Workarounds for getting new 2019.3 features in earlier versions

The 2019.3 beta is out and here's the list of new features. It's nice of Tableau to automate some of the things that could be done with workarounds in previous versions. If you like the new features but are stuck in an old version, or if you are a new kid wanting to know how things were done in ye olden days, follow the links below:
Distance between two points as a calculation
Working with data in UK national grid projected coordinates (haven't tried this script in TabPy to see how slow it would be, worked fine as a standalone python script for data preprocessing)
Pdf subscriptions (courtesy of Interworks)

Labelling polygons that go across the prime meridian and its antimeridian

Consider a dataset describing polygons like the one below:

lat	lon	path	polygons
1	1	1	1
-1	1	2	1
-1	-1	3	1
1	-1	4	1
1	1	5	1
1	179	1	2
-1	179	2	2
-1	-179	3	2
1	-179	4	2
1	179	5	2

Tableau doesn't allow you to label polygons directly, therefore the work around is to do dual axis on either latitude or longitude, and create a second layer using the average latitude and longitude of all the vertices of the polygon (key detail, remove the path from the level of detail, while you need it there for the polygon layer) to place the label. 

This works fine apart from any polygons that include points on either side of the antimeridian at 180 degrees longitude, using the convention of 0 to 180 for longitude east, and 0 to -180 for longitude west. The trick here is to detect if the maximum and minimum longitude have different signs and if difference between the maximum and the minimum longitude defines an angle smaller or greater than 180 degrees. In the latter case, the longitude convention needs to be changed to 0 to 360 for the averaging to give sensible results. To achieve that we create a calculated field as shown below and use it in place of the longitude.

if max([Lon])*min([Lon])<0 then

if max([Lon])-min([Lon])>180 then avg(if [Lon]<0 then 360+[Lon] else [Lon] end)

else avg([Lon]) end

else avg([Lon])

end

Spatial filtering by distance in km from a known point

 Tableau has supported the circular select tool for a few versions now. So the lazy way out is to use this select option, click at the known point on the map and then drag watching the radius of the circle until it reaches the desired value (tip: change the workbook locale to English Ireland for it to be in kilometres rather than miles). This only works for small distances though, what if you want to be a zoom level further out and look at hundreds and thousands of kilometres, or even use this distance to do something else such as filtering?

Let's port the haversine formula into Tableau

Assuming our fixed point is at 52N 0E (consider using parameters with lists of values if you have several points of interest), we create a calculated field step_1:

sin(radians(52-[Latitude])/2) * sin(radians(52-[Latitude])/2) +

        cos(radians([Latitude])) * cos(radians(52)) *

        sin(radians(0-[Longitude])/2) * sin(radians(0-[Longitude])/2)

Then the distance in km from the point defined in step 1 is given by:

2*[R] * atan2(sqrt([step_1]), sqrt(1-[step_1]))

Where R has been defined as the radius of the earth in km: 6371.

Converting UK national grid to latitude longitude Python function from Hannah Fry

I found Hannah Fry's python function useful with a dataset in national grid coordinates. It's not something that could be done easily in a Tableau calculated field as it is an iterative calculation that converges to the solution. Hannah Fry does have a Tableau connection though; she was a keynote speaker in the London Tableau conference on tour a few years ago. That presentation gave some key insights on Tom Cruise's central upper tooth, as well as showing pictures of two clones of Hannah with symmetrical faces, one based on the left side of her face and one based on the right hand side!

Layering marks and polygons on map

This is a trick that has become much simpler to perform since the introduction of union in Tableau 9.3.

We start with two data files, one with the vertices of our polygons and another with the locations where we want the marks. We create a union of those two when we create our tableau data source.

the wildcard union is particularly handy for multiple files so keep it in mind, in this case we don't really need it. What we then need to do is select the Latitude and the lat columns, right click and select 'Merge mismatched fields', likewise for the longitudes.

Then we can create our map with these merged latitudes and longitudes, but we really want to create two maps, one for each layer. Here's how to create the marks map:

Beware of the averaged coordinates, if you don't put all the dimensions in the level of detail you might not get a mark for each row in your dataset! And here's how to create the polygon:

Now we need to select dual axis and right click and hide the 'Null' location.This will give us the desired two layer map.

As it happens, my marks are the centroids of post-codes. So we can tell tableau that through the geographic role of the location field, and select filled maps as the type of mark to get the postcode polygon instead of the dot at the centroid. Note that the (generated) Latitude and Longitude is no good for this as it is not visible when editing the source and cannot be merged with the mismatched latitude longitude of the polygon source after the union, they can't even be used in calculated fields which could be another way round (the pre-9.3 way of doing things). So an original text only source might have to be imported into tableau and the generated coordinates will have to be copied to a new source to use for a union.

Alternative maps

Don't get me wrong. I don't read defense magazines. The extracts below are from the Radio User magazine, that I used to get both from a professional interest in maritime and aero communications and a hobby interest in shortwave and medium wave listening ( DX-ing). It is an excellent source of information on ADS-B and AIS as well as general search and rescue and safety comms, and of course long distance broadcasting and propagation conditions.

It does however also cater for plane spotters and scanner enthusiast following military exercises, hence the 'alternative' map of Scandinavia below. The description of the made up fringe country between Norway and Iceland is hilarious, any similarities to Brexit island are purely coincidental

The challenge as always of course is how to re-make the map in Tableau. To make my life easier I'm ignoring the made up internal borders inside Sweden and Finland, and focusing on relocating New Zealand to the North Atlantic.

Lets start with a dataset listing the necessary countries. Tableau is very good at mapping them in their usual locations.

But how do we move New Zealand? Let's have a go extracting the generated coordinates and fiddling with the numbers. We select the countries on the map, view the data and it's a rare case that the summary has more info (generated coordinates) than the full data. Select everything and copy into the csv creator of choise, any spreadsheet application will do.

Now move New Zealand to its 'alternative' position, lets say 60N 5W, remove the (generated) from the header, save the csv and re-import into Tableau.If you put the new coordinates on X and Y you get dots at the right places. But change to a filled map and New Zealand stubbornly refuses to move from the Pacific to the Atlantic.

Ok so clearly Tableau is doing things behind the scenes. Of course it has to as we haven't specified the relative size of the countries. Let's do that by adding the area of each country in another column. So can we escape by setting the geographic role to 'None'? 

It has flattened Norway, so it is sort of re-projecting the shape from Mercator projection. But everything is in the wrong place, and we've pushed the size as far up as possible. Bear in mind that we have another weapon in our disposal, zooming. Tableau doesn't zoom into the marks at the same magnification levels that it zooms into a map. You can see this by zooming into my Makeover Sunday II map. In this case, we want to zoom out to see if it gels the countries together. 

So it's not that simple. At this point we give up and leave it for another day.

Makeover Sunday II

This time I'm using a dataset from Jamie Laird, original workbook on Tableau Public . Having worked in remote sensing before, I like the idea of a map as pixels of so many kilometres by so many kilometres, or so many degrees by so many degrees latitude and longitude. This dataset behaves well with Tableau's default Mercator projection(only projection using built in maps) . If you have data closer to the poles you might want to force a geographic projection by using your own map background image.

As always, a couple of calculated fields come handy.
Truncated Latitude and Longitude:

int([Latitude])

int([Longitude]) 

Aggregate the number of responses and force a logarithmic colour scale, with appropriate legend:

case int(log(COUNTD([Response ID])))

when 0 then '<10'

when 1 then '10<x<100'

when 2 then '100<x<1000'

when 3 then '>1000'

end

Some footnotes on presentation: make sure you have no border or halo on the marks (controlled through colour), and if you really want to treat the marks as individual pixels use the square mark type.