Switching your Tableau accounts

As much as I love Tableau, their website(s) can be a bit confusing at times. Surfing around on them feels that you’re required to log in multiple times during one session. This is of course due to the site actually being many sites and you can have multiple identities on them, which might make things a little confusing…

As I’m about to change employer I wanted to make sure that my Tableau identity follows me along. Not that I have that much content on the Tableau site(s), but still. So I set about changing the emails.

The one’s I’m interested in “keeping” are the account on Tableau Community and the one on Tableau Public.

First, the Tableau Public account: Login to Tableu Public (note that you might have a separate password for this one, as they are NOT the same accounts!) and make the changes in the settings section. Again, you’ll need to verify the email via a confirmation email.

Then, the Tableau Community account: Log in – no, SIGN in, on the page http://www.tableau.com and make the necessary changes in the the “Edit account” menu. Make sure to verify the email via the confirmation email sent to the updates email address. You can find the instructions here.

So far so good. Except for the fact that changing your email on the community account also affects the account you have on your customer portal :/ So currently I can access my company account logging in with my private email… And apparently, if your customer portal account is deleted, so is your community account! This behaviour/dilemma doesn’t really seem to be recognised by Tableau. I’ve been in contact with both their Tech Support and their Customer Service, but neither has yet been able to help me. Let’s hope this can be resolved, as I am sure I am not the only one who wants to keep the community identity when changing employer.

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The 2018 presidential election in Finland, some observations from a news analytics perspective

The presidential elections 2018 in Finland were quite lame. The incumbent president, Sauli Niinistö, was a very strong candidate from the offset and was predicted to win in the first round, which he did. You can read more about the elections for instance on Wikipedia.

Boring election or not, from an analytics perspective there is always something interesting to learn. So I dug into the data and tried to understand how the elections had played out on our site, hbl.fi (which is the largest swedish language news site in Finland).

We published a total of 275 articles about the presidential election of 2018. 15 of these were published already in 2016, but the vast majority (123) was pubslished in January 2018.

Among the readers the interest for the elections grew over time, which might not be that extraordinery (for Finnish circumstances at least). Here are the pageviews per article over time (as Google Analytics samples the data heavily i used Supermetrics to retrieve the unsampled data – filtering on a custom dimension to get only the articles about the election):

President_2018_per_day

Not much interesting going on there. So, I also took a look at the traffic coming in via social media. Twitter is big in certain circles, but not really that important a driver of traffic to our site. Facebook, on the other hand, is quite interesting.

Using Supermetrics again, and doing some manual(!) work too, I matched the Facebook post reach for a selection of our articles to the unsampled pageviews measured by Google Analytics.  From this, it is apparent that approximately one in ten persons reached on Facebook ended up reading our articles on our site. Or more, as we know that some of the social media traffic is dark.

The problem with traffic that originates from Facebook is that people tend to jump in and read one article and then jump out again. Regarding the presidential elections this was painfully clear, the average pageviews was down to 1,2 for sessions originating from Facebook. You can picture this as: Four out of five people read only the one article that was linked to Facebook and then they leave our site. One out of five person reads an additional article and then decides to leave. But nobody reads three or more articles. This is something to think about – we get a good amount of traffic on these articles from Facebook but then we are not that good at keeping the readers on board. There’s certainly room for improvement.

What about the content then? Which articles interested the readers? Well, with good metadata this is not that difficult an analysis. Looking at the articles split by the candidate they covered and the time of day the article was published:

President_2018_per_candidate

(The legend of the graph is in swedish => “Allmän artikel” means a general article, i.e. either it covered many candidates or it didn’t cover any candidates at all.)

Apart from telling us which candidates attracted the most pageviews, this also clearly shows how many articles were written about which candidate. A quite simple graph in itself, a scatter diagram coloured by the metadata, but revealing a lot of information. From this graph there are several take aways; at what time should we (not) publish, which candidates did our readers find interesting, should we have written more/less about one candidate or the other. When you plot these graphs for all different kinds of meta data, you get a quite interesting story to tell the editors!

So even a boring election can be interesting when you look at the data. In fact, with data, nothing is ever boring 😉

 

A note about the graphs: The first graph in this post was made with Google Sheets’ chart function. It was an easy to use, and good enough, solution to tell the story of the pageviews. Why use something more fancy? The second graph I drew in Tableau, as the visualisation options are so much better there than in other tools. I like using the optimal tool for the task, not overkilling easy stuff with importing it to Tableau, but also not settling for lesser quality when there is a solution using a more advanced tool. If I had the need to plot the same graphs over and over again, I would go with an R-script to decrease the need of manual clicking and pointing.

 

Headache while trying to filter on a map in Tableau :/

This week’s MakeoverMonday delivered a data set on the accessibility of buildings in Singapore. For each building there is an index for the accessibility level and of course information on where this building is situated alongside with some information on that area (“subzone”). So I figured, why not plot each area on a map and then by clicking that area youl’d get a list of all the buildings in that area and their accessibility indeces? Seems straigth forward enough.

So I plotted the map, and let Tableau color the areas according to the average accessibility:

w50_singapore_averages.PNG

 

The darker the colour, the better the accessibility. Now I’d like the user to be able to click an area, for instance Alexandra Hill, and get the information about the buildings in this particular area. Like this:

w50_alexandrahill_table

But alas, this table is NOT shown when you click on the map, this action only shows one line per area, for some (for me) still unknown reason:

w50_alexandrahill_table_short

The entire list of buildings is shown only when you chose the area from a list on the side of the dashboard, but not when you click on the map. You can try it out on Tableau Public yourself.

I’ve tried different ways of filtering and different actions on the filters, but nada. I will, however, fix this! I want to understand why Tableau acts this way.  I just need to dig into it some more. So instead of serving you a nice #mmonday blog post, I shared some headache, but hey – this is not that uncommon when working with data after all 😉 Hang in there for the sequel!