (Sales by Quarter).
Drag the [Sub-Category Set] to the filter shelf on the “Sales by Quarter” worksheet
It should default to “Show Members in Set”, but just in case, click on the drop-down on the [Sub-Category Set] pill on the filter shelf and make sure that is the option selected
Add the dashboard action to update the [Sub-Category Set]
Navigate back to the dashboard
Go to Dashboard > Actions and click “Add Action”
Select “Change Set Values” from the list of options
Give your Action a descriptive Name (ex. SubCat Set Action Filter)
Under “Source Sheets” select the worksheet that will drive the action. In this example it is our bar chart “Sales by SubCat”.
Under “Target Set” select the set that was used as the filter in the previous steps. In this case it is our [Sub-Category Set]
Under “Run action on” choose “Select”
Under “Running the action will” choose “Assign values to set”
Under “Clearing the selection will” choose “Add all values to set”
Click “OK”
When you’re finished your “Add Set Action” box should look like this.
The way this Set Action configuration works is that each time you make a new selection, the contents of the set are being completely overwritten with the newly selected values. That’s what the “Assign values to set” option does. And when you clear the selection, by clicking on white space in the sheet, or the last selected value, the contents of the set are replaced again with all of the values. That’s what the “Add all values to set” option does.
I would recommend one additional step if you’re using this method to override the default highlighting. When using Set Actions you are somewhat limited on what techniques you can use for this, but the “Highlight Technique” works great. You can read about how to use that technique here. Once you’ve added the Highlight Action, just put the [Sub-Category Set] field on color on your “Sales by Subcat” sheet and select the colors you want to display for marks that are selected (in the set) and marks that are not selected (out of the set). When you’re done, your dashboard should look and function like this. Keep in mind that similar to Filter Actions, users will need to hold CTRL and click, or click and drag to select multiple marks.
Set Controls
Setting up our filter with Set Controls is going to be very similar to Set Actions, but with one major difference. The way Set Controls work is that they allow you to select marks one by one and either add them to your set or remove them from your set. This is a great feature, but it makes filtering with them a little tricky.
If we were to start with all of our values in the set, we couldn’t just click on a value in our bar chart to add it to the set, since it’s already there (as well as all of the other values). So we need to start with the set empty and then start adding values when we click on them. But if our set is empty, and we use that set as a filter, as we did in the previous example, then our line chart will be blank until we start adding values. And we don’t want that. We want the line chart to show all of the values, until we start selecting values, and then we want it to just show those values. And we can accomplish this with a calculated field. So first, we’re going to create our set, then we’ll create our calculated field, then we’ll add that field as a filter to our line chart, and then we’ll add the action to update the set.
Go to the sheet that will be affected by the action. In this case it is our line chart (Sales by Quarter).
Create a set for the field that will be used for the filter . In this case it is our [Sub-Category] field. (skip this step if you followed along with the Set Action example)
Right click on [Sub-Category]
Click on “Create”
Click on “Set”
Click the “All” option to select all values
Click “OK”
Create a calculated field called [SubCat Filter]
{ FIXED : COUNTD([Sub-Category Set])}=1 OR [Sub-Category Set]
Add the filter to the sheet that will be affected by the action (Sales by Quarter).
Drag the [SubCat Filter] field to the filter shelf on the “Sales by Quarter” worksheet
Filter on “True”
Add the dashboard action to update the [Sub-Category Set]
Navigate back to the dashboard
Go to Dashboard > Actions and click “Add Action”
Select “Change Set Values” from the list of options
Give your Action a descriptive Name (ex. SubCat Set Control Filter)
Under “Source Sheets” select the worksheet that will drive the action. In this example it is our bar chart “Sales by SubCat”.
Under “Target Set” select the set that was used as the filter in the previous steps. In this case it is our [Sub-Category Set]
Under “Run action on” choose “Select”
Under “Running the action will” choose “Add values to set”
Under “Clearing the selection will” choose “Remove all values from set”
Click “OK”
When you’re finished your “Add Set Action” box should look like this.
So there are a couple of things we should cover here, starting with the calculated field. Here it is again.
{ FIXED : COUNTD([Sub-Category Set])}=1 OR [Sub-Category Set]
Sets only have two possible values; IN or OUT. The set may contain hundreds or even thousands of values from the source field, but the sets themselves can only have these two values. So if we use a FIXED Level of Detail expression and count the distinct values, the result will be either 1 or 2. If the set is empty, the value for every record will be OUT, so the result of the LOD will be 1. Similarly, if the set contains all values, the value for every record will be IN, so the result of the LOD will still be 1. But if some values are in the set (IN) and other values are not in the set (OUT), then the result of the LOD will be 2 (IN and OUT).
So the first part of this calculated field ( FIXED : COUNTD([Sub-Category Set])}=1) will be true for all records when the set is empty, or if it contains all values. The second part of this calculated field (OR [Sub-Category Set]) will only be true for records in the set. So when we start with an empty set, the overall result of this calculated field will be True for every record, so everything will be included in our line chart. As soon as we add a value to our set, the first part becomes false for every record, but the second part becomes true for the values in our set. Because we are using an OR operator, the overall result, once we click on some values, will be true for the selected values and false for the rest of the values.
Next, let’s look at the Set Control options. We are starting with an empty set. Each time we click on a new mark, that value will be added to our set. That’s what the “Add values to set” option does. Unlike the “Assign values to set”, it does not override the set, it just adds new values to the existing ones. And then when we click on some white space in the sheet, or the last selected value, the set will go back to being empty. That’s what the “Remove all values from set” option does.
And just like in the previous example, I would recommend using the “Highlight Technique” covered here, and then adding the [SubCat Filter] field to color on the bar chart (Sales by SubCat).
And now your dashboard should look and function like this. Notice that you no longer need to CTRL click, or click and drag, to select multiple values. Nice!
Parameter Action
This method is by far the most complex, but if done correctly, it provides a really smooth user experience. The thing that I like most about this approach is that you can set it up so that you can “toggle” values in and out of the filter. There are a few extra steps in this approach, and some somewhat complex calculations. We need to create our parameter, create a calculated field that will be passed to that parameter, add that field to Detail on our bar chart, create a calculated field for our filter, add that filter to our line chart, and add the dashboard action that will update the parameter.
Create a string parameter called [SubCat Select] and set the “Current value” to the pipe character “|”
Create a calculated field called [SubCat String]
IF CONTAINS([SubCat Select], ‘|’ + [Sub-Category] + ‘|’) THEN REPLACE([SubCat Select],’|’ + [Sub-Category] + ‘|’,’|’)
ELSE [SubCat Select] + [Sub-Category] + ‘|’
END
Go to the sheet that will drive the parameter action and drag the [SubCat String] field to Detail. In this example, that is the “Sales by SubCat” sheet
Create a calculated field called [SubCat String Filter]
[SubCat Select]=’|’ OR CONTAINS([SubCat Select],’|’+[Sub-Category]+’|’)
Add the filter to the sheet that will be affected by the action (Sales by Quarter).
Drag the [SubCat String Filter] field to the filter shelf on the “Sales by Quarter” worksheet
Filter on “True”
Add the dashboard action to update the [Sub-Category Set]
Navigate back to the dashboard
Go to Dashboard > Actions and click “Add Action”
Select “Change Parameter” from the list of options
Give your Action a descriptive Name (ex. SubCat Parameter Filter)
Under “Source Sheets” select the worksheet that will drive the action. In this example it is our bar chart “Sales by SubCat”.
Under “Target Parameter” select the parameter that was set up in the previous steps. In this example it is [SubCat Select]
Under “Source Field” select the [SubCat String] Field
Under “Aggregation”, leave set to “None”
Under “Run action on” choose “Select”
Under “Clearing the selection will” choose “Keep Current Value”
Click “OK”
When you’re finished, your “Add Parameter Action” box should look like this
Alright, let’s talk through some of those calculations, starting with the [SubCat String] Field. Basically, what this calculation is doing is building and modifying a concatenated string of values. Here’s that calculation again.
IF CONTAINS([SubCat Select],’|’ + [Sub-Category] + ‘|’) THEN REPLACE([SubCat Select],’|’ + [Sub-Category] + ‘|’,’|’) ELSE [SubCat Select] + [Sub-Category] + ‘|’ END
We set up our parameter to have a default value of just the pipe character (“|”). I’ll explain the use of the pipes a little later on. The first line of the calculation looks to see if the selected value is already contained in our string. So it’s searching our entire concatenated string for the pipe + selected value + pipe (ex. |phones|). If it finds that value in our string, it will replace it with just a pipe. So for our example, if our parameter value was |phones|binders|storage| and we click on phones, it will replace “|phones|” with a pipe, leaving “|binders|storage|”
The second line of this calculation will add the selected value. The calc has already tested to see if it’s there in the previous step, and if it’s not, this line will add it along with a pipe. Now let’s look at our parameter action…in action.
Take a look at the parameter at the top. As we click on each Sub-Category, its added to our string. Now look what happens when we click on those selected marks again.
As we click on each previously selected mark, that mark is removed from our string, until we’re left with just our starting value, the pipe.
The reason I use pipes on both sides, instead of just a comma separated list is to avoid situations where one potential value could be equal to a part of another value (ex. Springfield and West Springfield). The pipes ensure we are matching exact values in the next step, our filter calculation. Here’s that filter calculation again.
[SubCat Select]=’|’ OR CONTAINS([SubCat Select],’|’+[Sub-Category]+’|’)
This is pretty similar to the filter that we created in our Set Controls example. The first part is checking to see if our concatenated string is “empty”, meaning nothing has been selected yet and it’s just a pipe. If that’s the case, the result of this calculated field will be true for every record. The second part of the calculated field is checking the [Sub-Category] field in each record and seeing if that value is contained within our parameter string (along with the starting and ending pipe). If it is, the result of this calculation will be True for those records and False for all of the other records.
When using this method, I would highly, highly, highly recommend using the “Filter Action” technique for disabling the default highlighting. You can find it here. This technique not only masks the selection, but it also automatically de-selects a mark after you click on it. That is really important when using this method. Once you add that Filter Action, just add the [SubCat String Filter] field to color, and you should be good to go. Here’s what it looks like in action.
And there you have it. Four different methods for filtering your dashboards on multiple selections. As I mentioned before, some of these are much more complex than others, but they provide a much better user experience. In my opinion, it’s worth it to put in the extra time and effort to save your users frustration later on. I hope you enjoyed this post, and keep an eye out for more installments of “It Depends”.
Welcome to our new series, Totally Useless Charts & How to Build Them. In each installment of this series we’ll look at one very custom chart, something with almost no real use cases, and we’ll walk through, step by step, how to build it. The purpose of this series isn’t necessarily to teach you how to build these specific useless charts, it’s more about talking through the techniques, the approach, and the thought process behind each chart. Our hope is that seeing how we went about building these will help you with your own custom charts. But if you do somehow find a great use case for one of these charts, by all means, please download the workbook and use it as your own.
In this first installment we’re going to learn how to build Lotus Flowers in Tableau. It’s not a requirement, but it may be a good idea to review Part 1 and Part 2 of the Fun With Curves Series before proceeding. To follow along, you can download the workbook here, and the data here.
Lotus Flower
First, let’s take a look at what we’re trying to build. Below is a lotus flower with 10 petals, which means we have a total of 11 polygons; 1 circle and 10 petals. The circle is fairly easy to build using the techniques in Part 1 mentioned above. The petals are a little more complicated. But first thing’s first…we need some data.
Building Your Data Source
Let’s start with our data. For this example we’re going to build 12 lotus flowers and we’re going to use the value from our data source to size the flowers appropriately. We’ll start with the tab titled ‘Source Data’.
Next, we’re going to do some densification to get the number of polygons needed for each of the flowers. Below I have 1 record for the Circle and 24 records for the petals. We’re going to build this in a way that will let you choose how many petals you want to display (up to 24). This data can be found in the ‘Polygons’ tab in the sample data.
Now we’re going to join these two sources together using a join calculation (value of 1 on each side). The result will be 25 records for each of our 12 ‘Base’ records.
Next, we need to do a little more densification, but this time it’s a little trickier. For our circle, we want at least 50 points for a relatively smooth curve. For our petals, we actually need to draw 2 lines for each petal, one for the left side of the petal (Min) and one for the right side of the petal (Max), and then join those together. Pretty confusing right? We’ll talk about this in a lot more detail. This table is a little too large to include a screenshot, but take a look at the ‘Densification’ tab in the sample data.
For our circles, we have 50 records. We have two numerical fields, [Points] and [Order], that both run from 1 to 50 and a [Type] field to identify that these points are for our circles. For our petals, we have 100 records. We still have the same two numerical fields, but the values are a little different. We have an [Order] field that runs from 1 to 100, and a [Points] field that runs from 1 to 50 and then back down from 50 to 1. We also have a [Side] field with values of Min or Max. The Min records will be used to draw the left side of our petals. The Max records will be used to draw the right side of our petals. And then we have a [Type] field to identify that these records are for our petals. Now we just need to join this table to our data source on the [Type] Field.
Building Your Circles
If you have read through Part 1 of the Fun With Curves series, then you may remember that in order to draw a circle in Tableau, we only need 2 inputs; the distance of each point from the center of the circle (the radius), and the position of each point around the circles (represented as a percentage).
Let’s start with the first input, the radius. We are going to size our circles based on the Value field in the Source Data. We want the area of our circles to represent the value in the data. So we have the area of each circle, we just need to use those values to calculate the radius of each circle. We can do this with the simple calculation below.
Radius = SQRT([Value]/PI())
Next, we need to calculate the position of each point around the circle. I’m not going to go into too much detail on this, but you can read more about it in the post mentioned above. To calculate this, we need the maximum number of points for our Circles (50), and we need the [Points] field (values 1 thru 50). For the max point calculation I am going to use an LOD because the max number of points for our circles, may not always align with the max number of points in our data source (but in this case it does).
Max_Point = {FIXED [PolygonType] : MAX([Points])}
Circle_Position = ([Points]-1)/([Max_Point]-1)
Next, we just need to plug the [Radius] and the [Circle_Position] values into our X and Y formulas for plotting points around a circle.
Right click on [Circle_X] and drag it to columns. When prompted, choose [Circle_X] without any aggregation
Right click on [Circle_Y] and drag it to rows. When prompted, choose [Circle_Y] without any aggregation
Right click on [Base_ID], change it to a Dimension, and drag it to Detail
Right click on [Order], change it to a Dimension, and drag it to Path
Drag [Type] to Filter Shelf and filter to ‘Circle’
Change the Mark Type to Polygon
Now you should have something that looks like this.
Although it looks like one big circle, we actually have all 12 circles in this view. They’re just stacked on top of each other. So next we need to space these out a little bit. There are a lot of different techniques to do this, but here’s one I like to use to create Trellis Charts. This technique works great when you have a sequential ID field, which we do (Base_ID).
First, we’re going to create a numeric parameter that will allow us to choose the number of columns we want to create. We’ll call the parameter [Column Count] and set the value to 3. Next, we’re goin to use the [Base_ID] field to break our circles into columns and rows, starting with row.
Row = CEILING([Base ID]/[Column Count])
Column = [Base ID]-(([Row]-1)*[Column Count])
Now right click on both of these fields, change them to Dimensions, and then drag them to the appropriate shelf (Row to Rows, Column to Columns). The result should look something like this.
Building Your Petals
Alright, so this part is a little more complicated. I’m going to start by reviewing the basics of how you build these shapes, but I’m going to skim over the calculations since those will change significantly once we try to build these petals around our circle. No need to follow along with the workbook during this section.
So here are the basics. Let’s start by drawing a Bezier Curve with 4 control points. Our line is going to start at 0,0 and end at 5,10. Wow, this is easy, we already have the coordinates for 2 of the points!
Let’s take a look at our inputs. Our line will have a height of 10 and a width of 5. I’ve also built 2 parameters that we’ll use to calculate the 2nd and 3rd set of points. You can experiment with different values here, but these seem to work pretty well. We need a total of 8 values (4 sets of X and Y).
Point 1 will be the start of the line. In this case, it’s 0,0
Point 2 will appear on the same X axis as Point 1, but will be somewhere between the start and end of the line on the Y axis. I like to place it two thirds of the way, or .67 (the value in the P2 Parameter Input above). So the coordinates for Point 2 will be 0 and 6.7 (P2 Parameter x Height of the line)
Point 3 will appear on the same X axis as Point 4, and will appear somewhere between the start and end of the line on the Y axis (similar to P2). I like to place it halfway, or .5 (the value in the P3 Parameter Input above). So the coordinates for Point 3 will be 5 and 5 (P3 Parameter x Height of the line).
Point 4 will be the end of the line. In this case, it’s 5,10
If you were to plot these 4 points, you would have a jagged line like you see in the image above. But look what happens when we plug those values into our Bezier calculations
X = (1-[T])^3*[P1_X] + 3*(1-[T])^2*[T]*[P2_X] + 3*(1-[T])*[T]^2*[P3_X] + [T]^3*[P4_X]
Y = (1-[T])^3*[P1_Y] + 3*(1-[T])^2*[T]*[P2_Y] + 3*(1-[T])*[T]^2*[P3_Y] + [T]^3*[P4_Y]
Alright, we are halfway there! Kind of. So now we have a line that will create 1/2 of one of our petals. But in order to turn this into a petal shaped polygon, we need another line that’s a mirror image of this one.
This is where the Min and Max records come in. We need to calculate our 4 sets of coordinates for both sides. Luckily, most of the values are actually the same. P3 and P4 are going to be identical for both lines. And the Y values for P1 and P2 are the same. The only differences are the X values for P1 and P2. And to calculate those we just add the width of the whole petal (width x2) to our starting point. And if we were to plug these coordinates into the same calculations, we have this.
Now this is where the [Order] field comes into play. To make this one single polygon instead of two separate lines, we can use the [Order] field on Path and change the Mark Type to polygon
On the Left (Min) side, we have points 1 thru 50, running from the bottom left up to the top middle. On the right (Max) side, we have points 1 thru 50 running from the bottom right up to the top middle. But then we have the [Order] field (on label in the image above). This field runs from the bottom left to the top middle to the bottom right, in one continuous flow, from value 1 to 100. This is what makes it a single continuous polygon.
Ok, so that’s how we would build 1 single petal shaped polygon, perfectly positioned facing directly upward. But that’s not what we’re trying to do. We’re trying to build a dynamic number of petals, evenly spread around a circle and facing outward in the appropriate directions. So let’s do that.
Building Your Petals (for real this time)
We’re going to use a similar approach to what was described above, but everything needs to be plotted around a circle. So any calculations we use to determine our 4 points are going to have to run through those X and Y calculations for plotting points around a circle. That means, for all of our points, P1 thru P4 for both sides, we need to calculate two things; the distance from the center of the circle, and the position around the circle. But before we calculate those specific points there are a few other calculations that we’ll need. We also need a parameter, called [Petal Count] that will allow us to select how many petals we want. This should be a numeric parameter, and let’s set the value to 10 (I recommend using a range, allowing values from 4 to 24)
T = ([Points]-1)/([Max_Point]-1) – this is the same as the [Position] calc used earlier. It’s used to evenly space points between 0% and 100%
Petal_Count_Filter = [Polygon ID]<=[Petal Count] – this will be used as a filter to limit the number of petals displayed to what is selected in the [Petal Count ] parameter
Petal_Width = 1/[Petal Count] – this calculates the total position around the circle that will be occupied by each petal. For example, if there were 10 petals, each one would occupy 1/10 of the space around the circle, or .10
Petal_Side_Position = IF [Side]=’MIN’ THEN ([Polygon ID]-1)*[Petal_Width] ELSE [Polygon ID]*[Petal_Width] END – this calculates the position of the start of each Min line and Max line. If there were 10 petals, the Min side of the 2nd petal would be at position .1 or (2-1)*.1, and the Max side of the petal would be at position .2, or 2*.1. The Min value will share the same position as the Max value of the previous petal. The max value will share the same position as the Min value of the next petal
Petal_Middle_Position = ([Polygon ID]/[Petal Count]) – ([Petal_Width]/2) – this calculates the position of the center of each petal. If there were 10 petals, the center of petal 3 would be at .25, or (3/10) – (.1/2). This is also halfway between the position of the Min line and the Max line.
Alright, now we can calculate all of our coordinates. Let’s start with P1. For the first input, we want this point to start right at the edge of our circle. So the distance from the center is going to be equal to the radius of the inner circle. So the first input is just [Radius]. For the second input, we’ll use the the [Petal_Side_Position] we calculated above.
If we were to plot these points for 12 petals, we would end up with 24 points, but it would appear that we only 12 because each is overlapping with another point. But this gives us the outside edges of each of our petals
Now onto P2. This one is a little more complicated. We’re going to use P1 as a starting point for this calculation, instead of the center of the inner circle. Now we need to calculate the distance from P1 where we want our next point to appear. First, we need to determine the length of the entire petal. I like to use a parameter for this so I can dynamically adjust the look of the flowers. So let’s create a parameter called [Petal_Length_Ratio]. This is going to be a number relative to the radius, so a ratio of 1 would set the length of the petal equal to the radius of the circle. A value of .8 would set the length of the petal equal to 80% of the radius, and so on. I usually go with a value somewhere between .5 and 1. We’ll use this along with the radius, so that the petals of each flower are sized appropriately based on the size of their inner circle. Next, we need to position this point somewhere between the start of the line and the end of the line. As I mentioned earlier, I like to place it two thirds of the way (P2_Parameter from the previous section). So the first input, the distance from P1, is going to be the radius x the length ratio x the P2 parameter. For the second input, we’re going to use the [Petal_Middle_Position] because we want this side of the line to follow the same path as the line with P3 and P4. If we were to use the [Petal_Side_Position] field, we would end up with really wide, strange looking petals. This will probably make more sense a little further along. For now, let’s plug those values into our X and Y calcs.
P3 is a little more straight forward. For the first input, we’re going to calculate the distance from the center of the inner circle. And then we’ll use a similar approach to what we did for P2. The first input will be the radius + (the radius x the length ratio x the P3 parameter). As I mentioned in the earlier section, I like to set this parameter to .5. And once again, we’re going to use the [Petal_Middle_Position] field for the second input.
P4 is almost identical to P3, except we don’t need the length ratio. We want this point to appear at the end of the line. So we can just remove that from the calc.
We’re almost there! If we were to plot these points for the first petal in our lotus flower, it would look like this. It looks very similar to what we reviewed in the previous section, but with one very important difference…everything is at an angle…which is what we wanted.
All that’s left to do is to plug all of these points in our Bezier calcs and then build our polygons!
Now the polygons. Let’s build this as a Trellis chart, just like we did with the Circles. So drag [Row] on to Rows and [Column] onto Columns. And then;
Right click on [Petal_X] and drag to Columns. When prompted, select [Petal_X] without aggregation
Right click on [Petal_Y] and drag to Rows. When prompted, select [Petal_Y] without aggregation
Drag [Type] to Filter Shelf and filter to ‘Petal’
Drag [Petal_Count_Filter] to Filter Shelf and filter to TRUE. Right click and ‘Add to Context’
Drag [Polygon_ID] to Detail
Drag [Order] to Path
Change Mark Type to Polygon
We’re so close! Your sheet should look like this
The only thing left to do is to combine the Circle polygons with the Petal Polygons. We have separate data for them, all we need to do is get them on the same sheet. So we’ll create two more simple calcs to bring it all together.
Final_X = IF [Type]=’Circle’ THEN [Circle_X] ELSE [Petal_X] END
Final_Y = IF [Type]=’Circle’ THEN [Circle_Y] ELSE [Petal_Y] END
Now just replace [Petal_X] and [Petal_Y] with [Final_X] and [Final_Y] and drag [Type] from the filter shelf on to Color and you should have your lotus flowers!
The Final Touches
The hard part is done, now to make it look pretty. Play around with some of the parameters until you get the look that you like. Adjust the [Petal Count], the [Column_Count], the [Petal_Length_Ratio], and even the [P2_Parameter] and [P3_Parameter] if you wanna get crazy.
Next, throw some color on there. You could make the color meaningful to encode some data, or you could do what I just did and color it randomly. I used the calc below and then just assigned one of the color palettes I have saved.
Color = [Type] + STR([Base ID])
And that’s it! If you made it this far, please reach out and let me know what you thought, and what you came up with. Thank you so much for reading, and keep an eye on the blog for more ‘Totally Useless Charts & How to Build Them’
The thing that I love most about Tableau is the incredible flexibility. No matter what you are trying to do, there is a way to do it. And more often than not, there are actually several ways to do it. That’s where this series comes in. There are so many incredible hacks and techniques floating out there in the Tableau Universe, it can be difficult to figure out which ones to use and when. In each installment of this series we’ll be focusing on one specific ‘question’ and discuss the pros, cons, and use cases of various techniques. And our first question of the series is… “How do I turn off the default highlighting in Tableau when I click on a mark?”. And the answer is, of course, “It Depends”.
First off, what are we talking about when we say ‘default highlighting’? As I’m sure you have noticed, when you click on a mark in Tableau, something happens to the mark you selected, and to all of the other marks in your view. When you click on a text mark, you get a blue box on the selected mark and all of the other marks fade. When you click on any other type of mark, that mark retains it’s formatting (sometimes with an extra black box around it) and the rest of the marks fade. And then everything in that view goes back to normal when you click on something else.
This behavior makes sense. When a mark is selected, you should know which mark that is. But the result, in my opinion, does not look great. It would be really nice if we could control what the selected and non-selected marks look like.
This post is going to focus on three techniques that will allow you to do just that. We’ll call them the ‘Highlight Technique’, the ‘Filter Technique’, and the ‘Transparent Technique’. First, let’s talk a little bit about each of these techniques, and then we’ll walk through how to apply them. If you’re familiar with the techniques and are just looking for a reminder on how to do one of them, feel free to skip ahead.
The Highlight Technique – This technique leverages a highlight action and essentially highlights every mark when any mark is selected. What I love about this technique is that it’s very simple to set up, and it can be applied to multiple worksheets. With a single action, you can ‘turn off’ highlighting for your entire dashboard…as long as your dashboard doesn’t contain a specific mark type. One of the major drawbacks of this approach is that it does not work with text marks. Instead of getting rid of the blue boxes on your BANs, this technique will turn them yellow. Another drawback of this approach is that it doesn’t actually de-select the mark, it just masks the selection. You can still see a black border around the selected mark, and if you have something that could be clicked on multiple times in a row (like a scroll button), it makes for a clunky user experience. Users would have to click the mark three times to run the action twice (once to run the action, a second time to de-select the mark, and a third time to run the action again). And one last drawback is if you have the opacity turned down on a mark, when you click on it, the mark will show at full opacity.
The Filter Technique – This technique leverages a filter action, and to be completely honest, I’m not entirely sure how it works. But it works great! I was first introduced to this technique by Yuri Fal, during a Twitter discussion on this exact topic, and several others have written about it since. What I love about this technique is that it actually de-selects the mark after you click on it, unlike the Highlight Technique. It also works on any chart type. The only downsides I have found with this approach are that it’s a little tricky to set up, you have to create a separate action for every worksheet on your dashboard, and it does not work well in conjunction with some other actions, mainly other filter actions and set controls. The issue with using this technique with other actions is that you cannot leverage the ‘Clearing the selection will…’ options. So basically there is no option to undo your action. This is a major drawback if you’re trying to use set controls, or another filter action, but not so much with parameter actions since you can replicate that ‘Clearing’ function with a calculated field.
The Transparent Technique – I haven’t used this technique much in the past (mainly because I learned the other techniques first), but it’s definitely something I will use more in the future. I first came across this approach in Kevin Flerlage’s blog post, 14 Use Cases for Transparent Shapes. It leverages a transparent shape that can be built in PowerPoint or other design tools and, unlike the other techniques, it does not rely on dashboard actions. Everything can be done in your worksheet. Because the transparent shape doesn’t have a border, or any fill, there is nothing for Tableau to highlight when it’s selected. Another way that it differs from the other approaches is that the Highlight and Filter techniques can be used almost universally (with the few exceptions we discussed earlier), but this approach has pretty limited applications when it comes to avoiding highlighting (but a wide variety of other awesome applications that you can read more about in Kevin’s post). The two use cases that we’ll focus on are text marks and buttons. Personally, I wouldn’t recommend trying this approach for any chart type where the marks aren’t uniform in size and spacing (but that doesn’t mean you can’t try it). Another downside of this approach is that it does not actually de-select a mark, it just masks it, similar to the Highlight Technique. But on the upside, it’s probably the easiest of the three methods to implement, at least for text marks.
Alright, so let’s bring that all together
Now it should be clear which technique you should use right? Of course not. It depends. So let’s look at a few specific common use cases.
Which Technique to Use When
Text Marks
As I mentioned earlier, the Highlight Technique does not work with text marks, so that leaves two options. Up until recently I would have said with 100% confidence that you should use the Filter Technique. The main difference here is that if you have more than one mark in your worksheet, the Transparent Technique will fade the other marks, and the Filter Technique will retain all formatting (so you’ll want to use other indicators, like color, to show which mark is selected). With most mark types you’re trying to avoid that fading, but it actually works pretty well with text marks. What we really want to do here is get rid of that blue box, which both options will do. Here are the two methods, the Transparent Technique as is, and the Filter Technique using color to indicate the selected mark
Transparent Technique
Filter Technique
Both look great, but I would give the edge to the Transparent Technique because of how easy it is to set up. But I encourage you to try both methods and see which one you prefer
Buttons
There aren’t really any limitations on this use case. All three methods will work, but what works best might depend on how your buttons are built and how they will be used. If you built your button in Tableau using the Circle or Square mark type, or if you built them in another tool and brought them in as custom shapes, I wouldn’t recommend using the Transparent Technique. For this technique to work you would have to create two worksheets, one with buttons and one with transparent shapes, and then layer the sheets so that the transparent shapes in your top sheet are aligned perfectly with the buttons on your bottom sheet. Not terribly difficult, but the other methods are easier in this case. The only time I would recommend using the Transparent Technique for buttons over the other techniques, is if your buttons are not actually in Tableau at all, and are instead, part of a background image. You could design and incorporate your buttons directly into your background with other design tools, and then use this technique to make them act and appear as buttons in Tableau.
The next consideration would be whether or not a button might be clicked multiple times in a row, like it would with a scroll button. In that case I would definitely go with the Filter Technique as that’s the only one of these techniques that actually de-selects the mark. As I mentioned earlier, without that de-selection, users will have to click the button three times to run the action twice in a row. If multiple clicks aren’t a concern, you can also use the Highlight Technique, but my vote goes to the Filter Technique. Overall it makes for the best user experience and can make running your dashboard actions smooth and app-like. It’s worth the little bit of extra effort to set it up.
Any Other Chart Type
If you’re running an action from any other type of chart, like a bar chart or a scatterplot, I would not recommend using the Transparent Technique. You may be able to get it to work (on a scatterplot at least), but similar to the Buttons use case, there is an easier way. I will always default to the Filter Technique, because I think it provides the best user experience. Again, it may take longer to set up, but in my experience, it’s usually worth it. However, there are times when that is not an option. As I mentioned before, with that technique, you lose the ability to ‘Clear the Selection’. That means no set controls, no filter actions, and extra work for parameter actions. If I’m running a parameter action, I will usually still put in the effort to use the Filter Technique (and use a calculated field in the action to clear the selection). If I’m running a filter action, or using set controls, I will use the Highlight Technique. So my vote here again goes to the Filter Technique (when possible).
Turn Off All Highlighting
Sometimes, I have a dashboard with no interactivity, but I still don’t want users to click on something and trigger that highlighting. If you aren’t using your tooltips you could just put a floating blank over your whole dashboard and call it a day. But in most cases I still want the users to see the tooltips. In that case, the Highlight Trick is fantastic. It’s quick to set up and it can be applied to every chart in your dashboard with a single action (except for text marks). You could set up the Filter Technique on each of your worksheets individually, or build duplicates of each and try to layer them with the Transparent Technique, but in this case, the Highlight Technique definitely gets the vote.
The Verdict
So to summarize; for text marks, like BANs, I recommend the Transparent Technique, for any other types of charts that are running a parameter action, I recommend the Filter Technique, and for charts running filter actions or using set controls, or for mass highlight removal, I recommend the Highlight Technique.
Perfect, now what exactly are these techniques?
The Three Techniques
Here’s a quick walk-through of each of the three techniques. One thing to keep in mind is that we are only going to cover how to remove the highlighting. In a lot of cases, once you remove the highlighting, it’s still important to indicate which mark is selected. This is especially true if you are using actions to filter your dashboard. I review a few ways to do this in a post I wrote a while back on the Highlight Technique, but this post is already long enough so I’m not going to repeat them here.
The Highlight Technique
Create a calculated field. This field can be called whatever you want and can contain any non-aggregate value. The key is that this field will be the same on every single row in your data (and every mark in your worksheet). I typically name my field ‘HL’ and use a blank value (in calc body, just enter ”). For this example, I’m going to use ‘I<3Tableau’
Drag your ‘HL’ field to Detail on the marks card on ALL worksheets where you want to disable highlighting
Go back to your dashboard and add a Highlight Action by clicking on ‘Dashboard’ in the upper section above the toolbar, and then selecting ‘Actions’. Then click ‘Add Action’ and select ‘Highlight’
Update your Highlight Action. In this example I want to turn off the highlighting on my bar chart and my scatterplot, so I’ve added the ‘HL’ field to detail on both of those worksheets
Give your action a descriptive name so it’s easy to find and edit later on
Under Source Sheets, select ALL of the worksheets where you want to disable highlighting
Under Target Sheets, also select ALL of the worksheets where you want to disable highlighting
Run the Action on Select
Under Target Highlighting choose ‘Selected Fields’
Choose the ‘HL’ field from the list of fields
Your updated action should look like this
Once that’s done, click ‘OK’ and test your action. Usually if the action is not working as expected it’s because the ‘HL’ field is not on detail, or because it’s a mark type that’s not supported by this technique (Text marks).
The Filter Technique
Create a calculated field called 0 and enter the number 0 in the body of the calculation
Repeat the step above but use 1 for the calculation name and the value in the body of the calculation
Right click on both new fields, 0 and 1, and change them to a dimension
Drag both new fields to Detail on the marks card on the worksheet where you want to disable highlighting (this technique can only support one sheet at a time)
Go back to your dashboard and add a Filter Action by clicking on ‘Dashboard’ in the upper section above the toolbar, and then selecting ‘Actions’. Then click ‘Add Action’ and select ‘Filter’
Update your Filter Action. In this example I want to turn off the highlighting on the BANs, so I’ve added the 0 and 1 fields to detail on that worksheet.
Give your action a descriptive name so it’s easy to find and edit later on
Under Source Sheets, keep the active dashboard selected in the drop-down and then select the worksheet where you want to disable highlighting from the options below (the list of sheets in your dashboard)
Under Target Sheets, select your worksheet from the drop-down. Make sure that you select it from the drop-down and not from the options below the drop-down
Run the Action on Select
Set the ‘Clearing the selection will’ option to ‘Show all values’
Under Filter, choose ‘Selected Fields’
In the table below, on the left side in the Source Field column, click ‘Click to add’ and choose 0 from the list of fields
On the right side, in the Target Field column, choose 1 from the list of fields
Your updated action should look like this
Once all of the options are updated, click OK and test your action. If your action is not working as expected, the most common issues are;
Under Target Sheets, you may have the dashboard selected in the drop-down and your worksheet selected in the options below that. You can fix this by selecting your worksheet in the drop-down instead of the dashboard
0 and 1 were added to detail as measures with aggregation. If this is the cause, you’ll see a warning at the bottom of the screenshot above that says ‘Missing fields from…’. The 0 and 1 need to be converted to dimensions, or added to detail without aggregation.
The Transparent Technique
So there are actually two different techniques here, one for text marks, and one for buttons. I am going to cover the technique for text marks because I think it’s the best technique for this mark type. But definitely check out Kevin’s post here to learn more about the technique with buttons and 12 other awesome uses cases for transparent shapes.
The first step is to create your transparent shape. I usually do this in PowerPoint but you can use any design program.
In PowerPoint, click on ‘Insert’ in the upper toolbar
Click on ‘Shapes’ and select a circle
Click anywhere on your slide to insert the circle
In the ‘Shape Format’ tab, click on ‘Shape Fill’ and select ‘No Fill’
In the ‘Shape Format’ tab, click on ‘Shape Outline’ and select ‘No Outline’
Right click on your transparent shape and select ‘Save Picture As’
Save the transparent shape to a sub-folder in the Shapes folder in your Tableau Repository
The path for this is usually C:\Users\Username\Documents\My Tableau Repository\Shapes unless you changed the location
Within the Shapes folder you can create sub-folders. I have a folder called ‘Transparent’ with just my transparent shape, so it’s easy to find
Once your transparent shape is created and saved you can apply it to your Text marks.
Go to the worksheet with the text marks
On the marks card, change the mark type to ‘Shape’
Click on ‘Shape’ then ‘More Shapes…’
At the bottom right of the window, click ‘Reload Shapes’
In the ‘Select Shape Palette’ drop-down, select the sub-folder where you saved your transparent shape
Click ‘Assign Palette’
When you do this, Tableau automatically moves all of your text to the Labels for the transparent shape, but if you have a field on color, you may notice that the color isn’t applied to those labels. To fix this, there’s one additional step.
Click on ‘Label’ on the marks card
Click on ‘Font’
Click the ‘Match Mark Color’ box
Now the color should be applied to each mark and if you click on any of the text marks, the blue box should be gone. You should see the selected mark retain it’s formatting, and the non-selected marks fade slightly.
The End
So that’s it for this installment of ‘It Depends’. Please keep in mind that these recommendations are all personal opinions based on my experiences. I encourage you to learn and try all of these different techniques on your own and figure out what works best for you.
And I hope you’ll join us for the next installment of ‘It Depends’ where we’ll discuss the different methods for filtering a dashboard with an action that supports multiple selections (Filter Action vs Set Action vs Set Control vs Parameter Action). Thanks everybody!
This is the third and final part of our series on creating curved elements in Tableau. Although this post covers new and different techniques, I would recommend checking out Part 1 of the series here as some of the concepts overlap. This post will focus on sigmoid curves and a few different techniques to build them, depending on what you’re trying to do and what your data looks like. To follow along, you can download the sample data and workbook here.
Sigmoid Curves
This is a sigmoid curve. Whether you realize it or not, these curves are everywhere on Tableau Public. Sankeys? Sigmoid Curves. Curvy lines on a map? Sigmoid Curves. Curvy bump charts, or curvy slope charts, or curvy dendograms, or curvy area charts? All Sigmoid Curves. The main difference between this type of curve and the Bezier curves we discussed in Part 2 of this series, is that only 2 points are needed to draw a Sigmoid Curve. If you have the start point, the end point, and the right formulas, the math will take over and create that nice symmetric s-shaped curve for you. So what formulas should you use? The answer is, as with most things in Tableau, it depends. We’re going to cover two different models for drawing Sigmoid curves, we’ll call them the ‘Standard’ model, and the ‘Dynamic’ model.
Think about most of the chart types that use sigmoid Curves. Sankeys, Curvy slope or bump charts, dendograms…they all have something in common. The start of the lines and the end of the lines are uniform. If they are running from left to right, the start of all of the lines share an X value, and the end of all of the lines share an X value (columns). If they are running from top to bottom, the start of all of the lines share a Y value, and the end of all of the lines share a Y value (rows). When these conditions are true, which they will be in 99% of the applications in Tableau, you can use the ‘Standard’ model. If either of those conditions are not true, you can use the ‘Dynamic’ model.
Since the majority of the time you will be using the ‘Standard’ model, let’s start with that one.
Building Your Data Source
To build our data source we are going to follow the same process that we did in Part 1 and Part 2 of this series. We are going to create additional points by joining our sample data to a densification table using join calculations (value of 1 on each side of the join). For this first example, we are going to use the sheet titled ‘Slope Chart’ for our sample data, and the sheet titled ‘SigmoidModel’ for our densification table. You can download the sample files here.
Here is our sample file that we will use to draw 12 curved lines, from left to right.
You may notice that the densification table looks a little different. In previous parts of this series, the densification table was a single column with numbers 1 thru however many points you wanted to create. For this model, we have 2 columns, ‘t’ and ‘Path’. The Path value will be used to tell Tableau how to connect the points (think connect the dots). The ‘t’ value is going to be used to draw the curve and will be a value evenly spaced between -6 and 6. For our example, we are using 24 points, so we will have a value at every .5 increment (-6, -5.5, -5, …). If you want your curve to be a little smoother, you could use 48 points and have a value at every .25 increment (-6, -5.75, -5.5, …). Really, you can use any number of points, you’ll just need to do the math to get t values that are evenly spaced between -6 and 6. Why -6 and 6? I have absolutely no idea and I’m not going to pretend to. I’m just here to show you how to make cool curvy things, and to do this cool curvy thing, you need values evenly spaced between -6 and 6. Here is our densification table
Building Your Calculations
Once we join those files together, there are only two calculations needed to turn the points from our sample data into curved lines.
Essentially what these calculations are doing are determining the total vertical distance that needs to be travelled (End Position – Start Position) and then the Sigmoid function spaces these points appropriately on the Y axis to create the S-shaped curve. Let’s build our view and then come back to this.
Build Your Curves
Follow the steps below to build your curves
Right click on the [T] field, drag it to columns, and when prompted, choose the top option ‘T’ without aggregation
Right click on the [Curve] field, drag it to rows, and when prompted, choose the top option ‘Curve’ without aggregation
Change [LineID] to a Dimension, and drag it to color
Change the Mark Type to ‘Line’
For this example, you may not need to address the ‘Path’, but I find it’s good practice when dealing with lines and polygons.
Right click on the [Path] field, drag it to Path, and when prompted, choose the top option ‘Path’ without aggregation
When you finish, your worksheet should look like this
With just two calculations, we were able to draw those nice curved lines. Let’s quickly re-visit those calcs. Our Sigmoid calculation is a mathematical function with values ranging between 0 and 1 depending on the ‘T’ value. The table below shows the Sigmoid Value for each T value and the second row shows the difference from the previous value. Notice that at the beginning and the end, the values don’t change much from one step to the next, but towards the middle the values change much more rapidly. And where T=0, the value is .5.
Now let’s look at that in context with our lines and with our Curve calculation. Notice that the vertical position for each of the points on the line doesn’t change much at the beginning or the end, but does change rapidly as it approaches the center. And the center of each line is exactly half way between the starting position and the ending position.
For example, Line 4 starts at position 4 and ends at position 10. At T=0, the vertical position is at 7, halfway between 4 and 10. Let’s plug this line into our calculation. Remember, from the table above, the value of Sigmoid at T=0 is .5
And let’s do this with one other point, just for good measure. If we look at the table above, we see that the Sigmoid value at T=-3 is .047
Curve = 4 + ((10 – 4) * .047) = 4.28
If you look at the image above, you’ll notice that at T=-3, the vertical position is just above the 4, or 4.28 to be exact. That’s enough math for now, let’s look at some other fun examples.
More Examples – Dendogram
Now let’s follow the same exact process for the curvy slope chart above, but change up our data a little bit. For this example we are going to use the sheet titled ‘Dendogram’ for our data, and we’ll use the same ‘SigmoidModel’ sheet for our densification. Here is our sample data.
In this example, our starting position is the same for all of our lines and is halfway between the minimum end position (1) and the maximum end position (12). If you follow the same process your sheet should look something like this.
Now just for fun, let’s make that starting point dynamic. Create a numeric parameter called [Dynamic_Start] and then create a new field called [Dynamic_Curve]. This will be exactly the same as our [Curve] calculation but we’ll replace the [Start_Position] field with [Dynamic_Start].
Now in your view, just replace the [Curve] field on Rows with the [Dynamic Curve] field and you should have something like this.
More Examples – Sankey
This is not going to be an in-depth tutorial on how to create Sankey diagrams. People much smarter than me (check out flerlagetwins.com) have written many posts and shared many templates for anyone interested in building one. This section will be dedicated more to the mechanics of a Sankey diagram and how we can take what we’ve already learned in the previous examples and apply them towards building a Sankey diagram. The one main difference between what we’ve done so far and what we’re going to do now lies in the Mark Type. It’s time for some polygon fun.
Essentially what we need to do is draw 2 lines for every 1 ‘Sankey Line’ and then connect them together. Typically, you would use table calculations in Tableau to figure out the positions of these lines, but we are going to keep it simple and use some data from our sample file. What we’re actually building is probably more of a Slope Chart, since each segment will be of equal width, but the fundamentals are the same as with a true Sankey Diagram. For this example we are going to use the sheet titled ‘Sankey’ and for our densification we’re going to use the sheet titled ‘SankeyModel’. Here is our sample data
You may notice that this densification table has some additional data. It has twice as many records and a new column called ‘Side’ with values ‘Min’ and ‘Max’. This is because for each segment, or ‘Sankey Line’ we will actually be drawing two lines, a Min Line and a Max Line. Here’s out densification data.
Building Your Calculations
We are still going to use the same 2 fields, Sigmoid and Curve, but in this case, the calculation for Curve will be a little different.
Sigmoid
1/(1+EXP(1)^-[T])
We have the two points needed for one of our lines (Start_Position and End_Position), we’ll call this the Min Line. But we need the two points for our Max Line as well. As I mentioned earlier, in this example all of our segments are going to be equal width, so we’ll just add 1 to our position fields.
Start_Position_Max
[Start_Position]+1
End_Position_Max
[End_Position]+1
And now we have the points needed to draw all of our curves.
Curve
IF [Side]=’Min’ then [Start_Position] + (([End_Position] – [Start_Position]) * [Sigmoid]) ELSE [Start_Position_Max] + (([End_Position_Max] – [Start_Position_Max]) * [Sigmoid]) END
This calculation may look complicated, but it’s almost identical to the Curve calculation we were using earlier. When we joined to the SankeyModel, it essentially created two sets of points for us. We have all of the same T values as we did earlier (24 points ranging from -6 thru 6), but now we have 2 of each, one where [Side] = ‘Min’ and one where [Side] = ‘Max’. So we can use this to draw two lines. The first part of the IF statement is for the ‘Min’ line. It is exactly the same as our previous Curve calculation. The second part of the IF statement (ELSE) is for the ‘Max’ line and it is the same calculation but using the [Start_Position_Max] and [End_Position_Max] fields.
Build Your Curves
To start, let’s follow the same process we did earlier, but with a couple of minor changes
Right click on the [T] field, drag it to columns, and when prompted, choose the top option ‘T’ without aggregation
Right click on the [Curve] field, drag it to rows, and when prompted, choose the top option ‘Curve’ without aggregation
Change [LineID] to a Dimension, and drag it to color
Change the Mark Type to ‘Line’
Drag [Side] to Detail
Your sheet should look something like this
This is starting to look like a Sankey diagram. For each LineID we have two lines that are moving together, we just need to ‘color them in’, and we’ll do that by converting them to polygons. So change the Mark Type to polygon.
Uh-oh. What happened?
So because we have [Side] on detail, it’s treating our Min and Max lines as separate polygons. We want them treated as 1 polygon, so surely removing the [Side] field will fix it.
$@!%!!!!
Alright, we can figure this out. Remember how earlier I said it was always good practice to address the Path on the Marks Card when using lines and polygons? Let’s try right clicking on our [Path] field, dragging that to Path and adding it without any aggregation.
Ahhh, that’s better.
Now let’s figure out what happened. We know why we had to remove the [Side] field, but how did the [Path] field fix our worksheet. If you look at the densification table, you’ll see that the T values go from -6 up to 6 and then repeat in the reverse order. The Path field on the other hand is sequential from 0 thru 49. Let’s take a look at the [Path] values in relation to the [T] values
So our [T] and [Curve] fields are telling Tableau where to plot all of the points, and then our [Path] field is telling Tableau the order in which to connect those points to form our polygon. And that is basically all you need to know about using Polygons in Tableau. Just Points and Path.
Let’s take a look at one more example of something you can do with the ‘Standard’ Model.
More Examples – Proportion Plot
The process for building a Proportion Plot is going to be very similar to what we did in the previous example. For this, we are going to use the sheet titled ‘Proportion’ for our sample data, and we’ll use the ‘SankeyModel’ sheet again for our densification. Here is our sample data.
You’ll notice that this table has a couple of extra columns. The ‘Run’ columns are just running totals of the Start_Value and End_Value columns. Again, this would typically be done with table calculations in Tableau, but to keep things simple we are doing those calculations in the data source. Now let’s build our calculations.
Building Your Calculations
Once again we are going to use those same two calculations, but with some minor modifications. First, our Sigmoid calculation, which is always the same.
Sigmoid
1/(1+EXP(1)^-[T])
Now, similar to the Sankey example, we need to draw two sets of line for each section. So we’ll need the start and end positions for our Min Line and the start and end positions for our Max Line. Because this is a Proportion Plot (part of a whole), we want all of our values represented as percentages. So we’ll calculate what our position should be and then divide that number by the total of the column (or the maximum value in the running total column) to translate that start position into a percentage of the whole.
Let’s take a closer look at this before we move on. We’ll use Section #2 from the table above. For the Starting Position (Start Position Min) we are going to subtract the value of that section from the running total to get the starting position.
So section #2 would start at 35 (90-55=35). Then we’ll divide that by 115, which is the total of the column, and the maximum value of the running total column, which gives us 30.4%. So this section of our Proportion Plot is going to start 30.4% of the way up our Y Axis. Now we’ll repeat for the Max Lines. These calculations will be the same except we do not need to subtract the value of the section. We just want the running total divided by the grand total.
Start Position Max
[Start_Value_Run]/{MAX([Start_Value_Run])}
End Position Max
[End_Value_Run]/{MAX([End_Value_Run])}
Using section #2 as an example again, we would divide 90 (the running total value) by 115 (the total of the column or the max value of the running total column) and get 78.3%. So we want this section of our Proportion Plot to start 30.4% of the way up the Y axis and end 78.3% of the way up the Y axis. And now for the Curve calculation, we’ll use the same calculation that we did in the Sankey Example
Curve
IF [Side]=’Min’ then [Start_Position_Min] + (([End_Position_Min] – [Start_Position_Min]) * [Sigmoid]) ELSE [Start_Position_Max] + (([End_Position_Max] – [Start_Position_Max]) * [Sigmoid]) END
Build Your Curves
Now we’ll follow the same process to build our view
Right click on the [T] field, drag it to columns, and when prompted, choose the top option ‘T’ without aggregation
Right click on the [Curve] field, drag it to rows, and when prompted, choose the top option ‘Curve’ without aggregation
Change [Section] to a Dimension, and drag it to color
Change the Mark Type to ‘Polygon’
Right click on the [Path] field, drag it to Path, and when prompted, choose the top option ‘Path’ without aggregation
When you’re finished, your sheet should look something like this.
If you look at section #2, the orange section, you’ll see that on the left side (the starting positions), it ranges from 30.4% to 78.3%, which is what we had calculated in our example. So it accounts for about 48% of the total (55 / 115=.48) and it starts at about 30% (35 / 115)
That’s it for the ‘Standard Model’. Now we are going to jump into one quick example with the ‘Dynamic Model’.
Dynamic Model
As I mentioned earlier, 99% of the time you are going to want to use the ‘Standard’ model, but there are instances where that may not work. One example use case, which has been popular in recent years, is drawing curved lines on a map that connect to another chart. So let’s use that.
Building Your Data Source
For this example we are going to draw curved lines from 9 U.S. states and have them end in a uniform column on the right side of our sheet, so we can connect them to another chart. For this we’ll use the sheet titled ‘Dynamic’ for our sample data and we’ll use the ‘SigmoidModel’ sheet for our densification. Here is our sample data.
So for each of our states we have the starting points (the lat and lon for the state), but we need to figure out our end points. This can be done in a lot of different ways. You can do it manually and plug those values into your data source. Personally, I like to calculate them in Tableau. But those calcs will be dramatically different depending on your use case. For this example, we are going to draw our lines from left to right and they will all end in a uniform column to the right of the U.S. and will be spaced evenly between the lowest state and highest state in our sample data. If your use case is different, you can most likely modify these calculations to get what you need, and as always, feel free to reach out to me if you have any questions. So let’s calculate our end points. Keep in mind that this step is completely optional. If you prefer to have static points in your data source for the end points, just name them End_Lat and End_Lon and skip ahead to the next calculation.
Build Your Calculations
This first calculation will be used to find the latitude for our end points. The calculation looks complicated but we will break it down.
The first portion of this calculation, {MAX([Lat])}, is used to find our ‘reference point’, which will be the maximum latitude in our sample data. The next portion of the calculation, ({MAX([Lat])}-{MIN([Lat])}) gets our ‘range’, or the maximum latitude minus the minimum latitude. Then we divide that range by the number of states -1 to get the spacing between points, or {COUNTD([State])-1}. And then we multiply that spacing by the Rank -1 (so that the first point starts at our max Lat).
So an easier version of this formula is Ref Point – ((Range/Number of States -1) *( Rank -1)
Now we need to calculate the longitude for our end points. This is much easier. We want all of our lines to end in the same column, so they will all have the same longitude. I like to use parameters rather than hardcoding values, so you can easily adjust the position of things. So let’s create a parameter called [Line End Offset] and set that value to 10. Then our calculation is just the maximum longitude value in our data + our offset parameter
End_Lon
{MAX([Lon])} + [Line End Offset]
So now we have the two sets of points that we’ll need to draw our lines. If we were to stop here and just map those two sets of points, we would have a point on the center of each of our states (blue) and then a column of points to the right of the U.S. (orange).
Now for the rest of our calculations. Once again we’ll need the Sigmoid calculation.
Sigmoid
1/(1+EXP(1)^-[T])
The Curve calculation is exactly the same as all of the previous examples just with different field names.
Curve
[Lat] + (([End Lat] – [Lat]) * [Sigmoid])
The main difference between the ‘Dynamic’ model and the ‘Standard’ model lies in the spacing of the points. In the Dynamic model, points are spaced evenly between -6 and 6 and we can just use the [T] field on columns. That obviously won’t work on a map. We have different longitudes for all of our starting points, so we’ll need to adjust the approach a little bit. Instead of evenly spacing points between -6 and 6, we need to evenly space them between our starting and ending longitudes. So we’ll take the total horizontal distance (ending longitude – starting longitude) and divide it by the number of points in our densification table -1.
Point Spacing
([End Lon]-[Lon])/({COUNTD([Path])-1})
Now we’ll use that calculation, along with the [Path] field and our starting longitude to create points that are equally distant between our starting and ending longitude.
Lon_Adjusted
[Lon] + [Path]*[Point Spacing]
Build Your Curves
Now that we have all of our calculations, let’s build our curve.
Right Click on [Lon_Adjusted], go to ‘Geographic Role’ and select ‘Longitude’
Right Click on [Curve], go to ‘Geographic Role’ and select ‘Latitude’
Right click on the [Lon_Adjusted] field, drag it to columns, and when prompted, choose the top option ‘Lon_Adjusted’ without aggregation
Right click on the [Curve] field, drag it to rows, and when prompted, choose the top option ‘Curve’ without aggregation
Drag [State] to Color
Change the Mark Type to ‘Line’
Right click on the [Path] field, drag it to Path, and when prompted, choose the top option ‘Path’ without aggregation
When you’re done, your sheet should look something like this.
Now with just a few modifications to the ‘Standard’ model, we were able to build our Sigmoid curves with different starting points. This example was for building lines that run left to right, but you can just as easily build them from top to bottom. The calculations and example for that can be found in the sample workbook here. Just use the fields with the ‘Ttb’ suffix.
If you’re still reading this, I apologize for the incredibly long post. There are just so many things you can do with Sigmoid Curves and I wanted to demonstrate how, with basically the same technique, you can build a huge variety of cool curvy things in Tableau. If you’re interested in learning more about Sigmoid Curves, definitely check out flerlagetwins.com. Ken and Kevin are incredible and everything I know about Sigmoid Curves (and a lot of other things that will eventually be covered in this blog), I learned from them.
Here are a few examples of where I’ve used Sigmoid Curves in my Tableau Public Work (click thumbnail to view on Tableau Public).
Do Mo(o)re with Data 