Game of Thrones, in Tableau’s Story Points

I know that Game of Thrones Season 4 ended quite a while ago, but I have presented this Story Points (dashboard? story?) a couple of times to different user groups and wanted to post it to my blog for others.

I collected tweets with #GameOfThrones, #GoT, and #GoT Season4 through ScraperWiki, which no longer offers this service, for several months in 2014. You’ll notice that the tweet volume is wildly inconsistent; this is both my fault and ScraperWiki’s. Twitter rate-limited their searches for some weeks, so I am missing a fair amount of tweets. (What better prompt for me to start using the Twitter API?)

Another reason I am missing tweets actually is something I admonish people about when I am training them in Tableau: case sensitivity! Turns out that hashtags are case-sensitive, too. While I searched for #GameOfThrones, I did not search for #Gameofthrones. (Tableau Public limits me to 1mm records, so it probably would not change much in this viz.) And in the Top 5 Hashtags list for each episode, I filtered out the hashtags for which I searched, because that would be redundant, and I normalized the tweets by using UPPER. (You should avoid showing members of a dimension in all upper-case in a visualization, if you can—it looks angry and makes people think that your MDM folks are lame.)

This data set is a good candidate for Story Points in Tableau because it is sequenced over time, and there are many opportunities to comment on the causation of the fluctuation in tweets. For instance, when Mark Gattis joined the cast as a very minor character, there was an unusual spike that had nothing to do with HBO, but rather with his popularity as Mycroft Holmes, on the BBC’s epic hit, “Sherlock”. (Maybe a good topic for using the Twitter API?)

There are some things I like about Story Points—it allows me to guide the user’s navigation very carefully, and it looks great. It does require analysts to think about what their audience really needs to take away from an analysis, which is part of the vocation of data analysis that often gets lost.

I don’t like the inability to modify the appearance of the Story Points controllers, and it doesn’t write changes back to the dashboard or visualization in use. I actually did not need to use it for this dashboard, because the horizontal bar of episode numbers serves as a filter, too. Building a dashboard also was more efficient for me, probably because I have done it so many times. I’m curious what you all think about Story Points.

I’m going to use the dashboard I built previously from this data set, and not Story Points, for my upcoming client demo. Story Points is good for presentations, but it’s not an enterprise analytics tool.

(I did redact some of the spoilers in this Story, for those of you who haven’t seen Season 4–like my husband, who has been ultra-helpful setting up this blog :))


Age is a number, but what does it mean?

The genesis for this blog post actually is my father. He had a birthday recently, which coincided with the announcement that he is engaged (congratulations, Dad!) to a lady who is from the country where he resides. So I was curious—how would his age compare in the country where they live? And what would his age in America be if he had been born elsewhere? And now that I’m entering middle-age, what would that look like in, for instance, Africa?

I used this World Bank data, which actually ships with Tableau, to create a multiplication factor for each country that relates its life expectancy, both for men and for women, to that of the US, and then I used a couple of parameters to allow you to select the country where you’re from—or one that holds your interest—and then input your age and gender. The labels over the countries tell you what your age would be if you lived there. If the local age is less than your current age, then their life expectancy is less than ours.

The countries are colored by the percentage of life that would be complete if you lived in a specific country. The news for our friends in Africa isn’t so good—their life expectancies are significantly shorter than ours in The Americas and in Europe. There’s a significant relationship between birth rate and life expectancy in each country. I added trend lines to the scatter plot below the map, and the relationships are logarithmic.

The birth rates translate into children per woman: for some perspective, 49 births per 1,000 in Niger translates into 7.6 births per woman; in the Netherlands, 9-ish births per 1,000 translates into 1.9 births per woman. (I found the births per woman data and will add it later this week.)

Click on a country or region to filter the scatter plot and the histogram. (I tested our friendly p-values and R2 values to confirm that this is the best model. If you love stats and Tableau, send me a message!)

Feel free to download the workbook and check out what I did with the parameters. This data is from 2010—I have found an updated data set, but it needs some major transformations; the metrics here haven’t changed significantly since published, but I will be updating it later in the week, and I plan to add more analyses of the infrastructure/educational/public health factors that contribute so such wide variations in birth rates and life expectancies over the next few weeks.